NatComm2020/In-vivo/Calibration and longitudinal assessment/Day1-2/Analysis scripts/PlotBode.py

# -*- coding: utf-8 -*-
"""
Created on Sun Apr 28 15:50:58 2019

@author: aemdlabs
"""
import deepdish as dd
import matplotlib.pyplot as plt
import numpy as np

plt.close('all')

MCSMapI={'SE1':'Ch03',
         'SE2':'Ch05',
         'SE3':'Ch01',
         'SE4':'Ch02',
         'SE5':'Ch22',
         'SE6':'Ch06',
         'SE7':'Ch16',
         'SE8':'Ch37',
         'SE9':'Ch20',
         'SE10':'Ch10',
         'SE11':'Ch24',
         'SE12':'Ch08',
         'SE13':'Ch14',
         'SE14':'Ch04',
         'SE15':'Ch18',
         'SE16':'Ch33',
         'SE17':'Ch34',
         'SE18':'Ch60',
         'SE19':'Ch38',
         'SE20':'Ch64',
         'SE21':'Ch40',
         'SE22':'Ch56',
         'SE23':'Ch42',
         'SE24':'Ch70',
         'SE25':'Ch66',
         'SE26':'Ch65',
         'SE27':'Ch68',
         'SE28':'Ch67',
         'SE29':'Ch55',
         'SE30':'Ch62',
         'SE31':'Ch58',
         'SE32':'Ch69',
         'ME1':'Ch57',
         'ME2':'Ch61',
         'ME3':'Ch53',
         'ME4':'Ch63',
         'ME5':'Ch52',
         'ME6':'Ch41',
         'ME7':'Ch49',
         'ME8':'Ch51',
         'ME9':'Ch46',
         'ME10':'Ch45',
         'ME11':'Ch44',
         'ME12':'Ch39',
         'ME13':'Ch54',
         'ME14':'Ch43',
         'ME15':'Ch50',
         'ME16':'Ch47',
         'ME17':'Ch32',
         'ME18':'Ch27',
         'ME19':'Ch30',
         'ME20':'Ch29',
         'ME21':'Ch28',
         'ME22':'Ch25',
         'ME23':'Ch26',
         'ME24':'Ch07',
         'ME25':'Ch21',
         'ME26':'Ch11',
         'ME27':'Ch17',
         'ME28':'Ch15',
         'ME29':'Ch13',
         'ME30':'Ch31',
         'ME31':'Ch19',
         'ME32':'Ch09'}

                          #Col, Row  
MCSMapFacingDown={'Ch58':(0,1),
                  'Ch57':(0,2),
                  'Ch56':(0,3),
                  'Ch55':(0,4),
                  'Ch54':(0,5),
                  'Ch53':(0,6),
                  'Ch52':(0,7),
                  'Ch51':(0,8),
                  'Ch50':(0,9),
                  'Ch49':(0,10),
                  'Ch60':(1,0),
                  'Ch61':(1,1),
                  'Ch62':(1,2),
                  'Ch63':(1,3),
                  'Ch64':(1,4),
                  'Ch65':(1,5),
                  'Ch43':(1,6),
                  'Ch44':(1,7),
                  'Ch45':(1,8),
                  'Ch46':(1,9),
                  'Ch47':(1,10),
                  'Ch70':(2,0),
                  'Ch69':(2,1),
                  'Ch68':(2,2),
                  'Ch67':(2,3),
                  'Ch66':(2,4),
                  'Ch42':(2,5),
                  'Ch41':(2,6),
                  'Ch40':(2,7),
                  'Ch39':(2,8),
                  'Ch38':(2,9),
                  'Ch37':(2,10),
                  'Ch01':(3,0),
                  'Ch02':(3,1),
                  'Ch03':(3,2),
                  'Ch04':(3,3),
                  'Ch05':(3,4),
                  'Ch06':(3,5),
                  'Ch30':(3,6),
                  'Ch31':(3,7),
                  'Ch32':(3,8),
                  'Ch33':(3,9),
                  'Ch34':(3,10),
                  'Ch11':(4,0),
                  'Ch10':(4,1),
                  'Ch09':(4,2),
                  'Ch08':(4,3),
                  'Ch07':(4,4),
                  'Ch29':(4,5),
                  'Ch28':(4,6),
                  'Ch27':(4,7),
                  'Ch26':(4,8),
                  'Ch25':(4,9),
                  'Ch24':(4,10),
                  'Ch12':None,
                  'Ch59':None,
                  'Ch13':(5,1),
                  'Ch14':(5,2),
                  'Ch15':(5,3),
                  'Ch16':(5,4),
                  'Ch17':(5,5),
                  'Ch18':(5,6),
                  'Ch19':(5,7),
                  'Ch20':(5,8),
                  'Ch21':(5,9),
                  'Ch22':(5,10)}


Directory = '../Analyzed data/2019-08-01T15-46-48/'

Fsigs,GmDict = dd.io.load(Directory+'GmChDict')


for key in GmDict.keys():
    plt.figure(1)
    plt.loglog(Fsigs[:-2], GmDict[key])

plt.xlabel('Frequency (Hz)')
plt.ylabel('|Gm Vsig| (A)')

plt.figure()
A=np.log10(np.ones((11,6))*5e-14)
    
    
for Trt in GmDict.keys():
    if Trt in ['M1','M2','S1','S2']:
        continue
    ch = MCSMapI[Trt]
#    if Trt in DCch:
#        continue
    
    A[MCSMapFacingDown[ch][1],MCSMapFacingDown[ch][0]] =np.log10(abs(GmDict[Trt][0]))

plt.imshow(A, interpolation='nearest', vmin=-8.5, vmax=-7.5)
plt.grid(True)
cbar=plt.colorbar()
plt.xlabel('column',fontsize=12)
plt.ylabel('row',fontsize=12)
cbar.set_label('I$_{ds-rms}$ (@1Hz)', rotation=270, labelpad=15,fontsize=13)
plt.title('Response to bipolar current stimulation')