X = sm.add_constant(np.column_stack((ele, X)))
#resulting columns in the following order: reciprocal_num_sp, spike_length, std_error of spike cut length, ones
the_fit = sm.OLS(y, X).fit()
print 'pvalues of multilinear regression:'
print '\treciprocal_num_sp, spike_length, std_error of spike cut length, ones'
print '\t', the_fit.pvalues
# Can print summary out put by
#print the_fit.summary()
# Perform multiple linear regression on the variable in question
multiple_regression(ev_LIFASC_list, matrix)
#--- calculate statistics of individual variables----
cre_dict = check_and_organize_data(all_neurons)
# create dataframes for easy median calculations
inhibit_df = pd.DataFrame(cre_dict['inhibitory'],
columns=[
'specimen_id', 'cre', 'std_err', 'length', 'n',
'1/n', 'ev_LIFASC'
])
excite_df = pd.DataFrame(cre_dict['excitatory'],
columns=[
'specimen_id', 'cre', 'std_err', 'length', 'n',
'1/n', 'ev_LIFASC'
])
inhibit = {}
excite = {}
LIF_dict['th_NOT_opt']['from_zero'] * 1e3, #4
LIF_dict['th_opt']['absolute'] * 1e3, #5
LIF_dict['th_opt']['from_zero'] * 1e3, #6
LIFR_dict['th_opt']['absolute'] * 1e3, #7
LIFR_dict['th_opt']['from_zero'] * 1e3, #8
LIFASC_dict['th_opt']['absolute'] * 1e3, #9
LIFASC_dict['th_opt']['from_zero'] * 1e3, #10
LIFRASC_dict['th_opt']['absolute'] *
1e3, #11
LIFRASC_dict['th_opt']['from_zero'] * 1e3, #12
LIFRASCAT_dict['th_opt']['absolute'] *
1e3, #13
LIFRASCAT_dict['th_opt']['from_zero'] * 1e3
]) #14
(cre_dict) = check_and_organize_data(
all_neurons) #organizes data into format used for distribution plotting
percentile_dict = distribution_plot(cre_dict,
3,
2,
xlabel='Resting potential (mV)',
ylabel=r'Measured $\theta_{\infty}$ (mV)')
percentile_dict = distribution_plot(
cre_dict,
3,
4,
xlabel='Resting potential (mV)',
ylabel=r'Measured $\Delta V$ $\theta_{\infty}$ (mV)')
plt.figure()
for cre in cre_dict:
for neuron in cre_dict[cre]: