A python package for making interactive as well as animated plots with matplotlib.
pip install animatplot
or using conda
conda install animatplot -c conda-forge
Documentation can be found here
git clone https://github.com/t-makaro/animatplot.git
pip install -e .
This project uses pycodestyle for linting. For testing, pytest is used.
Closes #11.
I wanted to understand how this library works a bit better so I had a go at writing a new block - the title block.
I wrote it using test-driven development so there are some unit tests included too.
I also grouped your existing comparison tests into a class.
I tried to use args and kwargs in such a way that any type of format string can be passed, and any kwargs intended for the call to matplotlib.pyplot.ax.set_title() are specified as a separate dictionary.
Let me know what you think.
I am not sure if it is a feature or a bug. When I was playing with the example code:
import numpy as np
import matplotlib.pyplot as plt
import animatplot as amp
x = np.linspace(-2, 2, 5)
y = np.linspace(-2, 2, 5)
t = np.linspace(0, 2*np.pi, 40)
X, Y, T = np.meshgrid(x, y, t)
Z = np.sin(X*X+Y*Y-T)
block = amp.blocks.Pcolormesh(X[:,:,0], Y[:,:,0], Z, t_axis=2, cmap='RdBu')
plt.colorbar(block.quad)
plt.gca().set_aspect('equal')
anim = amp.Animation([block], amp.Timeline(t))
anim.controls()
plt.show()
I used a very coarse grid 55 instead of the original 5050, so that it is more clear.
The data array Z is of size 55 at a certain time step. But the generated animation shows a grid of shape 44.
It seems blocks.Pcolormesh use the value in Z as the "node" value in animation, which is different in plt.pcolormesh whereZ value is treated as the "mesh cell" value.
.gif files created with animatplot only run once through, then stop on the final frame. They do loop properly when running interactively (in a Jupyter notebook) though.
I'm creating a gif in a jupyter notebook, using %matplotlib notebook. I save the gif, then try to open it using
This displays the gif once through, then it stops on the final frame. If I rerun the cell then it just opens a static image of the final frame.
If I open the .gif file with another program it also stops on the final frame, so I think it's a problem with the created file.
I've tried explicitly passing repeat=True to FuncAnimation() inside animatplot.Animation, and I've also tried using the imagemagick writer rather than PillowWriter, but the problem persists.
A reproducible example, which is basically just this example):
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import animatplot as amp
# Define LxL matrix
L = 55
# Initialize as random spin
M = 2*(np.random.rand(L,L)>.5)-1
J = 1
b = 2.5
