nkoch/model_mutations_2022
2
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity
c79560d191
model_mutations_2022/Code/Plotting_data_collection.py
nkoch1 c79560d191 Started working on Uli's comments
2022-09-04 22:45:56 -04:00

478 lines
24 KiB
Python
Raw Blame History

import pandas as pd
import h5py
import json
import os
import numpy as np
from ast import literal_eval
# todo: run some and test
# todo: develop consistent file structure
# todo: Model fI
# %% todo: Model fI
# for each model
# | (index) | mag | alt | type | F | I |
# | 0 | -10 | m | shift | array | array |
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_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'RS pyramidal +Kv1.1', 'RS inhibitory +Kv1.1', 'FS +Kv1.1',
'Cb stellate', 'Cb stellate +Kv1.1', 'Cb stellate $\Delta$Kv1.1', 'STN', 'STN +Kv1.1',
'STN $\Delta$Kv1.1']
# for each model get csv file with all shifts, scale and g
for model_name in models:
df = pd.DataFrame(columns=['mag', 'alt', 'type', 'F', 'I'])
folder = '../Neuron_models/{}'.format(model_name)
fname = os.path.join(folder, "{}.hdf5".format(model_name))
# for each alt in model
# with h5py.File(fname, "r+") as f:
# df.loc[model_name, '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]
# df.loc[model_name, 'mag'] = var
# df.loc[model_name, 'type'] = alt_type
# df.loc[model_name, 'F'] = f['analysis']['F_inf'][:]
# 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
# df.loc[model_name, 'I'] = I
# df.to_csv('./Model_fI/{}.csv'.format(model_name))
# %% todo: rheo/AUC_{}_corr
# | (index) | model | corr | p_value | g | 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')
# # AUC_shift = pd.DataFrame(columns=['alteration', 'RS Pyramidal','RS Inhibitory','FS','IB',
# # 'RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
# # 'FS +$K_V1.1$','IB +$K_V1.1$','Cb stellate','Cb stellate +$K_V1.1$',
# # 'Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$',
# # 'STN $\Delta$$K_V1.1$'])
# #
# # AUC_slope = pd.DataFrame(columns=['alteration','RS Pyramidal','RS Inhibitory','FS','IB','RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
# # 'FS +$K_V1.1$','IB +$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$'])
# #
# # AUC_g = pd.DataFrame(columns=['alteration','RS Pyramidal','RS Inhibitory','FS','IB','RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
# # 'FS +$K_V1.1$','IB +$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$'])
# #
# # script_dir = os.path.dirname(os.path.realpath("__file__"))
# # fname = os.path.join(script_dir, )
# # # f = h5py.File(fname, "r")
# #
# # models = ['RS_pyramidal', 'RS_inhib', 'FS', 'IB','Cb_stellate','Cb_stellate_Kv','Cb_stellate_Kv_only','STN','STN_Kv',
# # 'STN_Kv_only']
# # model_labels = ['RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
# # 'FS +$K_V1.1$','IB +$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$']
# # posp_models = ['RS_pyramidal', 'RS_inhib', 'FS', 'IB']
# # posp_model_labels = ['RS Pyramidal','RS Inhibitory', 'FS','IB']
# #
# #
# # shift_interest = 'n'
# # for i in range(len(models)):
# # with open('./SA_summary_df/{}_shift_AUC_rel_acc.json'.format(models[i])) as json_file:
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['0', :])/ data.loc['0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # AUC_shift[model_labels[i]] =data[shift_interest]
# # for i in range(len(posp_models)):
# # with open('./SA_summary_df_pospischil/{}_shift_AUC_rel_acc_pospischil.json'.format(models[i])) as json_file:
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['0', :])/ data.loc['0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # AUC_shift[posp_model_labels[i]] =data[shift_interest]
