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')