#Code to plot translocation statistics after the script - Event Detection

from sys import platform
import numpy as np
import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
import numpy as a
import pandas as pd
import scipy.signal as sig
import Functions as f
import shelve
from sklearn.mixture import GaussianMixture as GMM
import os
import h5py
import seaborn as sns
from tkinter import Tk
from tkinter.filedialog import askopenfilenames
root = Tk()
root.withdraw()
from scipy.stats import gaussian_kde


matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42

filenames = askopenfilenames(filetypes=[('data files', '*.db')], parent = root)
root.update()
print(filenames)

Name = ''
cm = plt.cm.get_cmap('RdYlBu')
voltageLimits = [0.49, 1.1]
dIrange = (0.25, 3.6)
dwellrange = (0, 2.24)
FracDIRange = (0, 100)

count = 0
dt = {}
dIF = {}
count = {}

dt = np.array([])
dIF = np.array([])
dI = np.array([])
tempvoltage = np.array([])
templocalbaseline = np.array([])
tempeventlength = np.array([])

localBase = np.array([])
v = np.array([])
t = np.array([])
t2 = np.array([])
count = 0

for file in filenames:
    print('file is:', file)
    fileNamewithourend, end = os.path.splitext(file)
    shelfFile = shelve.open(fileNamewithourend)
    event = shelfFile['TranslocationEvents']
    dIF = np.append(dIF, np.array([x.currentDrop/x.baseline for x in event.events if x.type == 'CUSUM'])*100) #if x.type == 'CUSUM'
    dI = np.append(dI, np.array([x.currentDrop for x in event.events if x.type == 'CUSUM' ])*1e9 ) #if x.type == 'CUSUM'
    v = np.append(v, [x.voltage for x in event.events if x.type == 'CUSUM' ]) #if x.type == 'CUSUM'
    localBase = np.append(localBase, [x.baseline*1e9/x.voltage for x in event.events if x.type == 'CUSUM' ]) #if x.type == 'CUSUM'
    tempeventlength = np.append(tempeventlength, [x.eventLength for x in event.events if x.type == 'CUSUM' ]) #if x.type == 'CUSUM'
    path, filen = os.path.split(file)
    print(path[:])
    filepath = path[:] + '/' + filen[:]
    st = os.stat(filepath.replace('\\', '/'))
    dt = np.append(dt, tempeventlength*1e3)
    t = np.append(t, st.st_birthtime * np.ones(len([x.currentDrop/x.baseline for x in event.events if x.type == 'CUSUM' ] ))) #if x.type == 'CUSUM'
    t2 = np.append(t2, st.st_mtime * np.ones(len([x.currentDrop/x.baseline for x in event.events if x.type == 'CUSUM' ]))) #if x.type == 'CUSUM'
    count += len([x.currentDrop/x.baseline for x in event.events if x.type == 'CUSUM' ]) #if x.type == 'CUSUM'


# Sort the voltages.
availableVoltages = np.unique(v[np.where((v > voltageLimits[0]) & (v < voltageLimits[1]))])
print(availableVoltages)
data = []
datadII0 = []
datalBase = []
dataDwell = []
dataRate = []
labels = []
NEvents = []

for vo in availableVoltages:
    ind1 = np.argwhere(v == vo)
    ind2 = np.intersect1d(ind1, np.argwhere((dt > dwellrange[0]) & (dt < dwellrange[1])))
    ind = np.intersect1d(ind2, np.argwhere((dI > dIrange[0]) & (dI < dIrange[1])))
    data.append(dI[ind])
    datadII0.append(dIF[ind])
    dataDwell.append(dt[ind])
    datalBase.append(localBase[ind])
    NEvents.append(len(ind))
    if 1:
        print('Modified-Written is applied')
        if (np.max(t2[ind])-np.min(t[ind])) != 0:
            dataRate.append(0)
        else:
            dataRate.append(len(ind)/(np.max(t2[ind])-np.min(t[ind])))
    else:
        if (np.max(t[ind])-np.min(t[ind])) != 0:
            dataRate.append(0)
        else:
            dataRate.append(len(ind)/(np.max(t[ind])-np.min(t[ind])))
    labels.append('{:0.0f}'.format(np.round(vo*1000)))

cond = np.array([])
for i,dati in enumerate(data):
    cond = np.append(cond, dati/availableVoltages[i])

print('plotting now ')


