Module Plotly
Expand source code
# Copyright (C) 2024
# Wassim Jabi <wassim.jabi@gmail.com>
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Affero General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more
# details.
#
# You should have received a copy of the GNU Affero General Public License along with
# this program. If not, see <https://www.gnu.org/licenses/>.
import topologic_core as topologic
import os
import warnings
try:
import plotly
import plotly.graph_objects as go
import plotly.offline as ofl
except:
print("Plotly - Installing required plotly library.")
try:
os.system("pip install plotly")
except:
os.system("pip install plotly --user")
try:
import plotly
import plotly.graph_objects as go
import plotly.offline as ofl
except:
warnings.warn("Plotly - Error: Could not import plotly.")
class Plotly:
@staticmethod
def AddColorBar(figure, values=[], nTicks=5, xPosition=-0.15, width=15, outlineWidth=0, title="", subTitle="", units="", colorScale="viridis", mantissa: int = 6):
"""
Adds a color bar to the input figure
Parameters
----------
figure : plotly.graph_objs._figure.Figure
The input plotly figure.
values : list , optional
The input list of values to use for the color bar. The default is [].
nTicks : int , optional
The number of ticks to use on the color bar. The default is 5.
xPosition : float , optional
The x location of the color bar. The default is -0.15.
width : int , optional
The width in pixels of the color bar. The default is 15
outlineWidth : int , optional
The width in pixels of the outline of the color bar. The default is 0.
title : str , optional
The title of the color bar. The default is "".
subTitle : str , optional
The subtitle of the color bar. The default is "".
units: str , optional
The units used in the color bar. The default is ""
colorScale : str , optional
The desired type of plotly color scales to use (e.g. "viridis", "plasma"). The default is "viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/.
In addition to these, three color-blind friendly scales are included. These are "protanopia", "deuteranopia", and "tritanopia" for red, green, and blue colorblindness respectively.
mantissa : int , optional
The desired length of the mantissa for the values listed on the color bar. The default is 6.
Returns
-------
plotly.graph_objs._figure.Figure
The input figure with the color bar added.
"""
if not isinstance(figure, plotly.graph_objs._figure.Figure):
return None
if units:
units = "Units: "+units
minValue = min(values)
maxValue = max(values)
step = (maxValue - minValue)/float(nTicks-1)
r = [round(minValue+i*step, mantissa) for i in range(nTicks)]
r[-1] = round(maxValue, mantissa)
# Define the minimum and maximum range of the colorbar
rs = [str(x) for x in r]
# Define the colorbar as a trace with no data, x or y coordinates
colorbar_trace = go.Scatter(
x=[0],
y=[0],
mode="markers",
showlegend=False,
marker=dict(
size=0,
colorscale=Plotly.ColorScale(colorScale), # choose the colorscale
cmin=minValue,
cmax=maxValue,
color=['rgba(0,0,0,0)'],
colorbar=dict(
x=xPosition,
title="<b>"+title+"</b><br>"+subTitle+"<br>"+units, # title of the colorbar
ticks="outside", # position of the ticks
tickvals=r, # values of the ticks
ticktext=rs, # text of the ticks
tickmode="array",
thickness=width,
outlinewidth=outlineWidth,
)
)
)
figure.add_trace(colorbar_trace)
return figure
@staticmethod
def Colors():
"""
Returns the list of named CSS colors that plotly can use.
Returns
-------
list
The list of named CSS colors.
"""
return ["aliceblue","antiquewhite","aqua",
"aquamarine","azure","beige",
"bisque","black","blanchedalmond",
"blue","blueviolet","brown",
"burlywood","cadetblue",
"chartreuse","chocolate",
"coral","cornflowerblue","cornsilk",
"crimson","cyan","darkblue",
"darkcyan","darkgoldenrod","darkgray",
"darkgrey","darkgreen","darkkhaki",
"darkmagenta","darkolivegreen","darkorange",
"darkorchid","darkred","darksalmon",
"darkseagreen","darkslateblue","darkslategray",
"darkslategrey","darkturquoise","darkviolet",
"deeppink","deepskyblue","dimgray",
"dimgrey","dodgerblue","firebrick",
"floralwhite","forestgreen","fuchsia",
"gainsboro","ghostwhite","gold",
"goldenrod","gray","grey",
"green"," greenyellow","honeydew",
"hotpink","indianred","indigo",
"ivory","khaki","lavender",
"lavenderblush","lawngreen","lemonchiffon",
"lightblue","lightcoral","lightcyan",
"lightgoldenrodyellow","lightgray","lightgrey",
"lightgreen","lightpink","lightsalmon",
"lightseagreen","lightskyblue","lightslategray",
"lightslategrey","lightsteelblue","lightyellow",
"lime","limegreen","linen",
"magenta","maroon","mediumaquamarine",
"mediumblue","mediumorchid","mediumpurple",
"mediumseagreen","mediumslateblue","mediumspringgreen",
"mediumturquoise","mediumvioletred","midnightblue",
"mintcream","mistyrose","moccasin",
"navajowhite","navy","oldlace",
"olive","olivedrab","orange",
"orangered","orchid","palegoldenrod",
"palegreen","paleturquoise","palevioletred",
"papayawhip","peachpuff","peru",
"pink","plum","powderblue",
"purple","red","rosybrown",
"royalblue","rebeccapurple","saddlebrown",
"salmon","sandybrown","seagreen",
"seashell","sienna","silver",
"skyblue","slateblue","slategray",
"slategrey","snow","springgreen",
"steelblue","tan","teal",
"thistle","tomato","turquoise",
"violet","wheat","white",
"whitesmoke","yellow","yellowgreen"]
@staticmethod
def ColorScale(colorScale: str = "viridis"):
# Colors recommended by various sources for color-blind-friendly palettes
protanopia_colors = [
"#E69F00", # orange
"#56B4E9", # sky blue
"#009E73", # bluish green
"#F0E442", # yellow
"#0072B2", # blue
"#D55E00", # vermillion
"#CC79A7", # reddish purple
]
deuteranopia_colors = [
"#377EB8", # blue
"#FF7F00", # orange
"#4DAF4A", # green
"#F781BF", # pink
"#A65628", # brown
"#984EA3", # purple
"#999999", # grey
]
tritanopia_colors = [
"#E69F00", # orange
"#56B4E9", # sky blue
"#009E73", # bluish green
"#F0E442", # yellow
"#0072B2", # blue
"#D55E00", # vermillion
"#CC79A7", # reddish purple
]
# Create colorscales for Plotly
def create_colorscale(colors):
colorscale = []
num_colors = len(colors)
for i, color in enumerate(colors):
position = i / (num_colors - 1)
colorscale.append((position, color))
return colorscale
if "prota" in colorScale.lower():
return create_colorscale(protanopia_colors)
elif "deutera" in colorScale.lower():
return create_colorscale(deuteranopia_colors)
elif "trita"in colorScale.lower():
return create_colorscale(tritanopia_colors)
else:
return colorScale
@staticmethod
def DataByDGL(data, labels):
"""
Returns a data frame from the DGL data.
Parameters
----------
data : list
The data to display.
labels : list
The labels to use for the data. The data with the labels in this list will be extracted and used in the returned dataFrame.
Returns
-------
pd.DataFrame
A pandas dataFrame
"""
try:
import pandas as pd
except:
print("Plotly - Installing required pandas library.")
try:
os.system("pip install pandas")
except:
os.system("pip install pandas --user")
try:
import pandas as pd
except:
warnings.warn("Plotly.DataByDGL - Error: Could not import pandas. Please install the pandas library manually. Returning None")
return None
if isinstance(data[labels[0]][0], int):
xAxis_list = list(range(1, data[labels[0]][0]+1))
else:
xAxis_list = data[labels[0]][0]
plot_data = [xAxis_list]
for i in range(1,len(labels)):
plot_data.append(data[labels[i]][0][:len(xAxis_list)])
dlist = list(map(list, zip(*plot_data)))
df = pd.DataFrame(dlist, columns=labels)
return df
@staticmethod
def DataByGraph(graph, vertexColor="black", vertexSize=6, vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[], showVertices=True, showVertexLegend=False, edgeColor="black", edgeWidth=1, edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[], showEdges=True, showEdgeLegend=False, colorScale="viridis"):
"""
Creates plotly vertex and edge data from the input graph.
Parameters
----------
graph : topologic_core.Graph
The input graph.
vertexColor : str , optional
The desired color of the output vertices. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is "black".
vertexSize : float , optional
The desired size of the vertices. The default is 6.
vertexLabelKey : str , optional
The dictionary key to use to display the vertex label. The default is None.
vertexGroupKey : str , optional
The dictionary key to use to display the vertex group. The default is None.
vertexGroups : list , optional
The list of vertex groups against which to index the color of the vertex. The default is [].
showVertices : bool , optional
If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True.
showVertexLegend : bool , optional
If set to True the vertex legend will be drawn. Otherwise, it will not be drawn. The default is False.
edgeColor : str , optional
The desired color of the output edges. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is "black".
edgeWidth : float , optional
The desired thickness of the output edges. The default is 1.
edgeLabelKey : str , optional
The dictionary key to use to display the edge label. The default is None.
edgeGroupKey : str , optional
The dictionary key to use to display the edge group. The default is None.
edgeGroups : list , optional
The list of groups to use for indexing the color of edges. The default is None.
showEdges : bool , optional
If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True.
showEdgeLegend : bool , optional
If set to True the edge legend will be drawn. Otherwise, it will not be drawn. The default is False.
colorScale : str , optional
The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/.
Returns
-------
list
The vertex and edge data list.
"""
from topologicpy.Vertex import Vertex
from topologicpy.Edge import Edge
from topologicpy.Dictionary import Dictionary
from topologicpy.Topology import Topology
from topologicpy.Graph import Graph
import plotly.graph_objs as go
if not Topology.IsInstance(graph, "Graph"):
return None
v_labels = []
v_groupList = []
data = []
if showVertices:
vertices = Graph.Vertices(graph)
if vertexLabelKey or vertexGroupKey:
for v in vertices:
Xn=[round(Vertex.X(v), 4) for v in vertices] # x-coordinates of nodes
Yn=[round(Vertex.Y(v), 4) for v in vertices] # y-coordinates of nodes
Zn=[round(Vertex.Z(v), 4) for v in vertices] # x-coordinates of nodes
v_label = ""
v_group = ""
d = Topology.Dictionary(v)
if d:
try:
v_label = str(Dictionary.ValueAtKey(d, key=vertexLabelKey)) or ""
except:
v_label = ""
try:
v_group = Dictionary.ValueAtKey(d, key=vertexGroupKey)
except:
v_group = None
try:
v_groupList.append(vertexGroups.index(v_group))
except:
v_groupList.append(len(vertexGroups))
if not v_label == "" and not v_group == "":
if v_group == 0:
v_label = v_label+" (0)"
else:
v_label = v_label+" ("+str(v_group)+")"
v_labels.append(v_label)
else:
for v in vertices:
Xn=[round(Vertex.X(v), 4) for v in vertices] # x-coordinates of nodes
Yn=[round(Vertex.Y(v), 4) for v in vertices] # y-coordinates of nodes
Zn=[round(Vertex.Z(v), 4) for v in vertices] # x-coordinates of nodes
if len(list(set(v_groupList))) < 2:
v_groupList = vertexColor
if len(v_labels) < 1:
v_labels = ""
v_trace=go.Scatter3d(x=Xn,
y=Yn,
z=Zn,
mode='markers',
name='Graph Vertices',
legendgroup=4,
legendrank=4,
showlegend=showVertexLegend,
marker=dict(symbol='circle',
size=vertexSize,
color=v_groupList,
colorscale=Plotly.ColorScale(colorScale),
line=dict(color=edgeColor, width=0.5)
),
text=v_labels,
hoverinfo='text'
)
data.append(v_trace)
if showEdges:
Xe=[]
Ye=[]
Ze=[]
e_labels = []
e_groupList = []
edges = Graph.Edges(graph)
if edgeLabelKey or edgeGroupKey:
for e in edges:
sv = Edge.StartVertex(e)
ev = Edge.EndVertex(e)
Xe+=[round(Vertex.X(sv), 4), round(Vertex.X(ev), 4), None] # x-coordinates of edge ends
Ye+=[round(Vertex.Y(sv), 4), round(Vertex.Y(ev), 4), None] # y-coordinates of edge ends
Ze+=[round(Vertex.Z(sv), 4), round(Vertex.Z(ev), 4), None] # z-coordinates of edge ends
e_label = ""
e_group = ""
d = Topology.Dictionary(e)
if d:
try:
e_label = str(Dictionary.ValueAtKey(d, key=edgeLabelKey)) or ""
except:
e_label = ""
try:
e_group = str(Dictionary.ValueAtKey(d, key=edgeGroupKey)) or ""
except:
e_group = ""
try:
e_groupList.append(edgeGroups.index(e_group))
except:
e_groupList.append(len(edgeGroups))
if not e_label == "" and not e_group == "":
e_label = e_label+" ("+e_group+")"
e_labels.append(e_label)
else:
for e in edges:
sv = Edge.StartVertex(e)
ev = Edge.EndVertex(e)
Xe+=[round(Vertex.X(sv), 4), round(Vertex.X(ev), 4), None] # x-coordinates of edge ends
Ye+=[round(Vertex.Y(sv), 4), round(Vertex.Y(ev), 4), None] # y-coordinates of edge ends
Ze+=[round(Vertex.Z(sv), 4), round(Vertex.Z(ev), 4), None] # z-coordinates of edge ends
if len(list(set(e_groupList))) < 2:
e_groupList = edgeColor
if len(e_labels) < 1:
e_labels = ""
e_trace=go.Scatter3d(x=Xe,
y=Ye,
z=Ze,
mode='lines',
name='Graph Edges',
legendgroup=5,
legendrank=5,
showlegend=showEdgeLegend,
line=dict(color=e_groupList, width=edgeWidth),
text=e_labels,
hoverinfo='text'
)
data.append(e_trace)
return data
@staticmethod
def DataByTopology(topology,
showVertices=True, vertexSize=1.1, vertexColor="black",
vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[],
vertexMinGroup=None, vertexMaxGroup=None,
showVertexLegend=False, vertexLegendLabel="Topology Vertices", vertexLegendRank=1,
vertexLegendGroup=1,
showEdges=True, edgeWidth=1, edgeColor="black",
edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[],
edgeMinGroup=None, edgeMaxGroup=None,
showEdgeLegend=False, edgeLegendLabel="Topology Edges", edgeLegendRank=2,
edgeLegendGroup=2,
showFaces=True, faceOpacity=0.5, faceColor="#FAFAFA",
faceLabelKey=None, faceGroupKey=None, faceGroups=[],
faceMinGroup=None, faceMaxGroup=None,
showFaceLegend=False, faceLegendLabel="Topology Faces", faceLegendRank=3,
faceLegendGroup=3,
intensityKey=None, intensities=[], colorScale="viridis", mantissa=6, tolerance=0.0001):
"""
Creates plotly face, edge, and vertex data.
Parameters
----------
topology : topologic_core.Topology
The input topology. This must contain faces and or edges.
showVertices : bool , optional
If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True.
vertexSize : float , optional
The desired size of the vertices. The default is 1.1.
vertexColor : str , optional
The desired color of the output vertices. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is "black".
vertexLabelKey : str , optional
The dictionary key to use to display the vertex label. The default is None.
vertexGroupKey : str , optional
The dictionary key to use to display the vertex group. The default is None.
vertexGroups : list , optional
The list of vertex groups against which to index the color of the vertex. The default is [].
vertexMinGroup : int or float , optional
For numeric vertexGroups, vertexMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the minimum value in vertexGroups. The default is None.
vertexMaxGroup : int or float , optional
For numeric vertexGroups, vertexMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the maximum value in vertexGroups. The default is None.
showVertexLegend : bool, optional
If set to True, the legend for the vertices of this topology is shown. Otherwise, it isn't. The default is False.
vertexLegendLabel : str , optional
The legend label string used to identify vertices. The default is "Topology Vertices".
vertexLegendRank : int , optional
The legend rank order of the vertices of this topology. The default is 1.
vertexLegendGroup : int , optional
The number of the vertex legend group to which the vertices of this topology belong. The default is 1.
showEdges : bool , optional
If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True.
edgeWidth : float , optional
The desired thickness of the output edges. The default is 1.
edgeColor : str , optional
The desired color of the output edges. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is "black".
edgeLabelKey : str , optional
The dictionary key to use to display the edge label. The default is None.
edgeGroupKey : str , optional
The dictionary key to use to display the edge group. The default is None.
edgeGroups : list , optional
The list of edge groups against which to index the color of the edge. The default is [].
edgeMinGroup : int or float , optional
For numeric edgeGroups, edgeMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the minimum value in edgeGroups. The default is None.
edgeMaxGroup : int or float , optional
For numeric edgeGroups, edgeMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the maximum value in edgeGroups. The default is None.
showEdgeLegend : bool, optional
If set to True, the legend for the edges of this topology is shown. Otherwise, it isn't. The default is False.
edgeLegendLabel : str , optional
The legend label string used to identify edges. The default is "Topology Edges".
edgeLegendRank : int , optional
The legend rank order of the edges of this topology. The default is 2.
edgeLegendGroup : int , optional
The number of the edge legend group to which the edges of this topology belong. The default is 2.
showFaces : bool , optional
If set to True the faces will be drawn. Otherwise, they will not be drawn. The default is True.
faceOpacity : float , optional
The desired opacity of the output faces (0=transparent, 1=opaque). The default is 0.5.
faceColor : str , optional
The desired color of the output faces. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is "#FAFAFA".
faceLabelKey : str , optional
The dictionary key to use to display the face label. The default is None.
faceGroupKey : str , optional
The dictionary key to use to display the face group. The default is None.
faceGroups : list , optional
The list of face groups against which to index the color of the face. This can bhave numeric or string values. This should match the type of value associated with the faceGroupKey. The default is [].
faceMinGroup : int or float , optional
For numeric faceGroups, minGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the minimum value in faceGroups. The default is None.
faceMaxGroup : int or float , optional
For numeric faceGroups, maxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the maximum value in faceGroups. The default is None.
showFaceLegend : bool, optional
If set to True, the legend for the faces of this topology is shown. Otherwise, it isn't. The default is False.
faceLegendLabel : str , optional
The legend label string used to idenitfy edges. The default is "Topology Faces".
faceLegendRank : int , optional
The legend rank order of the faces of this topology. The default is 3.
faceLegendGroup : int , optional
The number of the face legend group to which the faces of this topology belong. The default is 3.
intensityKey: str, optional
If not None, the dictionary of each vertex is searched for the value associated with the intensity key. This value is then used to color-code the vertex based on the colorScale. The default is None.
intensities : list , optional
The list of intensities against which to index the intensity of the vertex. The default is [].
colorScale : str , optional
The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/.
mantissa : int , optional
The desired length of the mantissa. The default is 6.
tolerance : float , optional
The desired tolerance. The default is 0.0001.
Returns
-------
list
The vertex, edge, and face data list.
"""
from topologicpy.Topology import Topology
from topologicpy.Dictionary import Dictionary
from topologicpy.Color import Color
from time import time
def closest_index(input_value, values):
return int(min(range(len(values)), key=lambda i: abs(values[i] - input_value)))
def vertexData(vertices, dictionaries=[], color="black", size=1.1, labelKey=None, groupKey=None, minGroup=None, maxGroup=None, groups=[], legendLabel="Topology Vertices", legendGroup=1, legendRank=1, showLegend=True, colorScale="Viridis"):
x = []
y = []
z = []
labels = []
groupList = []
label = ""
group = ""
if labelKey or groupKey:
if groups:
if len(groups) > 0:
if type(groups[0]) == int or type(groups[0]) == float:
if not minGroup:
minGroup = min(groups)
if not maxGroup:
maxGroup = max(groups)
else:
minGroup = 0
maxGroup = len(groups) - 1
else:
minGroup = 0
maxGroup = 1
for m, v in enumerate(vertices):
x.append(round(v[0], mantissa))
y.append(round(v[1], mantissa))
z.append(round(v[2], mantissa))
label = ""
group = ""
if len(dictionaries) > 0:
d = dictionaries[m]
if d:
try:
label = str(Dictionary.ValueAtKey(d, key=labelKey)) or ""
except:
label = ""
try:
group = Dictionary.ValueAtKey(d, key=groupKey) or None
except:
group = ""
try:
if type(group) == int or type(group) == float:
if group < minGroup:
group = minGroup
if group > maxGroup:
group = maxGroup
color = Color.ByValueInRange(group, minValue=minGroup, maxValue=maxGroup, colorScale=colorScale)
else:
color = Color.ByValueInRange(groups.index(group), minValue=minGroup, maxValue=maxGroup, colorScale=colorScale)
color = "rgb("+str(color[0])+","+str(color[1])+","+str(color[2])+")"
groupList.append(color)
except:
groupList.append(len(groups))
labels.append(label)
else:
for v in vertices:
x.append(round(v[0], mantissa))
y.append(round(v[1], mantissa))
z.append(round(v[2], mantissa))
if len(list(set(groupList))) < 2:
groupList = color
if len(labels) < 1:
labels = ""
vData= go.Scatter3d(x=x,
y=y,
z=z,
name=legendLabel,
showlegend=showLegend,
marker=dict(color=groupList, size=vertexSize),
mode='markers',
legendgroup=legendGroup,
legendrank=legendRank,
text=labels,
hoverinfo='text',
hovertext=labels
)
return vData
def edgeData(vertices, edges, dictionaries=None, color="black", width=1, labelKey=None, groupKey=None, minGroup=None, maxGroup=None, groups=[], legendLabel="Topology Edges", legendGroup=2, legendRank=2, showLegend=True, colorScale="Viridis"):
x = []
y = []
z = []
labels = []
groupList = []
label = ""
group = ""
if labelKey or groupKey:
if groups:
if len(groups) > 0:
if type(groups[0]) == int or type(groups[0]) == float:
if not minGroup:
minGroup = min(groups)
if not maxGroup:
maxGroup = max(groups)
else:
minGroup = 0
maxGroup = len(groups) - 1
else:
minGroup = 0
maxGroup = 1
for m, e in enumerate(edges):
sv = vertices[e[0]]
ev = vertices[e[1]]
x+=[round(sv[0], mantissa),round(ev[0], mantissa), None] # x-coordinates of edge ends
y+=[round(sv[1], mantissa),round(ev[1], mantissa), None] # y-coordinates of edge ends
z+=[round(sv[2], mantissa),round(ev[2], mantissa), None] # z-coordinates of edge ends
label = ""
group = ""
if len(dictionaries) > 0:
d = dictionaries[m]
if d:
try:
label = str(Dictionary.ValueAtKey(d, key=labelKey)) or ""
except:
label = ""
try:
group = Dictionary.ValueAtKey(d, key=groupKey) or None
except:
group = ""
try:
if type(group) == int or type(group) == float:
if group < minGroup:
group = minGroup
if group > maxGroup:
group = maxGroup
color = Color.ByValueInRange(group, minValue=minGroup, maxValue=maxGroup, colorScale=colorScale)
else:
color = Color.ByValueInRange(groups.index(group), minValue=minGroup, maxValue=maxGroup, colorScale=colorScale)
color = "rgb("+str(color[0])+","+str(color[1])+","+str(color[2])+")"
groupList.append(color)
except:
groupList.append(len(groups))
labels.append(label)
else:
for e in edges:
sv = vertices[e[0]]
ev = vertices[e[1]]
x+=[round(sv[0],mantissa),round(ev[0],mantissa), None] # x-coordinates of edge ends
y+=[round(sv[1],mantissa),round(ev[1],mantissa), None] # y-coordinates of edge ends
z+=[round(sv[2],mantissa),round(ev[2],mantissa), None] # z-coordinates of edge ends
if len(list(set(groupList))) < 2:
groupList = color
if len(labels) < 1:
labels = ""
eData = go.Scatter3d(x=x,
y=y,
z=z,
name=legendLabel,
showlegend=showLegend,
marker_size=0,
mode="lines",
line=dict(color=groupList, width=edgeWidth),
legendgroup=legendGroup,
legendrank=legendRank,
text=labels,
hoverinfo='text')
return eData
def faceData(vertices, faces, dictionaries=None, color="#FAFAFA",
opacity=0.5, labelKey=None, groupKey=None,
minGroup=None, maxGroup=None, groups=[], legendLabel="Topology Faces",
legendGroup=3, legendRank=3, showLegend=True, intensities=None, colorScale="viridis"):
x = []
y = []
z = []
for v in vertices:
x.append(round(v[0], mantissa))
y.append(round(v[1], mantissa))
z.append(round(v[2], mantissa))
i = []
j = []
k = []
labels = []
groupList = []
label = ""
group = ""
if labelKey or groupKey:
if groups:
if len(groups) > 0:
if type(groups[0]) == int or type(groups[0]) == float:
if not minGroup:
minGroup = min(groups)
if not maxGroup:
maxGroup = max(groups)
else:
minGroup = 0
maxGroup = len(groups) - 1
else:
minGroup = 0
maxGroup = 1
for m, f in enumerate(faces):
i.append(f[0])
j.append(f[1])
k.append(f[2])
label = ""
group = ""
if len(dictionaries) > 0:
d = dictionaries[m]
if d:
try:
label = str(Dictionary.ValueAtKey(d, key=labelKey)) or ""
except:
label = ""
try:
group = Dictionary.ValueAtKey(d, key=groupKey) or None
except:
group = ""
try:
if type(group) == int or type(group) == float:
if group < minGroup:
group = minGroup
if group > maxGroup:
group = maxGroup
color = Color.ByValueInRange(group, minValue=minGroup, maxValue=maxGroup, colorScale=colorScale)
else:
color = Color.ByValueInRange(groups.index(group), minValue=minGroup, maxValue=maxGroup, colorScale=colorScale)
color = "rgb("+str(color[0])+","+str(color[1])+","+str(color[2])+")"
groupList.append(color)
except:
groupList.append(len(groups))
labels.append(label)
else:
for f in faces:
i.append(f[0])
j.append(f[1])
k.append(f[2])
if len(list(set(groupList))) < 2:
groupList = None
if len(labels) < 1:
labels = ""
fData = go.Mesh3d(
x = x,
y = y,
z = z,
i = i,
j = j,
k = k,
name = legendLabel,
showlegend = showLegend,
legendgroup = legendGroup,
legendrank = legendRank,
color = color,
facecolor = groupList,
colorscale = Plotly.ColorScale(colorScale),
cmin = 0,
cmax = 1,
intensity = intensities,
opacity = opacity,
hoverinfo = 'text',
text = labels,
hovertext = labels,
flatshading = True,
showscale = False,
lighting = {"facenormalsepsilon": 0},
)
return fData
from topologicpy.Face import Face
from topologicpy.Cluster import Cluster
from topologicpy.Topology import Topology
from topologicpy.Dictionary import Dictionary
from time import time
if not Topology.IsInstance(topology, "Topology"):
return None
intensityList = []
alt_intensities = []
data = []
v_list = []
if Topology.Type(topology) == Topology.TypeID("Vertex"):
tp_vertices = [topology]
else:
tp_vertices = Topology.Vertices(topology)
if isinstance(intensities, list):
if len(intensities) == 0:
intensities = None
if not (tp_vertices == None or tp_vertices == []):
vertices = []
v_dictionaries = []
intensityList = []
if intensityKey:
for i, tp_v in enumerate(tp_vertices):
vertices.append([tp_v.X(), tp_v.Y(), tp_v.Z()])
d = Topology.Dictionary(tp_v)
if d:
v = Dictionary.ValueAtKey(d, key=intensityKey)
if not v == None:
alt_intensities.append(v)
v_list.append(v)
else:
alt_intensities.append(0)
v_list.append(0)
else:
alt_intensities.append(0)
v_list.append(0)
alt_intensities = list(set(alt_intensities))
alt_intensities.sort()
if isinstance(intensities, list):
if len(intensities) > 0:
alt_intensities = intensities
min_i = min(alt_intensities)
max_i = max(alt_intensities)
for i, tp_v in enumerate(tp_vertices):
v = v_list[i]
ci = closest_index(v_list[i], alt_intensities)
value = intensities[ci]
if (max_i - min_i) == 0:
value = 0
else:
value = (value - min_i)/(max_i - min_i)
intensityList.append(value)
if all(x == 0 for x in intensityList):
intensityList = None
if showVertices:
if len(vertices) == 0:
for i, tp_v in enumerate(tp_vertices):
if vertexLabelKey or vertexGroupKey:
d = Topology.Dictionary(tp_v)
v_dictionaries.append(d)
vertices.append([tp_v.X(), tp_v.Y(), tp_v.Z()])
data.append(vertexData(vertices, dictionaries=v_dictionaries, color=vertexColor, size=vertexSize, labelKey=vertexLabelKey, groupKey=vertexGroupKey, minGroup=vertexMinGroup, maxGroup=vertexMaxGroup, groups=vertexGroups, legendLabel=vertexLegendLabel, legendGroup=vertexLegendGroup, legendRank=vertexLegendRank, showLegend=showVertexLegend, colorScale=colorScale))
if showEdges and Topology.Type(topology) > Topology.TypeID("Vertex"):
if Topology.Type(topology) == Topology.TypeID("Edge"):
tp_edges = [topology]
else:
tp_edges = Topology.Edges(topology)
if not (tp_edges == None or tp_edges == []):
e_dictionaries = []
if edgeLabelKey or edgeGroupKey:
for tp_edge in tp_edges:
e_dictionaries.append(Topology.Dictionary(tp_edge))
e_cluster = Cluster.ByTopologies(tp_edges)
geo = Topology.Geometry(e_cluster, mantissa=mantissa)
vertices = geo['vertices']
edges = geo['edges']
data.append(edgeData(vertices, edges, dictionaries=e_dictionaries, color=edgeColor, width=edgeWidth, labelKey=edgeLabelKey, groupKey=edgeGroupKey, minGroup=edgeMinGroup, maxGroup=edgeMaxGroup, groups=edgeGroups, legendLabel=edgeLegendLabel, legendGroup=edgeLegendGroup, legendRank=edgeLegendRank, showLegend=showEdgeLegend, colorScale=colorScale))
if showFaces and Topology.Type(topology) >= Topology.TypeID("Face"):
if Topology.IsInstance(topology, "Face"):
tp_faces = [topology]
else:
tp_faces = Topology.Faces(topology)
if not(tp_faces == None or tp_faces == []):
# rebuild faces to remove any degenerate faces
#new_faces = []
#for i, f in enumerate(tp_faces):
#eb = Face.ExternalBoundary(f)
#eb = Topology.RemoveCollinearEdges(eb)
#if not eb == None:
#ibList = Face.InternalBoundaries(f)
#ibList = [Wire.RemoveCollinearEdges(ib) for ib in ibList]
#ibList = [ib for ib in ibList if not ib == None]
#new_f = Face.ByWires(eb, ibList, silent=False)
#if Topology.IsInstance(new_f, "Face"):
#if faceLabelKey or faceGroupKey:
#d = Topology.Dictionary(tp_faces[i])
#keys = Dictionary.Keys(d)
#if len(keys) > 0:
#new_f = Topology.SetDictionary(new_f, d)
#new_faces.append(new_f)
f_dictionaries = []
all_triangles = []
for tp_face in tp_faces:
triangles = Face.Triangulate(tp_face, tolerance=tolerance)
if isinstance(triangles, list):
for tri in triangles:
if faceLabelKey or faceGroupKey:
d = Topology.Dictionary(tp_face)
f_dictionaries.append(d)
if d:
_ = Topology.SetDictionary(tri, d)
all_triangles.append(tri)
if len(all_triangles) > 0:
f_cluster = Cluster.ByTopologies(all_triangles)
geo = Topology.Geometry(f_cluster, mantissa=mantissa)
vertices = geo['vertices']
faces = geo['faces']
data.append(faceData(vertices, faces, dictionaries=f_dictionaries, color=faceColor, opacity=faceOpacity, labelKey=faceLabelKey, groupKey=faceGroupKey, minGroup=faceMinGroup, maxGroup=faceMaxGroup, groups=faceGroups, legendLabel=faceLegendLabel, legendGroup=faceLegendGroup, legendRank=faceLegendRank, showLegend=showFaceLegend, intensities=intensityList, colorScale=colorScale))
return data
@staticmethod
def FigureByConfusionMatrix(matrix,
categories=[],
minValue=None,
maxValue=None,
title="Confusion Matrix",
xTitle = "Actual",
yTitle = "Predicted",
width=950,
height=500,
showScale = True,
colorScale='viridis',
colorSamples=10,
backgroundColor='rgba(0,0,0,0)',
marginLeft=0,
marginRight=0,
marginTop=40,
marginBottom=0):
"""
Returns a Plotly Figure of the input confusion matrix. Actual categories are displayed on the X-Axis, Predicted categories are displayed on the Y-Axis.
