def go():
df1 = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/718417069ead87650b90472464c7565dc8c2cb1c/sunburst-coffee-flavors-complete.csv'
)
df2 = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/718417069ead87650b90472464c7565dc8c2cb1c/coffee-flavors.csv'
)
fig = make_subplots(rows=1,
cols=2,
column_widths=[0.4, 0.4],
specs=[[{
'type': 'treemap',
'rowspan': 1
}, {
'type': 'treemap'
}]])
fig.add_trace(go.Treemap(ids=df1.ids,
labels=df1.labels,
parents=df1.parents),
col=1,
row=1)
fig.add_trace(go.Treemap(ids=df2.ids,
labels=df2.labels,
parents=df2.parents,
maxdepth=3),
col=2,
row=1)
fig.update_layout(margin={'t': 0, 'l': 0, 'r': 0, 'b': 0})
fig.show()
def covid_26():
global fig3
fig3 = go.Figure()
fig3.add_trace(
go.Treemap(ids=df["Confirmed"],
labels=df["States"],
parents=df["Active"],
values=df["Active_Value"],
textinfo='label+value',
domain=dict(column=0)))
fig3.add_trace(
go.Treemap(
ids=df["Confirmed"],
labels=df["States"],
parents=df["Deaths"],
values=df["Deaths_Value"],
textinfo='label+value',
domain=dict(column=1),
))
fig3.update_layout(
grid=dict(columns=2, rows=1),
margin=dict(t=0, l=0, r=0, b=0),
)
fig3.write_html("templates/choro22.html")
return render_template("choro22.html")
def test_pie_like_px():
# Pie
labels = ["Oxygen", "Hydrogen", "Carbon_Dioxide", "Nitrogen"]
values = [4500, 2500, 1053, 500]
fig = px.pie(names=labels, values=values)
trace = go.Pie(labels=labels, values=values)
_compare_figures(trace, fig)
labels = [
"Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"
]
parents = ["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"]
values = [10, 14, 12, 10, 2, 6, 6, 4, 4]
# Sunburst
fig = px.sunburst(names=labels, parents=parents, values=values)
trace = go.Sunburst(labels=labels, parents=parents, values=values)
_compare_figures(trace, fig)
# Treemap
fig = px.treemap(names=labels, parents=parents, values=values)
trace = go.Treemap(labels=labels, parents=parents, values=values)
_compare_figures(trace, fig)
# Funnel
x = ["A", "B", "C"]
y = [3, 2, 1]
fig = px.funnel(y=y, x=x)
trace = go.Funnel(y=y, x=x)
_compare_figures(trace, fig)
# Funnelarea
fig = px.funnel_area(values=y, names=x)
trace = go.Funnelarea(values=y, labels=x)
_compare_figures(trace, fig)
def plot_treemap_district(pallete="curl", df=df, template=sport_template):
fig = px.treemap(df,
path=["All", 'Stadsdeel', "Sport_en"],
color="sport_count_in_district",
color_continuous_scale=pallete,
range_color=[1, 22],
hover_name="Sport_en",
color_continuous_midpoint=np.average(
df["sport_count_in_district"]),
maxdepth=3)
fig.update_traces(
go.Treemap(
hovertemplate="%{label}",
texttemplate=
"%{label}<br><br>Percentage: %{percentParent:.1%} <br>Count: %{value}<br>",
outsidetextfont={"size": 20}))
fig.update_layout(
title=dict(
text="Sports Distribution in City Districts<br>(Click to Expand)",
),
coloraxis_colorbar=dict(title="Counts", ),
margin=dict(l=20, t=100),
template=template,
)
return fig
def plot_feature_importance(feature_importance):
"""Created a treemap plot based on feature importance and weights.
Args:
feature_importance (pandas.DataFrame): A DataFrame describing the
feature importances and weights of the trained model.
Returns:
plotly.graph_objects.Figure: A treemap of the features.
