MNT: replace removed NumPy aliases (np.float/int/object/complex, np.infty) for NumPy >=1.24/2.0 compatibility

pyCHX/Two_Time_Correlation_Function.py

@@ -289,7 +289,7 @@ def get_qedge2(qstart, qend, qwidth, noqs, return_int=False):
     if not return_int:
         return qedge, qcenter
     else:
-        return np.int(qedge), np.int(qcenter)
+        return int(qedge), int(qcenter)
 
 
 def get_qedge(qstart, qend, qwidth, noqs, return_int=False):
@@ -308,7 +308,7 @@ def get_qedge(qstart, qend, qwidth, noqs, return_int=False):
     if not return_int:
         return qedge, qcenter
     else:
-        return np.int(qedge), np.int(qcenter)
+        return int(qedge), int(qcenter)
 
 
 def get_time_edge(tstart, tend, twidth, nots, return_int=False):
@@ -328,7 +328,7 @@ def get_time_edge(tstart, tend, twidth, nots, return_int=False):
     if not return_int:
         return tedge, tcenter
     else:
-        return np.int(tedge), np.int(tcenter)
+        return int(tedge), int(tcenter)
 
 
 def rotate_g12q_to_rectangle(g12q):

pyCHX/chx_compress.py

@@ -452,7 +452,7 @@ def para_segment_compress_eigerdata(
     print("It will create %i temporary files for parallel compression." % Nf)
 
     if Nf > num_max_para_process:
-        N_runs = np.int(np.ceil(Nf / float(num_max_para_process)))
+        N_runs = int(np.ceil(Nf / float(num_max_para_process)))
         print("The parallel run number: %s is larger than num_max_para_process: %s" % (Nf, num_max_para_process))
     else:
         N_runs = 1

pyCHX/chx_crosscor.py

@@ -484,7 +484,7 @@ def fftconvolve_new(in1, in2, mode="full"):
 
     s1 = array(in1.shape)
     s2 = array(in2.shape)
-    complex_result = np.issubdtype(in1.dtype, np.complex) or np.issubdtype(in2.dtype, np.complex)
+    complex_result = np.issubdtype(in1.dtype, complex) or np.issubdtype(in2.dtype, complex)
     shape = s1 + s2 - 1
 
     if mode == "valid":

pyCHX/chx_generic_functions.py

@@ -2448,7 +2448,7 @@ def get_series_g2_taus(fra_max_list, acq_time=1, max_fra_num=None, log_taus=True
         if max_fra_num != None:
             L = max_fra_num
         else:
-            L = np.infty
+            L = np.inf
         if n > L:
             warnings.warn(
                 "Warning: the dose value is too large, and please"

pyCHX/chx_speckle.py

@@ -113,7 +113,7 @@ def xsvs(
     num_pixels = np.bincount(labels, minlength=(num_roi + 1))[1:]
 
     # probability density of detecting photons
-    prob_k_all = np.zeros([num_times, num_roi], dtype=np.object)
+    prob_k_all = np.zeros([num_times, num_roi], dtype=object)
 