nPer = 100
images = [M]
for i in range(100):
M = M.copy()
for dm in range(nPer):
jj = int(np.random.rand()*L - 1)
kk = int(np.random.rand()*L - 1)
dE = 2*J*(M[jj+1,kk] + M[jj-1,kk] + M[jj,kk+1] + M[jj,kk-1])*M[jj,kk]
if dE <= 0:
M[jj,kk]*=-1
else:
if(np.random.rand()<np.exp(-b*dE)):
M[jj,kk]*=-1
images.append(M)
M[:,-1] = M[:,0]
M[-1,:] = M[0,:]
fig1, ax1 = plt.subplots()
block = amp.blocks.Imshow(images, axis=ax1)
anim = amp.Animation([block])
anim.controls()
anim.save_gif('ising')
Also the output of using conda list to see which versions of everything I'm using:
# Name Version Build
alabaster 0.7.12 py36_0
anaconda custom py36hbbc8b67_0
anaconda-client 1.7.2 py36_0
anaconda-project 0.8.2 py36_0
animatplot 0.4.0.dev1 <pip>
appdirs 1.4.3 py36h28b3542_0
asn1crypto 0.24.0 py36_0
astroid 2.0.4 py36_0
astropy 3.0.5 py36h7b6447c_0
atomicwrites 1.2.1 py36_0
attrs 18.2.0 py36h28b3542_0
automat 0.7.0 py36_0
babel 2.6.0 py36_0
backcall 0.1.0 py36_0
backports 1.0 py36_1
backports.os 0.1.1 py36_0
backports.shutil_get_terminal_size 1.0.0 py36_2
beautifulsoup4 4.6.3 py36_0
bitarray 0.8.3 py36h14c3975_0
bkcharts 0.2 py36_0
blas 1.0 mkl
blaze 0.11.3 py36_0
bleach 3.0.2 py36_0
blosc 1.14.4 hdbcaa40_0
bokeh 1.0.1 py36_0
boto 2.49.0 py36_0
bottleneck 1.2.1 py36h035aef0_1
bzip2 1.0.6 h14c3975_5
ca-certificates 2018.03.07 0
cairo 1.14.12 h8948797_3
certifi 2018.10.15 py36_0
cffi 1.11.5 py36he75722e_1
cftime 1.0.2.1 py36hdd07704_0
chardet 3.0.4 py36_1
click 7.0 py36_0
cloudpickle 0.6.1 py36_0
clyent 1.2.2 py36_1
colorama 0.4.0 py36_0
constantly 15.1.0 py36h28b3542_0
contextlib2 0.5.5 py36_0
cryptography 2.3.1 py36hc365091_0
curl 7.61.0 h84994c4_0
cycler 0.10.0 py36_0
cython 0.29 py36he6710b0_0
cytoolz 0.9.0.1 py36h14c3975_1
dask 0.20.2 py36_0
dask-core 0.20.2 py36_0
datashape 0.5.4 py36_1
dbus 1.13.2 h714fa37_1
decorator 4.3.0 py36_0
defusedxml 0.5.0 py36_1
distributed 1.24.2 py36_0
docutils 0.14 py36_0
entrypoints 0.2.3 py36_2
et_xmlfile 1.0.1 py36_0
expat 2.2.6 he6710b0_0
experi 0.2.11 <pip>
fastcache 1.0.2 py36h14c3975_2
filelock 3.0.10 py36_0
flask 1.0.2 py36_1
flask-cors 3.0.7 py36_0
fontconfig 2.13.0 h9420a91_0
freetype 2.9.1 h8a8886c_1
fribidi 1.0.5 h7b6447c_0
get_terminal_size 1.0.0 haa9412d_0
gevent 1.3.7 py36h7b6447c_1
glib 2.56.2 hd408876_0
glob2 0.6 py36_1
gmp 6.1.2 h6c8ec71_1
gmpy2 2.0.8 py36h10f8cd9_2
graphite2 1.3.12 h23475e2_2
greenlet 0.4.15 py36h7b6447c_0
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 hb453b48_1
h5py 2.8.0 py36h989c5e5_3
harfbuzz 1.8.8 hffaf4a1_0
hdf4 4.2.13 h3ca952b_2
hdf5 1.10.2 hba1933b_1
heapdict 1.0.0 py36_2
html5lib 1.0.1 py36_0
hyperlink 18.0.0 py36_0
icu 58.2 h9c2bf20_1
idna 2.7 py36_0
imageio 2.4.1 py36_0
