本文整理汇总了Python中seaborn.despine方法的典型用法代码示例。如果您正苦于以下问题:Python seaborn.despine方法的具体用法?Python seaborn.despine怎么用?Python seaborn.despine使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类seaborn
的用法示例。
在下文中一共展示了seaborn.despine方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: image
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def image(path: str, costs: Dict[str, int]) -> str:
ys = ['0', '1', '2', '3', '4', '5', '6', '7+', 'X']
xs = [costs.get(k, 0) for k in ys]
sns.set_style('white')
sns.set(font='Concourse C3', font_scale=3)
g = sns.barplot(ys, xs, palette=['#cccccc'] * len(ys))
g.axes.yaxis.set_ticklabels([])
rects = g.patches
sns.set(font='Concourse C3', font_scale=2)
for rect, label in zip(rects, xs):
if label == 0:
continue
height = rect.get_height()
g.text(rect.get_x() + rect.get_width()/2, height + 0.5, label, ha='center', va='bottom')
g.margins(y=0, x=0)
sns.despine(left=True, bottom=True)
g.get_figure().savefig(path, transparent=True, pad_inches=0, bbox_inches='tight')
plt.clf() # Clear all data from matplotlib so it does not persist across requests.
return path
示例2: customize
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def customize(func):
"""
修饰器,设置输出图像内容与风格
"""
@wraps(func)
def call_w_context(*args, **kwargs):
set_context = kwargs.pop("set_context", True)
if set_context:
color_palette = sns.color_palette("colorblind")
with plotting_context(), axes_style(), color_palette:
sns.despine(left=True)
return func(*args, **kwargs)
else:
return func(*args, **kwargs)
return call_w_context
示例3: plot_read_count_dists
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def plot_read_count_dists(counts, h=8, n=50):
"""Boxplots of read count distributions """
scols,ncols = base.get_column_names(counts)
df = counts.sort_values(by='mean_norm',ascending=False)[:n]
df = df.set_index('name')[ncols]
t = df.T
w = int(h*(len(df)/60.0))+4
fig, ax = plt.subplots(figsize=(w,h))
if len(scols) > 1:
sns.stripplot(data=t,linewidth=1.0,palette='coolwarm_r')
ax.xaxis.grid(True)
else:
df.plot(kind='bar',ax=ax)
sns.despine(offset=10,trim=True)
ax.set_yscale('log')
plt.setp(ax.xaxis.get_majorticklabels(), rotation=90)
plt.ylabel('read count')
#print (df.index)
#plt.tight_layout()
fig.subplots_adjust(bottom=0.2,top=0.9)
return fig
示例4: plot_pca
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def plot_pca(pX, palette='Spectral', labels=None, ax=None, colors=None):
"""Plot PCA result, input should be a dataframe"""
if ax==None:
fig,ax=plt.subplots(1,1,figsize=(6,6))
cats = pX.index.unique()
colors = sns.mpl_palette(palette, len(cats)+1)
print (len(cats), len(colors))
for c, i in zip(colors, cats):
#print (i, len(pX.ix[i]))
#if not i in pX.index: continue
ax.scatter(pX.ix[i, 0], pX.ix[i, 1], color=c, s=90, label=i,
lw=.8, edgecolor='black', alpha=0.8)
ax.set_xlabel('PC1')
ax.set_ylabel('PC2')
i=0
if labels is not None:
for n, point in pX.iterrows():
l=labels[i]
ax.text(point[0]+.1, point[1]+.1, str(l),fontsize=(9))
i+=1
ax.legend(fontsize=10,bbox_to_anchor=(1.5, 1.05))
sns.despine()
plt.tight_layout()
return
示例5: barplot_messages_per_weekday
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def barplot_messages_per_weekday(msgs, your_name, target_name, path_to_save):
sns.set(style="whitegrid", palette="pastel")
messages_per_weekday = stools.get_messages_per_weekday(msgs)
labels = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
ax = sns.barplot(x=labels, y=[len(weekday) for weekday in messages_per_weekday.values()],
label=your_name, color="b")
sns.set_color_codes("muted")
sns.barplot(x=labels,
y=[len([msg for msg in weekday if msg.author == target_name])
for weekday in messages_per_weekday.values()],
label=target_name, color="b")
ax.legend(ncol=2, loc="lower right", frameon=True)
ax.set(ylabel="messages")
sns.despine(right=True, top=True)
fig = plt.gcf()
fig.set_size_inches(11, 8)
fig.savefig(os.path.join(path_to_save, barplot_messages_per_weekday.__name__ + ".png"), dpi=500)
# plt.show()
log_line(f"{barplot_messages_per_weekday.__name__} was created.")
