def main():
# Single field
simdata = load_pickle('results/sim/raw/squarematch.pickle')
single_field_preprocess_Romani(
simdata, save_pth='results/sim/singlefield_df.pickle')
# # Pair field
simdata = load_pickle('results/sim/raw/squarematch.pickle')
pair_field_preprocess_Romani(simdata,
save_pth='results/sim/pairfield_df.pickle')
def main():
save_dir = 'result_plots/sim'
os.makedirs(save_dir, exist_ok=True)
# single_simdf = load_pickle('results/sim/singlefield_df.pickle')
# singlefield_analysis_Romani(single_simdf, save_dir=save_dir)
pair_simdf = load_pickle('results/sim/pairfield_df.pickle')
pair_simdf['border'] = (pair_simdf['border1'] & pair_simdf['border2'])
pairfield_analysis_Romani(pair_simdf, save_dir=save_dir)
def calc_preprocessing_info():
# Get
# (1) Straightness distribution of passes,
# (2) Average spike counts per field per session
# (3) Pass counts per passes
simdata = load_pickle('results/network_proj/sim_result.pickle')
Indata = simdata['Indata']
SpikeData = simdata['SpikeData']
NeuronPos = simdata['NeuronPos']
all_spikecounts = []
all_straightness = []
radius = 10
minpasstime = 0.6
unique_neurons = SpikeData['neuronidx'].unique()
num_neurons = unique_neurons.shape[0]
for idx, nidx in enumerate(unique_neurons):
print('%d/%d'%(idx, num_neurons))
spks_count = SpikeData[SpikeData.neuronidx == nidx].shape[0]
all_spikecounts.append(spks_count)
# Get tok
neuronx, neurony = NeuronPos.loc[nidx, ['neuronx', 'neurony']]
dist = np.sqrt((neuronx - Indata.x) ** 2 + (neurony - Indata.y) ** 2)
tok = dist < radius
all_passidx = segment_passes(tok.to_numpy())
for pid1, pid2 in all_passidx:
pass_angles = Indata.loc[pid1:pid2, 'angle'].to_numpy()
pass_t = Indata.loc[pid1:pid2, 't'].to_numpy()
# Pass duration threshold
if pass_t.shape[0] < 5:
continue
if (pass_t[-1] - pass_t[0]) < minpasstime:
continue
straightness = compute_straightness(pass_angles)
all_straightness.append(straightness)
print('Average spk count per field = %0.2f'% (np.mean(all_spikecounts)))
print('0.1 quantile of straightness = %0.2f'% (np.quantile(all_straightness, 0.1)))
def sanity_check():
simdata = load_pickle('results/network_proj/sim_result.pickle')
indf = simdata['Indata']
spdf = simdata['SpikeData']
ntun = simdata['NeuronPos']
np.random.seed(1)
selected_nidxs = np.random.choice(spdf['neuronidx'].unique(), 4)
fig, ax = plt.subplots(2, 2, figsize=(10, 10))
ax = ax.ravel()
fig_den, ax_den = plt.subplots(2, 2, figsize=(10, 10))
ax_den = ax_den.ravel()
for idx, nidx in enumerate(selected_nidxs):
spdf_each = spdf[spdf.neuronidx == nidx]
neu_x, neu_y = ntun.loc[nidx, ['neuronx', 'neurony']]
tidxsp = spdf_each.tidxsp
xsp = indf.x[tidxsp].to_numpy()
ysp = indf.y[tidxsp].to_numpy()
# Scatter
ax[idx].plot(indf.x, indf.y)
ax[idx].scatter(xsp, ysp, c='r', marker='.', alpha=0.5)
ax[idx].scatter(neu_x, neu_y, c='k', marker='x')
# Density
xx, yy, zz = linear_gauss_density(xsp, ysp, 5, 5, 500, 500, (-40, 40), (-40, 40))
ax_den[idx].pcolormesh(xx, yy, zz)
ax_den[idx].scatter(neu_x, neu_y, c='k', marker='x')
fig.savefig('result_plots/network_proj/sanity_check.png')
fig_den.savefig('result_plots/network_proj/sanity_check_den.png')
def main():
input_df_pth = 'data/emankindata_processed_withwave.pickle'
output_dict_dir = 'results/sim/raw'
output_dict_name = 'squarematch.pickle'
print('Load experiment data')
input_df = pd.read_pickle(input_df_pth)
print('Extract experiment trajectory')
t, pos, _ = load_exp_trajectory(input_df)
print('Generate setting of Square environment')
neuron_theta, I_E, I_phase, ensemble_dict = gen_setting(t, pos, w_cos_scaled, gt_cos_scaled)
print('Simulate Squarematch environment')
simdata = simulate(t, pos, neuron_theta, I_E, I_phase, ensemble_dict, max_duration=None)
print('Save simulated results')
with open(join(output_dict_dir, output_dict_name), 'wb') as fh:
pickle.dump(simdata, fh)
print('Sanity check')
sanity_check(data=load_pickle(join(output_dict_dir, output_dict_name)),
save_pth=join(output_dict_dir, output_dict_name + '.png'))
fig_paireg.tight_layout()
fig_paireg.subplots_adjust(wspace=0.01, hspace=0.05)
fig_paireg.savefig(os.path.join(plot_dir, 'examples_pair.%s' % (figext)), dpi=dpi)
fig_kld.tight_layout()
fig_kld.subplots_adjust(wspace=0.05, hspace=0.2)
fig_kld.savefig(os.path.join(plot_dir, 'examples_kld.%s' % (figext)), dpi=dpi)
if __name__ == '__main__':
# Experiment's data preprocessing
data_pth = 'data/emankindata_processed_withwave.pickle'
save_pth = 'results/emankin/pairfield_df.pickle'
expdf = load_pickle(data_pth)
pair_field_preprocess_exp(expdf, vthresh=5, sthresh=3, NShuffles=200, save_pth=save_pth)
# # Plotting
# plot_dir = 'result_plots/pair_fields/'
# plot_pair_examples(expdf, vthresh=5, sthresh=3, plot_dir=plot_dir)
# simdata = load_pickle('results/sim/raw/squarematch.pickle')
# pair_field_preprocess_sim(simdata, save_pth='results/sim/pair_field/pairfield_df_square2.pickle')
dpi=dpi)
plt.close()
fieldid[ca] += 1
if __name__ == '__main__':
rawdf_pth = 'data/emankindata_processed_withwave.pickle'
save_pth = 'results/emankin/singlefield_df_NoShuffle.pickle'
# df = load_pickle(rawdf_pth)
# single_field_preprocess_exp(df, vthresh=5, sthresh=3, save_pth=save_pth)
# # # Plotting
plot_dir = 'result_plots/single_field/'
df = load_pickle(rawdf_pth)
plot_placefield_examples(df, plot_dir)
# # Simulation
# simdata = load_pickle('results/sim/raw/squarematch.pickle')
# single_field_preprocess_sim(simdata, save_pth='results/sim/singlefield_df_square2.pickle')
# # Network project
# simdata = load_pickle('results/network_proj/sim_result_dwdt.pickle')
# single_field_preprocess_networks(simdata, save_pth='results/network_proj/singlefield_df_networkproj_dwdt.pickle')
# df = load_pickle(rawdf_pth)
# precession_dist_inspection(df)
# test_pass_criteria(df)
# test_pass_minbins()