def plot_sunbirst_charts(solved_data: pd.DataFrame):
sb1 = px.sunburst(solved_data, values="expense", path=["type",
"entity"])._data
sb2 = px.sunburst(
solved_data[solved_data["solution"] == 1],
values="expense",
path=["type", "entity"],
)._data
# use data and structure FROM sb1 and sb2 (trick)
fig = make_subplots(rows=1,
cols=2,
specs=[[{
"type": "domain"
}, {
"type": "domain"
}]])
fig.add_trace(
go.Sunburst(
branchvalues="total",
labels=sb1[0]["labels"],
parents=sb1[0]["parents"],
values=sb1[0]["values"],
),
1,
1,
)
fig.add_trace(
go.Sunburst(
branchvalues="total",
labels=sb2[0]["labels"],
parents=sb2[0]["parents"],
values=sb2[0]["values"],
),
1,
2,
)
fig.update_layout(
title_text="Expenses distribution in solution, sunbirst chart",
# Add annotations in the center of the donut pies.
annotations=[
dict(text="Original", x=0.17, y=1.1, font_size=20,
showarrow=False),
dict(text="Optimized",
x=0.84,
y=1.1,
font_size=20,
showarrow=False),
],
)
# fig = go.Figure(data = [trace1, trace2], layout = layout)
fig.show()
def make_plot(white_moves, black_moves):
fig = make_subplots(rows=1, cols=2,
specs=[[{'type': 'domain'}, {'type': 'domain'}]],
subplot_titles=('White', 'Black'))
fig.add_trace(go.Sunburst(
name='M is white',
ids=white_moves['ids'],
labels=white_moves['labels'],
parents=white_moves['parents'],
values=white_moves['values'],
branchvalues="total",
opacity=1,
marker={'colors': white_moves['color'], 'line': {'color': '#000200'}},
hoverinfo='label+text+name+current path+value',
hovertext='Win: ' + white_moves['win'].apply(str) +
'\nDraw: ' + white_moves['draw'].apply(str) +
'\nLose: ' + white_moves['lose'].apply(str)
),
row=1, col=1)
fig.add_trace(go.Sunburst(
name='M is black',
ids=black_moves['ids'],
labels=black_moves['labels'],
parents=black_moves['parents'],
values=black_moves['values'],
branchvalues="total",
opacity=1,
marker={'colors': black_moves['color'], 'line': {'color': '#000200'}},
hoverinfo='label+text+name+current path+value',
hovertext='Win: ' + black_moves['win'].apply(str) +
'\nDraw: ' + black_moves['draw'].apply(str) +
'\nLose: ' + black_moves['lose'].apply(str)
),
row=1, col=2)
fig.update_layout(
height=750, width=1500,
margin={'t': 0, 'l': 2, 'r': 2, 'b': 0},
paper_bgcolor='#000200',
plot_bgcolor='#000200',
uniformtext={'minsize': 11, 'mode': 'hide'},
title={'text': 'First five moves of Magnus Carlsen games since he became the world champion',
'font': {'size': 18, 'color': '#ffffff'},
'y': 0.98,
'x': 0.5}
)
return fig
def build_sunburst():
story_id = ""
fig = go.Figure(
go.Sunburst(
ids=df.id,
labels=df.name,
parents=df.parent,
values=df.page_duration,
branchvalues="total",
customdata=customdata_df,
# domain=dict(column=0),
sort=False,
level=story_id,
rotation=90,
maxdepth=5,
insidetextfont=dict(size=10),
insidetextorientation="radial",
marker=dict(colors=df["color"]),
leaf=dict(opacity=1),
hovertemplate="<extra></extra><b>%{label} </b> <br> Night Duration: %{customdata[0]}</b> <br> Page Duration:%{value} </b> <br> Gender Mention Ratio (Female/Male):%{customdata[2]: .2f}<br>",
)
)
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
fig.update_layout(paper_bgcolor=bg_color)
return fig
def make_sunburst(df, path, values, title):
if len(path) > 2:
hover = "<b>%{label} </b><br> %{percentRoot:,.1%} </br> %{value:$,.0f} </br>"
else:
hover = "<b>%{label} </b><br> %{percentRoot:,.1%} </br> "
fig = px.sunburst(
df,
path=path,
values=values,
hover_data=["Per Capita_2017"],
color="Category",
color_discrete_map=du.sunburst_colors,
)
fig.update_traces(
go.Sunburst(hovertemplate=hover),
insidetextorientation="radial",
)
fig.update_layout(
title_text=title,
title_x=0.5,
title_xanchor="center",
title_yanchor="top",
margin=go.layout.Margin(b=10, t=30, l=10, r=10),
clickmode="event+select",
)
return fig
def create_sunburst_fig_title(caller):
"""Create the sunburst by sunburst_dict."""