Parameters
----------
matrix : list or numpy.array
The matrix to display.
categories : list
The list of categories to use on the X and Y axes.
minValue : float , optional
The desired minimum value to use for the color scale. If set to None, the minmum value found in the input matrix will be used. The default is None.
maxValue : float , optional
The desired maximum value to use for the color scale. If set to None, the maximum value found in the input matrix will be used. The default is None.
title : str , optional
The desired title to display. The default is "Confusion Matrix".
xTitle : str , optional
The desired X-axis title to display. The default is "Actual".
yTitle : str , optional
The desired Y-axis title to display. The default is "Predicted".
width : int , optional
The desired width of the figure. The default is 950.
height : int , optional
The desired height of the figure. The default is 500.
showScale : bool , optional
If set to True, a color scale is shown on the right side of the figure. The default is True.
colorScale : str , optional
The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/.
colorSamples : int , optional
The number of discrete color samples to use for displaying the data. The default is 10.
backgroundColor : str , optional
The desired background color. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is 'rgba(0,0,0,0)' (transparent).
marginLeft : int , optional
The desired left margin in pixels. The default is 0.
marginRight : int , optional
The desired right margin in pixels. The default is 0.
marginTop : int , optional
The desired top margin in pixels. The default is 40.
marginBottom : int , optional
The desired bottom margin in pixels. The default is 0.
"""
try:
import numpy as np
except:
print("Plotly.FigureByConfusionMatrix - Installing required numpy library.")
try:
os.system("pip install numpy")
except:
os.system("pip install numpy --user")
try:
import numpy as np
except:
warnings.warn("Plotly.FigureByConfusionMatrix - Error: Could not import numpy. Please install numpy manually. Returning None.")
return None
if not isinstance(matrix, list) and not isinstance(matrix, np.ndarray):
print("Plotly.FigureByConfusionMatrix - Error: The input matrix is not of the correct type. Returning None.")
return None
figure = Plotly.FigureByMatrix(matrix,
xCategories=categories,
minValue=minValue,
maxValue=maxValue,
title=title,
xTitle=xTitle,
yTitle=yTitle,
width=width,
height=height,
showScale=showScale,
colorScale=Plotly.ColorScale(colorScale),
colorSamples=colorSamples,
backgroundColor=backgroundColor,
marginLeft=marginLeft,
marginRight=marginRight,
marginTop=marginTop,
marginBottom=marginBottom)
layout = {
"yaxis": {"autorange": "reversed"},
}
figure.update_layout(layout)
return figure
@staticmethod
def FigureByMatrix(matrix,
xCategories=[],
yCategories=[],
minValue=None,
maxValue=None,
title="Matrix",
xTitle = "X Axis",
yTitle = "Y Axis",
width=950,
height=950,
showScale = False,
colorScale='gray',
colorSamples=10,
backgroundColor='rgba(0,0,0,0)',
marginLeft=0,
marginRight=0,
marginTop=40,
marginBottom=0,
mantissa: int = 6):
"""
Returns a Plotly Figure of the input matrix.
Parameters
----------
matrix : list or numpy.array
The matrix to display.
categories : list
The list of categories to use on the X and Y axes.
minValue : float , optional
The desired minimum value to use for the color scale. If set to None, the minmum value found in the input matrix will be used. The default is None.
maxValue : float , optional
The desired maximum value to use for the color scale. If set to None, the maximum value found in the input matrix will be used. The default is None.
title : str , optional
The desired title to display. The default is "Confusion Matrix".
xTitle : str , optional
The desired X-axis title to display. The default is "Actual".
yTitle : str , optional
The desired Y-axis title to display. The default is "Predicted".
width : int , optional
The desired width of the figure. The default is 950.
height : int , optional
The desired height of the figure. The default is 500.
showScale : bool , optional
If set to True, a color scale is shown on the right side of the figure. The default is True.
colorScale : str , optional
The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/.
colorSamples : int , optional
The number of discrete color samples to use for displaying the data. The default is 10.
backgroundColor : str , optional
The desired background color. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is 'rgba(0,0,0,0)' (transparent).
marginLeft : int , optional
The desired left margin in pixels. The default is 0.
marginRight : int , optional
The desired right margin in pixels. The default is 0.
marginTop : int , optional
The desired top margin in pixels. The default is 40.
marginBottom : int , optional
The desired bottom margin in pixels. The default is 0.
mantissa : int , optional
The desired number of digits of the mantissa. The default is 6.
"""
#import plotly.figure_factory as ff
import plotly.graph_objects as go
import plotly.express as px
try:
import numpy as np
except:
print("Plotly.FigureByMatrix - Installing required numpy library.")
try:
os.system("pip install numpy")
except:
os.system("pip install numpy --user")
try:
import numpy as np
except:
warnings.warn("Plotly.FigureByMatrix - Error: Could not import numpy. Please install numpy manually. Returning None.")
return None
if not isinstance(matrix, list) and not isinstance(matrix, np.ndarray):
print("Plotly.FigureByMatrix - Error: The input matrix is not of the correct type. Returning None.")
return None
annotations = []
if isinstance(matrix, list):
matrix = np.array(matrix)
colors = px.colors.sample_colorscale(Plotly.ColorScale(colorScale), [n/(colorSamples -1) for n in range(colorSamples)])
if not xCategories:
xCategories = [x for x in range(len(matrix[0]))]
if not yCategories:
yCategories = [y for y in range(len(matrix))]
if not maxValue or not minValue:
max_values = []
min_values = []
for i in range(len(matrix)):
row = matrix[i]
max_values.append(max(row))
min_values.append(min(row))
for j, value in enumerate(row):
annotations.append(
{
"x": xCategories[j],
"y": yCategories[i],
"font": {"color": "black"},
"bgcolor": "white",
"opacity": 0.5,
"text": str(round(value, mantissa)),
"xref": "x1",
"yref": "y1",
"showarrow": False
}
)
if not minValue:
minValueB = min(min_values)
if not maxValue:
maxValue = max(max_values)
else:
for i in range(len(matrix)):
row = matrix[i]
for j, value in enumerate(row):
annotations.append(
{
"x": xCategories[j],
"y": yCategories[i],
"font": {"color": "black"},
"bgcolor": "white",
"opacity": 0.5,
"text": str(round(value,mantissa)),
"xref": "x1",
"yref": "y1",
"showarrow": False
}
)
new_matrix = []
for i in range(len(matrix)):
row = matrix[i]
new_row = []
maxRow = sum(row)
for j in range(len(row)):
if maxRow == 0:
new_row.append(round(0, mantissa))
else:
new_row.append(round(float(row[j])/float(maxRow), mantissa))
new_matrix.append(new_row)
data = go.Heatmap(z=new_matrix, y=yCategories, x=xCategories, zmin=minValue, zmax=maxValue, showscale=showScale, colorscale=colors)
layout = {
"width": width,
"height": height,
"title": title,
"xaxis": {"title": xTitle},
"yaxis": {"title": yTitle, "autorange": "reversed"},
"annotations": annotations,
"paper_bgcolor": backgroundColor,
"plot_bgcolor": backgroundColor,
"margin":dict(l=marginLeft, r=marginRight, t=marginTop, b=marginBottom)
}
fig = go.Figure(data=data, layout=layout)
fig.update_xaxes( tickvals=xCategories)
fig.update_yaxes( tickvals=yCategories)
return fig
@staticmethod
def FigureByDataFrame(dataFrame,
labels=[],
width=950,
height=500,
title="Untitled",
xTitle="X Axis",
xSpacing=1,
yTitle="Y Axis",
ySpacing=1.0,
useMarkers=False,
chartType="Line",
backgroundColor='rgba(0,0,0,0)',
gridColor = 'lightgray',
marginLeft=0,
marginRight=0,
marginTop=40,
marginBottom=0):
"""
Returns a Plotly Figure of the input dataframe
Parameters
----------
df : pandas.df
The pandas dataframe to display.
data_labels : list
The labels to use for the data.
width : int , optional
The desired width of the figure. The default is 950.
height : int , optional
The desired height of the figure. The default is 500.
title : str , optional
The chart title. The default is "Training and Testing Results".
xTitle : str , optional
The X-axis title. The default is "Epochs".
xSpacing : float , optional
The X-axis spacing. The default is 1.0.
yTitle : str , optional
The Y-axis title. The default is "Accuracy and Loss".
ySpacing : float , optional
The Y-axis spacing. The default is 0.1.
useMarkers : bool , optional
If set to True, markers will be displayed. The default is False.
chartType : str , optional
The desired type of chart. The options are "Line", "Bar", or "Scatter". It is case insensitive. The default is "Line".
backgroundColor : str , optional
The desired background color. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is 'rgba(0,0,0,0)' (transparent).
grid : str , optional
The desired background color. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is 'lightgray'
marginLeft : int , optional
The desired left margin in pixels. The default is 0.
marginRight : int , optional
The desired right margin in pixels. The default is 0.
marginTop : int , optional
The desired top margin in pixels. The default is 40.
marginBottom : int , optional
The desired bottom margin in pixels. The default is 0.
Returns
-------
None.
"""
import plotly.express as px
if chartType.lower() == "line":
figure = px.line(dataFrame, x=labels[0], y=labels[1:], title=title, markers=useMarkers)
elif chartType.lower() == "bar":
figure = px.bar(dataFrame, x=labels[0], y=labels[1:], title=title)
elif chartType.lower() == "scatter":
figure = px.scatter(dataFrame, x=labels[0], y=labels[1:], title=title)
else:
raise NotImplementedError
layout = {
"width": width,
"height": height,
"title": title,
"xaxis": {"title": xTitle, "dtick": xSpacing, 'gridcolor': gridColor},
"yaxis": {"title": yTitle, "dtick": ySpacing, 'gridcolor': gridColor},
"paper_bgcolor": backgroundColor,
"plot_bgcolor": backgroundColor,
"margin":dict(l=marginLeft, r=marginRight, t=marginTop, b=marginBottom)
}
figure.update_layout(layout)
return figure
@staticmethod
def FigureByData(data, width=950, height=500,
xAxis=False, yAxis=False, zAxis=False,
axisSize=1, backgroundColor='rgba(0,0,0,0)',
marginLeft=0, marginRight=0,
marginTop=20, marginBottom=0,
tolerance = 0.0001):
"""
Creates a plotly figure.
Parameters
----------
data : list
The input list of plotly data.
width : int , optional
The width in pixels of the figure. The default value is 950.
height : int , optional
The height in pixels of the figure. The default value is 950.
xAxis : bool , optional
If set to True the x axis is drawn. Otherwise it is not drawn. The default is False.
yAxis : bool , optional
If set to True the y axis is drawn. Otherwise it is not drawn. The default is False.
zAxis : bool , optional
If set to True the z axis is drawn. Otherwise it is not drawn. The default is False.
axisSize : float , optional
The size of the X, Y, Z, axes. The default is 1.
backgroundColor : str , optional
The desired color of the background. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is "rgba(0,0,0,0)".
marginLeft : int , optional
The size in pixels of the left margin. The default value is 0.
marginRight : int , optional
The size in pixels of the right margin. The default value is 0.
marginTop : int , optional
The size in pixels of the top margin. The default value is 20.
marginBottom : int , optional
The size in pixels of the bottom margin. The default value is 0.
tolerance : float , optional
The desired tolerance. The default is 0.0001.
Returns
-------
plotly.graph_objs._figure.Figure
The created plotly figure.
"""
from topologicpy.Vertex import Vertex
from topologicpy.Edge import Edge
from topologicpy.Wire import Wire
if not isinstance(data, list):
return None
v0 = Vertex.ByCoordinates(0, 0, 0)
v1 = Vertex.ByCoordinates(axisSize,0,0)
v2 = Vertex.ByCoordinates(0,axisSize,0)
v3 = Vertex.ByCoordinates(0,0,axisSize)
if xAxis:
xEdge = Edge.ByVertices([v0,v1], tolerance=tolerance)
xData = Plotly.DataByTopology(xEdge, edgeColor="red", edgeWidth=6, showFaces=False, showEdges=True, showVertices=False, edgeLegendLabel="X-Axis")
data = data + xData
if yAxis:
yEdge = Edge.ByVertices([v0,v2], tolerance=tolerance)
yData = Plotly.DataByTopology(yEdge, edgeColor="green", edgeWidth=6, showFaces=False, showEdges=True, showVertices=False, edgeLegendLabel="Y-Axis")
data = data + yData
if zAxis:
zEdge = Edge.ByVertices([v0,v3], tolerance=tolerance)
zData = Plotly.DataByTopology(zEdge, edgeColor="blue", edgeWidth=6, showFaces=False, showEdges=True, showVertices=False, edgeLegendLabel="Z-Axis")
data = data + zData
figure = go.Figure(data=data)
figure.update_layout(
width=width,
height=height,
showlegend=True,
scene = dict(
xaxis = dict(visible=False),
yaxis = dict(visible=False),
zaxis =dict(visible=False),
),
scene_aspectmode='data',
paper_bgcolor=backgroundColor,
plot_bgcolor=backgroundColor,
margin=dict(l=marginLeft, r=marginRight, t=marginTop, b=marginBottom),
)
figure.update_xaxes(showgrid=False, zeroline=False, visible=False)
figure.update_yaxes(showgrid=False, zeroline=False, visible=False)
return figure
@staticmethod
def FigureByJSONFile(file):
"""
Imports a plotly figure from a JSON file.
Parameters
----------
file : file object
The JSON file.
Returns
-------
plotly.graph_objs._figure.Figure
The imported figure.
"""
figure = None
if not file:
return None
figure = plotly.io.read_json(file, output_type='Figure', skip_invalid=True, engine=None)
file.close()
return figure
@staticmethod
def FigureByJSONPath(path):
"""
Imports a plotly figure from a JSON file path.
Parameters
----------
path : str
The path to the BRep file.
Returns
-------
plotly.graph_objs._figure.Figure
The imported figure.
"""
if not path:
return None
try:
file = open(path)
except:
print("Plotly.FigureByJSONPath - Error: the JSON file is not a valid file. Returning None.")
return None
return Plotly.FigureByJSONFile(file)
@staticmethod
def FigureByPieChart(data, values, names):
"""
Creates a plotly pie chart figure.
Parameters
----------
data : list
The input list of plotly data.
values : list
The input list of values.
names : list
The input list of names.
"""
import plotly.express as px
try:
import pandas as pd
except:
print("Plotly.FigureByPieChart - Installing required pandas library.")
try:
os.system("pip install pandas")
except:
os.system("pip install pandas --user")
try:
import pandas as pd
except:
warnings.warn("Plotly.FigureByPieChart - Error: Could not import pandas. Please install the pandas library manually. Returning None")
return None
dlist = list(map(list, zip(*data)))
df = pd.DataFrame(dlist, columns=data['names'])
fig = px.pie(df, values=values, names=names)
return fig
@staticmethod
def FigureByTopology(topology,
showVertices=True, vertexSize=1.1, vertexColor="black",
vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[],
vertexMinGroup=None, vertexMaxGroup=None,
showVertexLegend=False, vertexLegendLabel="Topology Vertices", vertexLegendRank=1,
vertexLegendGroup=1,
showEdges=True, edgeWidth=1, edgeColor="black",
edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[],
edgeMinGroup=None, edgeMaxGroup=None,
showEdgeLegend=False, edgeLegendLabel="Topology Edges", edgeLegendRank=2,
edgeLegendGroup=2,
showFaces=True, faceOpacity=0.5, faceColor="#FAFAFA",
faceLabelKey=None, faceGroupKey=None, faceGroups=[],
faceMinGroup=None, faceMaxGroup=None,
showFaceLegend=False, faceLegendLabel="Topology Faces", faceLegendRank=3,
faceLegendGroup=3,
intensityKey=None,
width=950, height=500,
xAxis=False, yAxis=False, zAxis=False, axisSize=1, backgroundColor='rgba(0,0,0,0)',
marginLeft=0, marginRight=0, marginTop=20, marginBottom=0, showScale=False,
cbValues=[], cbTicks=5, cbX=-0.15, cbWidth=15, cbOutlineWidth=0, cbTitle="",
cbSubTitle="", cbUnits="", colorScale="viridis", mantissa=6, tolerance=0.0001):
"""
Creates a figure from the input topology.
Parameters
----------
topology : topologic_core.Topology
The input topology. This must contain faces and or edges.
showVertices : bool , optional
If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True.
vertexSize : float , optional
The desired size of the vertices. The default is 1.1.
vertexColor : str , optional
The desired color of the output vertices. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is "black".
vertexLabelKey : str , optional
The dictionary key to use to display the vertex label. The default is None.
vertexGroupKey : str , optional
The dictionary key to use to display the vertex group. The default is None.
vertexGroups : list , optional
The list of vertex groups against which to index the color of the vertex. The default is [].
vertexMinGroup : int or float , optional
For numeric vertexGroups, vertexMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the minimum value in vertexGroups. The default is None.
edgeMaxGroup : int or float , optional
For numeric vertexGroups, vertexMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the maximum value in vertexGroups. The default is None.
showVertexLegend : bool, optional
If set to True, the legend for the vertices of this topology is shown. Otherwise, it isn't. The default is False.
vertexLegendLabel : str , optional
The legend label string used to identify vertices. The default is "Topology Vertices".
vertexLegendRank : int , optional
The legend rank order of the vertices of this topology. The default is 1.
vertexLegendGroup : int , optional
The number of the vertex legend group to which the vertices of this topology belong. The default is 1.
showEdges : bool , optional
If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True.
edgeWidth : float , optional
The desired thickness of the output edges. The default is 1.
edgeColor : str , optional
The desired color of the output edges. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is "black".
edgeLabelKey : str , optional
The dictionary key to use to display the edge label. The default is None.
edgeGroupKey : str , optional
The dictionary key to use to display the edge group. The default is None.
edgeGroups : list , optional
The list of edge groups against which to index the color of the edge. The default is [].
edgeMinGroup : int or float , optional
For numeric edgeGroups, edgeMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the minimum value in edgeGroups. The default is None.
edgeMaxGroup : int or float , optional
For numeric edgeGroups, edgeMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the maximum value in edgeGroups. The default is None.
showEdgeLegend : bool, optional
If set to True, the legend for the edges of this topology is shown. Otherwise, it isn't. The default is False.
edgeLegendLabel : str , optional
The legend label string used to identify edges. The default is "Topology Edges".
edgeLegendRank : int , optional
The legend rank order of the edges of this topology. The default is 2.
edgeLegendGroup : int , optional
The number of the edge legend group to which the edges of this topology belong. The default is 2.
showFaces : bool , optional
If set to True the faces will be drawn. Otherwise, they will not be drawn. The default is True.
faceOpacity : float , optional
The desired opacity of the output faces (0=transparent, 1=opaque). The default is 0.5.
faceColor : str , optional
The desired color of the output faces. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is "#FAFAFA".
faceLabelKey : str , optional
The dictionary key to use to display the face label. The default is None.
faceGroupKey : str , optional
The dictionary key to use to display the face group. The default is None.
faceGroups : list , optional
The list of face groups against which to index the color of the face. This can bhave numeric or string values. This should match the type of value associated with the faceGroupKey. The default is [].
faceMinGroup : int or float , optional
For numeric faceGroups, minGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the minimum value in faceGroups. The default is None.
faceMaxGroup : int or float , optional
For numeric faceGroups, maxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the maximum value in faceGroups. The default is None.
showFaceLegend : bool, optional
If set to True, the legend for the faces of this topology is shown. Otherwise, it isn't. The default is False.
faceLegendLabel : str , optional
The legend label string used to idenitfy edges. The default is "Topology Faces".
faceLegendRank : int , optional
The legend rank order of the faces of this topology. The default is 3.
faceLegendGroup : int , optional
The number of the face legend group to which the faces of this topology belong. The default is 3.
width : int , optional
The width in pixels of the figure. The default value is 950.
height : int , optional
The height in pixels of the figure. The default value is 950.
xAxis : bool , optional
If set to True the x axis is drawn. Otherwise it is not drawn. The default is False.
yAxis : bool , optional
If set to True the y axis is drawn. Otherwise it is not drawn. The default is False.
zAxis : bool , optional
If set to True the z axis is drawn. Otherwise it is not drawn. The default is False.
backgroundColor : str , optional
The desired color of the background. This can be any plotly color string and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color.
The default is "rgba(0,0,0,0)".
marginLeft : int , optional
The size in pixels of the left margin. The default value is 0.
marginRight : int , optional
The size in pixels of the right margin. The default value is 0.
marginTop : int , optional
The size in pixels of the top margin. The default value is 20.
marginBottom : int , optional
The size in pixels of the bottom margin. The default value is 0.
camera : list , optional
The desired location of the camera). The default is [-1.25, -1.25, 1.25].
center : list , optional
The desired center (camera target). The default is [0, 0, 0].
up : list , optional
The desired up vector. The default is [0, 0, 1].
renderer : str , optional
The desired renderer. See Plotly.Renderers(). The default is "notebook".
intensityKey : str , optional
If not None, the dictionary of each vertex is searched for the value associated with the intensity key. This value is then used to color-code the vertex based on the colorScale. The default is None.
showScale : bool , optional
If set to True, the colorbar is shown. The default is False.
cbValues : list , optional
The input list of values to use for the colorbar. The default is [].
cbTicks : int , optional
The number of ticks to use on the colorbar. The default is 5.
cbX : float , optional
The x location of the colorbar. The default is -0.15.
cbWidth : int , optional
The width in pixels of the colorbar. The default is 15
cbOutlineWidth : int , optional
The width in pixels of the outline of the colorbar. The default is 0.
cbTitle : str , optional
The title of the colorbar. The default is "".
cbSubTitle : str , optional
The subtitle of the colorbar. The default is "".
cbUnits: str , optional
The units used in the colorbar. The default is ""
colorScale : str , optional
The desired type of plotly color scales to use (e.g. "viridis", "plasma"). The default is "viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/.
mantissa : int , optional
The desired length of the mantissa for the values listed on the colorbar. The default is 6.
tolerance : float , optional
The desired tolerance. The default is 0.0001.
