Discussion and changes to abstract
This commit is contained in:
nkoch1
153
Code/csv_generation/Plotting_data_collection.py
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153
Code/csv_generation/Plotting_data_collection.py
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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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297
Code/csv_generation/SA_collection.py
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297
Code/csv_generation/SA_collection.py
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import numpy as np
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import h5py
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import pandas as pd
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import os
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from ast import literal_eval
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import json
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import scipy.stats as stats
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# folder
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top_dir = '../Sensitivity_Analysis'
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#%% create JSON files for each alt type
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for dir_name in next(os.walk(top_dir))[1]:
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folder = os.path.join(top_dir, dir_name)
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print(folder)
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# for each alt_type create pd df
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shift_AUC = pd.DataFrame()
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shift_rheo = pd.DataFrame()
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shift_fI = pd.DataFrame(dtype=object)
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shift_I_mag = pd.DataFrame(dtype=object)
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slope_AUC = pd.DataFrame()
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slope_rheo = pd.DataFrame()
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slope_fI = pd.DataFrame(dtype=object)
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slope_I_mag = pd.DataFrame(dtype=object)
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g_AUC = pd.DataFrame()
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g_rheo = pd.DataFrame()
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g_fI = pd.DataFrame(dtype=object)
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g_I_mag = pd.DataFrame(dtype=object)
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for root, dirs, files in os.walk(folder):
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for file in files:
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if file.endswith('.hdf5'):
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with h5py.File(os.path.join(folder, file), "r+") as f:
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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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if alt_type == 'shift':
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shift_AUC.loc[alt, var] = f['analysis']['AUC'][()]
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try:
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shift_rheo.loc[alt, var] = f['analysis']['rheobase'][()]
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except:
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print('shift', var, alt)
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shift_fI.loc[alt, var] = 0
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shift_fI = shift_fI.astype(object)
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shift_fI.at[alt, var] = f['analysis']['F_inf'][:].tolist()
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shift_I_mag.loc[alt, var] = 0
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shift_I_mag = shift_I_mag.astype(object)
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shift_I_mag.at[alt, var] = ((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']) /
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f['data'].attrs['stim_num'])) * 1000).tolist() #nA
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elif alt_type == 'slope':
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slope_AUC.loc[alt, var] = f['analysis']['AUC'][()]
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try:
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slope_rheo.loc[alt, var] = f['analysis']['rheobase'][()]
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except:
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print('slope', var, alt)
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slope_fI.loc[alt, var] = 0
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slope_fI = slope_fI.astype(object)
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slope_fI.at[alt, var] = f['analysis']['F_inf'][:].tolist()
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slope_I_mag.loc[alt, var] = 0
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slope_I_mag = slope_I_mag.astype(object)
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slope_I_mag.at[alt, var] = ((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']) /
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f['data'].attrs['stim_num'])) * 1000).tolist()
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elif alt_type == 'g':
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g_AUC.loc[alt, var] = f['analysis']['AUC'][()]
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try:
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g_rheo.loc[alt, var] = f['analysis']['rheobase'][()]
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except:
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print('g', var, alt)
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g_fI.loc[alt, var] = 0
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g_fI = g_fI.astype(object)
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g_fI.at[alt, var] = f['analysis']['F_inf'][:].tolist()
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g_I_mag.loc[alt, var] = 0
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g_I_mag = g_I_mag.astype(object)
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g_I_mag.at[alt, var] = ((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']) /
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f['data'].attrs['stim_num'])) * 1000).tolist()
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else:
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print(file, 'Unknown alteration type')
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#save df with folder+alt_type
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save_folder = os.path.join(top_dir, 'SA_summary_df')
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if not os.path.isdir(save_folder):
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os.makedirs(save_folder)
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shift_AUC.to_json(os.path.join(save_folder, '{}_shift_AUC.json'.format(dir_name)))
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shift_rheo.to_json(os.path.join(save_folder, '{}_shift_rheo.json'.format(dir_name)))
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shift_fI.to_json(os.path.join(save_folder, '{}_shift_fI.json'.format(dir_name)))
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shift_I_mag.to_json(os.path.join(save_folder, '{}_shift_I_mag.json'.format(dir_name)))
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slope_AUC.to_json(os.path.join(save_folder, '{}_slope_AUC.json'.format(dir_name)))
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slope_rheo.to_json(os.path.join(save_folder, '{}_slope_rheo.json'.format(dir_name)))
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slope_fI.to_json(os.path.join(save_folder, '{}_slope_fI.json'.format(dir_name)))
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slope_I_mag.to_json(os.path.join(save_folder, '{}_slope_I_mag.json'.format(dir_name)))
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g_AUC.to_json(os.path.join(save_folder, '{}_g_AUC.json'.format(dir_name)))
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g_rheo.to_json(os.path.join(save_folder, '{}_g_rheo.json'.format(dir_name)))
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g_fI.to_json(os.path.join(save_folder, '{}_g_fI.json'.format(dir_name)))
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g_I_mag.to_json(os.path.join(save_folder, '{}_g_I_mag.json'.format(dir_name)))
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#%% AUC Correlation analysis
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alt_dict = {}
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alt_dict['m'] = 'Na activation'
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alt_dict['h'] = 'Na inactivation'
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alt_dict['n'] = 'K activation'
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alt_dict['s'] = '$K_V1.1$ activation'
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alt_dict['u'] = '$K_V1.1$inactivation'
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alt_dict['a'] = 'A activation'
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alt_dict['b'] = 'A inactivation'
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alt_dict['n_A'] = 'A activation'
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alt_dict['h_A'] = 'A inactivation'
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alt_dict['Na'] = 'Na'
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alt_dict['Kd'] = 'K'
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alt_dict['Kv'] = '$K_V1.1$'
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alt_dict['A'] = 'A'
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alt_dict['Leak'] = 'Leak'
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models =['RS_pyramidal', 'RS_inhib', 'FS', 'IB','Cb_stellate','Cb_stellate_Kv','Cb_stellate_Kv_only','STN','STN_Kv','STN_Kv_only']
