def run_item_clicked(self, item):
logging.info('Run item %s clicked' % item.text(0))
output = io.StringIO()
if item.parent() is not None:
if item.parent().text(0) == 'Variables':
cpt = self._concrete_model.find_component(item.text(0))
# create ggplot
df = mo.get_entity(cpt)
if item.text(0) == 'S':
ff = pn.ggplot(df, pn.aes('T', item.text(0))) + pn.ggtitle(cpt.doc) +\
pn.geom_step(pn.aes(color='States'), direction='hv') + pn.facet_wrap('States')
elif item.text(0) == 'Q':
ff = pn.ggplot(df, pn.aes('T', item.text(0))) + pn.ggtitle(cpt.doc) +\
pn.geom_step(pn.aes(color='J'), direction='hv') + pn.facet_grid('J~')
else:
ff = pn.ggplot(df, pn.aes('T', item.text(0))) + pn.ggtitle(cpt.doc) +\
pn.geom_step(pn.aes(color='J'), direction='hv') + pn.facet_grid('I~')
size = self.canvas.size()
ff += pn.theme(figure_size=(size.width() / 100, size.height() / 100))
# update to the new figure
fig = ff.draw()
self.canvas.figure = fig
self.canvas.draw()
output.close()
def test_labels():
"""
Test invalid arguments to chart components
"""
gg = ggplot(df, aes(x='x', y='y'))
gg = gg + geom_point()
gg = gg + xlab('xlab')
gg = gg + ylab('ylab')
gg = gg + ggtitle('title')
assert gg.labels['x'] == 'xlab'
assert gg.labels['y'] == 'ylab'
assert gg.labels['title'] == 'title'
gg = gg + labs(x='xlab2', y='ylab2', title='title2')
assert gg.labels['x'] == 'xlab2'
assert gg.labels['y'] == 'ylab2'
assert gg.labels['title'] == 'title2'
with pytest.raises(PlotnineError):
gg = gg + xlab(None)
with pytest.raises(PlotnineError):
gg = gg + ylab(None)
with pytest.raises(PlotnineError):
gg = gg + ggtitle(None)
with pytest.raises(PlotnineError):
gg = gg + labs('x', 'y')
def plot_hypothesis(hypothesis, file_name):
bin_types = list(hypothesis)
scores = list(hypothesis[bin_types[0]])
plots = []
for bin_type, score in product(bin_types, scores):
mean_name = "Mean: " + score
df = pd.DataFrame(columns=["Bin", "Dataset", mean_name])
df2 = pd.DataFrame(columns=["Bin", "t-statistic", 'p-value'])
for bin_ in hypothesis[bin_type][score]:
h = list(bin_.values())[0]
bin_name = list(bin_)[0]
parameter1 = h.p1
parameter2 = h.p2
mean1 = h.mean1
mean2 = h.mean2
row1 = {
"Bin": bin_name,
'Dataset': parameter1,
mean_name: str(round(float(mean1), 3))
}
row2 = {
"Bin": bin_name,
'Dataset': parameter2,
mean_name: str(round(float(mean2), 3))
}
df = df.append(row1, ignore_index=True)
df = df.append(row2, ignore_index=True)
t_statistic = h.t
p_value = h.p
row = {
"Bin":
bin_name,
't-statistic':
str(round(t_statistic, 3)),
'p-value':
str(p_value),
'95% Confidence':
"Significant" if p_value <= 0.05 else "Not Significant"
}
df2 = df2.append(row, ignore_index=True)
plots.append(
(ggplot(df, aes(x='Bin', y=mean_name, fill='Dataset')) +
geom_col(stat='identity', position='dodge') +
ggtitle("{0} bin distribution| {1}\nBin's Average Scores".format(
bin_type, score))))
plots.append(
(ggplot(df2, aes(x='Bin', y='p-value', fill='95% Confidence')) +
geom_col(stat='identity', width=0.2) + ggtitle(
"{0} bin distribution| {1}\nBin's 95% Confidence Level Test".