# # AUC_shift['alteration'] = AUC_shift.index
# #
# #
# #
# # slope_interest = 's'
# # for i in range(len(models)):
# # with open('./SA_summary_df/{}_slope_AUC_rel_acc.json'.format(models[i])) as json_file:
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['1.0', :])/ data.loc['1.0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # try:
# # AUC_slope[model_labels[i]] = data[slope_interest]
# # except:
# # pass
# # for i in range(len(posp_models)):
# # with open('./SA_summary_df_pospischil/{}_slope_AUC_rel_acc_pospischil.json'.format(models[i])) as json_file:
# #
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['1.0', :])/ data.loc['1.0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # try:
# # AUC_slope[posp_model_labels[i]] =data[slope_interest]
# # except:
# # pass
# # AUC_slope['alteration'] = AUC_slope.index
# #
# # g_interest = 'Kd'
# # for i in range(len(models)):
# # with open('./SA_summary_df/{}_g_AUC_rel_acc.json'.format(models[i])) as json_file:
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['1.0', :])/ data.loc['1.0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # AUC_g[model_labels[i]] =data[g_interest]
# # for i in range(len(posp_models)):
# # with open('./SA_summary_df_pospischil/{}_g_AUC_rel_acc_pospischil.json'.format(models[i])) as json_file:
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['1.0', :])/ data.loc['1.0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # AUC_g[posp_model_labels[i]] =data[g_interest]
# # AUC_g['alteration'] = AUC_g.index
# #
# # AUC_shift.to_csv('AUC_shift_ex.csv')
# # AUC_slope.to_csv('AUC_slope_ex.csv')
# # AUC_g.to_csv('AUC_g_ex.csv')
#
#
# # rheo_shift = pd.DataFrame(columns=['alteration', 'RS Pyramidal', 'RS Inhibitory', 'FS', 'IB',
# # 'RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
# # 'FS +$K_V1.1$','IB +$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$'])
# #
# # rheo_slope = pd.DataFrame(columns=['alteration', 'RS Pyramidal', 'RS Inhibitory', 'FS', 'IB','RS Pyramidal +$K_V1.1$',
# # 'RS Inhibitory +$K_V1.1$',
# # 'FS +$K_V1.1$','IB +$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$'])
# #
# # rheo_g = pd.DataFrame(columns=['alteration', 'RS Pyramidal', 'RS Inhibitory', 'FS', 'IB',
# # 'RS Pyramidal +$K_V1.1$','RS Inhibitory +$K_V1.1$',
# # 'FS +$K_V1.1$','IB +$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$'])
# #
# # script_dir = os.path.dirname(os.path.realpath("__file__"))
# # fname = os.path.join(script_dir, )
# # # f = h5py.File(fname, "r")
# #
# # models = ['RS_pyramidal', 'RS_inhib', 'FS', 'IB', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
# # 'STN_Kv',
# # 'STN_Kv_only']
# # model_labels = ['RS Pyramidal +$K_V1.1$', 'RS Inhibitory +$K_V1.1$', 'FS +$K_V1.1$', 'IB +$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$']
# # posp_models = ['RS_pyramidal', 'RS_inhib', 'FS', 'IB']
# # posp_model_labels = ['RS Pyramidal','RS Inhibitory', 'FS','IB']
# #
# # shift_interest = 's'
# # for i in range(len(models)):
# # with open('./SA_summary_df/{}_shift_rheo.json'.format(models[i])) as json_file:
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['0', :]) #/ data.loc['0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # try:
# # rheo_shift[model_labels[i]] = data[shift_interest]
# # except:
# # pass
# # for i in range(len(posp_models)):
# # with open('./SA_summary_df_pospischil/{}_shift_rheo_pospischil.json'.format(models[i])) as json_file:
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['0', :]) #/ data.loc['0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # try:
