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 make_non_outlier_interval(d1, d2):
"""
Returns the scatterplot fig of most of a violin plot.
"""
return graph_objs.Scatter(
x=[0, 0],
y=[d1, d2],
name="",
mode="lines",
line=graph_objs.scatter.Line(width=1.5, color="rgb(0,0,0)"),
)
def make_median(q2):
"""
Formats the 'median' hovertext for a violin plot.
"""
return graph_objs.Scatter(
x=[0],
y=[q2],
text=["median: " + "{:0.2f}".format(q2)],
mode="markers",
marker=dict(symbol="square", color="rgb(255,255,255)"),
hoverinfo="text",
)
def make_violin_rugplot(vals, pdf_max, distance, color="#1f77b4"):
"""
Returns a rugplot fig for a violin plot.
"""
return graph_objs.Scatter(
y=vals,
x=[-pdf_max - distance] * len(vals),
marker=graph_objs.scatter.Marker(color=color, symbol="line-ew-open"),
mode="markers",
name="",
showlegend=False,
hoverinfo="y",
)
def make_quartiles(q1, q3):
"""
Makes the upper and lower quartiles for a violin plot.
"""
return graph_objs.Scatter(
x=[0, 0],
y=[q1, q3],
text=[
"lower-quartile: " + "{:0.2f}".format(q1),
"upper-quartile: " + "{:0.2f}".format(q3),
],
mode="lines",
line=graph_objs.scatter.Line(width=4, color="rgb(0,0,0)"),
hoverinfo="text",
)
def make_half_violin(x, y, fillcolor="#1f77b4", linecolor="rgb(0, 0, 0)"):
"""
Produces a sideways probability distribution fig violin plot.
"""
text = [
"(pdf(y), y)=(" + "{:0.2f}".format(x[i]) + ", " +
"{:0.2f}".format(y[i]) + ")" for i in range(len(x))
]
return graph_objs.Scatter(
x=x,
y=y,
mode="lines",
name="",
text=text,
fill="tonextx",
fillcolor=fillcolor,
line=graph_objs.scatter.Line(width=0.5,
color=linecolor,
shape="spline"),
hoverinfo="text",
opacity=0.5,
)
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([1.0 / (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
def violin_colorscale(
data,
data_header,
group_header,
colors,
use_colorscale,
group_stats,
rugplot,
sort,
height,
width,
title,
):
"""
Refer to FigureFactory.create_violin() for docstring.
Returns fig for violin plot with colorscale.
"""
# collect all group names
group_name = []
for name in data[group_header]:
if name not in group_name:
group_name.append(name)
if sort:
group_name.sort()
# make sure all group names are keys in group_stats
for group in group_name:
if group not in group_stats:
raise exceptions.PlotlyError("All values/groups in the index "
"column must be represented "
"as a key in group_stats.")
gb = data.groupby([group_header])
L = len(group_name)
fig = make_subplots(rows=1,
cols=L,
shared_yaxes=True,
horizontal_spacing=0.025,
print_grid=False)
# prepare low and high color for colorscale
lowcolor = clrs.color_parser(colors[0], clrs.unlabel_rgb)
highcolor = clrs.color_parser(colors[1], clrs.unlabel_rgb)
# find min and max values in group_stats
group_stats_values = []
for key in group_stats:
group_stats_values.append(group_stats[key])
max_value = max(group_stats_values)
min_value = min(group_stats_values)
for k, gr in enumerate(group_name):
vals = np.asarray(gb.get_group(gr)[data_header], np.float)
# find intermediate color from colorscale
intermed = (group_stats[gr] - min_value) / (max_value - min_value)
intermed_color = clrs.find_intermediate_color(lowcolor, highcolor,
intermed)
plot_data, plot_xrange = violinplot(
vals, fillcolor="rgb{}".format(intermed_color), rugplot=rugplot)
layout = graph_objs.Layout()
for item in plot_data:
fig.append_trace(item, 1, k + 1)
fig["layout"].update(
{"xaxis{}".format(k + 1): make_XAxis(group_name[k], plot_xrange)})
# add colorbar to plot
trace_dummy = graph_objs.Scatter(
x=[0],
y=[0],
mode="markers",
marker=dict(
size=2,
cmin=min_value,
cmax=max_value,
colorscale=[[0, colors[0]], [1, colors[1]]],
showscale=True,
),
showlegend=False,
)
fig.append_trace(trace_dummy, 1, L)
# set the sharey axis style
fig["layout"].update({"yaxis{}".format(1): make_YAxis("")})
fig["layout"].update(
title=title,
showlegend=False,
hovermode="closest",
autosize=False,
height=height,
width=width,
)
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
Examples
--------
Example 1: Simple 2D Density Plot
>>> from plotly_study.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
>>> fig.show()
Example 2: Using Parameters
>>> from plotly_study.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
>>> 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_study.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_study.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 create_streamline(
x, y, u, v, density=1, angle=math.pi / 9, arrow_scale=0.09, **kwargs
):
"""
Returns data for a streamline plot.