if caller == 'ba':
sunburst_info, sunburst_title = hold_up_ba
elif caller == 'ma':
sunburst_info, sunburst_title = hold_up_ma
fig = go.Figure(
go.Sunburst(parents=sunburst_info['parent'],
labels=sunburst_info['character'],
values=sunburst_info['value'],
branchvalues="total",
marker=dict(
colors=sunburst_info['value'],
colorscale='Cividis',
)))
fig.update_layout(
# autosize=False,
margin={
"t": 0,
"l": 0,
"r": 0,
"b": 0,
"pad": 0,
},
showlegend=True,
height=512,
)
return fig, sunburst_title
def make_sunburst(df, path, values, title):
fig = px.sunburst(df,
path=path,
values=values,
color="Category",
color_discrete_map=du.sunburst_colors
# hover_data=['Population']
)
fig.update_traces(
go.Sunburst(
# hovertemplate='<b>%{label} </b> <br>%{customdata[0]}<br> $%{value:,.0f}'
hovertemplate="<b>%{label} </b> $%{value:,.0f}"),
insidetextorientation="radial",
)
fig.update_layout(
title_text=title,
title_x=0.5,
title_xanchor="center",
title_yanchor="top",
# title_y=0.95,
margin=go.layout.Margin(b=10, t=10, l=1, r=1),
# yaxis=go.layout.YAxis(tickprefix="$", fixedrange=True),
# xaxis=go.layout.XAxis(fixedrange=True),
# annotations=total_labels,
# paper_bgcolor="whitesmoke",
clickmode="event+select",
)
return fig
def graph():
fig = go.Figure(
go.Sunburst(labels=[
"Población Vulnerable", "3era Edad", "Discapacidad o Limitación",
"Condición Mental", "Visual o Auditiva", "Hablar o Comunicarse",
"Motriz", "Recordar o concentrarse", "Condición mental",
"Ver, aún usando lentes", "Oir, aún usando aparatos",
"Caminar, subir o bajar", "Bañarse, vestirse o comer"
],
parents=[
"", "Población Vulnerable", "Población Vulnerable",
"Población Vulnerable", "Población Vulnerable",
"Población Vulnerable", "Población Vulnerable",
"Condición Mental", "Condición Mental",
"Visual o Auditiva", "Visual o Auditiva", "Motriz",
"Motriz"
],
values=[
46200, 17600, 12568, 2466, 8140, 485, 4941, 982, 1484,
6311, 1829, 4147, 794
],
branchvalues="total",
textinfo='label+percent entry'))
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0), height=600)
return dcc.Graph(figure=fig)
def get_http_code_fig():
l, sum_2, sum_3, sum_4, sum_5, SUM = obj.get_http_status_data()
# print(l, sum_2, sum_3, sum_4, sum_5, SUM)
SUM += 15000
sum_3 += 700
l[2] += 237
l[3] += 243
l[4] += 20
fig = go.Figure(
go.Sunburst(
labels=[
"HTTP Status Code", "2xx", "3xx", "4xx", "5xx", "200", "202",
"301", "302", "307", "400", "401", "403", "404", '408', '500',
'502', '503'
],
parents=[
"", "HTTP Status Code", "HTTP Status Code", "HTTP Status Code",
"HTTP Status Code", "2xx", "2xx", "3xx", "3xx", "3xx", '4xx',
'4xx', '4xx', '4xx', '4xx', '5xx', '5xx', '5xx'
],
values=[SUM, sum_2, sum_3, sum_4, sum_5] + l,
branchvalues="total",
marker=dict(colorscale='Blackbody', ), # Rainbow Jet Blackbody
hovertemplate='<b>%{label} </b> <br> Count: %{value}',
name='', # 不显示trace0\trace1...
# maxdepth=2
))
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
return fig
def get_domain_host_type_fig():
labels = [
'Host Type', '企业', '事业单位', '政府机关', '社会团体', '民营非企业单位', '民办非企业单位', '个人',
'群众性团体组织', '律师执业机构', '群团组织', '基金会'
] + ['门户网站', '后台系统'] * 11
parents = [''] + ['Host Type'] * 11 + [
'企业', '企业', '事业单位', '事业单位', '政府机关', '政府机关', '社会团体', '社会团体', '民营非企业单位',
'民营非企业单位', '民办非企业单位', '民办非企业单位', '个人', '个人', '群众性团体组织', '群众性团体组织',
'律师执业机构', '律师执业机构', '群团组织', '群团组织', '基金会', '基金会'
]
res = obj.get_domain_host_type_fig_values([
'企业', '事业单位', '政府机关', '社会团体', '民营非企业单位', '民办非企业单位', '个人', '群众性团体组织',
'律师执业机构', '群团组织', '基金会'
])
values = [22875, 10630, 7578, 3504, 563, 275, 175, 110, 22, 12, 5, 1] + res
# print(len(labels), len(parents), len(values))
# print(labels)
# print(parents)
# print(values)
fig = go.Figure(
go.Sunburst(
labels=labels,
parents=parents,
values=values,
branchvalues="total",
hovertemplate='<b>%{label} </b> <br> Count: %{value}',
name='', # 不显示trace0\trace1...
))
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
return fig
def get_prod_share_trace(episode):
prod_types = episode.get_prod_types()
prod_type_values = list(prod_types.values()) if len(
prod_types.values()) > 0 else []
share_prod = observation_model.get_prod(episode)
df = share_prod.groupby("equipment_name")["value"].sum()
unique_prod_types = np.unique(prod_type_values)
labels = [*df.index.values, *np.unique(prod_type_values)]
parents = [prod_types.get(name) for name in df.index.values]
#labelTypes=[l if l in dic_colors_prod_types.keys() else prod_types.get(l) for l in labels]
colors=[dic_colors_prod_types[l] if l in dic_colors_prod_types.keys()
else dic_light_colors_prod_types[prod_types.get(l)] for l in labels]
values = list(df)
for prod_type in unique_prod_types:
parents.append("")
value = 0
for gen in df.index.values:
if prod_types.get(gen) == prod_type:
value = value + df.get(gen)
values.append(value)
return [go.Sunburst(labels=labels, values=values,
parents=parents, branchvalues="total", marker = dict(colors=colors,
colorscale='RdBu'))]
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 sunburst_plot(_stats: Dict, show=True, save=True):
data_comp = go.Figure(
go.Sunburst(
labels=LABELS,
parents=[
'RSNA_ICH',
'RSNA_ICH',
'training',
'training',
'any',
'any',
'any',
'any',
'any',
],
values=[_stats[_l].cnt for _l in LABELS],
branchvalues='remainder',
))
print([_stats[_l].cnt for _l in LABELS])
data_comp.update_layout(margin=dict(t=0, l=0, r=0, b=0),
height=1080,
width=1440)
if show:
data_comp.show()
if save:
data_comp.write_image(
os.path.join(ICH_DIR, 'data_composition_sunburst.png'))
def get_prod_share_trace(episode):
prod_types = episode.get_prod_types()
prod_type_values = list(
prod_types.values()) if len(prod_types.values()) > 0 else []
share_prod = observation_model.get_prod(episode)
df = share_prod.groupby("equipment_name")["value"].sum()
unique_prod_types = np.unique(prod_type_values)
labels = [*df.index.values, *np.unique(prod_type_values)]
parents = [prod_types.get(name) for name in df.index.values]
values = list(df)
for prod_type in unique_prod_types:
parents.append("")
value = 0
for gen in df.index.values:
if prod_types.get(gen) == prod_type:
value = value + df.get(gen)
values.append(value)
return [
go.Sunburst(labels=labels,
values=values,
parents=parents,
branchvalues="total")
]
def bloch_sunburst(vec, colormap):
"""Create a Bloch disc using a Plotly sunburst.
Parameters:
vec (ndarray): A vector of Bloch components.
colormap (Colormap): A matplotlib colormap.
Returns:
go.Figure: A Plotly figure instance,
Raises:
ValueError: Input vector is not normalized.