"""
feature_importance["parent"] = "Feature importance"
return go.Figure(
go.Treemap(
labels=feature_importance.index,
parents=feature_importance["parent"],
values=feature_importance["gain"],
customdata=feature_importance["weight"],
marker=dict(colors=feature_importance["weight"],
colorscale="greens"),
hovertemplate=("<b>%{label}</b><br>importance: %{value:.1%}"
"<br>weight: %{customdata:.1%}<extra></extra>"),
),
layout={"margin": {
"t": 0,
"r": 0,
"b": 0,
"l": 0,
}},
)
def plot_treemap_all(pallete="curl", df=df, template=sport_template):
fig = px.treemap(df,
path=["All", "Sport_en"],
color="Sport_count",
color_continuous_scale=pallete,
range_color=[1, 75],
hover_name="Sport_en",
color_continuous_midpoint=np.average(df["Sport_count"]),
maxdepth=3)
fig.update_traces(
go.Treemap(
textinfo="label",
texttemplate=
"%{label}<br><br>Percentage: %{percentParent:.1%} <br>Count: %{value}<br>",
hovertemplate=" %{label} ",
outsidetextfont={"size": 20},
))
fig.update_layout(title=dict(
text="Sports Distribution in Amsterdam by Type<br> (Click to Expand)"),
coloraxis_colorbar=dict(title="Counts",
tickvals=[10, 30, 50, 70]),
template=template)
return fig
def plot_treemap(self, port):
levels = ['ASSET', 'TYPE'] # levels used for the hierarchical chart
color_column = 'CHG'
aux_color_column = 'CURRENT_TOTAL'
value_column = 'TOTAL'
df_all_trees = self.__build_hierarchical_dataframe(
self.port, levels, value_column, color_column, aux_color_column)
trace = go.Treemap(
textinfo="label+percent root",
labels=df_all_trees['id'],
parents=df_all_trees['parent'],
values=df_all_trees['value'],
branchvalues='total',
marker=dict(colors=df_all_trees['color'],
colorscale='RdBu',
cmid=0),
hovertemplate=
'<b>%{label} </b> <br> Total: R$%{value:,.2f}<br> Chg: %{color:.2%}',
name='')
layout = go.Layout(autosize=False,
width=1000,
height=500,
margin=go.layout.Margin(l=10,
r=10,
b=10,
t=10,
pad=4))
fig = go.Figure(data=[trace], layout=layout)
fig.show()
def plot(nlocs, files, parents, ccns):
fig = go.Figure(
go.Treemap(
labels=files,
values=nlocs,
parents=parents,
marker=dict(colors=ccns,
colorscale='blues',
cmid=statistics.mean(ccns)),
))
fig.show()
def createTreeMap(self,labels=[],parents=[],values=[]):
fig = go.Figure(go.Treemap(
labels = labels,
parents = parents,
values = values
))
fig.update_layout(margin=dict(l=10, r=10, t=10, b=10),autosize=True)
fig.update_yaxes(automargin=True)
presult = plotly.offline.plot(fig, config={"displayModeBar": False},
show_link=False,
include_plotlyjs=False,
output_type='div')
return presult
def build_pkd_treemap(voivodeship=[]):
df = __df
if voivodeship is not None and voivodeship != []:
df = df.loc[np.isin(df.MainAddressVoivodeship, voivodeship), :].reset_index(drop=False)
pkd_names = __pkd_names
levels = ['PKDMainSection',
'PKDMainDivision'] # levels used for the hierarchical chart
color_columns = ['DurationOfExistenceInMonths']
value_column = 'Count'
df_all_trees = __build_hierarchical_dataframe(df, pkd_names, levels, value_column, color_columns)
df_all_trees = __format_strings(df_all_trees)
median_duration = df_all_trees['color'].median()
max_duration = df_all_trees['color'].max()
pkd_fig = go.Figure(go.Treemap(
labels=df_all_trees['id'],
parents=df_all_trees['parent'],
values=df_all_trees['value'],
branchvalues='total',
marker=dict(
colors=df_all_trees['color'],
colorscale='balance',
cmax=max_duration,
cmid=median_duration,
colorbar=dict(thickness=20, title='Przetrwane miesiące', titleside='right')),
maxdepth=2,
# texttemplate = "%{label}<br>%<b>%{customdata[2]}<\b><br>",
customdata=df_all_trees[['years', 'months', 'nazwa']],
