本文整理汇总了Python中seaborn.factorplot方法的典型用法代码示例。如果您正苦于以下问题:Python seaborn.factorplot方法的具体用法?Python seaborn.factorplot怎么用?Python seaborn.factorplot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.factorplot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: plot_mushra_boxplots
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def plot_mushra_boxplots(data, size=5, output_file=None):
"""
Plot the MUSHRA ratings as a grid of boxplots. If `output_file` is defined, then save the plot to file.
Parameters
----------
data: pandas.DataFrame
The ratings data obtained from `get_ratings_data`.
size : float
Height of each boxplot in inches. (default is 5)
output_file: str
Path to the output file location. (default is None)
Returns
-------
g : seaborn.axisgrid.FacetGrid
"""
g = sns.factorplot(x='stimulus', y='rating', data=data, row='condition_id', kind='box', notch=True, size=size)
g.set(ylim=(app.config['MIN_RATING_VALUE'], app.config['MAX_RATING_VALUE']))
if output_file is not None:
g.savefig(output_file)
示例2: _plotWeekdayStats
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def _plotWeekdayStats(stats, columns, groupBy=True):
dataToPlot = stats.copy()
# Group by weekday and rename date column
if groupBy:
dataToPlot = dataToPlot.groupby(stats['date'].dt.weekday).mean()
dataToPlot = dataToPlot.reset_index().rename(columns={'date':'weekday'})
# change stats from columns to row attribute
dataToPlot = pd.melt(dataToPlot, id_vars=['weekday'], value_vars=columns,
var_name='stats', value_name='val')
# Rename stats and weekdays
dataToPlot['stats'].replace(NAMES, inplace=True)
dataToPlot['weekday'].replace(dayOfWeek, inplace=True)
# Plot
g = sns.factorplot(data=dataToPlot, x="weekday", y="val", col="stats",
order=dayOfWeekOrder, kind="point", sharey=False, col_wrap=3)
g.set_xticklabels(rotation=45)
g.set(xlabel='')
return g
#sns.plt.show()
示例3: plotYearAndMonthStatsSleep
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def plotYearAndMonthStatsSleep(stats, columns=None):
"""
Plot aggregated (mean) stats by year and month.
:param stats: data to plot
"""
if not columns:
columns = ['sleep_efficiency', 'sleep_hours']
dataToPlot = _prepareYearAndMonthStats(stats, columns)
# Plot
g = sns.factorplot(data=dataToPlot, x="date", y="val", row="stats", kind="point", sharey=False)
g.set_xticklabels(rotation=45)
for ax in g.axes.flat:
ax.grid(b=True)
return g
#sns.plt.show()
示例4: plot_summary
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def plot_summary(summary_only, stats_dir, output):
import seaborn as sns
rows = parse_data(stats_dir)
g = sns.factorplot(y='result', x='score', col='experiment',
data=rows, kind='bar', ci=None,
order=ANSWER_PLOT_ORDER, size=4, col_wrap=4, sharex=False)
for ax in g.axes.flat:
for label in ax.get_xticklabels():
label.set_rotation(30)
plt.subplots_adjust(top=0.93)
g.fig.suptitle('Feature Ablation Study')
g.savefig(output, format='png', dpi=200)
示例5: factorplot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def factorplot(labels,data,name,):
sb.set_style("whitegrid",{'grid.linewidth': 3.,
'axes.edgecolor':'0.0','grid.edgecolor':'0.0'})
g = sb.factorplot(labels[0],labels[1],labels[2],kind='bar', data=data,legend_out=False,palette=cp,
hue_order = ['Oracle','TACO',"Naive (MLP)","Naive (GRU)"] )
sb.set_style("whitegrid")
g.set(ylim = (0,1))
g.set(xlim=(-0.45, 3.01))
g.fig.set_size_inches(15,8)
plt.legend(loc='best')
g.savefig(name+'.pdf')
示例6: tm_gene_family_plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def tm_gene_family_plot(tm_data, ordered_genomes, biotypes, gene_family_tgt):
"""transMap gene family collapse plots."""
try:
df = json_biotype_nested_counter_to_df(tm_data, 'Gene Family Collapse')
except ValueError: # no gene family collapse. probably the test set.
with gene_family_tgt.open('wb') as outf:
pass
return
df['Gene Family Collapse'] = pd.to_numeric(df['Gene Family Collapse'])
tot_df = df[['Gene Family Collapse', 'genome', 'count']].\
groupby(['genome', 'Gene Family Collapse']).aggregate(sum).reset_index()
tot_df = tot_df.sort_values('Gene Family Collapse')
af = luigi.local_target.atomic_file(gene_family_tgt.path)
with PdfPages(af.tmp_path) as pdf:
g = sns.factorplot(y='count', col='genome', x='Gene Family Collapse', data=tot_df, kind='bar',
col_order=ordered_genomes, col_wrap=4)
g.fig.suptitle('Number of genes collapsed during gene family collapse')
g.set_xlabels('Number of genes collapsed to one locus')
g.set_ylabels('Number of genes')
g.fig.subplots_adjust(top=0.9)
multipage_close(pdf, tight_layout=False)
for biotype in biotypes:
biotype_df = biotype_filter(df, biotype)
if biotype_df is None:
continue
biotype_df = biotype_df.sort_values('Gene Family Collapse')
g = sns.factorplot(y='count', col='genome', x='Gene Family Collapse', data=biotype_df, kind='bar',
col_order=[x for x in ordered_genomes if x in set(biotype_df.genome)], col_wrap=4)
g.fig.suptitle('Number of genes collapsed during gene family collapse for {}'.format(biotype))
g.set_xlabels('Number of genes collapsed to one locus')
g.set_ylabels('Number of genes')
g.fig.subplots_adjust(top=0.9)
multipage_close(pdf, tight_layout=False)
af.move_to_final_destination()
示例7: denovo_plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def denovo_plot(consensus_data, ordered_genomes, denovo_tgt):
af = luigi.local_target.atomic_file(denovo_tgt.path)
with PdfPages(af.tmp_path) as pdf:
try:
df = json_biotype_nested_counter_to_df(consensus_data, 'denovo')
except ValueError:
# No de novo results. Probably the test set.
return
# fix column names because json_biotype_nested_counter_to_df makes assumptions
df.columns = ['Result', 'Number of transcripts', 'Augustus mode', 'genome']
has_pb = len(set(df['Augustus mode'])) == 2
if len(set(df.genome)) > 1: # if we ran in PB only, we may not have multiple genomes
if has_pb is True:
ax = sns.factorplot(data=df, x='genome', y='Number of transcripts', kind='bar', col='Result',
hue='Augustus mode', col_wrap=2, row_order=ordered_genomes, sharex=True,
sharey=False)
else:
ax = sns.factorplot(data=df, x='genome', y='Number of transcripts', kind='bar', col='Result',
col_wrap=2, row_order=ordered_genomes, sharex=True, sharey=False)
else:
if has_pb is True:
ax = sns.factorplot(data=df, x='Result', y='Number of transcripts', kind='bar', hue='Augustus mode')
else:
ax = sns.factorplot(data=df, x='Result', y='Number of transcripts', kind='bar')
ax.set_xticklabels(rotation=90)
ax.fig.suptitle('Incorporation of de-novo predictions')
ax.fig.subplots_adjust(top=0.9)
multipage_close(pdf, tight_layout=False)
af.move_to_final_destination()
示例8: pb_support_plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def pb_support_plot(consensus_data, ordered_genomes, pb_genomes, pb_support_tgt):
af = luigi.local_target.atomic_file(pb_support_tgt.path)
with PdfPages(af.tmp_path) as pdf:
pb_genomes = [x for x in ordered_genomes if x in pb_genomes] # fix order
df = json_biotype_counter_to_df(consensus_data, 'IsoSeq Transcript Validation')
if len(df) == 0:
# no support information
return
df.columns = ['IsoSeq Transcript Validation', 'Number of transcripts', 'genome']
ax = sns.factorplot(data=df, x='genome', y='Number of transcripts', hue='IsoSeq Transcript Validation',
kind='bar', row_order=pb_genomes)
ax.set_xticklabels(rotation=90)
ax.fig.suptitle('Isoforms validated by at least one IsoSeq read')
multipage_close(pdf, tight_layout=False)
af.move_to_final_destination()
示例9: indel_plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def indel_plot(consensus_data, ordered_genomes, indel_plot_tgt):
af = luigi.local_target.atomic_file(indel_plot_tgt.path)
with PdfPages(af.tmp_path) as pdf:
tm_df = pd.concat([pd.DataFrame.from_dict(consensus_data[genome]['transMap Indels'], orient='index').T
for genome in ordered_genomes])
try: # this is a hack to deal with weird small input datasets
tm_df['genome'] = ordered_genomes
except:
return
tm_df['transcript set'] = ['transMap'] * len(tm_df)
consensus_df = pd.concat([pd.DataFrame.from_dict(consensus_data[genome]['Consensus Indels'], orient='index').T
for genome in ordered_genomes])
consensus_df['genome'] = ordered_genomes
consensus_df['transcript set'] = ['Consensus'] * len(consensus_df)
df = pd.concat([consensus_df, tm_df])
df = pd.melt(df, id_vars=['genome', 'transcript set'],
value_vars=['CodingDeletion', 'CodingInsertion', 'CodingMult3Indel'])
df.columns = ['Genome', 'Transcript set', 'Type', 'Percent of transcripts']
g = sns.factorplot(data=df, x='Genome', y='Percent of transcripts', col='Transcript set',
hue='Type', kind='bar', row_order=ordered_genomes,
col_order=['transMap', 'Consensus'])
g.set_xticklabels(rotation=90)
g.fig.subplots_adjust(top=.8)
g.fig.suptitle('Coding indels')
multipage_close(pdf, tight_layout=False)
af.move_to_final_destination()
###
# shared plotting functions
###
示例10: read_length_distributions
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def read_length_distributions(path, refs):
"""Get read lengths for all aligned files in path mapped
to ref db names, assumes the fasta files and sam files are present in the
target folder
"""
files = glob.glob(path+'/*.fa')
fnames = [os.path.splitext(os.path.basename(f))[0] for f in files]
fnames = [i for i in fnames if not i.endswith('_r')]
print (fnames)
res = []
for n in fnames:
x = get_aligned_reads_lengths(path, n, refs)
x = x.ix[10:40]
x['file'] = n
res.append(x)
res = pd.concat(res)
res = res.reset_index()
#print res[:60]
#plot histograms
m = pd.melt(res, id_vars=['file','length'], value_vars=refs,
var_name='ref', value_name='freq')
g = sns.factorplot(y='freq',x='length', data=m, row='ref', kind="bar",
size=2,aspect=4,sharex=False,palette='Blues_d')
plt.savefig(os.path.join(path,'read_lengths.png'))
return res
示例11: dist_from_hyperplane_plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def dist_from_hyperplane_plot(stats_output):
""" Plot SVM Classification Distance from Hyperplane
Args:
stats_output: a pandas file with prediction output
Returns:
fig: Will return a seaborn plot of distance from hyperplane
"""
if "dist_from_hyperplane_xval" in stats_output.columns:
sns.factorplot(
"subject_id",
"dist_from_hyperplane_xval",
hue="Y",
data=stats_output,
kind="point",
)
else:
sns.factorplot(
"subject_id",
"dist_from_hyperplane_all",
hue="Y",
data=stats_output,
kind="point",
)
plt.xlabel("Subject", fontsize=16)
plt.ylabel("Distance from Hyperplane", fontsize=16)
plt.title("Classification", fontsize=18)
return
示例12: plot_boxplot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def plot_boxplot(ds, cat, num):
sns.set()
plt.gcf().clear()
with sns.axes_style(style='ticks'):
sns.factorplot(cat, num, data=ds, kind="box")
from io import BytesIO
plt.xlabel(cat)
plt.ylabel(num)
figfile = BytesIO()
plt.savefig(figfile, format='png')
figfile.seek(0) # rewind to beginning of file
import base64
figdata_png = base64.b64encode(figfile.getvalue())
return figdata_png
示例13: plot_active_cells
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def plot_active_cells(active):
fg = sns.factorplot(data=active, x='segment_id', y='active', hue='group', col='session_id', sharey=True, order=['first', 'second', 'third', 'fourth', 'last'])
return fg
示例14: consensus_support_plot
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def consensus_support_plot(consensus_data, ordered_genomes, biotypes, modes, title, tgt):
"""grouped violin plots of original intron / intron annotation / exon annotation support"""
def adjust_plot(g, this_title):
g.set_xticklabels(rotation=90)
g.fig.suptitle(this_title)
g.fig.subplots_adjust(top=0.9)
for ax in g.axes.flat:
ax.set_ylabel('Percent supported')
ax.set_ylim(-1, 101)
dfs = []
for i, mode in enumerate(modes):
df = json_to_df_with_biotype(consensus_data, mode)
if i > 0:
df = df[mode]
dfs.append(df)
df = pd.concat(dfs, axis=1)
df = pd.melt(df, value_vars=modes, id_vars=['genome', 'biotype'])
af = luigi.local_target.atomic_file(tgt.path)
with PdfPages(af.tmp_path) as pdf:
if len(ordered_genomes) > 1:
g = sns.factorplot(data=df, y='value', x='genome', col='variable', col_wrap=2, kind='violin', sharex=True,
sharey=True, row_order=ordered_genomes, cut=0)
else:
g = sns.factorplot(data=df, y='value', x='variable', kind='violin', sharex=True,
sharey=True, row_order=ordered_genomes, cut=0)
adjust_plot(g, title)
multipage_close(pdf, tight_layout=False)
title += ' for {}'
for biotype in biotypes:
this_title = title.format(biotype)
biotype_df = biotype_filter(df, biotype)
if biotype_df is not None:
if len(ordered_genomes) > 1:
g = sns.factorplot(data=biotype_df, y='value', x='genome', col='variable', col_wrap=2,
kind='violin', sharex=True, sharey=True, row_order=ordered_genomes, cut=0)
else:
g = sns.factorplot(data=df, y='value', x='variable', kind='violin', sharex=True,
sharey=True, row_order=ordered_genomes, cut=0)
adjust_plot(g, this_title)
multipage_close(pdf, tight_layout=False)
af.move_to_final_destination()
示例15: _plotMonthlyStats
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import factorplot [as 别名]
def _plotMonthlyStats(stats, columns, groupBy=True):
dataToPlot = stats.copy()
# Group by month and rename date column
if groupBy:
dataToPlot = dataToPlot.groupby(stats['date'].dt.month).mean()
dataToPlot = dataToPlot.reset_index().rename(columns={'date': 'month'})
# change stats from columns to row attribute
dataToPlot = pd.melt(dataToPlot, id_vars=['month'], value_vars=columns,
var_name='stats', value_name='val')
# Rename stats and weekdays
dataToPlot['stats'].replace(NAMES, inplace=True)
dataToPlot['month'].replace(months, inplace=True)
order = [m for m in monthsOrder if m in dataToPlot['month'].unique()]
# Plot
g = sns.factorplot(data=dataToPlot, x="month", y="val", col="stats", order=order, kind="bar", sharey=False)
g.set_xticklabels(rotation=45)
g.set(xlabel='')
return g
#sns.plt.show()
# def _plotMonthlyStats(stats, columns):
# """
# Plot aggregated (mean) stats by month
# :param stats: data to plot
# :param columns: columns from stats to plot
# """
# MEASURE_NAME = 'month'
# months={1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun', 7:'Jul', 8:'Aug',
# 9:'Sep', 10:'Oct', 11:'Nov', 12:'Dec'}
# order = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
# stats[MEASURE_NAME] = stats[MEASURE_NAME].map(months)
#
# order = [m for m in order if m in stats[MEASURE_NAME].unique()]
#
# f, axes = getAxes(2,2)
# for i, c in enumerate(columns):
# if c in NAMES:
# c = NAMES[c]
# g = sns.barplot(x=MEASURE_NAME, y=c, data=stats, order=order, ax=axes[i])
# g.set_xlabel('')
# sns.plt.show()