467 lines
21 KiB
Python
467 lines
21 KiB
Python
import os
|
|
import glob
|
|
import pandas as pd
|
|
import nixio as nix
|
|
import numpy as np
|
|
import scipy.signal as sig
|
|
import matplotlib.pyplot as plt
|
|
from sklearn.metrics import roc_curve, roc_auc_score
|
|
from IPython import embed
|
|
|
|
from util import firing_rate, despine, extract_am, within_group_distance, across_group_distance
|
|
figure_folder = "figures"
|
|
data_folder = "data"
|
|
|
|
|
|
def read_baseline(block):
|
|
spikes = []
|
|
if "baseline" not in block.name:
|
|
print("Block %s does not appear to be a baseline block!" % block.name )
|
|
return spikes
|
|
spikes = block.data_arrays[0][:]
|
|
return spikes
|
|
|
|
|
|
def sort_blocks(nix_file):
|
|
block_map = {}
|
|
contrasts = []
|
|
deltafs = []
|
|
conditions = []
|
|
for b in nix_file.blocks:
|
|
if "baseline" not in b.name.lower():
|
|
name_parts = b.name.split("_")
|
|
cntrst = float(name_parts[1])
|
|
if cntrst not in contrasts:
|
|
contrasts.append(cntrst)
|
|
cndtn = name_parts[3]
|
|
if cndtn not in conditions:
|
|
conditions.append(cndtn)
|
|
dltf = float(name_parts[5])
|
|
if dltf not in deltafs:
|
|
deltafs.append(dltf)
|
|
block_map[(cntrst, dltf, cndtn)] = b
|
|
else:
|
|
block_map["baseline"] = b
|
|
return block_map, contrasts, deltafs, conditions
|
|
|
|
|
|
def get_spikes(block):
|
|
"""Get the spike trains.
|
|
|
|
Args:
|
|
block ([type]): [description]
|
|
|
|
Returns:
|
|
list of np.ndarray: the spike trains.
|
|
"""
|
|
response_map = {}
|
|
spikes = []
|
|
|
|
for da in block.data_arrays:
|
|
if "spike_times" in da.type and "response" in da.name:
|
|
resp_id = int(da.name.split("_")[-1])
|
|
response_map[resp_id] = da
|
|
for k in sorted(response_map.keys()):
|
|
spikes.append(response_map[k][:])
|
|
|
|
return spikes
|
|
|
|
|
|
def get_rates(spike_trains, duration, dt, kernel_width):
|
|
"""Convert the spike trains (list of spike_times) to rates using a Gaussian kernel of the given size.
|
|
|
|
Args:
|
|
spike_trains ([type]): [description]
|
|
duration ([type]): [description]
|
|
dt ([type]): [description]
|
|
kernel_width ([type]): [description]
|
|
|
|
Returns:
|
|
np.ndarray: Matrix of firing rates, 1. dimension is the number of trials
|
|
np.ndarray: the time vector
|
|
"""
|
|
time = np.arange(0.0, duration, dt)
|
|
rates = np.zeros((len(spike_trains), len(time)))
|
|
for i, sp in enumerate(spike_trains):
|
|
rates[i, :] = firing_rate(sp, duration, kernel_width, dt)
|
|
|
|
return rates, time
|
|
|
|
|
|
def get_firing_rate(block_map, df, contrast, condition, kernel_width=0.0005):
|
|
"""Retruns the firing rates and the spikes
|
|
|
|
Args:
|
|
block_map ([type]): [description]
|
|
df ([type]): [description]
|
|
contrast ([type]): [description]
|
|
condition ([type]): [description]
|
|
kernel_width (float, optional): [description]. Defaults to 0.0005.
|
|
|
|
Returns:
|
|
np.ndarray: the time vector.
|
|
np.ndarray: the rates with the first dimension representing the trials.
|
|
np.adarray: the spike trains.
|
|
"""
|
|
block = block_map[(contrast, df, condition)]
|
|
spikes = get_spikes(block)
|
|
duration = float(block.metadata["stimulus parameter"]["duration"])
|
|
dt = float(block.metadata["stimulus parameter"]["dt"])
|
|
|
|
rates, time = get_rates(spikes, duration, dt, kernel_width)
|
|
return time, rates, spikes
|
|
|
|
|
|
def get_signals(block):
|
|
"""Read the fish signals from block.
|
|
|
|
Args:
|
|
block ([type]): the block containing the data for a given df, contrast and condition
|
|
|
|
Raises:
|
|
ValueError: when the complete stimulus data is not found
|
|
ValueError: when the no-other animal data is not found
|
|
|
|
Returns:
|
|
np.ndarray: the complete signal
|
|
np.ndarray: the frequency profile of the recorded fish
|
|
np.ndarray: the frequency profile of the other fish
|
|
np.ndarray: the time axis
|
|
"""
|
|
self_freq = None
|
|
other_freq = None
|
|
signal = None
|
|
time = None
|
|
if "complete stimulus" not in block.data_arrays or "self frequency" not in block.data_arrays:
|
|
raise ValueError("Signals not stored in block!")
|
|
if "no-other" not in block.name and "other frequency" not in block.data_arrays:
|
|
raise ValueError("Signals not stored in block!")
|
|
|
|
signal = block.data_arrays["complete stimulus"][:]
|
|
time = np.asarray(block.data_arrays["complete stimulus"].dimensions[0].axis(len(signal)))
|
|
self_freq = block.data_arrays["self frequency"][:]
|
|
if "no-other" not in block.name:
|
|
other_freq = block.data_arrays["other frequency"][:]
|
|
return signal, self_freq, other_freq, time
|
|
|
|
|
|
def create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, current_df, figure_name=None):
|
|
conditions = ["no-other", "self", "other"]
|
|
condition_labels = ["soliloquy", "self chirping", "other chirping"]
|
|
min_time = 0.5
|
|
max_time = min_time + 0.5
|
|
|
|
fig = plt.figure(figsize=(6.5, 5.5))
|
|
fig_grid = (len(all_contrasts)*2 + 6, len(all_conditions)*3+2)
|
|
all_contrasts = sorted(all_contrasts, reverse=True)
|
|
|
|
for i, condition in enumerate(conditions):
|
|
# plot the signals
|
|
block = block_map[(all_contrasts[0], current_df, condition)]
|
|
signal, self_freq, other_freq, time = get_signals(block)
|
|
am = extract_am(signal)
|
|
|
|
self_eodf = block.metadata["stimulus parameter"]["eodfs"]["self"]
|
|
other_eodf = block.metadata["stimulus parameter"]["eodfs"]["other"]
|
|
|
|
# plot frequency traces
|
|
ax = plt.subplot2grid(fig_grid, (0, i * 3 + i), rowspan=2, colspan=3, fig=fig)
|
|
ax.plot(time[(time > min_time) & (time < max_time)], self_freq[(time > min_time) & (time < max_time)],
|
|
color="#ff7f0e", label="%iHz" % self_eodf)
|
|
ax.text(min_time-0.05, self_eodf, "%iHz" % self_eodf, color="#ff7f0e", va="center", ha="right", fontsize=9)
|
|
if other_freq is not None:
|
|
ax.plot(time[(time > min_time) & (time < max_time)], other_freq[(time > min_time) & (time < max_time)],
|
|
color="#1f77b4", label="%iHz" % other_eodf)
|
|
ax.text(min_time-0.05, other_eodf, "%iHz" % other_eodf, color="#1f77b4", va="center", ha="right", fontsize=9)
|
|
ax.set_title(condition_labels[i])
|
|
despine(ax, ["top", "bottom", "left", "right"], True)
|
|
|
|
# plot the am
|
|
ax = plt.subplot2grid(fig_grid, (3, i * 3 + i), rowspan=2, colspan=3, fig=fig)
|
|
ax.plot(time[(time > min_time) & (time < max_time)], signal[(time > min_time) & (time < max_time)],