# # 1. Zoomed Fractional Current Drop % Scatter Plot
# fig1 = plt.figure(1, figsize=(9, 6))
# ax = fig1.add_subplot(111)
# sc = ax.scatter(dt[np.where(v > 0)], dIF[np.where(v > 0)], c=v[np.where(v > 0)], vmin=min(v[np.where(v > 0)]), vmax=max(v[np.where(v > 0)]), s=35, cmap=cm)
# cbar = plt.colorbar(sc, ticks=availableVoltages)
# cbar.ax.set_yticklabels(labels)  # vertically oriented colorbar
# ax.set_xlabel('Time (ms)')
# ax.set_ylabel('Current Drop dI/I0 (%)')
# ax.set_title('{}\nScatter Plot, {} events'.format(Name, count))
# #plt.show()
# fig1.savefig(file[:-24] + 'ScatterFrac.pdf', transparent=True)
# # ax.set_xlim(dwellrange[0], dwellrange[1])
# # ax.set_ylim(FracDIRange[0], FracDIRange[1])
# fig1.savefig(file[:-24] + 'ScatterFracZoomed.pdf', transparent=True)
#
# #fig1.clear()
# #fig1.close()

# # 1.2.Fractional Current Drop Scatter Plot...
fig6 = plt.figure(6, figsize=(9, 6))
ax6 = fig6.add_subplot(111)
sc6 = ax6.scatter(dt[np.where(v > 0)], dI[np.where(v > 0)], c=v[np.where(v > 0)], vmin=min(v[np.where(v > 0)]), vmax=max(v[np.where(v > 0)]), s=35, cmap=cm)
cbar6 = plt.colorbar(sc6, ticks=availableVoltages)
cbar6.ax.set_yticklabels(labels)  # vertically oriented colorbar
ax6.set_xlabel('Time (ms)')
ax6.set_ylabel('Current Drop dI (nA)')
ax6.set_title('{}\nScatter Plot, {} events'.format(Name, count))
#plt.show()
fig6.savefig(file[:-24] + 'ScatterdI.pdf', transparent=True)
ax6.set_xlim(dwellrange[0], dwellrange[1])
ax6.set_ylim(dIrange[0], dIrange[1])
fig6.savefig(file[:-24] + 'ScatterdIZoomed.pdf', transparent=True)

# dati/availableVoltages[i]
#plt.show()


#fig1.clear()
#fig1.close()

# 2.BoxPlot Delta I vs Voltages
fig2 = plt.figure(2, figsize=(9, 6))
ax2 = fig2.add_subplot(111)
bp = ax2.boxplot(data, notch=True, autorange=True, sym='', whis=1)
ax2.set_xlabel('Voltage (mV)')
ax2.set_ylabel('Current Drop dI (nA)')
ax2.set_title('{}\nBoxplot (outliers removed)\n{} events'.format(Name, count))
ax2.set_xticklabels(labels)
ax2.get_xaxis().tick_bottom()
ax2.get_yaxis().tick_left()
fig2.savefig(file[:-24] + 'BoxplotdI.pdf', transparent=True)

# # 2.Violin plot Delta I vs Voltages
# fig70 = plt.figure(figsize=(9, 6))
# ax70 = fig70.add_subplot(111)
# bp = ax70.violinplot(data)
# ax70.set_xlabel('Voltage (mV)')
# ax70.set_ylabel('Current Drop dI (nA)')
# ax70.set_title('{}\nViolin (outliers removed)\n{} events'.format(Name, count))
# ax70.set_xticklabels(labels)
# ax70.get_xaxis().tick_bottom()
# ax70.get_yaxis().tick_left()
# fig70.savefig(file[:-24] + 'Violin_plotdI.pdf', transparent=True)

# # 2.Violin Delta I vs Voltages
# fig80 = plt.figure(figsize=(9, 6))
# ax80 = fig80.add_subplot(111)
# bp = ax80.violinplot(dataDwell)
# ax80.set_xlabel('Voltage (mV)')
# ax80.set_ylabel('Dwell time (dt)')
# ax80.set_title('{}\nViolin (outliers removed)\n{} events'.format(Name, count))
# ax80.set_xticklabels(labels)
# ax80.get_xaxis().tick_bottom()
# ax80.get_yaxis().tick_left()
# fig80.savefig(file[:-24] + 'Violin_plotdt.pdf', transparent=True)

# # 3.BoxPlot DwellTime vs Voltages
# fig3 = plt.figure(3, figsize=(9, 6))
# ax3 = fig3.add_subplot(111)
# bp = ax3.boxplot(dataDwell, notch=True, autorange=True, sym='', whis=1)
# ax3.set_xlabel('Voltage (mV)')
# ax3.set_ylabel('Dwell Time dt (ms)')
# ax3.set_title('{}\nBoxplot (outliers removed)\n{} events'.format(Name, count))
# ax3.set_xticklabels(labels)
# ax3.get_xaxis().tick_bottom()
# ax3.get_yaxis().tick_left()
# fig3.savefig(file[:-24] + 'BoxplotDwellTime.pdf', transparent=True)

# # 4.BoxPlot Event Rate vs Voltages
# fig4 = plt.figure(4, figsize=(9, 6))
# ax4 = fig4.add_subplot(111)
# bp = ax4.bar(np.arange(len(dataRate)), dataRate)
# ax4.set_xlabel('Voltage (mV)')
# ax4.set_ylabel('Event Rate (Hz)')
# ax4.set_title('{}\Barplot\n{} events'.format(Name, count))
# ax4.set_xticks(np.arange(len(dataRate)))
# ax4.set_xticklabels(labels)
# ax4.get_xaxis().tick_bottom()
# ax4.get_yaxis().tick_left()
# fig4.savefig(file[:-24] + 'BarplotEventRate.pdf', transparent=True)

# 5.Bar Plot Event Number vs Voltages
fig5 = plt.figure(5, figsize=(9, 6))
ax5 = fig5.add_subplot(111)
print(NEvents)
bp = ax5.bar(np.arange(len(NEvents)), NEvents)
ax5.set_xlabel('Voltage (mV)')
ax5.set_ylabel('Number Of Events')
ax5.set_title('{}\nBarplot\n{} events'.format(Name, count))
ax5.set_xticks(np.arange(len(NEvents)))
ax5.set_xticklabels(labels)
ax5.get_xaxis().tick_bottom()
ax5.get_yaxis().tick_left()
fig5.savefig(file[:-24] + 'BarplotEventNumber.pdf', transparent=True)

# # 7.BoxPlot Delta I vs Voltages
# fig7 = plt.figure(7, figsize=(9, 6))
# ax7 = fig7.add_subplot(111)
# bp = ax7.boxplot(datadII0, notch=True, autorange=True, sym='', whis=1)
# ax7.set_xlabel('Voltage (mV)')
# ax7.set_ylabel('Current Drop dI/I0 (%)')
# ax7.set_title('{}\nBoxplot (outliers removed)\n{} events'.format(Name, count))
# ax7.set_xticklabels(labels)
# ax7.get_xaxis().tick_bottom()
# ax7.get_yaxis().tick_left()
# fig7.savefig(file[:-24] + 'BoxplotdII0.pdf', transparent=True)


# 8.Histogram DwellTime vs Voltages
fig8 = plt.figure(8, figsize=(6, 12))
for i,dati in enumerate(dataDwell):
    ax8 = fig8.add_subplot(len(dataDwell), 1, i+1)
    n, bins, patches = ax8.hist(x=dati, bins = 100, alpha=0.7, rwidth=0.85)
    ax8.grid(axis='y', alpha=0.75)
    ax8.set_xlabel('Dwell Time (ms)')
    ax8.set_ylabel('Frequency')
    ax8.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
    #ax8.set_xlim(left=np.min(np.concatenate(dataDwell).ravel()), right=np.max(np.concatenate(dataDwell).ravel()))
    ax8.set_xlim(0, 2.24)
fig8.savefig(file[:-24] + 'HistogramDwellTime.pdf', transparent=True)