Returns
-------
Plotly figure
"""
from topologicpy.Topology import Topology
if not Topology.IsInstance(topology, "Topology"):
print("Plotly.FigureByTopology - Error: the input topology is not a valid topology. Returning None.")
return None
data = Plotly.DataByTopology(topology=topology,
showVertices=showVertices, vertexSize=vertexSize, vertexColor=vertexColor,
vertexLabelKey=vertexLabelKey, vertexGroupKey=vertexGroupKey, vertexGroups=vertexGroups,
vertexMinGroup=vertexMinGroup, vertexMaxGroup=vertexMaxGroup,
showVertexLegend=showVertexLegend, vertexLegendLabel=vertexLegendLabel, vertexLegendRank=vertexLegendRank,
vertexLegendGroup=vertexLegendGroup,
showEdges=showEdges, edgeWidth=edgeWidth, edgeColor=edgeColor,
edgeLabelKey=edgeLabelKey, edgeGroupKey=edgeGroupKey, edgeGroups=edgeGroups,
edgeMinGroup=edgeMinGroup, edgeMaxGroup=edgeMaxGroup,
showEdgeLegend=showEdgeLegend, edgeLegendLabel=edgeLegendLabel, edgeLegendRank=edgeLegendRank,
edgeLegendGroup=edgeLegendGroup,
showFaces=showFaces, faceOpacity=faceOpacity, faceColor=faceColor,
faceLabelKey=faceLabelKey, faceGroupKey=faceGroupKey, faceGroups=faceGroups,
faceMinGroup=faceMinGroup, faceMaxGroup=faceMaxGroup,
showFaceLegend=showFaceLegend, faceLegendLabel=faceLegendLabel, faceLegendRank=faceLegendRank,
faceLegendGroup=faceLegendGroup,
intensityKey=intensityKey, colorScale=colorScale, tolerance=tolerance)
figure = Plotly.FigureByData(data=data, width=width, height=height,
xAxis=xAxis, yAxis=yAxis, zAxis=zAxis, axisSize=axisSize,
backgroundColor=backgroundColor,
marginLeft=marginLeft, marginRight=marginRight,
marginTop=marginTop, marginBottom=marginBottom,
tolerance=tolerance)
if showScale:
figure = Plotly.AddColorBar(figure, values=cbValues, nTicks=cbTicks, xPosition=cbX, width=cbWidth, outlineWidth=cbOutlineWidth, title=cbTitle, subTitle=cbSubTitle, units=cbUnits, colorScale=colorScale, mantissa=mantissa)
return figure
@staticmethod
def FigureExportToJSON(figure, path, overwrite=False):
"""
Exports the input plotly figure to a JSON file.
Parameters
----------
figure : plotly.graph_objs._figure.Figure
The input plotly figure.
path : str
The input file path.
overwrite : bool , optional
If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't.
Returns
-------
bool
True if the export operation is successful. False otherwise.
"""
if not isinstance(figure, plotly.graph_objs._figure.Figure):
print("Plotly.FigureExportToJSON - Error: The input figure is not a plolty figure. Returning None.")
return None
if not isinstance(path, str):
print("Plotly.FigureExportToJSON - Error: The input path is not a string. Returning None.")
return None
# Make sure the file extension is .json
ext = path[len(path)-5:len(path)]
if ext.lower() != ".json":
path = path+".json"
f = None
try:
if overwrite == True:
f = open(path, "w")
else:
f = open(path, "x") # Try to create a new File
except:
print("Plotly.FigureExportToJSON - Error: Could not create a new file at the following location: "+path+". Returning None.")
return None
if (f):
plotly.io.write_json(figure, f, validate=True, pretty=False, remove_uids=True, engine=None)
f.close()
return True
if f:
try:
f.close()
except:
pass
return False
@staticmethod
def FigureExportToPDF(figure, path, width=1920, height=1200, overwrite=False):
"""
Exports the input plotly figure to a PDF file.
Parameters
----------
figure : plotly.graph_objs._figure.Figure
The input plotly figure.
path : str
The input file path.
width : int, optional
The width of the exported image in pixels. The default is 1920.
height : int , optional
The height of the exported image in pixels. The default is 1200.
overwrite : bool , optional
If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't.
Returns
-------
bool
True if the export operation is successful. False otherwise.
"""
import os
if not isinstance(figure, plotly.graph_objs._figure.Figure):
print("Plotly.FigureExportToPNG - Error: The input figure is not a plolty figure. Returning None.")
return None
if not isinstance(path, str):
print("Plotly.FigureExportToPNG - Error: The input path is not a string. Returning None.")
return None
# Make sure the file extension is .pdf
ext = path[len(path)-4:len(path)]
if ext.lower() != ".pdf":
path = path+".pdf"
if overwrite == False and os.path.exists(path):
print("Plotly.FigureExportToPDF - Error: A file already exists at this location and overwrite is set to False. Returning None.")
return None
plotly.io.write_image(figure, path, format='pdf', scale=1, width=width, height=height, validate=True, engine='auto')
return True
@staticmethod
def FigureExportToPNG(figure, path, width=1920, height=1200, overwrite=False):
"""
Exports the input plotly figure to a PNG file.
Parameters
----------
figure : plotly.graph_objs._figure.Figure
The input plotly figure.
path : str
The input file path.
width : int, optional
The width of the exported image in pixels. The default is 1920.
height : int , optional
The height of the exported image in pixels. The default is 1200.
overwrite : bool , optional
If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't.
Returns
-------
bool
True if the export operation is successful. False otherwise.
"""
import os
if not isinstance(figure, plotly.graph_objs._figure.Figure):
print("Plotly.FigureExportToPNG - Error: The input figure is not a plolty figure. Returning None.")
return None
if not isinstance(path, str):
print("Plotly.FigureExportToPNG - Error: The input path is not a string. Returning None.")
return None
# Make sure the file extension is .png
ext = path[len(path)-4:len(path)]
if ext.lower() != ".png":
path = path+".png"
if overwrite == False and os.path.exists(path):
print("Plotly.FigureExportToPNG - Error: A file already exists at this location and overwrite is set to False. Returning None.")
return None
plotly.io.write_image(figure, path, format='png', scale=1, width=width, height=height, validate=True, engine='auto')
return True
@staticmethod
def FigureExportToSVG(figure, path, width=1920, height=1200, overwrite=False):
"""
Exports the input plotly figure to a SVG file.
Parameters
----------
figure : plotly.graph_objs._figure.Figure
The input plotly figure.
path : str
The input file path.
width : int, optional
The width of the exported image in pixels. The default is 1920.
height : int , optional
The height of the exported image in pixels. The default is 1200.
overwrite : bool , optional
If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't.
Returns
-------
bool
True if the export operation is successful. False otherwise.
"""
import os
if not isinstance(figure, plotly.graph_objs._figure.Figure):
print("Plotly.FigureExportToSVG - Error: The input figure is not a plolty figure. Returning None.")
return None
if not isinstance(path, str):
print("Plotly.FigureExportToSVG - Error: The input path is not a string. Returning None.")
return None
# Make sure the file extension is .svg
ext = path[len(path)-4:len(path)]
if ext.lower() != ".svg":
path = path+".svg"
if overwrite == False and os.path.exists(path):
print("Plotly.FigureExportToSVG - Error: A file already exists at this location and overwrite is set to False. Returning None.")
return None
plotly.io.write_image(figure, path, format='svg', scale=1, width=width, height=height, validate=True, engine='auto')
return True
@staticmethod
def SetCamera(figure, camera=[-1.25, -1.25, 1.25], center=[0, 0, 0], up=[0, 0, 1], projection="perspective"):
"""
Sets the camera for the input figure.
Parameters
----------
figure : plotly.graph_objs._figure.Figure
The input plotly figure.
camera : list , optional
The desired location of the camera. The default is [-1.25, -1.25, 1.25].
center : list , optional
The desired center (camera target). The default is [0, 0, 0].
up : list , optional
The desired up vector. The default is [0, 0, 1].
projection : str , optional
The desired type of projection. The options are "orthographic" or "perspective". It is case insensitive. The default is "perspective"
Returns
-------
plotly.graph_objs._figure.Figure
The updated figure
"""
if not isinstance(camera, list):
camera = [-1.25, -1.25, 1.25]
if not isinstance(center, list):
center = [0, 0, 0]
if not isinstance(up, list):
up = [0, 0, 1]
projection = projection.lower()
if projection in "orthographic":
projection = "orthographic"
else:
projection = "perspective"
scene_camera = dict(
up=dict(x=up[0], y=up[1], z=up[2]),
eye=dict(x=camera[0], y=camera[1], z=camera[2]),
center=dict(x=center[0], y=center[1], z=center[2]),
projection=dict(type=projection)
)
figure.update_layout(scene_camera=scene_camera)
return figure
@staticmethod
def Show(figure, camera=[-1.25, -1.25, 1.25], center=[0, 0, 0], up=[0, 0, 1], renderer=None, projection="perspective"):
"""
Shows the input figure.
Parameters
----------
figure : plotly.graph_objs._figure.Figure
The input plotly figure.
camera : list , optional
The desired location of the camera. The default is [0, 0, 0].
center : list , optional
The desired center (camera target). The default is [0, 0, 0].
up : list , optional
The desired up vector. The default is [0, 0, 1].
renderer : str , optional
The desired renderer. See Plotly.Renderers(). If set to None, the code will attempt to discover the most suitable renderer. The default is None.
projection : str, optional
The desired type of projection. The options are "orthographic" or "perspective". It is case insensitive. The default is "perspective"
Returns
-------
None
"""
if figure == None:
print("Plotly.Show - Error: The input is NULL. Returning None.")
return None
if not isinstance(figure, plotly.graph_objs._figure.Figure):
print("Plotly.Show - Error: The input is not a figure. Returning None.")
return None
if renderer == None:
renderer = Plotly.Renderer()
if not renderer.lower() in Plotly.Renderers():
print("Plotly.Show - Error: The input renderer is not in the approved list of renderers. Returning None.")
return None
if not camera == None and not center == None and not up == None:
figure = Plotly.SetCamera(figure, camera=camera, center=center, up=up, projection=projection)
if renderer.lower() == "offline":
ofl.plot(figure)
else:
figure.show(renderer=renderer)
return None
@staticmethod
def Renderer():
"""
Return the renderer most suitable for the environment in which the script is running.
Parameters
----------
Returns
-------
str
The most suitable renderer type for the environment in which the script is running.
Currently, this is limited to:
- "vscode" if running in Visual Studio Code
- "colab" if running in Google Colab
- "iframe" if running in jupyter notebook or jupyterlab
- "browser" if running in anything else
"""
import sys
import os
if 'VSCODE_PID' in os.environ:
return 'vscode'
elif "google.colab" in sys.modules:
return "colab"
elif "ipykernel" in sys.modules:
return "iframe" #works for jupyter notebook and jupyterlab
else:
return "browser"
@staticmethod
def Renderers():
"""
Returns a list of the available plotly renderers.
Parameters
----------
Returns
-------
list
The list of the available plotly renderers.
"""
return ['plotly_mimetype', 'jupyterlab', 'nteract', 'vscode',
'notebook', 'notebook_connected', 'kaggle', 'azure', 'colab',
'cocalc', 'databricks', 'json', 'png', 'jpeg', 'jpg', 'svg',
'pdf', 'browser', 'firefox', 'chrome', 'chromium', 'iframe',
'iframe_connected', 'sphinx_gallery', 'sphinx_gallery_png', 'offline']
@staticmethod
def ExportToImage(figure, path, format="png", width="1920", height="1080"):
"""
Exports the plotly figure to an image.
Parameters
----------
figure : plotly.graph_objs._figure.Figure
The input plotly figure.
path : str
The image file path.
format : str , optional
The desired format. This can be any of "jpg", "jpeg", "pdf", "png", "svg", or "webp". It is case insensitive. The default is "png".
width : int , optional
The width in pixels of the figure. The default value is 1920.
height : int , optional
The height in pixels of the figure. The default value is 1080.
Returns
-------
bool
True if the image was exported sucessfully. False otherwise.
"""
if not isinstance(figure, plotly.graph_objs._figure.Figure):
return None
if not isinstance(path, str):
return None
if not format.lower() in ["jpg", "jpeg", "pdf", "png", "svg", "webp"]:
return None
returnStatus = False
try:
plotly.io.write_image(figure, path, format=format.lower(), scale=None, width=width, height=height, validate=True, engine='auto')
returnStatus = True
except:
returnStatus = False
return returnStatusClasses
class Plotly-
Expand source code
class Plotly: @staticmethod def AddColorBar(figure, values=[], nTicks=5, xPosition=-0.15, width=15, outlineWidth=0, title="", subTitle="", units="", colorScale="viridis", mantissa: int = 6): """ Adds a color bar to the input figure Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. values : list , optional The input list of values to use for the color bar. The default is []. nTicks : int , optional The number of ticks to use on the color bar. The default is 5. xPosition : float , optional The x location of the color bar. The default is -0.15. width : int , optional The width in pixels of the color bar. The default is 15 outlineWidth : int , optional The width in pixels of the outline of the color bar. The default is 0. title : str , optional The title of the color bar. The default is "". subTitle : str , optional The subtitle of the color bar. The default is "". units: str , optional The units used in the color bar. The default is "" colorScale : str , optional The desired type of plotly color scales to use (e.g. "viridis", "plasma"). The default is "viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. In addition to these, three color-blind friendly scales are included. These are "protanopia", "deuteranopia", and "tritanopia" for red, green, and blue colorblindness respectively. mantissa : int , optional The desired length of the mantissa for the values listed on the color bar. The default is 6. Returns ------- plotly.graph_objs._figure.Figure The input figure with the color bar added. """ if not isinstance(figure, plotly.graph_objs._figure.Figure): return None if units: units = "Units: "+units minValue = min(values) maxValue = max(values) step = (maxValue - minValue)/float(nTicks-1) r = [round(minValue+i*step, mantissa) for i in range(nTicks)] r[-1] = round(maxValue, mantissa) # Define the minimum and maximum range of the colorbar rs = [str(x) for x in r] # Define the colorbar as a trace with no data, x or y coordinates colorbar_trace = go.Scatter( x=[0], y=[0], mode="markers", showlegend=False, marker=dict( size=0, colorscale=Plotly.ColorScale(colorScale), # choose the colorscale cmin=minValue, cmax=maxValue, color=['rgba(0,0,0,0)'], colorbar=dict( x=xPosition, title="<b>"+title+"</b><br>"+subTitle+"<br>"+units, # title of the colorbar ticks="outside", # position of the ticks tickvals=r, # values of the ticks ticktext=rs, # text of the ticks tickmode="array", thickness=width, outlinewidth=outlineWidth, ) ) ) figure.add_trace(colorbar_trace) return figure @staticmethod def Colors(): """ Returns the list of named CSS colors that plotly can use. Returns ------- list The list of named CSS colors. """ return ["aliceblue","antiquewhite","aqua", "aquamarine","azure","beige", "bisque","black","blanchedalmond", "blue","blueviolet","brown", "burlywood","cadetblue", "chartreuse","chocolate", "coral","cornflowerblue","cornsilk", "crimson","cyan","darkblue", "darkcyan","darkgoldenrod","darkgray", "darkgrey","darkgreen","darkkhaki", "darkmagenta","darkolivegreen","darkorange", "darkorchid","darkred","darksalmon", "darkseagreen","darkslateblue","darkslategray", "darkslategrey","darkturquoise","darkviolet", "deeppink","deepskyblue","dimgray", "dimgrey","dodgerblue","firebrick", "floralwhite","forestgreen","fuchsia", "gainsboro","ghostwhite","gold", "goldenrod","gray","grey", "green"," greenyellow","honeydew", "hotpink","indianred","indigo", "ivory","khaki","lavender", "lavenderblush","lawngreen","lemonchiffon", "lightblue","lightcoral","lightcyan", "lightgoldenrodyellow","lightgray","lightgrey", "lightgreen","lightpink","lightsalmon", "lightseagreen","lightskyblue","lightslategray", "lightslategrey","lightsteelblue","lightyellow", "lime","limegreen","linen", "magenta","maroon","mediumaquamarine", "mediumblue","mediumorchid","mediumpurple", "mediumseagreen","mediumslateblue","mediumspringgreen", "mediumturquoise","mediumvioletred","midnightblue", "mintcream","mistyrose","moccasin", "navajowhite","navy","oldlace", "olive","olivedrab","orange", "orangered","orchid","palegoldenrod", "palegreen","paleturquoise","palevioletred", "papayawhip","peachpuff","peru", "pink","plum","powderblue", "purple","red","rosybrown", "royalblue","rebeccapurple","saddlebrown", "salmon","sandybrown","seagreen", "seashell","sienna","silver", "skyblue","slateblue","slategray", "slategrey","snow","springgreen", "steelblue","tan","teal", "thistle","tomato","turquoise", "violet","wheat","white", "whitesmoke","yellow","yellowgreen"] @staticmethod def ColorScale(colorScale: str = "viridis"): # Colors recommended by various sources for color-blind-friendly palettes protanopia_colors = [ "#E69F00", # orange "#56B4E9", # sky blue "#009E73", # bluish green "#F0E442", # yellow "#0072B2", # blue "#D55E00", # vermillion "#CC79A7", # reddish purple ] deuteranopia_colors = [ "#377EB8", # blue "#FF7F00", # orange "#4DAF4A", # green "#F781BF", # pink "#A65628", # brown "#984EA3", # purple "#999999", # grey ] tritanopia_colors = [ "#E69F00", # orange "#56B4E9", # sky blue "#009E73", # bluish green "#F0E442", # yellow "#0072B2", # blue "#D55E00", # vermillion "#CC79A7", # reddish purple ] # Create colorscales for Plotly def create_colorscale(colors): colorscale = [] num_colors = len(colors) for i, color in enumerate(colors): position = i / (num_colors - 1) colorscale.append((position, color)) return colorscale if "prota" in colorScale.lower(): return create_colorscale(protanopia_colors) elif "deutera" in colorScale.lower(): return create_colorscale(deuteranopia_colors) elif "trita"in colorScale.lower(): return create_colorscale(tritanopia_colors) else: return colorScale @staticmethod def DataByDGL(data, labels): """ Returns a data frame from the DGL data. Parameters ---------- data : list The data to display. labels : list The labels to use for the data. The data with the labels in this list will be extracted and used in the returned dataFrame. Returns ------- pd.DataFrame A pandas dataFrame """ try: import pandas as pd except: print("Plotly - Installing required pandas library.") try: os.system("pip install pandas") except: os.system("pip install pandas --user") try: import pandas as pd except: warnings.warn("Plotly.DataByDGL - Error: Could not import pandas. Please install the pandas library manually. Returning None") return None if isinstance(data[labels[0]][0], int): xAxis_list = list(range(1, data[labels[0]][0]+1)) else: xAxis_list = data[labels[0]][0] plot_data = [xAxis_list] for i in range(1,len(labels)): plot_data.append(data[labels[i]][0][:len(xAxis_list)]) dlist = list(map(list, zip(*plot_data))) df = pd.DataFrame(dlist, columns=labels) return df @staticmethod def DataByGraph(graph, vertexColor="black", vertexSize=6, vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[], showVertices=True, showVertexLegend=False, edgeColor="black", edgeWidth=1, edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[], showEdges=True, showEdgeLegend=False, colorScale="viridis"): """ Creates plotly vertex and edge data from the input graph. Parameters ---------- graph : topologic_core.Graph The input graph. vertexColor : str , optional The desired color of the output vertices. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". vertexSize : float , optional The desired size of the vertices. The default is 6. vertexLabelKey : str , optional The dictionary key to use to display the vertex label. The default is None. vertexGroupKey : str , optional The dictionary key to use to display the vertex group. The default is None. vertexGroups : list , optional The list of vertex groups against which to index the color of the vertex. The default is []. showVertices : bool , optional If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True. showVertexLegend : bool , optional If set to True the vertex legend will be drawn. Otherwise, it will not be drawn. The default is False. edgeColor : str , optional The desired color of the output edges. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". edgeWidth : float , optional The desired thickness of the output edges. The default is 1. edgeLabelKey : str , optional The dictionary key to use to display the edge label. The default is None. edgeGroupKey : str , optional The dictionary key to use to display the edge group. The default is None. edgeGroups : list , optional The list of groups to use for indexing the color of edges. The default is None. showEdges : bool , optional If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True. showEdgeLegend : bool , optional If set to True the edge legend will be drawn. Otherwise, it will not be drawn. The default is False. colorScale : str , optional The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. Returns ------- list The vertex and edge data list. """ from topologicpy.Vertex import Vertex from topologicpy.Edge import Edge from topologicpy.Dictionary import Dictionary from topologicpy.Topology import Topology from topologicpy.Graph import Graph import plotly.graph_objs as go if not Topology.IsInstance(graph, "Graph"): return None v_labels = [] v_groupList = [] data = [] if showVertices: vertices = Graph.Vertices(graph) if vertexLabelKey or vertexGroupKey: for v in vertices: Xn=[round(Vertex.X(v), 4) for v in vertices] # x-coordinates of nodes Yn=[round(Vertex.Y(v), 4) for v in vertices] # y-coordinates of nodes Zn=[round(Vertex.Z(v), 4) for v in vertices] # x-coordinates of nodes v_label = "" v_group = "" d = Topology.Dictionary(v) if d: try: v_label = str(Dictionary.ValueAtKey(d, key=vertexLabelKey)) or "" except: v_label = "" try: v_group = Dictionary.ValueAtKey(d, key=vertexGroupKey) except: v_group = None try: v_groupList.append(vertexGroups.index(v_group)) except: v_groupList.append(len(vertexGroups)) if not v_label == "" and not v_group == "": if v_group == 0: v_label = v_label+" (0)" else: v_label = v_label+" ("+str(v_group)+")" v_labels.append(v_label) else: for v in vertices: Xn=[round(Vertex.X(v), 4) for v in vertices] # x-coordinates of nodes Yn=[round(Vertex.Y(v), 4) for v in vertices] # y-coordinates of nodes Zn=[round(Vertex.Z(v), 4) for v in vertices] # x-coordinates of nodes if len(list(set(v_groupList))) < 2: v_groupList = vertexColor if len(v_labels) < 1: v_labels = "" v_trace=go.Scatter3d(x=Xn, y=Yn, z=Zn, mode='markers', name='Graph Vertices', legendgroup=4, legendrank=4, showlegend=showVertexLegend, marker=dict(symbol='circle', size=vertexSize, color=v_groupList, colorscale=Plotly.ColorScale(colorScale), line=dict(color=edgeColor, width=0.5) ), text=v_labels, hoverinfo='text' ) data.append(v_trace) if showEdges: Xe=[] Ye=[] Ze=[] e_labels = [] e_groupList = [] edges = Graph.Edges(graph) if edgeLabelKey or edgeGroupKey: for e in edges: sv = Edge.StartVertex(e) ev = Edge.EndVertex(e) Xe+=[round(Vertex.X(sv), 4), round(Vertex.X(ev), 4), None] # x-coordinates of edge ends Ye+=[round(Vertex.Y(sv), 4), round(Vertex.Y(ev), 4), None] # y-coordinates of edge ends Ze+=[round(Vertex.Z(sv), 4), round(Vertex.Z(ev), 4), None] # z-coordinates of edge ends e_label = "" e_group = "" d = Topology.Dictionary(e) if d: try: e_label = str(Dictionary.ValueAtKey(d, key=edgeLabelKey)) or "" except: e_label = "" try: e_group = str(Dictionary.ValueAtKey(d, key=edgeGroupKey)) or "" except: e_group = "" try: e_groupList.append(edgeGroups.index(e_group)) except: e_groupList.append(len(edgeGroups)) if not e_label == "" and not e_group == "": e_label = e_label+" ("+e_group+")" e_labels.append(e_label) else: for e in edges: sv = Edge.StartVertex(e) ev = Edge.EndVertex(e) Xe+=[round(Vertex.X(sv), 4), round(Vertex.X(ev), 4), None] # x-coordinates of edge ends Ye+=[round(Vertex.Y(sv), 4), round(Vertex.Y(ev), 4), None] # y-coordinates of edge ends Ze+=[round(Vertex.Z(sv), 4), round(Vertex.Z(ev), 4), None] # z-coordinates of edge ends if len(list(set(e_groupList))) < 2: e_groupList = edgeColor if len(e_labels) < 1: e_labels = "" e_trace=go.Scatter3d(x=Xe, y=Ye, z=Ze, mode='lines', name='Graph Edges', legendgroup=5, legendrank=5, showlegend=showEdgeLegend, line=dict(color=e_groupList, width=edgeWidth), text=e_labels, hoverinfo='text' ) data.append(e_trace) return data @staticmethod def DataByTopology(topology, showVertices=True, vertexSize=1.1, vertexColor="black", vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[], vertexMinGroup=None, vertexMaxGroup=None, showVertexLegend=False, vertexLegendLabel="Topology Vertices", vertexLegendRank=1, vertexLegendGroup=1, showEdges=True, edgeWidth=1, edgeColor="black", edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[], edgeMinGroup=None, edgeMaxGroup=None, showEdgeLegend=False, edgeLegendLabel="Topology Edges", edgeLegendRank=2, edgeLegendGroup=2, showFaces=True, faceOpacity=0.5, faceColor="#FAFAFA", faceLabelKey=None, faceGroupKey=None, faceGroups=[], faceMinGroup=None, faceMaxGroup=None, showFaceLegend=False, faceLegendLabel="Topology Faces", faceLegendRank=3, faceLegendGroup=3, intensityKey=None, intensities=[], colorScale="viridis", mantissa=6, tolerance=0.0001): """ Creates plotly face, edge, and vertex data. Parameters ---------- topology : topologic_core.Topology The input topology. This must contain faces and or edges. showVertices : bool , optional If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True. vertexSize : float , optional The desired size of the vertices. The default is 1.1. vertexColor : str , optional The desired color of the output vertices. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". vertexLabelKey : str , optional The dictionary key to use to display the vertex label. The default is None. vertexGroupKey : str , optional The dictionary key to use to display the vertex group. The default is None. vertexGroups : list , optional The list of vertex groups against which to index the color of the vertex. The default is []. vertexMinGroup : int or float , optional For numeric vertexGroups, vertexMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the minimum value in vertexGroups. The default is None. vertexMaxGroup : int or float , optional For numeric vertexGroups, vertexMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the maximum value in vertexGroups. The default is None. showVertexLegend : bool, optional If set to True, the legend for the vertices of this topology is shown. Otherwise, it isn't. The default is False. vertexLegendLabel : str , optional The legend label string used to identify vertices. The default is "Topology Vertices". vertexLegendRank : int , optional The legend rank order of the vertices of this topology. The default is 1. vertexLegendGroup : int , optional The number of the vertex legend group to which the vertices of this topology belong. The default is 1. showEdges : bool , optional If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True. edgeWidth : float , optional The desired thickness of the output edges. The default is 1. edgeColor : str , optional The desired color of the output edges. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". edgeLabelKey : str , optional The dictionary key to use to display the edge label. The default is None. edgeGroupKey : str , optional The dictionary key to use to display the edge group. The default is None. edgeGroups : list , optional The list of edge groups against which to index the color of the edge. The default is []. edgeMinGroup : int or float , optional For numeric edgeGroups, edgeMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the minimum value in edgeGroups. The default is None. edgeMaxGroup : int or float , optional For numeric edgeGroups, edgeMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the maximum value in edgeGroups. The default is None. showEdgeLegend : bool, optional If set to True, the legend for the edges of this topology is shown. Otherwise, it isn't. The default is False. edgeLegendLabel : str , optional The legend label string used to identify edges. The default is "Topology Edges". edgeLegendRank : int , optional The legend rank order of the edges of this topology. The default is 2. edgeLegendGroup : int , optional The number of the edge legend group to which the edges of this topology belong. The default is 2. showFaces : bool , optional If set to True the faces will be drawn. Otherwise, they will not be drawn. The default is True. faceOpacity : float , optional The desired opacity of the output faces (0=transparent, 1=opaque). The default is 0.5. faceColor : str , optional The desired color of the output faces. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "#FAFAFA". faceLabelKey : str , optional The dictionary key to use to display the face label. The default is None. faceGroupKey : str , optional The dictionary key to use to display the face group. The default is None. faceGroups : list , optional The list of face groups against which to index the color of the face. This can bhave numeric or string values. This should match the type of value associated with the faceGroupKey. The default is []. faceMinGroup : int or float , optional For numeric faceGroups, minGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the minimum value in faceGroups. The default is None. faceMaxGroup : int or float , optional For numeric faceGroups, maxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the maximum value in faceGroups. The default is None. showFaceLegend : bool, optional If set to True, the legend for the faces of this topology is shown. Otherwise, it isn't. The default is False. faceLegendLabel : str , optional The legend label string used to idenitfy edges. The default is "Topology Faces". faceLegendRank : int , optional The legend rank order of the faces of this topology. The default is 3. faceLegendGroup : int , optional The number of the face legend group to which the faces of this topology belong. The default is 3. intensityKey: str, optional If not None, the dictionary of each vertex is searched for the value associated with the intensity key. This value is then used to color-code the vertex based on the colorScale. The default is None. intensities : list , optional The list of intensities against which to index the intensity of the vertex. The default is []. colorScale : str , optional The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. mantissa : int , optional The desired length of the mantissa. The default is 6. tolerance : float , optional The desired tolerance. The default is 0.0001. Returns ------- list The vertex, edge, and face data list. """ from topologicpy.Topology import Topology from topologicpy.Dictionary import Dictionary from topologicpy.Color import Color from time import time def closest_index(input_value, values): return int(min(range(len(values)), key=lambda i: abs(values[i] - input_value))) def vertexData(vertices, dictionaries=[], color="black", size=1.1, labelKey=None, groupKey=None, minGroup=None, maxGroup=None, groups=[], legendLabel="Topology Vertices", legendGroup=1, legendRank=1, showLegend=True, colorScale="Viridis"): x = [] y = [] z = [] labels = [] groupList = [] label = "" group = "" if labelKey or groupKey: if groups: if len(groups) > 0: if type(groups[0]) == int or type(groups[0]) == float: if not minGroup: minGroup = min(groups) if not maxGroup: maxGroup = max(groups) else: minGroup = 0 maxGroup = len(groups) - 1 else: minGroup = 0 maxGroup = 1 for m, v in enumerate(vertices): x.append(round(v[0], mantissa)) y.append(round(v[1], mantissa)) z.append(round(v[2], mantissa)) label = "" group = "" if len(dictionaries) > 0: d = dictionaries[m] if d: try: label = str(Dictionary.ValueAtKey(d, key=labelKey)) or "" except: label = "" try: group = Dictionary.ValueAtKey(d, key=groupKey) or None except: group = "" try: if type(group) == int or type(group) == float: if group < minGroup: group = minGroup if group > maxGroup: group = maxGroup color = Color.ByValueInRange(group, minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) else: color = Color.ByValueInRange(groups.index(group), minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) color = "rgb("+str(color[0])+","+str(color[1])+","+str(color[2])+")" groupList.append(color) except: groupList.append(len(groups)) labels.append(label) else: for v in vertices: x.append(round(v[0], mantissa)) y.append(round(v[1], mantissa)) z.append(round(v[2], mantissa)) if len(list(set(groupList))) < 2: groupList = color if len(labels) < 1: labels = "" vData= go.Scatter3d(x=x, y=y, z=z, name=legendLabel, showlegend=showLegend, marker=dict(color=groupList, size=vertexSize), mode='markers', legendgroup=legendGroup, legendrank=legendRank, text=labels, hoverinfo='text', hovertext=labels ) return vData def edgeData(vertices, edges, dictionaries=None, color="black", width=1, labelKey=None, groupKey=None, minGroup=None, maxGroup=None, groups=[], legendLabel="Topology Edges", legendGroup=2, legendRank=2, showLegend=True, colorScale="Viridis"): x = [] y = [] z = [] labels = [] groupList = [] label = "" group = "" if labelKey or groupKey: if groups: if len(groups) > 0: if type(groups[0]) == int or type(groups[0]) == float: if not minGroup: minGroup = min(groups) if not maxGroup: maxGroup = max(groups) else: minGroup = 0 maxGroup = len(groups) - 1 else: minGroup = 0 maxGroup = 1 for m, e in enumerate(edges): sv = vertices[e[0]] ev = vertices[e[1]] x+=[round(sv[0], mantissa),round(ev[0], mantissa), None] # x-coordinates of edge ends y+=[round(sv[1], mantissa),round(ev[1], mantissa), None] # y-coordinates of edge ends z+=[round(sv[2], mantissa),round(ev[2], mantissa), None] # z-coordinates of edge ends label = "" group = "" if len(dictionaries) > 0: d = dictionaries[m] if d: try: label = str(Dictionary.ValueAtKey(d, key=labelKey)) or "" except: label = "" try: group = Dictionary.ValueAtKey(d, key=groupKey) or None except: group = "" try: if type(group) == int or type(group) == float: if group < minGroup: group = minGroup if group > maxGroup: group = maxGroup color = Color.ByValueInRange(group, minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) else: color = Color.ByValueInRange(groups.index(group), minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) color = "rgb("+str(color[0])+","+str(color[1])+","+str(color[2])+")" groupList.append(color) except: groupList.append(len(groups)) labels.append(label) else: for e in edges: sv = vertices[e[0]] ev = vertices[e[1]] x+=[round(sv[0],mantissa),round(ev[0],mantissa), None] # x-coordinates of edge ends y+=[round(sv[1],mantissa),round(ev[1],mantissa), None] # y-coordinates of edge ends z+=[round(sv[2],mantissa),round(ev[2],mantissa), None] # z-coordinates of edge ends if len(list(set(groupList))) < 2: groupList = color if len(labels) < 1: labels = "" eData = go.Scatter3d(x=x, y=y, z=z, name=legendLabel, showlegend=showLegend, marker_size=0, mode="lines", line=dict(color=groupList, width=edgeWidth), legendgroup=legendGroup, legendrank=legendRank, text=labels, hoverinfo='text') return eData def faceData(vertices, faces, dictionaries=None, color="#FAFAFA", opacity=0.5, labelKey=None, groupKey=None, minGroup=None, maxGroup=None, groups=[], legendLabel="Topology Faces", legendGroup=3, legendRank=3, showLegend=True, intensities=None, colorScale="viridis"): x = [] y = [] z = [] for v in vertices: x.append(round(v[0], mantissa)) y.append(round(v[1], mantissa)) z.append(round(v[2], mantissa)) i = [] j = [] k = [] labels = [] groupList = [] label = "" group = "" if labelKey or groupKey: if groups: if len(groups) > 0: if type(groups[0]) == int or type(groups[0]) == float: if not minGroup: minGroup = min(groups) if not maxGroup: maxGroup = max(groups) else: minGroup = 0 maxGroup = len(groups) - 1 else: minGroup = 0 maxGroup = 1 for m, f in enumerate(faces): i.append(f[0]) j.append(f[1]) k.append(f[2]) label = "" group = "" if len(dictionaries) > 0: d = dictionaries[m] if d: try: label = str(Dictionary.ValueAtKey(d, key=labelKey)) or "" except: label = "" try: group = Dictionary.ValueAtKey(d, key=groupKey) or None except: group = "" try: if type(group) == int or type(group) == float: if group < minGroup: group = minGroup if group > maxGroup: group = maxGroup color = Color.ByValueInRange(group, minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) else: color = Color.ByValueInRange(groups.index(group), minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) color = "rgb("+str(color[0])+","+str(color[1])+","+str(color[2])+")" groupList.append(color) except: groupList.append(len(groups)) labels.append(label) else: for f in faces: i.append(f[0]) j.append(f[1]) k.append(f[2]) if len(list(set(groupList))) < 2: groupList = None if len(labels) < 1: labels = "" fData = go.Mesh3d( x = x, y = y, z = z, i = i, j = j, k = k, name = legendLabel, showlegend = showLegend, legendgroup = legendGroup, legendrank = legendRank, color = color, facecolor = groupList, colorscale = Plotly.ColorScale(colorScale), cmin = 0, cmax = 1, intensity = intensities, opacity = opacity, hoverinfo = 'text', text = labels, hovertext = labels, flatshading = True, showscale = False, lighting = {"facenormalsepsilon": 0}, ) return fData from topologicpy.Face import Face from topologicpy.Cluster import Cluster from topologicpy.Topology import Topology from topologicpy.Dictionary import Dictionary from time import time if not Topology.IsInstance(topology, "Topology"): return None intensityList = [] alt_intensities = [] data = [] v_list = [] if Topology.Type(topology) == Topology.TypeID("Vertex"): tp_vertices = [topology] else: tp_vertices = Topology.Vertices(topology) if isinstance(intensities, list): if len(intensities) == 0: intensities = None if not (tp_vertices == None or tp_vertices == []): vertices = [] v_dictionaries = [] intensityList = [] if intensityKey: for i, tp_v in enumerate(tp_vertices): vertices.append([tp_v.X(), tp_v.Y(), tp_v.Z()]) d = Topology.Dictionary(tp_v) if d: v = Dictionary.ValueAtKey(d, key=intensityKey) if not v == None: alt_intensities.append(v) v_list.append(v) else: alt_intensities.append(0) v_list.append(0) else: alt_intensities.append(0) v_list.append(0) alt_intensities = list(set(alt_intensities)) alt_intensities.sort() if isinstance(intensities, list): if len(intensities) > 0: alt_intensities = intensities min_i = min(alt_intensities) max_i = max(alt_intensities) for i, tp_v in enumerate(tp_vertices): v = v_list[i] ci = closest_index(v_list[i], alt_intensities) value = intensities[ci] if (max_i - min_i) == 0: value = 0 else: value = (value - min_i)/(max_i - min_i) intensityList.append(value) if all(x == 0 for x in intensityList): intensityList = None if showVertices: if len(vertices) == 0: for i, tp_v in enumerate(tp_vertices): if vertexLabelKey or vertexGroupKey: d = Topology.Dictionary(tp_v) v_dictionaries.append(d) vertices.append([tp_v.X(), tp_v.Y(), tp_v.Z()]) data.append(vertexData(vertices, dictionaries=v_dictionaries, color=vertexColor, size=vertexSize, labelKey=vertexLabelKey, groupKey=vertexGroupKey, minGroup=vertexMinGroup, maxGroup=vertexMaxGroup, groups=vertexGroups, legendLabel=vertexLegendLabel, legendGroup=vertexLegendGroup, legendRank=vertexLegendRank, showLegend=showVertexLegend, colorScale=colorScale)) if showEdges and Topology.Type(topology) > Topology.TypeID("Vertex"): if Topology.Type(topology) == Topology.TypeID("Edge"): tp_edges = [topology] else: tp_edges = Topology.Edges(topology) if not (tp_edges == None or tp_edges == []): e_dictionaries = [] if edgeLabelKey or edgeGroupKey: for tp_edge in tp_edges: e_dictionaries.append(Topology.Dictionary(tp_edge)) e_cluster = Cluster.ByTopologies(tp_edges) geo = Topology.Geometry(e_cluster, mantissa=mantissa) vertices = geo['vertices'] edges = geo['edges'] data.append(edgeData(vertices, edges, dictionaries=e_dictionaries, color=edgeColor, width=edgeWidth, labelKey=edgeLabelKey, groupKey=edgeGroupKey, minGroup=edgeMinGroup, maxGroup=edgeMaxGroup, groups=edgeGroups, legendLabel=edgeLegendLabel, legendGroup=edgeLegendGroup, legendRank=edgeLegendRank, showLegend=showEdgeLegend, colorScale=colorScale)) if showFaces and Topology.Type(topology) >= Topology.TypeID("Face"): if Topology.IsInstance(topology, "Face"): tp_faces = [topology] else: tp_faces = Topology.Faces(topology) if not(tp_faces == None or tp_faces == []): # rebuild faces to remove any degenerate faces #new_faces = [] #for i, f in enumerate(tp_faces): #eb = Face.ExternalBoundary(f) #eb = Topology.RemoveCollinearEdges(eb) #if not eb == None: #ibList = Face.InternalBoundaries(f) #ibList = [Wire.RemoveCollinearEdges(ib) for ib in ibList] #ibList = [ib for ib in ibList if not ib == None] #new_f = Face.ByWires(eb, ibList, silent=False) #if Topology.IsInstance(new_f, "Face"): #if faceLabelKey or faceGroupKey: #d = Topology.Dictionary(tp_faces[i]) #keys = Dictionary.Keys(d) #if len(keys) > 0: #new_f = Topology.SetDictionary(new_f, d) #new_faces.append(new_f) f_dictionaries = [] all_triangles = [] for tp_face in tp_faces: triangles = Face.Triangulate(tp_face, tolerance=tolerance) if isinstance(triangles, list): for tri in triangles: if faceLabelKey or faceGroupKey: d = Topology.Dictionary(tp_face) f_dictionaries.append(d) if d: _ = Topology.SetDictionary(tri, d) all_triangles.append(tri) if len(all_triangles) > 0: f_cluster = Cluster.ByTopologies(all_triangles) geo = Topology.Geometry(f_cluster, mantissa=mantissa) vertices = geo['vertices'] faces = geo['faces'] data.append(faceData(vertices, faces, dictionaries=f_dictionaries, color=faceColor, opacity=faceOpacity, labelKey=faceLabelKey, groupKey=faceGroupKey, minGroup=faceMinGroup, maxGroup=faceMaxGroup, groups=faceGroups, legendLabel=faceLegendLabel, legendGroup=faceLegendGroup, legendRank=faceLegendRank, showLegend=showFaceLegend, intensities=intensityList, colorScale=colorScale)) return data @staticmethod def FigureByConfusionMatrix(matrix, categories=[], minValue=None, maxValue=None, title="Confusion Matrix", xTitle = "Actual", yTitle = "Predicted", width=950, height=500, showScale = True, colorScale='viridis', colorSamples=10, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=40, marginBottom=0): """ Returns a Plotly Figure of the input confusion matrix. Actual categories are displayed on the X-Axis, Predicted categories are displayed on the Y-Axis. Parameters ---------- matrix : list or numpy.array The matrix to display. categories : list The list of categories to use on the X and Y axes. minValue : float , optional The desired minimum value to use for the color scale. If set to None, the minmum value found in the input matrix will be used. The default is None. maxValue : float , optional The desired maximum value to use for the color scale. If set to None, the maximum value found in the input matrix will be used. The default is None. title : str , optional The desired title to display. The default is "Confusion Matrix". xTitle : str , optional The desired X-axis title to display. The default is "Actual". yTitle : str , optional The desired Y-axis title to display. The default is "Predicted". width : int , optional The desired width of the figure. The default is 950. height : int , optional The desired height of the figure. The default is 500. showScale : bool , optional If set to True, a color scale is shown on the right side of the figure. The default is True. colorScale : str , optional The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. colorSamples : int , optional The number of discrete color samples to use for displaying the data. The default is 10. backgroundColor : str , optional The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'rgba(0,0,0,0)' (transparent). marginLeft : int , optional The desired left margin in pixels. The default is 0. marginRight : int , optional The desired right margin in pixels. The default is 0. marginTop : int , optional The desired top margin in pixels. The default is 40. marginBottom : int , optional The desired bottom margin in pixels. The default is 0. """ try: import numpy as np except: print("Plotly.FigureByConfusionMatrix - Installing required numpy library.") try: os.system("pip install numpy") except: os.system("pip install numpy --user") try: import numpy as np except: warnings.warn("Plotly.FigureByConfusionMatrix - Error: Could not import numpy. Please install numpy manually. Returning None.") return None if not isinstance(matrix, list) and not isinstance(matrix, np.ndarray): print("Plotly.FigureByConfusionMatrix - Error: The input matrix is not of the correct type. Returning None.") return None figure = Plotly.FigureByMatrix(matrix, xCategories=categories, minValue=minValue, maxValue=maxValue, title=title, xTitle=xTitle, yTitle=yTitle, width=width, height=height, showScale=showScale, colorScale=Plotly.ColorScale(colorScale), colorSamples=colorSamples, backgroundColor=backgroundColor, marginLeft=marginLeft, marginRight=marginRight, marginTop=marginTop, marginBottom=marginBottom) layout = { "yaxis": {"autorange": "reversed"}, } figure.update_layout(layout) return figure @staticmethod def FigureByMatrix(matrix, xCategories=[], yCategories=[], minValue=None, maxValue=None, title="Matrix", xTitle = "X Axis", yTitle = "Y Axis", width=950, height=950, showScale = False, colorScale='gray', colorSamples=10, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=40, marginBottom=0, mantissa: int = 6): """ Returns a Plotly Figure of the input matrix. Parameters ---------- matrix : list or numpy.array The matrix to display. categories : list The list of categories to use on the X and Y axes. minValue : float , optional The desired minimum value to use for the color scale. If set to None, the minmum value found in the input matrix will be used. The default is None. maxValue : float , optional The desired maximum value to use for the color scale. If set to None, the maximum value found in the input matrix will be used. The default is None. title : str , optional The desired title to display. The default is "Confusion Matrix". xTitle : str , optional The desired X-axis title to display. The default is "Actual". yTitle : str , optional The desired Y-axis title to display. The default is "Predicted". width : int , optional The desired width of the figure. The default is 950. height : int , optional The desired height of the figure. The default is 500. showScale : bool , optional If set to True, a color scale is shown on the right side of the figure. The default is True. colorScale : str , optional The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. colorSamples : int , optional The number of discrete color samples to use for displaying the data. The default is 10. backgroundColor : str , optional The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'rgba(0,0,0,0)' (transparent). marginLeft : int , optional The desired left margin in pixels. The default is 0. marginRight : int , optional The desired right margin in pixels. The default is 0. marginTop : int , optional The desired top margin in pixels. The default is 40. marginBottom : int , optional The desired bottom margin in pixels. The default is 0. mantissa : int , optional The desired number of digits of the mantissa. The default is 6. """ #import plotly.figure_factory as ff import plotly.graph_objects as go import plotly.express as px try: import numpy as np except: print("Plotly.FigureByMatrix - Installing required numpy library.") try: os.system("pip install numpy") except: os.system("pip install numpy --user") try: import numpy as np except: warnings.warn("Plotly.FigureByMatrix - Error: Could not import numpy. Please install numpy manually. Returning None.") return None if not isinstance(matrix, list) and not isinstance(matrix, np.ndarray): print("Plotly.FigureByMatrix - Error: The input matrix is not of the correct type. Returning None.") return None annotations = [] if isinstance(matrix, list): matrix = np.array(matrix) colors = px.colors.sample_colorscale(Plotly.ColorScale(colorScale), [n/(colorSamples -1) for n in range(colorSamples)]) if not xCategories: xCategories = [x for x in range(len(matrix[0]))] if not yCategories: yCategories = [y for y in range(len(matrix))] if not maxValue or not minValue: max_values = [] min_values = [] for i in range(len(matrix)): row = matrix[i] max_values.append(max(row)) min_values.append(min(row)) for j, value in enumerate(row): annotations.append( { "x": xCategories[j], "y": yCategories[i], "font": {"color": "black"}, "bgcolor": "white", "opacity": 0.5, "text": str(round(value, mantissa)), "xref": "x1", "yref": "y1", "showarrow": False } ) if not minValue: minValueB = min(min_values) if not maxValue: maxValue = max(max_values) else: for i in range(len(matrix)): row = matrix[i] for j, value in enumerate(row): annotations.append( { "x": xCategories[j], "y": yCategories[i], "font": {"color": "black"}, "bgcolor": "white", "opacity": 0.5, "text": str(round(value,mantissa)), "xref": "x1", "yref": "y1", "showarrow": False } ) new_matrix = [] for i in range(len(matrix)): row = matrix[i] new_row = [] maxRow = sum(row) for j in range(len(row)): if maxRow == 0: new_row.append(round(0, mantissa)) else: new_row.append(round(float(row[j])/float(maxRow), mantissa)) new_matrix.append(new_row) data = go.Heatmap(z=new_matrix, y=yCategories, x=xCategories, zmin=minValue, zmax=maxValue, showscale=showScale, colorscale=colors) layout = { "width": width, "height": height, "title": title, "xaxis": {"title": xTitle}, "yaxis": {"title": yTitle, "autorange": "reversed"}, "annotations": annotations, "paper_bgcolor": backgroundColor, "plot_bgcolor": backgroundColor, "margin":dict(l=marginLeft, r=marginRight, t=marginTop, b=marginBottom) } fig = go.Figure(data=data, layout=layout) fig.update_xaxes( tickvals=xCategories) fig.update_yaxes( tickvals=yCategories) return fig @staticmethod def FigureByDataFrame(dataFrame, labels=[], width=950, height=500, title="Untitled", xTitle="X Axis", xSpacing=1, yTitle="Y Axis", ySpacing=1.0, useMarkers=False, chartType="Line", backgroundColor='rgba(0,0,0,0)', gridColor = 'lightgray', marginLeft=0, marginRight=0, marginTop=40, marginBottom=0): """ Returns a Plotly Figure of the input dataframe Parameters ---------- df : pandas.df The pandas dataframe to display. data_labels : list The labels to use for the data. width : int , optional The desired width of the figure. The default is 950. height : int , optional The desired height of the figure. The default is 500. title : str , optional The chart title. The default is "Training and Testing Results". xTitle : str , optional The X-axis title. The default is "Epochs". xSpacing : float , optional The X-axis spacing. The default is 1.0. yTitle : str , optional The Y-axis title. The default is "Accuracy and Loss". ySpacing : float , optional The Y-axis spacing. The default is 0.1. useMarkers : bool , optional If set to True, markers will be displayed. The default is False. chartType : str , optional The desired type of chart. The options are "Line", "Bar", or "Scatter". It is case insensitive. The default is "Line". backgroundColor : str , optional The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'rgba(0,0,0,0)' (transparent). grid : str , optional The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'lightgray' marginLeft : int , optional The desired left margin in pixels. The default is 0. marginRight : int , optional The desired right margin in pixels. The default is 0. marginTop : int , optional The desired top margin in pixels. The default is 40. marginBottom : int , optional The desired bottom margin in pixels. The default is 0. Returns ------- None. """ import plotly.express as px if chartType.lower() == "line": figure = px.line(dataFrame, x=labels[0], y=labels[1:], title=title, markers=useMarkers) elif chartType.lower() == "bar": figure = px.bar(dataFrame, x=labels[0], y=labels[1:], title=title) elif chartType.lower() == "scatter": figure = px.scatter(dataFrame, x=labels[0], y=labels[1:], title=title) else: raise NotImplementedError layout = { "width": width, "height": height, "title": title, "xaxis": {"title": xTitle, "dtick": xSpacing, 'gridcolor': gridColor}, "yaxis": {"title": yTitle, "dtick": ySpacing, 'gridcolor': gridColor}, "paper_bgcolor": backgroundColor, "plot_bgcolor": backgroundColor, "margin":dict(l=marginLeft, r=marginRight, t=marginTop, b=marginBottom) } figure.update_layout(layout) return figure @staticmethod def FigureByData(data, width=950, height=500, xAxis=False, yAxis=False, zAxis=False, axisSize=1, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=20, marginBottom=0, tolerance = 0.0001): """ Creates a plotly figure. Parameters ---------- data : list The input list of plotly data. width : int , optional The width in pixels of the figure. The default value is 950. height : int , optional The height in pixels of the figure. The default value is 950. xAxis : bool , optional If set to True the x axis is drawn. Otherwise it is not drawn. The default is False. yAxis : bool , optional If set to True the y axis is drawn. Otherwise it is not drawn. The default is False. zAxis : bool , optional If set to True the z axis is drawn. Otherwise it is not drawn. The default is False. axisSize : float , optional The size of the X, Y, Z, axes. The default is 1. backgroundColor : str , optional The desired color of the background. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "rgba(0,0,0,0)". marginLeft : int , optional The size in pixels of the left margin. The default value is 0. marginRight : int , optional The size in pixels of the right margin. The default value is 0. marginTop : int , optional The size in pixels of the top margin. The default value is 20. marginBottom : int , optional The size in pixels of the bottom margin. The default value is 0. tolerance : float , optional The desired tolerance. The default is 0.0001. Returns ------- plotly.graph_objs._figure.Figure The created plotly figure. """ from topologicpy.Vertex import Vertex from topologicpy.Edge import Edge from topologicpy.Wire import Wire if not isinstance(data, list): return None v0 = Vertex.ByCoordinates(0, 0, 0) v1 = Vertex.ByCoordinates(axisSize,0,0) v2 = Vertex.ByCoordinates(0,axisSize,0) v3 = Vertex.ByCoordinates(0,0,axisSize) if xAxis: xEdge = Edge.ByVertices([v0,v1], tolerance=tolerance) xData = Plotly.DataByTopology(xEdge, edgeColor="red", edgeWidth=6, showFaces=False, showEdges=True, showVertices=False, edgeLegendLabel="X-Axis") data = data + xData if yAxis: yEdge = Edge.ByVertices([v0,v2], tolerance=tolerance) yData = Plotly.DataByTopology(yEdge, edgeColor="green", edgeWidth=6, showFaces=False, showEdges=True, showVertices=False, edgeLegendLabel="Y-Axis") data = data + yData if zAxis: zEdge = Edge.ByVertices([v0,v3], tolerance=tolerance) zData = Plotly.DataByTopology(zEdge, edgeColor="blue", edgeWidth=6, showFaces=False, showEdges=True, showVertices=False, edgeLegendLabel="Z-Axis") data = data + zData figure = go.Figure(data=data) figure.update_layout( width=width, height=height, showlegend=True, scene = dict( xaxis = dict(visible=False), yaxis = dict(visible=False), zaxis =dict(visible=False), ), scene_aspectmode='data', paper_bgcolor=backgroundColor, plot_bgcolor=backgroundColor, margin=dict(l=marginLeft, r=marginRight, t=marginTop, b=marginBottom), ) figure.update_xaxes(showgrid=False, zeroline=False, visible=False) figure.update_yaxes(showgrid=False, zeroline=False, visible=False) return figure @staticmethod def FigureByJSONFile(file): """ Imports a plotly figure from a JSON file. Parameters ---------- file : file object The JSON file. Returns ------- plotly.graph_objs._figure.Figure The imported figure. """ figure = None if not file: return None figure = plotly.io.read_json(file, output_type='Figure', skip_invalid=True, engine=None) file.close() return figure @staticmethod def FigureByJSONPath(path): """ Imports a plotly figure from a JSON file path. Parameters ---------- path : str The path to the BRep file. Returns ------- plotly.graph_objs._figure.Figure The imported figure. """ if not path: return None try: file = open(path) except: print("Plotly.FigureByJSONPath - Error: the JSON file is not a valid file. Returning None.") return None return Plotly.FigureByJSONFile(file) @staticmethod def FigureByPieChart(data, values, names): """ Creates a plotly pie chart figure. Parameters ---------- data : list The input list of plotly data. values : list The input list of values. names : list The input list of names. """ import plotly.express as px try: import pandas as pd except: print("Plotly.FigureByPieChart - Installing required pandas library.") try: os.system("pip install pandas") except: os.system("pip install pandas --user") try: import pandas as pd except: warnings.warn("Plotly.FigureByPieChart - Error: Could not import pandas. Please install the pandas library manually. Returning None") return None dlist = list(map(list, zip(*data))) df = pd.DataFrame(dlist, columns=data['names']) fig = px.pie(df, values=values, names=names) return fig @staticmethod def FigureByTopology(topology, showVertices=True, vertexSize=1.1, vertexColor="black", vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[], vertexMinGroup=None, vertexMaxGroup=None, showVertexLegend=False, vertexLegendLabel="Topology Vertices", vertexLegendRank=1, vertexLegendGroup=1, showEdges=True, edgeWidth=1, edgeColor="black", edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[], edgeMinGroup=None, edgeMaxGroup=None, showEdgeLegend=False, edgeLegendLabel="Topology Edges", edgeLegendRank=2, edgeLegendGroup=2, showFaces=True, faceOpacity=0.5, faceColor="#FAFAFA", faceLabelKey=None, faceGroupKey=None, faceGroups=[], faceMinGroup=None, faceMaxGroup=None, showFaceLegend=False, faceLegendLabel="Topology Faces", faceLegendRank=3, faceLegendGroup=3, intensityKey=None, width=950, height=500, xAxis=False, yAxis=False, zAxis=False, axisSize=1, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=20, marginBottom=0, showScale=False, cbValues=[], cbTicks=5, cbX=-0.15, cbWidth=15, cbOutlineWidth=0, cbTitle="", cbSubTitle="", cbUnits="", colorScale="viridis", mantissa=6, tolerance=0.0001): """ Creates a figure from the input topology. Parameters ---------- topology : topologic_core.Topology The input topology. This must contain faces and or edges. showVertices : bool , optional If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True. vertexSize : float , optional The desired size of the vertices. The default is 1.1. vertexColor : str , optional The desired color of the output vertices. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". vertexLabelKey : str , optional The dictionary key to use to display the vertex label. The default is None. vertexGroupKey : str , optional The dictionary key to use to display the vertex group. The default is None. vertexGroups : list , optional The list of vertex groups against which to index the color of the vertex. The default is []. vertexMinGroup : int or float , optional For numeric vertexGroups, vertexMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the minimum value in vertexGroups. The default is None. edgeMaxGroup : int or float , optional For numeric vertexGroups, vertexMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the maximum value in vertexGroups. The default is None. showVertexLegend : bool, optional If set to True, the legend for the vertices of this topology is shown. Otherwise, it isn't. The default is False. vertexLegendLabel : str , optional The legend label string used to identify vertices. The default is "Topology Vertices". vertexLegendRank : int , optional The legend rank order of the vertices of this topology. The default is 1. vertexLegendGroup : int , optional The number of the vertex legend group to which the vertices of this topology belong. The default is 1. showEdges : bool , optional If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True. edgeWidth : float , optional The desired thickness of the output edges. The default is 1. edgeColor : str , optional The desired color of the output edges. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". edgeLabelKey : str , optional The dictionary key to use to display the edge label. The default is None. edgeGroupKey : str , optional The dictionary key to use to display the edge group. The default is None. edgeGroups : list , optional The list of edge groups against which to index the color of the edge. The default is []. edgeMinGroup : int or float , optional For numeric edgeGroups, edgeMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the minimum value in edgeGroups. The default is None. edgeMaxGroup : int or float , optional For numeric edgeGroups, edgeMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the maximum value in edgeGroups. The default is None. showEdgeLegend : bool, optional If set to True, the legend for the edges of this topology is shown. Otherwise, it isn't. The default is False. edgeLegendLabel : str , optional The legend label string used to identify edges. The default is "Topology Edges". edgeLegendRank : int , optional The legend rank order of the edges of this topology. The default is 2. edgeLegendGroup : int , optional The number of the edge legend group to which the edges of this topology belong. The default is 2. showFaces : bool , optional If set to True the faces will be drawn. Otherwise, they will not be drawn. The default is True. faceOpacity : float , optional The desired opacity of the output faces (0=transparent, 1=opaque). The default is 0.5. faceColor : str , optional The desired color of the output faces. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "#FAFAFA". faceLabelKey : str , optional The dictionary key to use to display the face label. The default is None. faceGroupKey : str , optional The dictionary key to use to display the face group. The default is None. faceGroups : list , optional The list of face groups against which to index the color of the face. This can bhave numeric or string values. This should match the type of value associated with the faceGroupKey. The default is []. faceMinGroup : int or float , optional For numeric faceGroups, minGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the minimum value in faceGroups. The default is None. faceMaxGroup : int or float , optional For numeric faceGroups, maxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the maximum value in faceGroups. The default is None. showFaceLegend : bool, optional If set to True, the legend for the faces of this topology is shown. Otherwise, it isn't. The default is False. faceLegendLabel : str , optional The legend label string used to idenitfy edges. The default is "Topology Faces". faceLegendRank : int , optional The legend rank order of the faces of this topology. The default is 3. faceLegendGroup : int , optional The number of the face legend group to which the faces of this topology belong. The default is 3. width : int , optional The width in pixels of the figure. The default value is 950. height : int , optional The height in pixels of the figure. The default value is 950. xAxis : bool , optional If set to True the x axis is drawn. Otherwise it is not drawn. The default is False. yAxis : bool , optional If set to True the y axis is drawn. Otherwise it is not drawn. The default is False. zAxis : bool , optional If set to True the z axis is drawn. Otherwise it is not drawn. The default is False. backgroundColor : str , optional The desired color of the background. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "rgba(0,0,0,0)". marginLeft : int , optional The size in pixels of the left margin. The default value is 0. marginRight : int , optional The size in pixels of the right margin. The default value is 0. marginTop : int , optional The size in pixels of the top margin. The default value is 20. marginBottom : int , optional The size in pixels of the bottom margin. The default value is 0. camera : list , optional The desired location of the camera). The default is [-1.25, -1.25, 1.25]. center : list , optional The desired center (camera target). The default is [0, 0, 0]. up : list , optional The desired up vector. The default is [0, 0, 1]. renderer : str , optional The desired renderer. See Plotly.Renderers(). The default is "notebook". intensityKey : str , optional If not None, the dictionary of each vertex is searched for the value associated with the intensity key. This value is then used to color-code the vertex based on the colorScale. The default is None. showScale : bool , optional If set to True, the colorbar is shown. The default is False. cbValues : list , optional The input list of values to use for the colorbar. The default is []. cbTicks : int , optional The number of ticks to use on the colorbar. The default is 5. cbX : float , optional The x location of the colorbar. The default is -0.15. cbWidth : int , optional The width in pixels of the colorbar. The default is 15 cbOutlineWidth : int , optional The width in pixels of the outline of the colorbar. The default is 0. cbTitle : str , optional The title of the colorbar. The default is "". cbSubTitle : str , optional The subtitle of the colorbar. The default is "". cbUnits: str , optional The units used in the colorbar. The default is "" colorScale : str , optional The desired type of plotly color scales to use (e.g. "viridis", "plasma"). The default is "viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. mantissa : int , optional The desired length of the mantissa for the values listed on the colorbar. The default is 6. tolerance : float , optional The desired tolerance. The default is 0.0001. Returns ------- Plotly figure """ from topologicpy.Topology import Topology if not Topology.IsInstance(topology, "Topology"): print("Plotly.FigureByTopology - Error: the input topology is not a valid topology. Returning None.") return None data = Plotly.DataByTopology(topology=topology, showVertices=showVertices, vertexSize=vertexSize, vertexColor=vertexColor, vertexLabelKey=vertexLabelKey, vertexGroupKey=vertexGroupKey, vertexGroups=vertexGroups, vertexMinGroup=vertexMinGroup, vertexMaxGroup=vertexMaxGroup, showVertexLegend=showVertexLegend, vertexLegendLabel=vertexLegendLabel, vertexLegendRank=vertexLegendRank, vertexLegendGroup=vertexLegendGroup, showEdges=showEdges, edgeWidth=edgeWidth, edgeColor=edgeColor, edgeLabelKey=edgeLabelKey, edgeGroupKey=edgeGroupKey, edgeGroups=edgeGroups, edgeMinGroup=edgeMinGroup, edgeMaxGroup=edgeMaxGroup, showEdgeLegend=showEdgeLegend, edgeLegendLabel=edgeLegendLabel, edgeLegendRank=edgeLegendRank, edgeLegendGroup=edgeLegendGroup, showFaces=showFaces, faceOpacity=faceOpacity, faceColor=faceColor, faceLabelKey=faceLabelKey, faceGroupKey=faceGroupKey, faceGroups=faceGroups, faceMinGroup=faceMinGroup, faceMaxGroup=faceMaxGroup, showFaceLegend=showFaceLegend, faceLegendLabel=faceLegendLabel, faceLegendRank=faceLegendRank, faceLegendGroup=faceLegendGroup, intensityKey=intensityKey, colorScale=colorScale, tolerance=tolerance) figure = Plotly.FigureByData(data=data, width=width, height=height, xAxis=xAxis, yAxis=yAxis, zAxis=zAxis, axisSize=axisSize, backgroundColor=backgroundColor, marginLeft=marginLeft, marginRight=marginRight, marginTop=marginTop, marginBottom=marginBottom, tolerance=tolerance) if showScale: figure = Plotly.AddColorBar(figure, values=cbValues, nTicks=cbTicks, xPosition=cbX, width=cbWidth, outlineWidth=cbOutlineWidth, title=cbTitle, subTitle=cbSubTitle, units=cbUnits, colorScale=colorScale, mantissa=mantissa) return figure @staticmethod def FigureExportToJSON(figure, path, overwrite=False): """ Exports the input plotly figure to a JSON file. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. path : str The input file path. overwrite : bool , optional If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't. Returns ------- bool True if the export operation is successful. False otherwise. """ if not isinstance(figure, plotly.graph_objs._figure.Figure): print("Plotly.FigureExportToJSON - Error: The input figure is not a plolty figure. Returning None.") return None if not isinstance(path, str): print("Plotly.FigureExportToJSON - Error: The input path is not a string. Returning None.") return None # Make sure the file extension is .json ext = path[len(path)-5:len(path)] if ext.lower() != ".json": path = path+".json" f = None try: if overwrite == True: f = open(path, "w") else: f = open(path, "x") # Try to create a new File except: print("Plotly.FigureExportToJSON - Error: Could not create a new file at the following location: "+path+". Returning None.") return None if (f): plotly.io.write_json(figure, f, validate=True, pretty=False, remove_uids=True, engine=None) f.close() return True if f: try: f.close() except: pass return False @staticmethod def FigureExportToPDF(figure, path, width=1920, height=1200, overwrite=False): """ Exports the input plotly figure to a PDF file. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. path : str The input file path. width : int, optional The width of the exported image in pixels. The default is 1920. height : int , optional The height of the exported image in pixels. The default is 1200. overwrite : bool , optional If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't. Returns ------- bool True if the export operation is successful. False otherwise. """ import os if not isinstance(figure, plotly.graph_objs._figure.Figure): print("Plotly.FigureExportToPNG - Error: The input figure is not a plolty figure. Returning None.") return None if not isinstance(path, str): print("Plotly.FigureExportToPNG - Error: The input path is not a string. Returning None.") return None # Make sure the file extension is .pdf ext = path[len(path)-4:len(path)] if ext.lower() != ".pdf": path = path+".pdf" if overwrite == False and os.path.exists(path): print("Plotly.FigureExportToPDF - Error: A file already exists at this location and overwrite is set to False. Returning None.") return None plotly.io.write_image(figure, path, format='pdf', scale=1, width=width, height=height, validate=True, engine='auto') return True @staticmethod def FigureExportToPNG(figure, path, width=1920, height=1200, overwrite=False): """ Exports the input plotly figure to a PNG file. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. path : str The input file path. width : int, optional The width of the exported image in pixels. The default is 1920. height : int , optional The height of the exported image in pixels. The default is 1200. overwrite : bool , optional If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't. Returns ------- bool True if the export operation is successful. False otherwise. """ import os if not isinstance(figure, plotly.graph_objs._figure.Figure): print("Plotly.FigureExportToPNG - Error: The input figure is not a plolty figure. Returning None.") return None if not isinstance(path, str): print("Plotly.FigureExportToPNG - Error: The input path is not a string. Returning None.") return None # Make sure the file extension is .png ext = path[len(path)-4:len(path)] if ext.lower() != ".png": path = path+".png" if overwrite == False and os.path.exists(path): print("Plotly.FigureExportToPNG - Error: A file already exists at this location and overwrite is set to False. Returning None.") return None plotly.io.write_image(figure, path, format='png', scale=1, width=width, height=height, validate=True, engine='auto') return True @staticmethod def FigureExportToSVG(figure, path, width=1920, height=1200, overwrite=False): """ Exports the input plotly figure to a SVG file. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. path : str The input file path. width : int, optional The width of the exported image in pixels. The default is 1920. height : int , optional The height of the exported image in pixels. The default is 1200. overwrite : bool , optional If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't. Returns ------- bool True if the export operation is successful. False otherwise. """ import os if not isinstance(figure, plotly.graph_objs._figure.Figure): print("Plotly.FigureExportToSVG - Error: The input figure is not a plolty figure. Returning None.") return None if not isinstance(path, str): print("Plotly.FigureExportToSVG - Error: The input path is not a string. Returning None.") return None # Make sure the file extension is .svg ext = path[len(path)-4:len(path)] if ext.lower() != ".svg": path = path+".svg" if overwrite == False and os.path.exists(path): print("Plotly.FigureExportToSVG - Error: A file already exists at this location and overwrite is set to False. Returning None.") return None plotly.io.write_image(figure, path, format='svg', scale=1, width=width, height=height, validate=True, engine='auto') return True @staticmethod def SetCamera(figure, camera=[-1.25, -1.25, 1.25], center=[0, 0, 0], up=[0, 0, 1], projection="perspective"): """ Sets the camera for the input figure. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. camera : list , optional The desired location of the camera. The default is [-1.25, -1.25, 1.25]. center : list , optional The desired center (camera target). The default is [0, 0, 0]. up : list , optional The desired up vector. The default is [0, 0, 1]. projection : str , optional The desired type of projection. The options are "orthographic" or "perspective". It is case insensitive. The default is "perspective" Returns ------- plotly.graph_objs._figure.Figure The updated figure """ if not isinstance(camera, list): camera = [-1.25, -1.25, 1.25] if not isinstance(center, list): center = [0, 0, 0] if not isinstance(up, list): up = [0, 0, 1] projection = projection.lower() if projection in "orthographic": projection = "orthographic" else: projection = "perspective" scene_camera = dict( up=dict(x=up[0], y=up[1], z=up[2]), eye=dict(x=camera[0], y=camera[1], z=camera[2]), center=dict(x=center[0], y=center[1], z=center[2]), projection=dict(type=projection) ) figure.update_layout(scene_camera=scene_camera) return figure @staticmethod def Show(figure, camera=[-1.25, -1.25, 1.25], center=[0, 0, 0], up=[0, 0, 1], renderer=None, projection="perspective"): """ Shows the input figure. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. camera : list , optional The desired location of the camera. The default is [0, 0, 0]. center : list , optional The desired center (camera target). The default is [0, 0, 0]. up : list , optional The desired up vector. The default is [0, 0, 1]. renderer : str , optional The desired renderer. See Plotly.Renderers(). If set to None, the code will attempt to discover the most suitable renderer. The default is None. projection : str, optional The desired type of projection. The options are "orthographic" or "perspective". It is case insensitive. The default is "perspective" Returns ------- None """ if figure == None: print("Plotly.Show - Error: The input is NULL. Returning None.") return None if not isinstance(figure, plotly.graph_objs._figure.Figure): print("Plotly.Show - Error: The input is not a figure. Returning None.") return None if renderer == None: renderer = Plotly.Renderer() if not renderer.lower() in Plotly.Renderers(): print("Plotly.Show - Error: The input renderer is not in the approved list of renderers. Returning None.") return None if not camera == None and not center == None and not up == None: figure = Plotly.SetCamera(figure, camera=camera, center=center, up=up, projection=projection) if renderer.lower() == "offline": ofl.plot(figure) else: figure.show(renderer=renderer) return None @staticmethod def Renderer(): """ Return the renderer most suitable for the environment in which the script is running. Parameters ---------- Returns ------- str The most suitable renderer type for the environment in which the script is running. Currently, this is limited to: - "vscode" if running in Visual Studio Code - "colab" if running in Google Colab - "iframe" if running in jupyter notebook or jupyterlab - "browser" if running in anything else """ import sys import os if 'VSCODE_PID' in os.environ: return 'vscode' elif "google.colab" in sys.modules: return "colab" elif "ipykernel" in sys.modules: return "iframe" #works for jupyter notebook and jupyterlab else: return "browser" @staticmethod def Renderers(): """ Returns a list of the available plotly renderers. Parameters ---------- Returns ------- list The list of the available plotly renderers. """ return ['plotly_mimetype', 'jupyterlab', 'nteract', 'vscode', 'notebook', 'notebook_connected', 'kaggle', 'azure', 'colab', 'cocalc', 'databricks', 'json', 'png', 'jpeg', 'jpg', 'svg', 'pdf', 'browser', 'firefox', 'chrome', 'chromium', 'iframe', 'iframe_connected', 'sphinx_gallery', 'sphinx_gallery_png', 'offline'] @staticmethod def ExportToImage(figure, path, format="png", width="1920", height="1080"): """ Exports the plotly figure to an image. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. path : str The image file path. format : str , optional The desired format. This can be any of "jpg", "jpeg", "pdf", "png", "svg", or "webp". It is case insensitive. The default is "png". width : int , optional The width in pixels of the figure. The default value is 1920. height : int , optional The height in pixels of the figure. The default value is 1080. Returns ------- bool True if the image was exported sucessfully. False otherwise. """ if not isinstance(figure, plotly.graph_objs._figure.Figure): return None if not isinstance(path, str): return None if not format.lower() in ["jpg", "jpeg", "pdf", "png", "svg", "webp"]: return None returnStatus = False try: plotly.io.write_image(figure, path, format=format.lower(), scale=None, width=width, height=height, validate=True, engine='auto') returnStatus = True except: returnStatus = False return returnStatusStatic methods
def AddColorBar(figure, values=[], nTicks=5, xPosition=-0.15, width=15, outlineWidth=0, title='', subTitle='', units='', colorScale='viridis', mantissa: int = 6)-
Adds a color bar to the input figure
Parameters
figure:plotly.graph_objs._figure.Figure- The input plotly figure.
values:list, optional- The input list of values to use for the color bar. The default is [].
nTicks:int, optional- The number of ticks to use on the color bar. The default is 5.
xPosition:float, optional- The x location of the color bar. The default is -0.15.
width:int, optional- The width in pixels of the color bar. The default is 15
outlineWidth:int, optional- The width in pixels of the outline of the color bar. The default is 0.
title:str, optional- The title of the color bar. The default is "".
subTitle:str, optional- The subtitle of the color bar. The default is "".
units:str, optional- The units used in the color bar. The default is ""
colorScale:str, optional- The desired type of plotly color scales to use (e.g. "viridis", "plasma"). The default is "viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. In addition to these, three color-blind friendly scales are included. These are "protanopia", "deuteranopia", and "tritanopia" for red, green, and blue colorblindness respectively.
mantissa:int, optional- The desired length of the mantissa for the values listed on the color bar. The default is 6.
Returns
plotly.graph_objs._figure.Figure- The input figure with the color bar added.
Expand source code
@staticmethod def AddColorBar(figure, values=[], nTicks=5, xPosition=-0.15, width=15, outlineWidth=0, title="", subTitle="", units="", colorScale="viridis", mantissa: int = 6): """ Adds a color bar to the input figure Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. values : list , optional The input list of values to use for the color bar. The default is []. nTicks : int , optional The number of ticks to use on the color bar. The default is 5. xPosition : float , optional The x location of the color bar. The default is -0.15. width : int , optional The width in pixels of the color bar. The default is 15 outlineWidth : int , optional The width in pixels of the outline of the color bar. The default is 0. title : str , optional The title of the color bar. The default is "". subTitle : str , optional The subtitle of the color bar. The default is "". units: str , optional The units used in the color bar. The default is "" colorScale : str , optional The desired type of plotly color scales to use (e.g. "viridis", "plasma"). The default is "viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. In addition to these, three color-blind friendly scales are included. These are "protanopia", "deuteranopia", and "tritanopia" for red, green, and blue colorblindness respectively. mantissa : int , optional The desired length of the mantissa for the values listed on the color bar. The default is 6. Returns ------- plotly.graph_objs._figure.Figure The input figure with the color bar added. """ if not isinstance(figure, plotly.graph_objs._figure.Figure): return None if units: units = "Units: "+units minValue = min(values) maxValue = max(values) step = (maxValue - minValue)/float(nTicks-1) r = [round(minValue+i*step, mantissa) for i in range(nTicks)] r[-1] = round(maxValue, mantissa) # Define the minimum and maximum range of the colorbar rs = [str(x) for x in r] # Define the colorbar as a trace with no data, x or y coordinates colorbar_trace = go.Scatter( x=[0], y=[0], mode="markers", showlegend=False, marker=dict( size=0, colorscale=Plotly.ColorScale(colorScale), # choose the colorscale cmin=minValue, cmax=maxValue, color=['rgba(0,0,0,0)'], colorbar=dict( x=xPosition, title="<b>"+title+"</b><br>"+subTitle+"<br>"+units, # title of the colorbar ticks="outside", # position of the ticks tickvals=r, # values of the ticks ticktext=rs, # text of the ticks tickmode="array", thickness=width, outlinewidth=outlineWidth, ) ) ) figure.add_trace(colorbar_trace) return figure def ColorScale(colorScale: str = 'viridis')-
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@staticmethod def ColorScale(colorScale: str = "viridis"): # Colors recommended by various sources for color-blind-friendly palettes protanopia_colors = [ "#E69F00", # orange "#56B4E9", # sky blue "#009E73", # bluish green "#F0E442", # yellow "#0072B2", # blue "#D55E00", # vermillion "#CC79A7", # reddish purple ] deuteranopia_colors = [ "#377EB8", # blue "#FF7F00", # orange "#4DAF4A", # green "#F781BF", # pink "#A65628", # brown "#984EA3", # purple "#999999", # grey ] tritanopia_colors = [ "#E69F00", # orange "#56B4E9", # sky blue "#009E73", # bluish green "#F0E442", # yellow "#0072B2", # blue "#D55E00", # vermillion "#CC79A7", # reddish purple ] # Create colorscales for Plotly def create_colorscale(colors): colorscale = [] num_colors = len(colors) for i, color in enumerate(colors): position = i / (num_colors - 1) colorscale.append((position, color)) return colorscale if "prota" in colorScale.lower(): return create_colorscale(protanopia_colors) elif "deutera" in colorScale.lower(): return create_colorscale(deuteranopia_colors) elif "trita"in colorScale.lower(): return create_colorscale(tritanopia_colors) else: return colorScale def Colors()-
Returns the list of named CSS colors that plotly can use.
Returns
list- The list of named CSS colors.
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@staticmethod def Colors(): """ Returns the list of named CSS colors that plotly can use. Returns ------- list The list of named CSS colors. """ return ["aliceblue","antiquewhite","aqua", "aquamarine","azure","beige", "bisque","black","blanchedalmond", "blue","blueviolet","brown", "burlywood","cadetblue", "chartreuse","chocolate", "coral","cornflowerblue","cornsilk", "crimson","cyan","darkblue", "darkcyan","darkgoldenrod","darkgray", "darkgrey","darkgreen","darkkhaki", "darkmagenta","darkolivegreen","darkorange", "darkorchid","darkred","darksalmon", "darkseagreen","darkslateblue","darkslategray", "darkslategrey","darkturquoise","darkviolet", "deeppink","deepskyblue","dimgray", "dimgrey","dodgerblue","firebrick", "floralwhite","forestgreen","fuchsia", "gainsboro","ghostwhite","gold", "goldenrod","gray","grey", "green"," greenyellow","honeydew", "hotpink","indianred","indigo", "ivory","khaki","lavender", "lavenderblush","lawngreen","lemonchiffon", "lightblue","lightcoral","lightcyan", "lightgoldenrodyellow","lightgray","lightgrey", "lightgreen","lightpink","lightsalmon", "lightseagreen","lightskyblue","lightslategray", "lightslategrey","lightsteelblue","lightyellow", "lime","limegreen","linen", "magenta","maroon","mediumaquamarine", "mediumblue","mediumorchid","mediumpurple", "mediumseagreen","mediumslateblue","mediumspringgreen", "mediumturquoise","mediumvioletred","midnightblue", "mintcream","mistyrose","moccasin", "navajowhite","navy","oldlace", "olive","olivedrab","orange", "orangered","orchid","palegoldenrod", "palegreen","paleturquoise","palevioletred", "papayawhip","peachpuff","peru", "pink","plum","powderblue", "purple","red","rosybrown", "royalblue","rebeccapurple","saddlebrown", "salmon","sandybrown","seagreen", "seashell","sienna","silver", "skyblue","slateblue","slategray", "slategrey","snow","springgreen", "steelblue","tan","teal", "thistle","tomato","turquoise", "violet","wheat","white", "whitesmoke","yellow","yellowgreen"] def DataByDGL(data, labels)-
Returns a data frame from the DGL data.
Parameters
data:list- The data to display.
labels:list- The labels to use for the data. The data with the labels in this list will be extracted and used in the returned dataFrame.