:param (list|ndarray) x: 1 dimensional, evenly spaced list or array
:param (list|ndarray) y: 1 dimensional, evenly spaced list or array
:param (ndarray) u: 2 dimensional array
:param (ndarray) v: 2 dimensional array
:param (float|int) density: controls the density of streamlines in
plot. This is multiplied by 30 to scale similiarly to other
available streamline functions such as matplotlib.
Default = 1
:param (angle in radians) angle: angle of arrowhead. Default = pi/9
:param (float in [0,1]) arrow_scale: value to scale length of arrowhead
Default = .09
:param kwargs: kwargs passed through plotly_study.graph_objs.Scatter
for more information on valid kwargs call
help(plotly_study.graph_objs.Scatter)
:rtype (dict): returns a representation of streamline figure.
Example 1: Plot simple streamline and increase arrow size
>>> from plotly_study.figure_factory import create_streamline
>>> import numpy as np
>>> import math
>>> # Add data
>>> x = np.linspace(-3, 3, 100)
>>> y = np.linspace(-3, 3, 100)
>>> Y, X = np.meshgrid(x, y)
>>> u = -1 - X**2 + Y
>>> v = 1 + X - Y**2
>>> u = u.T # Transpose
>>> v = v.T # Transpose
>>> # Create streamline
>>> fig = create_streamline(x, y, u, v, arrow_scale=.1)
>>> fig.show()
Example 2: from nbviewer.ipython.org/github/barbagroup/AeroPython
>>> from plotly_study.figure_factory import create_streamline
>>> import numpy as np
>>> import math
>>> # Add data
>>> N = 50
>>> x_start, x_end = -2.0, 2.0
>>> y_start, y_end = -1.0, 1.0
>>> x = np.linspace(x_start, x_end, N)
>>> y = np.linspace(y_start, y_end, N)
>>> X, Y = np.meshgrid(x, y)
>>> ss = 5.0
>>> x_s, y_s = -1.0, 0.0
>>> # Compute the velocity field on the mesh grid
>>> u_s = ss/(2*np.pi) * (X-x_s)/((X-x_s)**2 + (Y-y_s)**2)
>>> v_s = ss/(2*np.pi) * (Y-y_s)/((X-x_s)**2 + (Y-y_s)**2)
>>> # Create streamline
>>> fig = create_streamline(x, y, u_s, v_s, density=2, name='streamline')
>>> # Add source point
>>> point = Scatter(x=[x_s], y=[y_s], mode='markers',
... marker=Marker(size=14), name='source point')
>>> fig['data'].append(point)
>>> fig.show()
"""
utils.validate_equal_length(x, y)
utils.validate_equal_length(u, v)
validate_streamline(x, y)
utils.validate_positive_scalars(density=density, arrow_scale=arrow_scale)
streamline_x, streamline_y = _Streamline(
x, y, u, v, density, angle, arrow_scale
).sum_streamlines()
arrow_x, arrow_y = _Streamline(
x, y, u, v, density, angle, arrow_scale
).get_streamline_arrows()
streamline = graph_objs.Scatter(
x=streamline_x + arrow_x, y=streamline_y + arrow_y, mode="lines", **kwargs
)
data = [streamline]
layout = graph_objs.Layout(hovermode="closest")
return graph_objs.Figure(data=data, layout=layout)
def create_quiver(x,
y,
u,
v,
scale=0.1,
arrow_scale=0.3,
angle=math.pi / 9,
scaleratio=None,
**kwargs):
"""
Returns data for a quiver plot.
