def dash_report():
import pandas as pd
import plotly
import dash
import dash_table
import plotly.express as px
import dash_core_components as dcc
import dash_html_components as html
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
app = dash.Dash(__name__)
df4 = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv')
c = df4.columns
fig1 = px.bar(df4, x=df4.iloc[:, 1], y=df4.iloc[:, 2], hover_data=df4.columns, color=c[3],
labels={'x': c[1], 'y': c[2]})
fig2 = px.scatter(df4, x=df4.iloc[:, 1], y=df4.iloc[:, 2], height=700, hover_data=df4.columns, color=c[3],
size=df4.iloc[:, 4], size_max=65, log_x=True, labels={'x': c[1], 'y': c[2]})
fig3 = px.line(df4, x=df4.iloc[:, 1], y=df4.iloc[:, 2], color=c[3], line_group=c[0], hover_name=c[0])
fig4 = px.density_heatmap(df4, x=df4.iloc[:, 1], y=df4.iloc[:, 4], nbinsx=20, nbinsy=20,
labels={'x': c[1], 'y': c[4]}, hover_data=df4.columns)
fig5 = px.density_heatmap(df4, x=df4.iloc[:, 1], y=df4.iloc[:, 4], nbinsx=10, nbinsy=10, facet_row=df4.iloc[:, 3],
labels={'x': c[1], 'y': c[4]}, hover_data=df4.columns)
fig6 = px.treemap(df4, path=[df4.iloc[:, 1], df4.iloc[:, 3]], values=df4.iloc[:, 2], color=df4.iloc[:, 1],
color_continuous_scale=['red', 'yellow'])
plotly.offline.plot(fig6, filename='C:\\Ashwin\\SparkCode\\projects\\CodeWeek\\genie\\SR\\templates\\fig6.html')
def create_analysis_heatmap(yType, lsoa_selected):
if 'All' in lsoa_selected:
lsoa_selected = sectors
df_lsoa_selected, df_stores_selected = create_analysis_data(lsoa_selected)
df_display = df_stores_selected.merge(df_lsoa_selected[['LSOA', 'IMDRank', 'IMDDecile']], how='right', on='LSOA')
df_display.sort_values(by = ['IMDRank', 'Healthfulness'], inplace=True, ascending=False, ignore_index=True)
title = 'Store Distribution'
if yType == 0:
fig = px.density_heatmap(df_display, x='Healthfulness', y='LSOA', marginal_x='histogram',
color_continuous_scale=px.colors.sequential.Viridis)
elif yType == 1:
fig = px.density_heatmap(df_display, x='Healthfulness', y='IMDDecile', marginal_x='violin',
marginal_y='violin', color_continuous_scale=px.colors.sequential.Viridis)
fig.update_yaxes(range = [-1, 12])
else:
fig = go.Figure()
fig.update_layout(plot_bgcolor=colors['background'],
paper_bgcolor=colors['background'],
autosize=True,
height = 450,
#margin={'l': 20, 'b': 30, 'r': 10, 't': 40},
coloraxis_colorbar=dict(
title="Number of Stores",
thicknessmode="pixels", thickness=20,
yanchor="top", y=1,
ticks="outside"
),
font_color=colors['font'])
fig.update_xaxes(range = [-2, 2])
# fig.update_yaxes(title = 'LSOA (Arranged IMDRank)')
return fig
def plot_corner_cases(corner_cases, summary, special=None, CORNER_LIMIT=.1):
prefix = 'ALL'
if special is True:
prefix = 'Special Only'
corner_cases = corner_cases[corner_cases.spec].copy()
elif special is False:
prefix = 'NOT Special Only'
corner_cases = corner_cases[~corner_cases.spec].copy()
print(
f'Plotting results for {len(corner_cases)} corner cases for {summary["nb_samples"]} samples.'
)
print(
f'Corner cases are happening for {len(corner_cases.i.unique()) / summary["nb_samples"] * 100}% of the rejected cutomers'
f'Corner cases with perc_shift < 10% are happening for {len(corner_cases[corner_cases.perc_shift < CORNER_LIMIT].i.unique()) / summary["nb_samples"] * 100}% of the rejected cutomers'
)
fig = px.histogram(
corner_cases,
x='Feature',
title=f'{prefix} - AAR SHAP Corner - Cases Features',
hover_data=corner_cases.columns,
)
fig.update_layout(yaxis=dict(tickformat=',.2%', ), )
fig.show()
# fig = go.Figure([go.Bar(
# x=features_names,
# y=summ['corner_rate'],
# )])
# fig.update_layout(
# title=f'{prefix} - AAR SHAP Corner - Cases Features Rate (percentage)',
# yaxis=dict(tickformat=',.0%', ),
# )
# fig.show()
px.histogram(
corner_cases,
x='perc_shift',
title=f'{prefix} - AAR SHAP Corner Cases - Percentile Shifts',
hover_data=corner_cases.columns,
).show()
px.density_heatmap(
corner_cases,
x='Feature',
y='perc_shift',
nbinsy=50,
title=f'{prefix} - AAR SHAP Corner Cases',
).show()
px.density_heatmap(
corner_cases[corner_cases.perc_shift < CORNER_LIMIT],
x='Feature',
y='perc_shift',
nbinsy=50,
title=f'{prefix} - AAR SHAP Corner Cases / ONLY with perc_shift < 10%',
).show()
def test_auto_histfunc():
a = [1, 2]
assert px.histogram(x=a).data[0].histfunc is None
assert px.histogram(y=a).data[0].histfunc is None
assert px.histogram(x=a, y=a).data[0].histfunc == "sum"
assert px.histogram(x=a, y=a, histfunc="avg").data[0].histfunc == "avg"
assert px.density_heatmap(x=a, y=a).data[0].histfunc is None
assert px.density_heatmap(x=a, y=a, z=a).data[0].histfunc == "sum"
assert px.density_heatmap(x=a, y=a, z=a, histfunc="avg").data[0].histfunc == "avg"
def get_experiment_digest(experiment: pd.DataFrame):
df = experiment.copy()
if df.shape[0] > 0:
temp_date_time = pd.DatetimeIndex(df["date_time"])
df.insert(loc=4, column="time", value=temp_date_time.time)
df.insert(loc=4, column="date", value=temp_date_time.date)
df["hour"] = pd.to_numeric(
df.time.astype("str").str.split(pat=":", expand=True).iloc[:, 0]
).to_list()
count = df.shape[0]
desc_lines = {
f"{col.replace('_', ' ').capitalize()}s": f"{len(list(df[col].unique()))} unique"
for col in ["Plant", "date", "Camera", "view_option"]
}
fig = px.density_heatmap(
title="Observations per day and hour",
data_frame=df,
x="date",
y="hour",
height=400,
)
fig.update_yaxes(tick0=-0.5)
else:
count = 0
desc_lines = {col: "None" for col in ["plant", "date", "camera", "view_option"]}
fig = None
return count, desc_lines, fig
def get_figure(df):
# for an explanation
fig = px.density_heatmap(df)
fig.update_layout(width=500, height=500, margin=dict(l=20, r=0, t=15, b=5))
return fig
def build_graphBB1(dff,x_axis, y_axis,mode,trendline, marginal_sel,color,facet):
if marginal_sel == "None": marginal_sel = None
if color == "None": color = None
if facet == "None": facet = None
if trendline == "Ordinary Least Squares Regression": trendline = 'ols'
elif trendline == "Locally Weighted Smoothing": trendline = 'lowess'
fig = go.Figure()
if mode == "Scatter":
fig = px.scatter(dff, x=x_axis, y=y_axis, color=color, facet_col=facet, facet_col_wrap=3,
marginal_y=marginal_sel, marginal_x=marginal_sel, trendline="ols")
if mode == "Heat":
fig = px.density_heatmap(dff, x=x_axis, y=y_axis, marginal_x=marginal_sel, marginal_y=marginal_sel)
if mode == "Density":
#fig = px.density_contour(df, x="total_bill", y="tip", marginal_x=marginal_x, marginal_y=marginal_y)