Returns
pd.DataFrame- A pandas dataFrame
Expand source code
@staticmethod def DataByDGL(data, labels): """ Returns a data frame from the DGL data. Parameters ---------- data : list The data to display. labels : list The labels to use for the data. The data with the labels in this list will be extracted and used in the returned dataFrame. Returns ------- pd.DataFrame A pandas dataFrame """ try: import pandas as pd except: print("Plotly - Installing required pandas library.") try: os.system("pip install pandas") except: os.system("pip install pandas --user") try: import pandas as pd except: warnings.warn("Plotly.DataByDGL - Error: Could not import pandas. Please install the pandas library manually. Returning None") return None if isinstance(data[labels[0]][0], int): xAxis_list = list(range(1, data[labels[0]][0]+1)) else: xAxis_list = data[labels[0]][0] plot_data = [xAxis_list] for i in range(1,len(labels)): plot_data.append(data[labels[i]][0][:len(xAxis_list)]) dlist = list(map(list, zip(*plot_data))) df = pd.DataFrame(dlist, columns=labels) return df def DataByGraph(graph, vertexColor='black', vertexSize=6, vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[], showVertices=True, showVertexLegend=False, edgeColor='black', edgeWidth=1, edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[], showEdges=True, showEdgeLegend=False, colorScale='viridis')-
Creates plotly vertex and edge data from the input graph.
Parameters
graph:topologic_core.Graph- The input graph.
vertexColor:str, optional- The desired color of the output vertices. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black".
vertexSize:float, optional- The desired size of the vertices. The default is 6.
vertexLabelKey:str, optional- The dictionary key to use to display the vertex label. The default is None.
vertexGroupKey:str, optional- The dictionary key to use to display the vertex group. The default is None.
vertexGroups:list, optional- The list of vertex groups against which to index the color of the vertex. The default is [].
showVertices:bool, optional- If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True.
showVertexLegend:bool, optional- If set to True the vertex legend will be drawn. Otherwise, it will not be drawn. The default is False.
edgeColor:str, optional- The desired color of the output edges. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black".
edgeWidth:float, optional- The desired thickness of the output edges. The default is 1.
edgeLabelKey:str, optional- The dictionary key to use to display the edge label. The default is None.
edgeGroupKey:str, optional- The dictionary key to use to display the edge group. The default is None.
edgeGroups:list, optional- The list of groups to use for indexing the color of edges. The default is None.
showEdges:bool, optional- If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True.
showEdgeLegend:bool, optional- If set to True the edge legend will be drawn. Otherwise, it will not be drawn. The default is False.
colorScale:str, optional- The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/.
Returns
list- The vertex and edge data list.
Expand source code
@staticmethod def DataByGraph(graph, vertexColor="black", vertexSize=6, vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[], showVertices=True, showVertexLegend=False, edgeColor="black", edgeWidth=1, edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[], showEdges=True, showEdgeLegend=False, colorScale="viridis"): """ Creates plotly vertex and edge data from the input graph. Parameters ---------- graph : topologic_core.Graph The input graph. vertexColor : str , optional The desired color of the output vertices. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". vertexSize : float , optional The desired size of the vertices. The default is 6. vertexLabelKey : str , optional The dictionary key to use to display the vertex label. The default is None. vertexGroupKey : str , optional The dictionary key to use to display the vertex group. The default is None. vertexGroups : list , optional The list of vertex groups against which to index the color of the vertex. The default is []. showVertices : bool , optional If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True. showVertexLegend : bool , optional If set to True the vertex legend will be drawn. Otherwise, it will not be drawn. The default is False. edgeColor : str , optional The desired color of the output edges. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". edgeWidth : float , optional The desired thickness of the output edges. The default is 1. edgeLabelKey : str , optional The dictionary key to use to display the edge label. The default is None. edgeGroupKey : str , optional The dictionary key to use to display the edge group. The default is None. edgeGroups : list , optional The list of groups to use for indexing the color of edges. The default is None. showEdges : bool , optional If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True. showEdgeLegend : bool , optional If set to True the edge legend will be drawn. Otherwise, it will not be drawn. The default is False. colorScale : str , optional The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. Returns ------- list The vertex and edge data list. """ from topologicpy.Vertex import Vertex from topologicpy.Edge import Edge from topologicpy.Dictionary import Dictionary from topologicpy.Topology import Topology from topologicpy.Graph import Graph import plotly.graph_objs as go if not Topology.IsInstance(graph, "Graph"): return None v_labels = [] v_groupList = [] data = [] if showVertices: vertices = Graph.Vertices(graph) if vertexLabelKey or vertexGroupKey: for v in vertices: Xn=[round(Vertex.X(v), 4) for v in vertices] # x-coordinates of nodes Yn=[round(Vertex.Y(v), 4) for v in vertices] # y-coordinates of nodes Zn=[round(Vertex.Z(v), 4) for v in vertices] # x-coordinates of nodes v_label = "" v_group = "" d = Topology.Dictionary(v) if d: try: v_label = str(Dictionary.ValueAtKey(d, key=vertexLabelKey)) or "" except: v_label = "" try: v_group = Dictionary.ValueAtKey(d, key=vertexGroupKey) except: v_group = None try: v_groupList.append(vertexGroups.index(v_group)) except: v_groupList.append(len(vertexGroups)) if not v_label == "" and not v_group == "": if v_group == 0: v_label = v_label+" (0)" else: v_label = v_label+" ("+str(v_group)+")" v_labels.append(v_label) else: for v in vertices: Xn=[round(Vertex.X(v), 4) for v in vertices] # x-coordinates of nodes Yn=[round(Vertex.Y(v), 4) for v in vertices] # y-coordinates of nodes Zn=[round(Vertex.Z(v), 4) for v in vertices] # x-coordinates of nodes if len(list(set(v_groupList))) < 2: v_groupList = vertexColor if len(v_labels) < 1: v_labels = "" v_trace=go.Scatter3d(x=Xn, y=Yn, z=Zn, mode='markers', name='Graph Vertices', legendgroup=4, legendrank=4, showlegend=showVertexLegend, marker=dict(symbol='circle', size=vertexSize, color=v_groupList, colorscale=Plotly.ColorScale(colorScale), line=dict(color=edgeColor, width=0.5) ), text=v_labels, hoverinfo='text' ) data.append(v_trace) if showEdges: Xe=[] Ye=[] Ze=[] e_labels = [] e_groupList = [] edges = Graph.Edges(graph) if edgeLabelKey or edgeGroupKey: for e in edges: sv = Edge.StartVertex(e) ev = Edge.EndVertex(e) Xe+=[round(Vertex.X(sv), 4), round(Vertex.X(ev), 4), None] # x-coordinates of edge ends Ye+=[round(Vertex.Y(sv), 4), round(Vertex.Y(ev), 4), None] # y-coordinates of edge ends Ze+=[round(Vertex.Z(sv), 4), round(Vertex.Z(ev), 4), None] # z-coordinates of edge ends e_label = "" e_group = "" d = Topology.Dictionary(e) if d: try: e_label = str(Dictionary.ValueAtKey(d, key=edgeLabelKey)) or "" except: e_label = "" try: e_group = str(Dictionary.ValueAtKey(d, key=edgeGroupKey)) or "" except: e_group = "" try: e_groupList.append(edgeGroups.index(e_group)) except: e_groupList.append(len(edgeGroups)) if not e_label == "" and not e_group == "": e_label = e_label+" ("+e_group+")" e_labels.append(e_label) else: for e in edges: sv = Edge.StartVertex(e) ev = Edge.EndVertex(e) Xe+=[round(Vertex.X(sv), 4), round(Vertex.X(ev), 4), None] # x-coordinates of edge ends Ye+=[round(Vertex.Y(sv), 4), round(Vertex.Y(ev), 4), None] # y-coordinates of edge ends Ze+=[round(Vertex.Z(sv), 4), round(Vertex.Z(ev), 4), None] # z-coordinates of edge ends if len(list(set(e_groupList))) < 2: e_groupList = edgeColor if len(e_labels) < 1: e_labels = "" e_trace=go.Scatter3d(x=Xe, y=Ye, z=Ze, mode='lines', name='Graph Edges', legendgroup=5, legendrank=5, showlegend=showEdgeLegend, line=dict(color=e_groupList, width=edgeWidth), text=e_labels, hoverinfo='text' ) data.append(e_trace) return data def DataByTopology(topology, showVertices=True, vertexSize=1.1, vertexColor='black', vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[], vertexMinGroup=None, vertexMaxGroup=None, showVertexLegend=False, vertexLegendLabel='Topology Vertices', vertexLegendRank=1, vertexLegendGroup=1, showEdges=True, edgeWidth=1, edgeColor='black', edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[], edgeMinGroup=None, edgeMaxGroup=None, showEdgeLegend=False, edgeLegendLabel='Topology Edges', edgeLegendRank=2, edgeLegendGroup=2, showFaces=True, faceOpacity=0.5, faceColor='#FAFAFA', faceLabelKey=None, faceGroupKey=None, faceGroups=[], faceMinGroup=None, faceMaxGroup=None, showFaceLegend=False, faceLegendLabel='Topology Faces', faceLegendRank=3, faceLegendGroup=3, intensityKey=None, intensities=[], colorScale='viridis', mantissa=6, tolerance=0.0001)-
Creates plotly face, edge, and vertex data.
Parameters
topology:topologic_core.Topology- The input topology. This must contain faces and or edges.
showVertices:bool, optional- If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True.
vertexSize:float, optional- The desired size of the vertices. The default is 1.1.
vertexColor:str, optional- The desired color of the output vertices. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black".
vertexLabelKey:str, optional- The dictionary key to use to display the vertex label. The default is None.
vertexGroupKey:str, optional- The dictionary key to use to display the vertex group. The default is None.
vertexGroups:list, optional- The list of vertex groups against which to index the color of the vertex. The default is [].
vertexMinGroup:intorfloat, optional- For numeric vertexGroups, vertexMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the minimum value in vertexGroups. The default is None.
vertexMaxGroup:intorfloat, optional- For numeric vertexGroups, vertexMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the maximum value in vertexGroups. The default is None.
showVertexLegend:bool, optional- If set to True, the legend for the vertices of this topology is shown. Otherwise, it isn't. The default is False.
vertexLegendLabel:str, optional- The legend label string used to identify vertices. The default is "Topology Vertices".
vertexLegendRank:int, optional- The legend rank order of the vertices of this topology. The default is 1.
vertexLegendGroup:int, optional- The number of the vertex legend group to which the vertices of this topology belong. The default is 1.
showEdges:bool, optional- If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True.
edgeWidth:float, optional- The desired thickness of the output edges. The default is 1.
edgeColor:str, optional- The desired color of the output edges. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black".
edgeLabelKey:str, optional- The dictionary key to use to display the edge label. The default is None.
edgeGroupKey:str, optional- The dictionary key to use to display the edge group. The default is None.
edgeGroups:list, optional- The list of edge groups against which to index the color of the edge. The default is [].
edgeMinGroup:intorfloat, optional- For numeric edgeGroups, edgeMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the minimum value in edgeGroups. The default is None.
edgeMaxGroup:intorfloat, optional- For numeric edgeGroups, edgeMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the maximum value in edgeGroups. The default is None.
showEdgeLegend:bool, optional- If set to True, the legend for the edges of this topology is shown. Otherwise, it isn't. The default is False.
edgeLegendLabel:str, optional- The legend label string used to identify edges. The default is "Topology Edges".
edgeLegendRank:int, optional- The legend rank order of the edges of this topology. The default is 2.
edgeLegendGroup:int, optional- The number of the edge legend group to which the edges of this topology belong. The default is 2.
showFaces:bool, optional- If set to True the faces will be drawn. Otherwise, they will not be drawn. The default is True.
faceOpacity:float, optional- The desired opacity of the output faces (0=transparent, 1=opaque). The default is 0.5.
faceColor:str, optional- The desired color of the output faces. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "#FAFAFA".
faceLabelKey:str, optional- The dictionary key to use to display the face label. The default is None.
faceGroupKey:str, optional- The dictionary key to use to display the face group. The default is None.
faceGroups:list, optional- The list of face groups against which to index the color of the face. This can bhave numeric or string values. This should match the type of value associated with the faceGroupKey. The default is [].
faceMinGroup:intorfloat, optional- For numeric faceGroups, minGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the minimum value in faceGroups. The default is None.
faceMaxGroup:intorfloat, optional- For numeric faceGroups, maxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the maximum value in faceGroups. The default is None.
showFaceLegend:bool, optional- If set to True, the legend for the faces of this topology is shown. Otherwise, it isn't. The default is False.
faceLegendLabel:str, optional- The legend label string used to idenitfy edges. The default is "Topology Faces".
faceLegendRank:int, optional- The legend rank order of the faces of this topology. The default is 3.
faceLegendGroup:int, optional- The number of the face legend group to which the faces of this topology belong. The default is 3.
intensityKey:str, optional- If not None, the dictionary of each vertex is searched for the value associated with the intensity key. This value is then used to color-code the vertex based on the colorScale. The default is None.
intensities:list, optional- The list of intensities against which to index the intensity of the vertex. The default is [].
colorScale:str, optional- The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/.
mantissa:int, optional- The desired length of the mantissa. The default is 6.
tolerance:float, optional- The desired tolerance. The default is 0.0001.
Returns
list- The vertex, edge, and face data list.
Expand source code
@staticmethod def DataByTopology(topology, showVertices=True, vertexSize=1.1, vertexColor="black", vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[], vertexMinGroup=None, vertexMaxGroup=None, showVertexLegend=False, vertexLegendLabel="Topology Vertices", vertexLegendRank=1, vertexLegendGroup=1, showEdges=True, edgeWidth=1, edgeColor="black", edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[], edgeMinGroup=None, edgeMaxGroup=None, showEdgeLegend=False, edgeLegendLabel="Topology Edges", edgeLegendRank=2, edgeLegendGroup=2, showFaces=True, faceOpacity=0.5, faceColor="#FAFAFA", faceLabelKey=None, faceGroupKey=None, faceGroups=[], faceMinGroup=None, faceMaxGroup=None, showFaceLegend=False, faceLegendLabel="Topology Faces", faceLegendRank=3, faceLegendGroup=3, intensityKey=None, intensities=[], colorScale="viridis", mantissa=6, tolerance=0.0001): """ Creates plotly face, edge, and vertex data. Parameters ---------- topology : topologic_core.Topology The input topology. This must contain faces and or edges. showVertices : bool , optional If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True. vertexSize : float , optional The desired size of the vertices. The default is 1.1. vertexColor : str , optional The desired color of the output vertices. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". vertexLabelKey : str , optional The dictionary key to use to display the vertex label. The default is None. vertexGroupKey : str , optional The dictionary key to use to display the vertex group. The default is None. vertexGroups : list , optional The list of vertex groups against which to index the color of the vertex. The default is []. vertexMinGroup : int or float , optional For numeric vertexGroups, vertexMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the minimum value in vertexGroups. The default is None. vertexMaxGroup : int or float , optional For numeric vertexGroups, vertexMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the maximum value in vertexGroups. The default is None. showVertexLegend : bool, optional If set to True, the legend for the vertices of this topology is shown. Otherwise, it isn't. The default is False. vertexLegendLabel : str , optional The legend label string used to identify vertices. The default is "Topology Vertices". vertexLegendRank : int , optional The legend rank order of the vertices of this topology. The default is 1. vertexLegendGroup : int , optional The number of the vertex legend group to which the vertices of this topology belong. The default is 1. showEdges : bool , optional If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True. edgeWidth : float , optional The desired thickness of the output edges. The default is 1. edgeColor : str , optional The desired color of the output edges. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". edgeLabelKey : str , optional The dictionary key to use to display the edge label. The default is None. edgeGroupKey : str , optional The dictionary key to use to display the edge group. The default is None. edgeGroups : list , optional The list of edge groups against which to index the color of the edge. The default is []. edgeMinGroup : int or float , optional For numeric edgeGroups, edgeMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the minimum value in edgeGroups. The default is None. edgeMaxGroup : int or float , optional For numeric edgeGroups, edgeMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the maximum value in edgeGroups. The default is None. showEdgeLegend : bool, optional If set to True, the legend for the edges of this topology is shown. Otherwise, it isn't. The default is False. edgeLegendLabel : str , optional The legend label string used to identify edges. The default is "Topology Edges". edgeLegendRank : int , optional The legend rank order of the edges of this topology. The default is 2. edgeLegendGroup : int , optional The number of the edge legend group to which the edges of this topology belong. The default is 2. showFaces : bool , optional If set to True the faces will be drawn. Otherwise, they will not be drawn. The default is True. faceOpacity : float , optional The desired opacity of the output faces (0=transparent, 1=opaque). The default is 0.5. faceColor : str , optional The desired color of the output faces. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "#FAFAFA". faceLabelKey : str , optional The dictionary key to use to display the face label. The default is None. faceGroupKey : str , optional The dictionary key to use to display the face group. The default is None. faceGroups : list , optional The list of face groups against which to index the color of the face. This can bhave numeric or string values. This should match the type of value associated with the faceGroupKey. The default is []. faceMinGroup : int or float , optional For numeric faceGroups, minGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the minimum value in faceGroups. The default is None. faceMaxGroup : int or float , optional For numeric faceGroups, maxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the maximum value in faceGroups. The default is None. showFaceLegend : bool, optional If set to True, the legend for the faces of this topology is shown. Otherwise, it isn't. The default is False. faceLegendLabel : str , optional The legend label string used to idenitfy edges. The default is "Topology Faces". faceLegendRank : int , optional The legend rank order of the faces of this topology. The default is 3. faceLegendGroup : int , optional The number of the face legend group to which the faces of this topology belong. The default is 3. intensityKey: str, optional If not None, the dictionary of each vertex is searched for the value associated with the intensity key. This value is then used to color-code the vertex based on the colorScale. The default is None. intensities : list , optional The list of intensities against which to index the intensity of the vertex. The default is []. colorScale : str , optional The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. mantissa : int , optional The desired length of the mantissa. The default is 6. tolerance : float , optional The desired tolerance. The default is 0.0001. Returns ------- list The vertex, edge, and face data list. """ from topologicpy.Topology import Topology from topologicpy.Dictionary import Dictionary from topologicpy.Color import Color from time import time def closest_index(input_value, values): return int(min(range(len(values)), key=lambda i: abs(values[i] - input_value))) def vertexData(vertices, dictionaries=[], color="black", size=1.1, labelKey=None, groupKey=None, minGroup=None, maxGroup=None, groups=[], legendLabel="Topology Vertices", legendGroup=1, legendRank=1, showLegend=True, colorScale="Viridis"): x = [] y = [] z = [] labels = [] groupList = [] label = "" group = "" if labelKey or groupKey: if groups: if len(groups) > 0: if type(groups[0]) == int or type(groups[0]) == float: if not minGroup: minGroup = min(groups) if not maxGroup: maxGroup = max(groups) else: minGroup = 0 maxGroup = len(groups) - 1 else: minGroup = 0 maxGroup = 1 for m, v in enumerate(vertices): x.append(round(v[0], mantissa)) y.append(round(v[1], mantissa)) z.append(round(v[2], mantissa)) label = "" group = "" if len(dictionaries) > 0: d = dictionaries[m] if d: try: label = str(Dictionary.ValueAtKey(d, key=labelKey)) or "" except: label = "" try: group = Dictionary.ValueAtKey(d, key=groupKey) or None except: group = "" try: if type(group) == int or type(group) == float: if group < minGroup: group = minGroup if group > maxGroup: group = maxGroup color = Color.ByValueInRange(group, minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) else: color = Color.ByValueInRange(groups.index(group), minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) color = "rgb("+str(color[0])+","+str(color[1])+","+str(color[2])+")" groupList.append(color) except: groupList.append(len(groups)) labels.append(label) else: for v in vertices: x.append(round(v[0], mantissa)) y.append(round(v[1], mantissa)) z.append(round(v[2], mantissa)) if len(list(set(groupList))) < 2: groupList = color if len(labels) < 1: labels = "" vData= go.Scatter3d(x=x, y=y, z=z, name=legendLabel, showlegend=showLegend, marker=dict(color=groupList, size=vertexSize), mode='markers', legendgroup=legendGroup, legendrank=legendRank, text=labels, hoverinfo='text', hovertext=labels ) return vData def edgeData(vertices, edges, dictionaries=None, color="black", width=1, labelKey=None, groupKey=None, minGroup=None, maxGroup=None, groups=[], legendLabel="Topology Edges", legendGroup=2, legendRank=2, showLegend=True, colorScale="Viridis"): x = [] y = [] z = [] labels = [] groupList = [] label = "" group = "" if labelKey or groupKey: if groups: if len(groups) > 0: if type(groups[0]) == int or type(groups[0]) == float: if not minGroup: minGroup = min(groups) if not maxGroup: maxGroup = max(groups) else: minGroup = 0 maxGroup = len(groups) - 1 else: minGroup = 0 maxGroup = 1 for m, e in enumerate(edges): sv = vertices[e[0]] ev = vertices[e[1]] x+=[round(sv[0], mantissa),round(ev[0], mantissa), None] # x-coordinates of edge ends y+=[round(sv[1], mantissa),round(ev[1], mantissa), None] # y-coordinates of edge ends z+=[round(sv[2], mantissa),round(ev[2], mantissa), None] # z-coordinates of edge ends label = "" group = "" if len(dictionaries) > 0: d = dictionaries[m] if d: try: label = str(Dictionary.ValueAtKey(d, key=labelKey)) or "" except: label = "" try: group = Dictionary.ValueAtKey(d, key=groupKey) or None except: group = "" try: if type(group) == int or type(group) == float: if group < minGroup: group = minGroup if group > maxGroup: group = maxGroup color = Color.ByValueInRange(group, minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) else: color = Color.ByValueInRange(groups.index(group), minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) color = "rgb("+str(color[0])+","+str(color[1])+","+str(color[2])+")" groupList.append(color) except: groupList.append(len(groups)) labels.append(label) else: for e in edges: sv = vertices[e[0]] ev = vertices[e[1]] x+=[round(sv[0],mantissa),round(ev[0],mantissa), None] # x-coordinates of edge ends y+=[round(sv[1],mantissa),round(ev[1],mantissa), None] # y-coordinates of edge ends z+=[round(sv[2],mantissa),round(ev[2],mantissa), None] # z-coordinates of edge ends if len(list(set(groupList))) < 2: groupList = color if len(labels) < 1: labels = "" eData = go.Scatter3d(x=x, y=y, z=z, name=legendLabel, showlegend=showLegend, marker_size=0, mode="lines", line=dict(color=groupList, width=edgeWidth), legendgroup=legendGroup, legendrank=legendRank, text=labels, hoverinfo='text') return eData def faceData(vertices, faces, dictionaries=None, color="#FAFAFA", opacity=0.5, labelKey=None, groupKey=None, minGroup=None, maxGroup=None, groups=[], legendLabel="Topology Faces", legendGroup=3, legendRank=3, showLegend=True, intensities=None, colorScale="viridis"): x = [] y = [] z = [] for v in vertices: x.append(round(v[0], mantissa)) y.append(round(v[1], mantissa)) z.append(round(v[2], mantissa)) i = [] j = [] k = [] labels = [] groupList = [] label = "" group = "" if labelKey or groupKey: if groups: if len(groups) > 0: if type(groups[0]) == int or type(groups[0]) == float: if not minGroup: minGroup = min(groups) if not maxGroup: maxGroup = max(groups) else: minGroup = 0 maxGroup = len(groups) - 1 else: minGroup = 0 maxGroup = 1 for m, f in enumerate(faces): i.append(f[0]) j.append(f[1]) k.append(f[2]) label = "" group = "" if len(dictionaries) > 0: d = dictionaries[m] if d: try: label = str(Dictionary.ValueAtKey(d, key=labelKey)) or "" except: label = "" try: group = Dictionary.ValueAtKey(d, key=groupKey) or None except: group = "" try: if type(group) == int or type(group) == float: if group < minGroup: group = minGroup if group > maxGroup: group = maxGroup color = Color.ByValueInRange(group, minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) else: color = Color.ByValueInRange(groups.index(group), minValue=minGroup, maxValue=maxGroup, colorScale=colorScale) color = "rgb("+str(color[0])+","+str(color[1])+","+str(color[2])+")" groupList.append(color) except: groupList.append(len(groups)) labels.append(label) else: for f in faces: i.append(f[0]) j.append(f[1]) k.append(f[2]) if len(list(set(groupList))) < 2: groupList = None if len(labels) < 1: labels = "" fData = go.Mesh3d( x = x, y = y, z = z, i = i, j = j, k = k, name = legendLabel, showlegend = showLegend, legendgroup = legendGroup, legendrank = legendRank, color = color, facecolor = groupList, colorscale = Plotly.ColorScale(colorScale), cmin = 0, cmax = 1, intensity = intensities, opacity = opacity, hoverinfo = 'text', text = labels, hovertext = labels, flatshading = True, showscale = False, lighting = {"facenormalsepsilon": 0}, ) return fData from topologicpy.Face import Face from topologicpy.Cluster import Cluster from topologicpy.Topology import Topology from topologicpy.Dictionary import Dictionary from time import time if not Topology.IsInstance(topology, "Topology"): return None intensityList = [] alt_intensities = [] data = [] v_list = [] if Topology.Type(topology) == Topology.TypeID("Vertex"): tp_vertices = [topology] else: tp_vertices = Topology.Vertices(topology) if isinstance(intensities, list): if len(intensities) == 0: intensities = None if not (tp_vertices == None or tp_vertices == []): vertices = [] v_dictionaries = [] intensityList = [] if intensityKey: for i, tp_v in enumerate(tp_vertices): vertices.append([tp_v.X(), tp_v.Y(), tp_v.Z()]) d = Topology.Dictionary(tp_v) if d: v = Dictionary.ValueAtKey(d, key=intensityKey) if not v == None: alt_intensities.append(v) v_list.append(v) else: alt_intensities.append(0) v_list.append(0) else: alt_intensities.append(0) v_list.append(0) alt_intensities = list(set(alt_intensities)) alt_intensities.sort() if isinstance(intensities, list): if len(intensities) > 0: alt_intensities = intensities min_i = min(alt_intensities) max_i = max(alt_intensities) for i, tp_v in enumerate(tp_vertices): v = v_list[i] ci = closest_index(v_list[i], alt_intensities) value = intensities[ci] if (max_i - min_i) == 0: value = 0 else: value = (value - min_i)/(max_i - min_i) intensityList.append(value) if all(x == 0 for x in intensityList): intensityList = None if showVertices: if len(vertices) == 0: for i, tp_v in enumerate(tp_vertices): if vertexLabelKey or vertexGroupKey: d = Topology.Dictionary(tp_v) v_dictionaries.append(d) vertices.append([tp_v.X(), tp_v.Y(), tp_v.Z()]) data.append(vertexData(vertices, dictionaries=v_dictionaries, color=vertexColor, size=vertexSize, labelKey=vertexLabelKey, groupKey=vertexGroupKey, minGroup=vertexMinGroup, maxGroup=vertexMaxGroup, groups=vertexGroups, legendLabel=vertexLegendLabel, legendGroup=vertexLegendGroup, legendRank=vertexLegendRank, showLegend=showVertexLegend, colorScale=colorScale)) if showEdges and Topology.Type(topology) > Topology.TypeID("Vertex"): if Topology.Type(topology) == Topology.TypeID("Edge"): tp_edges = [topology] else: tp_edges = Topology.Edges(topology) if not (tp_edges == None or tp_edges == []): e_dictionaries = [] if edgeLabelKey or edgeGroupKey: for tp_edge in tp_edges: e_dictionaries.append(Topology.Dictionary(tp_edge)) e_cluster = Cluster.ByTopologies(tp_edges) geo = Topology.Geometry(e_cluster, mantissa=mantissa) vertices = geo['vertices'] edges = geo['edges'] data.append(edgeData(vertices, edges, dictionaries=e_dictionaries, color=edgeColor, width=edgeWidth, labelKey=edgeLabelKey, groupKey=edgeGroupKey, minGroup=edgeMinGroup, maxGroup=edgeMaxGroup, groups=edgeGroups, legendLabel=edgeLegendLabel, legendGroup=edgeLegendGroup, legendRank=edgeLegendRank, showLegend=showEdgeLegend, colorScale=colorScale)) if showFaces and Topology.Type(topology) >= Topology.TypeID("Face"): if Topology.IsInstance(topology, "Face"): tp_faces = [topology] else: tp_faces = Topology.Faces(topology) if not(tp_faces == None or tp_faces == []): # rebuild faces to remove any degenerate faces #new_faces = [] #for i, f in enumerate(tp_faces): #eb = Face.ExternalBoundary(f) #eb = Topology.RemoveCollinearEdges(eb) #if not eb == None: #ibList = Face.InternalBoundaries(f) #ibList = [Wire.RemoveCollinearEdges(ib) for ib in ibList] #ibList = [ib for ib in ibList if not ib == None] #new_f = Face.ByWires(eb, ibList, silent=False) #if Topology.IsInstance(new_f, "Face"): #if faceLabelKey or faceGroupKey: #d = Topology.Dictionary(tp_faces[i]) #keys = Dictionary.Keys(d) #if len(keys) > 0: #new_f = Topology.SetDictionary(new_f, d) #new_faces.append(new_f) f_dictionaries = [] all_triangles = [] for tp_face in tp_faces: triangles = Face.Triangulate(tp_face, tolerance=tolerance) if isinstance(triangles, list): for tri in triangles: if faceLabelKey or faceGroupKey: d = Topology.Dictionary(tp_face) f_dictionaries.append(d) if d: _ = Topology.SetDictionary(tri, d) all_triangles.append(tri) if len(all_triangles) > 0: f_cluster = Cluster.ByTopologies(all_triangles) geo = Topology.Geometry(f_cluster, mantissa=mantissa) vertices = geo['vertices'] faces = geo['faces'] data.append(faceData(vertices, faces, dictionaries=f_dictionaries, color=faceColor, opacity=faceOpacity, labelKey=faceLabelKey, groupKey=faceGroupKey, minGroup=faceMinGroup, maxGroup=faceMaxGroup, groups=faceGroups, legendLabel=faceLegendLabel, legendGroup=faceLegendGroup, legendRank=faceLegendRank, showLegend=showFaceLegend, intensities=intensityList, colorScale=colorScale)) return data def ExportToImage(figure, path, format='png', width='1920', height='1080')-
Exports the plotly figure to an image.
Parameters
figure:plotly.graph_objs._figure.Figure- The input plotly figure.
path:str- The image file path.
format:str, optional- The desired format. This can be any of "jpg", "jpeg", "pdf", "png", "svg", or "webp". It is case insensitive. The default is "png".
width:int, optional- The width in pixels of the figure. The default value is 1920.
height:int, optional- The height in pixels of the figure. The default value is 1080.
Returns
bool- True if the image was exported sucessfully. False otherwise.
Expand source code
@staticmethod def ExportToImage(figure, path, format="png", width="1920", height="1080"): """ Exports the plotly figure to an image. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. path : str The image file path. format : str , optional The desired format. This can be any of "jpg", "jpeg", "pdf", "png", "svg", or "webp". It is case insensitive. The default is "png". width : int , optional The width in pixels of the figure. The default value is 1920. height : int , optional The height in pixels of the figure. The default value is 1080. Returns ------- bool True if the image was exported sucessfully. False otherwise. """ if not isinstance(figure, plotly.graph_objs._figure.Figure): return None if not isinstance(path, str): return None if not format.lower() in ["jpg", "jpeg", "pdf", "png", "svg", "webp"]: return None returnStatus = False try: plotly.io.write_image(figure, path, format=format.lower(), scale=None, width=width, height=height, validate=True, engine='auto') returnStatus = True except: returnStatus = False return returnStatus def FigureByConfusionMatrix(matrix, categories=[], minValue=None, maxValue=None, title='Confusion Matrix', xTitle='Actual', yTitle='Predicted', width=950, height=500, showScale=True, colorScale='viridis', colorSamples=10, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=40, marginBottom=0)-
Returns a Plotly Figure of the input confusion matrix. Actual categories are displayed on the X-Axis, Predicted categories are displayed on the Y-Axis.
Parameters
matrix:listornumpy.array- The matrix to display.
categories:list- The list of categories to use on the X and Y axes.
minValue:float, optional- The desired minimum value to use for the color scale. If set to None, the minmum value found in the input matrix will be used. The default is None.
maxValue:float, optional- The desired maximum value to use for the color scale. If set to None, the maximum value found in the input matrix will be used. The default is None.
title:str, optional- The desired title to display. The default is "Confusion Matrix".
xTitle:str, optional- The desired X-axis title to display. The default is "Actual".
yTitle:str, optional- The desired Y-axis title to display. The default is "Predicted".
width:int, optional- The desired width of the figure. The default is 950.
height:int, optional- The desired height of the figure. The default is 500.
showScale:bool, optional- If set to True, a color scale is shown on the right side of the figure. The default is True.
colorScale:str, optional- The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/.
colorSamples:int, optional- The number of discrete color samples to use for displaying the data. The default is 10.
backgroundColor:str, optional- The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'rgba(0,0,0,0)' (transparent).
marginLeft:int, optional- The desired left margin in pixels. The default is 0.
marginRight:int, optional- The desired right margin in pixels. The default is 0.
marginTop:int, optional- The desired top margin in pixels. The default is 40.
marginBottom:int, optional- The desired bottom margin in pixels. The default is 0.
Expand source code
@staticmethod def FigureByConfusionMatrix(matrix, categories=[], minValue=None, maxValue=None, title="Confusion Matrix", xTitle = "Actual", yTitle = "Predicted", width=950, height=500, showScale = True, colorScale='viridis', colorSamples=10, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=40, marginBottom=0): """ Returns a Plotly Figure of the input confusion matrix. Actual categories are displayed on the X-Axis, Predicted categories are displayed on the Y-Axis. Parameters ---------- matrix : list or numpy.array The matrix to display. categories : list The list of categories to use on the X and Y axes. minValue : float , optional The desired minimum value to use for the color scale. If set to None, the minmum value found in the input matrix will be used. The default is None. maxValue : float , optional The desired maximum value to use for the color scale. If set to None, the maximum value found in the input matrix will be used. The default is None. title : str , optional The desired title to display. The default is "Confusion Matrix". xTitle : str , optional The desired X-axis title to display. The default is "Actual". yTitle : str , optional The desired Y-axis title to display. The default is "Predicted". width : int , optional The desired width of the figure. The default is 950. height : int , optional The desired height of the figure. The default is 500. showScale : bool , optional If set to True, a color scale is shown on the right side of the figure. The default is True. colorScale : str , optional The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. colorSamples : int , optional The number of discrete color samples to use for displaying the data. The default is 10. backgroundColor : str , optional The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'rgba(0,0,0,0)' (transparent). marginLeft : int , optional The desired left margin in pixels. The default is 0. marginRight : int , optional The desired right margin in pixels. The default is 0. marginTop : int , optional The desired top margin in pixels. The default is 40. marginBottom : int , optional The desired bottom margin in pixels. The default is 0. """ try: import numpy as np except: print("Plotly.FigureByConfusionMatrix - Installing required numpy library.") try: os.system("pip install numpy") except: os.system("pip install numpy --user") try: import numpy as np except: warnings.warn("Plotly.FigureByConfusionMatrix - Error: Could not import numpy. Please install numpy manually. Returning None.") return None if not isinstance(matrix, list) and not isinstance(matrix, np.ndarray): print("Plotly.FigureByConfusionMatrix - Error: The input matrix is not of the correct type. Returning None.") return None figure = Plotly.FigureByMatrix(matrix, xCategories=categories, minValue=minValue, maxValue=maxValue, title=title, xTitle=xTitle, yTitle=yTitle, width=width, height=height, showScale=showScale, colorScale=Plotly.ColorScale(colorScale), colorSamples=colorSamples, backgroundColor=backgroundColor, marginLeft=marginLeft, marginRight=marginRight, marginTop=marginTop, marginBottom=marginBottom) layout = { "yaxis": {"autorange": "reversed"}, } figure.update_layout(layout) return figure def FigureByData(data, width=950, height=500, xAxis=False, yAxis=False, zAxis=False, axisSize=1, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=20, marginBottom=0, tolerance=0.0001)-
Creates a plotly figure.
Parameters
data:list- The input list of plotly data.
width:int, optional- The width in pixels of the figure. The default value is 950.
height:int, optional- The height in pixels of the figure. The default value is 950.
xAxis:bool, optional- If set to True the x axis is drawn. Otherwise it is not drawn. The default is False.
yAxis:bool, optional- If set to True the y axis is drawn. Otherwise it is not drawn. The default is False.
zAxis:bool, optional- If set to True the z axis is drawn. Otherwise it is not drawn. The default is False.
axisSize:float, optional- The size of the X, Y, Z, axes. The default is 1.
backgroundColor:str, optional- The desired color of the background. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "rgba(0,0,0,0)".
marginLeft:int, optional- The size in pixels of the left margin. The default value is 0.
marginRight:int, optional- The size in pixels of the right margin. The default value is 0.
marginTop:int, optional- The size in pixels of the top margin. The default value is 20.
marginBottom:int, optional- The size in pixels of the bottom margin. The default value is 0.
tolerance:float, optional- The desired tolerance. The default is 0.0001.
Returns
plotly.graph_objs._figure.Figure- The created plotly figure.
Expand source code
@staticmethod def FigureByData(data, width=950, height=500, xAxis=False, yAxis=False, zAxis=False, axisSize=1, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=20, marginBottom=0, tolerance = 0.0001): """ Creates a plotly figure. Parameters ---------- data : list The input list of plotly data. width : int , optional The width in pixels of the figure. The default value is 950. height : int , optional The height in pixels of the figure. The default value is 950. xAxis : bool , optional If set to True the x axis is drawn. Otherwise it is not drawn. The default is False. yAxis : bool , optional If set to True the y axis is drawn. Otherwise it is not drawn. The default is False. zAxis : bool , optional If set to True the z axis is drawn. Otherwise it is not drawn. The default is False. axisSize : float , optional The size of the X, Y, Z, axes. The default is 1. backgroundColor : str , optional The desired color of the background. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "rgba(0,0,0,0)". marginLeft : int , optional The size in pixels of the left margin. The default value is 0. marginRight : int , optional The size in pixels of the right margin. The default value is 0. marginTop : int , optional The size in pixels of the top margin. The default value is 20. marginBottom : int , optional The size in pixels of the bottom margin. The default value is 0. tolerance : float , optional The desired tolerance. The default is 0.0001. Returns ------- plotly.graph_objs._figure.Figure The created plotly figure. """ from topologicpy.Vertex import Vertex from topologicpy.Edge import Edge from topologicpy.Wire import Wire if not isinstance(data, list): return None v0 = Vertex.ByCoordinates(0, 0, 0) v1 = Vertex.ByCoordinates(axisSize,0,0) v2 = Vertex.ByCoordinates(0,axisSize,0) v3 = Vertex.ByCoordinates(0,0,axisSize) if xAxis: xEdge = Edge.ByVertices([v0,v1], tolerance=tolerance) xData = Plotly.DataByTopology(xEdge, edgeColor="red", edgeWidth=6, showFaces=False, showEdges=True, showVertices=False, edgeLegendLabel="X-Axis") data = data + xData if yAxis: yEdge = Edge.ByVertices([v0,v2], tolerance=tolerance) yData = Plotly.DataByTopology(yEdge, edgeColor="green", edgeWidth=6, showFaces=False, showEdges=True, showVertices=False, edgeLegendLabel="Y-Axis") data = data + yData if zAxis: zEdge = Edge.ByVertices([v0,v3], tolerance=tolerance) zData = Plotly.DataByTopology(zEdge, edgeColor="blue", edgeWidth=6, showFaces=False, showEdges=True, showVertices=False, edgeLegendLabel="Z-Axis") data = data + zData figure = go.Figure(data=data) figure.update_layout( width=width, height=height, showlegend=True, scene = dict( xaxis = dict(visible=False), yaxis = dict(visible=False), zaxis =dict(visible=False), ), scene_aspectmode='data', paper_bgcolor=backgroundColor, plot_bgcolor=backgroundColor, margin=dict(l=marginLeft, r=marginRight, t=marginTop, b=marginBottom), ) figure.update_xaxes(showgrid=False, zeroline=False, visible=False) figure.update_yaxes(showgrid=False, zeroline=False, visible=False) return figure def FigureByDataFrame(dataFrame, labels=[], width=950, height=500, title='Untitled', xTitle='X Axis', xSpacing=1, yTitle='Y Axis', ySpacing=1.0, useMarkers=False, chartType='Line', backgroundColor='rgba(0,0,0,0)', gridColor='lightgray', marginLeft=0, marginRight=0, marginTop=40, marginBottom=0)-
Returns a Plotly Figure of the input dataframe
Parameters
df:pandas.df- The pandas dataframe to display.
data_labels:list- The labels to use for the data.
width:int, optional- The desired width of the figure. The default is 950.
height:int, optional- The desired height of the figure. The default is 500.
title:str, optional- The chart title. The default is "Training and Testing Results".
xTitle:str, optional- The X-axis title. The default is "Epochs".
xSpacing:float, optional- The X-axis spacing. The default is 1.0.
yTitle:str, optional- The Y-axis title. The default is "Accuracy and Loss".
ySpacing:float, optional- The Y-axis spacing. The default is 0.1.
useMarkers:bool, optional- If set to True, markers will be displayed. The default is False.
chartType:str, optional- The desired type of chart. The options are "Line", "Bar", or "Scatter". It is case insensitive. The default is "Line".
backgroundColor:str, optional- The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'rgba(0,0,0,0)' (transparent).
grid:str, optional- The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'lightgray'
marginLeft:int, optional- The desired left margin in pixels. The default is 0.
marginRight:int, optional- The desired right margin in pixels. The default is 0.
marginTop:int, optional- The desired top margin in pixels. The default is 40.
marginBottom:int, optional- The desired bottom margin in pixels. The default is 0.
Returns
None.
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@staticmethod def FigureByDataFrame(dataFrame, labels=[], width=950, height=500, title="Untitled", xTitle="X Axis", xSpacing=1, yTitle="Y Axis", ySpacing=1.0, useMarkers=False, chartType="Line", backgroundColor='rgba(0,0,0,0)', gridColor = 'lightgray', marginLeft=0, marginRight=0, marginTop=40, marginBottom=0): """ Returns a Plotly Figure of the input dataframe Parameters ---------- df : pandas.df The pandas dataframe to display. data_labels : list The labels to use for the data. width : int , optional The desired width of the figure. The default is 950. height : int , optional The desired height of the figure. The default is 500. title : str , optional The chart title. The default is "Training and Testing Results". xTitle : str , optional The X-axis title. The default is "Epochs". xSpacing : float , optional The X-axis spacing. The default is 1.0. yTitle : str , optional The Y-axis title. The default is "Accuracy and Loss". ySpacing : float , optional The Y-axis spacing. The default is 0.1. useMarkers : bool , optional If set to True, markers will be displayed. The default is False. chartType : str , optional The desired type of chart. The options are "Line", "Bar", or "Scatter". It is case insensitive. The default is "Line". backgroundColor : str , optional The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'rgba(0,0,0,0)' (transparent). grid : str , optional The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'lightgray' marginLeft : int , optional The desired left margin in pixels. The default is 0. marginRight : int , optional The desired right margin in pixels. The default is 0. marginTop : int , optional The desired top margin in pixels. The default is 40. marginBottom : int , optional The desired bottom margin in pixels. The default is 0. Returns ------- None. """ import plotly.express as px if chartType.lower() == "line": figure = px.line(dataFrame, x=labels[0], y=labels[1:], title=title, markers=useMarkers) elif chartType.lower() == "bar": figure = px.bar(dataFrame, x=labels[0], y=labels[1:], title=title) elif chartType.lower() == "scatter": figure = px.scatter(dataFrame, x=labels[0], y=labels[1:], title=title) else: raise NotImplementedError layout = { "width": width, "height": height, "title": title, "xaxis": {"title": xTitle, "dtick": xSpacing, 'gridcolor': gridColor}, "yaxis": {"title": yTitle, "dtick": ySpacing, 'gridcolor': gridColor}, "paper_bgcolor": backgroundColor, "plot_bgcolor": backgroundColor, "margin":dict(l=marginLeft, r=marginRight, t=marginTop, b=marginBottom) } figure.update_layout(layout) return figure def FigureByJSONFile(file)-
Imports a plotly figure from a JSON file.
Parameters
file:file object- The JSON file.
Returns
plotly.graph_objs._figure.Figure- The imported figure.
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@staticmethod def FigureByJSONFile(file): """ Imports a plotly figure from a JSON file. Parameters ---------- file : file object The JSON file. Returns ------- plotly.graph_objs._figure.Figure The imported figure. """ figure = None if not file: return None figure = plotly.io.read_json(file, output_type='Figure', skip_invalid=True, engine=None) file.close() return figure def FigureByJSONPath(path)-
Imports a plotly figure from a JSON file path.
Parameters
path:str- The path to the BRep file.
Returns
plotly.graph_objs._figure.Figure- The imported figure.
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@staticmethod def FigureByJSONPath(path): """ Imports a plotly figure from a JSON file path. Parameters ---------- path : str The path to the BRep file. Returns ------- plotly.graph_objs._figure.Figure The imported figure. """ if not path: return None try: file = open(path) except: print("Plotly.FigureByJSONPath - Error: the JSON file is not a valid file. Returning None.") return None return Plotly.FigureByJSONFile(file) def FigureByMatrix(matrix, xCategories=[], yCategories=[], minValue=None, maxValue=None, title='Matrix', xTitle='X Axis', yTitle='Y Axis', width=950, height=950, showScale=False, colorScale='gray', colorSamples=10, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=40, marginBottom=0, mantissa: int = 6)-
Returns a Plotly Figure of the input matrix.
Parameters
matrix:listornumpy.array- The matrix to display.
categories:list- The list of categories to use on the X and Y axes.
minValue:float, optional- The desired minimum value to use for the color scale. If set to None, the minmum value found in the input matrix will be used. The default is None.
maxValue:float, optional- The desired maximum value to use for the color scale. If set to None, the maximum value found in the input matrix will be used. The default is None.
title:str, optional- The desired title to display. The default is "Confusion Matrix".
xTitle:str, optional- The desired X-axis title to display. The default is "Actual".
yTitle:str, optional- The desired Y-axis title to display. The default is "Predicted".
width:int, optional- The desired width of the figure. The default is 950.
height:int, optional- The desired height of the figure. The default is 500.
showScale:bool, optional- If set to True, a color scale is shown on the right side of the figure. The default is True.
colorScale:str, optional- The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/.
colorSamples:int, optional- The number of discrete color samples to use for displaying the data. The default is 10.
backgroundColor:str, optional- The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'rgba(0,0,0,0)' (transparent).
marginLeft:int, optional- The desired left margin in pixels. The default is 0.
marginRight:int, optional- The desired right margin in pixels. The default is 0.
marginTop:int, optional- The desired top margin in pixels. The default is 40.
marginBottom:int, optional- The desired bottom margin in pixels. The default is 0.
mantissa:int, optional- The desired number of digits of the mantissa. The default is 6.
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@staticmethod def FigureByMatrix(matrix, xCategories=[], yCategories=[], minValue=None, maxValue=None, title="Matrix", xTitle = "X Axis", yTitle = "Y Axis", width=950, height=950, showScale = False, colorScale='gray', colorSamples=10, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=40, marginBottom=0, mantissa: int = 6): """ Returns a Plotly Figure of the input matrix. Parameters ---------- matrix : list or numpy.array The matrix to display. categories : list The list of categories to use on the X and Y axes. minValue : float , optional The desired minimum value to use for the color scale. If set to None, the minmum value found in the input matrix will be used. The default is None. maxValue : float , optional The desired maximum value to use for the color scale. If set to None, the maximum value found in the input matrix will be used. The default is None. title : str , optional The desired title to display. The default is "Confusion Matrix". xTitle : str , optional The desired X-axis title to display. The default is "Actual". yTitle : str , optional The desired Y-axis title to display. The default is "Predicted". width : int , optional The desired width of the figure. The default is 950. height : int , optional The desired height of the figure. The default is 500. showScale : bool , optional If set to True, a color scale is shown on the right side of the figure. The default is True. colorScale : str , optional The desired type of plotly color scales to use (e.g. "Viridis", "Plasma"). The default is "Viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. colorSamples : int , optional The number of discrete color samples to use for displaying the data. The default is 10. backgroundColor : str , optional The desired background color. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is 'rgba(0,0,0,0)' (transparent). marginLeft : int , optional The desired left margin in pixels. The default is 0. marginRight : int , optional The desired right margin in pixels. The default is 0. marginTop : int , optional The desired top margin in pixels. The default is 40. marginBottom : int , optional The desired bottom margin in pixels. The default is 0. mantissa : int , optional The desired number of digits of the mantissa. The default is 6. """ #import plotly.figure_factory as ff import plotly.graph_objects as go import plotly.express as px try: import numpy as np except: print("Plotly.FigureByMatrix - Installing required numpy library.") try: os.system("pip install numpy") except: os.system("pip install numpy --user") try: import numpy as np except: warnings.warn("Plotly.FigureByMatrix - Error: Could not import numpy. Please install numpy manually. Returning None.") return None if not isinstance(matrix, list) and not isinstance(matrix, np.ndarray): print("Plotly.FigureByMatrix - Error: The input matrix is not of the correct type. Returning None.") return None annotations = [] if isinstance(matrix, list): matrix = np.array(matrix) colors = px.colors.sample_colorscale(Plotly.ColorScale(colorScale), [n/(colorSamples -1) for n in range(colorSamples)]) if not xCategories: xCategories = [x for x in range(len(matrix[0]))] if not yCategories: yCategories = [y for y in range(len(matrix))] if not maxValue or not minValue: max_values = [] min_values = [] for i in range(len(matrix)): row = matrix[i] max_values.append(max(row)) min_values.append(min(row)) for j, value in enumerate(row): annotations.append( { "x": xCategories[j], "y": yCategories[i], "font": {"color": "black"}, "bgcolor": "white", "opacity": 0.5, "text": str(round(value, mantissa)), "xref": "x1", "yref": "y1", "showarrow": False } ) if not minValue: minValueB = min(min_values) if not maxValue: maxValue = max(max_values) else: for i in range(len(matrix)): row = matrix[i] for j, value in enumerate(row): annotations.append( { "x": xCategories[j], "y": yCategories[i], "font": {"color": "black"}, "bgcolor": "white", "opacity": 0.5, "text": str(round(value,mantissa)), "xref": "x1", "yref": "y1", "showarrow": False } ) new_matrix = [] for i in range(len(matrix)): row = matrix[i] new_row = [] maxRow = sum(row) for j in range(len(row)): if maxRow == 0: new_row.append(round(0, mantissa)) else: new_row.append(round(float(row[j])/float(maxRow), mantissa)) new_matrix.append(new_row) data = go.Heatmap(z=new_matrix, y=yCategories, x=xCategories, zmin=minValue, zmax=maxValue, showscale=showScale, colorscale=colors) layout = { "width": width, "height": height, "title": title, "xaxis": {"title": xTitle}, "yaxis": {"title": yTitle, "autorange": "reversed"}, "annotations": annotations, "paper_bgcolor": backgroundColor, "plot_bgcolor": backgroundColor, "margin":dict(l=marginLeft, r=marginRight, t=marginTop, b=marginBottom) } fig = go.Figure(data=data, layout=layout) fig.update_xaxes( tickvals=xCategories) fig.update_yaxes( tickvals=yCategories) return fig def FigureByPieChart(data, values, names)-
Creates a plotly pie chart figure.
Parameters
data:list- The input list of plotly data.
values:list- The input list of values.
names:list- The input list of names.
Expand source code
@staticmethod def FigureByPieChart(data, values, names): """ Creates a plotly pie chart figure. Parameters ---------- data : list The input list of plotly data. values : list The input list of values. names : list The input list of names. """ import plotly.express as px try: import pandas as pd except: print("Plotly.FigureByPieChart - Installing required pandas library.") try: os.system("pip install pandas") except: os.system("pip install pandas --user") try: import pandas as pd except: warnings.warn("Plotly.FigureByPieChart - Error: Could not import pandas. Please install the pandas library manually. Returning None") return None dlist = list(map(list, zip(*data))) df = pd.DataFrame(dlist, columns=data['names']) fig = px.pie(df, values=values, names=names) return fig def FigureByTopology(topology, showVertices=True, vertexSize=1.1, vertexColor='black', vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[], vertexMinGroup=None, vertexMaxGroup=None, showVertexLegend=False, vertexLegendLabel='Topology Vertices', vertexLegendRank=1, vertexLegendGroup=1, showEdges=True, edgeWidth=1, edgeColor='black', edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[], edgeMinGroup=None, edgeMaxGroup=None, showEdgeLegend=False, edgeLegendLabel='Topology Edges', edgeLegendRank=2, edgeLegendGroup=2, showFaces=True, faceOpacity=0.5, faceColor='#FAFAFA', faceLabelKey=None, faceGroupKey=None, faceGroups=[], faceMinGroup=None, faceMaxGroup=None, showFaceLegend=False, faceLegendLabel='Topology Faces', faceLegendRank=3, faceLegendGroup=3, intensityKey=None, width=950, height=500, xAxis=False, yAxis=False, zAxis=False, axisSize=1, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=20, marginBottom=0, showScale=False, cbValues=[], cbTicks=5, cbX=-0.15, cbWidth=15, cbOutlineWidth=0, cbTitle='', cbSubTitle='', cbUnits='', colorScale='viridis', mantissa=6, tolerance=0.0001)-
Creates a figure from the input topology.
