MNT: Remove depreciated function, pin to major versions of numpy and scipy.

.github/workflows/push_pr.yml

@@ -12,13 +12,13 @@ jobs:
     strategy:
       matrix:
         os: ["ubuntu-latest"]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v4
         with:
           submodules: true
           lfs: true
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: conda-incubator/setup-miniconda@v3
         with:
           auto-update-conda: true
           python-version: ${{ matrix.python-version }}

CHANGELOG.md

@@ -1,5 +1,11 @@
 This document contains the pymapvbvd release history in reverse chronological order.
 
+0.6.0 (Friday 28th June 2024)
+-----------------------------
+- Dropped support for Python 3.8
+- Testing for Python 3.12
+- Compatibility with Numpy version 2
+
 0.5.7 (Wednesday 31st January 2024)
 -----------------------------------
 - Added flag to disable line reflection. Thanks to FrankZijlstra for contributing.

mapvbvd/mapVBVD.py

@@ -132,9 +132,7 @@ def loop_mdh_read(fid, version, Nscans, scan, measOffset, measLength, print_prog
         #          the "DMA length"
         #              if mdh.ulPackBit
         #          it seems that the packbit is not always set correctly
-        tmp = data_u8[dmaIdx]
-        tmp.dtype = np.uint16
-        NCol_NCha = tmp  # was float [ushSamplesInScan  ushUsedChannels]
+        NCol_NCha = data_u8[dmaIdx].view(np.uint16).astype(int)
         ulDMALength = dmaOff + (8 * NCol_NCha[0] + dmaSkip) * NCol_NCha[1]
 
         n_acq = n_acq + 1

mapvbvd/read_twix_hdr.py

@@ -1,6 +1,6 @@
 import re
 import numpy as np
-from scipy.integrate import cumtrapz
+from scipy.integrate import cumulative_trapezoid
 
 from mapvbvd._attrdict import AttrDict
 
@@ -231,7 +231,7 @@ def read_twix_hdr(fid, prot):
 
             # make sure that gr_adc is always positive (rstraj needs to be strictly monotonic)
             gr_adc = np.maximum(gr_adc, 1e-4)
-            rstraj = (np.append(0, cumtrapz(gr_adc)) - ncol / 2) / np.sum(gr_adc)
+            rstraj = (np.append(0, cumulative_trapezoid(gr_adc)) - ncol / 2) / np.sum(gr_adc)
             rstraj -= np.mean(rstraj[int(ncol / 2) - 1:int(ncol / 2) + 1])
             # scale rstraj by kmax (only works if all slices have same FoV!!!)
             # TODO: these are examples of the keys not arranged correctly

requirements.yml

@@ -1,7 +1,7 @@
 dependencies:
- - numpy
+ - numpy>=1.26
  - tqdm
- - scipy
+ - scipy==1.13.*
  - matplotlib
  - h5py
  - six

setup.py

@@ -31,10 +31,10 @@
     license_file='LICENSE',
     classifiers=[
         "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
         "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent", ],
-    python_requires='>=3.8')
+    python_requires='>=3.9')