def scatterplot(dataframe, headers, diag, size, height, width, title,
**kwargs):
"""
Refer to FigureFactory.create_scatterplotmatrix() for docstring
Returns fig for scatterplotmatrix without index
"""
dim = len(dataframe)
fig = make_subplots(rows=dim, cols=dim, print_grid=False)
trace_list = []
# Insert traces into trace_list
for listy in dataframe:
for listx in dataframe:
if (listx == listy) and (diag == "histogram"):
trace = graph_objs.Histogram(x=listx, showlegend=False)
elif (listx == listy) and (diag == "box"):
trace = graph_objs.Box(y=listx, name=None, showlegend=False)
else:
if "marker" in kwargs:
kwargs["marker"]["size"] = size
trace = graph_objs.Scatter(x=listx,
y=listy,
mode="markers",
showlegend=False,
**kwargs)
trace_list.append(trace)
else:
trace = graph_objs.Scatter(x=listx,
y=listy,
mode="markers",
marker=dict(size=size),
showlegend=False,
**kwargs)
trace_list.append(trace)
trace_index = 0
indices = range(1, dim + 1)
for y_index in indices:
for x_index in indices:
fig.append_trace(trace_list[trace_index], y_index, x_index)
trace_index += 1
# Insert headers into the figure
for j in range(dim):
xaxis_key = "xaxis{}".format((dim * dim) - dim + 1 + j)
fig["layout"][xaxis_key].update(title=headers[j])
for j in range(dim):
yaxis_key = "yaxis{}".format(1 + (dim * j))
fig["layout"][yaxis_key].update(title=headers[j])
fig["layout"].update(height=height,
width=width,
title=title,
showlegend=True)
hide_tick_labels_from_box_subplots(fig)
return fig
def create_2d_density(x, y, colorscale='Earth', ncontours=20,
hist_color=(0, 0, 0.5), point_color=(0, 0, 0.5),
point_size=2, title='2D Density Plot',
height=600, width=600):
"""
Returns figure for a 2D density plot
:param (list|array) x: x-axis data for plot generation
:param (list|array) y: y-axis data for plot generation
:param (str|tuple|list) colorscale: either a plotly scale name, an rgb
or hex color, a color tuple or a list or tuple of colors. An rgb
color is of the form 'rgb(x, y, z)' where x, y, z belong to the
interval [0, 255] and a color tuple is a tuple of the form
(a, b, c) where a, b and c belong to [0, 1]. If colormap is a
list, it must contain the valid color types aforementioned as its
members.
:param (int) ncontours: the number of 2D contours to draw on the plot
:param (str) hist_color: the color of the plotted histograms
:param (str) point_color: the color of the scatter points
:param (str) point_size: the color of the scatter points
:param (str) title: set the title for the plot
:param (float) height: the height of the chart
:param (float) width: the width of the chart
Example 1: Simple 2D Density Plot
```
import plotly.plotly as py
from plotly.figure_factory create_2d_density
import numpy as np
# Make data points
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))
# Create a figure
fig = create_2D_density(x, y)
# Plot the data
py.iplot(fig, filename='simple-2d-density')
```
Example 2: Using Parameters
```
import plotly.plotly as py
from plotly.figure_factory create_2d_density
import numpy as np
# Make data points
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))
# Create custom colorscale
colorscale = ['#7A4579', '#D56073', 'rgb(236,158,105)',
(1, 1, 0.2), (0.98,0.98,0.98)]
# Create a figure
fig = create_2D_density(
x, y, colorscale=colorscale,
hist_color='rgb(255, 237, 222)', point_size=3)
# Plot the data
py.iplot(fig, filename='use-parameters')
```
"""
# validate x and y are filled with numbers only
for array in [x, y]:
if not all(isinstance(element, Number) for element in array):
raise exceptions.PlotlyError(
"All elements of your 'x' and 'y' lists must be numbers."
)
# validate x and y are the same length
if len(x) != len(y):
raise exceptions.PlotlyError(
"Both lists 'x' and 'y' must be the same length."
