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Nanopore-tools/QuickPlot_Muk.py
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QuickPlot_Muk.py
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import matplotlib
import numpy as np
import scipy
from scipy.optimize import curve_fit
from scipy import constants as cst
import Functions as uf
import os
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import h5py
import pandas as pd
from matplotlib.ticker import EngFormatter
from matplotlib import rc
import LoadData
#rc('text', usetex=True)
from tkinter import Tk
from tkinter.filedialog import askopenfilenames
import seaborn as sns
from matplotlib import gridspec
root = Tk()
root.withdraw()
formCurrent = EngFormatter(unit='A', places=3)
formA = EngFormatter(unit='A', places=3)
formB = EngFormatter(unit='s', places=3)
filenames = askopenfilenames()
root.update()
# SIMPLE PLOT: Ionic trace (Top) Voltage Trace (Below)
for i,file in enumerate(filenames):
dat = LoadData.OpenFile(file)
fig1 = plt.figure(1, figsize=(8, 4))
ax_part1 = fig1.add_subplot(2, 1, 1)
t = np.arange(len(dat['i1']))/dat['samplerate']
ax_part1.plot(t, dat['i1'] * 1e9)
ax_part1.set_ylabel('Ionic Current (nA)')
current_array = np.array(dat['i1'] * 1e9)
# print("mean/std at 0 mV : ", np.mean(current_array[0:47821], axis = 0), np.std(current_array[0:47821], axis = 0))
# print("mean/std at 50 mV : ", np.mean(current_array[49992:20274], axis=0), np.std(current_array[49992:20274], axis = 0))
# print("mean/std at 50 mV : ", np.mean(current_array[204383:209565], axis=0),np.std(current_array[204383:209565], axis=0))
# ax_part1.set_xlim(0, 50)
# ax_part1.set_ylim(-5, 5)
fig2 = plt.figure(1, figsize=(8, 4))
ax_part2 = fig1.add_subplot(2, 1, 2 , sharex=ax_part1)
t = np.arange(len(dat['v1']))/dat['samplerate']
ax_part2.plot(t, dat['v1']*1e3)
ax_part2.set_xlabel('Time (s)')
ax_part2.set_ylabel('Voltage (mV)')
# ax_part2.set_xlim(0, 50) §
# ax_part2.set_ylim(-60, 60)
plt.show()
# 2 Plot: Current-time (top), Voltage-time ON SAME graph
# for i,file in enumerate(filenames):
# dat = LoadData.OpenFile(file)
# fig1 = plt.figure(1, figsize=(8, 4))
# ax_part1 = fig1.add_subplot(111)
# t1 = np.arange(len(dat['i1']))/dat['samplerate']
# color1 = 'tab:green'
# ax_part1.plot(t1, dat['i1']*1e9, color = color1)
# ax_part1.set_ylabel('Ionic Current (nA)')
# ax_part1.set_xlabel('Time (s)')
# ax_part1.tick_params(labelcolor=color1)
#
# for i,file in enumerate(filenames):
# dat = LoadData.OpenFile(file)
# fig1 = plt.figure(1, figsize=(8, 4))
# ax_part2 = fig1.add_subplot(111, sharex=ax_part1)
# t2 = np.arange(len(dat['v1']))/dat['samplerate']
# color2 = 'tab:red'
# ax_part2 = ax_part1.twinx()
# ax_part2.plot(t2, dat['v1']*1e3, color=color2)
# ax_part2.set_ylabel('Voltage (mV)')
# ax_part2.tick_params( labelcolor=color2)
# ax_part2.set_xlim(0, 50)
# ax_part2.set_ylim(-60, 60)
# # # 3 Plot: Conductance-time (top), Voltage-time ON SAME graph 000 Check why noise level changes with voltage 0000
# for i,file in enumerate(filenames):
# dat = LoadData.OpenFile(file)
# fig1 = plt.figure(1, figsize=(8, 4))
# ax_part1 = fig1.add_subplot(2, 1, 1)
# t = np.arange(len(dat['i1']))/dat['samplerate']
# ax_part1.plot(t, dat['i1']/dat['v1']*1e9)
# # ax_part1.set_xlabel('Time (s)')
# ax_part1.set_ylabel('Conductance (nS)')
# fig2 = plt.figure(1, figsize=(8, 4))
# ax_part2 = fig1.add_subplot(2, 1, 2, sharex=ax_part1)
# t = np.arange(len(dat['v1']))/dat['samplerate']
# ax_part2.plot(t, dat['v1']*1e3)
# ax_part2.set_xlabel('Time (s)')
# ax_part2.set_ylabel('Voltage (mV)')
# # # ax_part2.set_xlim(0, 50)
# # # ax_part2.set_ylim(-60, 60)
#
# # 4 Plot: Current-time (top) and Histogram-counts (sideways)
# for i,file in enumerate(filenames):
# dat = LoadData.OpenFile(file)
# fig1 = plt.figure(1, figsize=(8, 4))
# ax_part1 = fig1.add_subplot(1,1,1)
# t = np.arange(len(dat['i1']))/dat['samplerate']
# ax_part1.plot(t, dat['i1'] * 1e9)
#
#
# fig2 = plt.figure(1, figsize=(1, 1))
# ax_part2 = fig1.add_subplot(1,2,2, sharey=ax_part1)
# current_array = np.array(dat['i1']*1e9)
# ax_part2 = sns.distplot(current_array, color="r", norm_hist = True, bins = 3000, vertical = True) #bins = 3000,
# # ax_part2 = sns.kdeplot(current_array, color="r", shade=True, vertical=True, kernel='gau') #bins = 3000,
#
#
# ax_part1.set_xlabel('Time (s)')
# ax_part1.set_ylabel('Ionic Current (nA)')
# # ax_part1.set_ylim(0, 1)
# # ax_part2.set_xlabel('Counts')
# plt.tight_layout()
# root.update()
# plt.show()
# # Plot: Pore Size (top), Voltage-time ON SAME graph
# for i,file in enumerate(filenames):
# dat = LoadData.OpenFile(file)
# fig1 = plt.figure(1, figsize=(8, 4))
# ax_part1 = fig1.add_subplot(2, 1, 1)
# t = np.arange(len(dat['i1']))/dat['samplerate']
# # t2 = t/60
# G = dat['i1']/dat['v1']*1e9
# size = (G+np.sqrt(G*(G+16*24/np.pi)))/(2*24)
# ax_part1.plot(t, size) #dat['i1']/dat['v1']*1e9 or #size
#
# # ax_part1.set_xlabel('Time (s)')
# ax_part1.set_ylabel('Pore size (nm)')
# fig2 = plt.figure(1, figsize=(8, 4))
# ax_part2 = fig1.add_subplot(2, 1, 2, sharex=ax_part1)
# t = np.arange(len(dat['v1']))/dat['samplerate']
#
# ax_part2.plot(t, dat['v1'])
# ax_part2.set_xlabel('Time (s)')
# ax_part2.set_ylabel('Voltage (V)')
# # # ax_part2.set_xlim(0, 50)
# # ax_part1.set_ylim(0, 5)
# # ax_part2.set_ylim(0, 0.5)
#
# # plt.savefig("Stability_300mV.svg")
# #1 Plot 211 and 212 type graph::: Current-time (top), Voltage-time (below), Histogram (Right)
#
#
# for i,file in enumerate(filenames):
# dat = LoadData.OpenFile(file)
# fig1 = plt.figure(1, figsize=(8, 4))
# ax_part1 = fig1.add_subplot(2, 2, 1)
# t = np.arange(len(dat['i1']))/dat['samplerate']
# ax_part1.plot(t, dat['i1'] * 1e9)
# ax_part1.set_ylabel('Ionic Current (nA)')
# current_array = np.array(dat['i1'] * 1e9)
# # print("mean/std at 0 mV : ", np.mean(current_array[0:47821], axis = 0), np.std(current_array[0:47821], axis = 0))
# # print("mean/std at 50 mV : ", np.mean(current_array[49992:20274], axis=0), np.std(current_array[49992:20274], axis = 0))
# # print("mean/std at 50 mV : ", np.mean(current_array[204383:209565], axis=0),np.std(current_array[204383:209565], axis=0))
#
# # ax_part1.set_xlim(0, 50)
# # ax_part1.set_ylim(-5, 5)
# fig2 = plt.figure(1, figsize=(8, 4))
# ax_part2 = fig1.add_subplot(2, 2, 3) #, sharex=ax_part1)
# t = np.arange(len(dat['v1']))/dat['samplerate']
# ax_part2.plot(t, dat['v1']*1e3)
# ax_part2.set_xlabel('Time (s)')
# ax_part2.set_ylabel('Voltage (mV)')
# # ax_part2.set_xlim(0, 50) §
# # ax_part2.set_ylim(-60, 60)
#
# fig3 = plt.figure(1, figsize = (1,1))
# ax_part3 = fig1.add_subplot(2,2,2, sharey=ax_part1)
# current_array = np.array(dat['i1']*1e9)
# ax_part3 = sns.distplot(current_array, color="r", norm_hist = True, vertical = True) #bins = 3000,
#
# plt.show()
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