def correlation(mat_file_1, mat_file_2):
"""
Draws the plot
"""
blockades_1 = read_mat(mat_file_1)
blockades_1 = sp._fractional_blockades(blockades_1)
blockades_1 = sp._filter_by_duration(blockades_1, 0.5, 20)
blockades_1 = map(lambda b: sp.discretize(sp._trim_flank_noise(b.eventTrace), 20), blockades_1)
blockades_2 = read_mat(mat_file_2)
blockades_2 = sp._fractional_blockades(blockades_2)
blockades_2 = sp._filter_by_duration(blockades_2, 0.5, 20)
blockades_2 = map(lambda b: sp.discretize(sp._trim_flank_noise(b.eventTrace), 20), blockades_2)
self_corr = []
cross_corr = []
for blockade in blockades_1:
block_self = []
for other in blockades_1:
block_self.append(1 - distance.correlation(blockade, other))
block_cross = []
for other in blockades_2:
block_cross.append(1 - distance.correlation(blockade, other))
self_corr.append(np.mean(block_self))
cross_corr.append(np.mean(block_cross))
mean_self = np.median(self_corr)
mean_cross = np.median(cross_corr)
matplotlib.rcParams.update({"font.size": 16})
fig = plt.subplot()
fig.spines["right"].set_visible(False)
fig.spines["top"].set_visible(False)
fig.get_xaxis().tick_bottom()
fig.get_yaxis().tick_left()
fig.set_xlim(-0.6, 0.6)
fig.set_ylim(-0.6, 0.6)
fig.set_xlabel("(H3 tail, H3 tail) correlation")
fig.set_ylabel("(H3 tail, CCL5) correlation")
for y in [-0.4, -0.2, 0, 0.2, 0.4]:
plt.plot((-0.6, 0.6), (y, y), "--",
lw=0.5, color="black")
plt.plot((y, y), (-0.6, 0.6), "--",
lw=0.5, color="black")
plt.plot((-0.6, 0.6), (mean_cross, mean_cross), "--",
lw=1.5, color="red")
plt.plot((mean_self, mean_self), (-0.6, 0.6), "--",
lw=1.5, color="red")
fig.scatter(self_corr, cross_corr, linewidth=0.5, c="dodgerblue",
s=30, edgecolor="blue")
plt.tight_layout()
plt.show()
def frequency_distribution(blockades_file, detailed):
"""
Plots the frequency distribution
"""
blockades = read_mat(blockades_file)
blockades = sp._fractional_blockades(blockades)
blockades = sp._filter_by_duration(blockades, 0.5, 20)
peaks_count = {}
for blockade in blockades:
if detailed:
detailed_plots(blockade)
signal = blockade.eventTrace[1000:-1000]
xx, yy = sp.find_peaks(signal)
peaks_count[blockade] = len(xx) / blockade.ms_Dwell * 5 / 4
mean = np.mean(peaks_count.values())
errors = map(lambda e: peaks_count[e] - mean, blockades)
lengths = map(lambda e: e.ms_Dwell, blockades)
f, (s1, s2) = plt.subplots(2)
s1.scatter(lengths, errors)
s2.hist(peaks_count.values(), bins=100)
plt.show()
def frequency_distribution(blockades_file, detailed):
"""
Plots the frequency distribution
"""
blockades = read_mat(blockades_file)
blockades = sp._fractional_blockades(blockades)
blockades = sp._filter_by_duration(blockades, 0.5, 20)
peaks_count = {}
for blockade in blockades:
if detailed:
detailed_plots(blockade)
signal = blockade.eventTrace[1000:-1000]
xx, yy = sp.find_peaks(signal)
peaks_count[blockade] = len(xx) / blockade.ms_Dwell * 5 / 4
mean = np.mean(peaks_count.values())
errors = map(lambda e: peaks_count[e] - mean, blockades)
lengths = map(lambda e: e.ms_Dwell, blockades)
f, (s1, s2) = plt.subplots(2)
s1.scatter(lengths, errors)
s2.hist(peaks_count.values(), bins=100)
plt.show()
def frequency_plot(blockade_files):
