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Update: code cleaned up

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nkoch1 2022-11-08 10:09:02 -05:00
parent ce46f8859a
commit 9037e1a016
46 changed files with 230 additions and 1131 deletions
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10
Code/Functions/Cb_stellate_fxns.py
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# -*- coding: utf-8 -*-
"""
Functions for Cb Stellate model
__author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
"""
import h5py import h5py
import gc import gc
import scipy import scipy

8
Code/Functions/Cb_stellate_fxns_Kv.py
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"""
Functions for Cb Stellate with Kv1.1 model
__author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
"""
import h5py import h5py
import scipy import scipy
import json import json

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Code/Functions/Pospischil_fxns.py
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"""
Functions for model from Pospischil et al. 2008 - RS Pyramidal, RS inhibitory, and FS
__author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
"""
import gc import gc
import h5py import h5py
import scipy import scipy

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Code/Functions/STN_fxns.py
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"""
Functions for STN model
__author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
"""
import scipy import scipy
import json import json
import copy import copy

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Code/Functions/STN_fxns_Kv.py
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"""
Functions for STN model with Kv1.1
__author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
"""
import scipy import scipy
import json import json
import copy import copy

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Code/Functions/Utility_fxns.py
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"""
General functions used throughout simulation scripts
__author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
"""
import json import json
import numpy as np import numpy as np
from numba import njit from numba import njit

6
Code/KCNA1_mutations/mut_Cb_stellate.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat May 8 16:20:11 2021 Script to simulate all KCNA1 mutations in Cb Stellate model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

6
Code/KCNA1_mutations/mut_Cb_stellate_Delta_Kv.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri Jun 18 10:47:40 2021 Script to simulate all KCNA1 mutations in Cb Stellate \Delta Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """

7
Code/KCNA1_mutations/mut_Cb_stellate_Kv.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat May 8 16:20:11 2021 Script to simulate all KCNA1 mutations in Cb Stellate +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np
from joblib import Parallel, delayed from joblib import Parallel, delayed
import json import json

7
Code/KCNA1_mutations/mut_FS_Kv.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat May 29 21:10:20 2021 Script to simulate all KCNA1 mutations in FS +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np
import os import os
import json import json

7
Code/KCNA1_mutations/mut_RS_inhib_Kv.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat May 29 21:10:48 2021 Script to simulate all KCNA1 mutations in RS Inhibitory +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np
from joblib import Parallel, delayed from joblib import Parallel, delayed
import os import os

7
Code/KCNA1_mutations/mut_RS_pyramidal_Kv.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat May 8 16:20:11 2021 Script to simulate all KCNA1 mutations in RS Pyramidal +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np
import os import os
import json import json

7
Code/KCNA1_mutations/mut_STN.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat May 8 16:20:11 2021 Script to simulate all KCNA1 mutations in STN model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np
from joblib import Parallel, delayed from joblib import Parallel, delayed
import json import json

7
Code/KCNA1_mutations/mut_STN_Delta_Kv.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri Jun 18 10:53:45 2021 Script to simulate all KCNA1 mutations in STN \Delta Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np
from joblib import Parallel, delayed from joblib import Parallel, delayed
import json import json

7
Code/KCNA1_mutations/mut_STN_Kv.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat May 8 16:20:11 2021 Script to simulate all KCNA1 mutations in STN +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np
from joblib import Parallel, delayed from joblib import Parallel, delayed
import json import json

6
Code/Neuron_models/Cb_stellate_Delta_Kv_model.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri Jun 18 10:47:40 2021 Script to run Cb Stellate \Delta Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """

6
Code/Neuron_models/Cb_stellate_Kv_model.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat May 8 16:20:11 2021 Script to run Cb Stellate +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

9
Code/Neuron_models/Cb_stellate_model.py
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# -*- coding: utf-8 -*-
"""
Script to run Cb Stellate model
__author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
"""
import numpy as np import numpy as np
import os import os
from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict

6
Code/Neuron_models/FS_Kv_model.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri May 7 23:37:22 2021 Script to run FS +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

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Code/Neuron_models/FS_model.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri May 7 23:37:22 2021 Script to run FS model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
Original Pospischil FS (no Kv1.1) __license__ = MIT
""" """
import numpy as np import numpy as np

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Code/Neuron_models/RS_inhib_Kv_model.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri May 7 23:37:22 2021 Script to run RS Inhibitory +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

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Code/Neuron_models/RS_inhib_model.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri May 7 23:37:22 2021 Script to run RS Inhibitory model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
Original Pospischil RS inhibitory (no Kv1.1) __license__ = MIT
""" """
import numpy as np import numpy as np
from numba import types from numba import types
from numba.typed import Dict from numba.typed import Dict

6
Code/Neuron_models/RS_pyramidal_Kv_model.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri May 7 23:37:22 2021 Script to run RS Pyramidal +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """

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Code/Neuron_models/RS_pyramidal_model.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri May 7 23:37:22 2021 Script to run RS Pyramidal model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

6
Code/Neuron_models/STN_Delta_Kv_model.py
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# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri Jun 18 10:53:45 2021 Script to run STN \Delta Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np
import os import os

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Code/Neuron_models/STN_Kv_model.py
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# -*- coding: utf-8 -*-
"""
Script to run STN +Kv1.1 model
__author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
"""
import numpy as np import numpy as np
import os import os
from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict

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Code/Neuron_models/STN_model.py
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# -*- coding: utf-8 -*-
"""
Script to run STN model
__author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
"""
import numpy as np import numpy as np
import os import os
from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict

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Code/Old/AUC_rheo_csv_gen.py
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import pandas as pd
import numpy as np
import h5py
import os
from ast import literal_eval
#%% create JSON files for each alt type
# folder
top_dir = '../Sensitivity_Analysis'
# for each alt_type create pd df
shift_AUC = pd.DataFrame()
shift_rheo = pd.DataFrame()
shift_fI = pd.DataFrame(dtype=object)
shift_I_mag = pd.DataFrame(dtype=object)
slope_AUC = pd.DataFrame()
slope_rheo = pd.DataFrame()
slope_fI = pd.DataFrame(dtype=object)
slope_I_mag = pd.DataFrame(dtype=object)
g_AUC = pd.DataFrame()
g_rheo = pd.DataFrame()
g_fI = pd.DataFrame(dtype=object)
g_I_mag = pd.DataFrame(dtype=object)
print('SA pd gen')
dir_names = ['RS_pyramidal', 'RS_inhib', 'FS','RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for dir_name in dir_names:
folder = os.path.join(top_dir, dir_name)
print(folder)
# for each alt_type create pd df
shift_AUC = pd.DataFrame()
shift_rheo = pd.DataFrame()
shift_fI = pd.DataFrame(dtype=object)
shift_I_mag = pd.DataFrame(dtype=object)
slope_AUC = pd.DataFrame()
slope_rheo = pd.DataFrame()
slope_fI = pd.DataFrame(dtype=object)
slope_I_mag = pd.DataFrame(dtype=object)
g_AUC = pd.DataFrame()
g_rheo = pd.DataFrame()
g_fI = pd.DataFrame(dtype=object)
g_I_mag = pd.DataFrame(dtype=object)
for root, dirs, files in os.walk(folder):
for file in files:
if file.endswith('.hdf5'):
with h5py.File(os.path.join(folder, file), "r+") as f:
alt = f['data'].attrs['alteration']
test = f['data'].attrs['alteration_info'].replace(' ', ',')
alt_info = literal_eval(test)
var = alt_info[0]
alt_type = alt_info[1]
if alt_type == 'shift':
shift_AUC.loc[alt, var] = f['analysis']['AUC'][()]
shift_rheo.loc[alt, var] = f['analysis']['rheobase'][()]
shift_fI.loc[alt, var] = 0
shift_fI = shift_fI.astype(object)
shift_fI.at[alt, var] = f['analysis']['F_inf'][:].tolist()
shift_I_mag.loc[alt, var] = 0
shift_I_mag = shift_I_mag.astype(object)
shift_I_mag.at[alt, var] = ((np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'],
(f['data'].attrs['I_high'] - f['data'].attrs['I_low']) /
f['data'].attrs['stim_num'])) * 1000).tolist() # nA
elif alt_type == 'slope':
slope_AUC.loc[alt, var] = f['analysis']['AUC'][()]
slope_rheo.loc[alt, var] = f['analysis']['rheobase'][()]
slope_fI.loc[alt, var] = 0
slope_fI = slope_fI.astype(object)
slope_fI.at[alt, var] = f['analysis']['F_inf'][:].tolist()
slope_I_mag.loc[alt, var] = 0
slope_I_mag = slope_I_mag.astype(object)
slope_I_mag.at[alt, var] = ((np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'],
(f['data'].attrs['I_high'] - f['data'].attrs['I_low']) /
f['data'].attrs['stim_num'])) * 1000).tolist()
elif alt_type == 'g':
g_AUC.loc[alt, var] = f['analysis']['AUC'][()]
g_rheo.loc[alt, var] = f['analysis']['rheobase'][()]
g_fI.loc[alt, var] = 0
g_fI = g_fI.astype(object)
g_fI.at[alt, var] = f['analysis']['F_inf'][:].tolist()
g_I_mag.loc[alt, var] = 0
g_I_mag = g_I_mag.astype(object)
g_I_mag.at[alt, var] = ((np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'],
(f['data'].attrs['I_high'] - f['data'].attrs['I_low']) /
f['data'].attrs['stim_num'])) * 1000).tolist()
else:
print(file, 'Unknown alteration type')
# save df with folder+alt_type
save_folder = os.path.join(top_dir, 'SA_summary_df')
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
shift_AUC.to_json(os.path.join(save_folder, '{}_shift_AUC.json'.format(dir_name)))
shift_rheo.to_json(os.path.join(save_folder, '{}_shift_rheo.json'.format(dir_name)))
shift_fI.to_json(os.path.join(save_folder, '{}_shift_fI.json'.format(dir_name)))
shift_I_mag.to_json(os.path.join(save_folder, '{}_shift_I_mag.json'.format(dir_name)))
slope_AUC.to_json(os.path.join(save_folder, '{}_slope_AUC.json'.format(dir_name)))
slope_rheo.to_json(os.path.join(save_folder, '{}_slope_rheo.json'.format(dir_name)))
slope_fI.to_json(os.path.join(save_folder, '{}_slope_fI.json'.format(dir_name)))
slope_I_mag.to_json(os.path.join(save_folder, '{}_slope_I_mag.json'.format(dir_name)))
g_AUC.to_json(os.path.join(save_folder, '{}_g_AUC.json'.format(dir_name)))
g_rheo.to_json(os.path.join(save_folder, '{}_g_rheo.json'.format(dir_name)))
g_fI.to_json(os.path.join(save_folder, '{}_g_fI.json'.format(dir_name)))
g_I_mag.to_json(os.path.join(save_folder, '{}_g_I_mag.json'.format(dir_name)))
# requires _shift_rheo.json
#%% correlation analysis ###########################################################################################
models = ['RS_pyramidal', 'RS_inhib', 'FS','RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
'STN_Kv', 'STN_Kv_only']
model_labels = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal +$K_V1.1$', 'RS_inhib +$K_V1.1$', 'FS +$K_V1.1$','Cb stellate', 'Cb stellate +$K_V1.1$',
'Cb stellate $\Delta$$K_V1.1$', 'STN', 'STN +$K_V1.1$', 'STN $\Delta$$K_V1.1$']
var_dict = {}
var_dict['RS_pyramidal'] = ['m', 'h', 'n']
var_dict['RS_inhib'] = ['m', 'h', 'n']
var_dict['FS'] = ['m', 'h', 'n']
var_dict['RS_pyramidal_Kv'] = ['m', 'h', 'n', 's', 'u']
var_dict['RS_inhib_Kv'] = ['m', 'h', 'n', 's', 'u']
var_dict['FS_Kv'] = ['m', 'h', 'n', 's', 'u']
var_dict['Cb_stellate'] = ['m', 'h', 'n', 'a', 'b']
var_dict['Cb_stellate_Kv'] = ['m', 'h', 'n', 'a', 'b', 's', 'u']
var_dict['Cb_stellate_Kv_only'] = ['m', 'h', 'n', 's', 'u']
var_dict['STN'] = ['m', 'h', 'n', 'n_A', 'h_A', 's', 'u']
var_dict['STN_Kv'] = ['m', 'h', 'n', 'n_A', 'h_A', 's', 'u']
var_dict['STN_Kv_only'] = ['m', 'h', 'n', 's', 'u']
folder = './Sensitivity_Analysis/corr_df'
if not os.path.isdir(folder):
os.makedirs(folder)
# % rheo
# % shift
shift_m = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_shift_rheo.json'.format(mod)) as json_file:
shift_m[mod] = pd.read_json(json_file)['m'].sort_index()
shift_m.sort_index(inplace=True)
shift_m.replace(0., np.NaN, inplace=True)
shift_m = (shift_m - shift_m.loc[0, :])
shift_m.to_json(os.path.join(folder, 'shift_m_rheo.json'))
shift_h = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_shift_rheo.json'.format(mod)) as json_file:
shift_h[mod] = pd.read_json(json_file)['h'].sort_index()
shift_h.sort_index(inplace=True)
shift_h.replace(0., np.NaN, inplace=True)
shift_h = (shift_h - shift_h.loc[0, :])
shift_h.to_json(os.path.join(folder, 'shift_h_rheo.json'))
shift_n = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_shift_rheo.json'.format(mod)) as json_file:
shift_n[mod] = pd.read_json(json_file)['n'].sort_index()
shift_n.sort_index(inplace=True)
shift_n.replace(0., np.NaN, inplace=True)
shift_n = (shift_n - shift_n.loc[0, :])
shift_n.to_json(os.path.join(folder, 'shift_n_rheo.json'))
shift_s = pd.DataFrame()
models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_shift_rheo.json'.format(mod)) as json_file:
shift_s[mod] = pd.read_json(json_file)['s'].sort_index()
shift_s.sort_index(inplace=True)
shift_s.replace(0., np.NaN, inplace=True)
shift_s = (shift_s - shift_s.loc[0, :])
shift_s.to_json(os.path.join(folder, 'shift_s_rheo.json'))
shift_u = pd.DataFrame()
models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_shift_rheo.json'.format(mod)) as json_file:
shift_u[mod] = pd.read_json(json_file)['u'].sort_index()
shift_u.sort_index(inplace=True)
shift_u.replace(0., np.NaN, inplace=True)
shift_u = (shift_u - shift_u.loc[0, :])
shift_u.to_json(os.path.join(folder, 'shift_u_rheo.json'))
shift_a = pd.DataFrame()
models = ['Cb_stellate', 'Cb_stellate_Kv']
for mod in models:
with open('./SA_summary_df/{}_shift_rheo.json'.format(mod)) as json_file:
shift_a[mod] = pd.read_json(json_file)['n_A'].sort_index()
models = ['STN', 'STN_Kv']
for mod in models:
with open('./SA_summary_df/{}_shift_rheo.json'.format(mod)) as json_file:
shift_a[mod] = pd.read_json(json_file)['a'].sort_index()
shift_a.sort_index(inplace=True)
shift_a.replace(0., np.NaN, inplace=True)
shift_a = (shift_a - shift_a.loc[0, :])
shift_a.to_json(os.path.join(folder, 'shift_a_rheo.json'))
shift_b = pd.DataFrame()
models = ['Cb_stellate', 'Cb_stellate_Kv']
for mod in models:
with open('./SA_summary_df/{}_shift_rheo.json'.format(mod)) as json_file:
shift_b[mod] = pd.read_json(json_file)['h_A'].sort_index()
models = ['STN', 'STN_Kv']
for mod in models:
with open('./SA_summary_df/{}_shift_rheo.json'.format(mod)) as json_file:
shift_b[mod] = pd.read_json(json_file)['b'].sort_index()
shift_b.sort_index(inplace=True)
shift_b.replace(0., np.NaN, inplace=True)
shift_b = (shift_b - shift_b.loc[0, :])
shift_b.to_json(os.path.join(folder, 'shift_b_rheo.json'))
# % slope
slope_m = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_slope_rheo.json'.format(mod)) as json_file:
