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paulo037
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starcoder

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specialized-skills/artificial-intelligence/statistics-and-machine-learning/correlation-and-regression-quick-recap-3.py
tobal/hackerrank
1
146286
<filename>specialized-skills/artificial-intelligence/statistics-and-machine-learning/correlation-and-regression-quick-recap-3.py x = [15 ,12, 8, 8, 7, 7, 7, 6, 5, 3] y = [10 ,25, 17, 11, 13, 17, 20, 13, 9, 15] n = len(x) xy = [x[i] * y[i] for i in range(n)] x_square = [x[i] * x[i] for i in range(n)] avg_x = sum(x) / n avg_y = sum(y) / n avg_xy = sum(xy) / n avg_xsqr = sum(x_square) / n m = ((avg_x * avg_y) - avg_xy) / (avg_x ** 2 - avg_xsqr) c = avg_y - (m * avg_x) print(round((m * 10 + c), 2))
[ 1, 529, 9507, 29958, 18732, 1891, 29899, 808, 6090, 29914, 442, 928, 616, 29899, 524, 28286, 29914, 6112, 6765, 29899, 392, 29899, 23523, 29899, 21891, 29914, 2616, 23445, 29899, 392, 29899, 276, 11476, 29899, 24561, 29899, 3757, 481, 29899, 29941, 29889, 2272, 13, 29916, 353, 518, 29896, 29945, 1919, 29896, 29906, 29892, 29871, 29947, 29892, 29871, 29947, 29892, 29871, 29955, 29892, 29871, 29955, 29892, 29871, 29955, 29892, 29871, 29953, 29892, 29871, 29945, 29892, 29871, 29941, 29962, 13, 29891, 353, 518, 29896, 29900, 1919, 29906, 29945, 29892, 29871, 29896, 29955, 29892, 29871, 29896, 29896, 29892, 29871, 29896, 29941, 29892, 29871, 29896, 29955, 29892, 29871, 29906, 29900, 29892, 29871, 29896, 29941, 29892, 29871, 29929, 29892, 29871, 29896, 29945, 29962, 13, 29876, 353, 7431, 29898, 29916, 29897, 13, 3594, 353, 518, 29916, 29961, 29875, 29962, 334, 343, 29961, 29875, 29962, 363, 474, 297, 3464, 29898, 29876, 4638, 13, 29916, 29918, 17619, 353, 518, 29916, 29961, 29875, 29962, 334, 921, 29961, 29875, 29962, 363, 474, 297, 3464, 29898, 29876, 4638, 13, 485, 29887, 29918, 29916, 353, 2533, 29898, 29916, 29897, 847, 302, 13, 485, 29887, 29918, 29891, 353, 2533, 29898, 29891, 29897, 847, 302, 13, 485, 29887, 29918, 3594, 353, 2533, 29898, 3594, 29897, 847, 302, 13, 485, 29887, 29918, 29916, 3044, 29878, 353, 2533, 29898, 29916, 29918, 17619, 29897, 847, 302, 13, 29885, 353, 5135, 485, 29887, 29918, 29916, 334, 1029, 29887, 29918, 29891, 29897, 448, 1029, 29887, 29918, 3594, 29897, 847, 313, 485, 29887, 29918, 29916, 3579, 29871, 29906, 448, 1029, 29887, 29918, 29916, 3044, 29878, 29897, 13, 29883, 353, 1029, 29887, 29918, 29891, 448, 313, 29885, 334, 1029, 29887, 29918, 29916, 29897, 13, 2158, 29898, 14486, 3552, 29885, 334, 29871, 29896, 29900, 718, 274, 511, 29871, 29906, 876, 13, 2 ]
bookworm/library/views.py
srilatha44/threepress
0
1614334
from django.utils.translation import ugettext as _ from django.core.mail import EmailMessage import logging, sys, urllib, urllib2, MySQLdb, os.path, unicodedata, traceback, urlparse from cStringIO import StringIO from zipfile import BadZipfile from xml.sax.saxutils import escape as xml_escape from django.contrib.auth.decorators import login_required from django.http import HttpResponseRedirect, Http404, HttpResponse from django.shortcuts import get_object_or_404 from django.core.urlresolvers import reverse from django.contrib.auth.forms import UserCreationForm from django.contrib import auth from django.template import RequestContext from django.core.paginator import Paginator, EmptyPage from django.views.generic.simple import direct_to_template from django.conf import settings from django.views.decorators.cache import cache_page, cache_control, never_cache from django.views.decorators.vary import vary_on_headers, vary_on_cookie from django_authopenid.views import signin from bookworm.library.epub import InvalidEpubException from bookworm.library.models import EpubArchive, HTMLFile, StylesheetFile, ImageFile, SystemInfo, get_file_by_item, order_fields, DRMEpubException, UserArchive from bookworm.library.forms import EpubValidateForm, ProfileForm from bookworm.library.epub import constants as epub_constants from bookworm.library.google_books.search import Request from bookworm.library.epub import epubcheck log = logging.getLogger('library.views') @never_cache def index(request): '''Public home page. This should be heavily cached (in fact eventually should be served only by the web server.)''' if request.user.is_authenticated(): return HttpResponseRedirect(reverse('library')) # If this is a mobile user, skip the public page if settings.MOBILE: return signin(request) return direct_to_template(request, "public.html", {}) @login_required @never_cache def library(request, page_number=settings.DEFAULT_START_PAGE, order=settings.DEFAULT_ORDER_FIELD, dir=settings.DEFAULT_ORDER_DIRECTION): '''Users's library listing. The page itself should not be cached although individual items in the library should be cached at the model level.''' if not dir in settings.VALID_ORDER_DIRECTIONS: raise Exception("Direction %s was not in our list of known ordering directions" % dir) if not order in settings.VALID_ORDER_FIELDS: raise Exception("Field %s was not in our list of known ordering fields" % order) if dir == 'desc': order_computed = '-%s' % order reverse_dir = 'asc' else: order_computed = order reverse_dir = 'desc' if page_number is None: page_number = settings.DEFAULT_START_PAGE user = request.user form = EpubValidateForm() paginator = Paginator(EpubArchive.objects.filter(user_archive__user=user).order_by(order_computed).distinct(), settings.DEFAULT_NUM_RESULTS) try: page = paginator.page(page_number) except EmptyPage: page_number = settings.DEFAULT_START_PAGE # If we somehow got to an invalid page number, start over return HttpResponseRedirect(reverse('library-paginate', args=[page_number])) for d in page.object_list: if d.orderable_author == '': # Populate the author field if it was empty before d.orderable_author = d.safe_author() d.save() sysinfo = SystemInfo() return direct_to_template(request, 'index.html', {'documents':paginator, 'page':page, 'form':form, 'order':order, 'dir':dir, 'total_users':sysinfo.get_total_users(), 'total_books':sysinfo.get_total_books(), 'reverse_dir':reverse_dir, 'order_text': order_fields[order], 'order_direction': _('ascending') if dir == 'asc' else _('descending'), 'order_adverb': _('alphabetically') if order != 'created_time' else _('by date value'), } ) # We can't cache this at the page level because the user may be # going to the last-read page rather than the document start @never_cache def view(request, title, key, first=False, resume=False): '''If we view just a document, we want to either see our last chapter, or see the first item in the <opf:spine>, as required by the epub specification.''' log.debug("Looking up title %s, key %s" % (title, key)) document = _get_document(request, title, key) # If we got 'None' from get_document with an anonymous user, then prompt them # to login; this is probably just a bookmark with an unauthenticated user if document is None and request.user.is_anonymous(): return HttpResponseRedirect(reverse('user_signin')) if not request.user.is_anonymous(): uprofile = request.user.get_profile() last_chapter_read = document.get_last_chapter_read(request.user) else: last_chapter_read = None uprofile = None if resume and last_chapter_read is not None: chapter = last_chapter_read elif not first and uprofile and uprofile.open_to_last_chapter and last_chapter_read: chapter = last_chapter_read else: try: toc = document.get_toc() first_item = toc.first_item() chapter = get_file_by_item(first_item, document) except InvalidEpubException: # We got some kind of catastrophic error while trying to # parse this document message = _('There was a problem reading the metadata for this document.') return view_chapter(request, title, key, None, message=message) if chapter is None: log.error('Could not find an item with the id of %s' % first_item) raise Http404 if first: log.debug("Forcing first chapter") # Force an HTTP redirect so we get a clean URL but go to the correct chapter ID return HttpResponseRedirect(reverse('view_chapter', kwargs={'title':document.safe_title(), 'key': document.id, 'chapter_id':chapter.filename})) return view_chapter(request, title, key, None, chapter=chapter, document=document) # Not cacheable either because it may be a different user @never_cache def view_chapter(request, title, key, chapter_id, chapter=None, document=None, google_books=None, message=None): if chapter is not None: chapter_id = chapter.id log.debug("Looking up title %s, key %s, chapter %s" % (title, key, chapter_id)) if not document: document = _get_document(request, title, key) if chapter is None: # Legacy objects may have more than one duplicate representation h = HTMLFile.objects.filter(archive=document, filename=chapter_id) if h.count() == 0: raise Http404 chapter = h[0] next = _chapter_next_previous(document, chapter, 'next') previous = _chapter_next_previous(document, chapter, 'previous') parent_chapter = None subchapter_href = None toc = document.get_top_level_toc() for t in toc: href = chapter.filename.encode(epub_constants.ENC) if href in [c.href() for c in t.find_descendants()]: parent_chapter = t subchapter_href = href break # Check whether this will render without throwing an exception try: chapter.render() stylesheets = chapter.stylesheets.all()[0:settings.MAX_CSS_FILES] # If we got 0 stylesheets, this may be a legacy book if len(stylesheets) == 0: stylesheets = StylesheetFile.objects.filter(archive=document)[0:settings.MAX_CSS_FILES] except InvalidEpubException, e: log.error(traceback.format_exc()) chapter = None stylesheets = None message = _(''' This book contained content that Bookworm couldn't read. You may need to check with the publisher that this is a valid ePub book that contains either XHTML or DTBook-formatted content.''') return direct_to_template(request, 'view.html', {'chapter':chapter, 'document':document, 'next':next, 'message':message, 'toc':toc, 'subchapter_href':subchapter_href, 'parent_chapter':parent_chapter, 'stylesheets':stylesheets, 'google_books':google_books, 'previous':previous}) @cache_page(60 * 15) @cache_control(private=True) def view_chapter_image(request, title, key, image): log.debug("Image request: looking up title %s, key %s, image %s" % (title, key, image)) document = _get_document(request, title, key) try: image_obj = ImageFile.objects.filter(archive=document, filename=image)[0] except IndexError: image = os.path.basename(image) try: image_obj = ImageFile.objects.filter(archive=document, filename=image)[0] except IndexError: raise Http404 response = HttpResponse(content_type=str(image_obj.content_type)) if image_obj.content_type == 'image/svg+xml': response.content = image_obj.file else: try: response.content = image_obj.get_data() except AttributeError: # Zero-length image raise Http404 return response @cache_page(60 * 15) @cache_control(public=True) def view_stylesheet(request, title, key, stylesheet_id): document = _get_document(request, title, key) log.debug('getting stylesheet %s' % stylesheet_id) stylesheets = StylesheetFile.objects.filter(archive=document,filename=stylesheet_id) if len(stylesheets) == 0: raise Http404 stylesheet = stylesheets[0] response = HttpResponse(content=stylesheet.file, content_type='text/css') return response @cache_control(private=True) def download_epub(request, title, key, nonce=None): document = _get_document(request, title, key, nonce=nonce) return _return_epub(document) def _return_epub(document): '''Return the epub archive content. If it's accidentally been deleted off the storage mechanism (usually this happens in developmif document is None: raise Http404 if document.get_content() is None: # This occurs if the file has been deleted unexpected from the filesystem raise Http404 elopment), return a 404 instead o.read of a zero-byte download.''' content = document.get_content() if content is None: raise Http404 response = HttpResposafe_name = unicodedata.normalize('NFKC', document.name).encode('ASCII', 'backslashreplace').replace(' ', '_') response['Content-Disposition'] = 'attachment; filename=%s' % safe_['Content-Disposition'] = 'at def view_document_metadata(request, title, key): log.debug("Looking up metadata %s, key %s" % (title, key)) document = _get_document(request, title, key) if not document: raise Http404 google_books = _get_google_books_info(document, request) form = EpubValidateForm() return direct_to_template(request, 'view.html', {'document':document, 'form': form, 'google_books':google_books}) @login_required def delete(request): '''Delete a book and associated metadata, and decrement our total books counter''' if 'key' in request.POST and 'title' in request.POST: title = request.POST['title'] key = request.POST['key'] log.debug("Deleting title %s, key %s" % (title, key)) if request.user.is_superuser: document = _get_document(request, title, key, override_owner=True) else: document = _get_document(request, title, key) # They should be the owner of this book to delete it if document is not None and document.is_owner(request.user): _delete_document(request, document) else: raise Http404 return HttpResponseRedirect(reverse('library')) def register(request): '''Register a new user on Bookworm''' form = UserCreationForm() if request.method == 'POST': data = request.POST.copy() errors = form.get_validation_errors(data) if not errors: new_user = form.save(data) user = auth.authenticate(username=new_user.username, password=request.POST['<PASSWORD>']) if user is not None and user.is_active: auth.login(request, user) return HttpResponseRedirect(reverse('library.views.index')) return direct_to_template(request, "auth/register.html", { 'form' : form }) @login_required @never_cache def profile(request): uprofile = RequestContext(request).get('profile') if request.openid: sreg = request.openid.sreg # If we have the email from OpenID and not in their profile, pre-populate it if not request.user.email and sreg.has_key('email'): request.user.email = sreg['email'] if sreg.has_key('fullname'): uprofile.fullname = sreg['fullname'] if sreg.has_key('nickname'): uprofile.nickname = sreg['nickname'] # These should only be updated if they haven't already been changed if uprofile.timezone is None and sreg.has_key('timezone'): uprofile.timezone = sreg['timezone'] if uprofile.language is None and sreg.has_key('language'): uprofile.language = sreg['language'] if uprofile.country is None and sreg.has_key('country'): uprofile.country = sreg['country'] uprofile.save() if settings.LANGUAGE_COOKIE_NAME in request.session: log.debug("Updating language to %s" % request.session.get(settings.LANGUAGE_COOKIE_NAME)) uprofile.language = request.session.get(settings.LANGUAGE_COOKIE_NAME) uprofile.save() if request.method == 'POST': form = ProfileForm(request.POST, instance=uprofile) if form.is_valid(): form.save() message = _("Your profile has been updated.") else: form = ProfileForm(instance=uprofile) message = None if 'msg' in request.GET: message = request.GET['msg'] return direct_to_template(request, 'auth/profile.html', {'form':form, 'prefs':uprofile, 'message':message}) @login_required def profile_delete(request): if not request.POST.has_key('delete'): # Extra sanity-check that this is a POST request log.error('Received deletion request but was not POST') message = _("There was a problem with your request to delete this profile.") return direct_to_template(request, 'profile.html', { 'message':message}) if not request.POST['delete'] == request.user.email: # And that we're POSTing from our own form (this is a sanity check, # not a security feature. The current logged-in user profile is always # the one to be deleted, regardless of the value of 'delete') log.error('Received deletion request but nickname did not match: received %s but current user is %s' % (request.POST['delete'], request.user.nickname())) message = _("There was a problem with your request to delete this profile.") return direct_to_template(request, 'profile.html', { 'message':message}) request.user.get_profile().delete() # Delete all their books (this is likely to time out for large numbers of books) documents = EpubArchive.objects.filter(user_archive__user=request.user) for d in documents: _delete_document(request, d) return HttpResponseRedirect('/') # fixme: actually log them out here tachment; filename=%sprofile_toggle_reading_mode(request): '''Toggle whether to use the simple reading more or the default mode.''' if request.method == 'POST': profile = request.user.get_profile() profile.simple_reading_mode = not(profile.simple_reading_mode) log.debug('setting reading mode to %s' % profile.simple_reading_mode) profile.save() url = request.META.get('HTTP_REFERER') if url is None: url = '/library/' return HttpResponseRedirect(url) @login_required def profile_change_font_size(request, size): '''Change the font size associated with the user's account''' if request.method == 'POST': profile = request.user.get_profile() profile.font_size = size; log.debug('setting font size to %s' % profile.font_size) profile.save() return HttpResponse('1') @login_required def profile_change_font_family(request, font): '''Change the font family associated with the user's account''' if request.method == 'POST': profile = request.user.get_profile() profile.font_family = font; log.debug('setting font family to %s' % profile.font_family) profile.save() return HttpResponse('1') tachment; filename=%s' % document.n, title=None, key=None): '''Uploads a new document and stores it in the database. If 'title' and 'key' are provided then this is a reload of an existing document, which should retain the same ID. The user must be an existing owner to reload a book.''' document = None ment and stores it in the database''' document = None if request.method == 'POST'': form = EpubValidat # The temporary file assigned by Django temp_file = request.FILES['epub'].temporary_file_path(in request.FILES['epub'].chunks(): data.write(c) if not key: log.debug("Creating new document: '%s'" % document_name) document = EpubArchive(name=document_name) document.save() else: log.debug("Reloading existing document: '%s'" % document_name) try: document = EpubArchive.objects.get(id__exact=key) log.debug(document.title) log.debug(request.user) # Is thie an owner of the document? if UserArchive.objects.filter(user=request.user, owner=True, archive=document).count() == 0: raise Http404 # Save off some metadata about it is_public = document.is_public # Delete the old one document.delete() # Create a new one with the possibly-new name document = EpubArchive(name=document_name,id=key) document.is_public = is_public document.save() successful_redirect = reverse('view_first', kwargs={'key':key, urn direct_to_template(request, 'upload.html', {'form':form, 'title':title, urn direct_to_template(request, 'upload.html', {'form':form, }) except EpubArchive.DoesNotExist: log.error("Key %s did not exist; creating new document" % (key)) document = EpubArchive(name=document_name) document.save() documenreturn add_data_to_document(request, document, open(temp_file, 'rb+'), form) # The form isn't valid (generally because we didn't actually upload anything) se from epubcheck, ignoring: %s' % d) _template(request, 'upload.html', { 'form':form}) else: form = EpubValidateForm() return add_data_to_document(request, document, data, form, redirect_success_to_page=True): '''Add epub data (as a file-like object) to a document, then explode it. If this returns True, return a successful redirect; otherwise return an error template. If the redirect_to_page parameter is True (default), the code will redirect ''' successful_redirect = reverse('library') document.set_content(data) try: document.explode() document.user_archive.create(archive=document, owner=True, user=request.user) document.save() except BadZipfile, e: # The user tried to upload something that wasn't a zip # file. This error isn't interesting; don't send email m _( log.error(sys.exc_value) message = 'The file you uploaded was not recogni)zed as anreturn _report_error(request, document, data, m, form, e, email=False) except MySQLdb.OperationalError, e: # This occurs normally when a single large transaction # is passed to MySQL. If you get many of these, # increase the value of the MySQL config value # max_allowed_packet (> 16M recommended). m = _(uebug("Got operational error %s" % e) message = "We detected a problem with your ebook that is most likely related to it being too big to display safely in a web browser. This can happen with very large images, or with extremely long chapters. Please check with the publisher that the book has been formatted correctly. Very large pages would require a lot of scrolling and load) first soreturn _report_error(request, document, data, m, form, e, email=True) except DRMEpubException, e: # DRM is evil m = _(u) document.delete() message = "It appears that you've uploaded a book which contains DRM (Digital Rights Management). This is a restriction that is meant to prevent illegal copying but also prevents legitimate owners from reading their ebooks wherever they like. You will probably need to use Adobe Digital Editions to read this ebook, but consider contacting the publisnot be addreturn _report_error(request, document, data, m, form, e, email=False) except Exception, e: # We got some unknown error (usually a malformed epub). We # want to know about these since they are sometimes actually Bookworm bugs. if settings.SKIP_EPUBCHECK: se from epubcheck, ignoring: %s' % d) return direct_to_template(request, 'upload.html', {'form':for 'message':str(e)}) _email_errors_to_admin(e, data, document) traceback.format_exc() log.error(tb) # Delete it first sodocument.delete() log.error(f) document.delete() # Let's see what's wrong with this by asking epvalid_resp = epubcheck.validate(data) error = _exception_message(e) if len(error) > 200: error = error[0:200] + u'...' message = [] message.append(_(u <p class='bw-upload-message'> error = error[0:200] + '...' message = "The file you uploaded looks like an ePub archive, but it has some problems that prevented it from being loaded. This may be a bug in Bookworm, or it may be a problem with th</p>")) message.append(_(u"<p class='bw-upload-errors'>%s</p>" % xml_escape(error))) log.error(f) document.delete() # Let's see what's wrong with this by asking epvalid_resp = epubcheck.validate(data) if valid_resp is None: # We got nothing useful from the validator (oops) pass elif len(valid_resp) == 0: message.append(_(u"<p>(epubcheck thinks this file is valid, so this may be a Bookworm error)</p>")) else: e = '\n'.join([i.text for i in valid_resp]) errors = ['<li>%s</li>' % i.replace('\n', '<br/>') for i in e.split('ERROR:') if i] message.append(_(u"<p class='bw-upload-message'>'.join(epub_error_list) message += "<p><a href='http://code.google.com/p/epubcheck/'>epubcheck</a> agrees that this is not a valid ePub file, so you <strong id='bw-num-errors'>%d</strong> error(s):</p>" % len(errors))) message.append(u" <ol id='bw-upload-error-list'>%s</ol>" % ''.join(errors)) se from epubcheck, ignoring: %s' % d) return direct_to_template(request, 'upload.html', {'form':for'message':u''.join(message)}) if redirect_success_to_page: return HttpResponseRedirect(successful_redirect) return document @login_required @never_cache def add_by_url(request): '''Accepts a GET request with parameter 'epub' which should be valid ePub URL. This will be added to the current logged-in user's library''' if not 'epub' in request.GET: raise Http404 epub_url = request.GET['epub'] return add_by_url_field(request, epub_url, redirect_success_to_page=True) def add_by_url_field(request, epub_url, redirect_success_to_page): form = EpubValidateForm() try: data = urllib2.urlopen(epub_url).read() data = StringIO(data) except urllib2.URLError: message = _("The address you provided does not point to an ePub book") se from epubcheck, ignoring: %s' % d) return direct_to_template(request, 'upload.html', {'form':for'message':message}) document = EpubArchive.objects.create(name=os.path.basename(urlparse.urlparse(epub_url).path)) document.save() return add_data_to_document(request, document, data, form, redirect_success_to_page) eturn dchapter_next_previous(document, chapter, dir='next'): '''Returns the next or previous data object from the OPF''' toc = document.get_toc() item = toc.find_item_by_id(chapter.idref) if dir == 'next': target_item = toc.find_next_item(item) else: target_item = toc.find_previous_item(item) if target_item is None: return None object = get_file_by_item(target_item, document) return object urn direct_to_template(request, 'upload.html', {'form':forl book epubindex toc = HTMLFile.objects.filter(archive=document) if toc: for t in toc: filter(archive=document) if toc: for t in toc: t.delete() # Delete all the stylesheets in the book css = StylesheetFile.objects.filter(archive=document) if css: for c in css: c.delete() # Delete all the images in the book images = ImageFile.objects.filter(archive=document) if images: for i in images: i.delete() # Delete the book itself document.delete() def _get_document(request, title, key, override_owner=False, nonce=None): '''Return a document by id and owner. Setting override_owner will search regardless of ownership, for use with admin accounts.''' user = requeest.user : if document.is_nonce_valid(nonce): return document else: log.error("Got an expired or invalid nonce: '%s', nonce='%s'" % (title, nonce)) raise Http404nce an# Anonymous users can never access non-public books if not document.is_public and user.is_anonymous(): log.error('Anonymous user tried to access non-public document %s' % (document.title)) return None if not document.is_public and not override_owner and not document.is_owner(user) andis_public and not override_owner and document.owner != user and not user.is_superuser: log.error('User %s tried to access document %s, which they do not own' % (user, title)) raise Http404 return document def _get_google_books_info(document, request): # Find all the words in the title and all the names in the author by splitting on # spaces and commas title_words = document.title.replace(',', '').split(' ') author_words = document.author.replace(',', '').split(' ') for t in title_words: query = 'intitle:%s+' % urllib.quote(t.encode('utf8')) for a in author_words: query += 'inauthor:%s+' % urllib.quote(a.encode('utf8')) if 'REMOTE_ADDR' in request.META: remote_addr = request.META['REMOTE_ADDR'] else: remdef _report_error(request, document, data, user_message, form, exception, email=False): '''Report an error with an uploaded book to the user. Required: - The request object - The document object created (will be deleted) - The data uploaded by the user - A message to the user - The form - The exception raised Setting 'email' to True will send a message to the value defined in settings.ERROR_EMAIL_RECIPIENTS (by default this is the first admin in settings.ADMINS). ''' log.error(exception) document.delete() if email: # Email it to the admins _email_errors_to_admin(exception, data, document) return direct_to_template(request, 'upload.html', { 'form':form, 'message':user_message}) def _email_errors_to_admin(exception, data, document): '''Send am email to the users registered to receive these messages (the value of settings.ERROR_EMAIL_RECIPIENTS (by default this is the first admin in settings.ADMINS). ''' # Email it to the admins message = _exception_message(exception) email = EmailMessage(u'[bookworm-error] %s (book=%s)' % (message, document.name), settings.REPLYTO_EMAIL, settings.ERROR_EMAIL_RECIPIENTS) email.attach(document.name, data, epub_constants.MIMETYPE) def _exception_message(exception): '''Return unicode string from exception.''' try: return exception.__unicode__() # Python 2.6 except AttributeError: return unicode(exception.message) # Python 2.5
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apps/04_journal/program.py
derrickyoo/python-jumpstart
0
196818
<gh_stars>0 import journal def main(): print_header() run_event_loop() pass def print_header(): print('------------------------------------------') print(' Personal Journal App ') print('------------------------------------------') print() def run_event_loop(): cmd = 'EMPTY' journal_name = 'default' journal_data = journal.load(journal_name) while cmd != 'x' and cmd: cmd = input('What do you want to do? [L]ist, [A]dd, or E[x]it? ') cmd = cmd.lower().strip() if cmd == 'l': list_entries(journal_data) elif cmd == 'a': add_entry(journal_data) elif cmd != 'x' and cmd: print('Sorry, we don\'t understand {}'.format(cmd)) print('Done, goodbye.') journal.save(journal_name, journal_data) def list_entries(data): print('Your journal entries: ') entries = reversed(data) for idx, entry in enumerate(entries): print('* [{0}] - {1}'.format(idx+1, entry)) def add_entry(data): text = input('Type your entry, <enter> to exit: ') journal.add_entry(text, data) if __name__ == '__main__': main()
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puma/helpers/testing/logging/decorator.py
gift-surg/puma
0
115315
import functools from typing import Any, Callable, TypeVar, Union, overload from unittest import TestCase from puma.context import Exit_1, Exit_2, Exit_3 from puma.helpers.testing.logging.capture_logs import CaptureLogs from puma.logging import LogLevel RT = TypeVar('RT') # _return type @overload # noqa: F811 def assert_no_warnings_or_errors_logged(func_or_testcase: Callable[..., RT]) -> Callable[..., RT]: """A decorator that checks that no errors or warnings are logged within the scope. For this to work, a logger must be configured at warning level to capture the log messages - this will usually be the case since default logging is at warning level. @assert_no_warnings_or_errors_logged def do_something(): ... """ ... @overload # noqa: F811 def assert_no_warnings_or_errors_logged(func_or_testcase: TestCase) -> 'AssertingCaptureLogs': """A function returning a context managed object which checks that no errors or warnings are logged within the scope. For this to work, a logger must be configured at warning level to capture the log messages - this will usually be the case since default logging is at warning level. def do_something(): assert_no_warnings_or_errors_logged(self) as log_context: ... """ ... def assert_no_warnings_or_errors_logged(func_or_testcase: Union[Callable[..., RT], TestCase]) -> Union[Callable[..., RT], 'AssertingCaptureLogs']: # noqa: F811 """Implementation of the assert_no_warnings_or_errors_logged overloads.""" if isinstance(func_or_testcase, TestCase): return AssertingCaptureLogs(func_or_testcase) elif callable(func_or_testcase): return assert_no_warnings_or_errors_logged_decorator(func_or_testcase) else: raise TypeError("assert_no_warnings_or_errors_logged called with unsupported parameter type") def assert_no_warnings_or_errors_logged_decorator(func: Callable[..., RT]) -> Callable[..., RT]: """A decorator that can be applied to test methods (i.e. methods of classes derived from TestCase). Causes a failure if the method logs any warning or error messages.""" @functools.wraps(func) def wrapper(self: TestCase, *args: Any, **kwargs: Any) -> RT: with AssertingCaptureLogs(self): return func(self, *args, **kwargs) return wrapper class AssertingCaptureLogs(CaptureLogs): def __init__(self, test_case: TestCase) -> None: self._test_case = test_case super().__init__(LogLevel.warn) def __exit__(self, exc_type: Exit_1, exc_value: Exit_2, traceback: Exit_3) -> None: records = self._context.pop_captured_records() super().__exit__(exc_type, exc_value, traceback) filtered_records = records.with_levels_in({LogLevel.warn, LogLevel.error, LogLevel.fatal}) if filtered_records: lines = filtered_records.get_lines(timestamp=False) self._test_case.fail(f"Unexpectedly raised warnings or errors: {lines}")
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APIs/Telegram API/telethon/crypto/authkey.py
Gregor-Davies/Awesome-Scripts-1
141
193634
""" This module holds the AuthKey class. """ import struct from hashlib import sha1 from ..extensions import BinaryReader class AuthKey: """ Represents an authorization key, used to encrypt and decrypt messages sent to Telegram's data centers. """ def __init__(self, data): """ Initializes a new authorization key. :param data: the data in bytes that represent this auth key. """ self.key = data @property def key(self): """ Returns the key of the key. Args: self: (todo): write your description """ return self._key @key.setter def key(self, value): """ Return the private key for the value. Args: self: (todo): write your description value: (todo): write your description """ if not value: self._key = self.aux_hash = self.key_id = None return if isinstance(value, type(self)): self._key, self.aux_hash, self.key_id = \ value._key, value.aux_hash, value.key_id return self._key = value with BinaryReader(sha1(self._key).digest()) as reader: self.aux_hash = reader.read_long(signed=False) reader.read(4) self.key_id = reader.read_long(signed=False) # TODO This doesn't really fit here, it's only used in authentication def calc_new_nonce_hash(self, new_nonce, number): """ Calculates the new nonce hash based on the current attributes. :param new_nonce: the new nonce to be hashed. :param number: number to prepend before the hash. :return: the hash for the given new nonce. """ new_nonce = new_nonce.to_bytes(32, 'little', signed=True) data = new_nonce + struct.pack('<BQ', number, self.aux_hash) # Calculates the message key from the given data return int.from_bytes(sha1(data).digest()[4:20], 'little', signed=True) def __bool__(self): """ Returns true if the boolean is true false otherwise. Args: self: (todo): write your description """ return bool(self._key) def __eq__(self, other): """ Return true if other is equal to other. Args: self: (todo): write your description other: (todo): write your description """ return isinstance(other, type(self)) and other.key == self._key
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python/unittest/maximumElementAfterDecrementingAndRearranging.py
geyuqiu/kata-bootstraps
0
120214
from typing import List class MaximumElementAfterDecrementingAndRearranging: def maximumElementAfterDecrementingAndRearranging(self, arr: List[int]) -> int: N = len(arr) arr.sort() if (arr[0] > 1): arr[0] = 1 for i in range(1, N): if (abs(arr[i] - arr[i - 1]) > 1): arr[i] = arr[i - 1] + 1 return max(arr)
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sdk/python/pulumi_aws/secretsmanager/outputs.py