# # rheo_shift[posp_model_labels[i]] = data[shift_interest]
# # except:
# # pass
# # rheo_shift['alteration'] = rheo_shift.index
# #
# # slope_interest = 'u'
# # for i in range(len(models)):
# # with open('./SA_summary_df/{}_slope_rheo.json'.format(models[i])) as json_file:
# #
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['1.0', :]) #/ data.loc['1.0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # try:
# # rheo_slope[model_labels[i]] = data[slope_interest]
# # except:
# # pass
# # for i in range(len(posp_models)):
# # with open('./SA_summary_df_pospischil/{}_slope_rheo_pospischil.json'.format(models[i])) as json_file:
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['1.0', :]) #/ data.loc['1.0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # # if models[i] == 'STN_Kv_only' or models[i] == 'Cb_stellate_Kv_only':
# # # data = data.drop(columns=['A'])
# # try:
# # rheo_slope[posp_model_labels[i]] = data[slope_interest]
# # except:
# # pass
# # rheo_slope['alteration'] = rheo_slope.index
# #
# # g_interest = 'Leak'
# # for i in range(len(models)):
# # with open('./SA_summary_df/{}_g_rheo.json'.format(models[i])) as json_file:
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['1.0', :]) #/ data.loc['1.0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # rheo_g[model_labels[i]] = data[g_interest]
# # for i in range(len(posp_models)):
# # with open('./SA_summary_df_pospischil/{}_g_rheo_pospischil.json'.format(models[i])) as json_file:
# # data = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # data.replace(0., np.NaN, inplace=True)
# # data = (data - data.loc['1.0', :]) #/ data.loc['1.0', :] # normalize AUC
# # data.sort_index(inplace=True)
# # rheo_g[posp_model_labels[i]] = data[g_interest]
# # rheo_g['alteration'] = rheo_g.index
# #
# #
# # rheo_shift.to_csv('rheo_shift_ex.csv')
# # rheo_slope.to_csv('rheo_slope_ex.csv')
# # rheo_g.to_csv('rheo_g_ex.csv')
#
# %% firing_values.csv,model_spiking.csv, model_F_inf.csv
import numpy as np
import pandas as pd
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_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'RS pyramidal +Kv1.1', 'RS inhibitory +Kv1.1', 'FS +Kv1.1',
'Cb stellate', 'Cb stellate +Kv1.1', 'Cb stellate $\Delta$Kv1.1', 'STN', 'STN +Kv1.1',
'STN $\Delta$Kv1.1']
firing_values = pd.DataFrame(columns=models, index=['spike_ind', 'ramp_up', 'ramp_down'])
# firing_values.loc['spike_ind', :] = np.array(
# [0.3, 0.04375, 0.25, 0.3, 0.0875, 0.25, 0.3, 0.65, 0.375, 0.125, 0.475, 0.4])
models = ['RS_pyr', 'RS_pyr_Kv', 'RS_inhib', 'RS_inhib_Kv', 'FS', 'FS_Kv',
'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Delta_Kv',
'STN', 'STN_Kv', 'STN_Delta_Kv']
col_names = ['I', 'I_inhib']
for mod in models: col_names.append(mod)
model_F_inf = pd.DataFrame(columns=col_names)
col_names = ['t']
for mod in models: col_names.append(mod)
spiking = pd.DataFrame(columns=col_names)
# index for example trace
spike_ind = {'RS_pyramidal': 60, 'RS_inhib':25, 'FS':50, 'RS_pyramidal_Kv':60,
'RS_inhib_Kv':50, 'FS_Kv':50, 'Cb_stellate':60, 'Cb_stellate_Kv':130,
'Cb_stellate_Kv_only':75, 'STN': 25, 'STN_Kv':95, 'STN_Kv_only':80}
for model_name in models:
folder = '../Neuron_models/{}'.format(model_name)
fname = os.path.join(folder, "{}.hdf5".format(model_name)) # RS_inhib
with h5py.File(fname, "r+") as f:
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'])
spiking[model_name] = f['data']['V_m'][spike_ind[model_name]][start:]
model_F_inf[model_name] = f['analysis']['F_inf'][:]
firing_values.loc['spike_ind', model_name] = I_mag[spike_ind[model_name]]
firing_values.loc['ramp_down', model_name] = f['analysis']['ramp_I_down'][()]
firing_values.loc['ramp_up', model_name] = f['analysis']['ramp_I_up'][()]
firing_values.to_csv('firing_values.csv')
spiking.to_csv('model_spiking.csv')
model_F_inf.to_csv('model_F_inf.csv')
# %% model_ramp.csv
# | (index) | t | models ....
import numpy as np
import pandas as pd
models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
'STN_Kv', 'STN_Kv_only']
model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate', 'Cb stellate +Kv1.1', 'Cb stellate $\Delta$Kv1.1',
'STN', 'STN +Kv1.1', 'STN $\Delta$Kv1.1']
col_names = ['t']
for mod in models: col_names.append(mod)
model_ramp = pd.DataFrame(columns=col_names)
sec = 4
dt = 0.01
ramp_len = int(sec * 1000 * 1 / dt)
t_ramp = np.arange(0, ramp_len) * dt
model_ramp.loc[:, 't'] = t_ramp
for model_name in models:
folder = '../Neuron_models/{}'.format(model_name)
fname = os.path.join(folder, "{}.hdf5".format(model_name)) # RS_inhib
with h5py.File(fname, "r+") as f:
model_ramp.loc[:, model_name] = f['analysis']['V_m_ramp'][()]
model_ramp.to_csv('model_ramp.csv')
# %% sim_mut_AUC.csv, sim_mut_rheo.csv
# generate mutation plot data
mutations = json.load(open("../mutations_effects_dict.json"))
keys_to_remove = ['V408L', 'T226R', 'R239S', 'R324T']
for key in keys_to_remove:
del mutations[key]
mutations_f = []
mutations_n = []
for mut in mutations:
mutations_n.append(mut)
mutations_f.append(mut.replace(" ", "_"))
models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
'STN_Kv', 'STN_Kv_only']
model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate', 'Cb stellate +Kv1.1', 'Cb stellate $\Delta$Kv1.1',
'STN', 'STN +Kv1.1', 'STN $\Delta$Kv1.1']
AUC = pd.DataFrame(columns=mutations_n)
rheobase = pd.DataFrame(columns=mutations_n)
save_folder = '../KCNA1_mutations'
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
for model_name in models:
folder = '../KCNA1_mutations/{}'.format(model_name)
for mut in list(mutations_n):
fname = os.path.join(folder, "{}.hdf5".format(mut.replace(" ", "_")))
with h5py.File(fname, "r+") as f:
rheobase.loc[mut.replace(" ", "_"), model_name] = f['analysis']['rheobase'][()]
AUC.loc[mut.replace(" ", "_"), model_name] = f['analysis']['AUC'][()]
AUC.replace(0., np.NaN, inplace=True)
rheobase.replace(0., np.NaN, inplace=True)
rheobase = (rheobase - rheobase.loc['WT', :]) /rheobase.loc['WT', :]
AUC = (AUC - AUC.loc['WT', :]) /AUC.loc['WT', :]
AUC.to_csv(os.path.join(save_folder, 'sim_mut_AUC.csv'))
rheobase.to_csv(os.path.join(save_folder, 'sim_mut_rheobase.csv'))
#########################################################################################################################
#########################################################################################################################
#########################################################################################################################
#########################################################################################################################
#########################################################################################################################
#########################################################################################################################
#########################################################################################################################
#########################################################################################################################
#%% sim_mut_rheo.csv, sim_mut_AUC.csv
# models = ['RS_pyramidal', 'RS_inhib', 'FS', 'IB','Cb_stellate','Cb_stellate_Kv','Cb_stellate_Kv_only','STN','STN_Kv','STN_Kv_only']
# mutations = json.load(open("./mutations_effects_dict.json"))
# mutations2 = []
# for mut in mutations:
# mutations2.append(mut.replace(" ", "_"))
# AUC_total = pd.DataFrame(columns=list(mutations2), index=models)
# AUC_rel_total = pd.DataFrame(columns=list(mutations2), index=models)
# rheo_total = pd.DataFrame(columns=list(mutations2), index=models)
# rheo_fit_total = pd.DataFrame(columns=list(mutations2), index=models)
# for mod in models:
# print(mod)
# with open('./mut_summary_df/{}_AUC.json'.format(mod)) as