hovertemplate='<b>%{label} </b> <br>%{customdata[2]} <br> - Liczba firm: %{value}<br> - Czas życia przeciętnej firmy: <b>%{customdata[0]}%{customdata[1]}</b>',
name=''
))
pkd_fig.update_layout(
title={
'text': "Czas życia przeciętnej firmy",
'y': 0.9,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
},
margin=dict(l=20, r=20, t=70, b=20),
)
return pkd_fig
def draw_treemap(data):
fig = go.Figure(
go.Treemap(
ids=data['id'],
parents=data['parent'],
labels=data['state'],
customdata=data['value'],
name='Zones',
pathbar={"visible": True},
hovertemplate=
'<b> Region: %{label}</b> <br> Zone: %{customdata}<extra></extra>',
marker=dict(colors=get_color(data['value']))))
fig.update_layout(title="Zones",
height=900,
margin=dict(t=30, b=10, r=0, l=0))
return fig
def get_fig():
unique_versions = list(df['version'].unique())
unique_versions.sort()
unique_simple_versions = list(
[infer_node_version(v) for v in unique_versions])
unique_simple_versions_parents = list(
["All versions" for _ in dict.fromkeys(unique_simple_versions)])
projects_names = list(df['name'])
versions = list(df['version'])
labels = list(dict.fromkeys(unique_simple_versions)) + \
unique_versions + projects_names
parents = unique_simple_versions_parents + unique_simple_versions + versions
layout = dict(
title=go.layout.Title(
text='NodeJS versions in use<br>(Generated on {})'.format(
creation_time),
font=graph_title_font),
paper_bgcolor=WHITE,
plot_bgcolor=TRANSPARENT,
treemapcolorway=colorway['GovUkColours'],
height=600,
)
return {
'data': [
go.Treemap(
labels=labels,
parents=parents,
textposition="middle center",
tiling=dict(packing="slice", ),
marker=dict(line=dict(width=0), ),
maxdepth=2,
)
],
'layout':
layout
}
def milestones_graph(value):
tags = list(milestones.keys())
labels = list(milestones.keys())
parents = [''] * len(tags)
# fixme: add colors?
color = []
counter = 0
stories_color = []
for index, milestone in enumerate(milestones.values()):
color = set_milestones_color(milestones, milestone, tags,
index, color)
for idx, title in enumerate(milestone['title']):
if milestone['label'][idx] == 'Bug':
stories_color.append('red')
elif milestone['label'][idx] == 'Test':
stories_color.append('purple')
elif milestone['label'][idx] == 'New Feature' or \
milestone['label'][idx] == 'Improvement':
stories_color.append('green')
else:
stories_color.append('gray')
parents.append(tags[index])
# When title is present in tags, set new id
labels.append(title)
if title in tags:
# Add issue to treemap but change it's identifier
tags.append(title + '_' + str(counter))
counter += 1
else:
# Add issue to treemap
tags.append(title)
# Chain colors for the titles
color += stories_color
fig = go.Figure(go.Treemap(
marker_colors=color,
labels=labels,
parents=parents,
ids=tags
))
return fig
def generate_treemap(filtered_df, geo_select):
if geo_select is None:
hashtag_list = filtered_df['hashtags'].tolist()
else:
hashtag_list = [point["customdata"] for point in geo_select["points"]]
k = 20
hashtags = [hashtag for sublist in hashtag_list for hashtag in sublist]
freq_df = pd.DataFrame(list(FreqDist(hashtags).items()),columns=["hashtag","count"])
freq_df = freq_df.sort_values('count',ascending=False)
fig = go.Figure(go.Treemap(
labels=freq_df['hashtag'][:k].tolist(),
values=freq_df['count'][:k].tolist(),
parents=['']*k,
marker_colorscale=px.colors.sequential.Teal,
hovertemplate='<b>%{label} </b> <br>Count: %{value}<extra></extra>'))
fig.update_layout(margin=dict(l=0, r=0, t=0, b=10),
height=250,
plot_bgcolor="#171b26",
paper_bgcolor="#171b26")
return fig
def treemap_capacity():
levels = ['Country', 'Region', 'Continent']
color_columns = ['capacity_mw', 'quantity']
value_column = 'capacity_mw'
df = total_capacity_per_country()
df_all_trees = build_hierarchical_dataframe(df, levels, value_column,
color_columns)
fig = go.Figure()
fig.add_trace(go.Treemap(**fig_treemap(df_all_trees)))
fig.update_layout(
title={
'text':
"Conutries Capacity (MW) Proportional to Numbers of Power Plant <br>(Hoover for more information)",
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
},
treemapcolorway=[continent_color(c) for c in get_continents()],
)
# fig.write_html('tmp3.html', auto_open=True)
return plotly.offline.plot(figure_or_data=fig,
include_plotlyjs=False,
output_type='div')
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
df2 = pd.read_csv('coffee-flavors.csv')
fig = make_subplots(rows=1,
cols=2,
column_widths=[0.4, 0.4],
specs=[[{
'type': 'treemap',
'rowspan': 1
}, {
'type': 'treemap'
}]])
fig.add_trace(go.Treemap(ids=df2.ids,
labels=df2.labels,
parents=df2.parents,
maxdepth=3),
col=1,
row=1)
fig.update_layout(margin={'t': 0, 'l': 0, 'r': 0, 'b': 0})
fig.show()
#%%
import plotly.graph_objects as go
labels = ["A1", "A2", "A3", "A4", "A5", "B1", "B2"]
parents = ["", "A1", "A2", "A3", "A4", "", "B1"]
fig = go.Figure(
go.Treemap(labels=labels,
parents=parents,
marker_colors=[
"pink", "royalblue", "lightgray", "purple", "cyan",
"lightgray", "lightblue"
]))
fig.show()
np_dfBooks[:, 0] == x[4], 1]:
parentList.update({x[1]: np_dfBooks[np_dfBooks[:, 0] == x[4], 1]})
else:
parentList.update({x[1]: ''})
print(count)
print(x[1])
if count == 3827:
print(x[1])
print(np_dfBooks[np_dfBooks[:, 0] == x[4], 1])
break
count = count + 1
print(parentList.keys())
print(parentList.values())
fig = go.Figure()
fig.add_trace(
go.Sunburst(labels=list(parentList.keys()),
parents=list(parentList.values()),
domain=dict(column=0)))
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
fig.show()
#treemap
fig1 = go.Figure(
go.Treemap(
labels=list(parentList.keys()),
parents=list(parentList.values()),
))
fig1.show()
for cat in categories:
modules_onecat = modules_df[modules_df.id.str.startswith(cat)]
fig_modules = go.Figure(
go.Treemap(
branchvalues="remainder",
ids=modules_onecat["id"],
labels="<b>" + modules_onecat["title"] + "</b> (" +
modules_onecat["id"] + ")",
parents=modules_onecat["parent"],
values=modules_onecat["priority"],
marker_colors=modules_onecat["assessment"],
text=(modules_onecat["text"]).apply(
lambda txt: '<br>'.join(textwrap.wrap(txt, width=100))),
textinfo="label+text",
hovertext=(
modules_onecat["text"] + " (" + modules_onecat["id"] + ")"
"<br>Priorität: " + (modules_onecat["priority"]).astype(str) +
"<br>Potential: " + (modules_onecat["potential"]).astype(str)
).apply(lambda txt: '<br>'.join(textwrap.wrap(txt, width=100))),
hoverinfo="text",
pathbar={"visible": True},
insidetextfont={"size": 75}))
fig_modules.update_layout(margin=dict(r=10, l=10)
# ~ height = 750
)
modules_plot_file.write(
import plotly.graph_objects as go
labels = ["A1", "A2", "A3", "A4", "A5", "B1", "B2"]
parents = ["", "A1", "A2", "A3", "A4", "", "B1"]
marker_colors = [
"pink", "royalblue", "lightgray", "purple", "cyan", "lightgreen",
"lightblue"
]
fig = go.Figure(
go.Treemap(labels=[
"Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"
],
parents=[
"", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan",
"Eve"
],
marker_colors=marker_colors))
fig.show()
import plotly.graph_objects as go
import pandas as pd
#create a dataframe from the Business Capability Model as defined.