RafalSumislawski/pulumi-aws
260
85590
<filename>sdk/python/pulumi_aws/secretsmanager/outputs.py<gh_stars>100-1000 # coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities __all__ = [ 'SecretReplica', 'SecretRotationRotationRules', 'SecretRotationRules', 'GetSecretRotationRotationRuleResult', 'GetSecretRotationRuleResult', ] @pulumi.output_type class SecretReplica(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "kmsKeyId": suggest = "kms_key_id" elif key == "lastAccessedDate": suggest = "last_accessed_date" elif key == "statusMessage": suggest = "status_message" if suggest: pulumi.log.warn(f"Key '{key}' not found in SecretReplica. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: SecretReplica.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: SecretReplica.__key_warning(key) return super().get(key, default) def __init__(__self__, *, region: str, kms_key_id: Optional[str] = None, last_accessed_date: Optional[str] = None, status: Optional[str] = None, status_message: Optional[str] = None): """ :param str region: Region for replicating the secret. :param str kms_key_id: ARN, Key ID, or Alias. :param str last_accessed_date: Date that you last accessed the secret in the Region. :param str status: Status can be `InProgress`, `Failed`, or `InSync`. :param str status_message: Message such as `Replication succeeded` or `Secret with this name already exists in this region`. """ pulumi.set(__self__, "region", region) if kms_key_id is not None: pulumi.set(__self__, "kms_key_id", kms_key_id) if last_accessed_date is not None: pulumi.set(__self__, "last_accessed_date", last_accessed_date) if status is not None: pulumi.set(__self__, "status", status) if status_message is not None: pulumi.set(__self__, "status_message", status_message) @property @pulumi.getter def region(self) -> str: """ Region for replicating the secret. """ return pulumi.get(self, "region") @property @pulumi.getter(name="kmsKeyId") def kms_key_id(self) -> Optional[str]: """ ARN, Key ID, or Alias. """ return pulumi.get(self, "kms_key_id") @property @pulumi.getter(name="lastAccessedDate") def last_accessed_date(self) -> Optional[str]: """ Date that you last accessed the secret in the Region. """ return pulumi.get(self, "last_accessed_date") @property @pulumi.getter def status(self) -> Optional[str]: """ Status can be `InProgress`, `Failed`, or `InSync`. """ return pulumi.get(self, "status") @property @pulumi.getter(name="statusMessage") def status_message(self) -> Optional[str]: """ Message such as `Replication succeeded` or `Secret with this name already exists in this region`. """ return pulumi.get(self, "status_message") @pulumi.output_type class SecretRotationRotationRules(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "automaticallyAfterDays": suggest = "automatically_after_days" if suggest: pulumi.log.warn(f"Key '{key}' not found in SecretRotationRotationRules. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: SecretRotationRotationRules.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: SecretRotationRotationRules.__key_warning(key) return super().get(key, default) def __init__(__self__, *, automatically_after_days: int): """ :param int automatically_after_days: Specifies the number of days between automatic scheduled rotations of the secret. """ pulumi.set(__self__, "automatically_after_days", automatically_after_days) @property @pulumi.getter(name="automaticallyAfterDays") def automatically_after_days(self) -> int: """ Specifies the number of days between automatic scheduled rotations of the secret. """ return pulumi.get(self, "automatically_after_days") @pulumi.output_type class SecretRotationRules(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "automaticallyAfterDays": suggest = "automatically_after_days" if suggest: pulumi.log.warn(f"Key '{key}' not found in SecretRotationRules. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: SecretRotationRules.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: SecretRotationRules.__key_warning(key) return super().get(key, default) def __init__(__self__, *, automatically_after_days: int): """ :param int automatically_after_days: Specifies the number of days between automatic scheduled rotations of the secret. """ pulumi.set(__self__, "automatically_after_days", automatically_after_days) @property @pulumi.getter(name="automaticallyAfterDays") def automatically_after_days(self) -> int: """ Specifies the number of days between automatic scheduled rotations of the secret. """ return pulumi.get(self, "automatically_after_days") @pulumi.output_type class GetSecretRotationRotationRuleResult(dict): def __init__(__self__, *, automatically_after_days: int): pulumi.set(__self__, "automatically_after_days", automatically_after_days) @property @pulumi.getter(name="automaticallyAfterDays") def automatically_after_days(self) -> int: return pulumi.get(self, "automatically_after_days") @pulumi.output_type class GetSecretRotationRuleResult(dict): def __init__(__self__, *, automatically_after_days: int): pulumi.set(__self__, "automatically_after_days", automatically_after_days) @property @pulumi.getter(name="automaticallyAfterDays") def automatically_after_days(self) -> int: return pulumi.get(self, "automatically_after_days")
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home/ftp.py
Jiunan73/flask
0
72376
<filename>home/ftp.py import time,os from ftplib import FTP fall_file='' ir_file='' def ftp_upload(filepath): IP = '127.0.0.1' user = 'johnlin' password = '<PASSWORD>' filename = os.path.basename(filepath) ftp=FTP() #ftp.set_debuglevel(2) ftp.connect(IP) ftp.login(user,password) ftp.cwd("/home/johnlin/ftp") ftp.storbinary('STOR %s'%filename, open(filepath, 'rb',8192)) print('success') if __name__ == '__main__': while True: b=os.listdir('/home/pi/flask/static/ir_cam/20200807/AD-HF048-P-192.168.1.20') a=sorted(b) ir_path='/home/pi/flask/static/ir_cam/20200807/AD-HF048-P-192.168.1.20/'+a[len(a)-1] if not ir_path==ir_file: ftp_upload(ir_path) ir_file=ir_path b= os.listdir('/home/pi/flask/static/ir_cam/fall/') for s in b: if s.endswith('.jpg')==False: print('del ',s) b.remove(s) a=sorted(b) fall_path='/home/pi/flask/static/ir_cam/fall/'+a[len(a)-1] print(fall_path) if not fall_path==fall_file: ftp_upload(fall_path) fall_file=fall_path time.sleep(5)
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lib/OSCP.py
streetlightvision/oscp-datalogger
0
48341
<filename>lib/OSCP.py import sqlite3 import sys import json import datetime import time import xml.etree.ElementTree as ET from lib.CMS import CMS def enum(*sequential, **named): enums = dict(zip(sequential, range(len(sequential))), **named) return type('Enum', (), enums) State = enum('INIT', 'CONNECT_LOCAL_DB', 'GATHER_DATA', 'DISCONNECT_LOCAL_DB', 'BUILD_MESSAGES', 'CMS_CONNECT', 'CMS_SEND_MESSAGES', 'CMS_DISCONNECT', 'UPDATE_LOCAL_DB', 'SLEEP') class OSCP: def __init__(self, config_file): with open(config_file) as data_file: config = json.load(data_file) if 'debug' in config: self.debug_flag = bool(config['debug']) else: self.debug_flag = True if 'refresh_interval' in config: self.refresh_interval = int(config['refresh_interval']) else: self.refresh_interval = 60 self.cms = CMS(config_file) self.state = State.INIT self.highest_id = 0 self.to_send = [] self.state_arr = [ self.init, self.connect_local_db, self.gather_data, self.disconnect_local_db, self.build_messages, self.cms_connect, self.cms_send_messages, self.cms_disconnect, self.update_local_db, self.sleep ] if self.debug_flag == True: print('Debug: '+str(self.debug_flag)) def run(self): print('Starting OSCP Gateway (Data Logger)...') running = True while running: self.state_arr[self.state]() def init(self): self.debug('Initializing...') self.debug('Loaded configuration file.') self.state = State.CONNECT_LOCAL_DB def connect_local_db(self): self.debug('Connecting to local database (sqlite)...') self.conn = sqlite3.connect('data.db', timeout=60) cursor = self.conn.execute("SELECT name FROM sqlite_master WHERE type='table'") found_data = False found_info = False for row in cursor: if row[0] == 'oscp_data': found_data = True if row[0] == 'oscp_info': found_info = True if found_data == False: print('Table `oscp_data` not found. Creating...') self.conn.execute("""CREATE TABLE "oscp_data" ( `id` INTEGER PRIMARY KEY AUTOINCREMENT, `controllerStrId` TEXT NOT NULL, `idOnController` TEXT NOT NULL, `meaning` TEXT, `value` TEXT NOT NULL, `time` INTEGER NOT NULL )""") self.conn.commit() if found_info == False: print('Table `oscp_info` not found. Creating...') self.conn.execute("""CREATE TABLE `oscp_info` ( `id` INTEGER PRIMARY KEY AUTOINCREMENT, `name` TEXT NOT NULL, `value` TEXT NOT NULL )""") self.conn.execute("INSERT INTO `oscp_info` (`name`, `value`) VALUES ('last_pushed', 0)") self.conn.commit() self.debug('Reading last `id` sent to CMS...') cursor = self.conn.execute("SELECT `value` FROM `oscp_info` WHERE `name`='last_pushed'") self.last_pushed = cursor.fetchone()[0] self.state = State.GATHER_DATA def gather_data(self): self.debug('Looking for new data...') cursor = self.conn.execute("SELECT `id`, `controllerStrId`, `idOnController`, `meaning`, `value`, `time` FROM `oscp_data` WHERE `id` > "+self.last_pushed) for row in cursor: if self.is_valid_entry(row) == True: self.to_send.append(row) self.state = State.DISCONNECT_LOCAL_DB def disconnect_local_db(self): if len(self.to_send) > 0: self.debug('Got new data!') self.state = State.BUILD_MESSAGES else: self.debug('No data to send!') self.state = State.SLEEP self.conn.close() self.debug('Disconnecting from local database (sqlite)') def build_messages(self): self.root = ET.Element("reporting") for entry in self.to_send: if int(entry[0]) > self.highest_id: self.highest_id = entry[0] value = ET.SubElement(self.root, "value", ctrlId=entry[1], id=entry[2], meaning=entry[3], date=entry[5] ) value.text = entry[4] self.state = State.CMS_CONNECT del self.to_send[:] def cms_connect(self): self.cms.connect() print('Connected to CMS!') if self.cms.auth() == True: self.debug('Authenticated!') self.state = State.CMS_SEND_MESSAGES else: print('Error connecting to CMS!') time.sleep(10) def cms_send_messages(self): self.debug('Sending messages to CMS') answer = self.cms.sendOSCP(ET.tostring(self.root).decode("utf-8")) error = False answer = ET.fromstring(answer) if self.debug == True: for entry in answer.findall('error'): error = True code = entry.get('code') print('Got error code '+code+': '+entry.text) if error == True: print("The entries that got an error won't be re-sent to the CMS.") self.state = State.CMS_DISCONNECT def cms_disconnect(self): self.debug('Disconnecting from CMS...') self.cms.getConnection().close() self.state = State.UPDATE_LOCAL_DB def update_local_db(self): self.debug('Writing last `id` sent to CMS...') self.conn = sqlite3.connect('data.db', timeout=60) self.conn.execute("UPDATE `oscp_info` SET `value` = "+str(self.highest_id)+" WHERE `name` = 'last_pushed'") self.conn.commit() self.conn.close() self.state = State.SLEEP def sleep(self): self.debug('Sleeping for '+str(self.refresh_interval)+'s...') time.sleep(self.refresh_interval) self.state = State.CONNECT_LOCAL_DB def is_valid_entry(self, entry): # `id`, `controllerStrId`, `idOnController`, `meaning`, `value`, `time` if len(entry[1]) == 0: return False if len(entry[2]) == 0: return False if len(entry[3]) == 0: return False try: datetime.datetime.strptime(str(entry[5]), '%Y-%m-%dT%H:%M:%S.%fZ') except ValueError: print('@@@@-> Invalid string format for `time` field!') return False return True def debug(self, string): if self.debug_flag == True: print(string)
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visualize.py
mac389/semantic-distance
2
31710
<reponame>mac389/semantic-distance import os, json, matplotlib matplotlib.use('Agg') import seaborn as sns import matplotlib.pyplot as plt import numpy as np import pandas as pd READ = 'rb' directory = json.load(open('directory.json',READ)) filename = os.path.join(directory['data-prefix'],'test-similarity-matrix.npy') data = np.load(filename).astype(float) data = (data-data.min())/(data.max()-data.min()) #Think more about how to scale f,ax = plt.subplots(figsize=(12,9)) #Only for control color_series = {i:color for i,color in enumerate(sns.color_palette("husl", 3))} colors = pd.Series([color_series[i%3] for i in xrange(data.shape[0])]) print colors hmap = sns.clustermap(np.corrcoef(data),col_colors = colors,row_colors=colors) #plt.tight_layout() plt.savefig('./results/clustermap-corr2.png')
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exerciciosEntrega/exercicioEntrega08.py
igorprati/python_modulo01_entrega
0
139991
#08 - Crie um programa que leia nome, ano de nascimento e carteira de trabalho e cadastre-os (com idade) em um dicionário. Se por acaso a CTPS for diferente de 0, o dicionário receberá também o ano de contratação e o salário. Calcule e acrescente , além da idade, com quantos anos a pessoa vai se aposentar. Considere que o trabalhador deve contribuir por 35 anos para se aposentar. ano = 2021 # ano atual que estamos dados = dict() # dicionário com os dados do usuário dados['Nome'] = input('Nome: ') # chave 'Nome' recebe valor dados['Idade'] = 2021 - int(input('Ano de nascimento: ')) # chave 'Idade' recebe ano de nascimento dados['CTPS'] = int(input('Carteira de trabalho: ')) # chave 'CTPS' recebe carteira de trabalho if dados['CTPS'] != 0: # se a carteira de trabalho for diferente de ZERO: dados['anoContratacao'] = int(input('Ano de contratação: ')) # o programa vai pedir ano de contratação e adicionar à chave 'anoContratacao' dados['Salário'] = int(input('Salário: ')) # o programa vai pedir salario e adicionar à chave 'Salário' aposentadoria = (dados['anoContratacao'] + 35) - 1998 # cria uma variável que recebe com quantos anos a pessoa vai se aposentar. Para isso, soma o ano de contratação + 35 anos de serviço - ano de nascimento print(f'Você irá se aposentar com {aposentadoria} anos.') else: print('Não sei quando você vai se aposentar...')
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dmutils/filters.py
DilwoarH/digitalmarketplace-utils
0
140672
<gh_stars>0 from __future__ import unicode_literals import re from six import string_types from jinja2 import evalcontextfilter, Markup, escape def smartjoin(input): list_to_join = list(input) if len(list_to_join) > 1: return '{} and {}'.format(', '.join(list_to_join[:-1]), list_to_join[-1]) elif len(list_to_join) == 1: return '{}'.format(list_to_join[0]) else: return '' def format_links(text): """ Filter that searches a given string (or other string-like object) for any URIs and wraps them with either an anchor link or a span, depending on whether the link contains a valid protocol. Python3's re library returns matches with type string rather than the arg's type, which causes problems for Markup() objects containing tags to be escaped later. Therefore we need to cast the matches and formatted links back to the original type before returning the value. """ url_match = re.compile(r"""( (?:https?://|www\.) # start with http:// or www. (?:[^\s<>"'/?#]+) # domain doesn't have these characters (?:[^\s<>"']+) # post-domain part of URL doesn't have these characters [^\s<>,"'\.] # no dot at end )""", re.X) matched_urls = [type(text)(substr) for substr in url_match.findall(text)] if matched_urls: link = '<a href="{0}" class="break-link" rel="external">{0}</a>' plaintext_link = '<span class="break-link">{0}</span>' text_array = [type(text)(substr) for substr in url_match.split(text)] formatted_text_array = [] for partial_text in text_array: if partial_text in matched_urls: if partial_text.startswith('www'): url = plaintext_link.format(Markup.escape(partial_text)) else: url = link.format(Markup.escape(partial_text)) formatted_text_array.append(url) else: partial_text = Markup.escape(partial_text) formatted_text_array.append(partial_text) formatted_text = Markup(''.join(formatted_text_array)) return formatted_text else: return text def nbsp(text): """Replace spaces with nbsp. If you want to use html with this filter you need to pass it in as marksafe ie. {{ "some text and <html>"|marksafe|nbsp }}""" text = escape(text) return text.replace(' ', Markup('&nbsp;')) def capitalize_first(maybe_text): """If it's a string capitalise the first character, unless it looks like a URL :param maybe_text: Could be anything :return: If maybe_text is a string it will be returned with an initial capital letter, otherwise unchanged """ if maybe_text and isinstance(maybe_text, string_types): if not maybe_text.startswith('http'): return maybe_text[0].capitalize() + maybe_text[1:] elif isinstance(maybe_text, (list, tuple)): return [capitalize_first(item) for item in maybe_text] return maybe_text # find repeated sequences of '\r\n\', optionally separated by other non-newline whitespace space characters _multiple_newlines_re = re.compile(r'(\r\n[ \t\f\v]*){2,}') # find \r\n sequences _single_newline_re = re.compile(r'(\r\n)') @evalcontextfilter def preserve_line_breaks(eval_ctx, value): # `escape()` returns Markdown objects in python2 # We want to cast the output value back into unicode strings value = u'{}'.format(escape(value)) # limit sequences of "\r\n\r\n ..."s to two value = _multiple_newlines_re.sub(u'\r\n\r\n', value) result = _single_newline_re.sub(u'<br>', value) if eval_ctx.autoescape: result = Markup(result) return result
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theano/sparse/__init__.py
michaelosthege/aesara
1
80798
<gh_stars>1-10 from warnings import warn try: import scipy enable_sparse = True except ImportError: enable_sparse = False warn("SciPy can't be imported. Sparse matrix support is disabled.") from theano.sparse.type import * if enable_sparse: from theano.sparse import opt, sharedvar from theano.sparse.basic import * from theano.sparse.sharedvar import sparse_constructor as shared
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src/python/create_folder_tree.py
natukikazemizo/Sedna1.0
4
165660
<reponame>natukikazemizo/Sedna1.0<filename>src/python/create_folder_tree.py # !BPY # -*- coding: UTF-8 -*- # Create Folder Tree # # Create Objects & Expnasion Motion # # 2019.03.12 Natukikazemizo # import bpy import os import math # CONSTANT OF PARAMETERS FOLDER_SPLITTER = "_" SEED_FOLDER_NAME = 'Folder' SEED_FOLDER_LEAF_NAME = 'Folder.Leaf' SEED_FILE_NAME = 'File' SEED_BRANCH_NAME = 'Branch' ROOT_FOLDER = "C:/SRC/blender" ROOT_FOLDER_ORG_NAME = "blender-2.79b" # DEF FOR TEST #ROOT_FOLDER = "C:/tmp/folder_tree_test/" #ROOT_FOLDER_ORG_NAME = "root" ROOT_FOLDER_SHORT_NAME = "Folder_0001" ACTION_SUFFIX = 'Action' SEED_ACTION = 'FolderAction' FILE_SEED_ACTION = 'FileAction' NEW_FOLDER_NAME = 'Folder_' FOLDER_ZFILL = 4 NEW_FILE_NAME = 'File_' FILE_ZFILL = 8 NEW_BRANCH_NAME = 'Branch_' NEW_BRANCH_H_NAME = 'Branch_h_' NEW_FOLDER_LEAF_NAME = "Folder.Leaf_" START_FRAME = 3000 FPS = 12 SCALE_STORAGE = 0.5 SCALE_NORMAL = 1.0 # Folder Constants FOLDER_X_POS_START = 0.02 FOLDER_X_POS_END = 0.2 FOLDER_X_MARGIN = 0.7 FOLDER_Y_MARGIN = 0.7 FOLDER_Z_MARGIN = 0.48 FOLDER_LEVEL1_Y_MARGIN = 2 FOLDER_STORAGE_SCALE = 0.02 FOLDER_FRAME_START_MARGIN = 1 FOLDER_FRAME_EXPAND_Z_WAIT = 0 FOLDER_FRAME_EXPAND_Z = 5 FOLDER_FRAME_EXPAND_X_WAIT = 1 FOLDER_FRAME_EXPAND_X = 6 FOLDER_FRAME_GO_OUT = 4 FOLDER_FRAME_RESIZE_WAIT = 1 FOLDER_FRAME_RESIZE = 4 FOLDER_FRAME_OPEN_WAIT = 1 FOLDER_FRAME_OPEN= 4 folder_motion_list = [ FOLDER_FRAME_START_MARGIN , FOLDER_FRAME_EXPAND_Z_WAIT , FOLDER_FRAME_EXPAND_Z , FOLDER_FRAME_EXPAND_X_WAIT , FOLDER_FRAME_EXPAND_X , FOLDER_FRAME_GO_OUT , FOLDER_FRAME_RESIZE_WAIT , FOLDER_FRAME_RESIZE , FOLDER_FRAME_OPEN_WAIT , FOLDER_FRAME_OPEN ] # Folder.Leaf Constants FOLDER_REAF_X = 0 FOLDER_REAF_Y = -0.04 FOLDER_REAF_Z = -0.15682 FOLDER_REAF_OPEN = math.pi / 4 # Folder Name Constants FOLDER_NAME_PREFIX = "FolderName_" FOLDER_NAME_X_MARGIN = -0.2 FOLDER_NAME_Y_MARGIN = -0.2 FOLDER_NAME_SCALE_STOREGE = 0.03 FOLDER_NAME_ROT_X = -1 * math.pi / 9 FOLDER_NAME_FRAME_START_MARGIN = 48 FOLDER_NAME_FRAME_MOVE_X = 8 FOLDER_NAME_FRAME_RESIZE_WAIT = 4 FOLDER_NAME_FRAME_RESIZE = 8 FOLDER_NAME_FRAME_ROT_WAIT = 4 FOLDER_NAME_FRAME_ROT = 8 # Branch Constants BRANCH_X_MARGIN = 0.5 BRANCH_Z_MARGIN = 0.2 # File Constants FILE_X_MARGIN = 0.4 FILE_Y_MARGIN = 0 FILE_Z_MARGIN = 0 FILE_FRAME_START_MARGIN = 4 FILE_FRAME_Z_EXPAND = 12 FILE_FRAME_RESIZE_WAIT = 4 FILE_FRAME_RESIZE = 8 FILE_FRAME_X_ROT_WAIT = 4 FILE_FRAME_X_ROT = 8 FILE_FRAME_Y_EXPAND_WAIT = 4 FILE_FRAME_Y_EXPAND = 18 F_CURVE_X_LOC = 0 F_CURVE_Y_LOC = 1 F_CURVE_Z_LOC = 2 F_CURVE_X_ROT = 3 F_CURVE_Y_ROT = 4 F_CURVE_Z_ROT = 5 F_CURVE_X_SCA = 6 F_CURVE_Y_SCA = 7 F_CURVE_Z_SCA = 8 F_CURVE_ARRAY = 9 # Global dic file_parent_dic = {} folder_pos_dic = {ROOT_FOLDER_SHORT_NAME:("", 0, 0, 0)} FOLDER_POS_PARENT_FOLDER_NAME = 0 FOLDER_POS_INDEX = 1 FOLDER_POS_LEVEL = 2 FOLDER_POS_ABSOLUTE_INDEX = 3 folder_num_name_dic = {"":"", ROOT_FOLDER_SHORT_NAME:ROOT_FOLDER_SHORT_NAME} file_cnt_dic = {} folder_end_frame_dic = {ROOT_FOLDER_SHORT_NAME:START_FRAME} # Global list level1_folder_list = [] # Global params folder_absolute_index = 0 # File I/O FOLDER_LIST_FILE_NAME="FolderList.txt" root_path = bpy.path.abspath("//") + "data/" text_file = open(root_path + FOLDER_LIST_FILE_NAME, mode = 'w') def get_file_num_name(folder_name): folder_num_name = folder_num_name_dic[folder_name] file_num_name = NEW_FILE_NAME + folder_num_name[-FOLDER_ZFILL:] return file_num_name def convert_folder_id(folder): ret = folder.replace(ROOT_FOLDER_ORG_NAME, ROOT_FOLDER_SHORT_NAME) ret = ret.replace(os.sep, FOLDER_SPLITTER) return ret def clear_old_breakpoints(fcurve): old_keyframe_index_list = [] for i, point in enumerate(fcurve.keyframe_points): if point.co[0] != START_FRAME: old_keyframe_index_list.append(i) if len(old_keyframe_index_list) > 0: old_keyframe_index_list.reverse() for i in old_keyframe_index_list: fcurve.keyframe_points.remove(fcurve.keyframe_points[i]) fcurve.update() def clear_all_old_breakpoints(fcurves): clear_old_breakpoints(fcurves[F_CURVE_X_LOC]) clear_old_breakpoints(fcurves[F_CURVE_Y_LOC]) clear_old_breakpoints(fcurves[F_CURVE_Z_LOC]) clear_old_breakpoints(fcurves[F_CURVE_X_ROT]) clear_old_breakpoints(fcurves[F_CURVE_Y_ROT]) clear_old_breakpoints(fcurves[F_CURVE_Z_ROT]) clear_old_breakpoints(fcurves[F_CURVE_X_SCA]) clear_old_breakpoints(fcurves[F_CURVE_Y_SCA]) clear_old_breakpoints(fcurves[F_CURVE_Z_SCA]) def clear_file_all_old_breakpoints(fcurves): clear_all_old_breakpoints(fcurves) clear_old_breakpoints(fcurves[F_CURVE_ARRAY]) def add_location_key_frame(fcurves, frame, x, y, z): add_keyframe_point(fcurves[F_CURVE_X_LOC], frame, x) add_keyframe_point(fcurves[F_CURVE_Y_LOC], frame, y) add_keyframe_point(fcurves[F_CURVE_Z_LOC], frame, z) def add_rotation_key_frame(fcurves, frame, x, y, z): add_keyframe_point(fcurves[F_CURVE_X_ROT], frame, x) add_keyframe_point(fcurves[F_CURVE_Y_ROT], frame, y) add_keyframe_point(fcurves[F_CURVE_Z_ROT], frame, z) def add_scale_key_frame(fcurves, frame, x, y, z): add_keyframe_point(fcurves[F_CURVE_X_SCA], frame, x) add_keyframe_point(fcurves[F_CURVE_Y_SCA], frame, y) add_keyframe_point(fcurves[F_CURVE_Z_SCA], frame, z) def add_array_key_frame(fcurves, frame, val): add_keyframe_point(fcurves[F_CURVE_ARRAY], frame, val) # get file list file_list = [] for root, folders, files in os.walk(ROOT_FOLDER): root = os.path.relpath(root, ROOT_FOLDER) if root == '.': root = '' parent_folder = convert_folder_id(root) file_list.append([root, sorted(folders), sorted(files), parent_folder]) def print_file(file_name, level_list, last): """ print fileName """ t = '' if len(level_list): t += ' ' if len(level_list) >= 2: for b in level_list[1:]: if b: t += ' ' else: t += '|' if len(level_list): if last: t += '-' else: t += '+' print(t + file_name) text_file.write(t + file_name + '\n') def dupObject(src_name, dup_name): """ duplidate object """ if bpy.data.objects.find(dup_name) < 0: src_obj = bpy.data.objects[src_name] # Select object bpy.ops.object.select_all(action='DESELECT') src_obj.select = True bpy.ops.object.duplicate(linked=True) new_obj = bpy.data.objects[src_name + '.001'] new_obj.name = dup_name def create_folder_num_name(folder_name): if folder_name not in folder_num_name_dic: folder_num_name_dic[folder_name] = NEW_FOLDER_NAME \ + str(len(folder_num_name_dic) + 1).zfill(FOLDER_ZFILL) def create_action(object_name): action_name = object_name + ACTION_SUFFIX if bpy.data.actions.find(action_name) < 0: new_action = bpy.data.actions[SEED_ACTION].copy() new_action.name = action_name bpy.data.objects[object_name].animation_data.action = new_action else: bpy.data.objects[object_name].animation_data.action = \ bpy.data.actions[action_name] def create_file_action(object_name): action_name = object_name + ACTION_SUFFIX # Enable Hire when f-cureve inclease .etc #if bpy.data.actions.find(action_name) > 0: # bpy.data.actions.remove(bpy.data.actions[action_name]) if bpy.data.actions.find(action_name) < 0: new_action = bpy.data.actions[FILE_SEED_ACTION].copy() new_action.name = action_name bpy.data.objects[object_name].animation_data.action = new_action else: bpy.data.objects[object_name].animation_data.action = \ bpy.data.actions[action_name] def get_branch_num_name(folder_name): folder_num_name = folder_num_name_dic[folder_name] return folder_num_name.replace(NEW_FOLDER_NAME, NEW_BRANCH_NAME) def get_branch_h_num_name(folder_name): folder_num_name = folder_num_name_dic[folder_name] return folder_num_name.replace(NEW_FOLDER_NAME, NEW_BRANCH_H_NAME) def get_folder_leaf_num_name(folder_name): folder_num_name = folder_num_name_dic[folder_name] return folder_num_name.replace(NEW_FOLDER_NAME, NEW_FOLDER_LEAF_NAME) def get_folder_index(folder_num_name): if folder_num_name == ROOT_FOLDER_SHORT_NAME or folder_num_name == "": return 0 return int(folder_num_name.replace(NEW_FOLDER_NAME, "")) def create_folder_mesh(parent_folder_name, folder_name): """ create folder mesh """ create_folder_num_name(folder_name) # duplicate object num_name = folder_num_name_dic[folder_name] # delete org # Enable hire when you want to delete objects #if bpy.data.objects.find(num_name) > 0: # bpy.ops.object.select_all(action= 'DESELECT') # bpy.data.objects[num_name].select = True # bpy.ops.object.delete() dupObject(SEED_FOLDER_NAME , num_name) # Enable Render bpy.data.objects[num_name].hide_render = False # Create new Action on new Object create_action(num_name) act = bpy.data.objects[num_name].animation_data.action # remove old keyframes clear_all_old_breakpoints(act.fcurves) # set start location add_location_key_frame(act.fcurves, START_FRAME, 0, 0, 0) # set start scale add_scale_key_frame(act.fcurves, START_FRAME, FOLDER_STORAGE_SCALE,\ FOLDER_STORAGE_SCALE, FOLDER_STORAGE_SCALE) def create_folder_leaf_mesh(parent_folder_name, folder_name): """ create folder leaf mesh """ # GetName folder_leaf_num_name = get_folder_leaf_num_name(folder_name) # delete org # Enable hire when you want to delete objects #if bpy.data.objects.find(folder_leaf_num_name) > 0: # bpy.ops.object.select_all(action='DESELECT') # bpy.data.objects[num_name].select = True # bpy.ops.object.delete() dupObject(SEED_FOLDER_LEAF_NAME , folder_leaf_num_name) print(folder_leaf_num_name) set_obj_parent(folder_num_name_dic[folder_name], folder_leaf_num_name) # Enable Render bpy.data.objects[folder_leaf_num_name].hide_render = False # Create new Action on new Object create_action(folder_leaf_num_name) act = bpy.data.objects[folder_leaf_num_name].animation_data.action # remove old keyframes clear_all_old_breakpoints(act.fcurves) # set start location add_location_key_frame(act.fcurves, START_FRAME, FOLDER_REAF_X, \ FOLDER_REAF_Y, FOLDER_REAF_Z) # set start rotation add_rotation_key_frame(act.fcurves, START_FRAME, 0, 0, 0) # set start scale add_scale_key_frame(act.fcurves, START_FRAME, 1, 1, 1) def create_branch_mesh(parent_folder_name, folder_name): """ create branch mesh """ # duplicate object branch_num_name = get_branch_num_name(folder_name) dupObject(SEED_BRANCH_NAME , branch_num_name) # Enable Render bpy.data.objects[branch_num_name].hide_render = False # Create new Action on new Object create_action(branch_num_name) act = bpy.data.objects[branch_num_name].animation_data.action # remove old keyframes clear_all_old_breakpoints(act.fcurves) # set start location add_location_key_frame(act.fcurves, START_FRAME, 0, 0, 0) # set start scale add_scale_key_frame(act.fcurves, START_FRAME, SCALE_STORAGE, SCALE_STORAGE,\ SCALE_STORAGE) def create_branch_h_mesh(parent_folder_name, folder_name): """ create branch mesh """ # duplicate object branch_h_num_name = get_branch_h_num_name(folder_name) dupObject(SEED_BRANCH_NAME , branch_h_num_name) # Enable Render bpy.data.objects[branch_h_num_name].hide_render = False # Create new Action on new Object create_action(branch_h_num_name) act = bpy.data.objects[branch_h_num_name].animation_data.action # remove old keyframes clear_all_old_breakpoints(act.fcurves) # set start location add_location_key_frame(act.fcurves, START_FRAME, 0, 0, 0) # set start scale add_scale_key_frame(act.fcurves, START_FRAME, SCALE_STORAGE, SCALE_STORAGE,\ SCALE_STORAGE) def create_file_mesh(file_name): """ create file mesh """ # delete org # Enable hire when you want to delete objects #if bpy.data.objects.find(file_name) > 0: # bpy.ops.object.select_all(action='DESELECT') # bpy.data.objects[file_name].select = True # bpy.ops.object.delete() # duplicate object dupObject(SEED_FILE_NAME , file_name) # Enable Render bpy.data.objects[file_name].hide_render = False # Create new Action on new Object create_file_action(file_name) act = bpy.data.objects[file_name].animation_data.action # remove old keyframes clear_file_all_old_breakpoints(act.fcurves) # set start location add_location_key_frame(act.fcurves, START_FRAME, 0, 0, 0) # set start rotation add_rotation_key_frame(act.fcurves, START_FRAME, 0, 0, 0) # set start scale add_scale_key_frame(act.fcurves, START_FRAME, SCALE_STORAGE, SCALE_STORAGE, SCALE_STORAGE) # set start array count add_array_key_frame(act.fcurves, START_FRAME, 1) def set_obj_parent(parent_name, obj_name): """ set parent on object """ # set parent if parent_name != "": obj = bpy.data.objects[obj_name] # set parent obj.parent = bpy.data.objects[parent_name] def func_main(arg, level_list): """ MAIN """ global folder_absolute_index root, folders, files, parent_folder = arg folder_len = len(folders) file_len = len(files) # Output subfolder for i, folder_name in enumerate(folders): nounder = (i == folder_len - 1 and file_len == 0) folder_id = folder_name # Replace root folder name to short name if folder_id == ROOT_FOLDER_ORG_NAME: folder_id = ROOT_FOLDER_SHORT_NAME if parent_folder != "": folder_id = parent_folder + FOLDER_SPLITTER + folder_id create_folder_mesh(parent_folder, folder_id) create_folder_leaf_mesh(parent_folder, folder_id) create_branch_mesh(parent_folder, folder_id) create_branch_h_mesh(parent_folder, folder_id) print_file('<' + folder_id + '>', level_list, nounder) level = len(level_list) folder_pos_dic[folder_id] = (parent_folder, i, level, \ folder_absolute_index) if level == 1: level1_folder_list.append(folder_id) folder_absolute_index += 1 # Output subfolder's subfolder under_root = os.path.join(root, folder_name) under_list = [] for t in file_list: if t[0] == under_root: under_list.append(t) for j, t in enumerate(under_list): if nounder and j == len(under_list) - 1: add = [True] else: add = [False] func_main(t, level_list + add) # Output files for i, file_name in enumerate(files): file_id = parent_folder + FOLDER_SPLITTER + file_name print_file(file_id, level_list, (i == file_len - 1)) file_cnt = len(files) if file_cnt > 0: file_num_name = get_file_num_name(parent_folder) create_file_mesh(file_num_name) file_cnt_dic[parent_folder] = (file_num_name, file_cnt) def add_keyframe_point(fcurve, frame, value): # find same frame keyframe_point index = 0 find_frame = False for i, point in enumerate(fcurve.keyframe_points): if point.co[0] == frame: index = i find_frame = True break if find_frame == False: # add keyframe_point when same frame not found fcurve.keyframe_points.add(1) index = len(fcurve.keyframe_points) - 1 # setup keyframe fcurve.keyframe_points[index].type = "BREAKDOWN" fcurve.keyframe_points[index].co = frame, value fcurve.keyframe_points[index].handle_left = frame - 0.5, value fcurve.keyframe_points[index].handle_right = frame + 0.5, value def setupFolders(max_start_frame): # reset root folder size act = bpy.data.objects[ROOT_FOLDER_SHORT_NAME].animation_data.action # set start scale add_scale_key_frame(act.fcurves, 1000, SCALE_NORMAL, SCALE_NORMAL, SCALE_NORMAL) for folder_name, pos in folder_pos_dic.items(): parent_folder_name, index, level, absolute_index = pos folder_num_name = folder_num_name_dic[folder_name] folder_index = get_folder_index(folder_num_name); # Setup folder Name on Level 0, 1 Folders if level <= 1: # Get Folder Name Object's ID folder_name_id = FOLDER_NAME_PREFIX + folder_num_name # Set parent object set_obj_parent(folder_num_name, folder_name_id) # get fcurves fcurves = bpy.data.objects[folder_name_id].animation_data.action.fcurves # Clear old breakpoints clear_all_old_breakpoints(fcurves) # add Initial KeyFrame frame = START_FRAME add_location_key_frame(fcurves, frame, 0, 0, 0) add_rotation_key_frame(fcurves, frame, 0, 0, 0) add_scale_key_frame(fcurves, frame, FOLDER_NAME_SCALE_STOREGE, \ FOLDER_NAME_SCALE_STOREGE, FOLDER_NAME_SCALE_STOREGE) # add Move x Start key Frame frame += (level + index) * sum(folder_motion_list) \ + FOLDER_NAME_FRAME_START_MARGIN add_location_key_frame(fcurves, frame, 0, 0, 0) # add Move x End key Frame frame += FOLDER_NAME_FRAME_MOVE_X add_location_key_frame(fcurves, frame, FOLDER_NAME_X_MARGIN, \ FOLDER_NAME_Y_MARGIN, 0) # add Resize wait key frame frame += FOLDER_NAME_FRAME_RESIZE_WAIT add_scale_key_frame(fcurves, frame, FOLDER_NAME_SCALE_STOREGE, \ FOLDER_NAME_SCALE_STOREGE, FOLDER_NAME_SCALE_STOREGE) # add Resize end key frame frame += FOLDER_NAME_FRAME_RESIZE add_scale_key_frame(fcurves, frame, 1, 1, 1) # add Rotation wait key frame frame += FOLDER_NAME_FRAME_ROT_WAIT add_rotation_key_frame(fcurves, frame, 0, 0, 0) # add Rotaion end key frame frame += FOLDER_NAME_FRAME_ROT add_rotation_key_frame(fcurves, frame, FOLDER_NAME_ROT_X, 0, 0) fcurves.update() # set parent object branch_num_name = get_branch_num_name(folder_name) branch_h_num_name = get_branch_h_num_name(folder_name) if folder_name != ROOT_FOLDER_SHORT_NAME: set_obj_parent(folder_num_name_dic[parent_folder_name], branch_num_name) set_obj_parent(folder_num_name_dic[parent_folder_name], branch_h_num_name) set_obj_parent(folder_num_name_dic[parent_folder_name], folder_num_name) # Calclate folder relative index parent_absolute_index = 0 if parent_folder_name != "": parent_absolute_index = \ folder_pos_dic[parent_folder_name][FOLDER_POS_ABSOLUTE_INDEX] relative_index = absolute_index - parent_absolute_index if relative_index == 0: relative_index = 1 # calc start frame parent_folder_index = 0 if parent_folder_name != ROOT_FOLDER_SHORT_NAME : parent_folder_index = get_folder_index(folder_num_name_dic[parent_folder_name]) frame = START_FRAME + (level + index) * sum(folder_motion_list) # get fcurves branch_fcurves = bpy.data.objects[branch_num_name].animation_data.action.fcurves brnc_h_fcurves = bpy.data.objects[branch_h_num_name].animation_data.action.fcurves folder_fcurves = bpy.data.objects[folder_num_name].animation_data.action.fcurves start_frame = frame # add move start key_frame frame += FOLDER_FRAME_START_MARGIN add_location_key_frame(branch_fcurves, frame, 0, 0, 0) add_rotation_key_frame(branch_fcurves, frame, 0, 0, 0) add_rotation_key_frame(brnc_h_fcurves, frame, 0, math.pi / 2, 0) add_location_key_frame(folder_fcurves, frame, 0, 0, 0) # add expand z wait key_frame frame += FOLDER_FRAME_EXPAND_Z_WAIT add_scale_key_frame(branch_fcurves, frame, 1, 1, 1) add_location_key_frame(brnc_h_fcurves, frame, 0, 0, 0) add_location_key_frame(folder_fcurves, frame, 0, 0, 0) # calc 1st Z add_location_key_frame z_loc_0010 = FOLDER_Z_MARGIN * index if folder_name == ROOT_FOLDER_SHORT_NAME: z_loc_0010 = FOLDER_Z_MARGIN * 2 # add expand z end key_frame frame += FOLDER_FRAME_EXPAND_Z add_scale_key_frame(branch_fcurves, frame, 1, 1, z_loc_0010 * 100) add_location_key_frame(brnc_h_fcurves, frame, 0, 0, z_loc_0010) add_location_key_frame(folder_fcurves, frame, 0, 0, z_loc_0010) # add expand x wait key_frame frame += FOLDER_FRAME_EXPAND_X_WAIT add_scale_key_frame(brnc_h_fcurves, frame, 1, 1, 1) add_location_key_frame(folder_fcurves, frame, 0, 0, z_loc_0010) # add expand x end key_frame frame += FOLDER_FRAME_EXPAND_X add_scale_key_frame(brnc_h_fcurves, frame, 1, 1, \ FOLDER_X_MARGIN * 100) add_location_key_frame(folder_fcurves, frame, FOLDER_X_MARGIN, 0, \ z_loc_0010) # add go out key_frame frame += FOLDER_FRAME_GO_OUT add_location_key_frame(folder_fcurves, frame, \ FOLDER_X_MARGIN + FOLDER_X_POS_START, 0, z_loc_0010) # add resize wait key_frame frame += FOLDER_FRAME_RESIZE_WAIT add_location_key_frame(folder_fcurves, frame, FOLDER_X_MARGIN + FOLDER_X_POS_START, 0, \ z_loc_0010) add_scale_key_frame(folder_fcurves, frame, FOLDER_STORAGE_SCALE, \ FOLDER_STORAGE_SCALE, FOLDER_STORAGE_SCALE) # add resize key_frame frame += FOLDER_FRAME_RESIZE add_location_key_frame(folder_fcurves, frame, \ FOLDER_X_MARGIN + FOLDER_X_POS_END, 0, z_loc_0010) add_scale_key_frame(folder_fcurves, frame, SCALE_NORMAL, \ SCALE_NORMAL, SCALE_NORMAL) # add folder motion end frame to dic folder_end_frame_dic[folder_name] = frame # add Z move end key frame z_loc_0020 = FOLDER_Z_MARGIN * relative_index if folder_name == ROOT_FOLDER_SHORT_NAME: z_loc_0020 = FOLDER_Z_MARGIN * 2 frame = max_start_frame add_location_key_frame(folder_fcurves, frame, \ FOLDER_X_MARGIN + FOLDER_X_POS_END, 0, z_loc_0020) add_location_key_frame(brnc_h_fcurves, frame, 0, 0, z_loc_0020) add_scale_key_frame(branch_fcurves, frame, 1, 1, z_loc_0020 * 100) # write log text_file.write(folder_num_name + "RelativeIndex:" + str(relative_index).zfill(4) \ + "Level:" + str(level).zfill(3) \ + "StartFrame:" + str(start_frame) + "EndFrame:" + str(frame) \ + "index:" + str(index).zfill(3) \ + "ParentFolder:" + parent_folder_name + " MyName:" + folder_name \ + '\n') # update fcurve branch_fcurves.update() folder_fcurves.update() def setupFiles(): for parent_folder, val in file_cnt_dic.items(): file_num_name, file_cnt = val folder_leaf_num_name = get_folder_leaf_num_name(parent_folder) # Set parent object set_obj_parent(folder_num_name_dic[parent_folder], file_num_name) # get fcurves fcurves = bpy.data.objects[file_num_name].animation_data.action.fcurves folder_leaf_fcurves = \ bpy.data.objects[folder_leaf_num_name].animation_data.action.fcurves # get start frame frame = folder_end_frame_dic[parent_folder] # add open wait key_frame frame += FOLDER_FRAME_OPEN_WAIT add_rotation_key_frame(folder_leaf_fcurves, frame, 0, 0, 0) # add open key_frame frame += FOLDER_FRAME_OPEN add_rotation_key_frame(folder_leaf_fcurves, frame, FOLDER_REAF_OPEN, \ 0, 0) # add move start key frame frame += FILE_FRAME_START_MARGIN add_location_key_frame(fcurves, frame, 0, 0, 0) # add z expand end keyFrame frame += FILE_FRAME_Z_EXPAND add_location_key_frame(fcurves, frame, FILE_X_MARGIN, 0, 0) # add resize wait keyFrame frame += FILE_FRAME_RESIZE_WAIT add_scale_key_frame(fcurves, frame, SCALE_STORAGE, SCALE_STORAGE, \ SCALE_STORAGE) # add resize end keyFrame frame += FILE_FRAME_RESIZE add_scale_key_frame(fcurves, frame, SCALE_NORMAL, SCALE_NORMAL, \ SCALE_NORMAL) # add x rot WAIT key frame frame += FILE_FRAME_X_ROT_WAIT add_rotation_key_frame(fcurves, frame, 0, 0, 0) # add x rot end key frame frame += FILE_FRAME_X_ROT add_location_key_frame(fcurves, frame, FILE_X_MARGIN, 0, 0) add_rotation_key_frame(fcurves, frame, math.pi, math.pi / 2, 0 ) # add y expand wait key frame frame += FILE_FRAME_Y_EXPAND_WAIT add_array_key_frame(fcurves, frame, 1) # add y expand end key frame frame += FILE_FRAME_Y_EXPAND add_array_key_frame(fcurves, frame, file_cnt) # update fcurve fcurves.update() # START print("### start ###") create_folder_mesh("", ROOT_FOLDER_SHORT_NAME) func_main(file_list.pop(0), []) # calc max start frame max_start_frame = 0 for folder_name, pos in folder_pos_dic.items(): parent_folder_name, index, level, absolute_index = pos frame = START_FRAME + (level + index) * sum(folder_motion_list) if frame > max_start_frame: max_start_frame = frame setupFolders(max_start_frame) setupFiles() text_file.close() print("### end ###")
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tests/test_gpuctl.py
Ed-Yang/gpuctl
2
117153
<gh_stars>1-10 #!/usr/bin/env python3 import time # from re import U import unittest from gpuctl import PciDev, GpuCtl, GpuDev, GpuAMD, GpuNV, GpuOk from gpuctl import GpuOk, GpuNak class TestGpuCtl(unittest.TestCase): def test_invalid_key(self): gpu_ctl = None try: gpu_ctl = GpuCtl(slots='aaaa') self.assertEqual(gpu_ctl, None) except: pass def test_discover_all(self): pci_devices = PciDev.discovery() gpu_devices = [] for pdev in pci_devices: gpu = None if pdev.is_amd(): gpu = GpuAMD(pdev) if pdev.is_nvidia(): gpu = GpuNV(pdev) if gpu and gpu.is_gpu(): gpu_devices.append(gpu) gpu_ctl = GpuCtl(gpu_devices=gpu_devices) def test_discover_vendor(self): vendors = ['AMD', 'NVIDIA'] pci_devices = PciDev.discovery(vendor_filter=vendors) gpu_devices = [] for pdev in pci_devices: gpu = None if pdev.is_amd(): gpu = GpuAMD(pdev) if pdev.is_nvidia(): gpu = GpuNV(pdev) if gpu and gpu.is_gpu(): gpu_devices.append(gpu) gpu_ctl = GpuCtl(gpu_devices=gpu_devices) def test_add_devices(self): vendors = ['AMD', 'NVIDIA'] pci_devices = PciDev.discovery(vendor_filter=vendors) gpu_devices = [] for pdev in pci_devices: gpu = None if pdev.is_amd(): gpu = GpuAMD(pdev) if pdev.is_nvidia(): gpu = GpuNV(pdev) if gpu and gpu.is_gpu(): gpu_devices.append(gpu) gpu_ctl = GpuCtl(gpu_devices=gpu_devices) cnt = gpu_ctl.add_gpu_devices(gpu_devices) self.assertEqual(cnt, 0) def test_over_temp(self): slot_name = '1111:11:11.1' pci_id = '1111:1111' pdev = PciDev(slot_name, pci_id, 'mock pci') self.assertIsNotNone(pdev) self.assertEqual(pdev.vendor_name(), 'Other') gpu_dev = GpuNak(pdev) self.assertIsNotNone(gpu_dev) gpu_ctl = GpuCtl(gpu_devices=[gpu_dev], fan=10, temp=20, tas='./tests/ok.sh') self.assertNotEqual(gpu_ctl, None) rv = gpu_ctl.set_interval(wait_period=10) self.assertTrue(rv) gpu_ctl.start() time.sleep(5) gpu_ctl.stop() def test_interval(self): slot_name = '1111:11:11.1' pci_id = '1111:1111' pdev = PciDev(slot_name, pci_id, 'mock pci') self.assertIsNotNone(pdev) self.assertEqual(pdev.vendor_name(), 'Other') gpu_dev = GpuOk(pdev) self.assertIsNotNone(gpu_dev) gpu_ctl = GpuCtl(gpu_devices=[gpu_dev]) rv = gpu_ctl.set_interval(intvl=1, wait_period=3) self.assertTrue(rv) rv = gpu_ctl.set_interval(intvl=2, wait_period=20) self.assertTrue(rv) rv = gpu_ctl.set_interval(intvl=2, wait_period=1) self.assertFalse(rv) def test_nak_gpu(self): slot_name = '1111:11:11.1' pci_id = '1111:1111' pdev = PciDev(slot_name, pci_id, 'mock pci') self.assertIsNotNone(pdev) self.assertEqual(pdev.vendor_name(), 'Other') gpu_dev = GpuNak(pdev) self.assertIsNotNone(gpu_dev) gpu_ctl = GpuCtl(gpu_devices=[gpu_dev], verbose=True) rv = gpu_ctl.set_interval(wait_period=10) self.assertTrue(rv) gpu_ctl.start() time.sleep(5) gpu_ctl.stop() if __name__ == '__main__': unittest.main()
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Legacy/quicksim/strategies/MonthlyRebalancing.py
romanbsd/QuantSoftwareToolkit
3
116872
''' (c) 2011, 2012 Georgia Tech Research Corporation This source code is released under the New BSD license. Please see http://wiki.quantsoftware.org/index.php?title=QSTK_License for license details. Created on Jan 1, 2011 @author:<NAME> @contact: <EMAIL> @summary: Contains tutorial for backtester and report. ''' # # MonthlyRebalancingExample.py # # Usage: python MonthlyRebalancingExample.py 1-1-2004' '1-1-2009' 'alloc.pkl' # # A strategy script which creates a monthly allocation table using # start date and end date along with the first 20 symbols of S&P500. # It then dumps the allocation table to a pickle file. # # #python imports import pickle from pylab import * from pandas import * import matplotlib.pyplot as plt import datetime as dt import os #qstk imports import qstkutil.DataAccess as da import qstkutil.qsdateutil as du if __name__ == "__main__": print("Running Monthly Rebalancing strategy starting "+sys.argv[1]+" and ending "+sys.argv[2]+".") #Get first 20 S&P Symbols symbols = list(np.loadtxt(os.environ['QS']+'/quicksim/strategies/S&P500.csv',dtype='str',delimiter=',',comments='#',skiprows=0)) symbols = symbols[0:19] #Set start and end boundary times t = list(map(int,sys.argv[1].split('-'))) startday = dt.datetime(t[2],t[0],t[1]) t = list(map(int,sys.argv[2].split('-'))) endday = dt.datetime(t[2],t[0],t[1]) #Get desired timestamps timeofday=dt.timedelta(hours=16) timestamps = du.getNYSEdays(startday,endday,timeofday) # Get the data from the data store dataobj = da.DataAccess('Norgate') historic = dataobj.get_data(timestamps, symbols, "close") # Setup the allocation table alloc_vals=.8/(len(historic.values[0,:])-1)*ones((1,len(historic.values[0,:]))) alloc=DataMatrix(index=[historic.index[0]], data=alloc_vals, columns=symbols) for date in range(1, len(historic.index)): if(historic.index[date].day==1): alloc=alloc.append(DataMatrix(index=[historic.index[date]], data=alloc_vals, columns=symbols)) alloc[symbols[0]] = .1 alloc['_CASH'] = .1 #Dump to a pkl file output=open(sys.argv[3],"wb") pickle.dump(alloc, output)
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cntapp/__init__.py
rgaudin/edupi
0
126465
<reponame>rgaudin/edupi<filename>cntapp/__init__.py default_app_config = 'cntapp.apps.CntappConfig'
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refinenet/__init__.py
Best-of-ACRV/pytorch-refinenet
3
48930
from .refinenet import RefineNet from .__main__ import main as run_from_args
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src/blog/posts/migrations/0008_post_category.py
ggeop/Django_blog
1
1612629
<reponame>ggeop/Django_blog # -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('posts', '0007_auto_20181231_0243'), ] operations = [ migrations.AddField( model_name='post', name='category', field=models.CharField(max_length=6, default='all', choices=[('all', 'ALL'), ('android', 'ANDROID'), ('iphone', 'IPHONE')]), ), ]
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login.py
cuifan1/test1
1
53995
for j in range(1, 9): for i in range(1,j+1): print("%d * %d = %d" % (i,j,i*j),end="\t") print()
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test.py
IldusTim/QAStudy
0
13452
# -*- coding: utf-8 -*- from selenium import webdriver from selenium.webdriver.support.ui import WebDriverWait import math from selenium.webdriver.support.ui import Select import os import time from selenium.webdriver.common.by import By from selenium.webdriver.support import expected_conditions as EC link = "http://suninjuly.github.io/explicit_wait2.html" opt = webdriver.ChromeOptions() opt.add_experimental_option('w3c', False) browser = webdriver.Chrome(chrome_options=opt) browser.implicitly_wait(5, 0.5) browser.get(link) button = browser.find_element_by_id("book") price = WebDriverWait(browser, 12).until(EC.text_to_be_present_in_element((By.ID, "price"),"10000 RUR")) button.click() def calc(x): return str(math.log(abs(12*math.sin(int(x))))) browser.find_element_by_class_name("btn-primary").click() # new_window = browser.window_handles[1] # browser.switch_to.window(new_window) x_element = browser.find_element_by_id("input_value") x = x_element.text y = calc(x) browser.find_element_by_id("answer").click() browser.find_element_by_id("answer").send_keys(y) browser.find_element_by_id("solve").click()
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app.py
taropun/nhentaifs-web
2
89252
#!/usr/bin/python3 import os import os.path import sys from bottle import abort, redirect, request, route, run, static_file, template SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__)) STATIC_DIR = '{}/static'.format(SCRIPT_DIR) NAVIGATION_SIZE = 7 PREFETCH_SIZE = 5 ROW_COUNT = 5 LARGE_GALLERY_SIZE = 100 def fs_path(path, *args): return os.path.join(sys.argv[1], path.format(*args)) @route('/static/<path:path>') def serve_static(path): return static_file(path, root=STATIC_DIR) @route('/img/<path:path>') def serve_image(path): response = static_file(path, root=sys.argv[1]) response.headers.pop('Content-Length') return response @route('/') def index(): return template('index.html.tpl') def window(index, width, items): if len(items) <= width: return items left_width = int(width / 2.0) + 1 right_width = width - left_width start = max(0, index - left_width) end = min(len(items), index + right_width) actual_width = end - start if (index - start) < left_width: end += width - actual_width if (end - index) < right_width: start -= width - actual_width return items[start:end] def chunks(items, count): for i in range(0, len(items), count): yield items[i:i + count] def make_nav(page, count): return { 'first': 1, 'previous': page - 1 if page > 1 else None, 'next': page + 1 if page < count else None, 'last': count, 'window': window(page, NAVIGATION_SIZE, list(range(1, count + 1))), 'current': page } def fs_content(path, *args): with open(fs_path(path, *args), 'r') as f: return f.read() def fs_peek(path, *args): with open(fs_path(path, *args), 'rb') as f: f.read(1) def results_metadata(base): per_page = int(fs_content('{}/per_page', base)) galleries = [] for i in range(per_page): try: ID = fs_content('{}/{}/id', base, i) except FileNotFoundError: break title = (fs_content('{}/{}/title/pretty', base, i) or fs_content('{}/{}/title/english', base, i)) files = os.listdir(fs_path('{}/{}', base, i)) thumb = ['{}/{}/{}'.format(base, i, f) for f in files if f.startswith('thumb.')][0] tags = {ID: fs_content('{}/{}/tags/{}', base, i, ID) for ID in os.listdir(fs_path('{}/{}/tags', base, i))} grouped_tags = group_tags(tags) language = 'language--{}'.format(guess_language(grouped_tags)) num_pages = int(fs_content('{}/{}/num_pages', base, i)) is_large = num_pages >= LARGE_GALLERY_SIZE classes = [language, 'gallery--large'] if is_large else [language] galleries.append({'id': ID, 'title': title, 'thumb': thumb, 'classes': ' '.join(classes)}) num_pages = int(fs_content('{}/num_pages', base)) return {'num_pages': num_pages, 'galleries': galleries} @route('/all') def frontpage(): title = 'Frontpage' page = int(request.query.page or '1') metadata = results_metadata('all/{}'.format(page)) num_pages = metadata['num_pages'] galleries = chunks(metadata['galleries'], ROW_COUNT) nav = make_nav(page, num_pages) base = '/all?page=' return template('results.html.tpl', title=title, base=base, galleries=galleries, nav=nav) @route('/search') def search(): query = request.query.query if not query: redirect('/all') title = 'Search: {}'.format(query) page = int(request.query.page or '1') metadata = results_metadata('search/{}/{}'.format(query, page)) num_pages = metadata['num_pages'] galleries = chunks(metadata['galleries'], ROW_COUNT) nav = make_nav(page, num_pages) base = '/search?query={}&page='.format(query) return template('results.html.tpl', title=title, base=base, galleries=galleries, nav=nav) @route('/tagged/<tag_id:int>') def tagged(tag_id): title = 'Tag: {}'.format(tag_id) page = int(request.query.page or '1') metadata = results_metadata('tagged/{}/{}'.format(tag_id, page)) num_pages = metadata['num_pages'] galleries = chunks(metadata['galleries'], ROW_COUNT) nav = make_nav(page, num_pages) base = '/tagged/{}?page='.format(tag_id) return template('results.html.tpl', title=title, base=base, galleries=galleries, nav=nav) def