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_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()