Mailman 3 July 2024 - Python-list

Installation of Slixfeed with pip fails
by Schimon Jehudah Aug. 12, 2024

Aug. 12, 2024

Greetings, to one and all! My name is Schimon, and I am the developer of a news chat bot for the XMPP network, called Slixfeed. I have recently added support for OMEMO encryption, and a friend of mine has reported that there is an issue installing it with pip. I suppoes this is a fault of a package at PyPi, or a fault at my pyproject.toml. This is the link to the project: https://git.xmpp-it.net/sch/Slixfeed#getting-started Please advise, Schimon

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Predicting an object over an pretrained model is not working
by marc nicole July 31, 2024

July 31, 2024

Hello all, I want to predict an object by given as input an image and want to have my model be able to predict the label. I have trained a model using tensorflow based on annotated database where the target object to predict was added to the pretrained model. the code I am using is the following where I set the target object image as input and want to have the prediction output: class MultiObjectDetection(): def __init__(self, classes_name): self._classes_name = classes_name self._num_classes = len(classes_name) self._common_params = {'image_size': 448, 'num_classes': self._num_classes, 'batch_size':1} self._net_params = {'cell_size': 7, 'boxes_per_cell':2, 'weight_decay': 0.0005} self._net = YoloTinyNet(self._common_params, self._net_params, test=True) def predict_object(self, image): predicts = self._net.inference(image) return predicts def process_predicts(self, resized_img, predicts, thresh=0.2): """ process the predicts of object detection with one image input. Args: resized_img: resized source image. predicts: output of the model. thresh: thresh of bounding box confidence. Return: predicts_dict: {"stick": [[x1, y1, x2, y2, scores1], [...]]}. """ cls_num = self._num_classes bbx_per_cell = self._net_params["boxes_per_cell"] cell_size = self._net_params["cell_size"] img_size = self._common_params["image_size"] p_classes = predicts[0, :, :, 0:cls_num] C = predicts[0, :, :, cls_num:cls_num+bbx_per_cell] # two bounding boxes in one cell. coordinate = predicts[0, :, :, cls_num+bbx_per_cell:] # all bounding boxes position. p_classes = np.reshape(p_classes, (cell_size, cell_size, 1, cls_num)) C = np.reshape(C, (cell_size, cell_size, bbx_per_cell, 1)) P = C * p_classes # confidencefor all classes of all bounding boxes (cell_size, cell_size, bounding_box_num, class_num) = (7, 7, 2, 1). predicts_dict = {} for i in range(cell_size): for j in range(cell_size): temp_data = np.zeros_like(P, np.float32) temp_data[i, j, :, :] = P[i, j, :, :] position = np.argmax(temp_data) # refer to the class num (with maximum confidence) for every bounding box. index = np.unravel_index(position, P.shape) if P[index] > thresh: class_num = index[-1] coordinate = np.reshape(coordinate, (cell_size, cell_size, bbx_per_cell, 4)) # (cell_size, cell_size, bbox_num_per_cell, coordinate)[xmin, ymin, xmax, ymax] max_coordinate = coordinate[index[0], index[1], index[2], :] xcenter = max_coordinate[0] ycenter = max_coordinate[1] w = max_coordinate[2] h = max_coordinate[3] xcenter = (index[1] + xcenter) * (1.0*img_size /cell_size) ycenter = (index[0] + ycenter) * (1.0*img_size /cell_size) w = w * img_size h = h * img_size xmin = 0 if (xcenter - w/2.0 < 0) else (xcenter - w/2.0) ymin = 0 if (xcenter - w/2.0 < 0) else (ycenter - h/2.0) xmax = resized_img.shape[0] if (xmin + w) > resized_img.shape[0] else (xmin + w) ymax = resized_img.shape[1] if (ymin + h) > resized_img.shape[1] else (ymin + h) class_name = self._classes_name[class_num] predicts_dict.setdefault(class_name, []) predicts_dict[class_name].append([int(xmin), int(ymin), int(xmax), int(ymax), P[index]]) return predicts_dict def non_max_suppress(self, predicts_dict, threshold=0.5): """ implement non-maximum supression on predict bounding boxes. Args: predicts_dict: {"stick": [[x1, y1, x2, y2, scores1], [...]]}. threshhold: iou threshold Return: predicts_dict processed by non-maximum suppression """ for object_name, bbox in predicts_dict.items(): bbox_array = np.array(bbox, dtype=np.float) x1, y1, x2, y2, scores = bbox_array[:,0], bbox_array[:,1], bbox_array[:,2], bbox_array[:,3], bbox_array[:,4] areas = (x2-x1+1) * (y2-y1+1) order = scores.argsort()[::-1] keep = [] while order.size > 0: i = order[0] keep.append(i) xx1 = np.maximum(x1[i], x1[order[1:]]) yy1 = np.maximum(y1[i], y1[order[1:]]) xx2 = np.minimum(x2[i], x2[order[1:]]) yy2 = np.minimum(y2[i], y2[order[1:]]) inter = np.maximum(0.0, xx2-xx1+1) * np.maximum(0.0, yy2-yy1+1) iou = inter/(areas[i]+areas[order[1:]]-inter) indexs = np.where(iou<=threshold)[0] order = order[indexs+1] bbox = bbox_array[keep] predicts_dict[object_name] = bbox.tolist() predicts_dict = predicts_dict return predicts_dict class_names = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor", "small_ball"] modelFile = ('models\\train\\model.ckpt-0') track_object = "small_ball"print("object detection and tracking...") multiObjectDetect = MultiObjectDetection(IP, class_names) image = tf.placeholder(tf.float32, (1, 448, 448, 3)) object_predicts = multiObjectDetect.predict_object(image) sess = tf.Session() saver = tf.train.Saver(multiObjectDetect._net.trainable_collection) saver.restore(sess, modelFile) index = 0while 1: src_img = cv2.imread("./weirdobject.jpg") resized_img = cv2.resize(src_img, (448, 448)) np_img = cv2.cvtColor(resized_img, cv2.COLOR_BGR2RGB) np_img = np_img.astype(np.float32) np_img = np_img / 255.0 * 2 - 1 np_img = np.reshape(np_img, (1, 448, 448, 3)) np_predict = sess.run(object_predicts, feed_dict={image: np_img}) predicts_dict = multiObjectDetect.process_predicts(resized_img, np_predict) predicts_dict = multiObjectDetect.non_max_suppress(predicts_dict) print ("predict dict = ", predicts_dict) The problem with this code is that the predicts_dict returns: predict dict = {'sheep': [[233.0, 92.0, 448.0, -103.0, 5.3531270027160645], [167.0, 509.0, 209.0, 101.0, 4.947688579559326], [0.0, 0.0, 448.0, 431.0, 3.393721580505371]], 'horse': [[374.0, 33.0, 282.0, 448.0, 5.277851581573486], [135.0, 688.0, -33.0, -14.0, 3.5144259929656982], [1.0, 117.0, 112.0, -138.0, 2.656987190246582]], 'bicycle': [[461.0, 781.0, 154.0, -381.0, 5.918102741241455], [70.0, 344.0, 391.0, -138.0, 3.031444787979126], [378.0, 497.0, 46.0, 149.0, 2.7629122734069824], [541.0, 583.0, 69.0, 307.0, 2.7170517444610596], [323.0, 22.0, 336.0, 448.0, 1.608760952949524]], 'bottle': [[390.0, 218.0, -199.0, 448.0, 4.582971096038818], [0.0, 0.0, 448.0, -410.0, 0.9097045063972473]], 'sofa': [[346.0, 102.0, 323.0, -38.0, 2.371835947036743]], 'dog': [[319.0, 254.0, -282.0, 373.0, 4.022889137268066]], 'cat': [[63.0, -195.0, 365.0, -92.0, 3.5134828090667725]], 'person': [[22.0, -122.0, 154.0, 448.0, 3.927537441253662], [350.0, 155.0, -36.0, -445.0, 2.679833173751831], [119.0, 416.0, -43.0, 292.0, 0.9529445171356201], [251.0, 445.0, 225.0, 188.0, 0.9001350402832031]], 'train': [[329.0, 485.0, -24.0, -235.0, 2.7050414085388184], [483.0, 362.0, 237.0, -86.0, 2.555817127227783], [13.0, 365.0, 373.0, 448.0, 0.6229299902915955]], 'small_ball': [[217.0, 737.0, 448.0, -315.0, 1.739920973777771], [117.0, 283.0, 153.0, 122.0, 1.5690066814422607]], 'boat': [[164.0, 805.0, 34.0, -169.0, 4.972668170928955], [0.0, 0.0, 397.0, 69.0, 2.353729486465454], [302.0, 605.0, 15.0, -22.0, 2.0259625911712646]], 'aeroplane': [[470.0, 616.0, -305.0, -37.0, 3.431873321533203], [0.0, 0.0, 448.0, -72.0, 2.836672306060791]], 'bus': [[0.0, 0.0, -101.0, -280.0, 1.2078320980072021]], 'pottedplant': [[620.0, -268.0, -124.0, 418.0, 2.158564805984497], [0.0, 0.0, 448.0, -779.0, 1.6623022556304932]], 'tvmonitor': [[0.0, 0.0, 448.0, 85.0, 3.238999128341675], [240.0, 772.0, 200.0, 91.0, 1.7443398237228394], [546.0, 155.0, 448.0, 448.0, 1.1334525346755981], [107.0, 441.0, 432.0, 219.0, 0.5971617698669434]], 'chair': [[470.0, -187.0, 106.0, 235.0, 3.8548083305358887], [524.0, 740.0, -103.0, 99.0, 3.636549234390259], [0.0, 0.0, 275.0, -325.0, 3.0997846126556396], [711.0, -231.0, -146.0, 392.0, 2.205275535583496]], 'diningtable': [[138.0, -310.0, 111.0, 448.0, 4.660728931427002], [317.0, -66.0, 313.0, 6.0, 4.535496234893799], [0.0, 0.0, -41.0, 175.0, 1.8571208715438843], [21.0, -92.0, 76.0, 172.0, 1.2035608291625977], [0.0, 0.0, 448.0, -250.0, 1.00322687625885]], 'car': [[312.0, 232.0, 132.0, 309.0, 3.205225706100464], [514.0, -76.0, 218.0, 448.0, 1.4289973974227905], [0.0, 0.0, 448.0, 142.0, 0.7124998569488525]]} WHile I expect only the dict to contain the small_ball key How's that is possible? where's the prediction output?How to fix the code?