     # square of probability density of detecting photons
     prob_k_pow_all = np.zeros_like(prob_k_all)

pyCHX/chx_specklecp.py

@@ -221,7 +221,7 @@ def xsvsp_single(
     # print(time_bin)
     # number of times in the time bin
     num_times = len(time_bin)
-    prob_k = np.zeros([num_times, num_roi], dtype=np.object)
+    prob_k = np.zeros([num_times, num_roi], dtype=object)
     prob_k_std_dev = np.zeros_like(prob_k)
     his_sum = np.zeros([num_times, num_roi])
     # print( len(res) )
@@ -430,7 +430,7 @@ def xsvsc_single(
     # number of pixels per ROI
     num_pixels = np.bincount(labels, minlength=(num_roi + 1))[1:]
     # probability density of detecting photons
-    prob_k = np.zeros([num_times, num_roi], dtype=np.object)
+    prob_k = np.zeros([num_times, num_roi], dtype=object)
     his_sum = np.zeros([num_times, num_roi])
     # square of probability density of detecting photons
     prob_k_pow = np.zeros_like(prob_k)
@@ -706,9 +706,9 @@ def get_his_std(data_pixel, rois, max_cts=None):
         max_cts = np.max(data_pixel) + 1
     qind, pixelist = roi.extract_label_indices(rois)
     noqs = len(np.unique(qind))
-    his = np.zeros([noqs], dtype=np.object)
-    std = np.zeros_like(his, dtype=np.object)
-    kmean = np.zeros_like(his, dtype=np.object)
+    his = np.zeros([noqs], dtype=object)
+    std = np.zeros_like(his, dtype=object)
+    kmean = np.zeros_like(his, dtype=object)
     for qi in range(noqs):
         pixelist_qi = np.where(qind == qi + 1)[0]
         # print(qi, max_cts)
@@ -779,10 +779,10 @@ def get_binned_his_std_qi(data_pixel_qi, lag_steps, max_cts=None):
     lag_steps = np.array(lag_steps)
     lag_steps = lag_steps[np.nonzero(lag_steps)]
     nologs = len(lag_steps)
-    his = np.zeros([nologs], dtype=np.object)
-    bins = np.zeros_like(his, dtype=np.object)
-    std = np.zeros_like(his, dtype=np.object)
-    kmean = np.zeros_like(his, dtype=np.object)
+    his = np.zeros([nologs], dtype=object)
+    bins = np.zeros_like(his, dtype=object)
+    std = np.zeros_like(his, dtype=object)
+    kmean = np.zeros_like(his, dtype=object)
     i = 0
     for lag in lag_steps:
         data_pixel_qi_ = np.sum(reshape_array(data_pixel_qi, lag), axis=1)
@@ -813,10 +813,10 @@ def get_binned_his_std(data_pixel, rois, lag_steps, max_cts=None):
     lag_steps = lag_steps[np.nonzero(lag_steps)]
 
     nologs = len(lag_steps)
-    his = np.zeros([nologs, noqs], dtype=np.object)
-    bins = np.zeros([nologs], dtype=np.object)
-    std = np.zeros_like(his, dtype=np.object)
-    kmean = np.zeros_like(his, dtype=np.object)
+    his = np.zeros([nologs, noqs], dtype=object)
+    bins = np.zeros([nologs], dtype=object)
+    std = np.zeros_like(his, dtype=object)
+    kmean = np.zeros_like(his, dtype=object)
     i = 0
     for lag in tqdm(lag_steps):
         data_pixel_ = np.sum(reshape_array(data_pixel, lag), axis=1)
@@ -1351,8 +1351,8 @@ def get_his_std_from_pds(spec_pds, his_shapes=None):
     if his_shapes is None:
         M, N = 2, int((len(spkeys) - 1) / 4)
     # print(M,N)
-    spec_his = np.zeros([M, N], dtype=np.object)
-    spec_std = np.zeros([M, N], dtype=np.object)
+    spec_his = np.zeros([M, N], dtype=object)
+    spec_std = np.zeros([M, N], dtype=object)
     for i in range(M):
         for j in range(N):
             spec_his[i, j] = np.array(spec_pds[spkeys[1 + i * N + j]][~np.isnan(spec_pds[spkeys[1 + i * N + j]])])

pyCHX/v2/_commonspeckle/Two_Time_Correlation_Function.py

@@ -289,7 +289,7 @@ def get_qedge2(qstart, qend, qwidth, noqs, return_int=False):
     if not return_int:
         return qedge, qcenter
     else:
-        return np.int(qedge), np.int(qcenter)
+        return int(qedge), int(qcenter)
 
 
 def get_qedge(qstart, qend, qwidth, noqs, return_int=False):
@@ -308,7 +308,7 @@ def get_qedge(qstart, qend, qwidth, noqs, return_int=False):
     if not return_int:
         return qedge, qcenter
     else:
-        return np.int(qedge), np.int(qcenter)
+        return int(qedge), int(qcenter)
 
 
 def get_time_edge(tstart, tend, twidth, nots, return_int=False):
@@ -328,7 +328,7 @@ def get_time_edge(tstart, tend, twidth, nots, return_int=False):
     if not return_int:
         return tedge, tcenter
     else:
-        return np.int(tedge), np.int(tcenter)
+        return int(tedge), int(tcenter)
 
