Update to upstream

.github/workflows/ci.yml

@@ -1,13 +1,18 @@
 name: nmrglue tests
 
-on: [push]
+on:
+  push:
+    branches: master
+  pull_request:
+    branches: master
 
 jobs:
   build:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+      fail-fast: false
 
     steps:
     - uses: actions/checkout@v3

CHANGELOG.md

@@ -1,7 +1,22 @@
-0.12 (Unreleased)
+0.13 (Unreleased)
 =================
 
 
+0.12 (2026-08-16)
+=================
+* Bruker to csdm conversion returns the correct dimension units (#227)
+* When reading bruker files both utf-8 and cp1252 encodings are tried (#239)
+* Correctly read Bruker data  in floating point format (#249)
+* Support reading partially acquired Bruker data (#252)
+* Add support for reading JEOL jdf format (#228)
+* Add support for reading RS2D/QUAD format (#230)
+* Add the update_uc function to update a unit_conversion object (#250)
+* Add a guess_tables function for use when reading Tecmat sequence tables (#257)
+* The read function in nmrml can optionally return processed data (#232)
+* Add support for Numpy 2.0+ (#224, #248)
+* Various minor fixes (#233, #236, #242, #238, #246, #245, #251)
+
+
 0.11 (2024-10-29)
 =================
 

examples/fitting_data/t1_measurements/extract_trajs.py

@@ -5,8 +5,8 @@
 import numpy as np
 
 # read in the integration limits and list of spectra
-peak_list = np.recfromtxt("boxes.in", encoding="UTF-8")
-spectra_list = np.recfromtxt("spectra.in", encoding="UTF-8")
+peak_list = np.genfromtxt("boxes.in", dtype=None, encoding="UTF-8")
+spectra_list = np.genfromtxt("spectra.in", dtype=None, encoding="UTF-8")
 
 # prepare the trajs records array
 num_spec = spectra_list.size

examples/fitting_data/t1_measurements/fit_exp_leastsq.py

@@ -7,9 +7,11 @@
 
 # read in the trajectories and times
 trajs = np.load("traj.npy")
-t1 = np.recfromtxt("time.dat")
+t1 = np.genfromtxt("time.dat", dtype=None)
 
 # fitting function and residual calculation
+
+
 def fit_func(p, x):
     A, R2 = p
     # bound A between 0.98 and 1.02 (although fits do not reflect this)
@@ -20,11 +22,13 @@ def fit_func(p, x):
 
     return A * np.exp(-1.0 * np.array(x) * R2 / 1.0e6)
 
+
 def residuals(p, y, x):
     err = y - fit_func(p, x)
     return err
 
-p0 = [1.0, 0.05] # initial guess
+
+p0 = [1.0, 0.05]  # initial guess
 
 fits = {}
 # loop over the peak trajectories

examples/fitting_data/t1_measurements/pt.py

@@ -6,6 +6,8 @@
 import numpy as np
 
 # the same fit_func as in fit_exp_leastsq.py
+
+
 def fit_func(p, x):
 
     A, R2 = p
@@ -18,10 +20,11 @@ def fit_func(p, x):
 
     return A * np.exp(-1.0 * np.array(x) * R2 / 1.0e6)
 
+
 # read in the trajectories, fitting results, and times
 fits = pickle.load(open("fits.pickle", "rb"))
 trajs = np.load("traj.npy")
-times = np.recfromtxt("time.dat")
+times = np.genfromtxt("time.dat", dtype=None)
 
 sim_times = np.linspace(times[0], times[-1], 2000)
 

examples/integration/integrate_1d/integrate_1d.py

@@ -10,7 +10,7 @@
 length = data.shape[0]
 
 # read in the integration limits
-peak_list = np.recfromtxt("limits.in")
+peak_list = np.genfromtxt("limits.in", dtype=None)
 
 # determine the ppm scale
 uc = ng.pipe.make_uc(dic, data)
@@ -40,7 +40,7 @@
     ax.plot(peak_scale, peak.cumsum() / 100. + peak.max(), 'g-')
     ax.plot(peak_scale, [0] * len(peak_scale), 'r-')
     ax.text(peak_scale[0], 0.5 * peak.sum() / 100. + peak.max(), name,
-                fontsize=8)
+            fontsize=8)
 