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model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'IB','Cb stellate','Cb stellate +$K_V1.1$','Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$','STN $\Delta$$K_V1.1$']
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shift_df = pd.DataFrame(columns=['model', 'corr', 'p_value', 'local corr', 'local p_value', 'ratio', '$\Delta V_{1/2}$', 'color']) # for boxplots
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for mod in range(len(models)):
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with open('./SA_summary_df/{}_shift_AUC.json'.format(models[mod])) as json_file:
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df = pd.read_json(json_file)
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df.sort_index(inplace=True)
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df.replace(0., np.NaN, inplace=True)
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df = (df - df.loc[0, :]) / df.loc[0, :]
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zero_ind = np.argwhere(df.index == 0)[0][0]
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ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
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df2 = df.loc[ind, :]
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for c in df.keys():
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tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
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tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
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ratio_tau = tau_l / tau
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shift_df = shift_df.append(pd.Series([model_names[mod], tau, p,tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=shift_df.columns), ignore_index=True)
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models =['RS_pyramidal', 'RS_inhib', 'FS', 'IB','Cb_stellate','Cb_stellate_Kv','Cb_stellate_Kv_only','STN','STN_Kv','STN_Kv_only']
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model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'IB','Cb stellate','Cb stellate +$K_V1.1$','Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$','STN $\Delta$$K_V1.1$']
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slope_df = pd.DataFrame(columns=['model', 'corr', 'p_value','local corr', 'local p_value', 'ratio', 'Slope (k)', 'color']) # for boxplots
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for mod in range(len(models)):
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with open('./SA_summary_df/{}_slope_AUC.json'.format(models[mod])) as json_file:
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df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
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df.index = df.index.map(float)
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df.sort_index(inplace=True)
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df.replace(0., np.NaN, inplace=True)
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df = (df - df.loc[1.0, :]) / df.loc[1.0, :]
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zero_ind = np.argwhere(df.index == 1)[0][0]
|
||||
ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
|
||||
df2 = df.loc[ind, :]
|
||||
for c in df.keys():
|
||||
tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
|
||||
tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
|
||||
ratio_tau = tau_l/tau
|
||||
slope_df = slope_df.append(pd.Series([model_names[mod], tau, p, tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=slope_df.columns), ignore_index=True)
|
||||
|
||||
models =['RS_pyramidal', 'RS_inhib', 'FS', 'IB','Cb_stellate','Cb_stellate_Kv','Cb_stellate_Kv_only','STN','STN_Kv','STN_Kv_only']
|
||||
model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'IB','Cb stellate','Cb stellate +$K_V1.1$','Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$','STN $\Delta$$K_V1.1$']
|
||||
g_df = pd.DataFrame(columns=['model', 'corr', 'p_value', 'local corr', 'local p_value', 'ratio', 'g', 'color']) # for boxplots
|
||||
for mod in range(len(models)):
|
||||
with open('./SA_summary_df/{}_g_AUC.json'.format(models[mod])) as json_file:
|
||||
df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
|
||||
df.index = df.index.map(float)
|
||||
df.sort_index(inplace=True)
|
||||
df.replace(0., np.NaN, inplace=True)
|
||||
df = (df - df.loc[1.0, :]) / df.loc[1.0, :]
|
||||
zero_ind = np.argwhere(df.index == 1)[0][0]
|
||||
ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
|
||||
df2 = df.loc[ind, :]
|
||||
for c in df.keys():
|
||||
tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
|
||||
tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
|
||||
ratio_tau = tau_l / tau
|
||||
g_df = g_df.append(pd.Series([model_names[mod], tau, p,tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=g_df.columns), ignore_index=True)
|
||||
|
||||
shift_df.to_json('./Sensitivity_analysis/shift_box_kendall_corr.json')
|
||||
slope_df.to_json('./Sensitivity_analysis/slope_box_kendall_corr.json')
|
||||
