154 lines
7.4 KiB
Python
154 lines
7.4 KiB
Python
import pandas as pd
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import h5py
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import json
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import os
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import numpy as np
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from ast import literal_eval
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# todo: run some and test
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# todo: Model fI, rheo_{}_corr, AUC_{}_corr
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# %% todo: Model fI
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# for each model
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# | (index) | mag | alt | type | F | I |
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# | 0 | -10 | m | shift | array | array |
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models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
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'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
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model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'RS pyramidal +Kv1.1', 'RS inhibitory +Kv1.1', 'FS +Kv1.1',
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'Cb stellate', 'Cb stellate +Kv1.1', 'Cb stellate $\Delta$Kv1.1', 'STN', 'STN +Kv1.1',
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'STN $\Delta$Kv1.1']
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# for each model get csv file with all shifts, scale and g
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for model_name in models:
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df = pd.DataFrame(columns=['mag', 'alt', 'type', 'F', 'I'])
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folder = '../Neuron_models/{}'.format(model_name)
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fname = os.path.join(folder, "{}.hdf5".format(model_name))
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# for each alt in model
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# with h5py.File(fname, "r+") as f:
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# df.loc[model_name, 'alt'] = f['data'].attrs['alteration']
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# test = f['data'].attrs['alteration_info'].replace(' ', ',')
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# alt_info = literal_eval(test)
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# var = alt_info[0]
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# alt_type = alt_info[1]
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# df.loc[model_name, 'mag'] = var
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# df.loc[model_name, 'type'] = alt_type
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# df.loc[model_name, 'F'] = f['analysis']['F_inf'][:]
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# I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'],
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# (f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num']) * 1000
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# df.loc[model_name, 'I'] = I
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# df.to_csv('./Model_fI/{}.csv'.format(model_name))
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# %% todo: rheo/AUC_{}_corr
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# | (index) | model | corr | p_value | g | color
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rheo_corr = pd.DataFrame(columns=['model', 'corr', 'p_value', 'g', 'color'])
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rheo_corr.to_csv('rheo_corr.csv')
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AUC_corr = pd.DataFrame(columns=['model', 'corr', 'p_value', 'g', 'color'])
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AUC_corr.to_csv('AUC_corr.csv')
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# %% firing_values.csv,model_spiking.csv, model_F_inf.csv # DONE ####################################################
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import numpy as np
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import pandas as pd
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models = ['RS_pyramidal', 'RS_inhib', 'FS', 'RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv',
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'Cb_stellate_Kv_only', 'STN', 'STN_Kv', 'STN_Kv_only']
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model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'RS pyramidal +Kv1.1', 'RS inhibitory +Kv1.1', 'FS +Kv1.1',
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'Cb stellate', 'Cb stellate +Kv1.1', 'Cb stellate $\Delta$Kv1.1', 'STN', 'STN +Kv1.1',
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'STN $\Delta$Kv1.1']
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firing_values = pd.DataFrame(columns=models, index=['spike_ind', 'ramp_up', 'ramp_down'])
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# firing_values.loc['spike_ind', :] = np.array(
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# [0.3, 0.04375, 0.25, 0.3, 0.0875, 0.25, 0.3, 0.65, 0.375, 0.125, 0.475, 0.4])
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models = ['RS_pyr', 'RS_pyr_Kv', 'RS_inhib', 'RS_inhib_Kv', 'FS', 'FS_Kv',
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'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Delta_Kv',
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'STN', 'STN_Kv', 'STN_Delta_Kv']
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col_names = ['I', 'I_inhib']
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for mod in models: col_names.append(mod)
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model_F_inf = pd.DataFrame(columns=col_names)
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col_names = ['t']
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for mod in models: col_names.append(mod)
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spiking = pd.DataFrame(columns=col_names)
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# index for example trace
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spike_ind = {'RS_pyramidal': 60, 'RS_inhib':25, 'FS':50, 'RS_pyramidal_Kv':60,
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'RS_inhib_Kv':50, 'FS_Kv':50, 'Cb_stellate':60, 'Cb_stellate_Kv':130,
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'Cb_stellate_Kv_only':75, 'STN': 25, 'STN_Kv':95, 'STN_Kv_only':80}
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for model_name in models:
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folder = '../Neuron_models/{}'.format(model_name)
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fname = os.path.join(folder, "{}.hdf5".format(model_name)) # RS_inhib
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with h5py.File(fname, "r+") as f:
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I_mag = np.arange(f['data'].attrs['I_low'], f['data'].attrs['I_high'],
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(f['data'].attrs['I_high'] - f['data'].attrs['I_low']) / f['data'].attrs['stim_num']) * 1000
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start = np.int(f['data'].attrs['initial_period'] * 1 / f['data'].attrs['dt'])
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stim_len = np.int((f['data'].attrs['stim_time'] - start) * f['data'].attrs['dt'])
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time = np.arange(0, stim_len, f['data'].attrs['dt'])
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spiking[model_name] = f['data']['V_m'][spike_ind[model_name]][start:]
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model_F_inf[model_name] = f['analysis']['F_inf'][:]
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firing_values.loc['spike_ind', model_name] = I_mag[spike_ind[model_name]]
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firing_values.loc['ramp_down', model_name] = f['analysis']['ramp_I_down'][()]
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firing_values.loc['ramp_up', model_name] = f['analysis']['ramp_I_up'][()]
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firing_values.to_csv('firing_values.csv')
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spiking.to_csv('model_spiking.csv')
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model_F_inf.to_csv('model_F_inf.csv')
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# %% model_ramp.csv # DONE #########################################################################
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# | (index) | t | models ....