|
|
color="#2ca02c", label="signal")
|
|
ax.plot(time[(time > min_time) & (time < max_time)], am[(time > min_time) & (time < max_time)],
|
|
color="#d62728", label="am")
|
|
despine(ax, ["top", "bottom", "left", "right"], True)
|
|
ax.set_ylim([-1.25, 1.25])
|
|
ax.legend(ncol=2, loc=(0.01, -0.5), fontsize=7, markerscale=0.5, frameon=False)
|
|
|
|
# for each contrast plot the firing rate
|
|
for j, contrast in enumerate(all_contrasts):
|
|
t, rates, _ = get_firing_rate(block_map, current_df, contrast, condition)
|
|
avg_resp = np.mean(rates, axis=0)
|
|
error = np.std(rates, axis=0)
|
|
ax = plt.subplot2grid(fig_grid, (j*2 + 6, i * 3 + i), rowspan=2, colspan=3)
|
|
ax.plot(t[(t > min_time) & (t < max_time)], avg_resp[(t > min_time) & (t < max_time)], color="k", lw=0.5)
|
|
ax.fill_between(t[(t > min_time) & (t < max_time)], (avg_resp - error)[(t > min_time) & (t < max_time)],
|
|
(avg_resp + error)[(t > min_time) & (t < max_time)], color="k", lw=0.0, alpha=0.25)
|
|
ax.set_ylim([0, 750])
|
|
ax.set_xlabel("")
|
|
ax.set_ylabel("")
|
|
ax.set_xticks(np.arange(min_time, max_time+.01, 0.250))
|
|
ax.set_xticklabels(map(int, (np.arange(min_time, max_time + .01, 0.250) - min_time) * 1000))
|
|
ax.set_xticks(np.arange(min_time, max_time+.01, 0.125), minor=True)
|
|
if j < len(all_contrasts) -1:
|
|
ax.set_xticklabels([])
|
|
ax.set_yticks(np.arange(0.0, 751., 500))
|
|
ax.set_yticks(np.arange(0.0, 751., 125), minor=True)
|
|
if i > 0:
|
|
ax.set_yticklabels([])
|
|
despine(ax, ["top", "right"], False)
|
|
if i == 2:
|
|
ax.text(max_time + 0.025*max_time, 350, "c=%.3f" % all_contrasts[j],
|
|
color="#d62728", ha="left", fontsize=7)
|
|
|
|
if i == 1:
|
|
ax.set_xlabel("time [ms]")
|
|
if i == 0:
|
|
ax.set_ylabel("frequency [Hz]", va="center")
|
|
ax.yaxis.set_label_coords(-0.45, 3.5)
|
|
|
|
name = figure_name if figure_name is not None else "chirp_responses.pdf"
|
|
name = (name + ".pdf") if ".pdf" not in name else name
|
|
plt.savefig(os.path.join(figure_folder, name))
|
|
plt.close()
|
|
|
|
|
|
def get_chirp_metadata(block):
|
|
trial_duration = float(block.metadata["stimulus parameter"]["duration"])
|
|
dt = float(block.metadata["stimulus parameter"]["dt"])
|
|
chirp_duration = block.metadata["stimulus parameter"]["chirp_duration"]
|
|
chirp_size = block.metadata["stimulus parameter"]["chirp_size"]
|
|
chirp_times = block.metadata["stimulus parameter"]["chirp_times"]
|
|
|
|
return trial_duration, dt, chirp_size, chirp_duration, chirp_times
|
|
|
|
|
|
def foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005, cell_name=""):
|
|
detection_performances = []
|
|
|
|
for contrast in all_contrasts:
|
|
print(" " * 50, end="\r")
|
|
print("Contrast: %.3f" % contrast, end="\r")
|
|
no_other_block = block_map[(contrast, df, "no-other")]
|
|
self_block = block_map[(contrast, df, "self")]
|
|
|
|
# get some metadata assuming they are all the same for each condition, which they should
|
|
duration, dt, _, chirp_duration, chirp_times = get_chirp_metadata(self_block)
|
|
|
|
interchirp_starts = np.add(chirp_times, 1.5 * chirp_duration)[:-1]
|
|
interchirp_ends = np.subtract(chirp_times, 1.5 * chirp_duration)[1:]
|
|
ici = np.floor(np.mean(np.subtract(interchirp_ends, interchirp_starts))*1000) / 1000
|
|
|
|
# get the spiking responses
|
