from typing import Union
import numpy as np
import pandas as pd
import plotly.graph_objs as go
from plotly.offline.offline import plot
[docs]def animate_init_(data: Union[np.ndarray, pd.DataFrame, list], filename: str) -> dict:
"""Plot the first (or zeroth) iteration of a population.
Intended as a frames object. Plots Scatter for 2D and 3D data.
Plots parallel coordinate plot for higher dimensional data.
Parameters
----------
data : Union[np.ndarray, pd.DataFrame, list]
Contains the data to be plotted. Each row is an individual's objective values.
filename : str
Contains the name of the file to which the plot is saved.
Returns
-------
dict
Plotly figure object
"""
numobj = data.shape[1]
if numobj == 2:
figure = animate_2d_init_(data, filename)
elif numobj == 3:
figure = animate_3d_init_(data, filename)
elif numobj >= 4:
figure = animate_parallel_coords_init_(data, filename)
print("Plot saved as: ", filename)
print("View the plot by opening the file in browser.")
print("To view the plot in Jupyter Notebook, use the IFrame command.")
return figure
[docs]def animate_next_(
data: Union[np.ndarray, pd.DataFrame, list],
figure: dict,
filename: str,
generation: int = None,
) -> dict:
"""Plot the next set of individuals in an animation.
Plots scatter for 2D and 3D data, parallel coordinate plot for 4D and up.
Parameters
----------
data : Union[np.ndarray, pd.DataFrame, list]
The objective values to be plotted
figure : dict
Plotly figure object compatible dict
filename : str
Name of the file to which the plot is saved
generation : int
Iteration Number
Returns
-------
dict
Plotly Figure Object
"""
numobj = data.shape[1]
if generation is None:
generation = len(figure["frames"])
if numobj == 2:
figure = animate_2d_next_(data, figure, filename, generation)
elif numobj == 3:
figure = animate_3d_next_(data, figure, filename, generation)
elif numobj >= 4:
figure = animate_parallel_coords_next_(data, figure, filename, generation)
return figure
[docs]def animate_2d_init_(
data: Union[np.ndarray, pd.DataFrame, list], filename: str
) -> dict:
"""Initiate a 2D scatter animation.
Only for 2D data.
Parameters
----------
data : Union[np.ndarray, pd.DataFrame, list]
Objective values
filename : str
Name of the file to which plot is saved
Returns
-------
dict
Plotly Figure Object
"""
figure = {"data": [], "layout": {}, "frames": []}
figure["layout"]["xaxis"] = {"autorange": True}
figure["layout"]["yaxis"] = {"autorange": True}
figure["layout"]["hovermode"] = "closest"
figure["layout"]["sliders"] = {
"args": ["transition", {"duration": 400, "easing": "cubic-in-out"}],
"initialValue": "1952",
"plotlycommand": "animate",
"visible": True,
}
sliders_dict = {
"active": 0,
"yanchor": "top",
"xanchor": "left",
"currentvalue": {
"font": {"size": 20},
"prefix": "Iteration:",
"visible": True,
"xanchor": "right",
},
"transition": {"duration": 300, "easing": "cubic-in-out"},
"pad": {"b": 10, "t": 50},
"len": 0.9,
"x": 0.1,
"y": 0,
"steps": [],
}
figure["layout"]["sliders"] = [sliders_dict]
data_dict = {
"x": list(data[:, 0]),
"y": list(data[:, 1]),
"mode": "markers",
"marker": {
"size": 5,
"color": "rgba(255, 182, 193, .9)",
"line": dict(width=2),
},
}
figure["data"].append(data_dict)
plot(figure, filename=filename)
animate_2d_next_(data, figure, filename, 0)
return figure
[docs]def animate_2d_next_(
data: Union[np.ndarray, pd.DataFrame, list],
figure: dict,
filename: str,
generation: int,
) -> dict:
"""Plot the next set of individuals in a 2D scatter animation.