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import numpy as np
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import pandas as pd
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models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
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'STN_Kv', 'STN_Kv_only']
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model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate', 'Cb stellate +Kv1.1', 'Cb stellate $\Delta$Kv1.1',
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'STN', 'STN +Kv1.1', 'STN $\Delta$Kv1.1']
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col_names = ['t']
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for mod in models: col_names.append(mod)
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model_ramp = pd.DataFrame(columns=col_names)
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sec = 4
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dt = 0.01
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ramp_len = int(sec * 1000 * 1 / dt)
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t_ramp = np.arange(0, ramp_len) * dt
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model_ramp.loc[:, 't'] = t_ramp
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for model_name in models:
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folder = '../Neuron_models/{}'.format(model_name)
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fname = os.path.join(folder, "{}.hdf5".format(model_name)) # RS_inhib
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with h5py.File(fname, "r+") as f:
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model_ramp.loc[:, model_name] = f['analysis']['V_m_ramp'][()]
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model_ramp.to_csv('model_ramp.csv')
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#%% sim_mut_AUC.csv, sim_mut_rheo.csv # DONE #########################################################################
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# generate mutation plot data
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mutations = json.load(open("../mutations_effects_dict.json"))
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keys_to_remove = ['V408L', 'T226R', 'R239S', 'R324T']
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for key in keys_to_remove:
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del mutations[key]
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mutations_f = []
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mutations_n = []
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for mut in mutations:
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mutations_n.append(mut)
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mutations_f.append(mut.replace(" ", "_"))
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models = ['RS_pyramidal_Kv', 'RS_inhib_Kv', 'FS_Kv', 'Cb_stellate', 'Cb_stellate_Kv', 'Cb_stellate_Kv_only', 'STN',
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'STN_Kv', 'STN_Kv_only']
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model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'Cb stellate', 'Cb stellate +Kv1.1', 'Cb stellate $\Delta$Kv1.1',
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'STN', 'STN +Kv1.1', 'STN $\Delta$Kv1.1']
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AUC = pd.DataFrame(columns=mutations_n)
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rheobase = pd.DataFrame(columns=mutations_n)
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save_folder = '../KCNA1_mutations'
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if not os.path.isdir(save_folder):
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os.makedirs(save_folder)
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for model_name in models:
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folder = '../KCNA1_mutations/{}'.format(model_name)
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for mut in list(mutations_n):
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fname = os.path.join(folder, "{}.hdf5".format(mut.replace(" ", "_")))
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with h5py.File(fname, "r+") as f:
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rheobase.loc[mut.replace(" ", "_"), model_name] = f['analysis']['rheobase'][()]
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AUC.loc[mut.replace(" ", "_"), model_name] = f['analysis']['AUC'][()]
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AUC.replace(0., np.NaN, inplace=True)
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rheobase.replace(0., np.NaN, inplace=True)
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rheobase = (rheobase - rheobase.loc['WT', :]) /rheobase.loc['WT', :]
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AUC = (AUC - AUC.loc['WT', :]) /AUC.loc['WT', :]
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AUC.to_csv(os.path.join(save_folder, 'sim_mut_AUC.csv'))
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rheobase.to_csv(os.path.join(save_folder, 'sim_mut_rheobase.csv'))
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#########################################################################################################################
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