     # write out the integration info
     tup = (name, peak_scale[0], peak_scale[-1], peak.sum())

examples/integration/integrate_2d/integrate_2d.py

@@ -8,10 +8,10 @@
 dic, data = ng.pipe.read("nmrpipe_2d/test.ft2")
 
 # read in the integration limits
-peak_list = np.recfromtxt("limits.in")
+peak_list = np.genfromtxt("limits.in", dtype=None)
 
 # prepare the output file
-f = open("volumes.out",'w')
+f = open("volumes.out", 'w')
 f.write("# Name\tVolume\n")
 
 # loop over the integration limits
@@ -23,7 +23,7 @@
         y0, y1 = y1, y0
 
     vol = data[y0:y1 + 1, x0:x1 + 1].sum()
-    f.write("%s\t%.3f\n"%(name, vol))
+    f.write("%s\t%.3f\n" % (name, vol))
 
 # close the output file
 f.close()

examples/jbnmr_examples/s12-s15_relaxation_analysis/extract_traj.py

@@ -2,8 +2,8 @@
 import numpy as np
 
 # read the integration limits and list of spectra
-peak_list = np.recfromtxt("boxes.in", names=True)
-spectra_list = np.recfromtxt("spectra.in")
+peak_list = np.genfromtxt("boxes.in", dtype=None, names=True)
+spectra_list = np.genfromtxt("spectra.in", dtype=None)
 
 # create an array to hold the trajectories
 trajectories = np.empty((peak_list.size, spectra_list.size), dtype='float')

examples/jbnmr_examples/s12-s15_relaxation_analysis/plot_boxes.py

@@ -12,8 +12,8 @@
 cl = 30000 * 1.20 ** np.arange(20)
 
 # read in the box limits and list of spectra
-peak_list = np.recfromtxt("boxes.in", names=True)
-spectra_list = np.recfromtxt("spectra.in")
+peak_list = np.genfromtxt("boxes.in", dtype=None, names=True)
+spectra_list = np.genfromtxt("spectra.in", dtype=None)
 
 # loop over the spectra
 for spec_number, spectra in enumerate(spectra_list):
@@ -30,16 +30,16 @@
             y0, y1 = y1, y0
 
         # slice the data around the peak
-        slice = data[y0 - ypad:y1 + 1 + ypad, x0 - xpad:x1 + 1 + xpad]
+        slice_ = data[y0 - ypad:y1 + 1 + ypad, x0 - xpad:x1 + 1 + xpad]
 
         # create the figure
         fig = plt.figure()
         ax = fig.add_subplot(111)
 
         # plot the contours
-        print "Plotting:", peak, spec_number
+        print("Plotting:", peak, spec_number)
         extent = (x0 - xpad + 1, x1 + xpad - 1, y0 - ypad + 1, y1 + ypad - 1)
-        ax.contour(slice, cl, cmap=cmap, extent=extent)
+        ax.contour(slice_, cl, cmap=cmap, extent=extent)
 
         # draw a box around the peak
         ax.plot([x0, x1, x1, x0, x0], [y0, y0, y1, y1, y0], 'k--')
@@ -51,6 +51,6 @@
                 [y0 + 1, y0 + 1, y1 - 1, y1 - 1, y0 + 1], 'k--', alpha=0.35)
 
         # set the title, save the figure
-        ax.set_title('Peak: %s Spectrum: %i'%(peak, spec_number))
-        fig.savefig('peak_%s_spectrum_%i'%(peak, spec_number))
-        del(fig)
+        ax.set_title('Peak: %s Spectrum: %i' % (peak, spec_number))
+        fig.savefig('peak_%s_spectrum_%i' % (peak, spec_number))
+        del fig

examples/jbnmr_examples/s12-s15_relaxation_analysis/plot_trajectories.py

@@ -2,18 +2,21 @@
 import matplotlib.pyplot as plt
 
 # exponential function used to fit the data
-def fit_func(p,x):
+
+
+def fit_func(p, x):
     A, R2 = p
     return A * np.exp(-1.0 * np.array(x) * R2 / 1.0e6)
 