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Issue with pip Installation on My Laptop
by Lizna Shah July 28, 2024

July 28, 2024

Hello, I am experiencing a problem with pip not being installed on my laptop and would appreciate any assistance you can provide. Here are the details of my issue: - Operating System: Windows Python Version: Python 3.10.10 Steps I have already taken to try and resolve the issue: 1. Verified that Python is installed by running `python --version` (output: [Python 3.10.10]). 2. Tried to install pip using the command `python -m ensurepip` and received the following error: Traceback (most recent call last): File "C:\Users\LIZNA SHAH\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "C:\Users\LIZNA SHAH\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 86, in _run_code exec(code, run_globals) File "C:\Users\LIZNA SHAH\AppData\Local\Programs\Python\Python310\lib\ensurepip\__main__.py", line 5, in <module> sys.exit(ensurepip._main()) File "C:\Users\LIZNA SHAH\AppData\Local\Programs\Python\Python310\lib\ensurepip\__init__.py", line 287, in _main return _bootstrap( File "C:\Users\LIZNA SHAH\AppData\Local\Programs\Python\Python310\lib\ensurepip\__init__.py", line 203, in _bootstrap return _run_pip([*args, *_PACKAGE_NAMES], additional_paths) File "C:\Users\LIZNA SHAH\AppData\Local\Programs\Python\Python310\lib\ensurepip\__init__.py", line 104, in _run_pip return subprocess.run(cmd, check=True).returncode File "C:\Users\LIZNA SHAH\AppData\Local\Programs\Python\Python310\lib\subprocess.py", line 503, in run with Popen(*popenargs, **kwargs) as process: File "C:\Users\LIZNA SHAH\AppData\Local\Programs\Python\Python310\lib\subprocess.py", line 971, in init self._execute_child(args, executable, preexec_fn, close_fds, File "C:\Users\LIZNA SHAH\AppData\Local\Programs\Python\Python310\lib\subprocess.py", line 1440, in _execute_child hp, ht, pid, tid = _winapi.CreateProcess(executable, args, OSError: [WinError 225] Operation did not complete successfully because the file contains a virus or potentially unwanted software 3. Attempted to use `get-pip.py` script from the official website, but encountered: ['get-pip.py' is not recognized as an internal or external command, operable program or batch file.]. Additional information: - I was able to use pip successfully with previous versions of Python on this laptop. I would be grateful for any guidance or troubleshooting steps you could suggest to resolve this issue. Thank you. Best regards, Lizna Shah Nepal

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I need a free LAMP hosting and a iMac
by Benjamin July 27, 2024

July 27, 2024

Hi, For several months I have searched free web hosing in Google, but have not find a satisfying result now. Any body know some good LAMP free web hosting? And, I have lost job since 2018, my macbook has only 2 intel core, I want to buy a new iMac for person programming, but I have only little money, any body donate me a iMac with ARM CPU? or donate some money at https://buymeacoffee.com/benjamin_yin <https://buymeacoffee.com/benjamin_yin> Thanks.

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[RELEASE] Python 3.13.0 beta 4 released.
by Thomas Wouters July 18, 2024

July 18, 2024

Python 3.13.0b4, the final beta of Python 3.13, is now available: https://www.python.org/downloads/release/python-3130b4/ *This is a beta preview of Python 3.13* Python 3.13 is still in development. This release, 3.13.0b4, is the *final* beta release preview of 3.13. Beta release previews are intended to give the wider community the opportunity to test new features and bug fixes and to prepare their projects to support the new feature release. We *strongly encourage* maintainers of third-party Python projects to *test with 3.13* during the beta phase and report issues found to the Python bug tracker <https://github.com/python/cpython/issues> as soon as possible. While the release is planned to be feature complete entering the beta phase, it is possible that features may be modified or, in rare cases, deleted up until the start of the release candidate phase (Tuesday 2024-07-30). Our goal is to have *no ABI changes* after this final beta release, and as few code changes as possible after 3.13.0rc1, the first release candidate. To achieve that, it will be *extremely important* to get as much exposure for 3.13 as possible during the beta phase. Please keep in mind that this is a preview release and its use is *not* recommended for production environments. <https://discuss.python.org/t/python-3-13-0b4-now-available/58565#major-new-…>Major new features of the 3.13 series, compared to 3.12 Some of the new major new features and changes in Python 3.13 are: <https://discuss.python.org/t/python-3-13-0b4-now-available/58565#new-featur…>New features - A new and improved interactive interpreter <https://docs.python.org/3.13/whatsnew/3.13.html#a-better-interactive-interp…>, based on PyPy <https://pypy.org>’s, featuring multi-line editing and color support, as well as colorized exception tracebacks <https://docs.python.org/3.13/whatsnew/3.13.html#improved-error-messages> . - An *experimental* free-threaded build mode <https://docs.python.org/3.13/whatsnew/3.13.html#free-threaded-cpython>, which disables the Global Interpreter Lock, allowing threads to run more concurrently. The build mode is available as an experimental feature in the Windows and macOS installers as well. - A preliminary, *experimental* JIT <https://docs.python.org/3.13/whatsnew/3.13.html#experimental-jit-compiler>, providing the ground work for significant performance improvements. - The locals() builtin function (and its C equivalent) now has well-defined semantics when mutating the returned mapping <https://docs.python.org/3.13/whatsnew/3.13.html#defined-mutation-semantics-…>, which allows debuggers to operate more consistently. - The (cyclic) garbage collector is now incremental <https://docs.python.org/3.13/whatsnew/3.13.html#incremental-garbage-collect…>, which should mean shorter pauses for collection in programs with a lot of objects. - A modified version of mimalloc <https://github.com/microsoft/mimalloc> is now included, optional but enabled by default if supported by the platform, and required for the free-threaded build mode. - Docstrings now have their leading indentation stripped <https://docs.python.org/3.13/whatsnew/3.13.html#other-language-changes>, reducing memory use and the size of .pyc files. (Most tools handling docstrings already strip leading indentation.) - The dbm module <https://docs.python.org/3.13/library/dbm.html> has a new dbm.sqlite3 backend <https://docs.python.org/3.13/whatsnew/3.13.html#dbm> that is used by default when creating new files. - The minimum supported macOS version was changed from 10.9 to *10.13 (High Sierra)*. Older macOS versions will not be supported going forward. - WASI is now a Tier 2 supported platform <https://peps.python.org/pep-0011/#tier-2>. Emscripten is no longer an officially supported platform <https://peps.python.org/pep-0011/#no-longer-supported-platforms> (but Pyodide <https://pyodide.org> continues to support Emscripten). <https://discuss.python.org/t/python-3-13-0b4-now-available/58565#typing-3> Typing - Support for type defaults in type parameters <https://peps.python.org/pep-0696/>. - A new type narrowing annotation <https://peps.python.org/pep-0742/>, typing.TypeIs. - A new annotation for read-only items in TypeDicts <https://peps.python.org/pep-0705/>. - A new annotation for marking deprecations in the type system <https://peps.python.org/pep-0702>. <https://discuss.python.org/t/python-3-13-0b4-now-available/58565#removals-a…>Removals and new deprecations - PEP 594 (Removing dead batteries from the standard library) <https://peps.python.org/pep-0594/> scheduled removals of many deprecated modules: aifc, audioop, chunk, cgi, cgitb, crypt, imghdr, mailcap, msilib, nis, nntplib, ossaudiodev, pipes, sndhdr, spwd, sunau, telnetlib, uu, xdrlib, lib2to3. - Many other removals <https://docs.python.org/3.13/whatsnew/3.13.html#removed> of deprecated classes, functions and methods in various standard library modules. - C API removals <https://docs.python.org/3.13/whatsnew/3.13.html#id10> and deprecations <https://docs.python.org/3.13/whatsnew/3.13.html#id9>. (Some removals present in alpha 1 were reverted in alpha 2, as the removals were deemed too disruptive at this time.) - New deprecations <https://docs.python.org/3.13/whatsnew/3.13.html#deprecated>, most of which are scheduled for removal from Python 3.15 or 3.16. (Hey, *fellow core developer,* if a feature you find important is missing from this list, let Thomas know <thomas(a)python.org>.) For more details on the changes to Python 3.13, see What’s new in Python 3.13 <https://docs.python.org/3.13/whatsnew/3.13.html>. The next pre-release of Python 3.13 will be 3.13.0rc1, *the first release candidate*, currently scheduled for 2024-07-30. <https://discuss.python.org/t/python-3-13-0b4-now-available/58565#more-resou…>More resources - Online Documentation <https://docs.python.org/3.13/> - PEP 719 <https://peps.python.org/pep-0719/>, 3.13 Release Schedule - Report bugs at https://github.com/python/cpython/issues. - Help fund Python directly <https://www.python.org/psf/donations/python-dev/> (or via GitHub Sponsors <https://github.com/sponsors/python>), and support the Python community <https://www.python.org/psf/donations/>. <https://discuss.python.org/t/python-3-13-0b4-now-available/58565#enjoy-the-…>Enjoy the new releases Thanks to all of the many volunteers who help make Python Development and these releases possible! Please consider supporting our efforts by volunteering yourself or through organization contributions to the Python Software Foundation. Finally, regards from a confusingly hot and rainy Munich, Your release team, Thomas Wouters Łukasz Langa Ned Deily Steve Dower