group_tags(tags): result = {} for ID, tag in tags.items(): key, value = tag.split(':') if key not in result: result[key] = {} result[key][value] = ID return result def guess_language(grouped_tags): if 'language' not in grouped_tags: return 'unknown' candidates = grouped_tags['language'] if 'translated' in candidates: candidates.pop('translated') languages = list(candidates.keys()) if 'english' in languages: return 'english' elif 'japanese' in languages: return 'japanese' elif 'chinese' in languages: return 'chinese' elif len(languages) > 0: return languages[0] else: return 'unknown' def gallery_metadata(gallery_id): base = 'gallery/{}'.format(gallery_id) files = os.listdir(fs_path(base)) tags = {ID: fs_content('{}/tags/{}', base, ID) for ID in os.listdir(fs_path('{}/tags', base))} return { 'id': gallery_id, 'title': { 'pretty': fs_content('{}/title/pretty', base), 'native': fs_content('{}/title/native', base), 'english': fs_content('{}/title/english', base) }, 'cover': ['{}/{}'.format(base, f) for f in files if f.startswith('cover.')][0], 'tags': group_tags(tags), 'filenames': fs_content('{}/filenames', base).split('\n'), 'num_pages': int(fs_content('{}/num_pages', base)), 'uploaded': int(fs_content('{}/uploaded', base)) } def related_metadata(gallery_id): base = 'related/{}'.format(gallery_id) directories = os.listdir(fs_path(base)) galleries = [] for directory in directories: base = 'related/{}/{}'.format(gallery_id, directory) files = os.listdir(fs_path(base)) ID = fs_content('{}/id', base, directory) tags = {ID: fs_content('{}/tags/{}', base, ID) for ID in os.listdir(fs_path('{}/tags', base))} grouped_tags = group_tags(tags) language = 'language--{}'.format(guess_language(grouped_tags)) num_pages = int(fs_content('{}/num_pages', base)) is_large = num_pages >= LARGE_GALLERY_SIZE classes = [language, 'gallery--large'] if is_large else [language] gallery = { 'id': ID, 'title': fs_content('{}/title/pretty', base), 'cover': ['gallery/{}/{}'.format(ID, f) for f in files if f.startswith('cover.')][0], 'classes': ' '.join(classes) } galleries.append(gallery) return galleries @route('/gallery/<gallery_id:int>') def gallery(gallery_id): metadata = gallery_metadata(gallery_id) title = metadata['title']['pretty'] or metadata['title']['english'] filenames = ['/img/gallery/{}/thumbs/{}'.format(gallery_id, f) for f in metadata['filenames']] thumbs = chunks(list(enumerate(filenames)), ROW_COUNT) related = related_metadata(gallery_id) return template('gallery.html.tpl', title=title, thumbs=thumbs, metadata=metadata, related=related) @route('/gallery/<gallery_id:int>/<page:int>') def gallery_page(gallery_id, page): index = page - 1 base = 'gallery/{}'.format(gallery_id) title = fs_content('{}/title/pretty', base) num_pages = int(fs_content('{}/num_pages', base)) if page < 1 or page > num_pages: abort(404) nav = make_nav(page, num_pages) nav.pop('window') filenames = fs_content('{}/filenames', base).split('\n') page_url = '/img/gallery/{}/pages/{}'.format(gallery_id, filenames[index]) base = '/gallery/{}/'.format(gallery_id) gallery_url = '/gallery/{}'.format(gallery_id) return template('gallery_page.html.tpl', title=title, base=base, nav=nav, page_url=page_url, gallery_url=gallery_url) @route('/gallery/<gallery_id:int>/<page:int>/prefetch') def gallery_page_prefetch(gallery_id, page): base = 'gallery/{}'.format(gallery_id) filenames = fs_content('{}/filenames', base).split('\n') pages = window(page, PREFETCH_SIZE, filenames) center = PREFETCH_SIZE // 2 pages.pop(center) for page in pages: fs_peek('{}/pages/{}', base, page) return {'status': 0} if __name__ == '__main__': if len(sys.argv) != 2: print('Usage: {} <mountpoint>'.format(sys.argv[0])) sys.exit(1) try: port = int(os.getenv('PORT') or '8080') run(port=port) except FileNotFoundError: abort(404) # TODO: # CSS: # - [ ] make it dark # - [X] highlight large galleries # JS: # - [X] load thumbs as they scroll into view # - [X] preload gallery images relative to the current one # - [X] bind keys in gallery viewer
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tests/test_box.py
mjclarke94/lammps-cython
6
65172
<gh_stars>1-10 from math import pi import pytest import numpy as np import lammps def test_lattice_const_to_lammps_box_cubic(): lengths = (5, 5, 5) angles = (pi/2, pi/2, pi/2) origin = (0, 0, 0) a, b, c = lengths xlo, ylo, zlo = origin bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles) assert np.all(np.isclose(bounds, [[xlo, xlo+a], [ylo, ylo+b], [zlo, zlo+c]])) assert np.all(np.isclose(tilts, (0, 0, 0))) assert np.all(np.isclose(rotation_matrix, np.eye(3))) def test_lattice_const_to_lammps_box_cubic_offset_origin(): lengths = (5, 5, 5) angles = (pi/2, pi/2, pi/2) origin = (4, 3, 2) a, b, c = lengths xlo, ylo, zlo = origin bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles, origin=origin) assert np.all(np.isclose(bounds, [[xlo, xlo+a], [ylo, ylo+b], [zlo, zlo+c]])) assert np.all(np.isclose(tilts, (0, 0, 0))) assert np.all(np.isclose(rotation_matrix, np.eye(3))) def test_lattice_to_lammps_box_cubic_transform(): lengths = (5, 5, 5) angles = (pi/2, pi/2, pi/2) origin = (4, 3, 2) a, b, c = lengths xlo, ylo, zlo = origin bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles, origin=origin) assert np.all(np.isclose(bounds, [[xlo, xlo+a], [ylo, ylo+b], [zlo, zlo+c]])) assert np.all(np.isclose(tilts, (0, 0, 0))) assert np.all(np.isclose(rotation_matrix, np.eye(3))) points = np.random.random((10, 3)) points_new_1 = lammps.core.transform_cartesian_vector_to_lammps_vector(points, rotation_matrix) assert np.all(np.isclose(points, points_new_1)) points_new_2 = lammps.core.transform_cartesian_vector_to_lammps_vector(points, rotation_matrix, origin) assert np.all(np.isclose(points + origin, points_new_2)) def test_lattice_const_to_lammps_box_rhomb(): # 3C-SiC lengths = (3.0968, 3.0968, 3.0968) angles = (pi/3, pi/3, pi/3) bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles) assert np.all(np.isclose(bounds, ((0, 3.0968), (0, 2.6819074704396493), (0, 2.528526611816982)), atol=1e-3)) assert np.all(np.isclose(tilts, (1.5484000000000004, 1.5484000000000004, 0.8939691568132165))) def test_lammps_box_to_lattice_const_cubic(): bounds = [[0, 5], [0, 5], [0, 5]] tilts = (0, 0, 0) origin = (0, 0, 0) lengths, angles, origin = lammps.core.lammps_box_to_lattice_const(bounds, tilts) assert np.all(np.isclose(lengths, (5, 5, 5))) assert np.all(np.isclose(angles, (pi/2, pi/2, pi/2))) def test_lammps_box_orthogonal_reversible(): lengths = (4, 4, 4) angles = (pi/2, pi/2, pi/2) origin = (1, 2, 3) bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles, origin=origin) lengths_r, angles_r, origin_r = lammps.core.lammps_box_to_lattice_const(bounds, tilts) assert np.all(np.isclose(lengths, lengths_r)) assert np.all(np.isclose(angles, angles_r)) assert np.all(np.isclose(origin, origin_r)) def test_lammps_box_tetrahedral_reversible(): # LiTaO3 lengths = (5.5338, 5.5338, 5.5338) angles = (56.14486291 * pi/180, 56.14486291 * pi/180, 56.14486291 * pi/180) origin = (1, 2, 3) bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles, origin=origin) lengths_r, angles_r, origin_r = lammps.core.lammps_box_to_lattice_const(bounds, tilts) assert np.all(np.isclose(lengths, lengths_r)) assert np.all(np.isclose(angles, angles_r)) assert np.all(np.isclose(origin, origin_r)) def test_lammps_initial_box(lmp): assert lmp.box.dimension == 3 assert np.all(np.isclose(lmp.box.lengths, (1., 1., 1.))) assert np.all(np.isclose(lmp.box.angles, (pi/2., pi/2., pi/2.))) assert np.all(np.isclose(lmp.box.bounds, [[-0.5, 0.5], [-0.5, 0.5], [-0.5, 0.5]])) assert np.all(np.isclose(lmp.box.tilts, [0, 0, 0])) assert np.all(np.isclose(lmp.box.lengths_angles, [[1, 1, 1], [pi/2, pi/2, pi/2]])) # lammps has some seriously weird initial behavior # has unit cell 1x1x1 with volume 0 ??? # actually has non-deterministic behavior 0 or inf # assert np.isclose(lmp.box.volume, 0.) def test_lammps_set_box_from_lattice_const(lmp): atom_types = 5 lengths = (10, 10, 10) angles = (pi/2., pi/2., pi/2.) lmp.box.from_lattice_const(atom_types, lengths, angles) assert np.all(np.isclose(lmp.box.lengths, lengths)) assert np.all(np.isclose(lmp.box.angles, angles)) assert lmp.system.total == 0 assert len(lmp.system.atom_types) == atom_types assert np.isclose(lmp.box.volume, 10**3) def test_lammps_update_lattice_const(lmp): lengths = (10, 10, 10) angles = (pi/2., pi/2., pi/2.) lmp.box.update_lattice_const(lengths, angles) assert np.all(np.isclose(lmp.box.lengths, lengths)) assert np.all(np.isclose(lmp.box.angles, angles)) assert np.isclose(lmp.box.volume, 10**3)
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hello-python/hello-world.py
florianbussmann/hello-world
2
89340
<filename>hello-python/hello-world.py<gh_stars>1-10 #!/usr/bin/python print "Hello, World."
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wz.py
DrGFreeman/multi-dash
0
101479
""" An alternative to wsgi.py to run with the werkzeug server for local development. Run with : $ python wg.py """ from werkzeug.serving import run_simple from werkzeug.wsgi import DispatcherMiddleware from flask_app import flask_app from app1 import app as app1 from app2 import app as app2 application = DispatcherMiddleware(flask_app, { '/app1': app1.server, '/app2': app2.server, }) run_simple('localhost', 8080, application, use_reloader=True)
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tests/test_auth_models.py
hydrobase/core_app
0
175238
<reponame>hydrobase/core_app import unittest from app import app from app.auth.models import User class TestAuthModels(unittest.TestCase): def setUp(self): self.app = app.test_client() self.user = User('admin') def tearDown(self): pass def test_is_authenticated(self): self.assertEquals(True, self.user.is_authenticated()) def test_is_active(self): self.assertEquals(True, self.user.is_active()) def test_is_anonyous(self): self.assertEquals(False, self.user.is_anonymous()) def test_get_id(self): self.assertEquals('admin', self.user.get_id()) if __name__ == '__main__': unittest.main()
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glinklater/watershed/watershed.py
RhodesCS2016/image-processing
0
1603306
<filename>glinklater/watershed/watershed.py<gh_stars>0 import numpy as np import cv2 as cv import sys from matplotlib import pyplot as plt def debug_display(image): cv.imshow('Debug', image) cv.waitKey(0) cv.destroyAllWindows() file = None try: print sys.argv[1] file = sys.argv[1] except: print 'Please specify an input file' sys.exit(1) img = cv.imread(file) gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) ret, thresh = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV+cv.THRESH_OTSU) debug_display(thresh) kernel = np.ones((3, 3), np.uint8) opening = cv.morphologyEx(thresh, cv.MORPH_OPEN, kernel, iterations=2) sure_bg = cv.dilate(opening, kernel, iterations=3) dist_transform = cv.distanceTransform(opening, cv.DIST_L2, 5) ret, sure_fg = cv.threshold(dist_transform, 0.7*dist_transform.max(), 255, 0) sure_fg = np.uint8(sure_fg) unknown = cv.subtract(sure_bg, sure_fg) debug_display(sure_bg) debug_display(sure_fg) debug_display(unknown) ret, markers = cv.connectedComponents(sure_fg) markers = markers + 1 markers[unknown==255] = 0 markers = cv.watershed(img, markers) img[markers == -1] = [255, 0, 0] debug_display(img)
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PyOpenGL-3.0.2/OpenGL/raw/GL/EXT/pixel_transform.py
frederica07/Dragon_Programming_Process
0
169653
<reponame>frederica07/Dragon_Programming_Process '''Autogenerated by get_gl_extensions script, do not edit!''' from OpenGL import platform as _p, constants as _cs, arrays from OpenGL.GL import glget import ctypes EXTENSION_NAME = 'GL_EXT_pixel_transform' def _f( function ): return _p.createFunction( function,_p.GL,'GL_EXT_pixel_transform',False) _p.unpack_constants( """GL_PIXEL_TRANSFORM_2D_EXT 0x8330 GL_PIXEL_MAG_FILTER_EXT 0x8331 GL_PIXEL_MIN_FILTER_EXT 0x8332 GL_PIXEL_CUBIC_WEIGHT_EXT 0x8333 GL_CUBIC_EXT 0x8334 GL_AVERAGE_EXT 0x8335 GL_PIXEL_TRANSFORM_2D_STACK_DEPTH_EXT 0x8336 GL_MAX_PIXEL_TRANSFORM_2D_STACK_DEPTH_EXT 0x8337 GL_PIXEL_TRANSFORM_2D_MATRIX_EXT 0x8338""", globals()) glget.addGLGetConstant( GL_PIXEL_TRANSFORM_2D_STACK_DEPTH_EXT, (1,) ) glget.addGLGetConstant( GL_MAX_PIXEL_TRANSFORM_2D_STACK_DEPTH_EXT, (1,) ) @_f @_p.types(None,_cs.GLenum,_cs.GLenum,_cs.GLint) def glPixelTransformParameteriEXT( target,pname,param ):pass @_f @_p.types(None,_cs.GLenum,_cs.GLenum,_cs.GLfloat) def glPixelTransformParameterfEXT( target,pname,param ):pass @_f @_p.types(None,_cs.GLenum,_cs.GLenum,arrays.GLintArray) def glPixelTransformParameterivEXT( target,pname,params ):pass @_f @_p.types(None,_cs.GLenum,_cs.GLenum,arrays.GLfloatArray) def glPixelTransformParameterfvEXT( target,pname,params ):pass @_f @_p.types(None,_cs.GLenum,_cs.GLenum,arrays.GLintArray) def glGetPixelTransformParameterivEXT( target,pname,params ):pass @_f @_p.types(None,_cs.GLenum,_cs.GLenum,arrays.GLfloatArray) def glGetPixelTransformParameterfvEXT( target,pname,params ):pass def glInitPixelTransformEXT(): '''Return boolean indicating whether this extension is available''' from OpenGL import extensions return extensions.hasGLExtension( EXTENSION_NAME )
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tests/mem/test_magic_memset_tlb_miss.py
capt-hb/cheritest
0
93174
<filename>tests/mem/test_magic_memset_tlb_miss.py #- # Copyright (c) 2018 <NAME> # All rights reserved. # # This software was developed by the University of Cambridge Computer # Laboratory as part of the Rigorous Engineering of Mainstream Systems (REMS) # project, funded by EPSRC grant EP/K008528/1. # # @BERI_LICENSE_HEADER_START@ # # Licensed to BERI Open Systems C.I.C. (BERI) under one or more contributor # license agreements. See the NOTICE file distributed with this work for # additional information regarding copyright ownership. BERI licenses this # file to you under the BERI Hardware-Software License, Version 1.0 (the # "License"); you may not use this file except in compliance with the # License. You may obtain a copy of the License at: # # http://www.beri-open-systems.org/legal/license-1-0.txt # # Unless required by applicable law or agreed to in writing, Work distributed # under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR # CONDITIONS OF ANY KIND, either express or implied. See the License for the # specific language governing permissions and limitations under the License. # # @BERI_LICENSE_HEADER_END@ # from beritest_tools import BaseBERITestCase, attr, HexInt @attr("qemu_magic_nops") class test_magic_memset_tlb_miss(BaseBERITestCase): EXPECTED_EXCEPTIONS = 1 def test_badvaddr(self): self.assertRegisterEqual(self.MIPS.s0, self.MIPS.a4, "Wrong BadVaddr") def test_context(self): self.assertRegisterEqual(self.MIPS.s1, (self.MIPS.a4 & 0xffffe000)>>9, "Wrong Context") # TODO test page table base def test_xcontext(self): self.assertRegisterEqual(self.MIPS.s2, (self.MIPS.a4 & 0xffffe000)>>9, "Wrong XContext") # TODO test page table base def test_entryhi(self): self.assertRegisterMaskEqual(self.MIPS.a4, 0xfffff000, self.MIPS.s3, "Wrong EntryHi") def test_status(self): self.assertRegisterMaskEqual(self.MIPS.s4, 2, 2, "Wrong EXL") def test_epc(self): '''Test EPC after TLB Invalid exception''' # plus 12 since check_instruction_traps uses 3 instructions before invoking the actual insn self.assertRegisterEqual(self.MIPS.a6 + 12, self.MIPS.s6, "Wrong EPC") def test_testdata(self): self.assertRegisterEqual(self.MIPS.a7, 0xfedcba9876543210, "Wrong testdata") def test_trap_info(self): self.assertCompressedTrapInfo(self.MIPS.s5, mips_cause=self.MIPS.Cause.TLB_Store, trap_count=1)
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handtracking/src/app/utils/CoordenateConverter.py
rafaeltoyo/unity-vr-server
3
1613999
<gh_stars>1-10 from copy import deepcopy class CoordenateConverter: """ Cordenate Converter """ bones = [(0, 1, 2, 3, 4), (0, 5, 6, 7, 8), (0, 9, 10, 11, 12), (0, 13, 14, 15, 16), (0, 17, 18, 19, 20)] def convert_to_relative(self, absolute_coordenates): relative_coordenates = deepcopy(absolute_coordenates) for hand_index in range(len(relative_coordenates)): for finger_connections in self.bones: for connection in finger_connections[1:]: coord = absolute_coordenates[hand_index].landmark[connection] if (connection - 1) % 4 == 0: last_finger_coordenates = absolute_coordenates[hand_index].landmark[0] else: last_finger_coordenates = absolute_coordenates[hand_index].landmark[connection - 1] coord_x = coord.x - last_finger_coordenates.x coord_y = coord.y - last_finger_coordenates.y coord_z = coord.z - last_finger_coordenates.z if connection != 0: relative_coordenates[hand_index].landmark[connection].x = coord_x relative_coordenates[hand_index].landmark[connection].y = coord_y relative_coordenates[hand_index].landmark[connection].z = coord_z return relative_coordenates def convert_to_absolute(self, relative_coordenates): absolute_coordenates = deepcopy(relative_coordenates) for hand_index in range(len(relative_coordenates)): for finger_connections in self.bones: last_finger_coordenates = deepcopy(relative_coordenates[hand_index].landmark[0]) last_finger_coordenates.x = 0 last_finger_coordenates.y = 0 last_finger_coordenates.z = 0 for connection in finger_connections: coord = relative_coordenates[hand_index].landmark[connection] coord_x = round(coord.x + last_finger_coordenates.x, 9) coord_y = round(coord.y + last_finger_coordenates.y, 9) coord_z = round(coord.z + last_finger_coordenates.z, 9) if connection != 0: absolute_coordenates[hand_index].landmark[connection].x = coord_x absolute_coordenates[hand_index].landmark[connection].y = coord_y absolute_coordenates[hand_index].landmark[connection].z = coord_z last_finger_coordenates.x = coord_x last_finger_coordenates.y = coord_y last_finger_coordenates.z = coord_z return absolute_coordenates