df = pd.read_csv('data/bcmL1L2L3.csv')
print(df)
#build a visual treemap of the business capability model via CSV
fig = go.Figure(go.Treemap(
ids = df.ids,
labels = df.labels,
parents = df.parents,
marker_colorscale = 'Blues'))
# marker_colors = ["pink", "royalblue", "lightgray", "purple", "cyan", "lightgray", "lightblue"]))
#fig.write_html("bcm.html")
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()
# ~ TOOLTIPS = [
# ~ ("Jahr", "@year"),
# ~ ("CO2 (tausend Tonnen)", "@CO2{0.00}"),
# ~ ("Prozent von 1990", "@percentage{0.0%}"),
# ~ ("Typ", "@type"),
# ~ ]
# TODO visualise modules
fig_modules = go.Figure(
go.Treemap(
branchvalues="total",
labels=[
"Wärme", "Strom", "Verkehr", "W1", "W2", "S1", "S2", "V1", "V2"
],
parents=[
"", "", "", "Wärme", "Wärme", "Strom", "Strom", "Verkehr",
"Verkehr"
],
values=[780, 690, 484, 700, 80, 600, 90, 400, 84],
marker_colors=[
"green", "yellow", "red", "green", "green", "red", "green", "red",
"red"
],
textinfo="label+value+percent parent+percent entry+percent root",
))
# ~ fig_modules.write_html('hugo/layouts/shortcodes/modules_' + city + '.html', include_plotlyjs = False,
# ~ config={'displayModeBar': False}, full_html = False, auto_open=True)
text=top_labels[i],
font=dict(family='Arial', size=14,
color='rgb(67, 67, 67)'),
showarrow=False))
space += xd[i]
fig.update_layout(
title="Age ratio distribution by gender",
annotations=annotations)
fig.show()
y = dist.Country.value_counts()
fig = go.Figure(go.Treemap(
labels = y.index,
parents = ['World'] * len(y),
values = y
))
fig.update_layout(title = '# of Survey Participants')
fig.show()
dist_ratio = dist.groupby('Country')['Gender'].value_counts().unstack().fillna(0)
dist_ratio['Female/Male'] = dist_ratio['Female'] / dist_ratio['Male']
print('Average female / male ratio')
print(sum(dist_ratio['Female']) / sum(dist_ratio['Male']))
dist_ratio['Country'] = dist_ratio.index
fig = px.choropleth(dist_ratio, locations='Country',
locationmode='country names',
color="Female/Male",
color_continuous_scale=[[0.0, "rgb(165,0,38)"],
for i in fields:
info['Summary'] = info['Summary'] + i + ': ' + info[i].astype(str) + '<br><br>'
args = dict(data_frame=info, values='Number of studies',
color='Number of studies', hover_data='',
path=['Focus', 'Tissue condition', 'Human/animal', 'Condition', 'Study'],
color_continuous_scale='Viridis')
args = px._core.build_dataframe(args, go.Treemap)
treemap_df = px._core.process_dataframe_hierarchy(args)['data_frame']
fig2 = go.Figure(go.Treemap(
ids=treemap_df['id'].tolist(),
labels=treemap_df['labels'].tolist(),
parents=treemap_df['parent'].tolist(),
values=treemap_df['Number of studies'].tolist(),
branchvalues='total',
text=info['Summary'],
hoverinfo='label',
textfont=dict(
size=15,
)
)
)
fig2 = fig2.update_layout(
autosize=False,
width=680,
height=600,
margin=dict(
l=0,
r=0,
t=0,
]
df = pd.DataFrame(
dict(States=States, Confirmed=Confirm, Deaths_Value=Deaths_value))
df["States_in_US"] = "States_in_US"
df["Deaths"] = "Deaths"
df["Active_Value"] = df["Active"] = df["Confirmed"] - df["Deaths_Value"]
df["Active"] = "Active"
df.head(60)
fig3 = go.Figure()
fig3.add_trace(
go.Treemap(ids=df["Confirmed"],
labels=df["States"],
parents=df["Active"],
values=df["Active_Value"],
textinfo='label+value',
domain=dict(column=0)))
fig3.add_trace(
go.Treemap(
ids=df["Confirmed"],
labels=df["States"],
parents=df["Deaths"],
values=df["Deaths_Value"],
textinfo='label+value',
domain=dict(column=1),
))
fig3.update_layout(
grid=dict(columns=2, rows=1),
rgbPallete = []
for counter in range(len(pallete)):
r = pallete[counter][0]
g = pallete[counter][1]
b = pallete[counter][2]
color = "rgb({},{},{})".format(r, g, b)
rgbPallete.append(color)
fig = go.Figure()
fig.add_trace(
go.Treemap(
labels=df_all_trees['id'],
parents=df_all_trees['parent'],
values=df_all_trees['value'],
branchvalues='total',
name="",
marker=dict(colors=df_all_trees['color'],
colorscale=rgbPallete,
cmid=average_score),
hovertemplate=