json_file:
# df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # df[mutations2].to_json('./mut_summary_df/{}_AUC.json'.format(mod))
# AUC_total.loc[mod, :] = df.loc['0',:]
# with open('./mut_summary_df/{}_AUC_rel.json'.format(mod)) as json_file:
# df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # df[mutations2].to_json('./mut_summary_df/{}_AUC.json'.format(mod))
# AUC_rel_total.loc[mod, :] = df.loc['0',:]
# with open('./mut_summary_df/{}_rheobase.json'.format(mod)) as json_file:
# df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # df[mutations2].to_json('./mut_summary_df/{}_AUC.json'.format(mod))
# rheo_total.loc[mod, :] = df.loc['0',:]
# with open('./mut_summary_df/{}_rheobase_fit.json'.format(mod)) as json_file:
# df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
# # df[mutations2].to_json('./mut_summary_df/{}_AUC.json'.format(mod))
# rheo_fit_total.loc[mod, :] = df.loc['0',:]
#
# # AUC_diff = (AUC_score.subtract(AUC_score['wt'], axis =0))#.divide(AUC_score['wt'], axis=0)
# AUC_total.to_json('mutation_AUC_summary.json')
# AUC_rel_total.to_json('mutation_AUC_rel_summary.json')
# rheo_total.to_json('mutation_rheo_summary.json')
# rheo_fit_total.to_json('mutation_rheo_fit_summary.json')
# models = ['RS_pyramidal', 'RS_inhib', 'FS', 'IB','Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
# 'STN_Kv', 'STN_Kv_only']
# model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'IB', '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}$']
#
# all_mut_rheo = pd.DataFrame(columns=[model_names])
# all_mut_AUC = pd.DataFrame(columns=[model_names])
# for mod in range(len(models)):
# AUC = pd.read_json('./CBZ_summary_df/{}_CBZ_AUC_rel_acc.json'.format(models[mod]), convert_axes=False,
# convert_dates=False)
# rheo = pd.read_json('./CBZ_summary_df/{}_CBZ_rheobase.json'.format(models[mod]), convert_axes=False,
# convert_dates=False)
# AUC.index = AUC.index.astype(float)
#
# rheo.index = rheo.index.astype(float)
#
# conc = np.array(AUC.index)
# mut_names = AUC.columns
# for m in mut_names:
# rheo[m] = rheo[m].map(lambda x: x[0])
# AUC[m] = AUC[m].map(lambda x: x[0])
# AUC.replace(0., np.NaN, inplace=True)
# rheo.replace(0., np.NaN, inplace=True)
# AUC = (AUC - AUC.loc[0, 'wt']) / AUC.loc[0, :] # normalize AUC
# AUC.sort_index(inplace=True)
# rheo = (rheo - rheo.loc[0, 'wt'])#/ rheo.loc[0, :] # normalize AUC
# rheo.sort_index(inplace=True)
#
#
# for mut in mut_names: # for each mutation
# x_mut = rheo.loc[:, mut.replace(" ", "_")]
# y_mut = AUC.loc[:, mut.replace(" ", "_")]
#
# all_mut_rheo[model_names[mod]] = rheo.loc[0.0, mut_names]
# all_mut_AUC[model_names[mod]] = AUC.loc[0.0, mut_names]
#
#
#
# all_mut_rheo.to_csv('sim_mut_rheo.csv')
# all_mut_AUC.to_csv('sim_mut_AUC.csv')
# # %% model_F_inf.csv
# # | (index) | I | I_inhib | models ....
#
# # models
# models = ['RS_pyr', 'RS_pyr_Kv', 'RS_inhib', 'RS_inhib_Kv', 'FS', 'FS_Kv',
# 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Delta_Kv',
# 'STN', 'STN_Kv', 'STN_Delta_Kv']
# col_names = ['I', 'I_inhib']
# for mod in models: col_names.append(mod)
# model_F_inf = pd.DataFrame(columns=col_names)
# folder = '../Neuron_models'
# with h5py.File(os.path.join(folder, "RS_pyr.hdf5"), "r+") as f:
# model_F_inf['I'] = np.arange(f['data'].attrs['low'][()], f['data'].attrs['high'][()],
# (f['data'].attrs['high'][()] - f['data'].attrs['low'][()]) /
# f['data'].attrs['stim_num'][()])
# with h5py.File(os.path.join(folder, "RS_inhib.hdf5"), "r+") as f:
# model_F_inf['I_inhib'] = np.arange(f['data'].attrs['low'][()], f['data'].attrs['high'][()],
# (f['data'].attrs['high'][()] - f['data'].attrs['low'][()]) /
# f['data'].attrs['stim_num'][()])
#
# for model_name in models:
# fname = os.path.join(folder, "{}.hdf5".format(model_name)) # RS_inhib
# with h5py.File(fname, "r+") as f:
# model_F_inf[model_name] = f['analysis']['F_inf'][()]
#
# # save df
# model_F_inf.to_csv('model_F_inf.csv')
Reference in New Issue View Git Blame Copy Permalink