# 9.Histogram CurrentDrop vs Voltages
fig9 = plt.figure(9, figsize=(6, 12))
for i,dati in enumerate(data):
    ax9 = fig9.add_subplot(len(data), 1, i+1)
    n, bins, patches = ax9.hist(x=dati, bins = 100, alpha=0.7, rwidth=0.85)
    ax9.grid(axis='y', alpha=0.75)
    ax9.set_xlabel('Current Drop (nA)')
    ax9.set_ylabel('Frequency')
    ax9.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
    #ax9.set_xlim(left=np.min(np.concatenate(data).ravel()), right=np.max(np.concatenate(data).ravel()))
    ax9.set_xlim(3.6, 0.25)
fig9.savefig(file[:-24] + 'HistogramCurrentDrop.pdf', transparent=True)
plt.style.use("seaborn-darkgrid")

# 10 KDE DwellTime vs Voltages
fig10 = plt.figure(10, figsize=(6, 12))
for i, dati in enumerate(dataDwell):
    ax10 = fig10.add_subplot(len(dataDwell), 1, i+1)
    ax10 = sns.distplot(dati, rug= False)
    ax10.set_xlabel('Dwell Time (ms)')
    ax10.set_ylabel('Frequency')
    ax10.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
    ax10.set_xlim(0, 2.24)
fig10.savefig(file[:-24] + 'KDEDwellTime.pdf')#, transparent=True)

# 11 KDE CurrentDrop vs Voltages
fig11 = plt.figure(11, figsize=(6, 12))
for i,dati in enumerate(data):
    ax11 = fig11.add_subplot(len(data), 1, i+1)
    ax11 = sns.distplot(dati)
    ax11.set_xlabel('Current Drop (nA)')
    ax11.set_ylabel('Frequency')
    # ax11.set_xlim(0, 2)
    ax11.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
    # ax11.set_xlim(left=np.min(np.concatenate(data).ravel()), right=np.max(np.concatenate(data).ravel()))
    ax11.set_xlim(0.25, 3.6)
fig11.savefig(file[:-24] + 'KDECurrentDrop.pdf')#, transparent=True)

# # 17 KDE DwellTime Fractional vs Voltages
# fig17 = plt.figure(17, figsize=(6, 12))
# for i,dati in enumerate(datadII0):
#     ax17 = fig17.add_subplot(len(datadII0), 1, i+1)
#     ax17 = sns.distplot(dati)
#     ax17.set_xlabel('Current Drop (nA)')
#     ax17.set_ylabel('Frequency')
#     ax17.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
#     ax17.set_xlim(left=0, right=100)
# fig17.savefig(file[:-24] + 'KDECurrentDropdII0.pdf')#, transparent=True)

# # 18 KDE Conductance Drop vs Voltages
# fig18 = plt.figure(18, figsize=(6, 12))
# for i, dati in enumerate(data):
#     ax18 = fig18.add_subplot(len(data), 1, i+1)
#     ax18 = sns.distplot(dati/availableVoltages[i])
#     ax18.set_xlabel('Conductance Drop (nS)')
#     ax18.set_ylabel('Frequency')
#     ax18.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
#     ax18.set_xlim(left=np.min(cond), right=np.max(cond))
# fig18.savefig(file[:-24] + 'KDEConductanceDrop.pdf')#, transparent=True)

# # 15 BiVariate KDE plot Individual
# fig15 = plt.figure(15, figsize=(9, 9))
# for i,dati in enumerate(data):
#     ax15 = fig15.add_subplot(2, 2, i + 1)
#     # cmap = sns.cubehelix_palette(as_cmap=True, dark=0, light=1, reverse=True)
#     ax15 = sns.kdeplot(dataDwell[i]*1e-3, dati*1e-9, shade=True, cbar=True, cut=0, cmap='rainbow', n_levels=60, reverse=False, shade_lowest=True, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
#     ax15.set_ylabel('Current Drop (nA)')
#     ax15.set_xlabel('Dwell Time (ms)')
#     ax15.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
#     ax15.set_xlim(left=np.min(np.concatenate(dataDwell).ravel())-1, right=np.max(np.concatenate(dataDwell).ravel())+1)
#     ax15.set_ylim(bottom=np.min(np.concatenate(data).ravel())-1, top=np.max(np.concatenate(data).ravel()))
# fig15.savefig(file[:-24] + 'KDEScatterIndividualdI.pdf')#, transparent=True)