"""
eps = 1e-6
vec = np.asarray(vec)
vec_norm = np.linalg.norm(vec)
if vec_norm > 1.0 + eps:
raise ValueError(
'Input vector has length {} greater than 1.0'.format(vec_norm))
for idx, val in enumerate(vec):
if abs(val) < 1e-15:
vec[idx] = 0
th = math.atan2(vec[1], vec[0])
if th < 0:
th = 2 * np.pi + th
z_hex = matplotlib.colors.rgb2hex(colormap(NORM(vec[2])))
z_color = "rgba({},{},{},{})".format(*hex_to_rgb(z_hex),
0.95 * vec_norm + 0.05)
ring_color = "rgba({},{},{},{})".format(*hex_to_rgb('#000000'),
0.95 * vec_norm + 0.05)
wedge_str = "\u2329X\u232A= {x}<br>"
wedge_str += "\u2329Y\u232A= {y}<br>"
wedge_str += "\u2329Z\u232A= {z}<br>"
wedge_str += " \u03B8 = {th}<br>"
wedge_str += "|\u03C8| = {pur}"
th_str = pi_check(th, ndigits=3)
th_str = th_str.replace('pi', '\u03C0')
hover_text = [
wedge_str.format(x=round(vec[0], 3),
y=round(vec[1], 3),
z=round(vec[2], 3),
th=th_str,
pur=np.round(vec_norm, 3))
] + [None]
bloch = go.Sunburst(labels=[" ", " "],
parents=["", " "],
values=[2 * np.pi - th, th],
hoverinfo="text",
hovertext=hover_text,
marker=dict(colors=[z_color, ring_color]))
return bloch
def fig(self):
fig = go.Figure(
go.Sunburst(ids=self.ids,
labels=self.labels,
parents=self.parents,
values=self.values))
return fig
def generate_sunburst(directories: Dict[Path, Tuple[int, int]],
root: Path) -> go.Sunburst:
"""Generate a sunburst chart from the directories given in input."""
max_depth = compute_max_depth(list(directories.keys()))
metrics_columns = ["contents", "known"]
levels_columns = ["lev" + str(i) for i in range(max_depth)]
df_columns = levels_columns + metrics_columns
dirs_df = generate_df_from_dirs(directories, df_columns, max_depth)
hierarchical_df = build_hierarchical_df(dirs_df, levels_columns,
metrics_columns, str(root))
sunburst = go.Sunburst(
labels=hierarchical_df["id"],
parents=hierarchical_df["parent"],
values=hierarchical_df["contents"],
branchvalues="total",
marker=dict(
colors=hierarchical_df["known"],
colorscale="matter",
cmid=50,
showscale=True,
),
hovertemplate="""<b>%{label}</b>
<br>Files: %{value}
<br>Known: <b>%{color:.2f}%</b>""",
name="",
)
return sunburst
def plotly():
x = list(range(10))
fig = go.Figure(data=go.Scatter(x=x, y=[i**2 for i in x]))
html1 = fig.to_html(include_plotlyjs="require", full_html=False)
fig = go.Figure(data=[
go.Scatter(x=[1, 2, 3, 4],
y=[10, 11, 12, 13],
mode='markers',
marker=dict(
color=[
'rgb(93, 164, 214)', 'rgb(255, 144, 14)',
'rgb(44, 160, 101)', 'rgb(255, 65, 54)'
],
opacity=[1, 0.8, 0.6, 0.4],
size=[40, 60, 80, 100],
))
])
html2 = fig.to_html(include_plotlyjs="require", full_html=False)
fig = go.Figure(
go.Sankey(
arrangement="snap",
node={
"label": ["A", "B", "C", "D", "E", "F"],
"x": [0.2, 0.1, 0.5, 0.7, 0.3, 0.5],
"y": [0.7, 0.5, 0.2, 0.4, 0.2, 0.3],
'pad': 10
}, # 10 Pixels
link={
"source": [0, 0, 1, 2, 5, 4, 3, 5],
"target": [5, 3, 4, 3, 0, 2, 2, 3],
"value": [1, 2, 1, 1, 1, 1, 1, 2]
}))
html3 = fig.to_html(include_plotlyjs="require", full_html=False)
fig = go.Figure(
go.Sunburst(
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],
))
# Update layout for tight margin
# See https://plotly.com/python/creating-and-updating-figures/
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
html4 = fig.to_html(include_plotlyjs="require", full_html=False)
put_grid([[put_html(html1), put_html(html2)],
[put_html(html3), put_html(html4)]],
cell_width='1fr',
cell_height='1fr')
def create_sunburst_fig(sunburst_info: dict): # --> fig:
"""Create the sunburst by sunburst_dict."""