slope_m[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['m'].sort_index()
slope_m.index = slope_m.index.map(float)
slope_m.sort_index(inplace=True)
slope_m.replace(0., np.NaN, inplace=True)
slope_m = (slope_m - slope_m.loc[1.0, :])
slope_m.to_json(os.path.join(folder, 'slope_m_rheo.json'))
slope_h = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_slope_rheo.json'.format(mod)) as json_file:
slope_h[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['h'].sort_index()
slope_h.index = slope_h.index.map(float)
slope_h.sort_index(inplace=True)
slope_h.replace(0., np.NaN, inplace=True)
slope_h = (slope_h - slope_h.loc[1.0, :])
slope_h.to_json(os.path.join(folder, 'slope_h_rheo.json'))
slope_n = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_slope_rheo.json'.format(mod)) as json_file:
slope_n[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['n'].sort_index()
slope_n.index = slope_n.index.map(float)
slope_n.sort_index(inplace=True)
slope_n.replace(0., np.NaN, inplace=True)
slope_n = (slope_n - slope_n.loc[1.0, :])
slope_n.to_json(os.path.join(folder, 'slope_n_rheo.json'))
slope_s = pd.DataFrame()
models = [ 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_slope_rheo.json'.format(mod)) as json_file:
slope_s[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['s'].sort_index()
slope_s.index = slope_s.index.map(float)
slope_s.sort_index(inplace=True)
slope_s.replace(0., np.NaN, inplace=True)
slope_s = (slope_s - slope_s.loc[1.0, :])
slope_s.to_json(os.path.join(folder, 'slope_s_rheo.json'))
slope_u = pd.DataFrame()
models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_slope_rheo.json'.format(mod)) as json_file:
slope_u[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['u'].sort_index()
slope_u.index = slope_u.index.map(float)
slope_u.sort_index(inplace=True)
slope_u.replace(0., np.NaN, inplace=True)
slope_u = (slope_u - slope_u.loc[1.0, :])
slope_u.to_json(os.path.join(folder, 'slope_u_rheo.json'))
slope_a = pd.DataFrame()
models = ['Cb_stellate', 'Cb_stellate_Kv']
for mod in models:
with open('./SA_summary_df/{}_slope_rheo.json'.format(mod)) as json_file:
slope_a[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['n_A'].sort_index()
models = ['STN', 'STN_Kv']
for mod in models:
with open('./SA_summary_df/{}_slope_rheo.json'.format(mod)) as json_file:
slope_a[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['a'].sort_index()
slope_a.index = slope_a.index.map(float)
slope_a.sort_index(inplace=True)
slope_a.replace(0., np.NaN, inplace=True)
slope_a = (slope_a - slope_a.loc[1.0, :])
slope_a.to_json(os.path.join(folder, 'slope_a_rheo.json'))
slope_b = pd.DataFrame()
models = ['Cb_stellate', 'Cb_stellate_Kv']
for mod in models:
with open('./SA_summary_df/{}_slope_rheo.json'.format(mod)) as json_file:
slope_b[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['h_A'].sort_index()
models = ['STN', 'STN_Kv']
for mod in models:
with open('./SA_summary_df/{}_slope_rheo.json'.format(mod)) as json_file:
slope_b[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['b'].sort_index()
slope_b.index = slope_b.index.map(float)
slope_b.sort_index(inplace=True)
slope_b.replace(0., np.NaN, inplace=True)
slope_b = (slope_b - slope_b.loc[1.0, :]) #
slope_b.to_json(os.path.join(folder, 'slope_b_rheo.json'))
# % g
g_Na = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_g_rheo.json'.format(mod)) as json_file:
g_Na[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['Na'].sort_index()
g_Na.index = g_Na.index.map(float)
g_Na.sort_index(inplace=True)
g_Na.replace(0., np.NaN, inplace=True)
g_Na = (g_Na - g_Na.loc[1.0, :])
g_Na.to_json(os.path.join(folder, 'g_Na_rheo.json'))
g_Kd = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_g_rheo.json'.format(mod)) as json_file:
g_Kd[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['Kd'].sort_index()
g_Kd.index = g_Kd.index.map(float)
g_Kd.sort_index(inplace=True)
g_Kd.replace(0., np.NaN, inplace=True)
g_Kd = (g_Kd - g_Kd.loc[1.0, :])
g_Kd.to_json(os.path.join(folder, 'g_Kd_rheo.json'))
g_Kv = pd.DataFrame()
models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_g_rheo.json'.format(mod)) as json_file:
g_Kv[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['Kv'].sort_index()
g_Kv.index = g_Kv.index.map(float)
g_Kv.sort_index(inplace=True)
g_Kv.replace(0., np.NaN, inplace=True)
g_Kv = (g_Kv - g_Kv.loc[1.0, :])
g_Kv.to_json(os.path.join(folder, 'g_Kv_rheo.json'))
g_A = pd.DataFrame()
models = ['Cb_stellate', 'Cb_stellate_Kv', 'STN', 'STN_Kv']
for mod in models:
with open('./SA_summary_df/{}_g_rheo.json'.format(mod)) as json_file:
g_A[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['A'].sort_index()
g_A.index = g_A.index.map(float)
g_A.sort_index(inplace=True)
g_A.replace(0., np.NaN, inplace=True)
g_A = (g_A - g_A.loc[1.0, :])
g_A.to_json(os.path.join(folder, 'g_A_rheo.json'))
g_Leak = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_g_rheo.json'.format(mod)) as json_file:
g_Leak[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['Leak'].sort_index()
g_Leak.index = g_Leak.index.map(float)
g_Leak.sort_index(inplace=True)
g_Leak.replace(0., np.NaN, inplace=True)
g_Leak = (g_Leak - g_Leak.loc[1.0, :])
g_Leak.to_json(os.path.join(folder, 'g_Leak_rheo.json'))
# % AUC
# % shift
shift_m = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_shift_AUC.json'.format(mod)) as json_file:
shift_m[mod] = pd.read_json(json_file)['m'].sort_index()
shift_m.sort_index(inplace=True)
shift_m.replace(0., np.NaN, inplace=True)
shift_m = (shift_m - shift_m.loc[0, :]) / shift_m.loc[0, :]
shift_m.to_json(os.path.join(folder, 'shift_m_AUC.json'))
shift_h = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_shift_AUC.json'.format(mod)) as json_file:
shift_h[mod] = pd.read_json(json_file)['h'].sort_index()
shift_h.sort_index(inplace=True)
shift_h.replace(0., np.NaN, inplace=True)
shift_h = (shift_h - shift_h.loc[0, :]) / shift_h.loc[0, :]
shift_h.to_json(os.path.join(folder, 'shift_h_AUC.json'))
shift_n = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_shift_AUC.json'.format(mod)) as json_file:
shift_n[mod] = pd.read_json(json_file)['n'].sort_index()
shift_n.sort_index(inplace=True)
shift_n.replace(0., np.NaN, inplace=True)
shift_n = (shift_n - shift_n.loc[0, :]) / shift_n.loc[0, :]
shift_n.to_json(os.path.join(folder, 'shift_n_AUC.json'))
shift_s = pd.DataFrame()
models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_shift_AUC.json'.format(mod)) as json_file:
shift_s[mod] = pd.read_json(json_file)['s'].sort_index()
shift_s.sort_index(inplace=True)
shift_s.replace(0., np.NaN, inplace=True)
shift_s = (shift_s - shift_s.loc[0, :]) / shift_s.loc[0, :]
shift_s.to_json(os.path.join(folder, 'shift_s_AUC.json'))
shift_u = pd.DataFrame()
models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_shift_AUC.json'.format(mod)) as json_file:
shift_u[mod] = pd.read_json(json_file)['u'].sort_index()
shift_u.sort_index(inplace=True)
shift_u.replace(0., np.NaN, inplace=True)
shift_u = (shift_u - shift_u.loc[0, :]) / shift_u.loc[0, :]
shift_u.to_json(os.path.join(folder, 'shift_u_AUC.json'))
shift_a = pd.DataFrame()
models = ['Cb_stellate', 'Cb_stellate_Kv']
for mod in models:
with open('./SA_summary_df/{}_shift_AUC.json'.format(mod)) as json_file:
shift_a[mod] = pd.read_json(json_file)['n_A'].sort_index()
models = ['STN', 'STN_Kv']
for mod in models:
with open('./SA_summary_df/{}_shift_AUC.json'.format(mod)) as json_file:
shift_a[mod] = pd.read_json(json_file)['a'].sort_index()
shift_a.sort_index(inplace=True)
shift_a.replace(0., np.NaN, inplace=True)
shift_a = (shift_a - shift_a.loc[0, :]) / shift_a.loc[0, :]
shift_a.to_json(os.path.join(folder, 'shift_a_AUC.json'))
shift_b = pd.DataFrame()
models = ['Cb_stellate', 'Cb_stellate_Kv']
for mod in models:
with open('./SA_summary_df/{}_shift_AUC.json'.format(mod)) as json_file:
shift_b[mod] = pd.read_json(json_file)['h_A'].sort_index()
models = ['STN', 'STN_Kv']
for mod in models:
with open('./SA_summary_df/{}_shift_AUC.json'.format(mod)) as json_file:
shift_b[mod] = pd.read_json(json_file)['b'].sort_index()
shift_b.sort_index(inplace=True)
shift_b.replace(0., np.NaN, inplace=True)
shift_b = (shift_b - shift_b.loc[0, :]) / shift_b.loc[0, :]
shift_b.to_json(os.path.join(folder, 'shift_b_AUC.json'))
# % slope
slope_m = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_slope_AUC.json'.format(mod)) as json_file:
slope_m[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['m'].sort_index()