# # BiVariate KDE plot Individual Fractional
# fig16 = plt.figure(16, figsize=(9, 9))
#
# for i,dati in enumerate(datadII0):
#     ax16 = fig16.add_subplot(2, 2, i + 1)
#     ax16 = sns.kdeplot(dataDwell[i], dati, shade=True, cbar=True, cmap='gist_heat', shade_lowest=False, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
#     ax16.set_ylabel('Fractional Current Drop (dI/I0)')
#     ax16.set_xlabel('Dwell Time (ms)')
#     ax16.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
#     ax16.set_xlim(left=np.min(np.concatenate(dataDwell).ravel())-1, right=np.max(np.concatenate(dataDwell).ravel())+1)
#     #ax16.set_ylim(bottom=0, top=100)
#     # Set graph limits manually
#     # ax16.set_xlim(0, 8)
#     # ax16.set_ylim(0, 8)
# fig16.savefig(file[:-24] + 'KDEScatterIndividualdIdI0.pdf')#, transparent=True)

# # BiVariate KDE plot Individual
# fig19 = plt.figure(19, figsize=(9, 9))
# for i,dati in enumerate(data):
#     ax19 = fig19.add_subplot(2, 2, i + 1)
#     ax19 = sns.kdeplot(dataDwell[i], dati/availableVoltages[i], cbar=True, cmap='gist_heat', shade=True, shade_lowest=False, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
#     ax19.set_ylabel('Conductance Drop (nS)')
#     ax19.set_xlabel('Dwell Time (ms)')
#     ax19.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
#     ax19.set_xlim(left=np.min(np.concatenate(dataDwell).ravel())-1, right=np.max(np.concatenate(dataDwell).ravel())+1)
#     ax19.set_ylim(bottom=np.min(cond)-1, top=np.max(cond))
#     # Set graph limits manually
#     # ax19.set_xlim(0, )
#     # ax19.set_ylim(0, )
# fig19.savefig(file[:-24] + 'KDEScatterConductanceDrop.pdf')#, transparent=True)

matplotlib.rcParams['font.size'] = 22

# KDE DwellTime vs Voltages ALL on One
fig12 = plt.figure(12, figsize=(16, 9))
ax12 = fig12.add_subplot(111)
for i, dati in enumerate(dataDwell):
    ax12 = sns.distplot(dati, label='{}mV'.format(str(int(availableVoltages[i]*1e3)), bins = 100, norm_hist=True))
ax12.legend()
ax12.set_xlabel('Dwell Time (ms)')
ax12.set_ylabel('Frequency')
ax12.set_title('Dwell Time Histograms')
#ax12.set_xlim(left=np.min(np.concatenate(dataDwell).ravel()), right=np.max(np.concatenate(dataDwell).ravel()))
ax12.set_xlim(0, 5)
fig12.savefig(file[:-24] + 'KDESingleDwellTime.pdf')#, transparent=True)

# KDE DwellTime vs Voltages ALL on One [Good plot]
fig13 = plt.figure(13, figsize=(16, 9))
ax13 = fig13.add_subplot(111)
for i,dati in enumerate(data):
    ax13 = sns.distplot(dati, label='{}mV'.format(str(int(availableVoltages[i]*1e3)), bins = 100, norm_hist=True))
ax13.legend()
ax13.set_xlabel('Current Drop (nA)')
ax13.set_ylabel('Frequency')
ax13.set_title('Current Drop Histograms')
ax13.set_xlim(0.25, 3.6)
# ax13.set_ylim(0, 4)
fig13.savefig(file[:-24] + 'KDESingleCurrentDrop.pdf')

# # KDE DwellTime vs Voltages ALL on One
# fig20 = plt.figure(20, figsize=(16, 9))
# ax20 = fig20.add_subplot(111)
# for i,dati in enumerate(data):
#     ax20 = sns.distplot(dati/availableVoltages[i], label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
# ax20.legend()
# ax20.set_xlabel('Conductance Drop (nS)')
# ax20.set_ylabel('Frequency')
# ax20.set_title('Conductance Drop Histograms')
# ax20.set_xlim(left=np.min(cond), right=np.max(cond))
# fig20.savefig(file[:-24] + 'KDESingleConductanceDrop.pdf')#, transparent=True)