Parameters
topology:topologic_core.Topology- The input topology. This must contain faces and or edges.
showVertices:bool, optional- If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True.
vertexSize:float, optional- The desired size of the vertices. The default is 1.1.
vertexColor:str, optional- The desired color of the output vertices. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black".
vertexLabelKey:str, optional- The dictionary key to use to display the vertex label. The default is None.
vertexGroupKey:str, optional- The dictionary key to use to display the vertex group. The default is None.
vertexGroups:list, optional- The list of vertex groups against which to index the color of the vertex. The default is [].
vertexMinGroup:intorfloat, optional- For numeric vertexGroups, vertexMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the minimum value in vertexGroups. The default is None.
edgeMaxGroup:intorfloat, optional- For numeric vertexGroups, vertexMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the maximum value in vertexGroups. The default is None.
showVertexLegend:bool, optional- If set to True, the legend for the vertices of this topology is shown. Otherwise, it isn't. The default is False.
vertexLegendLabel:str, optional- The legend label string used to identify vertices. The default is "Topology Vertices".
vertexLegendRank:int, optional- The legend rank order of the vertices of this topology. The default is 1.
vertexLegendGroup:int, optional- The number of the vertex legend group to which the vertices of this topology belong. The default is 1.
showEdges:bool, optional- If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True.
edgeWidth:float, optional- The desired thickness of the output edges. The default is 1.
edgeColor:str, optional- The desired color of the output edges. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black".
edgeLabelKey:str, optional- The dictionary key to use to display the edge label. The default is None.
edgeGroupKey:str, optional- The dictionary key to use to display the edge group. The default is None.
edgeGroups:list, optional- The list of edge groups against which to index the color of the edge. The default is [].
edgeMinGroup:intorfloat, optional- For numeric edgeGroups, edgeMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the minimum value in edgeGroups. The default is None.
edgeMaxGroup:intorfloat, optional- For numeric edgeGroups, edgeMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the maximum value in edgeGroups. The default is None.
showEdgeLegend:bool, optional- If set to True, the legend for the edges of this topology is shown. Otherwise, it isn't. The default is False.
edgeLegendLabel:str, optional- The legend label string used to identify edges. The default is "Topology Edges".
edgeLegendRank:int, optional- The legend rank order of the edges of this topology. The default is 2.
edgeLegendGroup:int, optional- The number of the edge legend group to which the edges of this topology belong. The default is 2.
showFaces:bool, optional- If set to True the faces will be drawn. Otherwise, they will not be drawn. The default is True.
faceOpacity:float, optional- The desired opacity of the output faces (0=transparent, 1=opaque). The default is 0.5.
faceColor:str, optional- The desired color of the output faces. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "#FAFAFA".
faceLabelKey:str, optional- The dictionary key to use to display the face label. The default is None.
faceGroupKey:str, optional- The dictionary key to use to display the face group. The default is None.
faceGroups:list, optional- The list of face groups against which to index the color of the face. This can bhave numeric or string values. This should match the type of value associated with the faceGroupKey. The default is [].
faceMinGroup:intorfloat, optional- For numeric faceGroups, minGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the minimum value in faceGroups. The default is None.
faceMaxGroup:intorfloat, optional- For numeric faceGroups, maxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the maximum value in faceGroups. The default is None.
showFaceLegend:bool, optional- If set to True, the legend for the faces of this topology is shown. Otherwise, it isn't. The default is False.
faceLegendLabel:str, optional- The legend label string used to idenitfy edges. The default is "Topology Faces".
faceLegendRank:int, optional- The legend rank order of the faces of this topology. The default is 3.
faceLegendGroup:int, optional- The number of the face legend group to which the faces of this topology belong. The default is 3.
width:int, optional- The width in pixels of the figure. The default value is 950.
height:int, optional- The height in pixels of the figure. The default value is 950.
xAxis:bool, optional- If set to True the x axis is drawn. Otherwise it is not drawn. The default is False.
yAxis:bool, optional- If set to True the y axis is drawn. Otherwise it is not drawn. The default is False.
zAxis:bool, optional- If set to True the z axis is drawn. Otherwise it is not drawn. The default is False.
backgroundColor:str, optional- The desired color of the background. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "rgba(0,0,0,0)".
marginLeft:int, optional- The size in pixels of the left margin. The default value is 0.
marginRight:int, optional- The size in pixels of the right margin. The default value is 0.
marginTop:int, optional- The size in pixels of the top margin. The default value is 20.
marginBottom:int, optional- The size in pixels of the bottom margin. The default value is 0.
camera:list, optional- The desired location of the camera). The default is [-1.25, -1.25, 1.25].
center:list, optional- The desired center (camera target). The default is [0, 0, 0].
up:list, optional- The desired up vector. The default is [0, 0, 1].
renderer:str, optional- The desired renderer. See Plotly.Renderers(). The default is "notebook".
intensityKey:str, optional- If not None, the dictionary of each vertex is searched for the value associated with the intensity key. This value is then used to color-code the vertex based on the colorScale. The default is None.
showScale:bool, optional- If set to True, the colorbar is shown. The default is False.
cbValues:list, optional- The input list of values to use for the colorbar. The default is [].
cbTicks:int, optional- The number of ticks to use on the colorbar. The default is 5.
cbX:float, optional- The x location of the colorbar. The default is -0.15.
cbWidth:int, optional- The width in pixels of the colorbar. The default is 15
cbOutlineWidth:int, optional- The width in pixels of the outline of the colorbar. The default is 0.
cbTitle:str, optional- The title of the colorbar. The default is "".
cbSubTitle:str, optional- The subtitle of the colorbar. The default is "".
cbUnits:str, optional- The units used in the colorbar. The default is ""
colorScale:str, optional- The desired type of plotly color scales to use (e.g. "viridis", "plasma"). The default is "viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/.
mantissa:int, optional- The desired length of the mantissa for the values listed on the colorbar. The default is 6.
tolerance:float, optional- The desired tolerance. The default is 0.0001.
Returns
Plotly figure
Expand source code
@staticmethod def FigureByTopology(topology, showVertices=True, vertexSize=1.1, vertexColor="black", vertexLabelKey=None, vertexGroupKey=None, vertexGroups=[], vertexMinGroup=None, vertexMaxGroup=None, showVertexLegend=False, vertexLegendLabel="Topology Vertices", vertexLegendRank=1, vertexLegendGroup=1, showEdges=True, edgeWidth=1, edgeColor="black", edgeLabelKey=None, edgeGroupKey=None, edgeGroups=[], edgeMinGroup=None, edgeMaxGroup=None, showEdgeLegend=False, edgeLegendLabel="Topology Edges", edgeLegendRank=2, edgeLegendGroup=2, showFaces=True, faceOpacity=0.5, faceColor="#FAFAFA", faceLabelKey=None, faceGroupKey=None, faceGroups=[], faceMinGroup=None, faceMaxGroup=None, showFaceLegend=False, faceLegendLabel="Topology Faces", faceLegendRank=3, faceLegendGroup=3, intensityKey=None, width=950, height=500, xAxis=False, yAxis=False, zAxis=False, axisSize=1, backgroundColor='rgba(0,0,0,0)', marginLeft=0, marginRight=0, marginTop=20, marginBottom=0, showScale=False, cbValues=[], cbTicks=5, cbX=-0.15, cbWidth=15, cbOutlineWidth=0, cbTitle="", cbSubTitle="", cbUnits="", colorScale="viridis", mantissa=6, tolerance=0.0001): """ Creates a figure from the input topology. Parameters ---------- topology : topologic_core.Topology The input topology. This must contain faces and or edges. showVertices : bool , optional If set to True the vertices will be drawn. Otherwise, they will not be drawn. The default is True. vertexSize : float , optional The desired size of the vertices. The default is 1.1. vertexColor : str , optional The desired color of the output vertices. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". vertexLabelKey : str , optional The dictionary key to use to display the vertex label. The default is None. vertexGroupKey : str , optional The dictionary key to use to display the vertex group. The default is None. vertexGroups : list , optional The list of vertex groups against which to index the color of the vertex. The default is []. vertexMinGroup : int or float , optional For numeric vertexGroups, vertexMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the minimum value in vertexGroups. The default is None. edgeMaxGroup : int or float , optional For numeric vertexGroups, vertexMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the vertexGroupKey. If set to None, it is set to the maximum value in vertexGroups. The default is None. showVertexLegend : bool, optional If set to True, the legend for the vertices of this topology is shown. Otherwise, it isn't. The default is False. vertexLegendLabel : str , optional The legend label string used to identify vertices. The default is "Topology Vertices". vertexLegendRank : int , optional The legend rank order of the vertices of this topology. The default is 1. vertexLegendGroup : int , optional The number of the vertex legend group to which the vertices of this topology belong. The default is 1. showEdges : bool , optional If set to True the edges will be drawn. Otherwise, they will not be drawn. The default is True. edgeWidth : float , optional The desired thickness of the output edges. The default is 1. edgeColor : str , optional The desired color of the output edges. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "black". edgeLabelKey : str , optional The dictionary key to use to display the edge label. The default is None. edgeGroupKey : str , optional The dictionary key to use to display the edge group. The default is None. edgeGroups : list , optional The list of edge groups against which to index the color of the edge. The default is []. edgeMinGroup : int or float , optional For numeric edgeGroups, edgeMinGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the minimum value in edgeGroups. The default is None. edgeMaxGroup : int or float , optional For numeric edgeGroups, edgeMaxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the edgeGroupKey. If set to None, it is set to the maximum value in edgeGroups. The default is None. showEdgeLegend : bool, optional If set to True, the legend for the edges of this topology is shown. Otherwise, it isn't. The default is False. edgeLegendLabel : str , optional The legend label string used to identify edges. The default is "Topology Edges". edgeLegendRank : int , optional The legend rank order of the edges of this topology. The default is 2. edgeLegendGroup : int , optional The number of the edge legend group to which the edges of this topology belong. The default is 2. showFaces : bool , optional If set to True the faces will be drawn. Otherwise, they will not be drawn. The default is True. faceOpacity : float , optional The desired opacity of the output faces (0=transparent, 1=opaque). The default is 0.5. faceColor : str , optional The desired color of the output faces. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "#FAFAFA". faceLabelKey : str , optional The dictionary key to use to display the face label. The default is None. faceGroupKey : str , optional The dictionary key to use to display the face group. The default is None. faceGroups : list , optional The list of face groups against which to index the color of the face. This can bhave numeric or string values. This should match the type of value associated with the faceGroupKey. The default is []. faceMinGroup : int or float , optional For numeric faceGroups, minGroup is the desired minimum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the minimum value in faceGroups. The default is None. faceMaxGroup : int or float , optional For numeric faceGroups, maxGroup is the desired maximum value for the scaling of colors. This should match the type of value associated with the faceGroupKey. If set to None, it is set to the maximum value in faceGroups. The default is None. showFaceLegend : bool, optional If set to True, the legend for the faces of this topology is shown. Otherwise, it isn't. The default is False. faceLegendLabel : str , optional The legend label string used to idenitfy edges. The default is "Topology Faces". faceLegendRank : int , optional The legend rank order of the faces of this topology. The default is 3. faceLegendGroup : int , optional The number of the face legend group to which the faces of this topology belong. The default is 3. width : int , optional The width in pixels of the figure. The default value is 950. height : int , optional The height in pixels of the figure. The default value is 950. xAxis : bool , optional If set to True the x axis is drawn. Otherwise it is not drawn. The default is False. yAxis : bool , optional If set to True the y axis is drawn. Otherwise it is not drawn. The default is False. zAxis : bool , optional If set to True the z axis is drawn. Otherwise it is not drawn. The default is False. backgroundColor : str , optional The desired color of the background. This can be any plotly color string and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color. The default is "rgba(0,0,0,0)". marginLeft : int , optional The size in pixels of the left margin. The default value is 0. marginRight : int , optional The size in pixels of the right margin. The default value is 0. marginTop : int , optional The size in pixels of the top margin. The default value is 20. marginBottom : int , optional The size in pixels of the bottom margin. The default value is 0. camera : list , optional The desired location of the camera). The default is [-1.25, -1.25, 1.25]. center : list , optional The desired center (camera target). The default is [0, 0, 0]. up : list , optional The desired up vector. The default is [0, 0, 1]. renderer : str , optional The desired renderer. See Plotly.Renderers(). The default is "notebook". intensityKey : str , optional If not None, the dictionary of each vertex is searched for the value associated with the intensity key. This value is then used to color-code the vertex based on the colorScale. The default is None. showScale : bool , optional If set to True, the colorbar is shown. The default is False. cbValues : list , optional The input list of values to use for the colorbar. The default is []. cbTicks : int , optional The number of ticks to use on the colorbar. The default is 5. cbX : float , optional The x location of the colorbar. The default is -0.15. cbWidth : int , optional The width in pixels of the colorbar. The default is 15 cbOutlineWidth : int , optional The width in pixels of the outline of the colorbar. The default is 0. cbTitle : str , optional The title of the colorbar. The default is "". cbSubTitle : str , optional The subtitle of the colorbar. The default is "". cbUnits: str , optional The units used in the colorbar. The default is "" colorScale : str , optional The desired type of plotly color scales to use (e.g. "viridis", "plasma"). The default is "viridis". For a full list of names, see https://plotly.com/python/builtin-colorscales/. mantissa : int , optional The desired length of the mantissa for the values listed on the colorbar. The default is 6. tolerance : float , optional The desired tolerance. The default is 0.0001. Returns ------- Plotly figure """ from topologicpy.Topology import Topology if not Topology.IsInstance(topology, "Topology"): print("Plotly.FigureByTopology - Error: the input topology is not a valid topology. Returning None.") return None data = Plotly.DataByTopology(topology=topology, showVertices=showVertices, vertexSize=vertexSize, vertexColor=vertexColor, vertexLabelKey=vertexLabelKey, vertexGroupKey=vertexGroupKey, vertexGroups=vertexGroups, vertexMinGroup=vertexMinGroup, vertexMaxGroup=vertexMaxGroup, showVertexLegend=showVertexLegend, vertexLegendLabel=vertexLegendLabel, vertexLegendRank=vertexLegendRank, vertexLegendGroup=vertexLegendGroup, showEdges=showEdges, edgeWidth=edgeWidth, edgeColor=edgeColor, edgeLabelKey=edgeLabelKey, edgeGroupKey=edgeGroupKey, edgeGroups=edgeGroups, edgeMinGroup=edgeMinGroup, edgeMaxGroup=edgeMaxGroup, showEdgeLegend=showEdgeLegend, edgeLegendLabel=edgeLegendLabel, edgeLegendRank=edgeLegendRank, edgeLegendGroup=edgeLegendGroup, showFaces=showFaces, faceOpacity=faceOpacity, faceColor=faceColor, faceLabelKey=faceLabelKey, faceGroupKey=faceGroupKey, faceGroups=faceGroups, faceMinGroup=faceMinGroup, faceMaxGroup=faceMaxGroup, showFaceLegend=showFaceLegend, faceLegendLabel=faceLegendLabel, faceLegendRank=faceLegendRank, faceLegendGroup=faceLegendGroup, intensityKey=intensityKey, colorScale=colorScale, tolerance=tolerance) figure = Plotly.FigureByData(data=data, width=width, height=height, xAxis=xAxis, yAxis=yAxis, zAxis=zAxis, axisSize=axisSize, backgroundColor=backgroundColor, marginLeft=marginLeft, marginRight=marginRight, marginTop=marginTop, marginBottom=marginBottom, tolerance=tolerance) if showScale: figure = Plotly.AddColorBar(figure, values=cbValues, nTicks=cbTicks, xPosition=cbX, width=cbWidth, outlineWidth=cbOutlineWidth, title=cbTitle, subTitle=cbSubTitle, units=cbUnits, colorScale=colorScale, mantissa=mantissa) return figure def FigureExportToJSON(figure, path, overwrite=False)-
Exports the input plotly figure to a JSON file.
Parameters
figure:plotly.graph_objs._figure.Figure- The input plotly figure.
path:str- The input file path.
overwrite:bool, optional- If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't.
Returns
bool- True if the export operation is successful. False otherwise.
Expand source code
@staticmethod def FigureExportToJSON(figure, path, overwrite=False): """ Exports the input plotly figure to a JSON file. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. path : str The input file path. overwrite : bool , optional If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't. Returns ------- bool True if the export operation is successful. False otherwise. """ if not isinstance(figure, plotly.graph_objs._figure.Figure): print("Plotly.FigureExportToJSON - Error: The input figure is not a plolty figure. Returning None.") return None if not isinstance(path, str): print("Plotly.FigureExportToJSON - Error: The input path is not a string. Returning None.") return None # Make sure the file extension is .json ext = path[len(path)-5:len(path)] if ext.lower() != ".json": path = path+".json" f = None try: if overwrite == True: f = open(path, "w") else: f = open(path, "x") # Try to create a new File except: print("Plotly.FigureExportToJSON - Error: Could not create a new file at the following location: "+path+". Returning None.") return None if (f): plotly.io.write_json(figure, f, validate=True, pretty=False, remove_uids=True, engine=None) f.close() return True if f: try: f.close() except: pass return False def FigureExportToPDF(figure, path, width=1920, height=1200, overwrite=False)-
Exports the input plotly figure to a PDF file.
Parameters
figure:plotly.graph_objs._figure.Figure- The input plotly figure.
path:str- The input file path.
width:int, optional- The width of the exported image in pixels. The default is 1920.
height:int, optional- The height of the exported image in pixels. The default is 1200.
overwrite:bool, optional- If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't.
Returns
bool- True if the export operation is successful. False otherwise.
Expand source code
@staticmethod def FigureExportToPDF(figure, path, width=1920, height=1200, overwrite=False): """ Exports the input plotly figure to a PDF file. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. path : str The input file path. width : int, optional The width of the exported image in pixels. The default is 1920. height : int , optional The height of the exported image in pixels. The default is 1200. overwrite : bool , optional If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't. Returns ------- bool True if the export operation is successful. False otherwise. """ import os if not isinstance(figure, plotly.graph_objs._figure.Figure): print("Plotly.FigureExportToPNG - Error: The input figure is not a plolty figure. Returning None.") return None if not isinstance(path, str): print("Plotly.FigureExportToPNG - Error: The input path is not a string. Returning None.") return None # Make sure the file extension is .pdf ext = path[len(path)-4:len(path)] if ext.lower() != ".pdf": path = path+".pdf" if overwrite == False and os.path.exists(path): print("Plotly.FigureExportToPDF - Error: A file already exists at this location and overwrite is set to False. Returning None.") return None plotly.io.write_image(figure, path, format='pdf', scale=1, width=width, height=height, validate=True, engine='auto') return True def FigureExportToPNG(figure, path, width=1920, height=1200, overwrite=False)-
Exports the input plotly figure to a PNG file.
Parameters
figure:plotly.graph_objs._figure.Figure- The input plotly figure.
path:str- The input file path.
width:int, optional- The width of the exported image in pixels. The default is 1920.
height:int, optional- The height of the exported image in pixels. The default is 1200.
overwrite:bool, optional- If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't.
Returns
bool- True if the export operation is successful. False otherwise.
Expand source code
@staticmethod def FigureExportToPNG(figure, path, width=1920, height=1200, overwrite=False): """ Exports the input plotly figure to a PNG file. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. path : str The input file path. width : int, optional The width of the exported image in pixels. The default is 1920. height : int , optional The height of the exported image in pixels. The default is 1200. overwrite : bool , optional If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't. Returns ------- bool True if the export operation is successful. False otherwise. """ import os if not isinstance(figure, plotly.graph_objs._figure.Figure): print("Plotly.FigureExportToPNG - Error: The input figure is not a plolty figure. Returning None.") return None if not isinstance(path, str): print("Plotly.FigureExportToPNG - Error: The input path is not a string. Returning None.") return None # Make sure the file extension is .png ext = path[len(path)-4:len(path)] if ext.lower() != ".png": path = path+".png" if overwrite == False and os.path.exists(path): print("Plotly.FigureExportToPNG - Error: A file already exists at this location and overwrite is set to False. Returning None.") return None plotly.io.write_image(figure, path, format='png', scale=1, width=width, height=height, validate=True, engine='auto') return True def FigureExportToSVG(figure, path, width=1920, height=1200, overwrite=False)-
Exports the input plotly figure to a SVG file.
Parameters
figure:plotly.graph_objs._figure.Figure- The input plotly figure.
path:str- The input file path.
width:int, optional- The width of the exported image in pixels. The default is 1920.
height:int, optional- The height of the exported image in pixels. The default is 1200.
overwrite:bool, optional- If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't.
Returns
bool- True if the export operation is successful. False otherwise.
Expand source code
@staticmethod def FigureExportToSVG(figure, path, width=1920, height=1200, overwrite=False): """ Exports the input plotly figure to a SVG file. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. path : str The input file path. width : int, optional The width of the exported image in pixels. The default is 1920. height : int , optional The height of the exported image in pixels. The default is 1200. overwrite : bool , optional If set to True the ouptut file will overwrite any pre-existing file. Otherwise, it won't. Returns ------- bool True if the export operation is successful. False otherwise. """ import os if not isinstance(figure, plotly.graph_objs._figure.Figure): print("Plotly.FigureExportToSVG - Error: The input figure is not a plolty figure. Returning None.") return None if not isinstance(path, str): print("Plotly.FigureExportToSVG - Error: The input path is not a string. Returning None.") return None # Make sure the file extension is .svg ext = path[len(path)-4:len(path)] if ext.lower() != ".svg": path = path+".svg" if overwrite == False and os.path.exists(path): print("Plotly.FigureExportToSVG - Error: A file already exists at this location and overwrite is set to False. Returning None.") return None plotly.io.write_image(figure, path, format='svg', scale=1, width=width, height=height, validate=True, engine='auto') return True def Renderer()-
Return the renderer most suitable for the environment in which the script is running.
Parameters
Returns
str- The most suitable renderer type for the environment in which the script is running. Currently, this is limited to: - "vscode" if running in Visual Studio Code - "colab" if running in Google Colab - "iframe" if running in jupyter notebook or jupyterlab - "browser" if running in anything else
Expand source code
@staticmethod def Renderer(): """ Return the renderer most suitable for the environment in which the script is running. Parameters ---------- Returns ------- str The most suitable renderer type for the environment in which the script is running. Currently, this is limited to: - "vscode" if running in Visual Studio Code - "colab" if running in Google Colab - "iframe" if running in jupyter notebook or jupyterlab - "browser" if running in anything else """ import sys import os if 'VSCODE_PID' in os.environ: return 'vscode' elif "google.colab" in sys.modules: return "colab" elif "ipykernel" in sys.modules: return "iframe" #works for jupyter notebook and jupyterlab else: return "browser" def Renderers()-
Returns a list of the available plotly renderers.
Parameters
Returns
list- The list of the available plotly renderers.
Expand source code
@staticmethod def Renderers(): """ Returns a list of the available plotly renderers. Parameters ---------- Returns ------- list The list of the available plotly renderers. """ return ['plotly_mimetype', 'jupyterlab', 'nteract', 'vscode', 'notebook', 'notebook_connected', 'kaggle', 'azure', 'colab', 'cocalc', 'databricks', 'json', 'png', 'jpeg', 'jpg', 'svg', 'pdf', 'browser', 'firefox', 'chrome', 'chromium', 'iframe', 'iframe_connected', 'sphinx_gallery', 'sphinx_gallery_png', 'offline'] def SetCamera(figure, camera=[-1.25, -1.25, 1.25], center=[0, 0, 0], up=[0, 0, 1], projection='perspective')-
Sets the camera for the input figure.
Parameters
figure:plotly.graph_objs._figure.Figure- The input plotly figure.
camera:list, optional- The desired location of the camera. The default is [-1.25, -1.25, 1.25].
center:list, optional- The desired center (camera target). The default is [0, 0, 0].
up:list, optional- The desired up vector. The default is [0, 0, 1].
projection:str, optional- The desired type of projection. The options are "orthographic" or "perspective". It is case insensitive. The default is "perspective"
Returns
plotly.graph_objs._figure.Figure- The updated figure
Expand source code
@staticmethod def SetCamera(figure, camera=[-1.25, -1.25, 1.25], center=[0, 0, 0], up=[0, 0, 1], projection="perspective"): """ Sets the camera for the input figure. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. camera : list , optional The desired location of the camera. The default is [-1.25, -1.25, 1.25]. center : list , optional The desired center (camera target). The default is [0, 0, 0]. up : list , optional The desired up vector. The default is [0, 0, 1]. projection : str , optional The desired type of projection. The options are "orthographic" or "perspective". It is case insensitive. The default is "perspective" Returns ------- plotly.graph_objs._figure.Figure The updated figure """ if not isinstance(camera, list): camera = [-1.25, -1.25, 1.25] if not isinstance(center, list): center = [0, 0, 0] if not isinstance(up, list): up = [0, 0, 1] projection = projection.lower() if projection in "orthographic": projection = "orthographic" else: projection = "perspective" scene_camera = dict( up=dict(x=up[0], y=up[1], z=up[2]), eye=dict(x=camera[0], y=camera[1], z=camera[2]), center=dict(x=center[0], y=center[1], z=center[2]), projection=dict(type=projection) ) figure.update_layout(scene_camera=scene_camera) return figure def Show(figure, camera=[-1.25, -1.25, 1.25], center=[0, 0, 0], up=[0, 0, 1], renderer=None, projection='perspective')-
Shows the input figure.
Parameters
figure:plotly.graph_objs._figure.Figure- The input plotly figure.
camera:list, optional- The desired location of the camera. The default is [0, 0, 0].
center:list, optional- The desired center (camera target). The default is [0, 0, 0].
up:list, optional- The desired up vector. The default is [0, 0, 1].
renderer:str, optional- The desired renderer. See Plotly.Renderers(). If set to None, the code will attempt to discover the most suitable renderer. The default is None.
projection:str, optional- The desired type of projection. The options are "orthographic" or "perspective". It is case insensitive. The default is "perspective"
Returns
None
Expand source code
@staticmethod def Show(figure, camera=[-1.25, -1.25, 1.25], center=[0, 0, 0], up=[0, 0, 1], renderer=None, projection="perspective"): """ Shows the input figure. Parameters ---------- figure : plotly.graph_objs._figure.Figure The input plotly figure. camera : list , optional The desired location of the camera. The default is [0, 0, 0]. center : list , optional The desired center (camera target). The default is [0, 0, 0]. up : list , optional The desired up vector. The default is [0, 0, 1]. renderer : str , optional The desired renderer. See Plotly.Renderers(). If set to None, the code will attempt to discover the most suitable renderer. The default is None. projection : str, optional The desired type of projection. The options are "orthographic" or "perspective". It is case insensitive. The default is "perspective" Returns ------- None """ if figure == None: print("Plotly.Show - Error: The input is NULL. Returning None.") return None if not isinstance(figure, plotly.graph_objs._figure.Figure): print("Plotly.Show - Error: The input is not a figure. Returning None.") return None if renderer == None: renderer = Plotly.Renderer() if not renderer.lower() in Plotly.Renderers(): print("Plotly.Show - Error: The input renderer is not in the approved list of renderers. Returning None.") return None if not camera == None and not center == None and not up == None: figure = Plotly.SetCamera(figure, camera=camera, center=center, up=up, projection=projection) if renderer.lower() == "offline": ofl.plot(figure) else: figure.show(renderer=renderer) return None