Hello everyone,
My first plotly question, so, apologies if any community formatting is missing.
However, a very quick question (I think/hope). I’m trying to created an animated quiver plot to help describe chemical kinetics for undergraduate students. In essence, I am using an individual quiver to act as an individual time point in a traditional scatter plot context. I would like to animate and have each quiver appear with each frame, either accumulatively, or as a single quiver for each point.
I would love to be able to animate this. I’ve found the ability to animate scatter plots a wonderful tool in plotly and so this was my first port of call.
Thanks, Dave
Code and data (i.e. pocbook_data1.csv file stated in code) is pasted below.
import pandas as pd
import numpy as np
import plotly.io as pio
import plotly.express as px
import plotly.figure_factory as ff
pio.renderers.default=‘browser’
df=pd.read_csv(r"C:\Users\Dave\Documents\pocbook_data1.csv")
df2=df.iloc[::2, :]
df2[‘gT’]=np.gradient(df2[‘Time’])
df2[‘gP’]=np.gradient(df2[‘P’])
x=df2[‘Time’]*0.1
y=df2[‘P’]
u=df2[‘gT’]
v=df2[‘gP’]*30
fig = ff.create_quiver(x, y, u, v)
fig.update_layout(
title=‘Quiver Kinetics’,
xaxis_title=“time*0.1 (s)”,
yaxis_title="[P] (M)")
fig.show()
Time | S | P |
---|---|---|
0 | 1 | 0 |
1 | 0.951229424 | 0.048770576 |
2 | 0.904837418 | 0.095162582 |
3 | 0.860707977 | 0.139292023 |
4 | 0.818730754 | 0.181269246 |
5 | 0.778800784 | 0.221199216 |
6 | 0.740818222 | 0.259181778 |
7 | 0.704688092 | 0.295311908 |
8 | 0.670320065 | 0.329679935 |
9 | 0.637628199 | 0.362371801 |
10 | 0.606530715 | 0.393469285 |
11 | 0.576949852 | 0.423050148 |
12 | 0.548811652 | 0.451188348 |
13 | 0.522045777 | 0.477954223 |
14 | 0.49658529 | 0.50341471 |
15 | 0.47236651 | 0.52763349 |
16 | 0.449328905 | 0.550671095 |
17 | 0.427414877 | 0.572585123 |
18 | 0.406569606 | 0.593430394 |
19 | 0.38674097 | 0.61325903 |
20 | 0.367879391 | 0.632120609 |
21 | 0.349937696 | 0.650062304 |
22 | 0.332871025 | 0.667128975 |
23 | 0.316636713 | 0.683363287 |
24 | 0.301194155 | 0.698805845 |
25 | 0.286504738 | 0.713495262 |
26 | 0.272531737 | 0.727468263 |
27 | 0.259240205 | 0.740759795 |
28 | 0.246596907 | 0.753403093 |
29 | 0.234570232 | 0.765429768 |
30 | 0.223130096 | 0.776869904 |
31 | 0.21224789 | 0.78775211 |
32 | 0.201896413 | 0.798103587 |
33 | 0.192049802 | 0.807950198 |
34 | 0.182683419 | 0.817316581 |
35 | 0.173773832 | 0.826226168 |
36 | 0.165298768 | 0.834701232 |
37 | 0.15723705 | 0.84276295 |
38 | 0.149568509 | 0.850431491 |
39 | 0.142273959 | 0.857726041 |
40 | 0.135335165 | 0.864664835 |
41 | 0.128734787 | 0.871265213 |
42 | 0.122456317 | 0.877543683 |
43 | 0.116484045 | 0.883515955 |
44 | 0.11080304 | 0.88919696 |
45 | 0.105399107 | 0.894600893 |
46 | 0.100258733 | 0.899741267 |
47 | 0.095369054 | 0.904630946 |
48 | 0.090717843 | 0.909282157 |
49 | 0.086293478 | 0.913706522 |
50 | 0.082084897 | 0.917915103 |
51 | 0.078081566 | 0.921918434 |
52 | 0.074273476 | 0.925726524 |
53 | 0.070651111 | 0.929348889 |
54 | 0.067205417 | 0.932794583 |
55 | 0.063927768 | 0.936072232 |
56 | 0.060809969 | 0.939190031 |
57 | 0.057844227 | 0.942155773 |
58 | 0.055023132 | 0.944976868 |
59 | 0.052339621 | 0.947660379 |
60 | 0.049786984 | 0.950213016 |
61 | 0.04735884 | 0.95264116 |
62 | 0.045049123 | 0.954950877 |
63 | 0.04285205 | 0.95714795 |
64 | 0.040762128 | 0.959237872 |
65 | 0.038774132 | 0.961225868 |
66 | 0.036883096 | 0.963116904 |
67 | 0.035084285 | 0.964915715 |
68 | 0.033373202 | 0.966626798 |
69 | 0.031745569 | 0.968254431 |
70 | 0.030197319 | 0.969802681 |
71 | 0.028724579 | 0.971275421 |
72 | 0.027323663 | 0.972676337 |
73 | 0.02599107 | 0.97400893 |
74 | 0.02472347 | 0.97527653 |
75 | 0.023517692 | 0.976482308 |
76 | 0.022370719 | 0.977629281 |
77 | 0.021279684 | 0.978720316 |
78 | 0.020241861 | 0.979758139 |
79 | 0.019254654 | 0.980745346 |
80 | 0.018315593 | 0.981684407 |
81 | 0.017422329 | 0.982577671 |
82 | 0.016572632 | 0.983427368 |
83 | 0.015764375 | 0.984235625 |
84 | 0.014995537 | 0.985004463 |
85 | 0.014264194 | 0.985735806 |
86 | 0.013568521 | 0.986431479 |
87 | 0.012906776 | 0.987093224 |
88 | 0.012277305 | 0.987722695 |
89 | 0.011678533 | 0.988321467 |
90 | 0.011108963 | 0.988891037 |
91 | 0.010567173 | 0.989432827 |
92 | 0.010051806 | 0.989948194 |
93 | 0.009561572 | 0.990438428 |
94 | 0.009095248 | 0.990904752 |
95 | 0.008651668 | 0.991348332 |
96 | 0.008229721 | 0.991770279 |
97 | 0.007828352 | 0.992171648 |
98 | 0.007446558 | 0.992553442 |
99 | 0.007083385 | 0.992916615 |
100 | 0.006737924 | 0.993262076 |