"""
Draws the plot
"""
datasets_names = []
frequencies = []
for file in blockade_files:
blockades = read_mat(file)
blockades = sp._fractional_blockades(blockades)
blockades = sp._filter_by_duration(blockades, 0.5, 20)
dataset_freqs = []
for blockade in blockades:
xx, yy = sp.find_peaks(blockade.eventTrace[1000:-1000])
dataset_freqs.append(len(xx) / blockade.ms_Dwell * 5 / 4)
frequencies.append(dataset_freqs)
datasets_names.append(os.path.basename(file).split(".")[0])
x_axis = np.arange(min(sum(frequencies, [])) - 10, max(sum(frequencies, [])) + 10, 0.1)
matplotlib.rcParams.update({"font.size": 16})
fig = plt.subplot()
colors = ["blue", "green", "red", "cyan"]
for distr, name, color in zip(frequencies, datasets_names, colors):
density = gaussian_kde(distr)
density.covariance_factor = lambda: 0.25
density._compute_covariance
gauss_dens = density(x_axis)
fig.spines["right"].set_visible(False)
fig.spines["top"].set_visible(False)
fig.get_xaxis().tick_bottom()
fig.get_yaxis().tick_left()
fig.set_ylim(0, 0.16)
fig.plot(x_axis, gauss_dens, antialiased=True, linewidth=2, color=color, alpha=0.7, label=name)
fig.fill_between(x_axis, gauss_dens, alpha=0.5, antialiased=True, color=color)
fig.set_xlabel("Fluctuation frequency, 1/ms")
legend = fig.legend(loc="upper left", frameon=False)
for label in legend.get_lines():
label.set_linewidth(3)
for label in legend.get_texts():
label.set_fontsize(16)
plt.show()
def correlation(mat_file_1, mat_file_2):
"""
Draws the plot
"""
blockades_1 = read_mat(mat_file_1)
blockades_1 = sp._fractional_blockades(blockades_1)
blockades_1 = sp._filter_by_duration(blockades_1, 0.5, 20)
blockades_1 = map(
lambda b: sp.discretize(sp._trim_flank_noise(b.eventTrace), 20),
blockades_1)
blockades_2 = read_mat(mat_file_2)
blockades_2 = sp._fractional_blockades(blockades_2)
blockades_2 = sp._filter_by_duration(blockades_2, 0.5, 20)
blockades_2 = map(
lambda b: sp.discretize(sp._trim_flank_noise(b.eventTrace), 20),
blockades_2)
self_corr = []
cross_corr = []
for blockade in blockades_1:
block_self = []
for other in blockades_1:
block_self.append(1 - distance.correlation(blockade, other))
block_cross = []
for other in blockades_2:
block_cross.append(1 - distance.correlation(blockade, other))
self_corr.append(np.mean(block_self))
cross_corr.append(np.mean(block_cross))
mean_self = np.median(self_corr)
mean_cross = np.median(cross_corr)
matplotlib.rcParams.update({"font.size": 16})
fig = plt.subplot()
fig.spines["right"].set_visible(False)
fig.spines["top"].set_visible(False)
fig.get_xaxis().tick_bottom()
fig.get_yaxis().tick_left()
fig.set_xlim(-0.6, 0.6)
fig.set_ylim(-0.6, 0.6)
fig.set_xlabel("(H3 tail, H3 tail) correlation")
fig.set_ylabel("(H3 tail, CCL5) correlation")
for y in [-0.4, -0.2, 0, 0.2, 0.4]:
plt.plot((-0.6, 0.6), (y, y), "--", lw=0.5, color="black")
plt.plot((y, y), (-0.6, 0.6), "--", lw=0.5, color="black")
plt.plot((-0.6, 0.6), (mean_cross, mean_cross), "--", lw=1.5, color="red")
plt.plot((mean_self, mean_self), (-0.6, 0.6), "--", lw=1.5, color="red")
fig.scatter(self_corr,
cross_corr,
linewidth=0.5,
c="dodgerblue",
s=30,
edgecolor="blue")
plt.tight_layout()
plt.show()