|
no_other_spikes = get_spikes(no_other_block)
|
|
self_spikes = get_spikes(self_block)
|
|
|
|
# get firing rates
|
|
no_other_rates, _ = get_rates(no_other_spikes, duration, dt, kernel_width)
|
|
self_rates, _ = get_rates(self_spikes, duration, dt, kernel_width)
|
|
|
|
# get the response snippets between chrips
|
|
no_other_snippets = np.zeros((len(interchirp_starts) * no_other_rates.shape[0], int(ici / dt)))
|
|
self_snippets = np.zeros_like(no_other_snippets)
|
|
for i in range(no_other_rates.shape[0]):
|
|
for j, start in enumerate(interchirp_starts):
|
|
start_index = int(start/dt)
|
|
end_index = start_index + no_other_snippets.shape[1]
|
|
index = i * len(interchirp_starts) + j
|
|
no_other_snippets[index, :] = no_other_rates[i, start_index:end_index]
|
|
self_snippets[index, :] = self_rates[i, start_index:end_index]
|
|
|
|
# get the distances
|
|
baseline_dist = within_group_distance(no_other_snippets)
|
|
comp_dist = across_group_distance(no_other_snippets, self_snippets)
|
|
|
|
# sort and perfom roc
|
|
triangle_indices = np.tril_indices_from(baseline_dist, -1)
|
|
valid_distances_baseline = baseline_dist[triangle_indices]
|
|
temp1 = np.zeros_like(valid_distances_baseline)
|
|
|
|
valid_distances_comparison = comp_dist.ravel()
|
|
temp2 = np.ones_like(valid_distances_comparison)
|
|
|
|
group = np.hstack((temp1, temp2))
|
|
score = np.hstack((valid_distances_baseline, valid_distances_comparison))
|
|
fpr, tpr, _ = roc_curve(group, score, pos_label=1)
|
|
auc = roc_auc_score(group, score)
|
|
detection_performances.append({"cell": cell_name, "detection_task": "beat", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
|
|
print("\n")
|
|
return detection_performances
|
|
|
|
|
|
def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005, cell_name=""):
|
|
detection_performances = []
|
|
|
|
for contrast in all_contrasts:
|
|
print(" " * 50, end="\r")
|
|
print("Contrast: %.3f" % contrast, end="\r")
|
|
no_other_block = block_map[(contrast, df, "no-other")]
|
|
self_block = block_map[(contrast, df, "self")]
|
|
other_block = block_map[(contrast, df, "self")]
|
|
|
|
# get some metadata assuming they are all the same for each condition, which they should
|
|
duration, dt, _, chirp_duration, chirp_times = get_chirp_metadata(self_block)
|
|
|
|
# get the spiking responses
|
|
no_other_spikes = get_spikes(no_other_block)
|
|
self_spikes = get_spikes(self_block)
|
|
other_spikes = get_spikes(other_block)
|
|
|
|
# get firing rates
|
|
no_other_rates, _ = get_rates(no_other_spikes, duration, dt, kernel_width)
|
|
self_rates, _ = get_rates(self_spikes, duration, dt, kernel_width)
|
|
other_rates, _ = get_rates(other_spikes, duration, dt, kernel_width)
|
|
|
|
# get the chirp response snippets
|
|
alone_chirping_snippets = np.zeros((len(chirp_times) * no_other_rates.shape[0], int(chirp_duration / dt)))
|
|
self_snippets = np.zeros_like(alone_chirping_snippets)
|
|
other_snippets = np.zeros_like(alone_chirping_snippets)
|
|
baseline_snippets = np.zeros_like(alone_chirping_snippets)
|
|
|
|
for i in range(no_other_rates.shape[0]):
|
|
for j, chirp_time in enumerate(chirp_times):
|
|
start_index = int((chirp_time - chirp_duration/2 + 0.003)/dt)
|
|
end_index = start_index + alone_chirping_snippets.shape[1]
|
|
index = i * len(chirp_times) + j
|
|
