本文整理汇总了Python中seaborn.lineplot方法的典型用法代码示例。如果您正苦于以下问题:Python seaborn.lineplot方法的具体用法?Python seaborn.lineplot怎么用?Python seaborn.lineplot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.lineplot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: evolution_over_time
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def evolution_over_time(self, column, **kwargs):
"""
Visualize the evolution over time of a column for all assets in group.
Parameters:
- column: The name of the column to visualize.
- kwargs: Additional keyword arguments to pass down
to the plotting function.
Returns:
A matplotlib Axes object.
"""
if 'ax' not in kwargs:
fig, ax = plt.subplots(1, 1, figsize=(10, 4))
else:
ax = kwargs.pop('ax')
return sns.lineplot(
x=self.data.index,
y=column,
hue=self.group_by,
data=self.data,
ax=ax,
**kwargs
)
示例2: analyze
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def analyze():
action_count = np.load(action_count_dir, allow_pickle=True)
print('action count loaded')
# action taken > 0
count_dict = {}
for i, v in enumerate(action_count):
if v > 0:
count_dict[i] = v
print('\n\n')
for k, v in sorted(count_dict.items()):
print('action: {}, count: {}'.format(k, v))
# y = np.load(data_dir + '/y_all_3695_score.npy')
# print(y[:20])
# plot barplot
seaborn.lineplot(list(count_dict.keys()), list(count_dict.values()))
plt.title('Action Count')
plt.xlabel('Action Index')
plt.ylabel('Count')
plt.show()
示例3: visualize_distribution_time_serie
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def visualize_distribution_time_serie(ts,value,path=None):
"""
Visualize the time-serie data in each individual dimensions.
Parameters
----------
ts: numpy array of shape (n_test, n_features)
The value of the test time serie data.
value: numpy array of shape (n_test, )
The outlier score of the test data.
path: string
The saving path for result figures.
"""
sns.set(style="ticks")
ts = pd.DatetimeIndex(ts)
value=value.to_numpy()[:,1:]
data = pd.DataFrame(value,ts)
data = data.rolling(2).mean()
sns_plot=sns.lineplot(data=data, palette="BuGn_r", linewidth=0.5)
if path:
sns_plot.figure.savefig(path+'/timeserie.png')
plt.show()
示例4: plot_data
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def plot_data(data, time="Iteration", value="AverageReturn", combine=False):
if isinstance(data, list):
data = pd.concat(data, ignore_index=True)
plt.figure(figsize=(16, 9))
sns.set(style="darkgrid", font_scale=1.5)
if not combine:
sns.tsplot(data=data, time=time, value=value, unit="Unit", condition="Condition")
else:
df1 = data.loc[:, [time, value[0], 'Condition']]
df1['Statistics'] = value[0]
df1.rename(columns={value[0]:'Value', 'Condition':'ExpName'}, inplace = True)
df2 = data.loc[:, [time, value[1], 'Condition']]
df2['Statistics'] = value[1]
df2.rename(columns={value[1]:'Value', 'Condition':'ExpName'}, inplace = True)
data = pd.concat([df1, df2], ignore_index=True)
sns.lineplot(x=time, y='Value', hue='ExpName', style='Statistics', data=data)
plt.legend(loc='best').draggable()
plt.savefig('result.png', bbox_inches='tight')
plt.show()
示例5: plot_mean_var
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def plot_mean_var(ad, title, ax):
ad = ad.copy()
sc.pp.filter_cells(ad, min_counts=1)
sc.pp.filter_genes(ad, min_counts=1)
m = ad.X.mean(axis=0)
v = ad.X.var(axis=0)
coefs, r2 = _fitquad(m, v)
ax.set(xscale="log", yscale="log")
ax.plot(m, v, 'o', c='black', markersize=1)
poly = np.poly1d(coefs)
sns.lineplot(m, poly(m), ax=ax, color='red')
ax.set_title(title)
ax.set_ylabel('Variance')
ax.set_xlabel(r'$\mu$')
sns.lineplot(m, m, ax=ax, color='blue')
ax.legend(['Genes', r'NB ($\theta=%.2f)\ r^2=%.3f$' % (coefs[0], r2), 'Poisson'])
return coefs[0]
示例6: plot_relative_error
