def plot_go_terms_up(filename, ontologies, case_name, df):
self._temp_df[case_name] = df.copy()
self._plus[case_name] = sum(df.plus_minus == '+')
self._minus[case_name] = sum(df.plus_minus == '-')
pylab.savefig(f"{config.output_dir}/Panther_up_{case_name}.png")
pylab.savefig(filename)
pylab.close()
def plotter(filename, key):
name = key.replace(" ", "_")
pylab.ioff()
histograms[key].plot(logy=False, lw=2, marker="o")
pylab.title(name + "(%s)" % count)
pylab.grid(True)
pylab.savefig(filename)
pylab.close() # need to close the figure otherwise warnings
def plotter(filename, key):
name = key.replace(" ", "_")
pylab.ioff()
histograms[key].plot(logy=False, lw=2, marker="o")
pylab.title(name + "(%s)" % count)
pylab.grid(True)
pylab.savefig(filename)
pylab.close() # need to close the figure otherwise warnings
def plot_go_terms_down(filename, ontologies, case_name, df):
df = self.pe.plot_go_terms("down", ontologies=ontologies,
compute_levels=self.enrichment_params['plot_compute_levels'],
log=self.enrichment_params['plot_logx'])
self._temp_df[case_name] = df.copy()
self._plus[case_name] = sum(df.plus_minus == '+')
self._minus[case_name] = sum(df.plus_minus == '-')
pylab.savefig(f"{config.output_dir}/Panther_down_{case_name}.png")
pylab.savefig(filename)
pylab.close()
# plot coverage found by blasr, with random y distribution (to see if there are overlaps)
ax = axarr[i * 2 + 1]
list_contigs = plot_contigs(res_best_ref, ax, colors, mode="random")
for area in genome_not_covered:
ax.axvspan(area[0], area[1], alpha=0.1, color='k')
ax.set_ylabel("Random")
ax.set_xlabel("Reference genome position")
ax.set_title(ref_found[i] + " : Random y")
if save_not_covered:
print(ref_found[i])
if len(genome_not_covered) > 0:
df = pd.DataFrame(genome_not_covered)
df.columns = ["start", "end"]
df["reference"] = ref_found[i]
df_not_covered_all = pd.concat([df_not_covered_all, df],
ignore_index=True)
#fig.subplots_adjust(bottom=0.1,top=0.9)
fig.tight_layout()
if save_plot:
pylab.savefig(file_plot)
else:
pylab.show()
pylab.close("all")
if save_not_covered:
df_not_covered_all.to_csv(filename_not_covered, index=None)
print("Create plots")
#colors = ['m','r','y','g','b','c','k']
cmap = pylab.cm.get_cmap(colormap)
#colors = [cmap(i) for i in np.linspace(0,1,len(list_analysis))]
# positions of genome
gen_pos = [[i, i + step - 1] for i in range(0, len_genome, step)]
y_pos = list(np.linspace(0, 1, len(analysis_names) + 2))
if custom_colors:
y_col = [colors[i] for i in range(len(analysis_names))]
else:
y_col = [cmap(i) for i in np.linspace(0, 1, len(analysis_names))]
pylab.close('all')
# create figure
fig, axarr = pylab.subplots(len(gen_pos),
1,
figsize=(int(step / 20000),
int(len(gen_pos)) * 1.1))
for i in range(len(gen_pos)):
subplot_variant_position(df_result, i, gen_pos, axarr, analysis_names,
y_pos, y_col, be_repeats_concat)
# add grey at the end (no genome)
ax = axarr[-1]
ax.axvspan(len_genome, gen_pos[-1][1], alpha=0.5, color='k')
#fig.subplots_adjust(bottom=0.2)