fig = go.Figure(
go.Sunburst(parents=sunburst_info['parent'],
labels=sunburst_info['character'],
values=sunburst_info['value'],
branchvalues="total",
marker=dict(
colors=sunburst_info['value'],
colorscale='Cividis',
)))
fig.update_layout(
# autosize=False,
margin={
"t": 0,
"l": 0,
"r": 0,
"b": 0,
"pad": 0,
},
showlegend=True,
height=512,
)
# print(sunburst_info)
return fig
def plot1(path):
data = pd.read_csv(path / 'raw_data.csv', index_col=0)
fig = make_subplots(rows=1,
cols=2,
specs=[[{
'type': 'domain'
}, {
'type': 'domain'
}]])
values = data['Gender'].value_counts(dropna=False).values.tolist()
labels = data['Gender'].value_counts(dropna=False).index.tolist()
labels = ['Missing', 'Male', 'Female']
fig.add_trace(
go.Pie(labels=labels,
values=values,
textinfo='label+percent',
insidetextorientation='radial'), 1, 1)
fig.update_traces(hole=0.35)
fig.update_layout(annotations=[
dict(text='Gender', x=0.15, y=0.5, font_size=18, showarrow=False)
])
data['Transmission_Type'] = data['Transmission_Type'].apply(
lambda x: 'Unknown' if str(x) == 'nan' else str(x).strip())
values = data['Transmission_Type'].value_counts().values.tolist()
labels = data['Transmission_Type'].value_counts().index.tolist()
for label, value in zip(labels, values):
if ((label == 'TBD') | (label == 'Unknown')):
values.append(value)
labels.append('Unknown')
labels[labels.index('Unknown')] = 'Missing'
values[-2] = values[-2] + values[-1]
values = values[:-1]
values.insert(0, sum(values[:-1]))
labels.insert(0, 'Transmission')
parents = [
'', 'Unknown', 'Transmission', 'Unknown', 'Transmission',
'Transmission'
]
fig.add_trace(
go.Sunburst(labels=labels,
parents=parents,
values=values,
branchvalues='total',
textinfo='label+percent root',
insidetextorientation='radial'), 1, 2)
fig.update_traces(hoverinfo='value', textfont_size=15)
fig.update_layout(font=dict(family="Courier New, monospace"),
showlegend=False,
margin=dict(l=20, r=20, t=50, b=50))
return fig
def plot_sources_hierarchical(source_info_1, source_info_2):
"""
Create a sunburst chart combining different kinds of information about the sources, e.g. the kinds of editions and the type of institution
or the filetypes and the sources.
Arguments:
source_info_1 (str): which kind of source information to plot as the inner circle / parents. Possible values: "sources_medium", "sources_filetype",
"sources_institution", "sources_edition", "institution_type"
source_info_2 (str): which kind of source information to plot as the outer circle / parents. Possible values are the same as for source_info_1.
"""
data_dir = "/home/ulrike/Git/data-nh/corpus/corpus-sources/"
data = pd.read_csv(join(data_dir, "../metadata_sources.csv"), index_col=0)
data_grouped_level_1 = data.groupby([source_info_1, source_info_2]).size()
data_grouped_level_0 = data_grouped_level_1.sum(level=0)
labels_level_0_set = list(data_grouped_level_1.keys().levels[0])
labels_level_0_all = list(data_grouped_level_1.index.get_level_values(0))
labels_level_1 = list(data_grouped_level_1.index.get_level_values(1))
labels = labels_level_0_set + labels_level_1
values = list(data_grouped_level_0) + list(data_grouped_level_1)
ids = labels_level_0_set + [
str(i[0]) + "-" + str(i[1])
for i in zip(labels_level_0_all, labels_level_1)
]
parents = ["" for i in range(len(labels_level_0_set))] + labels_level_0_all
colors = [
"rgb(31, 119, 180)", "rgb(255, 127, 14)", "rgb(44, 160, 44)",
"rgb(214, 39, 40)"
] #"rgb(44, 160, 44)", "rgb(214, 39, 40)"
fig = go.Figure(
go.Sunburst(ids=ids,
labels=labels,
parents=parents,
values=values,
marker=dict(colors=colors),
branchvalues="total",
textinfo="label+percent entry",
textfont=dict(size=16)))
fig.update_layout(autosize=False, width=800, height=800)
outfile = source_info_1 + "_" + source_info_2
fig.write_image(join(data_dir, outfile +
".png")) # scale=2 (increase physical resolution)
fig.write_html(
join(data_dir, outfile + ".html"
)) # include_plotlyjs="cdn" (don't include whole plotly library)
#fig.show()
print("done")
def victims_sunburst_update(input_race, input_region, input_sex, input_state):
df_local = filter_data(df_victims_sunburst,
input_race=input_race,
input_region=input_region,
input_sex=input_sex,
input_state=input_state
)
df_local = build_hierarchical_dataframe(df_local, ['Race4', 'Race3', 'Race2'], 'Victim')
fig = go.Figure()
# https://plotly.com/python/reference/sunburst/#sunburst-text
fig.add_trace(go.Sunburst(
labels=df_local["id"],
parents=df_local["parent"],
values=df_local["value"],
branchvalues="total",
text=df_local[['percentage']],
textinfo='label',
maxdepth=4,
marker=dict(
colors=df_local['color'],
colorscale='RdBu',
cmid=0.5,
line=dict(
width=1
)
),
insidetextfont=dict(
size=16,
family="Rockwell"
),
insidetextorientation='tangential',
hovertemplate='<b>%{label} </b> <br> <i>Victims: </i>%{value}<br>'+
'<i>Percentage: </i>%{text[0]} % <br>' +
'<extra></extra>',
))
fig.update_layout(
plot_bgcolor="white",
hoverlabel=dict(
bgcolor="white",
font_size=16,
font_family="Rockwell"
),
hoverlabel_align = 'right',
margin = dict(t=0, l=0, r=0, b=0),
height = 550,
width = 550
)
return fig
def sunburst_fig():
df = pd.read_csv(
'https://github.com/nchelaru/data-prep/raw/master/telco_cleaned_renamed.csv'
)
## Get categorical column names
cat_list = []
for col in df.columns:
if df[col].dtype == object:
cat_list.append(col)
## Get all possible levels of every categorical variable and number of data points in each level
cat_levels = {}
for col in cat_list:
levels = df[col].value_counts().to_dict()
cat_levels[col] = levels
## Convert nested dictionary to dataframe
nestdict = pd.DataFrame(cat_levels).stack().reset_index()
nestdict.columns = ['Level', 'Category', 'Population']
nestdict['Category'] = [s + ": " for s in nestdict['Category']]
cat_list = nestdict['Category'].unique()
empty_list = [None] * len(cat_list)
pop_list = ['0'] * len(cat_list)
df1 = pd.DataFrame()
df1['Level'] = cat_list
df1['Category'] = empty_list
df1['Population'] = pop_list
df = pd.concat([df1, nestdict])
df['Population'] = df['Population'].astype(int)
fig = go.Figure(
go.Sunburst(labels=df['Level'],
parents=df['Category'],
values=df['Population'],
leaf={"opacity": 0.4},
hoverinfo='skip'))
fig.update_layout(width=700, height=900, margin=dict(t=0, l=0, r=0, b=0))
return fig
def form(ids, labels, parents, values):
fig = go.Figure(
go.Sunburst(
ids=ids,
labels=labels,
parents=parents,
values=values,
branchvalues=
'total', # if children exceed parent, graph will crash and not show
insidetextorientation='horizontal' # text displays PP
))
return fig
def plot_sunburst_static(
df,
value_column,
levels=["land", "region_wb"],
color_columns=None,
_colors=colors.sequential.PuBu,
value_column_name="",
):
color = "#1f2630"
df_sunburst = build_hierarchical_dataframe(df, levels, value_column,
color_columns)
if color_columns is not None:
hovertemplate = ("<b>%{label} </b> <br> " + value_column_name +
": %{value}<br> per 100k: %{color:.0f}")
average_score = df_sunburst["color"].median()
min_score = df_sunburst["color"].quantile(0.1)
max_score = df_sunburst["color"].quantile(0.9)
fraction_series = df_sunburst["color"]
else:
hovertemplate = "<b>%{label} </b> <br> " + value_column_name + ": %{value}<br>"
average_score = df[value_column].median()
min_score = df[value_column].quantile(0.1)
max_score = df[value_column].quantile(0.9)
fraction_series = df[value_column]
fig = go.Figure()
fig.add_trace(
go.Sunburst(
labels=df_sunburst["id"],
parents=df_sunburst["parent"],
values=df_sunburst["value"],
branchvalues="total",
marker=dict(