)
colorscale = utils.validate_colors(colorscale, 'rgb')
colorscale = make_linear_colorscale(colorscale)
# validate hist_color and point_color
hist_color = utils.validate_colors(hist_color, 'rgb')
point_color = utils.validate_colors(point_color, 'rgb')
trace1 = graph_objs.Scatter(
x=x, y=y, mode='markers', name='points',
marker=dict(
color=point_color[0],
size=point_size,
opacity=0.4
)
)
trace2 = graph_objs.Histogram2dContour(
x=x, y=y, name='density', ncontours=ncontours,
colorscale=colorscale, reversescale=True, showscale=False
)
trace3 = graph_objs.Histogram(
x=x, name='x density',
marker=dict(color=hist_color[0]), yaxis='y2'
)
trace4 = graph_objs.Histogram(
y=y, name='y density',
marker=dict(color=hist_color[0]), xaxis='x2'
)
data = [trace1, trace2, trace3, trace4]
layout = graph_objs.Layout(
showlegend=False,
autosize=False,
title=title,
height=height,
width=width,
xaxis=dict(
domain=[0, 0.85],
showgrid=False,
zeroline=False
),
yaxis=dict(
domain=[0, 0.85],
showgrid=False,
zeroline=False
),
margin=dict(
t=50
),
hovermode='closest',
bargap=0,
xaxis2=dict(
domain=[0.85, 1],
showgrid=False,
zeroline=False
),
yaxis2=dict(
domain=[0.85, 1],
showgrid=False,
zeroline=False
)
)
fig = graph_objs.Figure(data=data, layout=layout)
return fig
def create_2d_density(
x,
y,
colorscale="Earth",
ncontours=20,
hist_color=(0, 0, 0.5),
point_color=(0, 0, 0.5),
point_size=2,
title="2D Density Plot",
height=600,
width=600,
):
"""
**deprecated**, use instead
:func:`plotly.express.density_heatmap`.
:param (list|array) x: x-axis data for plot generation
:param (list|array) y: y-axis data for plot generation
:param (str|tuple|list) colorscale: either a plotly scale name, an rgb
or hex color, a color tuple or a list or tuple of colors. An rgb
color is of the form 'rgb(x, y, z)' where x, y, z belong to the
interval [0, 255] and a color tuple is a tuple of the form
(a, b, c) where a, b and c belong to [0, 1]. If colormap is a
list, it must contain the valid color types aforementioned as its
members.
:param (int) ncontours: the number of 2D contours to draw on the plot
:param (str) hist_color: the color of the plotted histograms
:param (str) point_color: the color of the scatter points
:param (str) point_size: the color of the scatter points
:param (str) title: set the title for the plot
:param (float) height: the height of the chart
:param (float) width: the width of the chart
Examples
--------
Example 1: Simple 2D Density Plot
>>> from plotly.figure_factory import create_2d_density
>>> import numpy as np
>>> # Make data points
>>> 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))
>>> # Create a figure
>>> fig = create_2d_density(x, y)
>>> # Plot the data
>>> fig.show()
Example 2: Using Parameters
>>> from plotly.figure_factory import create_2d_density
>>> import numpy as np
>>> # Make data points
>>> 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))
>>> # Create custom colorscale
>>> colorscale = ['#7A4579', '#D56073', 'rgb(236,158,105)',
... (1, 1, 0.2), (0.98,0.98,0.98)]
>>> # Create a figure
>>> fig = create_2d_density(x, y, colorscale=colorscale,
... hist_color='rgb(255, 237, 222)', point_size=3)
>>> # Plot the data
>>> fig.show()
"""
# validate x and y are filled with numbers only
for array in [x, y]:
if not all(isinstance(element, Number) for element in array):
raise plotly.exceptions.PlotlyError(
"All elements of your 'x' and 'y' lists must be numbers.")
# validate x and y are the same length
if len(x) != len(y):
raise plotly.exceptions.PlotlyError(
"Both lists 'x' and 'y' must be the same length.")
colorscale = clrs.validate_colors(colorscale, "rgb")
colorscale = make_linear_colorscale(colorscale)
# validate hist_color and point_color
hist_color = clrs.validate_colors(hist_color, "rgb")
point_color = clrs.validate_colors(point_color, "rgb")
trace1 = graph_objs.Scatter(
x=x,
y=y,
mode="markers",
name="points",
marker=dict(color=point_color[0], size=point_size, opacity=0.4),
)
trace2 = graph_objs.Histogram2dContour(
x=x,
y=y,
name="density",
ncontours=ncontours,
colorscale=colorscale,
reversescale=True,
showscale=False,
)
trace3 = graph_objs.Histogram(x=x,
name="x density",
marker=dict(color=hist_color[0]),
yaxis="y2")
trace4 = graph_objs.Histogram(y=y,
name="y density",
marker=dict(color=hist_color[0]),
xaxis="x2")
data = [trace1, trace2, trace3, trace4]
layout = graph_objs.Layout(
showlegend=False,
autosize=False,
title=title,
height=height,
width=width,
xaxis=dict(domain=[0, 0.85], showgrid=False, zeroline=False),
yaxis=dict(domain=[0, 0.85], showgrid=False, zeroline=False),
margin=dict(t=50),
hovermode="closest",
bargap=0,
xaxis2=dict(domain=[0.85, 1], showgrid=False, zeroline=False),
yaxis2=dict(domain=[0.85, 1], showgrid=False, zeroline=False),
)
fig = graph_objs.Figure(data=data, layout=layout)
return fig
def scatterplot_theme(dataframe, headers, diag, size, height, width, title,
index, index_vals, endpts, colormap, colormap_type,
**kwargs):
"""
Refer to FigureFactory.create_scatterplotmatrix() for docstring
Returns fig for scatterplotmatrix with both index and colormap picked
"""
# Check if index is made of string values
if isinstance(index_vals[0], str):
unique_index_vals = []
for name in index_vals:
if name not in unique_index_vals:
unique_index_vals.append(name)
n_colors_len = len(unique_index_vals)
# Convert colormap to list of n RGB tuples
if colormap_type == 'seq':
foo = clrs.color_parser(colormap, clrs.unlabel_rgb)
foo = clrs.n_colors(foo[0], foo[1], n_colors_len)
theme = clrs.color_parser(foo, clrs.label_rgb)
if colormap_type == 'cat':
# leave list of colors the same way
theme = colormap
dim = len(dataframe)
fig = make_subplots(rows=dim, cols=dim, print_grid=False)
trace_list = []
legend_param = 0
# Work over all permutations of list pairs
for listy in dataframe:
for listx in dataframe:
# create a dictionary for index_vals
unique_index_vals = {}
for name in index_vals:
if name not in unique_index_vals:
unique_index_vals[name] = []
c_indx = 0 # color index
# Fill all the rest of the names into the dictionary
for name in sorted(unique_index_vals.keys()):
new_listx = []
new_listy = []
for j in range(len(index_vals)):
if index_vals[j] == name:
new_listx.append(listx[j])
new_listy.append(listy[j])
# Generate trace with VISIBLE icon
if legend_param == 1:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=new_listx,
marker=dict(
color=theme[c_indx]),
showlegend=True
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=new_listx,
name=None,
marker=dict(
color=theme[c_indx]),
showlegend=True
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
kwargs['marker']['color'] = theme[c_indx]
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
showlegend=True,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
marker=dict(
size=size,
color=theme[c_indx]),
showlegend=True,
**kwargs
)
# Generate trace with INVISIBLE icon
else:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=new_listx,
marker=dict(
color=theme[c_indx]),
showlegend=False
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=new_listx,
name=None,
marker=dict(
color=theme[c_indx]),
showlegend=False
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
kwargs['marker']['color'] = theme[c_indx]
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
showlegend=False,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
marker=dict(
size=size,
color=theme[c_indx]),
showlegend=False,
**kwargs
)
# Push the trace into dictionary
unique_index_vals[name] = trace
if c_indx >= (len(theme) - 1):
c_indx = -1
c_indx += 1
trace_list.append(unique_index_vals)
legend_param += 1
trace_index = 0
indices = range(1, dim + 1)
for y_index in indices:
for x_index in indices:
for name in sorted(trace_list[trace_index].keys()):
fig.append_trace(
trace_list[trace_index][name],
y_index,
x_index)
trace_index += 1
# Insert headers into the figure
for j in range(dim):
xaxis_key = 'xaxis{}'.format((dim * dim) - dim + 1 + j)
fig['layout'][xaxis_key].update(title=headers[j])
for j in range(dim):
yaxis_key = 'yaxis{}'.format(1 + (dim * j))
fig['layout'][yaxis_key].update(title=headers[j])
hide_tick_labels_from_box_subplots(fig)
if diag == 'histogram':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True,