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plugins/train/model/_base.py
oveis/DeepVideoFaceSwap
5
1609927
#!/usr/bin/env python3 """ Base class for Models. ALL Models should at least inherit from this class When inheriting model_data should be a list of NNMeta objects. See the class for details. """ import logging import os import sys import time from json import JSONDecodeError from shutil import copyfile, copytree import keras from keras import losses from keras import backend as K from keras.models import load_model, Model from keras.optimizers import Adam from keras.utils import get_custom_objects, multi_gpu_model from lib import Serializer from lib.model.losses import DSSIMObjective, PenalizedLoss from lib.model.nn_blocks import NNBlocks from lib.multithreading import MultiThread from lib.utils import get_folder from plugins.train._config import Config logger = logging.getLogger(__name__) # pylint: disable=invalid-name _CONFIG = None class ModelBase(): """ Base class that all models should inherit from """ def __init__(self, model_dir, gpus, no_logs=False, warp_to_landmarks=False, no_flip=False, training_image_size=256, alignments_paths=None, preview_scale=100, input_shape=None, encoder_dim=None, trainer="original", pingpong=False, memory_saving_gradients=False, predict=False): logger.debug("Initializing ModelBase (%s): (model_dir: '%s', gpus: %s, no_logs: %s" "training_image_size, %s, alignments_paths: %s, preview_scale: %s, " "input_shape: %s, encoder_dim: %s, trainer: %s, pingpong: %s, " "memory_saving_gradients: %s, predict: %s)", self.__class__.__name__, model_dir, gpus, no_logs, training_image_size, alignments_paths, preview_scale, input_shape, encoder_dim, trainer, pingpong, memory_saving_gradients, predict) self.predict = predict self.model_dir = model_dir self.gpus = gpus self.blocks = NNBlocks(use_subpixel=self.config["subpixel_upscaling"], use_icnr_init=self.config["icnr_init"], use_reflect_padding=self.config["reflect_padding"]) self.input_shape = input_shape self.output_shape = None # set after model is compiled self.encoder_dim = encoder_dim self.trainer = trainer self.state = State(self.model_dir, self.name, self.config_changeable_items, no_logs, pingpong, training_image_size) self.is_legacy = False self.rename_legacy() self.load_state_info() self.networks = dict() # Networks for the model self.predictors = dict() # Predictors for model self.history = dict() # Loss history per save iteration) # Training information specific to the model should be placed in this # dict for reference by the trainer. self.training_opts = {"alignments": alignments_paths, "preview_scaling": preview_scale / 100, "warp_to_landmarks": warp_to_landmarks, "no_flip": no_flip, "pingpong": pingpong} self.set_gradient_type(memory_saving_gradients) self.build() self.set_training_data() logger.debug("Initialized ModelBase (%s)", self.__class__.__name__) @property def config_section(self): """ The section name for loading config """ retval = ".".join(self.__module__.split(".")[-2:]) logger.debug(retval) return retval @property def config(self): """ Return config dict for current plugin """ global _CONFIG # pylint: disable=global-statement if not _CONFIG: model_name = self.config_section logger.debug("Loading config for: %s", model_name) _CONFIG = Config(model_name).config_dict return _CONFIG @property def config_changeable_items(self): """ Return the dict of config items that can be updated after the model has been created """ return Config(self.config_section).changeable_items @property def name(self): """ Set the model name based on the subclass """ basename = os.path.basename(sys.modules[self.__module__].__file__) retval = os.path.splitext(basename)[0].lower() logger.debug("model name: '%s'", retval) return retval @property def models_exist(self): """ Return if all files exist and clear session """ retval = all([os.path.isfile(model.filename) for model in self.networks.values()]) logger.debug("Pre-existing models exist: %s", retval) return retval @staticmethod def set_gradient_type(memory_saving_gradients): """ Monkeypatch Memory Saving Gradients if requested """ if not memory_saving_gradients: return logger.info("Using Memory Saving Gradients") from lib.model import memory_saving_gradients K.__dict__["gradients"] = memory_saving_gradients.gradients_memory def set_training_data(self): """ Override to set model specific training data. super() this method for defaults otherwise be sure to add """ logger.debug("Setting training data") self.training_opts["training_size"] = self.state.training_size self.training_opts["no_logs"] = self.state.current_session["no_logs"] self.training_opts["mask_type"] = self.config.get("mask_type", None) self.training_opts["coverage_ratio"] = self.calculate_coverage_ratio() self.training_opts["preview_images"] = 14 logger.debug("Set training data: %s", self.training_opts) def calculate_coverage_ratio(self): """ Coverage must be a ratio, leading to a cropped shape divisible by 2 """ coverage_ratio = self.config.get("coverage", 62.5) / 100 logger.debug("Requested coverage_ratio: %s", coverage_ratio) cropped_size = (self.state.training_size * coverage_ratio) // 2 * 2 coverage_ratio = cropped_size / self.state.training_size logger.debug("Final coverage_ratio: %s", coverage_ratio) return coverage_ratio def build(self): """ Build the model. Override for custom build methods """ self.add_networks() self.load_models(swapped=False) self.build_autoencoders() self.log_summary() self.compile_predictors(initialize=True) def build_autoencoders(self): """ Override for Model Specific autoencoder builds NB! ENSURE YOU NAME YOUR INPUTS. At least the following input names are expected: face (the input for image) mask (the input for mask if it is used) """ raise NotImplementedError def add_networks(self): """ Override to add neural networks """ raise NotImplementedError def load_state_info(self): """ Load the input shape from state file if it exists """ logger.debug("Loading Input Shape from State file") if not self.state.inputs: logger.debug("No input shapes saved. Using model config") return if not self.state.face_shapes: logger.warning("Input shapes stored in State file, but no matches for 'face'." "Using model config") return input_shape = self.state.face_shapes[0] logger.debug("Setting input shape from state file: %s", input_shape) self.input_shape = input_shape def add_network(self, network_type, side, network): """ Add a NNMeta object """ logger.debug("network_type: '%s', side: '%s', network: '%s'", network_type, side, network) filename = "{}_{}".format(self.name, network_type.lower()) name = network_type.lower() if side: side = side.lower() filename += "_{}".format(side.upper()) name += "_{}".format(side) filename += ".h5" logger.debug("name: '%s', filename: '%s'", name, filename) self.networks[name] = NNMeta(str(self.model_dir / filename), network_type, side, network) def add_predictor(self, side, model): """ Add a predictor to the predictors dictionary """ logger.debug("Adding predictor: (side: '%s', model: %s)", side, model) if self.gpus > 1: logger.debug("Converting to multi-gpu: side %s", side) model = multi_gpu_model(model, self.gpus) self.predictors[side] = model if not self.state.inputs: self.store_input_shapes(model) if not self.output_shape: self.set_output_shape(model) def store_input_shapes(self, model): """ Store the input and output shapes to state """ logger.debug("Adding input shapes to state for model") inputs = {tensor.name: K.int_shape(tensor)[-3:] for tensor in model.inputs} if not any(inp for inp in inputs.keys() if inp.startswith("face")): raise ValueError("No input named 'face' was found. Check your input naming. " "Current input names: {}".format(inputs)) self.state.inputs = inputs logger.debug("Added input shapes: %s", self.state.inputs) def set_output_shape(self, model): """ Set the output shape for use in training and convert """ logger.debug("Setting output shape") out = [K.int_shape(tensor)[-3:] for tensor in model.outputs] if not out: raise ValueError("No outputs found! Check your model.") self.output_shape = tuple(out[0]) logger.debug("Added output shape: %s", self.output_shape) def reset_pingpong(self): """ Reset the models for pingpong training """ logger.debug("Resetting models") # Clear models and graph self.predictors = dict() self.adversarial_autoencoders = dict() K.clear_session() # Load Models for current training run for model in self.networks.values(): model.network = Model.from_config(model.config) model.network.set_weights(model.weights) self.build_autoencoders() self.compile_predictors(initialize=False) logger.debug("Reset models") def compile_predictors(self, initialize=True): """ Compile the predictors """ logger.debug("Compiling Predictors") learning_rate = self.config.get("learning_rate", 5e-5) optimizer = self.get_optimizer(lr=learning_rate, beta_1=0.5, beta_2=0.999) for side, model in self.predictors.items(): mask = [inp for inp in model.inputs if inp.name.startswith("mask")] loss_names = ["loss"] loss_funcs = [self.loss_function(mask, side, initialize)] if mask: loss_names.append("mask_loss") loss_funcs.append(self.mask_loss_function(side, initialize)) model.compile(optimizer=optimizer, loss=loss_funcs) if len(loss_names) > 1: loss_names.insert(0, "total_loss") if initialize: self.state.add_session_loss_names(side, loss_names) self.history[side] = list() logger.debug("Compiled Predictors. Losses: %s", loss_names) def get_optimizer(self, lr=5e-5, beta_1=0.5, beta_2=0.999): # pylint: disable=invalid-name """ Build and return Optimizer """ opt_kwargs = dict(lr=lr, beta_1=beta_1, beta_2=beta_2) if (self.config.get("clipnorm", False) and keras.backend.backend() != "plaidml.keras.backend"): # NB: Clipnorm is ballooning VRAM useage, which is not expected behaviour # and may be a bug in Keras/TF. # PlaidML has a bug regarding the clipnorm parameter # See: https://github.com/plaidml/plaidml/issues/228 # Workaround by simply removing it. # TODO: Remove this as soon it is fixed in PlaidML. opt_kwargs["clipnorm"] = 1.0 logger.debug("Optimizer kwargs: %s", opt_kwargs) return Adam(**opt_kwargs) def loss_function(self, mask, side, initialize): """ Set the loss function Side is input so we only log once """ if self.config.get("dssim_loss", False): if side == "a" and not self.predict and initialize: logger.verbose("Using DSSIM Loss") loss_func = DSSIMObjective() else: if side == "a" and not self.predict and initialize: logger.verbose("Using Mean Absolute Error Loss") loss_func = losses.mean_absolute_error if mask and self.config.get("penalized_mask_loss", False): loss_mask = mask[0] if side == "a" and not self.predict and initialize: logger.verbose("Penalizing mask for Loss") loss_func = PenalizedLoss(loss_mask, loss_func) return loss_func def mask_loss_function(self, side, initialize): """ Set the mask loss function Side is input so we only log once """ if side == "a" and not self.predict and initialize: logger.verbose("Using Mean Squared Error Loss for mask") mask_loss_func = losses.mean_squared_error return mask_loss_func def converter(self, swap): """ Converter for autoencoder models """ logger.debug("Getting Converter: (swap: %s)", swap) if swap: model = self.predictors["a"] else: model = self.predictors["b"] if self.predict: # Must compile the model to be thread safe model._make_predict_function() # pylint: disable=protected-access retval = model.predict logger.debug("Got Converter: %s", retval) return retval @property def iterations(self): "Get current training iteration number" return self.state.iterations def map_models(self, swapped): """ Map the models for A/B side for swapping """ logger.debug("Map models: (swapped: %s)", swapped) models_map = {"a": dict(), "b": dict()} sides = ("a", "b") if not swapped else ("b", "a") for network in self.networks.values(): if network.side == sides[0]: models_map["a"][network.type] = network.filename if network.side == sides[1]: models_map["b"][network.type] = network.filename logger.debug("Mapped models: (models_map: %s)", models_map) return models_map def log_summary(self): """ Verbose log the model summaries """ if self.predict: return for side in sorted(list(self.predictors.keys())): logger.verbose("[%s %s Summary]:", self.name.title(), side.upper()) self.predictors[side].summary(print_fn=lambda x: logger.verbose("R|%s", x)) for name, nnmeta in self.networks.items(): if nnmeta.side is not None and nnmeta.side != side: continue logger.verbose("%s:", name.title()) nnmeta.network.summary(print_fn=lambda x: logger.verbose("R|%s", x)) def load_models(self, swapped): """ Load models from file """ logger.debug("Load model: (swapped: %s)", swapped) if not self.models_exist and not self.predict: logger.info("Creating new '%s' model in folder: '%s'", self.name, self.model_dir) return None if not self.models_exist and self.predict: logger.error("Model could not be found in folder '%s'. Exiting", self.model_dir) exit(0) if not self.is_legacy: K.clear_session() model_mapping = self.map_models(swapped) for network in self.networks.values(): if not network.side: is_loaded = network.load() else: is_loaded = network.load(fullpath=model_mapping[network.side][network.type]) if not is_loaded: break if is_loaded: logger.info("Loaded model from disk: '%s'", self.model_dir) return is_loaded def save_models(self, snapshot_iteration): """ Backup and save the models """ logger.debug("Backing up and saving models") should_backup = self.get_save_averages() save_threads = list() for network in self.networks.values(): name = "save_{}".format(network.name) save_threads.append(MultiThread(network.save, name=name, should_backup=should_backup)) save_threads.append(MultiThread(self.state.save, name="save_state", should_backup=should_backup)) for thread in save_threads: thread.start() for thread in save_threads: if thread.has_error: logger.error(thread.errors[0]) thread.join() logger.info("saved models") if snapshot_iteration: self.snapshot_models() def snapshot_models(self): """ Take a snapshot of the model at current state and back up """ logger.info("Saving snapshot") src = self.model_dir dst = get_folder("{}_{}".format(self.model_dir, self.iterations)) for filename in os.listdir(src): if filename.endswith(".bk"): continue srcfile = os.path.join(src, filename) dstfile = os.path.join(dst, filename) copyfunc = copytree if os.path.isdir(srcfile) else copyfile logger.debug("Saving snapshot: '%s' > '%s'", srcfile, dstfile) copyfunc(srcfile, dstfile) logger.info("Saved snapshot") def get_save_averages(self): """ Return the loss averages since last save and reset historical losses This protects against model corruption by only backing up the model if any of the loss values have fallen. TODO This is not a perfect system. If the model corrupts on save_iteration - 1 then model may still backup """ logger.debug("Getting Average loss since last save") avgs = dict() backup = True for side, loss in self.history.items(): if not loss: backup = False break avgs[side] = sum(loss) / len(loss) self.history[side] = list() # Reset historical loss if not self.state.lowest_avg_loss.get(side, None): logger.debug("Setting initial save iteration loss average for '%s': %s", side, avgs[side]) self.state.lowest_avg_loss[side] = avgs[side] continue if backup: # Only run this if backup is true. All losses must have dropped for a valid backup backup = self.check_loss_drop(side, avgs[side]) logger.debug("Lowest historical save iteration loss average: %s", self.state.lowest_avg_loss) logger.debug("Average loss since last save: %s", avgs) if backup: # Update lowest loss values to the state for side, avg_loss in avgs.items(): logger.debug("Updating lowest save iteration average for '%s': %s", side, avg_loss) self.state.lowest_avg_loss[side] = avg_loss logger.debug("Backing up: %s", backup) return backup def check_loss_drop(self, side, avg): """ Check whether total loss has dropped since lowest loss """ if avg < self.state.lowest_avg_loss[side]: logger.debug("Loss for '%s' has dropped", side) return True logger.debug("Loss for '%s' has not dropped", side) return False def rename_legacy(self): """ Legacy Original, LowMem and IAE models had inconsistent naming conventions Rename them if they are found and update """ legacy_mapping = {"iae": [("IAE_decoder.h5", "iae_decoder.h5"), ("IAE_encoder.h5", "iae_encoder.h5"), ("IAE_inter_A.h5", "iae_intermediate_A.h5"), ("IAE_inter_B.h5", "iae_intermediate_B.h5"), ("IAE_inter_both.h5", "iae_inter.h5")], "original": [("encoder.h5", "original_encoder.h5"), ("decoder_A.h5", "original_decoder_A.h5"), ("decoder_B.h5", "original_decoder_B.h5"), ("lowmem_encoder.h5", "original_encoder.h5"), ("lowmem_decoder_A.h5", "original_decoder_A.h5"), ("lowmem_decoder_B.h5", "original_decoder_B.h5")]} if self.name not in legacy_mapping.keys(): return logger.debug("Renaming legacy files") set_lowmem = False updated = False for old_name, new_name in legacy_mapping[self.name]: old_path = os.path.join(str(self.model_dir), old_name) new_path = os.path.join(str(self.model_dir), new_name) if os.path.exists(old_path) and not os.path.exists(new_path): logger.info("Updating legacy model name from: '%s' to '%s'", old_name, new_name) os.rename(old_path, new_path) if old_name.startswith("lowmem"): set_lowmem = True updated = True if not updated: logger.debug("No legacy files to rename") return self.is_legacy = True logger.debug("Creating state file for legacy model") self.state.inputs = {"face:0": [64, 64, 3]} self.state.training_size = 256 self.state.config["coverage"] = 62.5 self.state.config["subpixel_upscaling"] = False self.state.config["reflect_padding"] = False self.state.config["mask_type"] = None self.state.config["lowmem"] = False self.encoder_dim = 1024 if set_lowmem: logger.debug("Setting encoder_dim and lowmem flag for legacy lowmem model") self.encoder_dim = 512 self.state.config["lowmem"] = True self.state.replace_config(self.config_changeable_items) self.state.save() class NNMeta(): """ Class to hold a neural network and it's meta data filename: The full path and filename of the model file for this network. type: The type of network. For networks that can be swapped The type should be identical for the corresponding A and B networks, and should be unique for every A/B pair. Otherwise the type should be completely unique. side: A, B or None. Used to identify which networks can be swapped. network: Define network to this. """ def __init__(self, filename, network_type, side, network): logger.debug("Initializing %s: (filename: '%s', network_type: '%s', side: '%s', " "network: %s", self.