fig = px.density_contour(dff, x=x_axis, y=y_axis, color=color, marginal_x=marginal_sel,
marginal_y=marginal_sel, trendline=trendline)
if mode == "Density Fill":
fig = px.density_contour(dff, x=x_axis, y=y_axis)
fig.update_traces(contours_coloring="fill", contours_showlabels = True)
fig.update_layout(
#margin=dict(l=0, r=0, t=0, b=0),
#paper_bgcolor="lightcyan",
#plot_bgcolor='gainsboro' #gainsboro, lightsteelblue lightsalmon lightgreen lightpink lightcyan lightblue black
)
graph = dcc.Graph(id="scatterB", figure=fig)
return graph
def make_heatmap(**params):
if params['hist_x']:
params['hist_x'] = "histogram"
else:
params['hist_x'] = None
if params['hist_y']:
params['hist_y'] = "histogram"
else:
params['hist_y'] = None
fig = px.density_heatmap(
df,
x=Xs[-1],
y=Y,
marginal_x=params['hist_x'],
marginal_y=params['hist_y'],
title=params['Title'],
width=params['Figsize_x'] * 100,
height=params['Figsize_y'] * 100,
)
fig.update_layout(
xaxis_title=params['xlabel'],
yaxis_title=params['ylabel'],
)
fig.show()
if params['save']:
fig.write_image("saved_plotly_heatmap.png")
def DiversityMap():
fig = px.density_heatmap(HR_df,
x="Position",
y="RaceDesc",
facet_row="Sex")
return fig.write_html(
"/Users/christiannielsen/Documents/GitHub/christiannielsen98.github.io/docs/Python/HTML/DiversityMap.html"
)
def g8(batch_id, x, y, colorset):
input_model = models.BatchInput(batch_id)
df = input_model.as_pandas_dataframe()
fig = px.density_heatmap(df, x=x, y=y, marginal_x="rug", marginal_y="histogram",
color_continuous_scale=get_colorset(colorset))
div = opy.plot(fig, auto_open=False, output_type='div')
return div
def log_plotly(step):
df = px.data.tips()
fig = px.density_heatmap(df,
x="total_bill",
y="tip",
facet_row="sex",
facet_col="smoker")
tracking.log_plotly_chart(name="2d-hist", figure=fig, step=step)
def analyze(plot_against_income):
fig = px.density_heatmap(_data, x=plot_against_income, y="V2026")
fig = px.density_heatmap(
_data,
x=plot_against_income,
y="V2026",
title='<b>Expected Income</b>\r\n(Hover for details)',
height=400,
width=400,
nbinsx=pd.Series(_data[plot_against_income]).nunique(),
nbinsy=pd.Series(_data['V2026']).nunique(),
range_color=heatmap_z_min_max(fig))
fig.layout['xaxis']['title']['text'] = mf.dataDictionary[
plot_against_income]['Desc'].capitalize()
fig.layout['yaxis']['title']['text'] = 'Income Bracket'
fig.layout['coloraxis']['colorbar']['title']['text'] = 'Count'
graph = dcc.Graph(figure=fig)
return graph
def heatmap(df: pd.DataFrame, x: str, y: str, criteria: str = None) -> go.Figure:
if criteria is not None:
df = filter_records(df, criteria)
if not x in df.columns:
logger.log_error('x not found in columns')
return
if not y in df.columns:
logger.log_error('y not found in columns')
return
return px.density_heatmap(df, x=x, y=y)
def plotting(data):
# scatter plot of petal lengths vs. petal widths
petal_length_v_width_plot = px.scatter(data, x=2, y=3)
petal_length_v_width_plot.update_layout(
title_text="petal lengths vs. petal widths")
petal_length_v_width_plot.update_xaxes(ticks="inside",
title_text="Petal Lengths (cm)")
petal_length_v_width_plot.update_yaxes(ticks="inside",
title_text="Petal Widths (cm)")
# violin plot of sepal lengths
# sepal_lengths_violin_plot = px.violin(data, y=0:4)
sepal_lengths_violin_plot = px.violin(data,
y=0,
color=4,
violinmode="overlay")
sepal_lengths_violin_plot.update_layout(
title_text="violin plot of sepal lengths", )