alone_chirping_snippets[index, :] = no_other_rates[i, start_index:end_index]
|
|
self_snippets[index, :] = self_rates[i, start_index:end_index]
|
|
other_snippets[index, :] = other_rates[i, start_index:end_index]
|
|
baseline_start_index = int((chirp_time + 1.5 * chirp_duration)/dt)
|
|
baseline_end_index = baseline_start_index + alone_chirping_snippets.shape[1]
|
|
baseline_snippets[index, :] = no_other_rates[i, baseline_start_index:baseline_end_index]
|
|
|
|
# get the distances
|
|
# 1. Soliloquy
|
|
# 2. Nobody chirps, all alone aka baseline response
|
|
# 3. I chirp while the other is present compared to self chirping without the other one present
|
|
# 4. the otherone chrips to me compared to baseline with anyone chirping
|
|
alone_chirping_dist = within_group_distance(alone_chirping_snippets)
|
|
baseline_dist = within_group_distance(baseline_snippets)
|
|
self_vs_alone_dist = across_group_distance(alone_chirping_snippets, self_snippets)
|
|
other_vs_baseline_dist = across_group_distance(baseline_snippets, other_snippets)
|
|
|
|
# sort and perfom roc for two comparisons
|
|
# 1. soliloquy vs. self chirping in company
|
|
# 2. other chirping vs. nobody is chirping
|
|
triangle_indices = np.tril_indices_from(alone_chirping_dist, -1)
|
|
valid_no_other_distances = alone_chirping_dist[triangle_indices]
|
|
no_other_temp = np.zeros_like(valid_no_other_distances)
|
|
|
|
valid_baseline_distances = baseline_dist[triangle_indices]
|
|
baseline_temp = np.zeros_like(valid_baseline_distances)
|
|
|
|
valid_self_vs_alone_distances = self_vs_alone_dist.ravel()
|
|
self_vs_alone_temp = np.ones_like(valid_self_vs_alone_distances)
|
|
|
|
valid_other_vs_baseline_distances = other_vs_baseline_dist.ravel()
|
|
other_vs_baseline_temp = np.ones_like(valid_other_vs_baseline_distances)
|
|
|
|
group = np.hstack((no_other_temp, self_vs_alone_temp))
|
|
score = np.hstack((valid_no_other_distances, valid_self_vs_alone_distances))
|
|
fpr, tpr, _ = roc_curve(group, score, pos_label=1)
|
|
auc = roc_auc_score(group, score)
|
|
detection_performances.append({"cell": cell_name, "detection_task": "self vs soliloquy", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
|
|
|
|
group = np.hstack((baseline_temp, other_vs_baseline_temp))
|
|
score = np.hstack((valid_baseline_distances, valid_other_vs_baseline_distances))
|
|
fpr, tpr, _ = roc_curve(group, score, pos_label=1)
|
|
auc = roc_auc_score(group, score)
|
|
detection_performances.append({"cell": cell_name, "detection_task": "other vs quietness", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
|
|
|
|
print("\n")
|
|
return detection_performances
|
|
|
|
|
|
def plot_detection_results(data_frame, df, kernel_width, cell):
|
|
cell_results = data_frame[(data_frame.cell == cell) & (data_frame.df == df)]
|
|
conditions = sorted(cell_results.detection_task.unique())
|
|
kernels = sorted(cell_results.kernel_width.unique())
|
|
|
|
fig = plt.figure(figsize=(6.5, 5.5))
|
|
fig_grid = (8, 7)
|
|
for i, c in enumerate(conditions):
|
|
condition_results = cell_results[cell_results.detection_task == c]
|
|
|
|
roc_ax = plt.subplot2grid(fig_grid, (i * 2 + i, 0), colspan=3, rowspan=2)
|
|
auc_ax = plt.subplot2grid(fig_grid, (i * 2 + i, 4), colspan=3, rowspan=2)
|
|
|
|
roc_data = condition_results[condition_results.kernel_width == kernel_width]