# # BiVariate KDE plot All In One
# fig14 = plt.figure(14, figsize=(9, 9))
# ax14 = fig14.add_subplot(111)
# for i,dati in enumerate(data):
#     ax14 = sns.kdeplot(dataDwell[i], dati, shade=True, shade_lowest=False, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
# ax14.legend()
# ax14.set_ylabel('Current Drop (nA)')
# ax14.set_xlabel('Dwell Time (ms)')
# ax14.set_title('KDE Scatter')
# # Set graph limits
# # ax14.set_xlim(0, 8)
# # ax14.set_ylim(0, 5)
# #ax14.set_xlim(left=np.min(np.concatenate(data).ravel()), right=np.max(np.concatenate(data).ravel()))
# fig14.savefig(file[:-24] + 'KDEScatterdI.pdf')#, transparent=True)

# # BiVariate KDE plot All In One
# fig21 = plt.figure(21, figsize=(9, 9))
# ax21 = fig21.add_subplot(111)
# for i,dati in enumerate(data):
#     ax21 = sns.kdeplot(dataDwell[i], dati/availableVoltages[i], shade=True, shade_lowest=False, label='{}mV'.format(str(int(availableVoltages[i]*1e3))))
# ax21.legend()
# ax21.set_ylabel('Conductance Drop (nA)')
# ax21.set_xlabel('Dwell Time (ms)')
# ax21.set_title('KDE Scatter')
# # ax21.set_xlim(0, 8)
# ax21.set_ylim(0, 5)
# #ax21.set_xlim(left=np.min(np.concatenate(data).ravel()), right=np.max(np.concatenate(data).ravel()))
# fig21.savefig(file[:-24] + 'KDEScatterConductance.pdf')#, transparent=True)

# # KDE DwellTime vs Voltages
# fig22 = plt.figure(22, figsize=(6, 12))
# for i, dati in enumerate(datalBase):
#     ax22 = fig22.add_subplot(len(data), 1, i+1)
#     ax22 = sns.distplot(dati)
#     ax22.set_xlabel('Conductance (nS)')
#     ax22.set_ylabel('Frequency')
#     # ax22.set_xlim(0, 50)
#     ax22.set_title('{}mV'.format(int(availableVoltages[i]*1e3)))
#     ax22.set_xlim(left= 0, right=50)#np.min(np.concatenate(datalBase).ravel()), right=np.max(np.concatenate(datalBase).ravel()))
# fig22.savefig(file[:-24] + 'LocalBaseline.pdf')#, transparent=True)


# #Only KDEplot
#
# # KDE DwellTime vs Voltages
# fig32 = plt.figure(32, figsize=(16, 9))
# ax32 = fig32.add_subplot(111)
# for i, dati in enumerate(dataDwell):
#     ax32 = sns.kdeplot(dati, label='{}mV'.format(str(int(availableVoltages[i]*1e3)), bins = 100))
# ax32.legend()
# ax32.set_xlabel('Dwell Time (ms)')
# ax32.set_ylabel('Prob. density')
# ax32.set_title('Dwell Time Histograms')
# ax32.set_xlim(0, 1)
# ax32.set_xlim(left=np.min(np.concatenate(dataDwell).ravel()), right=np.max(np.concatenate(dataDwell).ravel()))
# fig32.savefig(file[:-24] + 'DwellTime_KDEPlot.pdf')
#
# # KDE Current Drop vs Voltages
# fig33 = plt.figure(33, figsize=(16, 9))
# ax33 = fig33.add_subplot(111)
# for i,dati in enumerate(data):
#     ax33 = sns.kdeplot(dati, label='{}mV'.format(str(int(availableVoltages[i]*1e3))), bw=0.2, shade = False)
# ax33.legend()
# ax33.set_xlabel('Current Drop (nA)')
# ax33.set_ylabel('Prob. density')
# ax33.set_title('Current Drop Histograms')
# ax33.set_xlim(0, 4)
# fig33.savefig(file[:-24] + 'CurrentDrop_KDEPlot.pdf')