:param (list|ndarray) x: x coordinates of the arrow locations
:param (list|ndarray) y: y coordinates of the arrow locations
:param (list|ndarray) u: x components of the arrow vectors
:param (list|ndarray) v: y components of the arrow vectors
:param (float in [0,1]) scale: scales size of the arrows(ideally to
avoid overlap). Default = .1
:param (float in [0,1]) arrow_scale: value multiplied to length of barb
to get length of arrowhead. Default = .3
:param (angle in radians) angle: angle of arrowhead. Default = pi/9
:param (positive float) scaleratio: the ratio between the scale of the y-axis
and the scale of the x-axis (scale_y / scale_x). Default = None, the
scale ratio is not fixed.
:param kwargs: kwargs passed through plotly_study.graph_objs.Scatter
for more information on valid kwargs call
help(plotly_study.graph_objs.Scatter)
:rtype (dict): returns a representation of quiver figure.
Example 1: Trivial Quiver
>>> from plotly_study.figure_factory import create_quiver
>>> import math
>>> # 1 Arrow from (0,0) to (1,1)
>>> fig = create_quiver(x=[0], y=[0], u=[1], v=[1], scale=1)
>>> fig.show()
Example 2: Quiver plot using meshgrid
>>> from plotly_study.figure_factory import create_quiver
>>> import numpy as np
>>> import math
>>> # Add data
>>> x,y = np.meshgrid(np.arange(0, 2, .2), np.arange(0, 2, .2))
>>> u = np.cos(x)*y
>>> v = np.sin(x)*y
>>> #Create quiver
>>> fig = create_quiver(x, y, u, v)
>>> fig.show()
Example 3: Styling the quiver plot
>>> from plotly_study.figure_factory import create_quiver
>>> import numpy as np
>>> import math
>>> # Add data
>>> x, y = np.meshgrid(np.arange(-np.pi, math.pi, .5),
... np.arange(-math.pi, math.pi, .5))
>>> u = np.cos(x)*y
>>> v = np.sin(x)*y
>>> # Create quiver
>>> fig = create_quiver(x, y, u, v, scale=.2, arrow_scale=.3, angle=math.pi/6,
... name='Wind Velocity', line=dict(width=1))
>>> # Add title to layout
>>> fig['layout'].update(title='Quiver Plot')
>>> fig.show()
Example 4: Forcing a fix scale ratio to maintain the arrow length
>>> from plotly_study.figure_factory import create_quiver
>>> import numpy as np
>>> # Add data
>>> x,y = np.meshgrid(np.arange(0.5, 3.5, .5), np.arange(0.5, 4.5, .5))
>>> u = x
>>> v = y
>>> angle = np.arctan(v / u)
>>> norm = 0.25
>>> u = norm * np.cos(angle)
>>> v = norm * np.sin(angle)
>>> # Create quiver with a fix scale ratio
>>> fig = create_quiver(x, y, u, v, scale = 1, scaleratio = 0.5)
>>> fig.show()
"""
utils.validate_equal_length(x, y, u, v)
utils.validate_positive_scalars(arrow_scale=arrow_scale, scale=scale)
if scaleratio is None:
quiver_obj = _Quiver(x, y, u, v, scale, arrow_scale, angle)
else:
quiver_obj = _Quiver(x, y, u, v, scale, arrow_scale, angle, scaleratio)
barb_x, barb_y = quiver_obj.get_barbs()
arrow_x, arrow_y = quiver_obj.get_quiver_arrows()
quiver_plot = graph_objs.Scatter(x=barb_x + arrow_x,
y=barb_y + arrow_y,
mode="lines",
**kwargs)
data = [quiver_plot]
if scaleratio is None:
layout = graph_objs.Layout(hovermode="closest")
else:
layout = graph_objs.Layout(hovermode="closest",
yaxis=dict(scaleratio=scaleratio,
scaleanchor="x"))
return graph_objs.Figure(data=data, layout=layout)