plt.close("all")
示例6: bar_box_violin_dot_plots
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def bar_box_violin_dot_plots(data, category_col, numeric_col, axes, file_name=None):
sns.barplot(category_col, numeric_col, data=data, ax=axes[0])
sns.boxplot(
category_col, numeric_col, data=data[data[numeric_col].notnull()], ax=axes[2]
)
sns.violinplot(
category_col,
numeric_col,
data=data,
kind="violin",
inner="quartile",
scale="count",
split=True,
ax=axes[3],
)
sns.stripplot(category_col, numeric_col, data=data, jitter=True, ax=axes[1])
sns.despine(left=True)
示例7: learning_curve
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def learning_curve(self, idxs=[2,3,5,6]):
import seaborn as sns
import matplotlib.pyplot as plt
plt.switch_backend('agg')
# set style
sns.set_context("paper", font_scale=1.5,)
# sns.set_style("ticks", {
# "font.family": "Times New Roman",
# "font.serif": ["Times", "Palatino", "serif"]})
for idx in idxs:
plt.plot(self.logs[self.args.trigger],
self.logs[self.header[idx]], label=self.header[idx])
plt.ylabel(" {} / {} ".format(repr(self.criterion), repr(self.evaluator)))
if self.args.trigger == 'epoch':
plt.xlabel("Epochs")
else:
plt.xlabel("Iterations")
plt.suptitle("Training log of {}".format(self.method))
# remove top&left line
# sns.despine()
plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
plt.savefig(os.path.join(Logs_DIR, 'curve', '{}.png'.format(self.repr)),
format='png', bbox_inches='tight', dpi=144)
示例8: customize
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def customize(func):
@wraps(func)
def call_w_context(*args, **kwargs):
if not PlotConfig.FONT_SETTED:
_use_chinese(True)
set_context = kwargs.pop('set_context', True)
if set_context:
with plotting_context(), axes_style():
sns.despine(left=True)
return func(*args, **kwargs)
else:
return func(*args, **kwargs)
return call_w_context
示例9: __init__
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def __init__(self, save_animation=False, fps=30):
"""Initialize the helper class.
:param save_animation: Whether the animation should be saved as a gif. Requires the ImageMagick library.
:param fps: The number of frames per second when saving the gif animation.
"""
self.save_animation = save_animation
self.fps = fps
self.figure, (self.ax1, self.ax2) = plt.subplots(1, 2, figsize=(8, 4))
self.figure.suptitle("1D GAN")
sns.set(color_codes=True, style='white', palette='colorblind')
sns.despine(self.figure)
plt.show(block=False)
if self.save_animation:
self.writer = ImageMagickWriter(fps=self.fps)
self.writer.setup(self.figure, 'demo.gif', dpi=100)
示例10: fig6
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def fig6():
""" violin plot for the physicochemical proerties comparison.
A: molecules generated by pre-trained model v.s. ZINC set.
B: molecules generated by fine-tuned model v.s. A2AR set.
"""
plt.figure(figsize=(12, 6))
plt.subplot(121)
sns.set(style="white", palette="pastel", color_codes=True)
df = properties(['data/ZINC_B.txt', 'mol_p.txt'], ['ZINC Dataset', 'Pre-trained Model'])
sns.violinplot(x='Property', y='Number', hue='Set', data=df, linewidth=1, split=True, bw=1)
sns.despine(left=True)
plt.ylim([0.0, 18.0])
plt.xlabel('Structural Properties')
plt.subplot(122)
df = properties(['data/CHEMBL251.txt', 'mol_ex.txt'], ['A2AR Dataset', 'Fine-tuned Model'])
sns.set(style="white", palette="pastel", color_codes=True)
sns.violinplot(x='Property', y='Number', hue='Set', data=df, linewidth=1, split=True, bw=1)
sns.despine(left=True)
plt.ylim([0.0, 18.0])
plt.xlabel('Structural Properties')
plt.tight_layout()
plt.savefig('Figure_6.tif', dpi=300)
示例11: fig9
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def fig9():
""" violin plot for the physicochemical proerties comparison.
1: molecules generated by DrugEx with pre-trained model as exploration network.
2: molecules generated by DrugEx with fine-tuned model as exploration network.