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def plot_relative_error(data_dir="heterogeneous_example_data"):
if data_dir[-1] != "/":
data_dir += "/"
data_filename = data_dir + "heterogeneous_example_error_data.json"
data = json.load(open(data_filename))
plotting_data = []
for n in data.keys():
for i in data[n].keys():
d = {"Sample Size": int(n), "Iteration": int(i)}
true_effects = data[n][str(i)]["true_effects"]
estimated_effects = data[n][str(i)]["estimated_effects"]
error = np.array(true_effects) - np.array(estimated_effects)
relative_error = np.linalg.norm(error) / np.linalg.norm(true_effects)
d["Relative Error"] = relative_error
plotting_data.append(d)
plotting_df = pd.DataFrame(plotting_data)
plt.figure(figsize=(18, 8))
ax = plt.gca()
grid = sns.lineplot(
x="Sample Size", y="Relative Error", data=plotting_df, marker="o", ax=ax
)
sample_sizes = [int(n) for n in data.keys()]
ax.set_xticks(sample_sizes)
ax.set_xticklabels([""] + sample_sizes[1:], rotation=45)
ax.set_xlim([0, max(sample_sizes) + 100])
sns.despine()
plt.title("Relative Error of Causal Forest")
plt.show()
示例7: lineplot_multi_fig
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def lineplot_multi_fig(
outer_key,
data,
xcol,
ycols,
ci,
in_split_keys,
plot_cfg=None,
data_fns=None,
plot_fns=None,
**kwargs,
):
"""General method for line plotting TensorBoard datasets with smoothing and subsampling.
Returns one figure for each plot."""
if plot_fns is None:
plot_fns = []
# Aggregate data and convert to 'tidy' or longform format Seaborn expects
longform = _aggregate_data(data, xcol, ycols, in_split_keys, data_fns)
# Plot one figure per ycol
for ycol in ycols:
gridspec = {
"left": 0.22,
"bottom": 0.22,
}
fig, ax = plt.subplots(gridspec_kw=gridspec)
sns.lineplot(x=xcol, y=ycol, data=longform, ci=ci, linewidth=1, label="Adv", **kwargs)
for plot_fn in plot_fns:
plot_fn(locals(), ax)
yield (ycol,), fig
示例8: plot_train_result
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def plot_train_result(self):
"""Function to plot the training result."""
algo = self.algo_list
path = self.config.path_result
result = self.config.path_figures
data = [self.config.dataset_name]
files = os.listdir(str(path))
files_lwcase = [f.lower() for f in files]
for d in data:
df = pd.DataFrame()
for a in algo:
file_no = len([c for c in files_lwcase if a.lower() in c if 'training' in c])
if file_no < 1:
continue
file_path = str(path / (a.lower() + '_Training_results_' + str(file_no - 1) + '.csv'))
if os.path.exists(file_path):
with open(str(path / (a.lower() + '_Training_results_' + str(file_no - 1) + '.csv')), 'r') as fh:
df_2 = pd.read_csv(fh)
if df.empty:
df['Epochs'] = df_2['Epochs']
df['Loss'] = df_2['Loss']
df['Algorithm'] = [a] * len(df_2)
else:
df_3 = pd.DataFrame()
df_3['Epochs'] = df_2['Epochs']
df_3['Loss'] = df_2['Loss']
df_3['Algorithm'] = [a] * len(df_2)
frames = [df, df_3]
df = pd.concat(frames)
plt.figure()
ax = seaborn.lineplot(x="Epochs", y="Loss", hue="Algorithm", markers=True, dashes=False, data=df)
files = os.listdir(str(result))
files_lwcase = [f.lower() for f in files]
file_no = len([c for c in files_lwcase if d.lower() in c if 'training' in c])
plt.savefig(str(result / (d + '_training_loss_plot_' + str(file_no) + '.pdf')), bbox_inches='tight', dpi=300)
# plt.show()
示例9: plot_average
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def plot_average(self, event_type, center='Peak', time_before=1,
time_after=1, filt=(None, None), figsize=(6, 4.5),
**kwargs):
"""plot_average (not for REM, spindles & SW only)"""
import seaborn as sns
import matplotlib.pyplot as plt
df_sync = self.get_sync_events(center=center, time_before=time_before,
time_after=time_after,
filt=filt)
assert not df_sync.empty, "Could not calculate event-locked data."