imagesize 1.1.0 py36_0
importlib_metadata 0.6 py36_0
incremental 17.5.0 py36_0
intel-openmp 2019.1 144
ipykernel 5.1.0 py36h39e3cac_0
ipython 7.1.1 py36h39e3cac_0
ipython_genutils 0.2.0 py36_0
ipywidgets 7.4.2 py36_0
isort 4.3.4 py36_0
itsdangerous 1.1.0 py36_0
jbig 2.1 hdba287a_0
jdcal 1.4 py36_0
jedi 0.13.1 py36_0
jeepney 0.4 py36_0
jinja2 2.10 py36_0
jpeg 9b h024ee3a_2
jsonschema 2.6.0 py36_0
jupyter 1.0.0 py36_7
jupyter_client 5.2.3 py36_0
jupyter_console 6.0.0 py36_0
jupyter_core 4.4.0 py36_0
jupyterlab 0.35.3 py36_0
jupyterlab_launcher 0.13.1 py36_0
jupyterlab_server 0.2.0 py36_0
keyring 16.1.0 py36_0
kiwisolver 1.0.1 py36hf484d3e_0
lazy-object-proxy 1.3.1 py36h14c3975_2
libcurl 7.61.0 h1ad7b7a_0
libedit 3.1.20170329 h6b74fdf_2
libffi 3.2.1 hd88cf55_4
libgcc-ng 8.2.0 hdf63c60_1
libgfortran-ng 7.3.0 hdf63c60_0
libnetcdf 4.6.1 h10edf3e_1
libpng 1.6.35 hbc83047_0
libsodium 1.0.16 h1bed415_0
libssh2 1.8.0 h9cfc8f7_4
libstdcxx-ng 8.2.0 hdf63c60_1
libtiff 4.0.9 he85c1e1_2
libtool 2.4.6 h7b6447c_5
libuuid 1.0.3 h1bed415_2
libxcb 1.13 h1bed415_1
libxml2 2.9.8 h26e45fe_1
libxslt 1.1.32 h1312cb7_0
llvmlite 0.25.0 py36hd408876_0
locket 0.2.0 py36_1
lxml 4.2.5 py36hefd8a0e_0
lzo 2.10 h49e0be7_2
markupsafe 1.1.0 py36h7b6447c_0
matplotlib 3.0.1 py36h5429711_0
mccabe 0.6.1 py36_1
mistune 0.8.4 py36h7b6447c_0
mkl 2018.0.3 1
mkl-service 1.1.2 py36h90e4bf4_5
mkl_fft 1.0.6 py36h7dd41cf_0
mkl_random 1.0.1 py36h4414c95_1
more-itertools 4.3.0 py36_0
mpc 1.1.0 h10f8cd9_1
mpfr 4.0.1 hdf1c602_3
mpmath 1.0.0 py36_2
msgpack-python 0.5.6 py36h6bb024c_1
multipledispatch 0.6.0 py36_0
natsort 5.5.0 <pip>
nbconvert 5.3.1 py36_0
nbformat 4.4.0 py36_0
ncurses 6.1 he6710b0_1
netcdf4 1.4.2 py36h4b4f87f_0
networkx 2.2 py36_1
nltk 3.3.0 py36_0
nose 1.3.7 py36_2
notebook 5.7.2 py36_0
numba 0.40.0 py36h962f231_0
numexpr 2.6.8 py36hd89afb7_0
numpy 1.15.4 py36h1d66e8a_0
numpy-base 1.15.4 py36h81de0dd_0
numpydoc 0.8.0 py36_0
odo 0.5.1 py36_0
olefile 0.46 py36_0
openpyxl 2.5.9 py36_0
openssl 1.0.2p h14c3975_0
packaging 18.0 py36_0
pandas 0.23.4 py36h04863e7_0
pandoc 2.2.3.2 0
pandocfilters 1.4.2 py36_1
pango 1.42.4 h049681c_0
parso 0.3.1 py36_0
partd 0.3.9 py36_0
patchelf 0.9 he6710b0_3
path.py 11.5.0 py36_0
pathlib2 2.3.2 py36_0
patsy 0.5.1 py36_0
pcre 8.42 h439df22_0
pep8 1.7.1 py36_0
pexpect 4.6.0 py36_0
pickleshare 0.7.5 py36_0
pillow 5.3.0 py36h34e0f95_0
pip 18.1 py36_0
pixman 0.34.0 hceecf20_3
pkginfo 1.4.2 py36_1
pluggy 0.8.0 py36_0
ply 3.11 py36_0
prometheus_client 0.4.2 py36_0
prompt_toolkit 2.0.7 py36_0
psutil 5.4.8 py36h7b6447c_0
ptyprocess 0.6.0 py36_0
py 1.7.0 py36_0
pyasn1 0.4.4 py36h28b3542_0
pyasn1-modules 0.2.2 py36_0
pycodestyle 2.4.0 py36_0
pycosat 0.6.3 py36h14c3975_0
pycparser 2.19 py36_0
pycrypto 2.6.1 py36h14c3975_9
pycurl 7.43.0.2 py36hb7f436b_0
pyflakes 2.0.0 py36_0
pygments 2.2.0 py36_0
pyhamcrest 1.9.0 py36_2
pylint 2.1.1 py36_0
pyodbc 4.0.24 py36he6710b0_0
pyopenssl 18.0.0 py36_0
pyparsing 2.3.0 py36_0
pyqt 5.9.2 py36h05f1152_2
pysocks 1.6.8 py36_0
pytables 3.4.4 py36ha205bf6_0
pytest 4.0.0 py36_0
pytest-arraydiff 0.2 py36h39e3cac_0
pytest-astropy 0.4.0 py36_0
pytest-doctestplus 0.2.0 py36_0
pytest-openfiles 0.3.0 py36_0
pytest-remotedata 0.3.1 py36_0
python 3.6.6 h6e4f718_2
python-dateutil 2.7.5 py36_0
pytz 2018.7 py36_0
pywavelets 1.0.1 py36hdd07704_0
pyyaml 3.13 py36h14c3975_0
pyzmq 17.1.2 py36h14c3975_0
qt 5.9.6 h8703b6f_2
qtawesome 0.5.3 py36_0
qtconsole 4.4.2 py36_0
qtpy 1.5.2 py36_0
readline 7.0 h7b6447c_5
requests 2.20.1 py36_0
rope 0.11.0 py36_0
ruamel_yaml 0.15.46 py36h14c3975_0
scikit-image 0.14.0 py36hf484d3e_1
scikit-learn 0.20.1 py36h4989274_0
scipy 1.1.0 py36hfa4b5c9_1
seaborn 0.9.0 py36_0
secretstorage 3.1.0 py36_0
send2trash 1.5.0 py36_0
service_identity 17.0.0 py36h28b3542_0
setuptools 40.6.2 py36_0
simplegeneric 0.8.1 py36_2
singledispatch 3.4.0.3 py36_0
sip 4.19.8 py36hf484d3e_0
six 1.11.0 py36_1
snappy 1.1.7 hbae5bb6_3
snowballstemmer 1.2.1 py36_0
sortedcollections 1.0.1 py36_0
sortedcontainers 2.0.5 py36_0
sphinx 1.8.2 py36_0
sphinxcontrib 1.0 py36_1
sphinxcontrib-websupport 1.1.0 py36_1
spyder 3.3.2 py36_0
spyder-kernels 0.3.0 py36_0
sqlalchemy 1.2.14 py36h7b6447c_0
sqlite 3.25.3 h7b6447c_0
statsmodels 0.9.0 py36h035aef0_0
sympy 1.3 py36_0
tblib 1.3.2 py36_0
terminado 0.8.1 py36_1
testpath 0.4.2 py36_0
tk 8.6.8 hbc83047_0
toolz 0.9.0 py36_0
tornado 5.1.1 py36h7b6447c_0
tqdm 4.28.1 py36h28b3542_0
traitlets 4.3.2 py36_0
twisted 18.9.0 py36h7b6447c_0
typed-ast 1.1.0 py36h14c3975_0
unicodecsv 0.14.1 py36_0
unixodbc 2.3.7 h14c3975_0
urllib3 1.23 py36_0
wcwidth 0.1.7 py36_0
webencodings 0.5.1 py36_1
werkzeug 0.14.1 py36_0
wheel 0.32.3 py36_0
widgetsnbextension 3.4.2 py36_0
wrapt 1.10.11 py36h14c3975_2
wurlitzer 1.0.2 py36_0
xarray 0.10.9+50.g17815b4 <pip>
xBOUT 0.1 <pip>
xcollect 0.1 <pip>
xlrd 1.1.0 py36_1
xlsxwriter 1.1.2 py36_0
xlwt 1.3.0 py36_0
xstorm 0.1 <pip>
xz 5.2.4 h14c3975_4
yaml 0.1.7 had09818_2
zeromq 4.2.5 hf484d3e_1
zict 0.1.3 py36_0
zlib 1.2.11 h7b6447c_3
zope 1.0 py36_1
zope.interface 4.6.0 py36h7b6447c_0
While this is a pure python package, pip likes to mangle conda packages sometimes. So, if someone tries to install Animatplot with a version of matplotlib < 2.2 which was installed with conda, then pip attempts to update matplotlib and butcher the environment (sometimes multiple since conda symlinks packages of the same version across environments).
