我对使用plotly python api制作3D轨迹动画感兴趣。
通过混合有关plotly animation和plotly 3d line plot的文档中的一些代码，我提出了以下实现，它应该在jupyter笔记本中为3d中的布朗运动设置动画。
# Cell 1
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.grid_objs import Grid, Column
这里我使用numpy创建随机运动。每行代表具有3个坐标的轨迹点。 （我随后放了一个随机种子来缩放轴）
# Cell 2
np.random.seed(0)
brownian_motion = np.cumsum(np.random.uniform(low=-0.5, high=0.5, size=(25,3)), axis=0)
我尝试直接使用此np.array作为输入来创建动画，但是我收到错误，指出我无法使用原始数据创建动画，而是应该使用网格。我正在使用情节Column和Grid类在笔记本的下一个单元格中构建此网格。
# Cell 3
my_columns = []
nr_frames = brownian_motion.shape[0]
for k in range(nr_frames):
my_columns.extend(
[Column(brownian_motion[:k+1,0], 'x{}'.format(k+1)),
Column(brownian_motion[:k+1,1], 'y{}'.format(k+1)),
Column(brownian_motion[:k+1,2], 'z{}'.format(k+1))
])
grid = Grid(my_columns)
最后，我尝试构建动画scatter3d线图。
# Cell 4
data = [dict(
type='scatter3d',
xsrc = grid.get_column_reference('x1'),
ysrc = grid.get_column_reference('y1'),
zsrc = grid.get_column_reference('z1'),
marker=dict(
size=4
),
line=dict(
color='#1f77b4',
width=1
)
)]
frames = []
for k in range(nr_frames):
frames.append(
dict(
data = dict(
xsrc=grid.get_column_reference('x{}'.format(k+1)),
ysrc=grid.get_column_reference('y{}'.format(k+1)),
zsrc=grid.get_column_reference('z{}'.format(k+1))
)
)
)
layout = dict(
width=800,
height=700
)
fig = dict(data=data, frames= frames, layout=layout)
py.create_animations(fig)
然而，我得到了这个难以理解的错误：
---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\plotly\api\v2\utils.py in validate_response(response)
65 try:
---> 66 parsed_content = response.json()
67 except ValueError:
~\AppData\Local\Continuum\anaconda3\lib\site-packages\requests\models.py in json(self, **kwargs)
891 pass
--> 892 return complexjson.loads(self.text, **kwargs)
893
~\AppData\Local\Continuum\anaconda3\lib\json\__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
353 parse_constant is None and object_pairs_hook is None and not kw):
--> 354 return _default_decoder.decode(s)
355 if cls is None:
~\AppData\Local\Continuum\anaconda3\lib\json\decoder.py in decode(self, s, _w)
338 """
--> 339 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
340 end = _w(s, end).end()
~\AppData\Local\Continuum\anaconda3\lib\json\decoder.py in raw_decode(self, s, idx)
356 except StopIteration as err:
--> 357 raise JSONDecodeError("Expecting value", s, err.value) from None
358 return obj, end
JSONDecodeError: Expecting value: line 1 column 1 (char 0)
During handling of the above exception, another exception occurred:
PlotlyRequestError Traceback (most recent call last)
<ipython-input-3-dc743f0d5686> in <module>()
33
34 fig = dict(data=data, frames= frames, layout=layout)
---> 35 py.create_animations(fig)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\plotly\plotly\plotly.py in create_animations(figure, filename, sharing, auto_open)
1796 )
1797
-> 1798 response = v2.plots.create(body)
1799 parsed_content = response.json()
1800
~\AppData\Local\Continuum\anaconda3\lib\site-packages\plotly\api\v2\plots.py in create(body)
16 """
17 url = build_url(RESOURCE)
---> 18 return request('post', url, json=body)
19
20
~\AppData\Local\Continuum\anaconda3\lib\site-packages\plotly\api\v2\utils.py in request(method, url, **kwargs)
151 content = response.content if response else 'No content'
152 raise exceptions.PlotlyRequestError(message, status_code, content)
--> 153 validate_response(response)
154 return response
~\AppData\Local\Continuum\anaconda3\lib\site-packages\plotly\api\v2\utils.py in validate_response(response)
67 except ValueError:
68 message = content if content else 'No Content'
---> 69 raise exceptions.PlotlyRequestError(message, status_code, content)
70
71 message = ''
PlotlyRequestError: <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<title>Plotly</title>
<style>
body { background-color: #f3f3f3; }
.error-page {
margin-top: 5%;
}
.error-page-logo {
width: 350px;
margin: auto;
}
.error-page-logo img {
max-width: 100%;
}
.error-page-text,
.error-page-subtext {
text-align: center;
color: #1d3b84;
font-family: 'Open Sans', verdana, arial, sans-serif;
font-size: 24px;
font-weight: 400;
line-height: 1.5;
}
.error-page-subtext {
color: #69738a;
font-size: 16px;
}
a {
font-size: 14px;
text-decoration: none;
color: #447bdc;
}
a:hover {
color: #1d3b84;
}
</style>
</head>
<body>
<div class="error-page">
<div class="error-page-logo">
<img alt="plotly" 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" 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知道我怎么能做到吗？