format(bin_type, score)) +
scale_fill_manual(values={
'Significant': "#214517",
'Not Significant': '#c62f2d'
})))
save_as_pdf_pages(plots, file_name)
return
def round_2_plot():
if not os.path.exists(round_2_df_path):
eprint(f'Downloading {round_2_df_url} to {round_2_df_path}')
urlretrieve(round_2_df_url, round_2_df_path)
verify_checksum(round_2_df_checksum, round_2_df_path)
df = pd.read_json(round_2_df_path)
p = (
ggplot(df) + aes(x='char_percent', y='correct', color='Dataset') +
facet_wrap('Guessing_Model', nrow=1) + stat_summary_bin(
fun_data=mean_no_se, bins=20, shape='.', linetype='None',
size=0.5) + scale_y_continuous(breaks=np.linspace(0, 1, 6)) +
scale_x_continuous(breaks=[0, .5, 1]) +
coord_cartesian(ylim=[0, 0.7]) +
ggtitle('Round 2 Attacks and Models') +
xlab('Percent of Question Revealed') + ylab('Accuracy') + theme(
#legend_position='top', legend_box_margin=0, legend_title=element_blank(),
strip_text_x=element_text(margin={
't': 6,
'b': 6,
'l': 1,
'r': 5
})) +
scale_color_manual(values=['#FF3333', '#66CC00', '#3333FF', '#FFFF33'],
name='Questions'))
p.save('2019_tacl_trick/auto_fig/round_2_json.pdf', width=7.0, height=1.7)
def round_1_plot():
df = pd.read_csv('2019_tacl_trick/data/round_1.csv')
model_dtype = CategoricalDtype(['DAN', 'RNN', 'IR'], ordered=True)
df['Model'] = df['Model'].astype(model_dtype)
# This following is a hack so that the legend widths are the same across plots
def rename(x):
if x == 'Round 1 - IR Adversarial':
return 'Round 1 - IR Adversarial '
else:
return x
df['Dataset'] = df['Dataset'].map(rename)
p = (ggplot(df) + aes(x='x', y='y', color='Dataset') +
facet_wrap('Model', nrow=1) + geom_point(size=1.0, shape='o') +
scale_y_continuous(breaks=np.linspace(0, 1, 6), limits=[0, 0.6]) +
scale_x_continuous(breaks=[0, .5, 1]) +
xlab('Percent of Question Revealed') + ylab('Accuracy') +
ggtitle('Round 1 Attacks and Models') +
theme(strip_text_x=element_text(margin={
't': 6,
'b': 6,
'l': 1,
'r': 5
})) + scale_color_manual(
values=['#FF3333', '#66CC00', '#3333FF', '#FFFF33'],
name='Questions'))
p.save('2019_tacl_trick/auto_fig/round_1_csv.pdf', width=7.0, height=1.7)
def plot_replicate_density(
df,
batch,
plate,
output_file_base=None,
output_file_extensions=[".png", ".pdf", ".svg"],
dpi=300,
height=1.5,
width=2,
):
density_gg = (
gg.ggplot(df, gg.aes(x="pairwise_correlation", fill="replicate_info"))
+ gg.geom_density(alpha=0.3) + gg.scale_fill_manual(
name="Replicate",
labels={
"True": "True",
"False": "False"
},
values=["#B99638", "#2DB898"],
) + gg.xlab("Pearson Correlation") + gg.ylab("Density") +
gg.ggtitle("{}: {}".format(batch, plate)) + gg.theme_bw() + gg.theme(
title=gg.element_text(size=9),
axis_text=gg.element_text(size=5),
axis_title=gg.element_text(size=8),
legend_text=gg.element_text(size=6),
legend_title=gg.element_text(size=7),
strip_text=gg.element_text(size=4, color="black"),
strip_background=gg.element_rect(colour="black", fill="#fdfff4"),
))
if output_file_base:
save_figure(density_gg, output_file_base, output_file_extensions, dpi,
height, width)
return density_gg
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 density(X, y, sreg, treg):
"""
Plot the 2d-density of the size vs correlation data.
Parameters:
- - - - -
X: float, array
independent variable
y: float, array
dependent variable
Returns:
- - - -
g: figure
density plot
"""
df = pd.DataFrame({'Size': X, 'Correlation': y})
g = (ggplot(df, aes('Size', 'Correlation')) +
geom_point(alpha=0.5, size=0.25) +
geom_density_2d(size=1, color='r') + plotnine.ggtitle(
'Dispersion Correlations\n{:} --> {:}'.format(sreg, treg)))
return g
def plot_overlap_duration(self, data, options):
matches = data["matches"]
matches = matches.loc[matches.tag_overlap > 0]
# matches.loc[:, "log_dur"] = log()
plt = ggplot(data=matches, mapping=aes(x="tag_duration", y="tag_overlap",),)
plt = (
plt
+ geom_point()
+ xlab("Tag duration")
+ ylab("Proportion tag overlapping with matching event")
+ theme_classic()
+ theme(
axis_text_x=element_text(angle=90, vjust=1, hjust=1, margin={"r": -30}),
plot_title=element_text(
weight="bold", size=14, margin={"t": 10, "b": 10}
),
figure_size=(10, 10),
text=element_text(size=12, weight="bold"),
)
+ ggtitle(
(
"Proportion of tag overlapping with matching event depending on duration "
+ "size for model {}, database {}, class {}\n"
+ "with detector options {}"
).format(
options["scenario_info"]["model"],
options["scenario_info"]["database"],
options["scenario_info"]["class"],
options,
)
)
)
return plt
def predictionContour(fit, data, y, title, density=51):
data = data.copy()
y = y.copy().astype(str)
def predictor(g, h):
dfgh = pd.DataFrame({data.columns[0]: [g]})
dfgh[data.columns[1]] = [h]
return fit.predict_proba(dfgh)[0, 1]
data["class"] = y
xrng = (0.5 * np.floor(2.0 * min(data.iloc[:, 0])),
0.5 * np.ceil(2.0 * max(data.iloc[:, 0])))
yrng = (0.5 * np.floor(2.0 * min(data.iloc[:, 1])),
0.5 * np.ceil(2.0 * max(data.iloc[:, 1])))
out = ggfuntile(predictor,
data,
xrng=xrng,
yrng=yrng,
density=density,
xlab=data.columns[0],
ylab=data.columns[1],
zlab="P(Y=1)",
breaks=[-np.inf, 0.5, np.inf])
out += ggtitle(title)
return out
def p(N=3):
"""Return *N* distinct plot objects."""
