def plot_profile(lab, element):
plt.figure()
plt.plot(lab.profiles[element], -lab.x,
sns.xkcd_rgb["denim blue"], lw=3, label=element)
if element == 'Temperature':
plt.title('Temperature profile')
plt.xlabel('Temperature, C')
elif element == 'pH':
plt.title('pH profile')
plt.xlabel('pH')
else:
plt.title('%s concentration' % (element, ))
plt.xlabel('Concentration')
plt.ylabel('Depth')
ax = plt.gca()
ax.ticklabel_format(useOffset=False)
ax.grid(linestyle='-', linewidth=0.2)
plt.legend()
plt.tight_layout()
return ax
python类xkcd_rgb()的实例源码
def plot_evaluation_episode_reward():
pylab.clf()
sns.set_context("poster")
pylab.plot(0, 0)
episodes = [0]
average_scores = [0]
median_scores = [0]
for n in xrange(len(csv_evaluation)):
params = csv_evaluation[n]
episodes.append(params[0])
average_scores.append(params[1])
median_scores.append(params[2])
pylab.plot(episodes, average_scores, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("average score")
pylab.savefig("%s/evaluation_episode_average_reward.png" % args.plot_dir)
pylab.clf()
pylab.plot(0, 0)
pylab.plot(episodes, median_scores, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("median score")
pylab.savefig("%s/evaluation_episode_median_reward.png" % args.plot_dir)
def plot_evaluation_episode_reward():
pylab.clf()
sns.set_context("poster")
pylab.plot(0, 0)
episodes = [0]
average_scores = [0]
median_scores = [0]
for n in xrange(len(csv_evaluation)):
params = csv_evaluation[n]
episodes.append(params[0])
average_scores.append(params[1])
median_scores.append(params[2])
pylab.plot(episodes, average_scores, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("average score")
pylab.savefig("%s/evaluation_episode_average_reward.png" % args.plot_dir)
pylab.clf()
pylab.plot(0, 0)
pylab.plot(episodes, median_scores, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("median score")
pylab.savefig("%s/evaluation_episode_median_reward.png" % args.plot_dir)
def plot_episode_reward():
pylab.clf()
sns.set_context("poster")
pylab.plot(0, 0)
episodes = [0]
scores = [0]
for n in xrange(len(csv_episode)):
params = csv_episode[n]
episodes.append(params[0])
scores.append(params[1])
pylab.plot(episodes, scores, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("score")
pylab.savefig("%s/episode_reward.png" % args.plot_dir)
def plot_training_episode_highscore():
pylab.clf()
sns.set_context("poster")
pylab.plot(0, 0)
episodes = [0]
highscore = [0]
for n in xrange(len(csv_training_highscore)):
params = csv_training_highscore[n]
episodes.append(params[0])
highscore.append(params[1])
pylab.plot(episodes, highscore, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("highscore")
pylab.savefig("%s/training_episode_highscore.png" % args.plot_dir)
def plot_crbh_fit(model_df, hits_df, model_plot_fn, show=False,
figsize=(10,10), feature_col='E', length_col='s_aln_len',
**fig_kwds):
plt.style.use('seaborn-ticks')
with FigureManager(model_plot_fn, show=show,
figsize=figsize, **fig_kwds) as (fig, ax):
sample_size = min(len(hits_df), 5000)
hits_df, scaled_col = scale_evalues(hits_df, name=feature_col,
inplace=False)
ax.scatter(hits_df[length_col], hits_df[scaled_col], s=10, alpha=0.7,
c=sns.xkcd_rgb['ruby'], marker='o', label='Query Hits')
ax.scatter(model_df['center'], model_df['fit'], label='CRBL Fit',
c=sns.xkcd_rgb['twilight blue'], marker='o', s=5, alpha=0.7)
leg = ax.legend(fontsize='medium', scatterpoints=3, frameon=True)
leg.get_frame().set_linewidth(1.0)
ax.set_xlim(model_df['center'].min(), model_df['center'].max())
ax.set_ylim(0, max(model_df['fit'].max(), hits_df[scaled_col].max()) + 50)
ax.set_ylabel('Score ($E_{scaled}$)' if scaled_col == 'E_scaled'\
else 'Score ({0})'.format(scaled_col))
ax.set_xlabel('Alignment Length')
def plot_episode_reward():
pylab.clf()
sns.set_context("poster")
pylab.plot(0, 0)
episodes = [0]
scores = [0]
for n in xrange(len(csv_episode)):
params = csv_episode[n]
episodes.append(params[0])
scores.append(params[1])
pylab.plot(episodes, scores, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("score")
pylab.savefig("%s/episode_reward.png" % args.plot_dir)
def plot_training_episode_highscore():
pylab.clf()
sns.set_context("poster")
pylab.plot(0, 0)
episodes = [0]
highscore = [0]
for n in xrange(len(csv_training_highscore)):
params = csv_training_highscore[n]
episodes.append(params[0])
highscore.append(params[1])
pylab.plot(episodes, highscore, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("highscore")
pylab.savefig("%s/training_episode_highscore.png" % args.plot_dir)