if event_type == 'spindles':
title = "Average spindle"
else: # "sw":
title = "Average SW"
# Start figure
fig, ax = plt.subplots(1, 1, figsize=figsize)
sns.lineplot(data=df_sync, x='Time', y='Amplitude', hue='Channel',
ax=ax, **kwargs)
# ax.legend(frameon=False, loc='lower right')
ax.set_xlim(df_sync['Time'].min(), df_sync['Time'].max())
ax.set_title(title)
ax.set_xlabel('Time (sec)')
ax.set_ylabel('Amplitude (uV)')
return ax
#############################################################################
# SPINDLES DETECTION
#############################################################################
示例10: create_threshold_graph
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def create_threshold_graph(data):
"""
display threshold analysis graph
:param data:
:return: None
"""
sns.set(rc={"figure.figsize": (20.7, 10.27)})
plt.ylim(0, 1.1)
plt.axvline(0.2, 0, 1)
plot = sns.lineplot(data=data, palette="tab10", linewidth=3.5)
plt.setp(plot.legend().get_texts(), fontsize="22")
plot.set_xlabel("Threshold T", fontsize=18)
plot.set_ylabel("Metrics mentioned above", fontsize=18)
开发者ID:watson-developer-cloud,项目名称:assistant-dialog-skill-analysis,代码行数:15,代码来源:confidence_analyzer.py
示例11: get_df_for_env
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def get_df_for_env(gym_id):
env_total_timesteps = envs[gym_id+"total_timesteps"]
env_increment = env_total_timesteps / 500
envs_same_x_axis = []
for sampled_run in envs[gym_id]:
df = pd.DataFrame(columns=sampled_run.columns)
x_axis = [i*env_increment for i in range(500-2)]
current_row = 0
for timestep in x_axis:
while sampled_run.iloc[current_row]["global_step"] < timestep:
current_row += 1
if current_row > len(sampled_run)-2:
break
if current_row > len(sampled_run)-2:
break
temp_row = sampled_run.iloc[current_row].copy()
temp_row["global_step"] = timestep
df = df.append(temp_row)
envs_same_x_axis += [df]
return pd.concat(envs_same_x_axis, ignore_index=True)
# uncommenet the following to generate all figures
# for env in set(all_df["gym_id"]):
# data = get_df_for_env(env)
# sns.lineplot(data=data, x="global_step", y=feature_of_interest, hue="algo", ci='sd')
# plt.legend(fontsize=6)
# plt.title(env)
# plt.savefig(f"{env}.svg")
# plt.clf()
# debugging
示例12: plot_spread
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def plot_spread(df, ticker1, ticker2, idx, th, stop):
px1 = df[ticker1].iloc[idx] / df[ticker1].iloc[idx[0]]
px2 = df[ticker2].iloc[idx] / df[ticker2].iloc[idx[0]]
sns.set(style='white')
# Set plotting figure
fig, ax = plt.subplots(2, 1, gridspec_kw={'height_ratios': [2, 1]})
# Plot the 1st subplot
sns.lineplot(data=[px1, px2], linewidth=1.2, ax=ax[0])
ax[0].legend(loc='upper left')
# Calculate the spread and other thresholds
spread = df[ticker1].iloc[idx] - df[ticker2].iloc[idx]
mean_spread = spread.mean()
sell_th = mean_spread + th
buy_th = mean_spread - th
sell_stop = mean_spread + stop
buy_stop = mean_spread - stop
# Plot the 2nd subplot
sns.lineplot(data=spread, color='#85929E', ax=ax[1], linewidth=1.2)
ax[1].axhline(sell_th, color='b', ls='--', linewidth=1, label='sell_th')
ax[1].axhline(buy_th, color='r', ls='--', linewidth=1, label='buy_th')
ax[1].axhline(sell_stop, color='g', ls='--', linewidth=1, label='sell_stop')
ax[1].axhline(buy_stop, color='y', ls='--', linewidth=1, label='buy_stop')
ax[1].fill_between(idx, sell_th, buy_th, facecolors='r', alpha=0.3)