colors=fraction_series,
colorscale=_colors,
# reversescale=True,
cmid=average_score,
cmax=max_score,
cmin=min_score,
),
hovertemplate=hovertemplate,
name="",
))
fig.update_layout(
margin=dict(t=10, b=10, r=10, l=10),
plot_bgcolor=color,
paper_bgcolor=color,
)
return fig
def update_graph_live(n):
# input source for data - BEGIN
f = open('edgebox1.txt', 'r')
# num_edgebox1 = f.read()
col_edgebox1 = define_color(f.read())
f.close()
f = open('edgebox1_vm1.txt', 'r')
col_edgebox1_vm1 = define_color(f.read())
f.close()
f = open('edgebox1_vm2.txt', 'r')
col_edgebox1_vm2 = define_color(f.read())
f.close()
f = open('edgebox2.txt', 'r')
col_edgebox2 = define_color(f.read())
f.close()
f = open('edgebox2_vm1.txt', 'r')
col_edgebox2_vm1 = define_color(f.read())
f.close()
f = open('edgebox2_vm2.txt', 'r')
col_edgebox2_vm2 = define_color(f.read())
f.close()
f = open('edgebox2_vm3.txt', 'r')
col_edgebox2_vm3 = define_color(f.read())
f.close()
f = open('kube1.txt', 'r')
col_kube1 = define_color(f.read())
f.close()
f = open('kube2.txt', 'r')
col_kube2 = define_color(f.read())
f.close()
f = open('master.txt','r')
col_master = define_color(f.read())
f.close()
# input source for data - END
fig = go.Figure(go.Sunburst(
textfont = {"size":15},
labels=[" ","K-Fabric","K-Cube1","K-Cube2","K-Cube3","K-Cube4","K-Cube5","eno1","eno2","eno3","eno1 ","eno2 ","eno3 ","eno1 ","eno2 ","eno3 ","eno1 ","eno2 ","eno3 ","eno1 ","eno2 ","eno3 ","vSW1","vSW2","vSW3","VM1","VM2","VM3","vSW1 ","vSW2 ","vSW3 ","VM1 ","VM2 ","VM3 "],#9
# all the settings below probably follows the order of the labels above
parents=[""," ","K-Fabric","K-Fabric","K-Fabric","K-Fabric","K-Fabric","K-Cube1","K-Cube1","K-Cube1","K-Cube2","K-Cube2","K-Cube2","K-Cube3","K-Cube3","K-Cube3","K-Cube4","K-Cube4","K-Cube4","K-Cube5","K-Cube5","K-Cube5","eno1 ","vSW1","vSW1","vSW3","vSW3","vSW2","eno1","vSW1 ","vSW1 ","vSW3 ","vSW3 ","vSW2 ",],
# values = [BPF2, BPF3, BPF1] : BFP1 size doesn't really change
marker = {"colors":[WHITE,WHITE,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,GRAY,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,],
"line":{'color':[BLACK,BLACK,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,WHITE,]}}, # in the order of BPF2, BPF3
)) # set row / col here
# fig.update_layout(grid=dict(columns=1,rows=1),margin = dict(t=0, l=0, r=0, b=0)) # maybe this is where they change the subplot
f.close()
return fig
def levels_visualization(data, levels_name):
labels, parents, values, colors = generate_values_for_visualization(data, levels_name)
trace = go.Sunburst(labels=labels, parents=parents, values=values,
maxdepth = 2, branchvalues="total",
textinfo="label", hoverinfo="value",
textfont = {"size":16},
marker = {"line": {"width": 1}, "colors":colors})
layout = go.Layout(width=600, height=600, margin = go.layout.Margin(t=0, l=0, r=0, b=0))
return go.Figure([trace], layout)
def sunburst(countries=None):
if countries:
temp = data[data['Country'].isin(countries)]
else:
temp = data.copy()
fig = px.sunburst(temp,
path=['Department', 'Classification'],
color_discrete_sequence=['#4a4bc7'])
fig.update_traces(go.Sunburst(hovertemplate='Number of artworks=%{value}'))
fig.update_layout(
title=dict(text='Artworks Classification Arranged by Department',
font=dict(color='black')))
return fig
def do_sunburst(html_flpth, png_flpth):
logging.info('Generating sunburst')
ana.TaxTree.build(app_file.get_fix_filtered_id2tax())
names, parents, counts, paths, color_ids = ana.TaxTree.get_sunburst_lists(
) # first el is Root
palette = px.colors.qualitative.Alphabet
id2ind = {
color_id: i
for i, color_id in enumerate(
sorted(list(set(color_ids))) # deterministic unique color_ids
)
} # map color ids to int
colors = ['#FFFFFF'] + [
palette[id2ind[color_id] % len(palette)] for color_id in color_ids[1:]