 
 def rotate_g12q_to_rectangle(g12q):

pyCHX/v2/_commonspeckle/chx_compress.py

@@ -251,8 +251,8 @@ def read_compressed_eigerdata(
             CAL = True
     if CAL:
         FD = Multifile(filename, beg, end)
-        imgsum = np.zeros(FD.end - FD.beg, dtype=np.float)
-        avg_img = np.zeros([FD.md["ncols"], FD.md["nrows"]], dtype=np.float)
+        imgsum = np.zeros(FD.end - FD.beg, dtype=float)
+        avg_img = np.zeros([FD.md["ncols"], FD.md["nrows"]], dtype=float)
         imgsum, bad_frame_list_ = get_each_frame_intensityc(
             FD,
             sampling=1,
@@ -461,7 +461,7 @@ def para_segment_compress_eigerdata(
     print("It will create %i temporary files for parallel compression." % Nf)
 
     if Nf > num_max_para_process:
-        N_runs = np.int(np.ceil(Nf / float(num_max_para_process)))
+        N_runs = int(np.ceil(Nf / float(num_max_para_process)))
         print("The parallel run number: %s is larger than num_max_para_process: %s" % (Nf, num_max_para_process))
     else:
         N_runs = 1
@@ -544,7 +544,7 @@ def segment_compress_eigerdata(
 
     Nimg_ = len(images)
     M, N = images[0].shape
-    avg_img = np.zeros([M, N], dtype=np.float)
+    avg_img = np.zeros([M, N], dtype=float)
     Nopix = float(avg_img.size)
     n = 0
     good_count = 0
@@ -791,7 +791,7 @@ def init_compress_eigerdata(
     fp.write(Header)
 
     Nimg_ = len(images)
-    avg_img = np.zeros_like(images[0], dtype=np.float)
+    avg_img = np.zeros_like(images[0], dtype=float)
     Nopix = float(avg_img.size)
     n = 0
     good_count = 0

pyCHX/v2/_commonspeckle/chx_correlationc.py

@@ -1530,14 +1530,14 @@ def get_data(self):
         Return: 2-D array, shape as (len(images), len(pixellist))
         """
 
-        data_array = np.zeros([self.length, len(self.pixelist)], dtype=np.float)
+        data_array = np.zeros([self.length, len(self.pixelist)], dtype=float)
         # fra_pix = np.zeros_like( pixelist, dtype=np.float64)
         timg = np.zeros(self.FD.md["ncols"] * self.FD.md["nrows"], dtype=np.int32)
         timg[self.pixelist] = np.arange(1, len(self.pixelist) + 1)
 
         if self.norm_inten is not None:
             # Mean_Int_Qind = np.array( self.qind.copy(), dtype=np.float)
-            Mean_Int_Qind = np.ones(len(self.qind), dtype=np.float)
+            Mean_Int_Qind = np.ones(len(self.qind), dtype=float)
             noqs = len(np.unique(self.qind))
             nopr = np.bincount(self.qind - 1)
             noprs = np.concatenate([np.array([0]), np.cumsum(nopr)])
@@ -1645,14 +1645,14 @@ def get_data(self):
         Return: 2-D array, shape as (len(images), len(pixellist))
         """
 
-        data_array = np.zeros([self.length, len(self.pixelist)], dtype=np.float)
+        data_array = np.zeros([self.length, len(self.pixelist)], dtype=float)
         # fra_pix = np.zeros_like( pixelist, dtype=np.float64)
         timg = np.zeros(self.FD.md["ncols"] * self.FD.md["nrows"], dtype=np.int32)
         timg[self.pixelist] = np.arange(1, len(self.pixelist) + 1)
 
         if self.mean_int_sets is not None:
             # Mean_Int_Qind = np.array( self.qind.copy(), dtype=np.float)
-            Mean_Int_Qind = np.ones(len(self.qind), dtype=np.float)
+            Mean_Int_Qind = np.ones(len(self.qind), dtype=float)
             noqs = len(np.unique(self.qind))
             nopr = np.bincount(self.qind - 1)
             noprs = np.concatenate([np.array([0]), np.cumsum(nopr)])

pyCHX/v2/_commonspeckle/chx_generic_functions.py

@@ -1438,7 +1438,7 @@ def get_waxs_beam_center(gamma, origin=[432, 363], Ldet=1495, pixel_size=75 * 1e
         beam center: for the target gamma, in pixel
     """
     return [
-        np.int(origin[0] + np.tan(np.radians(gamma)) * Ldet / pixel_size),
+        int(origin[0] + np.tan(np.radians(gamma)) * Ldet / pixel_size),
         origin[1],
     ]
 
