Fortunately, it seems to work out of the box if I just download the source and run your example. I don't know about the results, but at least it's not throwing any exception
>wget https://github.com/brews/baysplinepy/archive/refs/tags/v0.0.2.tar.gz
--2025-07-31 15:25:04-- https://github.com/brews/baysplinepy/archive/refs/tags/v0.0.2.tar.gz
Resolving github.com (github.com)... 140.82.121.4
Connecting to github.com (github.com)|140.82.121.4|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://codeload.github.com/brews/baysplinepy/tar.gz/refs/tags/v0.0.2 [following]
--2025-07-31 15:25:05-- https://codeload.github.com/brews/baysplinepy/tar.gz/refs/tags/v0.0.2
Resolving codeload.github.com (codeload.github.com)... 140.82.121.10
Connecting to codeload.github.com (codeload.github.com)|140.82.121.10|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: unspecified [application/x-gzip]
Saving to: ‘v0.0.2.tar.gz’
[ <=> ] 101,303 --.-K/s in 0.01s
2025-07-31 15:25:05 (6.57 MB/s) - ‘v0.0.2.tar.gz’ saved [101303]
>tar xvf v0.0.2.tar.gz
baysplinepy-0.0.2/
baysplinepy-0.0.2/.gitignore
baysplinepy-0.0.2/.travis.yml
baysplinepy-0.0.2/LICENSE
baysplinepy-0.0.2/README.rst
baysplinepy-0.0.2/bayspline/
baysplinepy-0.0.2/bayspline/__init__.py
baysplinepy-0.0.2/bayspline/example_data/
baysplinepy-0.0.2/bayspline/example_data/tierney2016-p178-15p.csv
baysplinepy-0.0.2/bayspline/modelparams/
baysplinepy-0.0.2/bayspline/modelparams/__init__.py
baysplinepy-0.0.2/bayspline/modelparams/bayes_posterior.mat
baysplinepy-0.0.2/bayspline/modelparams/core.py
baysplinepy-0.0.2/bayspline/plot.py
baysplinepy-0.0.2/bayspline/predict.py
baysplinepy-0.0.2/bayspline/tests/
baysplinepy-0.0.2/bayspline/tests/__init__.py
baysplinepy-0.0.2/bayspline/tests/test_predict_sst.py
baysplinepy-0.0.2/bayspline/tests/test_predict_uk.py
baysplinepy-0.0.2/bayspline/tests/test_utils.py
baysplinepy-0.0.2/bayspline/utils.py
baysplinepy-0.0.2/ci/
baysplinepy-0.0.2/ci/requirements-py35.yml
baysplinepy-0.0.2/ci/requirements-py36.yml
baysplinepy-0.0.2/setup.py
baysplinepy-0.0.2/whats-new.rst
>cd baysplinepy-0.0.2/
>ls
bayspline/ ci/ LICENSE README.rst setup.py whats-new.rst
(base) >conda activate my_very_complete_env
(my_very_complete_env) >python
Python 3.12.4 | packaged by conda-forge | (main, Jun 17 2024, 10:23:07) [GCC 12.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> import bayspline as bsl
>>> example_file = bsl.get_example_data('tierney2016-p178-15p.csv')
>>> d = np.genfromtxt(example_file, delimiter=',', names=True)
>>>
>>> prediction = bsl.predict_sst(d['uk37'], prior_std=10)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 500/500 [00:33<00:00, 14.87it/s]
>>> type(prediction)
<class 'bayspline.predict.SSTPrediction'>
>>> prediction
SSTPrediction(ensemble=array([[43.00995563, 32.83456583, 29.41568629, ..., 56.05279752,
32.5673014 , 37.69329088],
[25.5316321 , 34.83117254, 34.56163057, ..., 32.62638468,
27.94681856, 29.08531471],
[32.07855444, 25.59368864, 26.73683853, ..., 40.68898904,
26.97243237, 37.7751331 ],
...,
[22.12087532, 28.13367503, 22.64527099, ..., 26.5950704 ,
26.22114106, 25.42657283],
[23.44125776, 25.53569734, 25.79566057, ..., 25.4587341 ,
24.11952543, 23.62833184],
[23.28643255, 28.16401372, 29.39495386, ..., 26.28692475,
28.17450948, 28.1976014 ]], shape=(97, 125000)), prior_mean=array([27.85294118, 27.29411765, 27.23529412, 27.32352941, 27.32352941,
27.44117647, 27.26470588, 27.52941176, 27.44117647, 27.47058824,
27.52941176, 27.44117647, 27.26470588, 27.32352941, 26.97058824,
27.23529412, 27.08823529, 27.32352941, 27.11764706, 27.08823529,
27.08823529, 27.17647059, 27.17647059, 27.08823529, 27.02941176,
26.97058824, 27.32352941, 27.14705882, 27.29411765, 27.20588235,
27.17647059, 27.41176471, 27.35294118, 26.91176471, 27.26470588,
27.29411765, 27.17647059, 26.79411765, 26.47058824, 26.76470588,
27.02941176, 26.79411765, 26.73529412, 26.73529412, 26.79411765,
26.85294118, 26.73529412, 26.73529412, 26.67647059, 26.55882353,
26.47058824, 26.41176471, 26.5 , 26.38235294, 26.38235294,
26.32352941, 26.41176471, 26.38235294, 26.32352941, 26.32352941,
26.29411765, 26.29411765, 26.32352941, 26.08823529, 26.08823529,
25.82352941, 25.5 , 25.32352941, 25.11764706, 25.58823529,
25.5 , 25.38235294, 25.64705882, 25.55882353, 25.23529412,
25.35294118, 25.97058824, 25.67647059, 25.97058824, 25.67647059,
25.85294118, 25.61764706, 25.76470588, 26.08823529, 26. ,
25.55882353, 26. , 25.97058824, 26. , 25.58823529,
25.52941176, 25.11764706, 25.38235294, 25.55882353, 25.61764706,
25.35294118, 25.11764706]), prior_std=10, jump_distance=np.float64(6.350899999999999), acceptance=np.float64(0.46950119587628864), rhat=array([1.14268629]))
>>>
>>>
>>> sst = np.arange(1, 25)
>>> prediction_2 = bsl.predict_uk(sst)
>>> prediction_2
UKPrediction(ensemble=array([[0.10494627, 0.04294787, 0.15532562, ..., 0.0587097 , 0.20169074,
0.09581955],
[0.19066846, 0.04635925, 0.15683951, ..., 0.1004465 , 0.18069335,
0.15126002],
[0.204884 , 0.09502602, 0.19157386, ..., 0.05443234, 0.20863643,
0.15645332],
...,
[0.76992931, 0.70046949, 0.85805503, ..., 0.78276341, 0.79814979,
0.78590304],
[0.80936849, 0.87841609, 0.70769468, ..., 0.81879373, 0.76117749,
0.79661735],
[0.85334466, 0.82227373, 0.86390017, ..., 0.88562306, 0.82070975,
0.89398335]], shape=(24, 1500)), prior_mean=None, prior_std=None)
I have provided below the listing of my environment, but looking at what you import, your dependencies seem to be very standard ones, mostly: numpy, scipy, tqdm and matplotlib
@brews I have tried to install baysplinepy from your special channel, for one of our PhD students who wanted to experiment with it.
Unfortunately, you must have hard-coded some out-of-date dependencies that seem to want my conda to install a wayland X server or something into my fairly complete existing environment
Fortunately, it seems to work out of the box if I just download the source and run your example. I don't know about the results, but at least it's not throwing any exception
I have provided below the listing of my environment, but looking at what you import, your dependencies seem to be very standard ones, mostly:
numpy,scipy,tqdmandmatplotlib