"""
fig = plt.figure(figsize=(12, 12))
ax1 = fig.add_subplot(211)
sns.set(style="white", palette="pastel", color_codes=True)
df = properties(mol_paths + real_path, labels + real_label, is_active=True)
sns.violinplot(x='Property', y='Number', hue='Set', data=df, linewidth=1, bw=0.8)
sns.despine(left=True)
ax1.set(ylim=[0.0, 15.0], xlabel='Structural Properties')
ax2 = fig.add_subplot(212)
df = properties(mol_paths1 + real_path, labels + real_label, is_active=True)
sns.set(style="white", palette="pastel", color_codes=True)
sns.violinplot(x='Property', y='Number', hue='Set', data=df, linewidth=1, bw=0.8)
sns.despine(left=True)
ax2.set(ylim=[0.0, 15.0], xlabel='Structural Properties')
fig.tight_layout()
fig.savefig('Figure_9.tif', dpi=300)
示例12: plot_all_models_gmm
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def plot_all_models_gmm(models, data, l, u, bins, out_file):
"""
Plot a bunch of GMMs.
:param models: list of GMM model objects
:param data: Ks array
:param l: lower Ks limit
:param u: upper Ks limit
:param bins: number of histogram bins
:param out_file: output file
:return: nada
"""
fig, axes = plt.subplots(len(models), 3, figsize=(15, 3 * len(models)))
for i, model in enumerate(models):
plot_mixture(model, data, axes[i, 0], l, u, bins=bins)
plot_mixture(model, data, axes[i, 1], log=True, l=np.log(l + 0.0001),
u=np.log(u), bins=bins)
plot_probs(model, axes[i, 2], l, u)
sns.despine(offset=5)
fig.tight_layout()
fig.savefig(out_file)
示例13: plot_all_models_bgmm
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def plot_all_models_bgmm(models, data, l, u, bins, out_file):
"""
Plot a bunch of BGMMs.
:param models: list of GMM model objects
:param data: Ks array
:param l: lower Ks limit
:param u: upper Ks limit
:param bins: number of histogram bins
:param out_file: output file
:return: nada
"""
fig, axes = plt.subplots(len(models), 4, figsize=(20, 3 * len(models)))
for i, model in enumerate(models):
plot_mixture(model, data, axes[i, 0], l, u, bins=bins)
plot_mixture(model, data, axes[i, 1], log=True, l=np.log(l + 0.0001),
u=np.log(u), bins=bins)
plot_probs(model, axes[i, 2], l, u)
plot_bars_weights(model, axes[i, 3])
sns.despine(offset=5)
fig.tight_layout()
fig.savefig(out_file)
示例14: geoValueWeightedVisulization
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def geoValueWeightedVisulization(valueDes):
valueDes["ID"]=valueDes.index
sns.set(style="whitegrid")
# Make the PairGrid
extractedColumns=["count","mean","std","max"]
g=sns.PairGrid(valueDes.sort_values("count", ascending=False),x_vars=extractedColumns, y_vars=["ID"],height=10, aspect=.25)
# Draw a dot plot using the stripplot function
g.map(sns.stripplot, size=10, orient="h",palette="ch:s=1,r=-.1,h=1_r", linewidth=1, edgecolor="w")
# Use the same x axis limits on all columns and add better labels
g.set(xlabel="value", ylabel="") #g.set(xlim=(0, 25), xlabel="Crashes", ylabel="")
# Use semantically meaningful titles for the columns
titles=valueDes.columns.tolist()
for ax, title in zip(g.axes.flat, titles):
# Set a different title for each axes
ax.set(title=title)
# Make the grid horizontal instead of vertical
ax.xaxis.grid(False)
ax.yaxis.grid(True)
sns.despine(left=True, bottom=True)
示例15: BoxPlot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import despine [as 别名]
def BoxPlot(self, feature):
fig, ax = plt.subplots()
ax = sns.boxplot(y=self.df[feature], ax=ax)
box = ax.artists[0]
indices = random.sample(range(len(self.SelectedColors)), 2)
colors=[self.SelectedColors[i] for i in sorted(indices)]
box.set_facecolor(colors[0])
box.set_edgecolor(colors[1])
sns.despine(offset=10, trim=True)
this_dir, this_filename = os.path.split(__file__)
OutFileName = os.path.join(this_dir, 'HTMLTemplate/dist/output/'+feature + '.png')
if platform.system() =='Linux':
OutFileName = os.path.join(this_dir, 'HTMLTemplate/dist/output/' + feature + '.png')
plt.savefig(OutFileName)
return OutFileName