@@ -2543,7 +2543,7 @@ def get_series_g2_taus(fra_max_list, acq_time=1, max_fra_num=None, log_taus=True
         if max_fra_num is not None:
             L = max_fra_num
         else:
-            L = np.infty
+            L = np.inf
         if n > L:
             warnings.warn(
                 "Warning: the dose value is too large, and please"
@@ -2638,8 +2638,8 @@ def combine_images(filenames, outputfile, outsize=(2000, 2400)):
     # nx = np.int( np.ceil( np.sqrt(N)) )
     # ny = np.int( np.ceil( N / float(nx)  ) )
 
-    ny = np.int(np.ceil(np.sqrt(N)))
-    nx = np.int(np.ceil(N / float(ny)))
+    ny = int(np.ceil(np.sqrt(N)))
+    nx = int(np.ceil(N / float(ny)))
 
     # print(nx,ny)
     result = Image.new("RGB", outsize, color=(255, 255, 255, 0))

pyCHX/v2/_commonspeckle/chx_speckle.py

@@ -113,7 +113,7 @@ def xsvs(
     num_pixels = np.bincount(labels, minlength=(num_roi + 1))[1:]
 
     # probability density of detecting photons
-    prob_k_all = np.zeros([num_times, num_roi], dtype=np.object)
+    prob_k_all = np.zeros([num_times, num_roi], dtype=object)
 
     # square of probability density of detecting photons
     prob_k_pow_all = np.zeros_like(prob_k_all)

pyCHX/v2/_commonspeckle/chx_specklecp.py

@@ -232,7 +232,7 @@ def xsvsp_single(
     # print(time_bin)
     # number of times in the time bin
     num_times = len(time_bin)
-    prob_k = np.zeros([num_times, num_roi], dtype=np.object)
+    prob_k = np.zeros([num_times, num_roi], dtype=object)
     prob_k_std_dev = np.zeros_like(prob_k)
     his_sum = np.zeros([num_times, num_roi])
     # print( len(res) )
@@ -441,7 +441,7 @@ def xsvsc_single(
     # number of pixels per ROI
     num_pixels = np.bincount(labels, minlength=(num_roi + 1))[1:]
     # probability density of detecting photons
-    prob_k = np.zeros([num_times, num_roi], dtype=np.object)
+    prob_k = np.zeros([num_times, num_roi], dtype=object)
     his_sum = np.zeros([num_times, num_roi])
     # square of probability density of detecting photons
     prob_k_pow = np.zeros_like(prob_k)
@@ -727,9 +727,9 @@ def get_his_std(data_pixel, rois, max_cts=None):
         max_cts = np.max(data_pixel) + 1
     qind, pixelist = roi.extract_label_indices(rois)
     noqs = len(np.unique(qind))
-    his = np.zeros([noqs], dtype=np.object)
-    std = np.zeros_like(his, dtype=np.object)
-    kmean = np.zeros_like(his, dtype=np.object)
+    his = np.zeros([noqs], dtype=object)
+    std = np.zeros_like(his, dtype=object)
+    kmean = np.zeros_like(his, dtype=object)
     for qi in range(noqs):
         pixelist_qi = np.where(qind == qi + 1)[0]
         # print(qi, max_cts)
@@ -800,10 +800,10 @@ def get_binned_his_std_qi(data_pixel_qi, lag_steps, max_cts=None):
     lag_steps = np.array(lag_steps)
     lag_steps = lag_steps[np.nonzero(lag_steps)]
     nologs = len(lag_steps)
-    his = np.zeros([nologs], dtype=np.object)
-    bins = np.zeros_like(his, dtype=np.object)
-    std = np.zeros_like(his, dtype=np.object)
-    kmean = np.zeros_like(his, dtype=np.object)
+    his = np.zeros([nologs], dtype=object)
+    bins = np.zeros_like(his, dtype=object)
+    std = np.zeros_like(his, dtype=object)
+    kmean = np.zeros_like(his, dtype=object)
     i = 0
     for lag in lag_steps:
         data_pixel_qi_ = np.sum(reshape_array(data_pixel_qi, lag), axis=1)
@@ -834,10 +834,10 @@ def get_binned_his_std(data_pixel, rois, lag_steps, max_cts=None):
     lag_steps = lag_steps[np.nonzero(lag_steps)]
 