'<b>%{label} </b> <br> Número de drones: %{value}<br> Porcentagem do total: %{color:.2f}',
maxdepth=2))
fig.update_layout(margin=dict(t=15, b=15, r=15, l=15), autosize=True)
plotly.io.write_html(
fig,
file="./../website/webviews/treemap-proprietarios.html",
full_html=False,
default_height="80%")
color='R^2',
hover_data='',
path=['Measure', 'Study'],
color_continuous_scale='Viridis')
args = px._core.build_dataframe(args, go.Treemap)
treemap_df = px._core.process_dataframe_hierarchy(args)['data_frame']
fig4 = go.Figure(
go.Treemap(ids=treemap_df['id'].tolist(),
labels=treemap_df['labels'].tolist(),
parents=treemap_df['parent'].tolist(),
values=treemap_df['Sample points'].tolist(),
branchvalues='total',
text='R<sup>2</sup>: ' + filtered_df['R^2'].astype(str) +
'<br>' + filtered_df['Details'],
hovertext=filtered_df['Study'] + '<br>R<sup>2</sup>: ' +
filtered_df['R^2'].astype(str) + '<br>Number of samples: ' +
filtered_df['Sample points'].astype(str),
hoverinfo='text',
textfont=dict(size=15, ),
marker=dict(colors=filtered_df['R^2'],
colorscale='Viridis',
colorbar=dict(title='R<sup>2</sup>'),
showscale=True)))
fig4 = fig4.update_layout(
title=dict(text="Figure 4 - R<sup>2</sup> values across studies"),
autosize=False,
width=650,
height=600,
margin=dict(l=0))
def get(token: str, file_id: int):
def log(*args):
print(f'[{token[:4]}]', *args)
client = putiopy.Client(token)
files: Dict[int, File] = {}
processed = 0
try:
root = client.File.get(file_id)
except putiopy.ClientError as e:
if e.response.status_code == 401 and file_id == 0:
raise ExpiredToken
raise
total_size = root.size
log("total size of file(%d): %d gb" % (file_id, total_size // 2**30))
def append_children_recursive(putio_file, total_sizes: Queue,
level: int) -> None:
nonlocal processed
children = list_children(putio_file.id, client)
threads = []
children_sizes: Queue[int] = Queue()
for child in children:
if child.folder_type == 'SHARED_ROOT':
continue
if (level < MAX_LEVEL
and child.content_type == 'application/x-directory'):
t = threading.Thread(target=append_children_recursive,
args=(child, children_sizes, level + 1))
t.start()
threads.append(t)
else:
children_sizes.put(child.size)
files[child.id] = File(child, child.size)
processed += child.size
log("processed %d of %d gb" %
(processed // 2**30, total_size // 2**30))
for t in threads:
t.join()
children_size = 0
while True:
try:
children_size += children_sizes.get_nowait()
except Empty:
break
dir_size = putio_file.size
if children_size > dir_size:
dir_size = children_size
total_sizes.put(dir_size)
files[putio_file.id] = File(putio_file, dir_size)
start = time.time()
append_children_recursive(root, Queue(), level=1)
end = time.time()
log('file tree traversed in %s seconds' % (end - start))
ids = []
labels = []
values = []
parents = []
for id, file in files.items():
ids.append(file.id)
labels.append(file.name)
values.append(file.size)
try:
parents.append(files[file.parent_id].id)
except KeyError:
parents.append('')
fig = go.Figure(
go.Treemap(
ids=ids,
labels=labels,
values=values,
parents=parents,
branchvalues='total',
texttemplate='%{label}',
hovertemplate='%{label}<br>%{value:.2s}<extra></extra>',
))
return fig.to_html(include_plotlyjs='cdn')
def visualize_plotly(acts, title=None, treemap=True, sunburst=True):
# @warn this is not async, and it takes rather long to complete
##
out_links = []
out_files = []
if timedelta_total_seconds(acts.total_duration) == 0:
return out_links, out_files
all_acts = get_acts(acts)
acts_agg = all_acts[0]
# print(acts_agg)
ids = [act.name for act in all_acts]
labels = [
f"{act.shortname} {(timedelta_total_seconds(act.total_duration)*100/timedelta_total_seconds(acts_agg.total_duration)):.1f}%"
for act in all_acts
]
texts = [timedelta_str(act.total_duration) for act in all_acts]
##
parents = [(act.parent and act.parent.name) or "" for act in all_acts]
values = [(timedelta_total_seconds(act.total_duration) / (3600))
for act in all_acts]