template = (
ggplot(aes(x='wt', y='mpg', label='name'), data=mtcars) +
geom_text()
)
for i in range(1, N+1):
yield template + ggtitle('%d of %d' % (i, N))
def plot_scores(df, title=None, xlab=None, ylab=None):
g = (gg.ggplot(df, gg.aes(x=cfg.SCORE_COLNAME_X, y=cfg.SCORE_COLNAME_Y)) +
gg.geom_line())
if title is not None:
g += gg.ggtitle(title)
if xlab is not None:
g += gg.xlab(xlab)
if ylab is not None:
g += gg.ylab(ylab)
return g
def comparison_plot(self,
df: pd.DataFrame,
xmin=None,
xmax=None,
bw="normal_reference",
**kwargs):
return (ggplot(df, aes(df.columns[1], fill=df.columns[0])) +
scale_fill_brewer(type="qual", palette="Pastel1") +
geom_density(bw=bw, alpha=0.8) + ggtitle(self.plot_title) +
self._scale_x(xmin, xmax) + ergo_theme)
def create(self, file_path: str) -> None:
(ggplot(self._data, aes(x="pattern", y="count", label="fraction")) +
geom_bar(stat="identity", fill="#1e4f79") +
geom_text(va='bottom', size=24, format_string='{:.1%}') +
scale_x_discrete(limits=self._data["pattern"]) +
scale_y_continuous(labels=comma_format(), expand=[0.1, 0]) +
ggtitle("Design Pattern Counts") + xlab("Design Pattern") +
ylab("Count") + theme_classic(base_size=32, base_family="Helvetica") +
theme(text=element_text(size=32),
axis_text_x=element_text(rotation=45, ha="right"))).save(
file_path, width=24, height=8)
def create(self, file_path: str) -> None:
(ggplot(self._data, aes(x="count", label="..count..")) +
geom_bar(fill="#1e4f79") +
geom_text(stat="count", va='bottom', size=24) +
scale_x_discrete(limits=[
"1", "2", "3", "5", "26", "52", "97", "100", "300", "537"
]) + scale_y_continuous(breaks=[0, 5, 10], limits=[0, 10]) +
ggtitle("Case Study Sizes") + xlab("Number of Projects") +
ylab("Number of Case Studies") +
theme_classic(base_size=28, base_family="Helvetica") +
theme(text=element_text(size=28))).save(file_path, width=14, height=7)
def density_plot(
self,
df: pd.DataFrame,
xmin=None,
xmax=None,
fill: str = "#fbb4ae",
bw="normal_reference",
**kwargs,
):
return (ggplot(df, aes(df.columns[0])) +
geom_density(fill=fill, alpha=0.8) + ggtitle(self.plot_title) +
self._scale_x(xmin, xmax) + ergo_theme)
def plot_replicate_correlation(
df,
batch,
plate,
facet_string=None,
split_samples=False,
output_file_base=None,
output_file_extensions=[".png", ".pdf", ".svg"],
dpi=500,
height=4,
width=5,
return_plot=False,
):
correlation_gg = (
gg.ggplot(
df,
gg.aes(x="group_replicate", y="similarity_metric", fill="group_replicate"),
)
+ gg.geom_boxplot(
alpha=0.3, outlier_alpha=0, width=0.8, notchwidth=0.25, fatten=1.5
)
+ gg.geom_jitter(shape=".", size=0.001, alpha=0.3, width=0.3, height=0)
+ gg.scale_fill_manual(
name="Replicate",
labels={"True": "True", "False": "False"},
values=["#B99638", "#2DB898"],
)
+ gg.xlab("Replicates")
+ gg.ylab("Pearson Correlation")
+ gg.ggtitle("{}: {}".format(batch, plate))
+ gg.theme_bw()
+ gg.theme(
subplots_adjust={"wspace": 0.2},
title=gg.element_text(size=5),
axis_text=gg.element_text(size=4),
axis_title=gg.element_text(size=5),
legend_text=gg.element_text(size=4),
legend_title=gg.element_text(size=5),
strip_text=gg.element_text(size=4, color="black"),
strip_background=gg.element_rect(colour="black", fill="#fdfff4"),
)
)
if split_samples:
assert facet_string, "To split samples, specify a facet_string"
correlation_gg += gg.facet_wrap(facet_string)
if output_file_base:
save_figure(
correlation_gg, output_file_base, output_file_extensions, dpi, height, width
)
if return_plot:
return correlation_gg
def create(self, file_path: str) -> None:
(ggplot(self._data, aes(x="category", y="count", label="percent")) +
geom_bar(stat="identity", fill="#1e4f79") +
geom_text(va='bottom', size=24) +
scale_x_discrete(limits=self._data["category"]) +
scale_y_continuous(labels=comma_format(), expand=[0.1, 0]) +
ggtitle("Classes per Category") + xlab("Category") +
ylab("Number of Classes") +
theme_classic(base_size=32, base_family="Helvetica") +
theme(text=element_text(size=32),
axis_text_x=element_text(rotation=45, ha="right"))).save(
