diff --git a/pyCHX/Two_Time_Correlation_Function.py b/pyCHX/Two_Time_Correlation_Function.py index a110211..b88b626 100644 --- a/pyCHX/Two_Time_Correlation_Function.py +++ b/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): diff --git a/pyCHX/chx_compress.py b/pyCHX/chx_compress.py index 97ca96a..9735d49 100644 --- a/pyCHX/chx_compress.py +++ b/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 diff --git a/pyCHX/chx_crosscor.py b/pyCHX/chx_crosscor.py index c95d417..8b41239 100644 --- a/pyCHX/chx_crosscor.py +++ b/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": diff --git a/pyCHX/chx_generic_functions.py b/pyCHX/chx_generic_functions.py index 6a67dd2..589f89f 100644 --- a/pyCHX/chx_generic_functions.py +++ b/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" diff --git a/pyCHX/chx_speckle.py b/pyCHX/chx_speckle.py index a6eb8f3..b87a1a9 100644 --- a/pyCHX/chx_speckle.py +++ b/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) diff --git a/pyCHX/chx_specklecp.py b/pyCHX/chx_specklecp.py index d03ea3b..b49540f 100644 --- a/pyCHX/chx_specklecp.py +++ b/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]])]) diff --git a/pyCHX/v2/_commonspeckle/Two_Time_Correlation_Function.py b/pyCHX/v2/_commonspeckle/Two_Time_Correlation_Function.py index 6d05898..09ae2af 100644 --- a/pyCHX/v2/_commonspeckle/Two_Time_Correlation_Function.py +++ b/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): diff --git a/pyCHX/v2/_commonspeckle/chx_compress.py b/pyCHX/v2/_commonspeckle/chx_compress.py index f6c1bf3..329f813 100644 --- a/pyCHX/v2/_commonspeckle/chx_compress.py +++ b/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 diff --git a/pyCHX/v2/_commonspeckle/chx_correlationc.py b/pyCHX/v2/_commonspeckle/chx_correlationc.py index fb31982..eeb3062 100644 --- a/pyCHX/v2/_commonspeckle/chx_correlationc.py +++ b/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)]) diff --git a/pyCHX/v2/_commonspeckle/chx_generic_functions.py b/pyCHX/v2/_commonspeckle/chx_generic_functions.py index fb6db14..f63b281 100644 --- a/pyCHX/v2/_commonspeckle/chx_generic_functions.py +++ b/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)) diff --git a/pyCHX/v2/_commonspeckle/chx_speckle.py b/pyCHX/v2/_commonspeckle/chx_speckle.py index 75ab068..1444989 100644 --- a/pyCHX/v2/_commonspeckle/chx_speckle.py +++ b/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) diff --git a/pyCHX/v2/_commonspeckle/chx_specklecp.py b/pyCHX/v2/_commonspeckle/chx_specklecp.py index 771e51f..a96d312 100644 --- a/pyCHX/v2/_commonspeckle/chx_specklecp.py +++ b/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]])]) diff --git a/pyCHX/v2/_commonspeckle/xpcs_timepixel.py b/pyCHX/v2/_commonspeckle/xpcs_timepixel.py index 6c594a9..6427dd5 100644 --- a/pyCHX/v2/_commonspeckle/xpcs_timepixel.py +++ b/pyCHX/v2/_commonspeckle/xpcs_timepixel.py @@ -336,7 +336,7 @@ 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"]) @@ -344,7 +344,7 @@ def init_compress_timepix_data(pos, t, binstep, filename, mask=None, md=None, no 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)) diff --git a/pyCHX/v2/_futurepyCHX/Create_Report.py b/pyCHX/v2/_futurepyCHX/Create_Report.py index 4c7a560..6e84633 100644 --- a/pyCHX/v2/_futurepyCHX/Create_Report.py +++ b/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: diff --git a/pyCHX/v2/_futurepyCHX/Two_Time_Correlation_Function.py b/pyCHX/v2/_futurepyCHX/Two_Time_Correlation_Function.py index b3d7899..3a5e4f9 100644 --- a/pyCHX/v2/_futurepyCHX/Two_Time_Correlation_Function.py +++ b/pyCHX/v2/_futurepyCHX/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): diff --git a/pyCHX/v2/_futurepyCHX/chx_compress.py b/pyCHX/v2/_futurepyCHX/chx_compress.py index 8ac7184..d81a3e6 100644 --- a/pyCHX/v2/_futurepyCHX/chx_compress.py +++ b/pyCHX/v2/_futurepyCHX/chx_compress.py @@ -248,8 +248,8 @@ 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, @@ -458,7 +458,7 @@ 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 @@ -541,7 +541,7 @@ 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 @@ -788,7 +788,7 @@ 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 diff --git a/pyCHX/v2/_futurepyCHX/chx_correlationc.py b/pyCHX/v2/_futurepyCHX/chx_correlationc.py index fb31982..eeb3062 100644 --- a/pyCHX/v2/_futurepyCHX/chx_correlationc.py +++ b/pyCHX/v2/_futurepyCHX/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)]) diff --git a/pyCHX/v2/_futurepyCHX/chx_crosscor.py b/pyCHX/v2/_futurepyCHX/chx_crosscor.py index 28e839b..deae401 100644 --- a/pyCHX/v2/_futurepyCHX/chx_crosscor.py +++ b/pyCHX/v2/_futurepyCHX/chx_crosscor.py @@ -492,7 +492,7 @@ 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": diff --git a/pyCHX/v2/_futurepyCHX/chx_generic_functions.py b/pyCHX/v2/_futurepyCHX/chx_generic_functions.py index 0e3c577..d58d0cc 100644 --- a/pyCHX/v2/_futurepyCHX/chx_generic_functions.py +++ b/pyCHX/v2/_futurepyCHX/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)) diff --git a/pyCHX/v2/_futurepyCHX/chx_speckle.py b/pyCHX/v2/_futurepyCHX/chx_speckle.py index 75ab068..1444989 100644 --- a/pyCHX/v2/_futurepyCHX/chx_speckle.py +++ b/pyCHX/v2/_futurepyCHX/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) diff --git a/pyCHX/v2/_futurepyCHX/chx_specklecp.py b/pyCHX/v2/_futurepyCHX/chx_specklecp.py index a4e5029..b109e2d 100644 --- a/pyCHX/v2/_futurepyCHX/chx_specklecp.py +++ b/pyCHX/v2/_futurepyCHX/chx_specklecp.py @@ -226,7 +226,7 @@ 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) ) @@ -435,7 +435,7 @@ 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) @@ -721,9 +721,9 @@ 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) @@ -794,10 +794,10 @@ 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) @@ -828,10 +828,10 @@ 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) @@ -1384,8 +1384,8 @@ 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]])]) diff --git a/pyCHX/v2/_futurepyCHX/xpcs_timepixel.py b/pyCHX/v2/_futurepyCHX/xpcs_timepixel.py index 264da7e..eb3130a 100644 --- a/pyCHX/v2/_futurepyCHX/xpcs_timepixel.py +++ b/pyCHX/v2/_futurepyCHX/xpcs_timepixel.py @@ -336,7 +336,7 @@ 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"]) @@ -344,7 +344,7 @@ def init_compress_timepix_data(pos, t, binstep, filename, mask=None, md=None, no 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)) diff --git a/pyCHX/xpcs_timepixel.py b/pyCHX/xpcs_timepixel.py index 85080c5..af4a364 100644 --- a/pyCHX/xpcs_timepixel.py +++ b/pyCHX/xpcs_timepixel.py @@ -301,7 +301,7 @@ 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"])