def event_counts_date(request_disc=None):
'''
Plot the average timeline of a certain institution
request should be given as a dictionary
'''
request = np.ones(df.shape[0], dtype=bool)
for key in request_disc.keys():
if key == "institution":
request = request & (df[key].str.contains(request_disc[key]))
else:
request = request & (df[key] == request_disc[key])
df_selected = df[request]
df_selected["date_md"] = df_selected["admission_date"].apply(
lambda dt: dt.replace(year=1980))
df_selected["year"] = df_selected["admission_date"].apply(
lambda dt: dt.year)
samp = df[request].iloc[0]
title = ""
for key in request_disc.keys():
title += samp[key]
title += " "
gg = p9.ggplot(df_selected)
gg += p9.aes(x="date_md", y="admission_status")
gg += p9.scale_x_datetime(date_breaks='10 days',
date_labels="%m-%d",
limits=np.array([
np.min(df_selected["date_md"]),
pd.to_datetime("1980-4-20")
]))
gg += p9.geom_count()
gg += p9.ggtitle(title)
return gg
def test_continuous_x_y():
p = ggplot(df, aes('y', 'y')) + geom_count()
assert p + _theme == 'continuous_x_y'
def test_continuous_x_y():
p = ggplot(df, aes('y', 'y')) + geom_count()
assert p + _theme == 'continuous_x_y'
def test_discrete_y():
p = ggplot(df, aes('y', 'x')) + geom_count()
assert p + _theme == 'discrete_y'
def test_discrete_y():
p = ggplot(df, aes('y', 'x')) + geom_count()
assert p + _theme == 'discrete_y'