file_path, width=7, height=7)
def plot(self,
plotDat,
tag=None,
log=True,
by='cell_type',
data_set=None,
title=None,
alpha=.4):
pDat = plotDat.copy()
gcorr = pearsonr(pDat.measured, pDat.prediction)[0]
corrs = pDat.groupby(
pDat[by]).apply(lambda x: pearsonr(x.measured, x.prediction)[0])
pDat['corr'] = corrs[pDat[by]].values
by_str = '{}_pearson'.format(by)
pDat[by_str] = pDat.apply(
lambda x: '{} {:.2f}'.format(x[by], corrs[x[by]]), axis=1)
if data_set:
pDat = pDat.loc[pDat['dataset_name'] == data_set]
pl = (pn.ggplot(pn.aes('measured', 'prediction', color=by_str), pDat) +
pn.geom_point(alpha=alpha) + pn.stat_smooth(mapping=pn.aes(
'measured', 'prediction', color=by_str),
method='lm',
geom='line',
alpha=0.5,
se=False,
inherit_aes=False))
if len(pDat['sample'].unique()) < 10:
pl = pl + pn.aes(shape='sample')
else:
pl = pl + pn.aes(shape='dataset_name')
if log is True:
pl = pl + pn.scale_x_log10() + pn.scale_y_log10()
if title is not None:
pl = pl + pn.ggtitle(title)
elif tag is not None:
pl = pl + pn.ggtitle('{} pearson={}'.format(tag, gcorr))
else:
pl = pl + pn.ggtitle('pearson={}'.format(gcorr))
return pl
def create(self, file_path: str) -> None:
(ggplot(self._data, aes("value")) +
geom_histogram(bins=100, fill="#1e4f79") +
facet_wrap(facets="variable", scales="free", ncol=3) +
scale_x_continuous(trans=asinh_trans(), labels=asinh_labels) +
scale_y_continuous(labels=comma_format()) +
ggtitle("Distributions of QMOOD Quality Attributes") +
xlab("Quality Attribute Value") + ylab("Number of Projects") +
theme_classic(base_size=32, base_family="Helvetica") +
theme(text=element_text(size=32),
subplots_adjust={
"wspace": 0.35,
"hspace": 0.35
})).save(file_path, width=24, height=12)
def export_graph(graphname, df, columns, plot):
print('Not provided:')
for c in columns:
outstr = "{} events, {}".format(
*len_and_pct(df[filter_notprovided(df[c], keep=True)], df))
with open(
"../report/src/numbers/" + "np-" + graphname + "-" + c +
".tex", "w") as f:
f.write(outstr)
print(f'- {c}: {outstr}')
(plot + p9.ggtitle("")).save("../report/src/images/" + graphname + ".png",
dpi=300)
print(plot)
def create(self, file_path: str) -> None:
(ggplot(self._data, aes("loc")) +
geom_histogram(bins=100, fill="#1e4f79") +
facet_grid(facets="category ~ .", scales='free_y') +
scale_x_continuous(trans=asinh_trans(), labels=asinh_labels) +
scale_y_continuous(labels=comma_format())
#+ scale_y_continuous(labels=lambda l: ["%.2f%%" % (v * 100 / len(self._data)) for v in l])
+ ggtitle("Class Sizes") + xlab("Lines of Code") +
ylab("Number of Classes") +
theme_classic(base_size=32, base_family="Helvetica") +
theme(text=element_text(size=32), subplots_adjust={"hspace": 0.1
})).save(file_path,
width=8,
height=18)
def plot_result_stats(results, title):
stats = results.describe().unstack().reset_index().rename(columns={
"level_0": "metric",
"level_1": "group",
0: "value"
})
stats = stats[~stats["group"].isin(["count", "min", "max"])]
stats["value_presentation"] = round(stats["value"], 2)
plot = (p9.ggplot(stats) + p9.aes("metric", "value", fill="group") +
p9.geom_col(position="dodge") + p9.theme_bw() +
p9.coord_cartesian(ylim=[0, 1.0]) + p9.ggtitle(title) +
p9.geom_text(p9.aes(label="value_presentation"),
position=p9.position_dodge(width=0.9),
va="bottom"))
return plot
def create(self, file_path: str) -> None:
(ggplot(self._data, aes("value")) +
geom_histogram(bins=100, fill="#1e4f79") +
facet_wrap(facets="variable", scales="free", ncol=3) + xlim(0, 1) +
scale_y_continuous(labels=comma_format()) +
ggtitle("Intensity of Design Pattern Use") +
xlab("Percentage of Classes Participating in Design Pattern") +
ylab("Number of Projects") +
theme_classic(base_size=32, base_family="Helvetica") +
theme(text=element_text(size=32),
axis_title_y=element_text(margin={"r": 40}),
subplots_adjust={
"wspace": 0.3,
"hspace": 0.5
})).save(file_path, width=24, height=24)
def plot_overlap_duration_bar(self, data, options):
matches = data["matches"]