barmode='stack')
return fig
elif diag == 'box':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
else:
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
else:
if endpts:
intervals = utils.endpts_to_intervals(endpts)
# Convert colormap to list of n RGB tuples
if colormap_type == 'seq':
foo = clrs.color_parser(colormap, clrs.unlabel_rgb)
foo = clrs.n_colors(foo[0], foo[1], len(intervals))
theme = clrs.color_parser(foo, clrs.label_rgb)
if colormap_type == 'cat':
# leave list of colors the same way
theme = colormap
dim = len(dataframe)
fig = make_subplots(rows=dim, cols=dim, print_grid=False)
trace_list = []
legend_param = 0
# Work over all permutations of list pairs
for listy in dataframe:
for listx in dataframe:
interval_labels = {}
for interval in intervals:
interval_labels[str(interval)] = []
c_indx = 0 # color index
# Fill all the rest of the names into the dictionary
for interval in intervals:
new_listx = []
new_listy = []
for j in range(len(index_vals)):
if interval[0] < index_vals[j] <= interval[1]:
new_listx.append(listx[j])
new_listy.append(listy[j])
# Generate trace with VISIBLE icon
if legend_param == 1:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=new_listx,
marker=dict(
color=theme[c_indx]),
showlegend=True
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=new_listx,
name=None,
marker=dict(
color=theme[c_indx]),
showlegend=True
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
(kwargs['marker']
['color']) = theme[c_indx]
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=str(interval),
showlegend=True,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=str(interval),
marker=dict(
size=size,
color=theme[c_indx]),
showlegend=True,
**kwargs
)
# Generate trace with INVISIBLE icon
else:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=new_listx,
marker=dict(
color=theme[c_indx]),
showlegend=False
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=new_listx,
name=None,
marker=dict(
color=theme[c_indx]),
showlegend=False
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
(kwargs['marker']
['color']) = theme[c_indx]
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=str(interval),
showlegend=False,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=str(interval),
marker=dict(
size=size,
color=theme[c_indx]),
showlegend=False,
**kwargs
)
# Push the trace into dictionary
interval_labels[str(interval)] = trace
if c_indx >= (len(theme) - 1):
c_indx = -1
c_indx += 1
trace_list.append(interval_labels)
legend_param += 1
trace_index = 0
indices = range(1, dim + 1)
for y_index in indices:
for x_index in indices:
for interval in intervals:
fig.append_trace(
trace_list[trace_index][str(interval)],
y_index,
x_index)
trace_index += 1
# Insert headers into the figure
for j in range(dim):
xaxis_key = 'xaxis{}'.format((dim * dim) - dim + 1 + j)
fig['layout'][xaxis_key].update(title=headers[j])
for j in range(dim):
yaxis_key = 'yaxis{}'.format(1 + (dim * j))
fig['layout'][yaxis_key].update(title=headers[j])
hide_tick_labels_from_box_subplots(fig)
if diag == 'histogram':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True,
barmode='stack')
return fig
elif diag == 'box':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
else:
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
else:
theme = colormap
# add a copy of rgb color to theme if it contains one color
if len(theme) <= 1:
theme.append(theme[0])
color = []
for incr in range(len(theme)):
color.append([1. / (len(theme) - 1) * incr, theme[incr]])
dim = len(dataframe)
fig = make_subplots(rows=dim, cols=dim, print_grid=False)
trace_list = []
legend_param = 0
# Run through all permutations of list pairs
for listy in dataframe:
for listx in dataframe:
# Generate trace with VISIBLE icon
if legend_param == 1:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=listx,
marker=dict(
color=theme[0]),
showlegend=False
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=listx,
marker=dict(
color=theme[0]),
showlegend=False
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
kwargs['marker']['color'] = index_vals
kwargs['marker']['colorscale'] = color
kwargs['marker']['showscale'] = True
trace = graph_objs.Scatter(
x=listx,
y=listy,
mode='markers',
showlegend=False,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=listx,
y=listy,
mode='markers',
marker=dict(
size=size,
color=index_vals,
colorscale=color,
showscale=True),
showlegend=False,
**kwargs
)
# Generate trace with INVISIBLE icon
else:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=listx,
marker=dict(
color=theme[0]),
showlegend=False