sepal_lengths_violin_plot.update_yaxes(title_text="sepal length (cm)")
# pie chart of different classes observed
setosa = data[:, 4].tolist().count("Iris-setosa")
versicolor = data[:, 4].tolist().count("Iris-versicolor")
virginica = data[:, 4].tolist().count("Iris-virginica")
labels = ["Setosa", "Versicolor", "Virginica"]
values = [setosa, versicolor, virginica]
pie_chart_classes = go.Figure(data=[
go.Pie(
labels=labels,
values=values,
textinfo="label+text+value+percent",
insidetextorientation="auto",
hole=0.3,
)
])
# box plot of petal lengths of each class
box_plot_of_petal_lengths = px.box(data, x=4, y=2, points="all")
box_plot_of_petal_lengths.update_xaxes(title="Unique Species")
box_plot_of_petal_lengths.update_yaxes(title="Petal Lengths (cm)")
# 2-D histogram (heatmap) of petal width and sepal width
hist_2d = px.density_heatmap(data, x=3, y=1)
hist_2d.update_xaxes(title="Petal Width (cm)")
hist_2d.update_yaxes(title="Sepal Width (cm)")
# show plots
petal_length_v_width_plot.show()
sepal_lengths_violin_plot.show()
pie_chart_classes.show()
box_plot_of_petal_lengths.show()
hist_2d.show()
return
def signal_heatmap_plot(cluster_pd_2D_pre_2, sel_binning):
fig = px.density_heatmap(x=cluster_pd_2D_pre_2.cl_pos_x,
y=cluster_pd_2D_pre_2.cl_pos_y,
marginal_x="histogram",
marginal_y="histogram",
nbinsx=int(128 / sel_binning),
nbinsy=int(128 / sel_binning))
fig.update_layout(yaxis_title="Y strips ",
xaxis_title="X strips",
height=800)
return fig
def genre_year_position_heatmap(genre_per_act_df, smooth, category_orders):
# Position on lineup x Musical Genre
df = genre_per_act_df.copy()
binsize = int(100 / smooth)
df.loc[:, 'importance_order'] = df['order_in_lineup'].apply(
lambda x: min([x // binsize, smooth - 1]))
df.loc[:, 'importance_order'] = df['importance_order'].apply(lambda x: "{:.0f}%-{:.0f}%".format(x*binsize, (x+1)*binsize)\
if x < smooth - 1 else "{:.0f}%-100%".format(x*binsize))
df = df.sort_values('importance_order')
df.loc[:, 'value'] = 100 * df['value']
df.loc[:,
'rank_on_order'] = df.groupby(['importance_order', 'genre', 'year'
])['value'].rank(ascending=False)
df_artists = df\
.loc[df['rank_on_order'] <= 5]\
.groupby(['genre', 'importance_order', 'year'])['artist_name'].apply(lambda x: '<br>'.join(x))\
.to_frame().reset_index()
df = df\
.groupby(['genre', 'importance_order', 'year'], as_index=False)\
.agg({'value':'mean'})
df = pd.merge(left=df,
right=df_artists,
on=['year', 'genre', 'importance_order'],
how='left')
fig = px.density_heatmap(df,
animation_frame='genre',
z='value',
y='importance_order',
hover_data=['artist_name'],
category_orders=category_orders,
x='year',
color_continuous_scale=px.colors.sequential.RdPu,
nbinsx=9,
range_color=[0, 50])
fig = format_fig(fig)
fig.update_layout(
title=
'Evolução da relevância de cada gênero musical por horário do Lineup',
coloraxis_colorbar_title='Participação<br>',
coloraxis_colorbar_ticksuffix='%',
coloraxis_colorbar_tickvals=[0, 15, 30, 45])
return fig
def update_grad_yr_histogram(selected_country):
filtered_df = df_grad_years[df_grad_years["COUNTRY_ISO"] ==
selected_country]
maxyr = df_grad_years["YEAR"].max()
fig = px.density_heatmap(filtered_df,
x="FOREIGN_WORKER_YRS_ED_COMP",
y="YEAR", z="COUNT",
range_x=[1980, maxyr])
fig.update_traces(xbins_size=1)
return fig
def basic_image(self):
"""Create a heat-map plot and update the figure attribute. Requires that x, y and z attributes are set.
z must be a 2D image.
"""
self.figure = px.density_heatmap(
self.data,
x=self.x,
y=self.y,
z=self.z,
color_continuous_scale=px.colors.sequential.Viridis,
hover_data=self.labels,
)
def index():
# extract data needed for visuals
# TODO: Below is an example - modify to extract data for your own visuals
genre_counts = df.groupby('genre').count()['message']
genre_names = list(genre_counts.index)
# create visuals
# TODO: Below is an example - modify to create your own visuals
graphs = [{
'data': [Bar(x=genre_names, y=genre_counts)],
'layout': {
'title': 'Distribution of Message Genres',
'yaxis': {
'title': "Count"
},
'xaxis': {
'title': "Genre"
}
}
}]
df['summ'] = df[df.columns[4:]].sum(axis=1)
fig = px.density_heatmap(
df,
x="genre",
y="summ",
marginal_x="histogram",
marginal_y="histogram",
labels={
"genre": "Message genre",
"summ": "Number of categories each message is classified as"
},
title="Density heatmap of message category vs genre")
graphs.append(fig)
fig = px.imshow(
df[df.columns[4:-1]],
labels=dict(x="Message category",
y="Message number",
color="classified as"),
title='Visualization of the categories, each message is classified as')
graphs.append(fig)
# encode plotly graphs in JSON
ids = ["graph-{}".format(i) for i, _ in enumerate(graphs)]
graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)
# render web page with plotly graphs
return render_template('master.html', ids=ids, graphJSON=graphJSON)
def graf_ods_2(local):
df = ROYALTIES
local = trata_local(local)
if len(local) > 0:
df = df.loc[df['municipio'].isin(local)]
fig = px.density_heatmap(df,
x='ano',
z='royalties per capita',
y="municipio",
hover_data=['municipio', 'ano'],
title="Graf. 1 - Metas ODS")
fig.update_layout(xaxis=dict(tickmode='linear', dtick=1))
return fig
def update_graph(events_json, player, value) :
if player :
events = pd.read_json(events_json, orient='split')
player_events = events[events["player"] == player]
newxbins = math.floor(120 / value)
newybins = math.floor(80 / value)
print("loc_x: ")
print(player_events["loc_x"])
return px.density_heatmap(player_events, x="loc_x", y="loc_y", nbinsx=newxbins, nbinsy=newybins, color_continuous_scale="Viridis")
else :
return {}
def get_html(cfg, mode=None):
df = HeatMap.compute()
fig = px.density_heatmap(df,
y="Rolle-Kontext",
x="Gesamtbewertung",
nbinsy=20,
color_continuous_scale="Viridis")
fig.update_layout(title="Verteilung Teilnehmer",
paper_bgcolor='rgb(243, 243, 243)',
plot_bgcolor='rgb(243, 243, 243)')
return fig.to_html(**cfg)
def doTomography(bins, mdf, title):
fig = px.density_heatmap(mdf,
x="xx",
y="yy",
title=title,
nbinsx=bins,
nbinsy=bins,
marginal_x="histogram",
marginal_y="histogram")
fig.update_layout(
xaxis_title="XView (cm)",
yaxis_title="YView (cm)",
)
fig.show()
def signal_heatmap_plot(cluster_pd_2D_pre_2, sel_binning):
fig = px.density_heatmap(
x=cluster_pd_2D_pre_2.cl_pos_x * 0.0650,
y=cluster_pd_2D_pre_2.cl_pos_y * 0.0650,
marginal_x="histogram",
marginal_y="histogram",
nbinsx=int(128 / sel_binning),
nbinsy=int(128 / sel_binning),
color_continuous_scale=px.colors.sequential.Viridis)
fig.update_layout(yaxis_title="Y pos [cm] ",
xaxis_title="X pos [cm]",
height=800,
width=800)
return fig
def get_figure5(df, x_col, y_col):
print("HEATMAP")
print(df)
fig = px.density_heatmap(df,
x=x_col,
y=y_col,
marginal_x="histogram",
marginal_y="histogram")
fig.update_layout(autosize=True,
margin=dict(l=30, r=30, b=20, t=40),
hovermode="closest",
plot_bgcolor="#F9F9F9",
paper_bgcolor="#E9E9E9",
legend=dict(font=dict(size=10), orientation="h"))
return fig
def charge_vs_time_plot(hit_pd_APV):
fig = px.density_heatmap(hit_pd_APV,
x="GemHit_time",
y="GemHit_q",
title="Charge vs time",
marginal_x="histogram",
marginal_y="histogram",
color_continuous_scale=colorscale,
nbinsx=150,
nbinsy=150)
fig.update_layout(xaxis_title="Time ",
yaxis_title="Charge [adc]",
height=800)
# fig.update_xaxes(range=[1300, 1600])
# fig.update_yaxes(range=[0, 60])
return fig
def showgraph(graph_selection, liste_annee, liste_ville, liste_specialite):
if graph_selection == "Carte Thermique":
df = pd.read_csv('data/2_aggregates/agg_latest_spot.csv', index_col=0)
df = df[df.ville.isin(liste_ville)
& df.specialite.isin(liste_specialite)
& df.annee.isin(liste_annee)]
fig = px.density_heatmap(df,
x="ville",
y="specialite",
z="classement",
histfunc="avg")
st.plotly_chart(fig, use_container_width=True)
if graph_selection == "Boites à Moustaches":
st.write("WIP")
if graph_selection == "Résulats par Ville à travers le temps":
df = df.groupby(['ville', 'annee']).max()
df = df.reset_index(level=['ville', 'annee'])
st.write(df)
fig = px.line(df, x="annee", y="classement", color='ville')
st.plotly_chart(fig, use_container_width=True)
if graph_selection == "Résulats par Spécialité à travers le temps":
fig = px.line(df, x="annee", y="classement", color='specialite')
st.plotly_chart(fig, use_container_width=True)
if graph_selection == "Lignes de crêtes par Ville":
df_full = pd.read_csv('data/2_aggregates/full.csv', index_col=0)
df_full = df_full[df_full.ville.isin(liste_ville)
& df_full.specialite.isin(liste_specialite)
& df_full.annee.isin(liste_annee)]
plt.figure()
joyplot(data=df_full[['classement', 'ville']],
by='ville',
figsize=(12, 8),
linecolor="blue",
colormap=cm.autumn_r)
plt.title('Ligne de crête des classements par ville', fontsize=20)
st.pyplot(plt, use_container_width=True)
if graph_selection == "Lignes de crêtes par Spécialité":
st.write("WIP")
def fig_density_heatmap(df,
df_x,
df_y,
title,
marginal_x=None,
marginal_y=None):
"""
Density heatmap for the first feature of the dataframe
"""
fig = px.density_heatmap(
df,
x=df_x,
y=df_y,
marginal_x=marginal_x,
marginal_y=marginal_y,
title=title,
)
fig = fig_update_layout(fig)
return fig
def interactive_heatmap2D(df):
"""
Interactive Histogram2D Plot
params:
df: input data frame
return:
plots histogram2D plot in streamlit
"""
global column2idx
# Choose X, Y and Color Axis
cols = st.beta_columns([1, 3])
with cols[0]:
st.markdown("### X-axis")
x_col = st.selectbox("Choose X-Axis Feature", df.columns,
column2idx["age"])
nbinsx = st.slider("Number of Bins", 10, 100, 20)
st.markdown("### Y-axis")
y_col = st.selectbox("Choose Y-Axis Feature", df.columns,
column2idx["fnlwgt"])
nbinsy = st.slider("Number of Bins (Y-Axis)", 10, 100, 20)
st.markdown("### Z-axis")
z_col = st.selectbox("Choose Z-Axis Feature", df.columns,
column2idx["hours_per_week"])
agg_func = st.selectbox("Aggregation Function",
["avg", "sum", "min", "sum", "count"], 0)
with cols[1]:
fig = px.density_heatmap(df,
x=x_col,
y=y_col,
z=z_col,
nbinsx=nbinsx,
nbinsy=nbinsy,
histfunc=agg_func)
fig.update_layout(width=1000, height=800, font_size=20)
st.plotly_chart(fig)
def scatter_browser_dimensions(self,
df,
type_plot='scatter',
save_file=True):
"""
Output scatter plot of browser dimensions.
Args:
df (dataframe): dataframe with data from heroku.
type_plot (str, optional): type of plot: scatter, density_heatmap.
save_file (bool, optional): flag for saving an html file with plot.
"""
logger.info('Creating plot of type_plot %s for browser dimensions.',
type_plot)
# scatter plot with histograms
if type_plot == 'scatter':
fig = px.scatter(df,
x='window_width',
y='window_height',
marginal_x='violin',
marginal_y='violin',
color='browser_name')
# density map with histograms
elif type_plot == 'density_heatmap':
fig = px.density_heatmap(df,
x='window_width',
y='window_height',
marginal_x='violin',
marginal_y='violin')
# unsopported type
else:
logger.error('Wrong type of plot %s given.', type_plot)
return -1
# update layout
fig.update_layout(template=self.template)
# save file
if save_file:
self.save_plotly(fig, 'scatter_browser_dimensions', self.folder)
# open it in localhost instead
else:
fig.show()
