out_i['dir'] = 'Low $[S^{**}]$'
out = pandas.concat([out, out_i[out.columns]])
for i in range(len(sim_res_rev)):
out_i = pandas.DataFrame(sim_res_rev[i], columns=out.columns[3:])
out_i['time'] = t
out_i['signal'] = numpy.flip(C3_scan)[i]
out_i['dir'] = 'High $[S^{**}]$'
out = pandas.concat([out, out_i[out.columns]])
out.to_csv("./num_cont_nuts_model/sim2.txt", sep="\t", index=False)
###################### plotting ##################################
g = (
ggplot(out, aes('time', response, group='signal', color='signal')) +
geom_line(size=0.5)
# + ylim(0, 202)
+ labs(x="time", y="$[S^{**}]$") + scale_color_distiller(
palette='RdYlBu', type="diverging", name="$B_{tot}$") +
facet_wrap('~dir') + theme_bw())
g.save(filename="./num_cont_nuts_model/sim_fwd_rev2.png",
format="png",
width=8,
height=4,
units='in',
verbose=False)
eq = out[out.time == max(out.time)]
# with pandas.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also
# print(eq)
g = (ggplot(eq) + aes(x='signal', y=response, color='dir') +
labs(x="$B_{tot}$", y="$[S^{**}]$", color="") +
out_i['time'] = t
out_i['signal'] = C3_scan[i]
out_i['dir'] = 'Low $[S^{**}]$'
out = pandas.concat([out, out_i[out.columns]])
for i in range(len(sim_res_rev)):
out_i = pandas.DataFrame(sim_res_rev[i], columns=out.columns[3:])
out_i['time'] = t
out_i['signal'] = numpy.flip(C3_scan)[i]
out_i['dir'] = 'High $[S^{**}]$'
out = pandas.concat([out, out_i[out.columns]])
out.to_csv("./num_cont_graphs/sim2.txt", sep="\t", index=False)
###################### plotting ##################################
g = (ggplot(out, aes('time', response, group='signal', color='signal')) +
geom_line(size=0.5) + ylim(0, 202) + labs(x="time", y="$[S^{**}]$") +
scale_color_distiller(
palette='RdYlBu', type="diverging", name="$B_{tot}$") +
facet_wrap('~dir') + theme_bw())
g.save(filename="./num_cont_graphs/sim_fwd_rev2.png",
format="png",
width=8,
height=4,
units='in',
verbose=False)
eq = out[out.time == max(out.time)]
g = (ggplot(eq) + aes(x='signal', y=response, color='dir') +
labs(x="$B_{tot}$", y="$[S^{**}]$", color="") +
geom_path(size=2, alpha=0.5) + geom_point(color="black") + theme_bw() +
geom_point(color="black") + annotate("point",
x=plot_specifications[2][0][0],
out_i['time'] = t
out_i['signal'] = C3_scan[i]
out_i['dir'] = 'fwd'
out = pandas.concat([out, out_i[out.columns]])
for i in range(len(sim_res_rev)):
out_i = pandas.DataFrame(sim_res_rev[i], columns=out.columns[3:])
out_i['time'] = t
out_i['signal'] = numpy.flip(C3_scan)[i]
out_i['dir'] = 'rev'
out = pandas.concat([out, out_i[out.columns]])
out.to_csv("sim.txt", sep="\t", index=False)
###################### plotting ##################################
g = (ggplot(out, aes('time', 's2', group='signal', color='signal')) +
geom_line(size=0.5) + ylim(0, 20000) +
scale_color_distiller(palette='RdYlBu', type="diverging") +
facet_wrap('~dir') + theme_bw())
g.save(filename="./num_cont_graphs/sim_fwd_rev.png",
format="png",
width=8,
height=4,
units='in',
verbose=False)
eq = out[out.time == max(out.time)]
g = (ggplot(eq) + aes(x='signal', y='s2', color='dir') +
geom_path(size=2, alpha=0.5) + geom_point(color="black") + theme_bw())
g.save(filename="./num_cont_graphs/sim_bif_diag.png",
format="png",
width=8,
height=4,