I'd like to be able to install Animatplot with conda.
packagingA simple example showing some of the advantages of providing convenience functions for plotting xarray DataArrays. xarray integration means that you could streamline the visualisation of time-evolving data down to just a few lines, e.g.:
data = xr.load_dataset('3d_data.nc')
anim, block, timeline = amp.animated_plot(da)
anim.save_gif('./xarray_imshow')
plt.show()
Although I think full seamless integration into xarray's system would be quite involved (and not something I'm qualified to do), then providing a set of convenience functions something like this shouldn't be very difficult.
Notice there are several # TODOs in the code showing how this could be extended.
Let me know what you think!
matplotlib-3.3 adds a new option shading="nearest" for pcolormesh. This PR makes animatplot compatible with that option. Now also works with shading="gouraud" when matplotlib=3.3. Don't know why shading="gouraud" still doesn't work with matplotlib<=3.2, I guess they changed something in the internals.
I added some unit tests for blocks.Line(), and extended it to also accept a 1D array input for x (the most natural format for use in xarray).
I also tried to clarify the logic for parsing the input, and make it raise more informative errors.
One high-level question - why allow users to input a list of numpy arrays at all? It makes the internal logic more complex and I don't really see what the benefit is. The only time a list of numpy arrays is a more suitable representation of data than a numpy array with an extra dimension is if the length of the array changes over time, but I don't know if that's actually supported by the plotting functions? Also if you do want to support do you want to keep the data internally as a list of arrays? Or is it okay to do what I did here and convert all lists of arrays straight to numpy arrays using np.asanyarray()?
It appears that the animatplot.testing package is not installed; this means that tests cannot be run against an installed copy because the import doesn't work.
Allows you to have a timeline which has a label other than "Time". Useful for when you want to animate your data over a non-temporal dimension, e.g. space.
Implemented as an optional argument to 'timeline_slider()', so usage looks like:
anim.controls(timeline_slider_args={'time_label': 'x position'})
Not sure if this should instead be implemented as an extra attribute on the timeline?
Hi there
As far as I can tell there is currently no way to control the format of the label to the progress bar in animations?
I have this currently...
import animatplot as amp
t = (np.arange(speed.shape[0])/12.+1950)
tt = t.astype(int)
.
.
.
anim = amp.Animation([im], amp.Timeline(tt))
anim.controls()
anim.save_gif('surface-ocean-circulation')
This gives 1950,1951,... but the labels on the progress bar are to 2 decimal places, see attached screenshot.
Is it possible to have further control over this, for example integer labels or even strings?
Many thanks
Jonny Williams NIWA, New Zealand
Adapting the pcolormesh example, and using the new shading="nearest" option from matplotlib-3.3.2
import numpy as np
import matplotlib.pyplot as plt
import animatplot as amp
x = np.linspace(-2, 2, 50)
y = np.linspace(-2, 2, 50)
t = np.linspace(0, 2*np.pi, 40)
X, Y, T = np.meshgrid(x, y, t)
Z = np.sin(X*X+Y*Y-T)
shading = "nearest"
#shading = "flat"
block = amp.blocks.Pcolormesh(X[:,:,0], Y[:,:,0], Z, t_axis=2, cmap='RdBu', shading=shading)
plt.colorbar(block.quad)
plt.gca().set_aspect('equal')
anim = amp.Animation([block], amp.Timeline(t))
anim.controls()
anim.save_gif('pcolormesh')
plt.show()
gives
instead of the expected result, which we get with shading="flat"
I suspect this is related to flattening the array here https://github.com/t-makaro/animatplot/blob/632d988687fca7e7415e9fa49fe7eebc3f0991c6/animatplot/blocks/image_like.py#L57-L60
Running your example of vect_plot (0.4.2, matplotlib 3.5.2) I get:
"name": "ValueError",
"message": "Passing parameters norm and vmin/vmax simultaneously is not supported.