Parameters
----------
data : Union[np.ndarray, pd.DataFrame, list]
The objective values to be plotted
figure : dict
Plotly figure object compatible dict
filename : str
Name of the file to which the plot is saved
generation : int
Iteration Number
Returns
-------
dict
Plotly Figure Object
"""
frame = {"data": [], "name": str(generation)}
sliders_dict = figure["layout"]["sliders"][0]
data_dict = {
"x": list(data[:, 0]),
"y": list(data[:, 1]),
"mode": "markers",
"marker": {
"size": 5,
"color": "rgba(255, 182, 193, .9)",
"line": dict(width=2),
},
}
frame["data"].append(data_dict)
figure["frames"].append(frame)
slider_step = {
"args": [
[generation],
{
"frame": {"duration": 300, "redraw": False},
"mode": "immediate",
"transition": {"duration": 300},
},
],
"label": generation,
"method": "animate",
}
sliders_dict["steps"].append(slider_step)
figure["layout"]["sliders"] = [sliders_dict]
plot(figure, auto_open=False, filename=filename)
return figure
[docs]def animate_3d_init_(
data: Union[np.ndarray, pd.DataFrame, list], filename: str
) -> dict:
"""Plot the first (or zeroth) iteration of a population.
Intended as a frames object. Plots Scatter 3D data.
Parameters
----------
data : Union[np.ndarray, pd.DataFrame, list]
Contains the data to be plotted. Each row is an individual's objective values.
filename : str
Contains the name of the file to which the plot is saved.
Returns
-------
dict
Plotly figure object
"""
figure = {"data": [], "layout": {}, "frames": []}
figure["layout"]["hovermode"] = "closest"
figure["layout"]["sliders"] = {
"args": ["transition", {"duration": 400, "easing": "cubic-in-out"}],
"initialValue": "1",
"plotlycommand": "animate",
"visible": True,
}
sliders_dict = {
"active": 0,
"yanchor": "top",
"xanchor": "left",
"currentvalue": {
"font": {"size": 20},
"prefix": "Iteration:",
"visible": True,
"xanchor": "right",
},
"transition": {"duration": 300, "easing": "cubic-in-out"},
"pad": {"b": 10, "t": 50},
"len": 0.9,
"x": 0.1,
"y": 0,
"steps": [],
}
figure["layout"]["sliders"] = [sliders_dict]
figure["layout"]["scene"] = dict(
xaxis=dict(
backgroundcolor="rgb(200, 200, 230)",
gridcolor="rgb(255, 255, 255)",
showbackground=True,
zerolinecolor="rgb(255, 255, 255)",
),
yaxis=dict(
backgroundcolor="rgb(230, 200,230)",
gridcolor="rgb(255, 255, 255)",
showbackground=True,
zerolinecolor="rgb(255, 255, 255)",
),
zaxis=dict(
backgroundcolor="rgb(230, 230,200)",
gridcolor="rgb(255, 255, 255)",
showbackground=True,
zerolinecolor="rgb(255, 255, 255)",
),
)
data_dict = go.Scatter3d(
x=list(data[:, 0]),
y=list(data[:, 1]),
z=list(data[:, 2]),
mode="markers",
marker=dict(
size=8,
color=data[:, 2],
colorscale="Viridis",
opacity=0.5,
line=dict(width=2, color="black", colorscale="Viridis"),
),
)
figure["data"].append(data_dict)
plot(figure, filename=filename)
animate_3d_next_(data, figure, filename, 0)
return figure
[docs]def animate_3d_next_(
data: Union[np.ndarray, pd.DataFrame, list],
figure: dict,
filename: str,
generation: int,
) -> dict:
"""Plot the next set of individuals in an animation.
Plots scatter for 3D data.
Parameters
----------
data : Union[np.ndarray, pd.DataFrame, list]
The objective values to be plotted
figure : dict
Plotly figure object compatible dict
filename : str
Name of the file to which the plot is saved
generation : int
Iteration Number
Returns
-------
dict
Plotly Figure Object
"""
frame = {"data": [], "name": str(generation)}
sliders_dict = figure["layout"]["sliders"][0]
data_dict = go.Scatter3d(
x=list(data[:, 0]),
y=list(data[:, 1]),
z=list(data[:, 2]),
mode="markers",
marker=dict(
size=8,
color=data[:, 2],
colorscale="Viridis",
opacity=0.5,
line=dict(width=2, color="black", colorscale="Viridis"),
),
)
frame["data"].append(data_dict)
figure["frames"].append(frame)
slider_step = {
"args": [
[generation],
{
"frame": {"duration": 300, "redraw": True},
"mode": "immediate",
"transition": {"duration": 300},
},
],
"label": generation,
"method": "animate",
}
sliders_dict["steps"].append(slider_step)
figure["layout"]["sliders"] = [sliders_dict]
plot(figure, auto_open=False, filename=filename)
return figure
[docs]def animate_parallel_coords_init_(
data: Union[np.ndarray, pd.DataFrame, list], filename: str
) -> dict:
"""Plot the first (or zeroth) iteration of a population.