slope_m.index = slope_m.index.map(float)
slope_m.sort_index(inplace=True)
slope_m.replace(0., np.NaN, inplace=True)
slope_m = (slope_m - slope_m.loc[1.0, :]) / slope_m.loc[1.0, :]
slope_m.to_json(os.path.join(folder, 'slope_m_AUC.json'))
slope_h = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_slope_AUC.json'.format(mod)) as json_file:
slope_h[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['h'].sort_index()
slope_h.index = slope_h.index.map(float)
slope_h.sort_index(inplace=True)
slope_h.replace(0., np.NaN, inplace=True)
slope_h = (slope_h - slope_h.loc[1.0, :]) / slope_h.loc[1.0, :]
slope_h.to_json(os.path.join(folder, 'slope_h_AUC.json'))
slope_n = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_slope_AUC.json'.format(mod)) as json_file:
slope_n[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['n'].sort_index()
slope_n.index = slope_n.index.map(float)
slope_n.sort_index(inplace=True)
slope_n.replace(0., np.NaN, inplace=True)
slope_n = (slope_n - slope_n.loc[1.0, :]) / slope_n.loc[1.0, :]
slope_n.to_json(os.path.join(folder, 'slope_n_AUC.json'))
slope_s = pd.DataFrame()
models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_slope_AUC.json'.format(mod)) as json_file:
slope_s[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['s'].sort_index()
slope_s.index = slope_s.index.map(float)
slope_s.sort_index(inplace=True)
slope_s.replace(0., np.NaN, inplace=True)
slope_s = (slope_s - slope_s.loc[1.0, :]) / slope_s.loc[1.0, :]
slope_s.to_json(os.path.join(folder, 'slope_s_AUC.json'))
slope_u = pd.DataFrame()
models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_slope_AUC.json'.format(mod)) as json_file:
slope_u[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['u'].sort_index()
slope_u.index = slope_u.index.map(float)
slope_u.sort_index(inplace=True)
slope_u.replace(0., np.NaN, inplace=True)
slope_u = (slope_u - slope_u.loc[1.0, :]) / slope_u.loc[1.0, :]
slope_u.to_json(os.path.join(folder, 'slope_u_AUC.json'))
slope_a = pd.DataFrame()
models = ['Cb_stellate', 'Cb_stellate_Kv']
for mod in models:
with open('./SA_summary_df/{}_slope_AUC.json'.format(mod)) as json_file:
slope_a[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['n_A'].sort_index()
models = ['STN', 'STN_Kv']
for mod in models:
with open('./SA_summary_df/{}_slope_AUC.json'.format(mod)) as json_file:
slope_a[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['a'].sort_index()
slope_a.index = slope_a.index.map(float)
slope_a.sort_index(inplace=True)
slope_a.replace(0., np.NaN, inplace=True)
slope_a = (slope_a - slope_a.loc[1.0, :]) / slope_a.loc[1.0, :]
slope_a.to_json(os.path.join(folder, 'slope_a_AUC.json'))
slope_b = pd.DataFrame()
models = ['Cb_stellate', 'Cb_stellate_Kv']
for mod in models:
with open('./SA_summary_df/{}_slope_AUC.json'.format(mod)) as json_file:
slope_b[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['h_A'].sort_index()
models = ['STN', 'STN_Kv']
for mod in models:
with open('./SA_summary_df/{}_slope_AUC.json'.format(mod)) as json_file:
slope_b[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['b'].sort_index()
slope_b.index = slope_b.index.map(float)
slope_b.sort_index(inplace=True)
slope_b.replace(0., np.NaN, inplace=True)
slope_b = (slope_b - slope_b.loc[1.0, :]) / slope_b.loc[1.0, :]
slope_b.to_json(os.path.join(folder, 'slope_b_AUC.json'))
# % g
g_Na = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_g_AUC.json'.format(mod)) as json_file:
g_Na[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['Na'].sort_index()
g_Na.index = g_Na.index.map(float)
g_Na.sort_index(inplace=True)
g_Na.replace(0., np.NaN, inplace=True)
g_Na = (g_Na - g_Na.loc[1.0, :]) / g_Na.loc[1.0, :]
g_Na.to_json(os.path.join(folder, 'g_Na_AUC.json'))
g_Kd = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_g_AUC.json'.format(mod)) as json_file:
g_Kd[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['Kd'].sort_index()
g_Kd.index = g_Kd.index.map(float)
g_Kd.sort_index(inplace=True)
g_Kd.replace(0., np.NaN, inplace=True)
g_Kd = (g_Kd - g_Kd.loc[1.0, :]) / g_Kd.loc[1.0, :]
g_Kd.to_json(os.path.join(folder, 'g_Kd_AUC.json'))
g_Kv = pd.DataFrame()
models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_g_AUC.json'.format(mod)) as json_file:
g_Kv[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['Kv'].sort_index()
g_Kv.index = g_Kv.index.map(float)
g_Kv.sort_index(inplace=True)
g_Kv.replace(0., np.NaN, inplace=True)
g_Kv = (g_Kv - g_Kv.loc[1.0, :]) / g_Kv.loc[1.0, :]
g_Kv.to_json(os.path.join(folder, 'g_Kv_AUC.json'))
g_A = pd.DataFrame()
models = ['Cb_stellate', 'Cb_stellate_Kv', 'STN', 'STN_Kv']
for mod in models:
with open('./SA_summary_df/{}_g_AUC.json'.format(mod)) as json_file:
g_A[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['A'].sort_index()
g_A.index = g_A.index.map(float)
g_A.sort_index(inplace=True)
g_A.replace(0., np.NaN, inplace=True)
g_A = (g_A - g_A.loc[1.0, :]) / g_A.loc[1.0, :]
g_A.to_json(os.path.join(folder, 'g_A_AUC.json'))
g_Leak = pd.DataFrame()
models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
for mod in models:
with open('./SA_summary_df/{}_g_AUC.json'.format(mod)) as json_file:
g_Leak[mod] = pd.read_json(json_file, convert_axes=False, convert_dates=False)['Leak'].sort_index()
g_Leak.index = g_Leak.index.map(float)
g_Leak.sort_index(inplace=True)
g_Leak.replace(0., np.NaN, inplace=True)
g_Leak = (g_Leak - g_Leak.loc[1.0, :]) / g_Leak.loc[1.0, :]
g_Leak.to_json(os.path.join(folder, 'g_Leak_AUC.json'))
#%% todo: CREATE CSV FILES ###################################################################################################
#% AUC
# from ./Code/From_Neuronal_models/tau_analysis/AUC_corr_df_gen.py
AUC_shift_df = pd.read_json('./Sensitivity_analysis/shift_box_kendall_corr_rel.json', orient='records')
AUC_slope_df = pd.read_json('./Sensitivity_analysis/slope_box_kendall_corr_rel.json', orient='records') #, lines=True)
AUC_g_df = pd.read_json('./Sensitivity_analysis/g_box_kendall_corr_rel.json', orient='records')
# for i in np.array(['FS', 'IB', 'RS inhibitory', 'RS pyramidal']):
# AUC_shift_df.loc[AUC_shift_df['model'] == i, 'model'] = i + ' +$K_V1.1$' # ''+$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'
# AUC_slope_df.loc[AUC_slope_df['model'] == i, 'model'] = i + ' +$K_V1.1$' #' +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'
# AUC_g_df.loc[AUC_g_df['model'] == i, 'model'] = i + ' +$K_V1.1$' #' +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'
AUC_shift_df_posp = pd.read_json('./Sensitivity_analysis/shift_box_kendall_corr_rel_pospischil.json', orient='records')
AUC_slope_df_posp = pd.read_json('./Sensitivity_analysis/slope_box_kendall_corr_rel_pospischil.json', orient='records') #, lines=True)
AUC_g_df_posp = pd.read_json('./Sensitivity_analysis/g_box_kendall_corr_rel_pospischil.json', orient='records')
AUC_shift_df = AUC_shift_df.append(AUC_shift_df_posp)
AUC_slope_df = AUC_slope_df.append(AUC_slope_df_posp)
AUC_g_df = AUC_g_df.append(AUC_g_df_posp)
AUC_shift_df.to_csv('AUC_shift_corr.csv')
AUC_slope_df.to_csv('AUC_scale_corr.csv')
AUC_g_df.to_csv('AUC_g_corr.csv')
#% rheo
rheo_shift_df = pd.read_json('./Sensitivity_analysis/rheo_shift_box_kendall_corr.json', orient='records')
rheo_slope_df = pd.read_json('./Sensitivity_analysis/rheo_slope_box_kendall_corr.json', orient='records') #, lines=True)
rheo_g_df = pd.read_json('./Sensitivity_analysis/rheo_g_box_kendall_corr.json', orient='records')
for i in np.array(['FS', 'IB', 'RS inhibitory', 'RS pyramidal']):
rheo_shift_df.loc[rheo_shift_df['model'] == i, 'model'] = i + ' +$K_V1.1$' #' +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'
rheo_slope_df.loc[rheo_slope_df['model'] == i, 'model'] = i + ' +$K_V1.1$' #' +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'
rheo_g_df.loc[rheo_g_df['model'] == i, 'model'] = i + ' +$K_V1.1$' #' +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'
rheo_shift_df_posp = pd.read_json('./Sensitivity_analysis/rheo_shift_box_kendall_corr_pospischil.json', orient='records')
rheo_slope_df_posp = pd.read_json('./Sensitivity_analysis/rheo_slope_box_kendall_corr_pospischil.json', orient='records') #, lines=True)
rheo_g_df_posp = pd.read_json('./Sensitivity_analysis/rheo_g_box_kendall_corr_pospischil.json', orient='records')
rheo_shift_df = rheo_shift_df.append(rheo_shift_df_posp)
rheo_slope_df = rheo_slope_df.append(rheo_slope_df_posp)
rheo_g_df = rheo_g_df.append(rheo_g_df_posp)
rheo_shift_df.to_csv('rheo_shift_corr.csv')
rheo_slope_df.to_csv('rheo_scale_corr.csv')
rheo_g_df.to_csv('rheo_g_corr.csv')