|
|
|
|
contrasts = roc_data.contrast.unique()
|
|
for c in contrasts:
|
|
tpr = roc_data.true_positives[roc_data.contrast == c].values[0]
|
|
fpr = roc_data.false_positives[roc_data.contrast == c].values[0]
|
|
roc_ax.plot(fpr, tpr, label="%.3f" % c, zorder=2)
|
|
roc_ax.legend(loc="best", fontsize=6, ncol=2, frameon=False)
|
|
roc_ax.plot([0., 1.],[0., 1.], color="k", lw=0.5, ls="--", zorder=0)
|
|
roc_ax.set_xlabel("false positive rate", fontsize=9)
|
|
roc_ax.set_ylabel("true positive rate", fontsize=9)
|
|
roc_ax.set_xticks(np.arange(0.0, 1.01, 0.5))
|
|
roc_ax.set_xticks(np.arange(0.0, 1.01, 0.25), minor=True)
|
|
roc_ax.set_xticklabels(np.arange(0.0, 1.01, 0.5), fontsize=8)
|
|
roc_ax.set_yticks(np.arange(0.0, 1.01, 0.5))
|
|
roc_ax.set_yticks(np.arange(0.0, 1.01, 0.25), minor=True)
|
|
roc_ax.set_yticklabels(np.arange(0.0, 1.01, 0.5), fontsize=8)
|
|
|
|
for k in kernels:
|
|
contrasts = np.asarray(condition_results.contrast[condition_results.kernel_width == k])
|
|
aucs = np.asarray(condition_results.auc[condition_results.kernel_width == k])
|
|
aucs_sorted = aucs[np.argsort(contrasts)]
|
|
contrasts_sorted = np.sort(contrasts)
|
|
auc_ax.plot(contrasts_sorted, aucs_sorted, marker=".", label=r"$\sigma$: %.4f" % k)
|
|
|
|
auc_ax.set_xlabel("contrast [%]")
|
|
auc_ax.set_ylim([0.25, 1.0])
|
|
auc_ax.set_ylabel("discriminability")
|
|
auc_ax.legend(ncol=2, fontsize=6)
|
|
auc_ax.plot([min(contrasts), max(contrasts)], [0.5, 0.5], lw=0.5, ls"--",)
|
|
fig.savefig("discrimination.pdf")
|
|
|
|
|
|
def foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=None, cell_name=""):
|
|
dfs = [current_df] if current_df is not None else all_dfs
|
|
kernels = [0.00025, 0.0005, 0.001, 0.0025, 0.005]
|
|
result_dicts = []
|
|
for df in dfs:
|
|
for kw in kernels:
|
|
print("df: %i, kernel: %.4f" % (df, kw))
|
|
print("Foreign fish detection during beat:")
|
|
result_dicts.extend(foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, kw, cell_name))
|
|
print("Foreign fish detection during chirp:")
|
|
result_dicts.extend(foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, kw, cell_name))
|
|
|
|
|
|
break
|
|
|
|
embed()
|
|
|
|
return result_dicts
|
|
|
|
|
|
def process_cell(filename, dfs=[], contrasts=[], conditions=[]):
|
|
nf = nix.File.open(filename, nix.FileMode.ReadOnly)
|
|
block_map, all_contrasts, all_dfs, all_conditions = sort_blocks(nf)
|
|
if "baseline" in block_map.keys():
|
|
baseline_spikes = read_baseline(block_map["baseline"])
|
|
else:
|
|
print("ERROR: no baseline data for file %s!" % filename)
|
|
# fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_20Hz.pdf"
|
|
# create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, 20, figure_name=fig_name)
|
|
# fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_-100Hz.pdf"
|
|
# create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, -100, figure_name=fig_name)
|
|
results = foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=20,
|
|
cell_name=filename.split(os.path.sep)[-1].split(".nix")[0])
|
|
|
|
|
|
nf.close()
|
|
|
|
|
|
def main():
|
|
nix_files = sorted(glob.glob(os.path.join(data_folder, "cell*.nix")))
|
|
for nix_file in nix_files:
|
|
process_cell(nix_file, dfs=[20], contrasts=[20], conditions=["self"])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() |