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Relatively prime integers in NumPy
by Popov, Dmitry Yu July 13, 2024

July 13, 2024

Dear Sirs. Does NumPy provide a simple mechanism to identify relatively prime integers, i.e. integers which don't have a common factor other than +1 or -1? For example, in case of this array: [[1,5,8], [2,4,8], [3,3,9]] I can imagine a function which would return array of common factors along axis 0: [1,2,3]. Those triples of numbers along axis 1 with the factor of1 or -1 would be relatively prime integers. Regards, Dmitry Popov Argonne, IL USA

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Best use of "open" context manager
by Rob Cliffe July 12, 2024

July 12, 2024

Consider this scenario (which I ran into in real life): I want to open a text file and do a lot of processing on the lines of that file. If the file does not exist I want to take appropriate action, e.g. print an error message and abort the program. I might write it like this: try: with open(FileName) as f: for ln in f: print("I do a lot of processing here") # Many lines of code here ..... except FileNotFoundError: print(f"File {FileName} not found") sys.exit() but this violates the principle that a "try" suite should be kept small, so that only targeted exceptions are trapped, not to mention that having "try" and "except" far apart decreases readability. Or I might write it like this: try: f = open(FileName) as f: FileLines = f.readlines() except FileNotFoundError: print(f"File {FileName} not found") sys.exit() # I forgot to put "f.close()" here -:) for ln in File Lines: print("I do a lot of processing here") # Many lines of code here ..... but this loses the benefits of using "open" as a context manager, and would also be unacceptable if the file was too large to read into memory. Really I would like to write something like try: with open(FileName) as f: except FileNotFoundError: print(f"File {FileName} not found") sys.exit() else: # or "finally:" for ln in f: print("I do a lot of processing here") # Many lines of code here ..... but this of course does not work because by the time we get to "for ln in f:" the file has been closed so we get ValueError: I/O operation on closed file I could modify the last attempt to open the file twice, which would work, but seems like a kludge (subject to race condition, inefficient). Is there a better / more Pythonic solution? Best wishes Rob Cliffe

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Re: Password Hash Validation (Posting On Python-List Prohibited)
by Lawrence D'Oliveiro July 12, 2024

July 12, 2024

On Fri, 21 Jun 2024 06:32:58 -0000 (UTC), I wrote: > On Fri, 21 Jun 2024 03:40:55 -0000 (UTC), I wrote: > >> I think I will create my own wrapper using ctypes. > > Done <https://gitlab.com/ldo/nixcrypt>. The repo now includes an example script that exercises the various functions of the module.

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python repl vi mode line editing not working.
by Tobiah July 10, 2024

July 10, 2024

Kubuntu 24.04. sinewave:toby ~(1)> cat .inputrc set editing-mode vi set keymap vi sinewave:toby ~(1)> cat .editrc bind -v bind \\t rl_complete sinewave:toby ~(1)> python Python 2.7.18 (default, Jul 8 2024, 12:49:12) [GCC 13.2.0] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> 1 1 >>> 2 2 >>> ^[k I see the literal 'escape' character + 'k', when it should let me edit previous commands. I did have to compile my own python because I'm using 2.7 on this machine. Thanks for any help. Toby

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Re: python repl vi mode line editing not working.
by Tobiah July 10, 2024

July 10, 2024

> For this to work, the Python implementation should use the same > readline library as your shell, I guess. It works in python3, so I guess my problem is that I'm compiling python (I think kubuntu dropped python2), but I don't see any relevant options in the configure help.

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