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=listx,
marker=dict(
color=theme[0]),
showlegend=False
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
kwargs['marker']['color'] = index_vals
kwargs['marker']['colorscale'] = color
kwargs['marker']['showscale'] = False
trace = graph_objs.Scatter(
x=listx,
y=listy,
mode='markers',
showlegend=False,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=listx,
y=listy,
mode='markers',
marker=dict(
size=size,
color=index_vals,
colorscale=color,
showscale=False),
showlegend=False,
**kwargs
)
# Push the trace into list
trace_list.append(trace)
legend_param += 1
trace_index = 0
indices = range(1, dim + 1)
for y_index in indices:
for x_index in indices:
fig.append_trace(trace_list[trace_index],
y_index,
x_index)
trace_index += 1
# Insert headers into the figure
for j in range(dim):
xaxis_key = 'xaxis{}'.format((dim * dim) - dim + 1 + j)
fig['layout'][xaxis_key].update(title=headers[j])
for j in range(dim):
yaxis_key = 'yaxis{}'.format(1 + (dim * j))
fig['layout'][yaxis_key].update(title=headers[j])
hide_tick_labels_from_box_subplots(fig)
if diag == 'histogram':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True,
barmode='stack')
return fig
elif diag == 'box':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
else:
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
def scatterplot_dict(dataframe, headers, diag, size,
height, width, title, index, index_vals,
endpts, colormap, colormap_type, **kwargs):
"""
Refer to FigureFactory.create_scatterplotmatrix() for docstring
Returns fig for scatterplotmatrix with both index and colormap picked.
Used if colormap is a dictionary with index values as keys pointing to
colors. Forces colormap_type to behave categorically because it would
not make sense colors are assigned to each index value and thus
implies that a categorical approach should be taken
"""
theme = colormap
dim = len(dataframe)
fig = make_subplots(rows=dim, cols=dim, print_grid=False)
trace_list = []
legend_param = 0
# Work over all permutations of list pairs
for listy in dataframe:
for listx in dataframe:
# create a dictionary for index_vals
unique_index_vals = {}
for name in index_vals:
if name not in unique_index_vals:
unique_index_vals[name] = []
# Fill all the rest of the names into the dictionary
for name in sorted(unique_index_vals.keys()):
new_listx = []
new_listy = []
for j in range(len(index_vals)):
if index_vals[j] == name:
new_listx.append(listx[j])
new_listy.append(listy[j])
# Generate trace with VISIBLE icon
if legend_param == 1:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=new_listx,
marker=dict(
color=theme[name]),
showlegend=True
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=new_listx,
name=None,
marker=dict(
color=theme[name]),
showlegend=True
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
kwargs['marker']['color'] = theme[name]
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
showlegend=True,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
marker=dict(
size=size,
color=theme[name]),
showlegend=True,
**kwargs
)
# Generate trace with INVISIBLE icon
else:
if (listx == listy) and (diag == 'histogram'):
trace = graph_objs.Histogram(
x=new_listx,
marker=dict(
color=theme[name]),
showlegend=False
)
elif (listx == listy) and (diag == 'box'):
trace = graph_objs.Box(
y=new_listx,
name=None,
marker=dict(
color=theme[name]),
showlegend=False
)
else:
if 'marker' in kwargs:
kwargs['marker']['size'] = size
kwargs['marker']['color'] = theme[name]
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
showlegend=False,
**kwargs
)
else:
trace = graph_objs.Scatter(
x=new_listx,
y=new_listy,
mode='markers',
name=name,
marker=dict(
size=size,
color=theme[name]),
showlegend=False,
**kwargs
)
# Push the trace into dictionary
unique_index_vals[name] = trace
trace_list.append(unique_index_vals)
legend_param += 1
trace_index = 0
indices = range(1, dim + 1)
for y_index in indices:
for x_index in indices:
for name in sorted(trace_list[trace_index].keys()):
fig.append_trace(
trace_list[trace_index][name],
y_index,
x_index)
trace_index += 1
# Insert headers into the figure
for j in range(dim):
xaxis_key = 'xaxis{}'.format((dim * dim) - dim + 1 + j)
fig['layout'][xaxis_key].update(title=headers[j])
for j in range(dim):
yaxis_key = 'yaxis{}'.format(1 + (dim * j))
fig['layout'][yaxis_key].update(title=headers[j])
hide_tick_labels_from_box_subplots(fig)
if diag == 'histogram':
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True,
barmode='stack')
return fig
else:
fig['layout'].update(
height=height, width=width,
title=title,
showlegend=True)
return fig