# Do NOT round the values. Plotly expects them to sum correctly or something.
## Test out input values:
# lim=19
# labels = labels[:lim]
# parents = parents[:lim]
# values = values[:lim]
# print(labels)
# print(texts)
# print(parents)
# print(values)
##
# https://plotly.com/python/treemaps/
# https://plotly.com/python/reference/treemap/
plot_opts = dict(
branchvalues="total",
ids=ids,
labels=labels,
parents=parents,
values=values,
text=texts,
# %{value:.1f}
texttemplate=
"%{label}<br>%{text}<br>%{percentParent:.1%} of %{parent}<br>%{percentEntry:.1%} of %{entry}<br>%{percentRoot:.1%} of %{root}",
# textinfo = "label+value+percent parent+percent entry+percent root",
# %{currentPath}%{label}
hovertemplate=
"%{label}<br>%{text}<br>%{percentParent:.1%} of %{parent}<br>%{percentEntry:.1%} of %{entry}<br>%{percentRoot:.1%} of %{root}<extra>%{id}</extra>",
# https://community.plotly.com/t/how-to-explicitly-set-colors-for-some-sectors-in-a-treemap/51162
# color="day",
# color_discrete_map={'(?)':'gold', 'Study':'green', 'wasted':'black'},
)
## colors @unresolved https://community.plotly.com/t/how-to-explicitly-set-colors-for-some-sectors-in-a-treemap/51162
def choose_color(i, act, parent_names=None, cmap=cmaps["alphabet"]):
if parent_names == None:
parent_names = act.name.split("_")
longname = "_".join(parent_names)
if longname in categories:
return categories[longname]
parent_names = parent_names[:-1]
if len(parent_names) >= 1:
return choose_color(i, act, parent_names)
else:
return cmap[abs(
(hash(longname) % len(cmap))
)] # abs is redundant: Python modulo operator always return the remainder having the same sign as the divisor.
cs = [choose_color(i, act) for i, act in enumerate(all_acts)]
if treemap:
fig = go.Figure(go.Treemap(**plot_opts))
# fig.update_layout(margin = dict(t=0, l=0, r=0, b=0))
fig.update_layout(margin=dict(t=30, l=0, r=30, b=30))
# fig.update_layout(uniformtext=dict(minsize=6, mode='hide'))
fig.update_traces(marker_colors=cs)
fig.update_traces(marker_depthfade=True)
if title:
fig.update_layout(title_text=title)
fig.update_layout(title_font_size=11)
# fig.update_layout(title_x=0.1)
is_local and fig.show()
l, f = fig_export(fig,
"treemap",
width=400,
height=400,
svg_export=False,
pdf_export=False)
out_links += l # is list
out_files += f
##
if sunburst:
# @unresolved https://community.plotly.com/t/show-the-current-path-bar-in-sunburst-plots-just-like-treemap-plots/51155
plot_opts["labels"] = [act.shortname for act in all_acts]
plot_opts["texttemplate"] = "%{label}<br>%{text}, %{percentRoot:%}"
fig = go.Figure(go.Sunburst(**plot_opts))
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
# fig.update_layout(uniformtext=dict(minsize=6, mode='hide'))
fig.update_traces(marker_colors=cs)
if title:
fig.update_layout(title_text=title)
fig.update_layout(title_font_size=11)
fig.update_layout(title_y=0.99)
is_local and fig.show()
l, f = fig_export(fig,
"sunburst",
width=400,
height=400,
svg_export=False,
pdf_export=False)
out_links += l # is list
out_files += f
return out_links, out_files
import plotly.graph_objects as go
import plotly.offline as py
#values = ["12", "12", "13", "14", "15", "20", "30"]parents = ["", "Lung cancer", "Colorectal cancer", "Non-melanoma skin cancer", "prostate cancer ", "breast cancer", "", "the leading cause of cancer deaths", "Second most common cause of Cancer deaths"]
import plotly.graph_objects as go
import plotly.io as pio
labels = ["Lung cancer", "Colorectal cancer", "Non-melanoma skin cancer", "Prostate cancer", "Breast cancer"]
parents = ["", "Lung cancer", "Colorectal cancer", "Non-melanoma skin cancer", "Prostate cancer", "Breast cancer"]
values = ["2749", "1900", "10000", "3665", "3100"]
fig = go.Figure(go.Treemap(
labels = labels,
parents = parents,
values = values,
marker_colors = ["beige", "royalblue", "black", "lightblue", "pink", "lightgray", "pink"]
))
fig.show()
py.iplot(fig)
py.plot(fig, filename='tre.html')
pio.write_html(fig, file= 'index.html', auto_open=True)