matches = matches.loc[matches.tag_overlap > 0]
matches.loc[:, "tag_overlap_bin"] = pd.cut(
matches.tag_overlap, [0, 0.25, 0.5, 0.75, 1]
)
matches.loc[:, "tag_duration_bin"] = pd.cut(
matches.tag_duration, [0, 0.25, 0.5, 0.75, 1, 1.5, 2, float("inf")]
)
matches.loc[matches.tag_overlap < 0.3].to_csv("small_overlap.csv")
# matches.loc[:, "log_dur"] = log()
plt = ggplot(
data=matches, mapping=aes(x="tag_duration_bin", fill="tag_overlap_bin",),
)
plt = (
plt
+ geom_bar()
+ xlab("Tag duration")
+ ylab("Proportion tag overlapping with matching event")
+ theme_classic()
+ theme(
axis_text_x=element_text(angle=90, vjust=1, hjust=1, margin={"r": -30}),
plot_title=element_text(
weight="bold", size=14, margin={"t": 10, "b": 10}
),
figure_size=(10, 10),
text=element_text(size=12, weight="bold"),
)
+ ggtitle(
(
"Proportion of tag overlapping with matching event depending on duration "
+ "size for model {}, database {}, class {}\n"
+ "with detector options {}"
).format(
options["scenario_info"]["model"],
options["scenario_info"]["database"],
options["scenario_info"]["class"],
options,
)
)
)
return plt
def plot_dist_with_ci(dist):
return (pn.ggplot(dist, pn.aes(x='estimates')) +
pn.geom_histogram(bins=25) + pn.geom_vline(
xintercept=dist.quantile(0.025),
color="#FF5500",
size=2,
linetype='dotted',
) + pn.geom_vline(
xintercept=dist.quantile(0.975),
color="#FF5500",
size=2,
linetype='dotted',
) + pn.ggtitle("${0:,.0f} ({1:,.0f}, {2:,.0f})".format(
np.mean(dist.estimates),
dist.estimates.quantile(0.025),
dist.estimates.quantile(0.975),
)))
def create(self, file_path: str) -> None:
metrics = self._data["metric"].unique()
for metric in metrics:
data = self._data[self._data["metric"] == metric]
q75, q25 = np.percentile(data["value"], [98, 2])
(ggplot(data, aes(x="category", y="value")) +
geom_boxplot(outlier_shape="") +
coord_cartesian(ylim=(q75 * 0.8, q25 * 1.2))
#+ facet_wrap(facets="metric", scales="free", ncol=3)
+ ggtitle(metric)
#+ ggtitle("QMOOD Quality Attributes")
+ xlab("Category") + ylab("Value") +
theme_classic(base_size=28, base_family="Helvetica")
#+ theme(subplots_adjust={"wspace": 0.25, "hspace": 0.2})
).save(f"{file_path}.{metric}.pdf", width=24, height=24)
def plot_replicate_density(
df,
batch,
plate,
cutoff,
percent_strong,
output_file_base=None,
output_file_extensions=[".png", ".pdf", ".svg"],
dpi=300,
height=1.5,
width=2,
return_plot=False,
):
density_gg = (
gg.ggplot(df, gg.aes(x="similarity_metric", fill="group_replicate"))
+ gg.geom_density(alpha=0.3)
+ gg.scale_fill_manual(
name="Replicate",
labels={"True": "True", "False": "False"},
values=["#B99638", "#2DB898"],
)
+ gg.xlab("Pearson Correlation")
+ gg.ylab("Density")
+ gg.geom_vline(xintercept=cutoff, color="red", linetype="dashed")
+ gg.ggtitle(
f"{batch}; Plate: {plate}\n\nPercent Replicating: {np.round(percent_strong * 100, 2)}%"
)
+ gg.theme_bw()
+ gg.theme(
title=gg.element_text(size=3.5),
axis_text=gg.element_text(size=4),
axis_title=gg.element_text(size=4),
legend_text=gg.element_text(size=4),
legend_title=gg.element_text(size=4),
strip_text=gg.element_text(size=4, color="black"),
strip_background=gg.element_rect(colour="black", fill="#fdfff4"),
)
)
if output_file_base:
save_figure(
density_gg, output_file_base, output_file_extensions, dpi, height, width
)
if return_plot:
return density_gg
def image_histogram():
# create windows
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.namedWindow('image_bw', cv2.WINDOW_NORMAL)
cv2.namedWindow('image_bw_eq', cv2.WINDOW_NORMAL)
# read and work with image
image = cv2.imread(r"image.jpg")
image_bw = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image_bw_eq = cv2.equalizeHist(image_bw)
# display images
cv2.imshow('image', image)
cv2.imshow('image_bw', image_bw)
cv2.imshow('image_bw_eq', image_bw_eq)
# calculate histogram
# np_hist_y, bins = np.histogram(image_bw.ravel(), 256, [0, 256])
# hist = np.bincount(image_bw.ravel(), minlength=256) # faster version of np.histogram
# plt.hist(image_bw.ravel(), bins=256)
hist_bw = cv2.calcHist([image_bw], [0], None, [256], [0, 255])
hist_bw_eq = cv2.calcHist([image_bw_eq], [0], None, [256], [0, 255])
np_hist_x = np.arange(len(hist_bw))
d = {
'np_hist_x': np_hist_x,
'hist_bw': hist_bw.flatten(),
'hist_bw_eq': hist_bw_eq.flatten()
}
df = pd.DataFrame(data=d)
# plot histogram
pn_handle = pn.ggplot(df) + pn.geom_col(pn.aes(x='np_hist_x', y='hist_bw'), color=None, fill='red', alpha=0.5) + pn.ylab('occurences') \
+ pn.geom_col(pn.aes(x='np_hist_x', y='hist_bw_eq'), color=None, fill='green', alpha=0.5) \
+ pn.ggtitle('Histograms of bw images')
pn_handle.draw()
plt.show()
while True:
pressed_key = cv2.waitKey(16)
if pressed_key == ord('q'):
break
# cleanup opencv
cv2.destroyAllWindows()
def main(argv: List[str]) -> None:
parser = argparse.ArgumentParser()
parser.add_argument("roll_rule", type=RollRule, choices=list(RollRule))
parser.add_argument("--num_iterations", type=int, default=10000)
parser.add_argument("--seed", type=int, default=None)
parser.add_argument("--plot_file", default="ability_roll_distribution.png")
args = parser.parse_args(argv)
if args.seed is not None:
random.seed(args.seed)
# Run the simulation and process the data
roll_counts = simulate(args.roll_rule, args.num_iterations)
data = process_data(roll_counts)
# Calculate statistics
mean = sum(data["value"] * data["percent"] / 100.0)
mode = data.iloc[data["count"].idxmax()]["value"]
stddev = math.sqrt(
sum(data["percent"] / 100.0 * (data["value"] - mean)**2.0))
skewness = pearson_first_skewness(mean, mode, stddev)
# Print out result information
print(data)
print()
print("Mean:", mean)
print("Mode:", mode)
print("Standard deviation:", stddev)
print("Skewness:", skewness)
# Plot the data
plot = (plt9.ggplot(data, plt9.aes("value", "percent")) +
plt9.geom_bar(stat="identity") +
plt9.geom_vline(xintercept=mean, color="black") +
plt9.xlim(0, 21) + plt9.ylab("Chance (%)") +
plt9.xlab("Ability Score") +
plt9.ggtitle("Ability Score Distribution ({} iterations)".format(
args.num_iterations)))
plot.save(args.plot_file, dpi=300)
print("Wrote plot image to:", args.plot_file)
def main():
mpl.rc('mathtext', fontset='cm')
warnings.filterwarnings('ignore',
r'(geom|position)_\w+ ?: Removed \d+ rows')
warnings.filterwarnings('ignore', r'Saving .+ x .+ in image')
warnings.filterwarnings('ignore', r'Filename: .+\.png')
df = concat_map(Pf_Ob_Ol, 'P_f', np.linspace(0.1, 1, 10))
save_both(my_plot(df, 'O_b', 'O_l', 'P_f')
+ titles('P_f(O_b, O_l)')
+ limits((1, 10))
+ gg.geom_abline(slope=1, intercept=0,
linetype='dashed', color='grey')
+ gg.geom_line()
, 'Pf_Ob_Ol')
df = concat_map(Pf_Ob_σ, 'P_f', np.linspace(0.1, 1, 10))
save_both(my_plot(df, 'O_b', 'σ', 'P_f')
+ titles('P_f(O_b, σ)')
+ limits((1, 10), (0, 5))
+ gg.geom_line()
, 'Pf_Ob_σ')
df = concat_map(Pq_Ob_Ol, 'P_q', np.linspace(-0.9, 0, 10))
save_both(my_plot(df, 'O_b', 'O_l', 'P_q')
+ titles('P_q(O_b, O_l)')
+ limits((1, 10))
+ gg.geom_abline(slope=1, intercept=0,
linetype='dashed', color='grey')
+ gg.geom_line()
, 'Pq_Ob_Ol')
df = concat_map(Pq_Ob_σ, 'P_q', np.linspace(-0.9, 0, 10))
save_both(my_plot(df, 'O_b', 'σ', 'P_q')
+ titles('P_q(O_b, σ)')
+ limits((1, 10), (0, 5))
+ gg.geom_line()
, 'Pq_Ob_σ')
df = concat_map(Opr_Ob_Ol, 'Opr', np.linspace(1, 5, 9))
save_both(my_plot(df, 'O_b', 'O_l', 'Opr')
+ titles("O'(O_b, O_l)")
+ limits((1, 10), (1, 10))
+ gg.geom_line()
+ gg.geom_abline(slope=1, intercept=0,
linetype='dashed', color='grey')
, 'Opr_Ob_Ol')
df = concat_map(Opr_Ob_σ, 'Opr', np.linspace(1, 5, 9))
save_both(my_plot(df, 'O_b', 'σ', 'Opr')
+ titles("O'(O_b, σ)")
+ limits((1, 10), (0, 5))
+ gg.geom_line()
, 'Opr_Ob_σ')
df = (pd.DataFrame({'Opr': np.linspace(1, 21, 101)})
.assign(Pf=lambda x: Opr_Pf(x.Opr)))
save_both(my_plot(df, 'Opr', 'Pf')
+ titles("P_f(O')")
+ labs("O'", 'P_f')
+ limits((1, 20), (0, 1),
xbreaks=np.linspace(2, 20, 10),
ybreaks=np.linspace(0, 1, 11))
+ gg.geom_line()
+ gg.geom_hline(yintercept=C, linetype='dashed', color='grey')
, 'Pf_Opr')
