def build_2d_density(self, **data):
df = data['df']
n_records = self.set_size(**data)
columns = data['columns']
return ff.create_2d_density(df[:n_records][columns[0]],
df[:n_records][columns[1]])
def drawHistogram(data, accuracyName, variableName):
x = [d[variableName] for d in data]
y = [d[accuracyName] for d in data]
colorscale = ['rgb(255,0,0)', (1,1,1)]
f = ff.create_2d_density(
x, y, colorscale=colorscale,
hist_color='rgb(255, 237, 222)', point_size=3,
title='',
height=1080,
width=1920)
return f
def CC_viz(df,
charter='Plotly',
output_chart=False,
output_dir=None,
resolution=150,
display=False):
''' Takes two continuous feature names and generates a 2D Kernel Density Plot '''
U = list(df.columns)[0]
V = list(df.columns)[1]
if charter == 'Plotly':
fig = ff.create_2d_density(df[U],
df[V],
colorscale=px.colors.sequential.Blues_r,
hist_color=(135 / 255, 206 / 255,
250 / 255),
title='')
fig.update_layout(xaxis_showgrid=False,
xaxis_zeroline=False,
xaxis_title=U.replace('_', ' ').title(),
yaxis_title=V.replace('_', ' ').title(),
plot_bgcolor="rgba(0, 0, 0, 0)",
paper_bgcolor="rgba(0, 0, 0, 0)",
showlegend=False)
if display: fig.show()
if output_chart:
fig.update_xaxes(tickcolor='white', tickfont=dict(color='white'))
fig.update_yaxes(tickcolor='white', tickfont=dict(color='white'))
fig.update_layout(font=dict(color="white"))
fig.write_image(str(output_dir / 'charts' /
(U + '_' + V + '.png')),
scale=resolution // 72)
else:
sns.kdeplot(df[U],
df[V],
color='blue',
shade=True,
alpha=0.3,
shade_lowest=False)
if len(df[U]) < 500:
sns.scatterplot(
x=df[U], y=df[V], color='blue', alpha=0.5, linewidth=0
) # Only show a scatter plot if there are fewer than 500 data points
plt.xlabel(U.replace('_', ' ').title())
plt.ylabel(V.replace('_', ' ').title())
if display: plt.show()
if output_chart:
plt.savefig(output_dir / 'charts' / (U + '_' + V + '.png'),
dpi=resolution)
plt.close('all')
import pandas as pd
iris = load_iris()
columns = ["label"] + iris.feature_names
label = iris.target.reshape([150, 1])
labeled = np.hstack([label, iris.data])
# make dataframe
df = pd.DataFrame(labeled, columns=columns)
# setosaのsepal-lengthとsepal-widthを使います．
setosa = df[df["label"] == 0]
x = setosa['sepal length (cm)']
y = setosa['sepal width (cm)']
# define colorscale
colorscale = [
'#7A4579', '#D56073', 'rgb(236,158,105)', (1, 1, 0.2), (0.98, 0.98, 0.98)
]
fig = ff.create_2d_density(
x,
y,
colorscale=colorscale,
hist_color='rgb(0, 128, 222)',
point_size=3,
)
offline.plot(fig, filename="2d-density", image="png")
def create_2D_density(*args, **kwargs):
FigureFactory._deprecated('create_2D_density', 'create_2d_density')
from plotly.figure_factory import create_2d_density
return create_2d_density(*args, **kwargs)
buildTagData(tags, plots)
"""
-------------------------------------------------------------------------------
--------------------------Creating the plotly figures--------------------------
-------------------------------------------------------------------------------
"""
scatterPointFigure = px.scatter(combinedFrame,
x="x",
y="y",
color="tag",
title="Scatter attempt")
heatmapFigure = create_2d_density(combinedFrame.x,
combinedFrame.y,
colorscale=colorscale,
hist_color='rgb(255, 237, 222)',
point_size=1,
title='2D Density Plot',
height=1200,
width=1200)
trackingFigureAnimated = go.Figure(
data=getAllInitialData(MinuteFrameList, len(MinuteFrameList)),
layout=go.Layout(
xaxis=dict(range=[-xMinMax - 10, xMinMax + 10], autorange=False),
yaxis=dict(range=[-yMinMax - 10, yMinMax + 10], autorange=False),
title="All Tag Tracker",
updatemenus=[
dict(type="buttons",
buttons=[dict(label="Play", method="animate", args=[None])])
],
height=800),
##############################################################################
# Scatter Histogram
# ---------------------------------------------------------------------------
# Source:
# https://plot.ly/python/density-plots/
# https://plot.ly/python/2d-density-plots/
##############################################################################
import numpy as np
import plotly
import plotly.figure_factory as ff
t = np.linspace(-1, 1.2, 2000)
x = (t**3) + (0.3 * np.random.randn(2000))
y = (t**6) + (0.3 * np.random.randn(2000))
colorscale = ['#426d9f', (1, 1, 1)]
fig = ff.create_2d_density(x,
y,
colorscale=colorscale,
ncontours=20,
hist_color='#c4e0f9',
point_size=1.5,
point_color=(0.0, 0.0, 0.0))
plotly.offline.iplot(fig, filename='histogram_subplots')
plotly.offline.plot(fig, filename='scatterHistogram.html')
def create_2D_density(*args, **kwargs):
FigureFactory._deprecated('create_2D_density', 'create_2d_density')
from plotly.figure_factory import create_2d_density
return create_2d_density(*args, **kwargs)
def scatter(x,
y,
legacy,
names,
path,
plots,
color,
colormap,
settings,
stat=None,
log=False,
minvalx=0,
minvaly=0,
title=None,
xmax=None,
ymax=None):
"""->
create marginalised scatterplots and KDE plot with marginalized histograms
-> update from scatter_legacy function to utilise plotly package
- scatterplot with histogram on both axes
- kernel density plot with histograms on both axes
- hexbin not implemented yet
- pauvre plot temporarily not available
"""
logging.info(
f"NanoPlot: Creating {names[0]} vs {names[1]} plots using {x.size} reads."
)
if not contains_variance([x, y], names):
return []
plots_made = []
idx = np.random.choice(x.index, min(10000, len(x)), replace=False)
maxvalx = xmax or np.amax(x[idx])
maxvaly = ymax or np.amax(y[idx])
if plots["dot"]:
if log:
dot_plot = Plot(path=path + "_loglength_dot.html",
title=f"{names[0]} vs {names[1]} plot using dots "
"after log transformation of read lengths")
else:
dot_plot = Plot(path=path + "_dot.html",
title=f"{names[0]} vs {names[1]} plot using dots")
fig = px.scatter(x=x[idx],
y=y[idx],
marginal_x="histogram",
marginal_y="histogram",
range_x=[minvalx, maxvalx],
range_y=[minvaly, maxvaly])
fig.update_traces(marker=dict(color=color))
fig.update_yaxes(rangemode="tozero")
fig.update_xaxes(rangemode="tozero")
fig.update_layout(xaxis_title=names[0],
yaxis_title=names[1],
title=title or dot_plot.title,
title_x=0.5)
if log:
ticks = [
10**i for i in range(10) if not 10**i > 10 * (10**maxvalx)
]
fig.update_layout(xaxis=dict(tickmode='array',
tickvals=np.log10(ticks),
ticktext=ticks,
tickangle=45))
dot_plot.fig = fig
dot_plot.html = dot_plot.fig.to_html(full_html=False,
include_plotlyjs='cdn')
dot_plot.save(settings)
plots_made.append(dot_plot)
if plots["kde"]:
kde_plot = Plot(path=path + "_loglength_kde.html" if log else path +
"_kde.html",
title=f"{names[0]} vs {names[1]} kde plot")
col = hex_to_rgb_scale_0_1(color)
fig = ff.create_2d_density(x[idx],
y[idx],
point_size=3,
hist_color=col,
point_color=col,
colorscale=colormap)
fig.update_layout(xaxis_title=names[0],
yaxis_title=names[1],
title=title or kde_plot.title,
title_x=0.5,
xaxis=dict(tickangle=45))
if log:
ticks = [
10**i for i in range(10) if not 10**i > 10 * (10**maxvalx)
]
fig.update_layout(xaxis=dict(tickmode='array',
tickvals=np.log10(ticks),
ticktext=ticks,
tickangle=45))
kde_plot.fig = fig
kde_plot.html = kde_plot.fig.to_html(full_html=False,
include_plotlyjs='cdn')
kde_plot.save(settings)
plots_made.append(kde_plot)
if 1 in legacy.values():
settings, args = utils.get_args()
plots_made += scatter_legacy(x=x[idx],
y=y[idx],
names=names,
path=path,
plots=legacy,
color=color,
settings=settings,
stat=stat,
log=log,
minvalx=minvalx,
minvaly=minvaly,
title=title)
return plots_made
########### This is about using figure_factory module instead of graph_objs module of plotly to plot a 2D density plot ################
import plotly.offline as offline
import plotly.figure_factory as ff
pubg = pd.read_csv('PUBG.csv')
df_pubg = pubg.apply(pd.to_numeric, errors="ignore")
df_bar_pubg = df_pubg.head(10)
x = df_bar_pubg['solo_Wins']
y = df_bar_pubg['solo_TimeSurvived']
#z = df_bar_pubg['squad_Heals']
colorscale = ['#C09055', '#1B5709', '#FF8237', '#FF65A4', '#C8B4FF']
fig = ff.create_2d_density(x,
y,
colorscale=colorscale,
hist_color='rgb(255,237,222)',
point_size='3')
offline.plot(fig, filename='histogram_subplots')