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: Model fI, rheo_{}_corr, AUC_{}_corr
# %% 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')

# %% firing_values.csv,model_spiking.csv, model_F_inf.csv # DONE ####################################################
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 # DONE #########################################################################
# | (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 # DONE #########################################################################
# 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'))


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