df = concat_map(σpr_Ob_σ, 'σpr', np.linspace(0, 5, 11))
save_both(my_plot(df, 'O_b', 'σ', 'σpr')
+ titles("σ'(O_b, σ)")
+ limits((1, 10), (0, 5))
+ gg.geom_line()
, 'σpr_Ob_σ')
df = (pd.DataFrame({'σpr': np.linspace(0, 21, 106)})
.assign(Pq=lambda x: σpr_Pq(x.σpr)))
save_both(my_plot(df, 'σpr', 'Pq')
+ titles("P_q(σ')")
+ labs("σ'", 'P_q')
+ limits((0, 20), (-1, 0),
xbreaks=np.linspace(0, 20, 11),
ybreaks=np.linspace(-1, 0, 11))
+ gg.geom_line()
, 'Pq_σpr')
df = concat_map(liab_Ob_Ol_free, 'liab', np.linspace(0, 10, 11))
save_both(my_plot(df, 'O_b', 'O_l', 'liab', clab='-R_{bl}')
+ titles("-R_{bl}(O_b, O_l)", "S_b = 1, C_b = 0, C_l = 0.02",
mathrm('Free bet', dollars=False))
+ limits((1,20), (1, 10))
+ gg.geom_line()
+ gg.geom_abline(slope=1, intercept=0,
linetype='dashed', color='grey')
, 'liab_Ob_Ol_free')
df = concat_map(liab_Ob_Ol_free, 'liab', np.linspace(0, 10, 11))
save_both(my_plot(df, 'O_b', 'σ', 'liab', clab='-R_{bl}')
+ titles("-R_{bl}(O_b, σ)", "S_b = 1, C_b = 0, C_l = 0.02",
mathrm('Free bet', dollars=False))
+ limits((1,20), (1, 10))
+ gg.geom_line()
, 'liab_Ob_σ_free')
df = concat_map(liab_Ob_Ol_qual, 'liab', np.linspace(0, 10, 11))
save_both(my_plot(df, 'O_b', 'O_l', 'liab', clab='-R_{bl}')
+ titles("-R_{bl}(O_b, O_l)", "S_b = 1, C_b = 0, C_l = 0.02",
mathrm('Qualifying bet', dollars=False))
+ limits((1,20), (1, 10))
+ gg.geom_line()
+ gg.geom_abline(slope=1, intercept=0,
linetype='dashed', color='grey')
, 'liab_Ob_Ol_qual')
df = concat_map(liab_Ob_Ol_qual, 'liab', np.linspace(0, 10, 11))
save_both(my_plot(df, 'O_b', 'σ', 'liab', clab='-R_{bl}')
+ titles("-R_{bl}(O_b, σ)", "S_b = 1, C_b = 0, C_l = 0.02",
mathrm('Qualifying bet', dollars=False))
+ limits((1,20), (1, 10))
+ gg.geom_line()
, 'liab_Ob_σ_qual')
df_Pf = Pf_Ob_σ(0.6).assign(profit=dollars('P_f'))
df_Pq = Pq_Ob_σ(-0.3).assign(profit=dollars('P_q'))
df = pd.concat((df_Pf, df_Pq), ignore_index=True)
df.drop_duplicates('O_b', inplace=True)
Opr = df_Pf.query('σ==0').O_b[0]
σpr = df_Pq.query('O_b==1').σ[0]
labels = pd.DataFrame({
'x': [Opr+0.1, 1, 9.8], 'y': [4.8, σpr, σpr + 0.3],
'label': ["$O'$", "$σ'$", mathrm('More profit')]
})
lab_aes = gg.aes('x', 'y', label='label')
save_both(
gg.ggplot(df, gg.aes(x='O_b', y='σ'))
+ gg.geom_area(gg.aes(fill='profit'), alpha=0.3)
+ gg.geom_vline(xintercept=Opr, linetype='dashed')
+ gg.geom_hline(yintercept=σpr, linetype='dashed')
# text alignment can't be specified in an aes
+ gg.geom_text(lab_aes, data=labels.ix[:0], ha='left', va='top')
+ gg.geom_text(lab_aes, data=labels.ix[1:1], ha='left', va='bottom')
+ gg.geom_text(lab_aes, data=labels.ix[2:], ha='right', va='bottom')
+ gg.scale_fill_discrete(name=mathrm('Bet type'),
labels=[mathrm('Free'), mathrm('Qualifying')])
+ limits((1, 10), (0, 5))
+ gg.ggtitle('%s "%s" %s' % (mathrm('Shape of the'),
mathrm('more profitable'),
mathrm('space')))
+ labs('O_b', 'σ')
, 'Px_shapes')
def titles(t, *s): # title, hacky subtitles
if not s:
s = [commission_string]
s = ['${}_{%s}$' % (x,) for x in s]
return gg.ggtitle('\n'.join(['$%s$' % (t,)] + s))
def plot_char_percent_vs_accuracy_smooth(self, expo=False, no_models=False, columns=False):
if self.y_max is not None:
limits = [0, float(self.y_max)]
eprint(f'Setting limits to: {limits}')
else:
limits = [0, 1]
if expo:
if os.path.exists('data/external/all_human_gameplay.json') and not self.no_humans:
with open('data/external/all_human_gameplay.json') as f:
all_gameplay = json.load(f)
frames = []
for event, name in [('parents', 'Intermediate'), ('maryland', 'Expert'), ('live', 'National')]:
if self.merge_humans:
name = 'Human'
gameplay = all_gameplay[event]
if event != 'live':
control_correct_positions = gameplay['control_correct_positions']
control_wrong_positions = gameplay['control_wrong_positions']
control_positions = control_correct_positions + control_wrong_positions
control_positions = np.array(control_positions)
control_result = np.array(len(control_correct_positions) * [1] + len(control_wrong_positions) * [0])
argsort_control = np.argsort(control_positions)