] # Root is white
fig = go.Figure(
go.Sunburst(
ids=paths,
labels=names,
parents=parents,
values=counts,
marker=dict(colors=colors),
hovertemplate=(
'Taxon: %{label}<br>'
'Path: %{id}<br>'
'Amplicon count: %{value}'
'<extra></extra>' # hide secondary hover box
),
branchvalues='total',
sort=False,
))
fig.update_layout(
title_text='Taxonomy (conf=%s)' % Var.params.get_prose_args()['conf'],
title_x=0.5,
)
fig.write_html(html_flpth)
if DO_STATIC and len(
names) * 4 > MAX_DATA: # whether to hide behind static image
# 4 because name+path+count+color
fig.write_image(png_flpth, width=IMG_HEIGHT, height=IMG_HEIGHT)
return True
else:
return False
def sundata():
df_characters = pd.read_csv("data/character_data.csv")
df_parent = pd.read_csv("data/parent_data.csv")
parent_values = list(df_parent["parent"])
core_values = list(df_characters["Inner circle"].dropna())
environment_ecology = list(df_characters["Environment & Ecology"].dropna())
economy_technology = list(df_characters["Economy & Technology"].dropna())
social_values = list(df_characters["Social"].dropna())
species_values = list(df_characters["Species"].dropna())
parent=[""]+parent_values
character= core_values+environment_ecology+economy_technology+social_values+species_values
values = [values_birds_migration_route[-1],values_sea_and_archipelago_birds[-1],values_mussels[-1],values_fish[-1]]
last_four = []
species_color = round(sum(values)/4)
five_values = values
five_values.append(species_color)
print(five_values, " FIVE VALUS")
for num in five_values:
if num == 0:
last_four.append("lightgrey")
elif num == 1:
last_four.append("red")
elif num == 2:
last_four.append("orange")
elif num == 3:
last_four.append("yellow")
elif num == 4:
last_four.append("lightgreen")
elif num == 5:
last_four.append("green")
species_color = [last_four[-1]]
last_four = last_four[0:4]
colors_d = {"line": {"width":3},"colors":['lightgrey']*7+species_color+['lightgrey']*42+last_four}
fig = go.Sunburst(
labels=character,
parents=parent,
opacity=0.8,
textfont = {'size': 15},
insidetextorientation = 'horizontal',
marker = colors_d,
)
layout = go.Layout(hovermode='closest', height=1300, width=1300,margin=go.layout.Margin(t=250, l=250, r=250, b=250))
values.clear()
return {'data': [fig], 'layout': layout}
def _sunburst_os(db, output):
print("Collecting data...")
def generate_os_wrappers():
q = """SELECT os_response.value AS os,
wrapper_response.flags AS wrapper_flags,
wrapper_response.value AS wrapper_original,
wrapper_sanitize.value AS wrapper_sanitized
FROM responses os_response
LEFT JOIN responses wrapper_response ON wrapper_response.question = 7 AND
wrapper_response.session = os_response.session
LEFT JOIN sanitize wrapper_sanitize ON wrapper_sanitize.idx = wrapper_response.idx
WHERE os_response.question = 4;"""
for result in iter_results(db, q):
if result["wrapper_flags"] & ResponseFlags.sanitized:
wrapper = result["wrapper_sanitized"]
else:
wrapper = result["wrapper_original"]
yield result["os"], wrapper
# ensure we count this for the case of the OS in general as well.
if wrapper:
yield result["os"], ""
counter = collections.Counter()
counter.update(generate_os_wrappers())
ids, labels, parents, values = [], [], [], []
for (os, wrapper), count in counter.items():
if wrapper:
ids.append(f"{os} - {wrapper}")
labels.append(wrapper)
parents.append(os)
values.append(count)
else:
ids.append(os)
labels.append(os)
parents.append("Preferred OS")
values.append(count)
print("Generating graph...")
import plotly.graph_objects as go
fig = go.Figure(data=go.Sunburst(
ids=ids,
labels=labels,
parents=parents,
values=values,
branchvalues="total",
hoverinfo="label+text+value+name+percent parent"),
layout_title_text="OS and Wrapper Usage")
_output_fig(fig, output)