     nologs = len(lag_steps)
-    his = np.zeros([nologs, noqs], dtype=np.object)
-    bins = np.zeros([nologs], dtype=np.object)
-    std = np.zeros_like(his, dtype=np.object)
-    kmean = np.zeros_like(his, dtype=np.object)
+    his = np.zeros([nologs, noqs], dtype=object)
+    bins = np.zeros([nologs], dtype=object)
+    std = np.zeros_like(his, dtype=object)
+    kmean = np.zeros_like(his, dtype=object)
     i = 0
     for lag in tqdm(lag_steps):
         data_pixel_ = np.sum(reshape_array(data_pixel, lag), axis=1)
@@ -1390,8 +1390,8 @@ def get_his_std_from_pds(spec_pds, his_shapes=None):
     if his_shapes is None:
         M, N = 2, int((len(spkeys) - 1) / 4)
     # print(M,N)
-    spec_his = np.zeros([M, N], dtype=np.object)
-    spec_std = np.zeros([M, N], dtype=np.object)
+    spec_his = np.zeros([M, N], dtype=object)
+    spec_std = np.zeros([M, N], dtype=object)
     for i in range(M):
         for j in range(N):
             spec_his[i, j] = np.array(spec_pds[spkeys[1 + i * N + j]][~np.isnan(spec_pds[spkeys[1 + i * N + j]])])

pyCHX/v2/_commonspeckle/xpcs_timepixel.py

@@ -336,15 +336,15 @@ def init_compress_timepix_data(pos, t, binstep, filename, mask=None, md=None, no
     )
     fp.write(Header)
 
-    N_ = np.int(np.ceil((t.max() - t.min()) / binstep))
+    N_ = int(np.ceil((t.max() - t.min()) / binstep))
     print("There are %s frames to be compressed..." % (N_ - 1))
 
     ps, vs, cs = get_pvlist_from_post(pos, t, binstep, detx=md["sx"], dety=md["sy"])
     N = len(cs) - 1  # the last one might don't have full number for bings, so kick off
     css = np.cumsum(cs)
     imgsum = np.zeros(N)
     good_count = 0
-    avg_img = np.zeros([md["sy"], md["sx"]], dtype=np.float)
+    avg_img = np.zeros([md["sy"], md["sx"]], dtype=float)
 
     for i in tqdm(range(0, N)):
         if i == 0:
@@ -436,7 +436,7 @@ def init_compress_timepix_data_light_duty(
     imgsum = np.zeros(N - 1)
     print("There are %s frames to be compressed..." % (N - 1))
     good_count = 0
-    avg_img = np.zeros([md["sy"], md["sx"]], dtype=np.float)
+    avg_img = np.zeros([md["sy"], md["sx"]], dtype=float)
     for i in tqdm(range(N - 1)):
         ind1 = np.argmin(np.abs(tx[i] - t))
         ind2 = np.argmin(np.abs(tx[i + 1] - t))

pyCHX/v2/_futurepyCHX/Create_Report.py

@@ -2018,7 +2018,7 @@ def recursively_save_dict_contents_to_group(h5file, path, dic):
         if not isinstance(key, str):
             raise ValueError("dict keys must be strings to save to hdf5")
         # save strings, numpy.int64, and numpy.float64 types
-        if isinstance(item, (np.int64, np.float64, str, np.float, float, np.float32, int)):
+        if isinstance(item, (np.int64, np.float64, str, float, float, np.float32, int)):
             # print( 'here' )
             h5file[path + key] = item
             if not h5file[path + key].value == item: