def test_rect_nofill():
p = (ggplot(df)
+ aes(xmin='xmin', xmax='xmax', ymin='ymin', ymax='ymax')
+ geom_rect(color='red', fill=None, size=2)
+ geom_rect(aes(ymin='ymin+2', ymax='ymax+2'),
color='blue', fill='None', size=2)
+ geom_rect(aes(ymin='ymin+4', ymax='ymax+4'),
color='green', fill='', size=2)
+ geom_rect(aes(ymin='ymin+6', ymax='ymax+6'),
color='yellow', fill='gray', size=2))
assert p == 'rect-nofill'
def test_rect_nofill():
p = (ggplot(df)
+ aes(xmin='xmin', xmax='xmax', ymin='ymin', ymax='ymax')
+ geom_rect(color='red', fill=None, size=2)
+ geom_rect(aes(ymin='ymin+2', ymax='ymax+2'),
color='blue', fill='None', size=2)
+ geom_rect(aes(ymin='ymin+4', ymax='ymax+4'),
color='green', fill='', size=2)
+ geom_rect(aes(ymin='ymin+6', ymax='ymax+6'),
color='yellow', fill='gray', size=2))
assert p == 'rect-nofill'
def test_dodge_empty_data():
empty_df = pd.DataFrame({'x': [], 'y': []})
p = (ggplot(df1, aes('x', 'y')) + geom_point() +
geom_rect(empty_df,
aes(xmin='x', xmax='x+1', ymin='y', ymax='y+1'),
position='dodge'))
p.draw_test()
def test_rect_aesthetics():
p = (
ggplot(df, aes(xmin='xmin', xmax='xmax', ymin='ymin', ymax='ymax')) +
geom_rect() + geom_rect(aes(ymin='ymin+2', ymax='ymax+2', alpha='z'),
show_legend=False) +
geom_rect(aes(ymin='ymin+4', ymax='ymax+4', fill='factor(z)')) +
geom_rect(aes(ymin='ymin+6', ymax='ymax+6', color='factor(z+1)'),
size=2) +
geom_rect(aes(ymin='ymin+8', ymax='ymax+8', linetype='factor(z+2)'),
color='yellow',
size=2) + _theme +
# for comparison with geom_tile which
# has labels by default
labs(x='x', y='y'))
assert p == 'rect-aesthetics'
def plot_calibrations():
params = data.sample_calibrations()
return (
pn.ggplot(
params,
pn.aes(xmin='boardsize-.25',
xmax='boardsize+.25',
group='boardsize',
fill='factor(boardsize)')) +
pn.geom_hline(yintercept=.5, alpha=.2) + pn.geom_rect(
pn.aes(ymin='lower', ymax='upper'), show_legend=False, color='k') +
pn.geom_rect(pn.aes(ymin='mid', ymax='mid'),
show_legend=False,
color='k',
size=2) + pn.scale_y_continuous(labels=percent_format()) +
pn.scale_fill_hue(l=.4) + pn.coord_cartesian(ylim=(.4, .6)) +
pn.labs(y='Win rate v. perfect play', x='Board size') + plot.IEEE())
def ranges_graphics(target, signatures_long, ranges, hydro_state, year,
algorithm):
if ranges is None:
return
alg = ''
if algorithm == "boruta":
alg = "Boruta"
elif algorithm == "lasso":
alg = "LASSO"
elif algorithm == "kbestcorr":
alg = "SelectKBest (correlation)"
elif algorithm == "kbestmi":
alg = "SelectKBest (mutual information)"
elif algorithm == "ga":
alg = "Genetic Algorithm"
signatures_long["wavelength"] = pandas.to_numeric(
signatures_long["wavelength"])
# signatures_long["value"] = pandas.to_numeric(signatures_long["value"])
y_max = signatures_long["value"].max()
graph_signatures = ggplot(signatures_long) \
+ theme(legend_position = "none") \
+ aes(x = "wavelength", y = "value", color = "variable") \
+ labs(
x = "Wavelength (nm)",
y = "Reflectance (%)",
title = f"Ranges in signature: {target} {hydro_state} {alg}, {year}.",
subtitle = f"{alg}, {hydro_state} set."
)
if ranges is not None:
for i in range(len(ranges)):
i_range = []
for j in range(len(ranges[i])):
# graph_signatures = graph_signatures + geom_vline(xintercept = ranges[i][j], color="black", alpha = 0.2)
i_range.append(ranges[i][j])
graph_signatures = graph_signatures + geom_rect(aes(
xmin=i_range[0], xmax=i_range[1], ymin=0.0, ymax=y_max),
fill="steelblue",
alpha=0.1,
color=None)
graph_signatures = graph_signatures + geom_line()
else:
graph_signatures = graph_signatures + geom_line()
print(graph_signatures)
graph_signatures.save(filename=os.path.join(
PLOT_DIR, f"{hydro_state}-{year}-{algorithm}-ranges-{target}"))
return
def test_rect_aesthetics():
p = (ggplot(df, aes(xmin='xmin', xmax='xmax',
ymin='ymin', ymax='ymax')) +
geom_rect() +
geom_rect(aes(ymin='ymin+2', ymax='ymax+2',
alpha='z'),
show_legend=False) +
geom_rect(aes(ymin='ymin+4', ymax='ymax+4',
fill='factor(z)')) +
geom_rect(aes(ymin='ymin+6', ymax='ymax+6',
color='factor(z+1)'), size=2) +
geom_rect(aes(ymin='ymin+8', ymax='ymax+8',
linetype='factor(z+2)'),
color='yellow', size=2) +
_theme +
# for comparison with geom_tile which
# has labels by default
labs(x='x', y='y'))
assert p == 'rect-aesthetics'
def test_coord_trans_groups():
df = pd.DataFrame({
'xmin': [0, 2, 4],
'xmax': [1, 3, 5],
'ymin': [0, 2, 4],
'ymax': [1, 3, 5],
'c': list('abc')
})
p = (ggplot(df)
+ geom_rect(
aes(xmin='xmin', xmax='xmax', ymin='ymin', ymax='ymax', fill='c'))
+ coord_trans()
)
assert p == 'coord-trans-groups'
def test_infinite_rects():
df = pd.DataFrame({'x': range(10), 'y': range(10)})
rdf = pd.DataFrame({
'xmin': [3],
'xmax': 7,
'ymin': -np.inf,
'ymax': np.inf,
})
p = (ggplot(df, aes('x', 'y')) + geom_point() + geom_rect(
data=rdf,
mapping=aes(xmin='xmin', xmax='xmax', ymin='ymin', ymax='ymax'),
alpha=0.2,
inherit_aes=False))
assert p == 'infinite-rects'
def plot_windows(self, query=""):
"""Plot selection windows with 'plotnine'.
Install plotnine separately.
Args:
query (str): a query used for subselection in "self.windows"
Returns:
ggplot: a plot with selection windows
"""
from plotnine import ggplot, aes, geom_rect, theme_minimal, xlab, ylab, labs
D = self.windows.reset_index().query(query) if query else self.windows[1:].reset_index()
plot = (ggplot(aes(), data=D) +
geom_rect(aes(xmin='mz_left', xmax='mz_right',
ymin='IM_min', ymax='IM_max',
fill='pd.Categorical(window_gr)'),
alpha=.5, color='black')+
theme_minimal() +
xlab('mass/charge') +
ylab('1/K0') +
labs(fill='Window Group'))
return plot
def plot_cor(df):
# drop missing correlations
out = df[~df['corr'].isnull()]
# add pair column
out = out.assign(pair=out.col_1 + '&' + out.col_2)
# add a sign column
sign = ((out['corr'] > 0).astype('int')).to_list()
sign = [['Negative', 'Positive'][i] for i in sign]
out['sign'] = sign
#out = out.sort_values('pair', ascending = False).reset_index(drop = True)
# add ind column
out['ind'] = [out.shape[0] - i for i in range(out.shape[0])]
# plot using bands
ggplt = p9.ggplot(data = out, mapping = p9.aes(x = 'pair', y = 'corr')) \
+ p9.geom_hline(
yintercept = 0,
linetype = "dashed",
color = "#c2c6cc"
) \
+ p9.geom_rect(
alpha = 0.4,
xmin = out.ind.values - 0.4,
xmax = out.ind.values + 0.4,
ymin = out.lower.values,
ymax = out.upper.values,
fill = [['b', '#abaeb3'][int(x > 0.05)] for x in out.p_value]
) \
+ p9.geom_segment(
x = out.ind.values - 0.4,
y = out['corr'].values,
xend = out.ind.values + 0.4,
yend = out['corr'].values
) \
+ p9.coord_flip() \
+ p9.ylim(np.min(out.lower.values), np.max(out.upper.values)) \
+ p9.labs(x = "", y = "Correlation")
return ggplt
abortion.loc[(abortion.wht==0) & (abortion.male==1), 'bm'] = 1
abortion['bf'] = 0
abortion.loc[(abortion.wht==0) & (abortion.male==0), 'bf'] = 1
abortion_filt = abortion[(abortion.bf==1) & (abortion.age.isin([15,25]))]
reg = (
smf
.wls("""lnr ~ C(repeal)*C(year) + C(younger)*C(repeal) + C(younger)*C(year) +
C(yr)*C(year) + C(fip)*t + acc + ir + pi + alcohol + crack + poverty + income + ur""",
data=abortion_filt, weights=abortion_filt.totpop.values)
.fit(
cov_type='cluster',
cov_kwds={'groups': abortion_filt.fip.values},
method='pinv')
)
abortion_plot = pd.DataFrame({'sd': reg.bse['C(yr)[T.1]:C(year)[T.1986.0]':'C(yr)[T.1]:C(year)[T.2000.0]'],
'mean': reg.params['C(yr)[T.1]:C(year)[T.1986.0]':'C(yr)[T.1]:C(year)[T.2000.0]'],
'year':np.arange(1986, 2001)})
abortion_plot['lb'] = abortion_plot['mean'] - abortion_plot['sd']*1.96
abortion_plot['ub'] = abortion_plot['mean'] + abortion_plot['sd']*1.96
p.ggplot(abortion_plot, p.aes(x = 'year', y = 'mean')) + p.geom_rect(p.aes(xmin=1986, xmax=1991, ymin=-np.inf, ymax=np.inf), fill = "cyan", alpha = 0.01)+ p.geom_point()+ p.geom_text(p.aes(label = 'year'), ha='right')+ p.geom_hline(yintercept = 0) + p.geom_errorbar(p.aes(ymin = 'lb', ymax = 'ub'), width = 0.2,
position = p.position_dodge(0.05)) +\
p.labs(title= "Estimated effect of abortion legalization on gonorrhea")
.wls(formula, data=abortion_bf15, weights=abortion_bf15.totpop.values)
.fit(
cov_type='cluster',
cov_kwds={'groups': abortion_bf15.fip.values},
method='pinv')
)
reg.summary()
abortion_plot = pd.DataFrame(
{
'sd': reg.bse['C(repeal)[T.1.0]:C(year)[T.1986.0]':'C(repeal)[T.1.0]:C(year)[T.2000.0]'],
'mean': reg.params['C(repeal)[T.1.0]:C(year)[T.1986.0]':'C(repeal)[T.1.0]:C(year)[T.2000.0]'],
'year': np.arange(1986, 2001)
})
abortion_plot['lb'] = abortion_plot['mean'] - abortion_plot['sd']*1.96
abortion_plot['ub'] = abortion_plot['mean'] + abortion_plot['sd']*1.96
(
p.ggplot(abortion_plot, p.aes(x = 'year', y = 'mean')) +
p.geom_rect(p.aes(xmin=1985, xmax=1992, ymin=-np.inf, ymax=np.inf), fill="cyan", alpha = 0.01) +
p.geom_point() +
p.geom_text(p.aes(label = 'year'), ha='right') +
p.geom_hline(yintercept = 0) +
p.geom_errorbar(p.aes(ymin = 'lb', ymax = 'ub'), width = 0.2,
position = p.position_dodge(0.05)) +
p.labs(title= "Estimated effect of abortion legalization on gonorrhea")
)