g_df.to_json('./Sensitivity_analysis/g_box_kendall_corr.json')
|
||||
|
||||
|
||||
#%% rheobase correlation analysis
|
||||
|
||||
models =['RS_pyramidal', 'RS_inhib', 'FS', 'IB','Cb_stellate','Cb_stellate_Kv','Cb_stellate_Kv_only','STN','STN_Kv','STN_Kv_only']
|
||||
model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'IB','Cb stellate','Cb stellate +$K_V1.1$','Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$','STN $\Delta$$K_V1.1$']
|
||||
shift_df = pd.DataFrame(columns=['model', 'corr', 'p_value', 'local corr', 'local p_value', 'ratio', '$\Delta V_{1/2}$', 'color']) # for boxplots
|
||||
for mod in range(len(models)):
|
||||
with open('./SA_summary_df/{}_shift_rheo.json'.format(models[mod])) as json_file:
|
||||
df = pd.read_json(json_file)
|
||||
df.sort_index(inplace=True)
|
||||
df.replace(0., np.NaN, inplace=True)
|
||||
df = (df - df.loc[0, :]) / df.loc[0, :]
|
||||
zero_ind = np.argwhere(df.index == 0)[0][0]
|
||||
ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
|
||||
df2 = df.loc[ind, :]
|
||||
for c in df.keys():
|
||||
tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
|
||||
tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
|
||||
ratio_tau = tau_l / tau
|
||||
shift_df = shift_df.append(pd.Series([model_names[mod], tau, p,tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=shift_df.columns), ignore_index=True)
|
||||
|
||||
|
||||
models =['RS_pyramidal', 'RS_inhib', 'FS', 'IB','Cb_stellate','Cb_stellate_Kv','Cb_stellate_Kv_only','STN','STN_Kv','STN_Kv_only']
|
||||
model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'IB','Cb stellate','Cb stellate +$K_V1.1$','Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$','STN $\Delta$$K_V1.1$']
|
||||
slope_df = pd.DataFrame(columns=['model', 'corr', 'p_value','local corr', 'local p_value', 'ratio', 'Slope (k)', 'color']) # for boxplots
|
||||
for mod in range(len(models)):
|
||||
with open('./SA_summary_df/{}_slope_rheo.json'.format(models[mod])) as json_file:
|
||||
df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
|
||||
df.index = df.index.map(float)
|
||||
df.sort_index(inplace=True)
|
||||
df.replace(0., np.NaN, inplace=True)
|
||||
df = (df - df.loc[1.0, :]) / df.loc[1.0, :]
|
||||
zero_ind = np.argwhere(df.index == 1)[0][0]
|
||||
ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
|
||||
df2 = df.loc[ind, :]
|
||||
for c in df.keys():
|
||||
tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
|
||||
tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
|
||||
ratio_tau = tau_l/tau
|
||||
slope_df = slope_df.append(pd.Series([model_names[mod], tau, p, tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=slope_df.columns), ignore_index=True)
|
||||
|
||||
models =['RS_pyramidal', 'RS_inhib', 'FS', 'IB','Cb_stellate','Cb_stellate_Kv','Cb_stellate_Kv_only','STN','STN_Kv','STN_Kv_only']
|
||||
model_names = ['RS pyramidal', 'RS inhibitory', 'FS', 'IB','Cb stellate','Cb stellate +$K_V1.1$','Cb stellate $\Delta$$K_V1.1$','STN','STN +$K_V1.1$','STN $\Delta$$K_V1.1$']
|
||||
g_df = pd.DataFrame(columns=['model', 'corr', 'p_value', 'local corr', 'local p_value', 'ratio', 'g', 'color']) # for boxplots
|
||||
for mod in range(len(models)):
|
||||
with open('./SA_summary_df/{}_g_rheo.json'.format(models[mod])) as json_file:
|
||||
df = pd.read_json(json_file, convert_dates=False, convert_axes=False)
|
||||
df.index = df.index.map(float)
|
||||
df.sort_index(inplace=True)
|
||||
df.replace(0., np.NaN, inplace=True)
|
||||
df = (df - df.loc[1.0, :]) / df.loc[1.0, :]
|
||||
zero_ind = np.argwhere(df.index == 1)[0][0]
|
||||
ind = [df.index[zero_ind - 1], df.index[zero_ind], df.index[zero_ind + 1]]
|
||||
df2 = df.loc[ind, :]
|
||||
for c in df.keys():
|
||||
tau, p = stats.kendalltau(df.index, df[c], nan_policy='omit')
|
||||
tau_l, p_l = stats.kendalltau(df2.index, df2[c], nan_policy='omit')
|
||||
ratio_tau = tau_l / tau
|
||||
g_df = g_df.append(pd.Series([model_names[mod], tau, p,tau_l, p_l, ratio_tau, alt_dict[c],clr_dict[models[mod]]], index=g_df.columns), ignore_index=True)
|
||||
|
||||
shift_df.to_json('./Sensitivity_analysis/rheo_shift_box_kendall_corr.json')
|
||||
slope_df.to_json('./Sensitivity_analysis/rheo_slope_box_kendall_corr.json')
|
||||
g_df.to_json('./Sensitivity_analysis/rheo_g_box_kendall_corr.json')
|
||||
|
||||
|
||||
#%% todo: CREATE CSV FILES ###################################################################################################
|
||||
|
||||
# from ./Code/From_Neuronal_models/tau_analysis/AUC_corr_df_gen.py
|
||||
AUC_shift_df = pd.read_json('./Sensitivity_analysis/shift_box_kendall_corr_rel.json', orient='records')
|
||||
AUC_slope_df = pd.read_json('./Sensitivity_analysis/slope_box_kendall_corr_rel.json', orient='records') #, lines=True)
|
||||
AUC_g_df = pd.read_json('./Sensitivity_analysis/g_box_kendall_corr_rel.json', orient='records')
|
||||
# for i in np.array(['FS', 'IB', 'RS inhibitory', 'RS pyramidal']):
|
||||
# AUC_shift_df.loc[AUC_shift_df['model'] == i, 'model'] = i + ' +$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')
|
||||
Reference in New Issue
Block a user