control_x = control_positions[argsort_control]
control_sorted_result = control_result[argsort_control]
control_y = control_sorted_result.cumsum() / control_sorted_result.shape[0]
control_df = pd.DataFrame({'correct': control_y, 'char_percent': control_x})
control_df['Dataset'] = 'Regular Test'
control_df['Guessing_Model'] = f' {name}'
frames.append(control_df)
adv_correct_positions = gameplay['adv_correct_positions']
adv_wrong_positions = gameplay['adv_wrong_positions']
adv_positions = adv_correct_positions + adv_wrong_positions
adv_positions = np.array(adv_positions)
adv_result = np.array(len(adv_correct_positions) * [1] + len(adv_wrong_positions) * [0])
argsort_adv = np.argsort(adv_positions)
adv_x = adv_positions[argsort_adv]
adv_sorted_result = adv_result[argsort_adv]
adv_y = adv_sorted_result.cumsum() / adv_sorted_result.shape[0]
adv_df = pd.DataFrame({'correct': adv_y, 'char_percent': adv_x})
adv_df['Dataset'] = 'IR Adversarial'
adv_df['Guessing_Model'] = f' {name}'
frames.append(adv_df)
if len(gameplay['advneural_correct_positions']) > 0:
adv_correct_positions = gameplay['advneural_correct_positions']
adv_wrong_positions = gameplay['advneural_wrong_positions']
adv_positions = adv_correct_positions + adv_wrong_positions
adv_positions = np.array(adv_positions)
adv_result = np.array(len(adv_correct_positions) * [1] + len(adv_wrong_positions) * [0])
argsort_adv = np.argsort(adv_positions)
adv_x = adv_positions[argsort_adv]
adv_sorted_result = adv_result[argsort_adv]
adv_y = adv_sorted_result.cumsum() / adv_sorted_result.shape[0]
adv_df = pd.DataFrame({'correct': adv_y, 'char_percent': adv_x})
adv_df['Dataset'] = 'RNN Adversarial'
adv_df['Guessing_Model'] = f' {name}'
frames.append(adv_df)
human_df = pd.concat(frames)
human_vals = sort_humans(list(human_df['Guessing_Model'].unique()))
human_dtype = CategoricalDtype(human_vals, ordered=True)
human_df['Guessing_Model'] = human_df['Guessing_Model'].astype(human_dtype)
dataset_dtype = CategoricalDtype(['Regular Test', 'IR Adversarial', 'RNN Adversarial'], ordered=True)
human_df['Dataset'] = human_df['Dataset'].astype(dataset_dtype)
if no_models:
p = ggplot(human_df) + geom_point(shape='.')
else:
df = self.char_plot_df
if 1 not in self.rounds:
df = df[df['Dataset'] != 'Round 1 - IR Adversarial']
if 2 not in self.rounds:
df = df[df['Dataset'] != 'Round 2 - IR Adversarial']
df = df[df['Dataset'] != 'Round 2 - RNN Adversarial']
p = ggplot(df)
if self.save_df is not None:
eprint(f'Saving df to: {self.save_df}')
df.to_json(self.save_df)
if os.path.exists('data/external/all_human_gameplay.json') and not self.no_humans:
eprint('Loading human data')
p = p + geom_line(data=human_df)
if columns:
facet_conf = facet_wrap('Guessing_Model', ncol=1)
else:
facet_conf = facet_wrap('Guessing_Model', nrow=1)
if not no_models:
if self.mvg_avg_char:
chart = stat_smooth(method='mavg', se=False, method_args={'window': 400})
else:
chart = stat_summary_bin(fun_data=mean_no_se, bins=20, shape='.', linetype='None', size=0.5)
else:
chart = None
p = (
p + facet_conf
+ aes(x='char_percent', y='correct', color='Dataset')
)
if chart is not None:
p += chart
p = (
p
+ scale_y_continuous(breaks=np.linspace(0, 1, 6))
+ scale_x_continuous(breaks=[0, .5, 1])
+ coord_cartesian(ylim=limits)
+ xlab('Percent of Question Revealed')
+ ylab('Accuracy')
+ theme(
#legend_position='top', legend_box_margin=0, legend_title=element_blank(),
strip_text_x=element_text(margin={'t': 6, 'b': 6, 'l': 1, 'r': 5})
)
+ scale_color_manual(values=['#FF3333', '#66CC00', '#3333FF', '#FFFF33'], name='Questions')
)
if self.title != '':
p += ggtitle(self.title)
return p
else:
if self.save_df is not None:
eprint(f'Saving df to: {self.save_df}')
df.to_json(self.save_df)
return (
ggplot(self.char_plot_df)
+ aes(x='char_percent', y='correct', color='Guessing_Model')
+ stat_smooth(method='mavg', se=False, method_args={'window': 500})
+ scale_y_continuous(breaks=np.linspace(0, 1, 6))
+ coord_cartesian(ylim=limits)
)