Intended as a frames object. Plots parallel coordinate plot for >3D data.
Parameters
----------
data : Union[np.ndarray, pd.DataFrame, list]
Contains the data to be plotted. Each row is an individual's objective values.
filename : str
Contains the name of the file to which the plot is saved.
Returns
-------
dict
Plotly figure object
"""
figure = {"data": [], "layout": {}, "frames": []}
objectives = pd.DataFrame(data)
figure["layout"]["hovermode"] = "closest"
figure["layout"]["sliders"] = {
"args": ["transition", {"duration": 400, "easing": "cubic-in-out"}],
"initialValue": "1952",
"plotlycommand": "animate",
"visible": True,
}
sliders_dict = {
"active": 0,
"yanchor": "top",
"xanchor": "left",
"currentvalue": {
"font": {"size": 20},
"prefix": "Iteration:",
"visible": True,
"xanchor": "right",
},
"transition": {"duration": 300, "easing": "cubic-in-out"},
"pad": {"b": 10, "t": 50},
"len": 0.9,
"x": 0.1,
"y": 0,
"steps": [],
}
figure["layout"]["sliders"] = [sliders_dict]
dimensions = [
dict(
range=[min(objectives[column]), max(objectives[column])],
label="f" + str(column),
values=objectives[column],
)
for column in objectives
]
line = dict(
color=objectives[0],
colorscale="Viridis",
showscale=True,
cmin=min(objectives[objectives.columns[0]]),
cmax=max(objectives[objectives.columns[0]]),
)
data_dict = go.Parcoords(line=line, dimensions=dimensions)
figure["data"].append(data_dict)
plot(figure, filename=filename)
animate_parallel_coords_next_(data, figure, filename, 0)
return figure
[docs]def animate_parallel_coords_next_(
data: Union[np.ndarray, pd.DataFrame, list],
figure: dict,
filename: str,
generation: int,
) -> dict:
"""Plot the next set of individuals in an animation.
Plots parallel coordinate plot for 4D and up.
Parameters
----------
data : Union[np.ndarray, pd.DataFrame, list]
The objective values to be plotted
figure : dict
Plotly figure object compatible dict
filename : str
Name of the file to which the plot is saved
generation : int
Iteration Number
Returns
-------
dict
Plotly Figure Object
"""
frame = {"data": [], "name": str(generation)}
objectives = pd.DataFrame(data)
sliders_dict = figure["layout"]["sliders"][0]
dimensions = [
dict(
range=[min(objectives[column]), max(objectives[column])],
label="f" + str(column),
values=objectives[column],
)
for column in objectives
]
line = dict(
color=objectives[0],
colorscale="Viridis",
showscale=True,
cmin=min(objectives[objectives.columns[0]]),
cmax=max(objectives[objectives.columns[0]]),
)
data_dict = go.Parcoords(line=line, dimensions=dimensions)
frame["data"].append(data_dict)
figure["frames"].append(frame)
slider_step = {
"args": [
[generation],
{
"frame": {"duration": 300, "redraw": True},
"mode": "immediate",
"transition": {"duration": 300},
},
],
"label": generation,
"method": "animate",
}
sliders_dict["steps"].append(slider_step)
figure["layout"]["sliders"] = [sliders_dict]
plot(figure, auto_open=False, filename=filename)
return figure
[docs]def test():
data1 = np.random.rand(100, 3)
data2 = np.square(data1)
data3 = np.square(data2)
data4 = np.square(data3)
data = [data2, data3, data4]
filename = "firsttest.html"
figure = animate_3d_init_(data1, filename)
for i in range(1, 4):
figure = animate_3d_next_(data[i - 1], figure, filename, i)
[docs]def test2():
data = np.random.rand(100, 1) * np.pi
obj = np.hstack((data, np.sin(data), np.cos(data), np.log(data)))
figure = animate_parallel_coords_init_(obj, "partest.html")
for i in range(1, 10):
obj = np.hstack((data, np.sin(data / i), np.cos(data / i), np.log(data / i)))
figure = animate_parallel_coords_next_(obj, figure, "partest.html", i)
