Fixed #1116 Avoid Import Fastmath Functions

[seanlaw]

See #1116

Pull Request Checklist

Below is a simple checklist but please do not hesitate to ask for assistance!

• Fork, clone, and checkout the newest version of the code
• Create a new branch
• Make necessary code changes
• Install black (i.e., python -m pip install black or conda install -c conda-forge black)
• Install flake8 (i.e., python -m pip install flake8 or conda install -c conda-forge flake8)
• Install pytest-cov (i.e., python -m pip install pytest-cov or conda install -c conda-forge pytest-cov)
• Run black --exclude=".*\.ipynb" --extend-exclude=".venv" --diff ./ in the root stumpy directory
• Run flake8 --extend-exclude=.venv ./ in the root stumpy directory
• Run ./setup.sh dev && ./test.sh in the root stumpy directory
• Reference a Github issue (and create one if one doesn't already exist)

[gitnotebooks]

Review these changes at https://app.gitnotebooks.com/stumpy-dev/stumpy/pull/1117

[seanlaw]

@NimaSarajpoor This is ready to be reviewed at your earliest convenience, thanks!

[NimaSarajpoor]

@seanlaw
Looks good to me. Nice fix!

[seanlaw]

It's really strange that all tests were passing before the commit. However, after the code was committed, some of the tests are failing but nothing has changed.

[NimaSarajpoor]

It's really strange that all tests were passing before the commit. However, after the code was committed, some of the tests are failing but nothing has changed.

And the error says:

# in core.preprocess_non_normalized

T[~np.isfinite(T)] = np.nan
        ^^^^^^^^^^^^^^^^^^
E       ValueError: assignment destination is read-only

I cannot understand why it shows "read-only".

---

Update: Apparently the error appears when input time series is of type pandas

[NimaSarajpoor]

Update: Apparently the error appears when input time series is of type pandas

I was able to reproduce this with pandas 3.0.0, released in January 21, 2026.

import pandas as pd 
import numpy as np

print(pd.__version__)  # 3.0.0
print(np.__version__)  # 2.3.5


T_B = np.array([ 584.,  -11.,   23.,   79., 1001.,    0.,  -19.])
ps = pd.Series(T_B)
T = np.asarray(ps)
T[0] = 100

And this gives:

ValueError: assignment destination is read-only

---

Update: For the above example, np.shares_memory(T, ps.array) returns True, and according to read-only array section in pandas Copy-on-Write (CoW):

Accessing the underlying NumPy array of a DataFrame will return a read-only array if the array shares data with the initial DataFrame: