本文整理汇总了Python中seaborn.swarmplot方法的典型用法代码示例。如果您正苦于以下问题:Python seaborn.swarmplot方法的具体用法?Python seaborn.swarmplot怎么用?Python seaborn.swarmplot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.swarmplot方法的10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: test_swarmspan
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import swarmplot [as 别名]
def test_swarmspan():
print('Testing swarmspans')
base_mean = np.random.randint(10, 101)
seed, ptp, df = create_dummy_dataset(base_mean=base_mean)
print('\nSeed = {}; base mean = {}'.format(seed, base_mean))
for c in df.columns[1:-1]:
print('{}...'.format(c))
f1, swarmplt = plt.subplots(1)
sns.swarmplot(data=df[[df.columns[0], c]], ax=swarmplt)
sns_yspans = []
for coll in swarmplt.collections:
sns_yspans.append(get_swarm_yspans(coll))
f2, b = _api.plot(data=df, idx=(df.columns[0], c))
dabest_yspans = []
for coll in f2.axes[0].collections:
dabest_yspans.append(get_swarm_yspans(coll))
for j, span in enumerate(sns_yspans):
assert span == pytest.approx(dabest_yspans[j])
示例2: plot_number_subjects_by_domain
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import swarmplot [as 别名]
def plot_number_subjects_by_domain(df, save_cfg=cfg.saving_config):
"""Plot number of subjects in studies by domain.
"""
# Split values into separate rows and remove invalid values
col = 'Data - subjects'
nb_subj_df = ut.split_column_with_multiple_entries(
df, col, ref_col='Main domain')
nb_subj_df = nb_subj_df.loc[~nb_subj_df[col].isin(['n/m', 'tbd'])]
nb_subj_df[col] = nb_subj_df[col].astype(int)
nb_subj_df = nb_subj_df.loc[nb_subj_df[col] > 0, :]
nb_subj_df['Main domain'] = nb_subj_df['Main domain'].apply(
ut.wrap_text, max_char=13)
fig, ax = plt.subplots(
figsize=(save_cfg['text_width'] / 3 * 2, save_cfg['text_height'] / 3))
ax.set(xscale='log', yscale='linear')
sns.swarmplot(
y='Main domain', x=col, data=nb_subj_df,
ax=ax, size=3, order=nb_subj_df.groupby(['Main domain'])[
col].median().sort_values().index)
ax.set_xlabel('Number of subjects')
ax.set_ylabel('')
logger.info('Stats on number of subjects per model: {}'.format(
nb_subj_df[col].describe()))
plt.tight_layout()
if save_cfg is not None:
fname = os.path.join(save_cfg['savepath'], 'nb_subject_per_domain')
fig.savefig(fname + '.' + save_cfg['format'], **save_cfg)
return ax
示例3: plot_imp_swarm
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import swarmplot [as 别名]
def plot_imp_swarm(d, mi, imp_col, palette=None,
title="Imputation Swarm", **plot_kwgs):
"""Create the swarm plot for multiply imputed data.
Args:
d (list): dataset returned from multiple imputation.
mi (MultipleImputer): multiple imputer used to generate d.
imp_col (str): column to plot. Should be a column with imputations.
title (str, Optional): title of plot. Default is "Imputation Swarm".
palette (list, tuple, Optional): colors for the imps and observed.
Default is None. if None, colors default to ["r","c"].
**plot_kwgs: keyword arguments used by sns.set.
Returns:
sns.distplot: swarmplot for imputed data
Raises:
ValueError: see _validate_data method.
"""
# set plot type, validate, and define names necessary
_default_plot_args(**plot_kwgs)
_validate_data(d, mi, imp_col)
datasets_merged = _melt_df(d, mi, imp_col)
if palette is None:
palette = ["r", "c"]
# swarmplot example
sns.swarmplot(
x="imp_num", y=imp_col, hue="imputed", palette=palette,
data=datasets_merged, hue_order=["yes", "no"]
).set(xlabel="Imputation Number", title=title)
示例4: plot_swarm_evenness
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import swarmplot [as 别名]
def plot_swarm_evenness(df, output, verbose=False):
if verbose:
sys.stderr.write(f"{bcolors.GREEN}Plotting swarmed evenness{bcolors.ENDC}\n")
sns.violinplot(data=df, x='kmer', y='Evenness')
sns.swarmplot(data=df, x='kmer', y='Evenness')
sns.despine(offset=10, trim=True)
plt.savefig(f"{output}.swarm.evenness.png")
plt.clf()
示例5: plot_shannon
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import swarmplot [as 别名]
def plot_shannon(df, output, verbose=False):
if verbose:
sys.stderr.write(f"{bcolors.GREEN}Plotting swarmed shannon{bcolors.ENDC}\n")
sns.violinplot(data=df, x='kmer', y='Shannon')
sns.swarmplot(data=df, x='kmer', y='Shannon')
sns.despine(offset=10, trim=True)
plt.savefig(f"{output}.shannon.png")
plt.clf()
示例6: write
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import swarmplot [as 别名]
def write(self):
output_file = self.settings['ageing_wip_chart']
if not output_file:
logger.debug("No output file specified for ageing WIP chart")
return
chart_data = self.get_result()
if len(chart_data.index) == 0:
logger.warning("Unable to draw ageing WIP chart with zero completed items")
return
fig, ax = plt.subplots()
if self.settings['ageing_wip_chart_title']:
ax.set_title(self.settings['ageing_wip_chart_title'])
sns.swarmplot(x='status', y='age', order=chart_data.columns[4:], data=chart_data, ax=ax)
ax.set_xlabel("Status")
ax.set_ylabel("Age (days)")
ax.set_xticklabels(ax.xaxis.get_majorticklabels(), rotation=90)
_, top = ax.get_ylim()
ax.set_ylim(0, top)
set_chart_style()
# Write file
logger.info("Writing ageing WIP chart to %s", output_file)
fig.savefig(output_file, bbox_inches='tight', dpi=300)
plt.close(fig)
示例7: ageing_wip_chart
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import swarmplot [as 别名]
def ageing_wip_chart(cycle_data, start_column, end_column, done_column=None, now=None, title=None, ax=None):
if len(cycle_data.index) == 0:
raise UnchartableData("Cannot draw ageing WIP chart with no data")
if ax is None:
fig, ax = plt.subplots()
if title is not None:
ax.set_title(title)
if now is None:
now = pd.Timestamp.now()
if done_column is None:
done_column = cycle_data.columns[-1]
today = now.date()
# remove items that are done
cycle_data = cycle_data[pd.isnull(cycle_data[done_column])]
# Check that we still have some data to proceed with.
if len(cycle_data.index) == 0:
raise UnchartableData("Cannot draw ageing WIP chart with no data - All items done!")
cycle_data = pd.concat((
cycle_data[['key', 'summary']],
cycle_data.ix[:, start_column:end_column]
), axis=1)
def extract_status(row):
last_valid = row.last_valid_index()
if last_valid is None:
return np.NaN
return last_valid
def extract_age(row):
started = row[start_column]
if pd.isnull(started):
return np.NaN
return (today - started.date()).days
wip_data = cycle_data[['key', 'summary']].copy()
wip_data['status'] = cycle_data.apply(extract_status, axis=1)
wip_data['age'] = cycle_data.apply(extract_age, axis=1)
wip_data.dropna(how='any', inplace=True)
sns.swarmplot(x='status', y='age', order=cycle_data.columns[2:], data=wip_data, ax=ax)
ax.set_xlabel("Status")
ax.set_ylabel("Age (days)")
ax.set_xticklabels(ax.xaxis.get_majorticklabels(), rotation=90)
bottom, top = ax.get_ylim()
ax.set_ylim(0, top)
return ax
示例8: _plot_results_accuracy_per_domain
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import swarmplot [as 别名]
def _plot_results_accuracy_per_domain(results_df, diff_df, save_cfg):
"""Make scatterplot + boxplot to show accuracy difference by domain.
"""
fig, axes = plt.subplots(
nrows=2, ncols=1, sharex=True,
figsize=(save_cfg['text_width'], save_cfg['text_height'] / 3),
gridspec_kw = {'height_ratios':[5, 1]})
results_df['Main domain'] = results_df['Main domain'].apply(
ut.wrap_text, max_char=20)
sns.catplot(y='Main domain', x='acc_diff', s=3, jitter=True,
data=results_df, ax=axes[0])
axes[0].set_xlabel('')
axes[0].set_ylabel('')
axes[0].axvline(0, c='k', alpha=0.2)
sns.boxplot(x='acc_diff', data=diff_df, ax=axes[1])
sns.swarmplot(x='acc_diff', data=diff_df, color="0", size=2, ax=axes[1])
axes[1].axvline(0, c='k', alpha=0.2)
axes[1].set_xlabel('Accuracy difference')
fig.subplots_adjust(wspace=0, hspace=0.02)
plt.tight_layout()
logger.info('Number of studies included in the accuracy improvement analysis: {}'.format(
results_df.shape[0]))
median = diff_df['acc_diff'].median()
iqr = diff_df['acc_diff'].quantile(.75) - diff_df['acc_diff'].quantile(.25)
logger.info('Median gain in accuracy: {:.6f}'.format(median))
logger.info('Interquartile range of the gain in accuracy: {:.6f}'.format(iqr))
best_improvement = diff_df.nlargest(3, 'acc_diff')
logger.info('Best improvement in accuracy: {}, in {}'.format(
best_improvement['acc_diff'].values[0],
best_improvement['Citation'].values[0]))
logger.info('Second best improvement in accuracy: {}, in {}'.format(
best_improvement['acc_diff'].values[1],
best_improvement['Citation'].values[1]))
logger.info('Third best improvement in accuracy: {}, in {}'.format(
best_improvement['acc_diff'].values[2],
best_improvement['Citation'].values[2]))
if save_cfg is not None:
savename = 'reported_accuracy_per_domain'
fname = os.path.join(save_cfg['savepath'], savename)
fig.savefig(fname + '.' + save_cfg['format'], **save_cfg)
return axes
示例9: plot_swarm
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import swarmplot [as 别名]
def plot_swarm(df, x, y, hue, tag='eda', directory=None):
r"""Display a Swarm Plot.
Parameters
----------
df : pandas.DataFrame
The dataframe containing the ``x`` and ``y`` features.
x : str
Variable name in ``df`` to display along the x-axis.
y : str
Variable name in ``df`` to display along the y-axis.
hue : str
Variable name to be used as hue, i.e., another data dimension.
tag : str
Unique identifier for the plot.
directory : str, optional
The full specification of the plot location.
Returns
-------
None : None.
References
----------
http://seaborn.pydata.org/generated/seaborn.swarmplot.html
"""
logger.info("Generating Swarm Plot")
# Generate the swarm plot
swarm_plot = sns.swarmplot(x=x, y=y, hue=hue, data=df)
swarm_fig = swarm_plot.get_figure()
# Save the plot
write_plot('seaborn', swarm_fig, 'swarm_plot', tag, directory)
#
# Time Series Plots
#
#
# Function plot_time_series
#
示例10: motion_plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import swarmplot [as 别名]
def motion_plot(group_conf_summary):
# Plot style setup
plt.style.use('seaborn-white')
plt.rcParams['font.family'] = 'Helvetica'
colour = ["#fe6863", "#00a074"]
palette = sns.set_palette(colour)
small = 15
plt.rc('font', size=small) # controls default text sizes
plt.rc('axes', titlesize=small) # fontsize of the axes title
plt.rc('axes', linewidth=2.2)
plt.rc('axes', labelsize=small) # fontsize of the x and y labels
plt.rc('xtick', labelsize=small) # fontsize of the tick labels
plt.rc('ytick', labelsize=small) # fontsize of the tick labels
plt.rc('legend', fontsize=small) # legend fontsize
plt.rc('lines', linewidth=2.2, color='gray')
# ------------------------------------------
motion_dict = {'Mean FD': ['mean_fd', 0.2],
'Max FD': ['max_fd', 5],
'Percent of outlier dataframes (%)': ['perc_spikes', 20]}
fig, axes = plt.subplots(1, 3, figsize=(16, 7))
fig.subplots_adjust(wspace=0.4, hspace=0.4)
i = 0
for key, value in motion_dict.items():
plt.figure(figsize=(4, 6))
p = sns.swarmplot(y=value[0],
x="task",
hue="include",
data=group_conf_summary,
alpha=0.8,
s=10,
color=palette,
ax=axes[i]
)
p = sns.boxplot(y=value[0],
x="task",
data=group_conf_summary,
showcaps=False,
boxprops={'facecolor': 'None'},
showfliers=False, ax=axes[i])
p.title.set_text(f"Threshold = {value[1]}")
p.axhline(value[1], ls='--', color="#fe6863")
p.set(xlabel='')
p.set(ylabel=key)
p.get_legend().set_visible(False)
p.tick_params(axis='both', which='both', length=6, width=2.2)
i += 1
fig.suptitle(f"Excluding high motion subjects", va="top")
return fig