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Code/Old/FS_mut_pd.py
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@ -1,134 +0,0 @@
# -*- coding: utf-8 -*-
"""
Created on Sat May 29 21:10:20 2021
@author: nils
"""
import numpy as np
import os
import h5py
import json
from Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 10
num_current = 7
C, surf_area = capacitance(56.9, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
tau_max_p = 502
V_init = -70
V_T = -57.9
# initialize arrays
current = np.zeros((num_current, stim_num))
gating_out = np.zeros((num_gating, stim_num))
# gating parameters
b_param = {}
b_param['m'] = np.array([-34.33054521, -8.21450277, 1.42295686])
b_param['h'] = np.array([-34.51951036, 4.04059373, 1., 0.])
b_param['n'] = np.array([-63.76096946, -13.83488194, 7.35347425])
b_param['q'] = np.array([-39.03684525, -5.57756176, 2.25190197])
b_param['r'] = np.array([-57.37, 20.98, 1.])
b_param['p'] = np.array([-45., -9.9998807337, 1.])
b_param['s'] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
mut_act_Vhalf_wt = -30.01851851851851
mut_act_k_wt = -7.7333333333333325
s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
s_diff_k = mut_act_k_wt - b_param['s'][1]
b_param['s'][1] = b_param['s'][1] + s_diff_k
b_param['u'][1] = b_param['u'][1] + s_diff_k
b_param['s'][0] = b_param['s'][0] + s_diff_Vhalf
b_param['u'][0] = b_param['u'][0] + s_diff_Vhalf
b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
b_param['s_mut'][0] = b_param['s_mut'][0] + s_diff_Vhalf
b_param['u_mut'][0] = b_param['u_mut'][0] + s_diff_Vhalf
# reversal potentials
E["Na"] = 50.
E["K"] = -90.
E["Ca"] = 120.
E["Leak"] = -70.4
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = True
currents_included["L"] = False
currents_included["M"] = True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.1
g["Na"] = 58. * surf_area
g["Kd"] = 3.9 * (1 - Kv_ratio) * surf_area
g["M"] = 0.075 * surf_area
if currents_included["Kv_mut"] == True:
g["Kv"] = 3.9 * Kv_ratio / 2 * surf_area
else:
g["Kv"] = 3.9 * Kv_ratio / 2 * surf_area * 2
g["Kv_mut"] = 3.9 * Kv_ratio / 2 * surf_area
g["L"] = 0. * surf_area
g["Leak"] = 0.038 * surf_area
# save folder
folder = './KCNA1_mutations/FS'
if not os.path.isdir(folder):
os.makedirs(folder)
# mutation properties
mutations = json.load(open("../KCNA1_mutations/mutations_effects_dict.json"))
# prominence = 50
# # min_spike_height = 0
# desired_AUC_width = high/5
#
# Parallel(n_jobs=8, verbose=9)(
# delayed(Pospischil_multi)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p,
# shift, scale, b_param, slope_shift, gating_out, current, prominence,
# desired_AUC_width, mutations, mut, folder, high,low, number_steps, initial_period, sec, save_gating=True)
# for mut in list(mutations.keys()))
#%% Get pd Dataframes for certain variables
import pandas as pd
AUC = pd.DataFrame(columns=mutations.keys())
AUC_rel = pd.DataFrame(columns=mutations.keys())
rheobase = pd.DataFrame(columns=mutations.keys())
rheobase_fit = pd.DataFrame(columns=mutations.keys())
rheobase_null_fit = pd.DataFrame(columns=mutations.keys())
for mut in list(mutations.keys()):
fname = os.path.join(folder, "{}.hdf5".format(mut.replace(" ", "_")))
f = h5py.File(fname, "r")
AUC['{}'.format(mut.replace(" ", "_"))] = f['analysis']['AUC']
AUC_rel['{}'.format(mut.replace(" ", "_"))] = f['analysis']['AUC_rel']
rheobase['{}'.format(mut.replace(" ", "_"))] = f['analysis']['rheobase']
rheobase_fit['{}'.format(mut.replace(" ", "_"))] = f['analysis']['rheobase_fit']
rheobase_null_fit['{}'.format(mut.replace(" ", "_"))] = f['analysis']['rheobase_null_fit']
top_dir = '../KCNA1_mut'
model_name = 'FS'
save_folder = os.path.join(top_dir, 'mut_summary_df')
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
AUC.to_json(os.path.join(save_folder, '{}_AUC.json'.format(model_name)))
AUC_rel.to_json(os.path.join(save_folder, '{}_AUC_rel.json'.format(model_name)))
rheobase.to_json(os.path.join(save_folder, '{}_rheobase.json'.format(model_name)))
rheobase_fit.to_json(os.path.join(save_folder, '{}_rheobase_fit.json'.format(model_name)))
rheobase_null_fit.to_json(os.path.join(save_folder, '{}_rheobase_null_fit.json'.format(model_name)))