Please pass vmin/vmax directly to the norm when creating it.",
can be solved by replacing:
pargs = {
'norm' : mpl.colors.LogNorm(),
'vmax': 1000,
'vmin': .1,
'cmap': 'pink',
}
with:
pargs = {
'norm' : colors.Normalize(vmin = 0, vmax = 1000),
'cmap': 'bwr',
}
The implementation of matplotlib.animation.FileMovieWriter seems to have changed in matplotlib-3.4, and the _frame_sink member no longer exists. This makes the unit tests fail. I guess BunchOfFiles needs to be updated, maybe following the patters in matplotlib.animation https://matplotlib.org/stable/_modules/matplotlib/animation.html?
Adds a new Block subclass Surface to make surface plots using matplotlib's plot_surface() method.
Makes animation by clearing plot and re-drawing. It was suggested that this is the best solution as majority of time is drawing rather than updating the array anyway, see https://stackoverflow.com/a/45713451/13577592.
~Making the vertical axis scale fixed doesn't work at the moment. Seems to be a bug with matplotlib's vmin and vmax arguments, see https://github.com/matplotlib/matplotlib/issues/19870.~
~Still needs tests and documentation.~
This PR attempts to make the animation controls (time-slider and play/pause button) compatible with use of tight_layout().
Previously, calling tight_layout could arrange the animations in a figure, but would not move or take into account the controls, which would end up overlapping the lower part of the animations.
This PR seems to work, but requires accessing a private variable of matplotlib.figure.Figure to get the gridspec, in order to modify it to add the controls. Also modifies a private member of matplotlib.gridspec.Gridspec to modify the number of rows. It would be nice if matplotlib provided public methods to access and update gridspecs, but as far as I can see they do not at the moment.
One issue is that the current implementation seems to reserve too much whitespace around the controls. It would be nice to find a way to allow tight_layout() to modify the amount of space above/below the slider.
This library is great, you've done a really nice job with it so far @t-makaro .
I'm using it pretty regularly, and getting close to having it integrated within xarray through https://github.com/pydata/xarray/pull/2729.
This will hopefully increase the number of people using animatplot (both directly and indirectly through xarray), and will probably mean you get more suggestions and bugs found.
Would you like any help with the maintainance of the library? Me and @dcherian are both keen to use this, and if one of us also had commit access then we could push bug fixes for you. We could have some kind of "all PRs need a review" rule, and you retain control over the scope? What do you think?
The changelog needs to be updated for release.
valfmt to None. If set to None, it will use %1.2% for most cases, but %s for np.datetime.Backwards incompatible changes:
axis kwargs that deprecated in favor of ax in 0.3.0Dev changes:
This release has multiple contributors (and the first release to have such): t-makaro TomNicholas* dcherian*
*indicates new contributor.
Source code(tar.gz)Fix packaging of the .animations and .blocks subpackages.
Source code(tar.gz)Animatplot now features a whole new API.
Built on the concept of blocks.
Now has documentation!
Source code(tar.gz)The first release of 0.2.0 to PyPI. This release is close to future complete, but Pcolormesh and Quiver blocks need some polishing, and tutorials need to be written.
Source code(tar.gz)The original version.
Source code(tar.gz)
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Plot, scatter plots and histograms in the terminal using braille dots, with (almost) no dependancies. Plot with color or make complex figures - similar to a very small sibling to matplotlib. Or use the canvas to plot dots and lines yourself.
Bohemian Eigenvalue Plotting Examples This repository contains examples of generating plots of Bohemian eigenvalues. The examples in this repository a
Построение графиков на основе публичных данных о ДЭГ в Москве в 2021г. Описание Скрипты в данном репозитории позволяют собственноручно построить графи
This plugin plots the time you spent on a tag as a histogram.
jupiter Generate "Jupiter" plots for circular genomes Description Python scripts to generate plots from ViennaRNA output. Written in "pidgin" python w
YOPO is an interactive dashboard which generates various standard plots.you can create various graphs and charts with a click of a button. This tool uses Dash and Flask in backend.
streamlit-shap This component provides a wrapper to display SHAP plots in Streamlit.
shadyquant ?? This python package allows you to quantile and plot lines where you have multiple samples, typically for visualizing uncertainty. Your d
ts2vg: Time series to visibility graphs The Python ts2vg package provides high-performance algorithm implementations to build visibility graphs from t
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