-fitting_results = np.recfromtxt('fits.txt')
+
+fitting_results = np.genfromtxt('fits.txt', dtype=None)
 experimental_relaxation_times = np.loadtxt("relaxation_times.in")
-simulated_relaxation_times = np.linspace(0,4000000,2000)
+simulated_relaxation_times = np.linspace(0, 4000000, 2000)
 
 # loop over the fitting results
 for peak, A, R2, ier in fitting_results:
 
-    print "Plotting:", peak
+    print("Plotting:", peak)
 
     # load the experimental and simulated relaxation trajectories
     experimental_trajectory = np.loadtxt(peak + '.dat')

examples/plotting/plot_2d/plot_assignments.py

@@ -20,15 +20,15 @@
 dic, data = ng.pipe.read("nmrpipe_2d/test.ft2")
 
 # read in the integration limits
-peak_list = np.recfromtxt("limits.in")
+peak_list = np.genfromtxt("limits.in", dtype=None)
 
 # create the figure
 fig = plt.figure()
 ax = fig.add_subplot(111)
 
 # plot the contours
 ax.contour(data, cl, cmap=cmap,
-            extent=(0, data.shape[1] - 1, 0, data.shape[0] - 1))
+           extent=(0, data.shape[1] - 1, 0, data.shape[0] - 1))
 
 # loop over the peaks
 for name, x0, y0, x1, y1 in peak_list:

examples/plotting/plot_2d/plot_boxes.py

@@ -21,7 +21,7 @@
 dic, data = ng.pipe.read("nmrpipe_2d/test.ft2")
 
 # read in the integration limits
-peak_list = np.recfromtxt("limits.in")
+peak_list = np.genfromtxt("limits.in", dtype=None)
 
 # loop over the peaks
 for name, x0, y0, x1, y1 in peak_list:
@@ -32,16 +32,16 @@
         y0, y1 = y1, y0
 
     # slice the data around the peak
-    slice = data[y0 - ypad:y1 + 1 + ypad, x0 - xpad:x1 + 1 + xpad]
+    slice_ = data[y0 - ypad:y1 + 1 + ypad, x0 - xpad:x1 + 1 + xpad]
 
     # create the figure
     fig = plt.figure()
     ax = fig.add_subplot(111)
 
     # plot the contours
-    print "Plotting:", name
+    print("Plotting:", name)
     etup = (x0 - xpad + 1, x1 + xpad - 1, y0 - ypad + 1, y1 + ypad - 1)
-    ax.contour(slice, cl, cmap=cmap, extent=etup)
+    ax.contour(slice_, cl, cmap=cmap, extent=etup)
 
     # draw a box around the peak
     ax.plot([x0, x1, x1, x0, x0], [y0, y0, y1, y1, y0], 'k--')
@@ -50,11 +50,11 @@
     ax.plot([x0 - 1, x1 + 1, x1 + 1, x0 - 1, x0 - 1],
             [y0 - 1, y0 - 1, y1 + 1, y1 + 1, y0 - 1], 'k--', alpha=0.35)
     ax.plot([x0 + 1, x1 - 1, x1 - 1, x0 + 1, x0 + 1],
-            [y0 + 1, y0 + 1, y1 - 1, y1 - 1, y0 + 1], 'k--',alpha=0.35)
+            [y0 + 1, y0 + 1, y1 - 1, y1 - 1, y0 + 1], 'k--', alpha=0.35)
 
     # set the title
     ax.set_title(name)
 
     # save the figure
     fig.savefig(f"{name}.png")
-    del(fig)
+    del fig

examples/simulation/simulate_ucsf/make_ucsf.py

@@ -32,7 +32,7 @@
 data = np.empty((512, 1024), dtype='float32')
 
 # read in the peak list
-peak_list = np.recfromtxt('peaks.txt', names=True)
+peak_list = np.genfromtxt('peaks.txt', dtype=None, names=True)
 npeaks = len(peak_list)
 
 # convert the peak list from PPM to points

nmrglue/__init__.py

@@ -3,4 +3,4 @@
 from .util import *
 from .analysis import *
 
-__version__ = '0.12-dev'
+__version__ = '0.13-dev'