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Code/Old/Firing_csv_generation.py
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@ -1,227 +0,0 @@
import h5py
import numpy as np
import os
import pandas as pd
spiking = pd.DataFrame(columns=['t', 'RS Pyramidal','RS Inhibitory','FS','IB',
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'IB +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'Cb stellate','Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$','STN','STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'])
values = pd.DataFrame(index = ['spike_ind', 'ramp_up','ramp_down'], columns=['RS Pyramidal','RS Inhibitory','FS','IB',
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'IB +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$','Cb stellate','Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$','STN','STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'])
F = pd.DataFrame(columns=['I','I_inhib', 'RS Pyramidal','RS Inhibitory','FS','IB',
'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'IB +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$','Cb stellate','Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$','STN','STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'])
# RS Pyramidal #########################################################################################################
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/RS_pyramidal.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 60
spiking['RS Pyramidal'] = f['data']['V_m'][ind][start:]
spiking['t'] = time
F['I'] = I_mag
F['RS Pyramidal'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'RS Pyramidal'] = I_mag[ind]
values.loc['ramp_up', 'RS Pyramidal'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'RS Pyramidal'] = f['analysis']['ramp_I_down'][()]
# RS Inhibitory #########################################################################################################
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/RS_inhib.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 25
spiking['RS Inhibitory'] = f['data']['V_m'][ind][start:]
F['I_inhib'] = I_mag
F['RS Inhibitory'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'RS Inhibitory'] = I_mag[ind]
values.loc['ramp_up', 'RS Inhibitory'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'RS Inhibitory'] = f['analysis']['ramp_I_down'][()]
# FS#####################################################################################################################
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/FS.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 50
spiking['FS'] = f['data']['V_m'][ind][start:]
F['FS'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'FS'] = I_mag[ind]
values.loc['ramp_up', 'FS'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'FS'] = f['analysis']['ramp_I_down'][()]
# RS Pyramidal Kv #########################################################################################################
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/RS_pyramidal_Kv.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 60
spiking['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['data']['V_m'][ind][start:]
spiking['t'] = time
F['I'] = I_mag
F['RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = I_mag[ind]
values.loc['ramp_up', 'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'RS Pyramidal +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_down'][()]
# RS Inhibitory #########################################################################################################
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/RS_inhib_Kv.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 50
spiking['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['data']['V_m'][ind][start:]
F['I_inhib'] = I_mag
F['RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = I_mag[ind]
values.loc['ramp_up', 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'RS Inhibitory +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_down'][()]
# FS#####################################################################################################################
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/FS_Kv.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 50
spiking['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['data']['V_m'][ind][start:]
F['FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = I_mag[ind]
values.loc['ramp_up', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'FS +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_down'][()]
# Cb Stellate cell ######################################################################################################
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/Cb_stellate.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 60
spiking['Cb stellate'] = f['data']['V_m'][ind][start:]
F['Cb stellate'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'Cb stellate'] = I_mag[ind]
values.loc['ramp_up', 'Cb stellate'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'Cb stellate'] = f['analysis']['ramp_I_down'][()]
# 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'
# Cb stellate Kv #############################################################################################
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/Cb_stellate_Kv.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 130
spiking['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['data']['V_m'][ind][start:]
F['Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = I_mag[ind]
values.loc['ramp_up', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'Cb stellate +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_down'][()]
# 'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'
# Cb stellate Kv only ####################################################################################
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/Cb_stellate_Delta_Kv.hdf5.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 75
spiking['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['data']['V_m'][ind][start:]
F['Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = I_mag[ind]
values.loc['ramp_up', 'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'Cb stellate $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_down'][()]
# STN ###################################################################################################################
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/STN.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 25
spiking['STN'] = f['data']['V_m'][ind][start:]
F['STN'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'STN'] = I_mag[ind]
values.loc['ramp_up', 'STN'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'STN'] = f['analysis']['ramp_I_down'][()]
# 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$',
# STN Kv ##################################################################################################
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/STN_Kv.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 95
spiking['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['data']['V_m'][ind][start:]
F['STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = I_mag[ind]
values.loc['ramp_up', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'STN +$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_down'][()]
# 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'
# STN Kv only #############################################################################################3
script_dir = os.path.dirname(os.path.realpath("__file__"))
fname = os.path.join(script_dir, '../Neuron_models/STN_Delta_Kv.hdf5')
f = h5py.File(fname, "r")
I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'], (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num'])*1000
start = np.int(f['data'].attrs['initial_period']*1/f['data'].attrs['dt'])
stim_len = np.int((f['data'].attrs['stim_time'] - start) *f['data'].attrs['dt'])
time = np.arange(0, stim_len,f['data'].attrs['dt'])
ind = 80
spiking['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['data']['V_m'][ind][start:]
F['STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['F_inf'][:]
values.loc['spike_ind', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = I_mag[ind]
values.loc['ramp_up', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_up'][()]
values.loc['ramp_down', 'STN $\Delta$$\mathrm{K}_{\mathrm{V}}\mathrm{1.1}$'] = f['analysis']['ramp_I_down'][()]
#%%
spiking.to_csv('model_spiking.csv')
values.to_csv('firing_values.csv')
F.to_csv('model_F_inf.csv')