ax[1].legend(loc='upper left', labels=['Spread', 'sell_th', 'buy_th', 'sell_stop', 'buy_stop'], prop={'size':6.5})
示例13: visualize
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def visualize(result, name):
# (N_samples=5, timesteps=200, types)
result = np.array(result)
assert result.shape[2] == 2
# ax = sns.tsplot(data=result, condition=['OptNet', 'Adam'], linestyle='--')
def trans(series):
# (N_samples, timesteps)
x = np.tile(np.arange(series.shape[1]) + 1,
(series.shape[0], 1)).flatten()
y = series.flatten()
return {'x': x, 'y': y}
ax = sns.lineplot(label='OptNet', **trans(result[:, :, 0]))
ax = sns.lineplot(label='Adam', ax=ax, **trans(result[:, :, 1]))
ax.lines[-1].set_linestyle('-')
ax.legend()
plt.yscale('log'), plt.xlabel('steps')
plt.ylabel('loss'), plt.title('MNIST')
plt.ylim(0.09, 3.0)
plt.xlim(1, result.shape[1])
plt.grid(which='both', alpha=0.6, color='black', linewidth=0.1,
linestyle='-')
ax.tick_params(which='both', direction='in')
ax.tick_params(which='major', length=8)
ax.tick_params(which='minor', length=3)
ax.xaxis.set_minor_locator(AutoMinorLocator(5))
plt.show()
plt.savefig(name)
plt.close()
示例14: lineplot_message_length
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def lineplot_message_length(msgs, your_name, target_name, path_to_save):
sns.set(style="whitegrid")
(x, y_total), (xticks, xticks_labels, xlabel) = _get_plot_data(msgs), _get_xticks(msgs)
y_your = [avg([len(msg.text) for msg in period if msg.author == your_name]) for period in y_total]
y_target = [avg([len(msg.text) for msg in period if msg.author == target_name]) for period in y_total]
plt.fill_between(x, y_your, alpha=0.3)
ax = sns.lineplot(x=x, y=y_your, palette="denim blue", linewidth=2.5, label=your_name)
plt.fill_between(x, y_target, alpha=0.3)
sns.lineplot(x=x, y=y_target, linewidth=2.5, label=target_name)
ax.set(xlabel=xlabel, ylabel="average message length (characters)")
ax.set_xticklabels(xticks_labels)
ax.tick_params(axis='x', bottom=True, color="#A9A9A9")
plt.xticks(xticks, rotation=65)
ax.margins(x=0, y=0)
# plt.tight_layout()
fig = plt.gcf()
fig.set_size_inches(13, 7)
fig.savefig(os.path.join(path_to_save, lineplot_message_length.__name__ + ".png"), dpi=500)
# plt.show()
plt.close("all")
log_line(f"{lineplot_message_length.__name__} was created.")
示例15: lineplot_messages
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import lineplot [as 别名]
def lineplot_messages(msgs, your_name, target_name, path_to_save):
sns.set(style="whitegrid")
(x, y_total), (xticks, xticks_labels, xlabel) = _get_plot_data(msgs), _get_xticks(msgs)
y_your = [len([msg for msg in period if msg.author == your_name]) for period in y_total]
y_target = [len([msg for msg in period if msg.author == target_name]) for period in y_total]
plt.fill_between(x, y_your, alpha=0.3)
ax = sns.lineplot(x=x, y=y_your, palette="denim blue", linewidth=2.5, label=your_name)
plt.fill_between(x, y_target, alpha=0.3)
sns.lineplot(x=x, y=y_target, linewidth=2.5, label=target_name)
ax.set(xlabel=xlabel, ylabel="messages")
ax.set_xticklabels(xticks_labels)
ax.tick_params(axis='x', bottom=True, color="#A9A9A9")
plt.xticks(xticks, rotation=65)
ax.margins(x=0, y=0)
# plt.tight_layout()
fig = plt.gcf()
fig.set_size_inches(13, 7)
fig.savefig(os.path.join(path_to_save, lineplot_messages.__name__ + ".png"), dpi=500)
# plt.show()
plt.close("all")
log_line(f"{lineplot_messages.__name__} was created.")