18
Code/Old/README.md
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@ -1,18 +0,0 @@
### Python code repository for Koch et al. 2022
Modelling on the effects of ion current property changes on firing behaviour in diverse neuronal models and in with Kv1.1 mutations that cause episodic ataxia type 1.
## ./Functions
- Python function files for models
- RS pyramidal, RS inhibitory and FS from Pospischil et al. (2008)
- Cb stellate from Alexander et al. (2019) with and without added Kv1.1
- STN from Otsuka et al. (2004) with and without added Kv1.1
- Utility_fxns.py for common useful functions
## ./Neuron_models
- Scripts for simulation of each model to current steps and ramp currents
## ./Sensitivity_Analysis
- Scripts for sensitivity analysis in each model
## ./KCNA1_mutations
- Scripts for simulation of KCNA1 mutations described in Lauxmann et al. (2021)

6
Code/Sensitivity_Analysis/SA_Cb_stellate.py
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@ -1,8 +1,10 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri Jun 4 08:24:33 2021 Script to run sensitivity analysis for Cb Stellate model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

6
Code/Sensitivity_Analysis/SA_Cb_stellate_Delta_Kv.py
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@ -1,8 +1,10 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri Jun 4 08:24:33 2021 Script to run sensitivity analysis for Cb Stellate \Delta Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

6
Code/Sensitivity_Analysis/SA_Cb_stellate_Kv.py
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@ -1,8 +1,10 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri Jun 4 08:24:33 2021 Script to run sensitivity analysis for Cb Stellate +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

8
Code/Sensitivity_Analysis/SA_FS.py
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@ -1,10 +1,10 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat Jun 5 18:14:31 2021 Script to run sensitivity analysis for FS model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
Original Pospischil FS Sensitivity analysis (no Kv1.1) __license__ = MIT
""" """
import numpy as np import numpy as np

7
Code/Sensitivity_Analysis/SA_FS_Kv.py
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@ -1,11 +1,12 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat Jun 5 18:14:31 2021 Script to run sensitivity analysis for FS +Kv model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np
from numba import types from numba import types
from numba.typed import Dict from numba.typed import Dict

6
Code/Sensitivity_Analysis/SA_RS_inhib.py
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@ -1,8 +1,10 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat Jun 5 18:14:31 2021 Script to run sensitivity analysis for RS Inhibitory model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

6
Code/Sensitivity_Analysis/SA_RS_inhib_Kv.py
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@ -1,8 +1,10 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat Jun 5 18:14:31 2021 Script to run sensitivity analysis for RS Inhibitory +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

6
Code/Sensitivity_Analysis/SA_RS_pyramidal.py
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@ -1,8 +1,10 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri Jun 4 08:24:33 2021 Script to run sensitivity analysis for RS Pyramidal model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

6
Code/Sensitivity_Analysis/SA_RS_pyramidal_Kv.py
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@ -1,8 +1,10 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Fri Jun 4 08:24:33 2021 Script to run sensitivity analysis for RS Pyramidal +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np

8
Code/Sensitivity_Analysis/SA_STN.py
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@ -1,10 +1,10 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat Jun 5 19:44:04 2021 Script to run sensitivity analysis for STN model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
original STN model SA __license__ = MIT
""" """

6
Code/Sensitivity_Analysis/SA_STN_Delta_Kv.py
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@ -1,8 +1,10 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat Jun 5 19:44:04 2021 Script to run sensitivity analysis for STN \Delta Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """

7
Code/Sensitivity_Analysis/SA_STN_Kv.py
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@ -1,11 +1,12 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Sat Jun 5 19:44:04 2021 Script to run sensitivity analysis for STN +Kv1.1 model
@author: nils __author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
""" """
import numpy as np import numpy as np
from numba import types from numba import types
from numba.typed import Dict from numba.typed import Dict

29
Code/csv_generation/Plotting_data_collection.py
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@ -1,3 +1,11 @@
"""
Script to analyse and collect data from simulations
__author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
"""
import pandas as pd import pandas as pd
import h5py import h5py
import json import json
@ -6,8 +14,7 @@ import numpy as np
from ast import literal_eval from ast import literal_eval
import string import string
import matplotlib.cm as cm import matplotlib.cm as cm
# import Code.csv_generation.SA_collection
# todo: run some and test
# %% rheo_{}_ex.csv, AUC_{}_ex.csv # %% rheo_{}_ex.csv, AUC_{}_ex.csv
## AUC ################### ## AUC ###################
@ -147,7 +154,7 @@ rheo_slope.to_csv('rheo_slope_ex.csv')
rheo_g.to_csv('rheo_g_ex.csv') rheo_g.to_csv('rheo_g_ex.csv')
# %% todo: Model fI # %% Model fI
# for each model # for each model
# | (index) | mag | alt | type | F | I | # | (index) | mag | alt | type | F | I |
# | 0 | -10 | m | shift | array | array | # | 0 | -10 | m | shift | array | array |
@ -221,19 +228,9 @@ for m in models:
os.makedirs(folder) os.makedirs(folder)
df.to_csv(os.path.join(folder, '{}_fI.csv'.format(model_save_name[m]))) df.to_csv(os.path.join(folder, '{}_fI.csv'.format(model_save_name[m])))
# #%% todo: rheo/AUC_{}_corr - done in SA_collection.py
# # | (index) | model | corr | p_value | g | color
# # %% firing_values.csv, model_spiking.csv, model_F_inf.csv
# # in rheo_[]_corr.csv and AUC_[]_corr.csv
# # columns=['model', 'corr', 'p_value', 'local corr', 'local p_value', 'ratio', '$\Delta V_{1/2}$', 'color']
#
# rheo_corr = pd.DataFrame(columns=['model', 'corr', 'p_value', 'g', 'color'])
# rheo_corr.to_csv('rheo_corr.csv')
#
# AUC_corr = pd.DataFrame(columns=['model', 'corr', 'p_value', 'g', 'color'])
# AUC_corr.to_csv('AUC_corr.csv')
# %% firing_values.csv,model_spiking.csv, model_F_inf.csv
import numpy as np import numpy as np
import pandas as pd import pandas as pd

15
Code/csv_generation/SA_collection.py
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@ -1,3 +1,11 @@
"""
Script to analyse and collect data from sensitivity analysis simulations
__author__ = Nils A. Koch
__copyright__ = Copyright 2022, Nils A. Koch
__license__ = MIT
"""
import numpy as np import numpy as np
import h5py import h5py
import pandas as pd import pandas as pd
@ -13,7 +21,7 @@ top_dir = '../Sensitivity_Analysis/Data'
for dir_name in next(os.walk(top_dir))[1]: # for each folder in top_file_dir (each model) for dir_name in next(os.walk(top_dir))[1]: # for each folder in top_file_dir (each model)
folder = os.path.join(top_dir, dir_name) folder = os.path.join(top_dir, dir_name)
print(folder) print(folder)
# for each alt_type create pd df # for each alt_type create pandas dataframe
shift_AUC = pd.DataFrame() shift_AUC = pd.DataFrame()
shift_rheo = pd.DataFrame() shift_rheo = pd.DataFrame()
shift_fI = pd.DataFrame(dtype=object) shift_fI = pd.DataFrame(dtype=object)
@ -277,9 +285,8 @@ shift_df.to_json('./Sensitivity_analysis/rheo_shift_box_kendall_corr.json')
slope_df.to_json('./Sensitivity_analysis/rheo_slope_box_kendall_corr.json') slope_df.to_json('./Sensitivity_analysis/rheo_slope_box_kendall_corr.json')
g_df.to_json('./Sensitivity_analysis/rheo_g_kendall_corr.json') g_df.to_json('./Sensitivity_analysis/rheo_g_kendall_corr.json')
#%% create csv file
#%% todo: CREATE CSV FILES ################################################################################################### # | (index) | model | corr | p_value | g | color
#% AUC #% AUC
AUC_shift_json = pd.read_json('./Sensitivity_analysis/shift_kendall_corr_rel.json', orient='records') AUC_shift_json = pd.read_json('./Sensitivity_analysis/shift_kendall_corr_rel.json', orient='records')
AUC_slope_json = pd.read_json('./Sensitivity_analysis/slope_kendall_corr_rel.json', orient='records') #, lines=True) AUC_slope_json = pd.read_json('./Sensitivity_analysis/slope_kendall_corr_rel.json', orient='records') #, lines=True)

1
Code/run_simulations.sh
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@ -1,3 +1,4 @@
# Bash shell script to run all simulations and analysis
# Individual Neuronal models # Individual Neuronal models
python ./Neuron_models/RS_pyramidal_model.py python ./Neuron_models/RS_pyramidal_model.py