__class__.__name__, filename, network_type, side, network) self.filename = filename self.type = network_type.lower() self.side = side self.name = self.set_name() self.network = network self.network.name = self.name self.config = network.get_config() # For pingpong restore self.weights = network.get_weights() # For pingpong restore logger.debug("Initialized %s", self.__class__.__name__) def set_name(self): """ Set the network name """ name = self.type if self.side: name += "_{}".format(self.side) return name def load(self, fullpath=None): """ Load model """ fullpath = fullpath if fullpath else self.filename logger.debug("Loading model: '%s'", fullpath) try: network = load_model(self.filename, custom_objects=get_custom_objects()) except ValueError as err: if str(err).lower().startswith("cannot create group in read only mode"): self.convert_legacy_weights() return True logger.warning("Failed loading existing training data. Generating new models") logger.debug("Exception: %s", str(err)) return False except OSError as err: # pylint: disable=broad-except logger.warning("Failed loading existing training data. Generating new models") logger.debug("Exception: %s", str(err)) return False self.config = network.get_config() self.network = network # Update network with saved model self.network.name = self.type return True def save(self, fullpath=None, should_backup=False): """ Save model """ fullpath = fullpath if fullpath else self.filename if should_backup: self.backup(fullpath=fullpath) logger.debug("Saving model: '%s'", fullpath) self.weights = self.network.get_weights() self.network.save(fullpath) def backup(self, fullpath=None): """ Backup Model """ origfile = fullpath if fullpath else self.filename backupfile = origfile + ".bk" logger.debug("Backing up: '%s' to '%s'", origfile, backupfile) if os.path.exists(backupfile): os.remove(backupfile) if os.path.exists(origfile): os.rename(origfile, backupfile) def convert_legacy_weights(self): """ Convert legacy weights files to hold the model topology """ logger.info("Adding model topology to legacy weights file: '%s'", self.filename) self.network.load_weights(self.filename) self.save(should_backup=False) self.network.name = self.type class State(): """ Class to hold the model's current state and autoencoder structure """ def __init__(self, model_dir, model_name, config_changeable_items, no_logs, pingpong, training_image_size): logger.debug("Initializing %s: (model_dir: '%s', model_name: '%s', " "config_changeable_items: '%s', no_logs: %s, pingpong: %s, " "training_image_size: '%s'", self.__class__.__name__, model_dir, model_name, config_changeable_items, no_logs, pingpong, training_image_size) self.serializer = Serializer.get_serializer("json") filename = "{}_state.{}".format(model_name, self.serializer.ext) self.filename = str(model_dir / filename) self.name = model_name self.iterations = 0 self.session_iterations = 0 self.training_size = training_image_size self.sessions = dict() self.lowest_avg_loss = dict() self.inputs = dict() self.config = dict() self.load(config_changeable_items) self.session_id = self.new_session_id() self.create_new_session(no_logs, pingpong) logger.debug("Initialized %s:", self.__class__.__name__) @property def face_shapes(self): """ Return a list of stored face shape inputs """ return [tuple(val) for key, val in self.inputs.items() if key.startswith("face")] @property def mask_shapes(self): """ Return a list of stored mask shape inputs """ return [tuple(val) for key, val in self.inputs.items() if key.startswith("mask")] @property def loss_names(self): """ Return the loss names for this session """ return self.sessions[self.session_id]["loss_names"] @property def current_session(self): """ Return the current session dict """ return self.sessions[self.session_id] def new_session_id(self): """ Return new session_id """ if not self.sessions: session_id = 1 else: session_id = max(int(key) for key in self.sessions.keys()) + 1 logger.debug(session_id) return session_id def create_new_session(self, no_logs, pingpong): """ Create a new session """ logger.debug("Creating new session. id: %s", self.session_id) self.sessions[self.session_id] = {"timestamp": time.time(), "no_logs": no_logs, "pingpong": pingpong, "loss_names": dict(), "batchsize": 0, "iterations": 0} def add_session_loss_names(self, side, loss_names): """ Add the session loss names to the sessions dictionary """ logger.debug("Adding session loss_names. (side: '%s', loss_names: %s", side, loss_names) self.sessions[self.session_id]["loss_names"][side] = loss_names def add_session_batchsize(self, batchsize): """ Add the session batchsize to the sessions dictionary """ logger.debug("Adding session batchsize: %s", batchsize) self.sessions[self.session_id]["batchsize"] = batchsize def increment_iterations(self): """ Increment total and session iterations """ self.iterations += 1 self.sessions[self.session_id]["iterations"] += 1 def load(self, config_changeable_items): """ Load state file """ logger.debug("Loading State") try: with open(self.filename, "rb") as inp: state = self.serializer.unmarshal(inp.read().decode("utf-8")) self.name = state.get("name", self.name) self.sessions = state.get("sessions", dict()) self.lowest_avg_loss = state.get("lowest_avg_loss", dict()) self.iterations = state.get("iterations", 0) self.training_size = state.get("training_size", 256) self.inputs = state.get("inputs", dict()) self.config = state.get("config", dict()) logger.debug("Loaded state: %s", state) self.replace_config(config_changeable_items) except IOError as err: logger.warning("No existing state file found. Generating.") logger.debug("IOError: %s", str(err)) except JSONDecodeError as err: logger.debug("JSONDecodeError: %s:", str(err)) def save(self, should_backup=False): """ Save iteration number to state file """ logger.debug("Saving State") if should_backup: self.backup() try: with open(self.filename, "wb") as out: state = {"name": self.name, "sessions": self.sessions, "lowest_avg_loss": self.lowest_avg_loss, "iterations": self.iterations, "inputs": self.inputs, "training_size": self.training_size, "config": _CONFIG} state_json = self.serializer.marshal(state) out.write(state_json.encode("utf-8")) except IOError as err: logger.error("Unable to save model state: %s", str(err.strerror)) logger.debug("Saved State") def backup(self): """ Backup state file """ origfile = self.filename backupfile = origfile + ".bk" logger.debug("Backing up: '%s' to '%s'", origfile, backupfile) if os.path.exists(backupfile): os.remove(backupfile) if os.path.exists(origfile): os.rename(origfile, backupfile) def replace_config(self, config_changeable_items): """ Replace the loaded config with the one contained within the state file Check for any fixed=False parameters changes and log info changes """ global _CONFIG # pylint: disable=global-statement # Add any new items to state config for legacy purposes for key, val in _CONFIG.items(): if key not in self.config.keys(): logger.info("Adding new config item to state file: '%s': '%s'", key, val) self.config[key] = val self.update_changed_config_items(config_changeable_items) logger.debug("Replacing config. Old config: %s", _CONFIG) _CONFIG = self.config logger.debug("Replaced config. New config: %s", _CONFIG) logger.info("Using configuration saved in state file") def update_changed_config_items(self, config_changeable_items): """ Update any parameters which are not fixed and have been changed """ if not config_changeable_items: logger.debug("No changeable parameters have been updated") return for key, val in config_changeable_items.items(): old_val = self.config[key] if old_val == val: continue self.config[key] = val logger.info("Config item: '%s' has been updated from '%s' to '%s'", key, old_val, val)
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cuberoot_of_a_number.py
matosBrent/PythonScripts
3
142078
<filename>cuberoot_of_a_number.py def diff(n, mid) : if (n > (mid * mid * mid)) : return (n - (mid * mid * mid)) else : return ((mid * mid * mid) - n) # Returns cube root of a no n def cubicRoot(n) : # Set start and end for binary # search start = 0 end = n # Set precision e = 0.0000001 while (True) : mid = (start + end) / 2 error = diff(n, mid) # If error is less than e # then mid is our answer # so return mid if (error <= e) : return mid # If mid*mid*mid is greater # than n set end = mid if ((mid * mid * mid) > n) : end = mid # If mid*mid*mid is less # than n set start = mid else : start = mid # Driver code n = 3 print("Cubic root of", n, "is", round(cubicRoot(n),6))
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protonets/config.py
filangelos/protonets
2
76336
import os import yaml class Config(object): """Script configuration file parser. Attributes ---------- dataset: str Name of the dataset to train on (i.e., 'omniglot'). num_epochs: int Number of training epochs. num_episodes: int Number of episodes per epoch. num_ways_train: int Number of random classes per episode for training. num_support_train: int Number of samples per class to use as support for training. num_query_train: int Number of samples per class to use as query for training. num_ways_val: int Number of random classes per episode for validation. num_support_val: int Number of samples per class to use as support for validation. num_query_val: int Number of samples per class to use as query for validation. seed: int Random seed. """ def __init__(self, config_yaml: str) -> None: if not os.path.exists(config_yaml): raise ValueError( f"The config file at {config_yaml} is missing.") config = yaml.load(open(config_yaml, "r")) self.dataset = config["dataset"] self.num_epochs = config.get("num_epochs", 100) self.num_episodes = config.get("num_episodes", 100) self.num_ways_train = config.get("num_ways_train", 60) self.num_support_train = config.get("num_support_train", 5) self.num_query_train = config.get("num_query_train", 5) self.num_ways_val = config.get("num_ways_val", 5) self.num_support_val = config.get("num_support_val", 5) self.num_query_val = config.get("num_query_val", 15) self.seed = config.get("seed", 0)
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scripts/ingest.py
adjspecies/sfdata
1
170589
import beautifulsoup4 import cookielib import mechanize br = mechanize.Browser() jar = cookielib.LWPCookieJar() br.set_cookiejar(jar) br.set_handle_equiv( True ) br.set_handle_gzip( True ) br.set_handle_redirect( True ) br.set_handle_referer( True ) br.set_handle_robots( False )
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polling_stations/apps/data_collection/management/commands/import_torbay.py
chris48s/UK-Polling-Stations
0
24
from data_collection.management.commands import BaseXpressDemocracyClubCsvImporter class Command(BaseXpressDemocracyClubCsvImporter): council_id = 'E06000027' addresses_name = 'parl.2017-06-08/Version 1/Torbay Democracy_Club__08June2017.tsv' stations_name = 'parl.2017-06-08/Version 1/Torbay Democracy_Club__08June2017.tsv' elections = ['parl.2017-06-08'] csv_delimiter = '\t'
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cinder/volume/drivers/violin/v6000_common.py
rlucio/cinder-violin-driver-icehouse
0
183302
# vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright 2014 Violin Memory, Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """ Violin Memory 6000 Series All-Flash Array Common Driver for Openstack Cinder Uses Violin REST API via XG-Tools to manage a standard V6000 series flash array to provide network block-storage services. by <NAME> Senior Software Engineer Violin Memory """ import re import time from oslo.config import cfg from cinder import context from cinder import exception from cinder.openstack.common import log as logging from cinder import utils from cinder.volume.drivers.san import san from cinder.volume import volume_types LOG = logging.getLogger(__name__) # support vmos versions V6.3.0.4 or newer # support vmos versions V6.3.1 or newer VMOS_SUPPORTED_VERSION_PATTERNS = ['V6.3.0.[4-9]', 'V6.3.[1-9].?[0-9]?'] try: import vxg except ImportError: LOG.exception( _("The Violin V6000 driver for Cinder requires the presence of " "the Violin 'XG-Tools', python libraries for facilitating " "communication between applications and the v6000 XML API. " "The libraries can be downloaded from the Violin Memory " "support website at http://www.violin-memory.com/support")) raise else: LOG.info(_("Running with xg-tools version: %s"), vxg.__version__) violin_opts = [ cfg.StrOpt('gateway_vip', default='', help='IP address or hostname of the v6000 master VIP'), cfg.StrOpt('gateway_mga', default='', help='IP address or hostname of mg-a'), cfg.StrOpt('gateway_mgb', default='', help='IP address or hostname of mg-b'), cfg.StrOpt('gateway_user', default='admin', help='User name for connecting to the Memory Gateway'), cfg.StrOpt('gateway_password', default='', help='User name for connecting to the Memory Gateway', secret=True), cfg.BoolOpt('use_igroups', default=False, help='Use igroups to manage targets and initiators'), cfg.BoolOpt('use_thin_luns', default=False, help='Use thin luns instead of thick luns'), ] CONF = cfg.CONF CONF.register_opts(violin_opts) class InvalidBackendConfig(exception.CinderException): message = _("Volume backend config is invalid: %(reason)s") class RequestRetryTimeout(exception.CinderException): message = _("Backend service retry timeout hit: %(timeout)s sec") class ViolinBackendErr(exception.CinderException): message = _("Backend reports: %(message)s") class ViolinBackendErrExists(exception.CinderException): message = _("Backend reports: item already exists") class ViolinBackendErrNotFound(exception.CinderException): message = _("Backend reports: item not found") class V6000CommonDriver(san.SanDriver): """Executes commands relating to Violin Memory Arrays.""" def __init__(self, *args, **kwargs): super(V6000CommonDriver, self).__init__(*args, **kwargs) self.request_timeout = 300 self.vmem_vip = None self.vmem_mga = None self.vmem_mgb = None self.container = "" self.stats = {} self.config = kwargs.get('configuration', None) self.context = None self.lun_tracker = LunIdList(self.db) if self.config: self.config.append_config_values(violin_opts) def do_setup(self, context): """Any initialization the driver does while starting.""" if not self.config.gateway_vip: raise exception.InvalidInput( reason=_('Gateway VIP is not set')) if not self.config.gateway_mga: raise exception.InvalidInput( reason=_('Gateway IP for mg-a is not set')) if not self.config.gateway_mgb: raise exception.InvalidInput( reason=_('Gateway IP for mg-b is not set')) self.vmem_vip = vxg.open(self.config.gateway_vip, self.config.gateway_user, self.config.gateway_password, keepalive=True) self.vmem_mga = vxg.open(self.config.gateway_mga, self.config.gateway_user, self.config.gateway_password, keepalive=True) self.vmem_mgb = vxg.open(self.config.gateway_mgb, self.config.gateway_user, self.config.gateway_password, keepalive=True) self.context = context vip = self.vmem_vip.basic ret_dict = vip.get_node_values("/vshare/state/local/container/*") if ret_dict: self.container = ret_dict.items()[0][1] ret_dict = vip.get_node_values( "/vshare/state/local/container/%s/lun/*" % self.container) if ret_dict: self.lun_tracker.update_from_volume_ids(ret_dict.values()) ret_dict = vip.get_node_values( "/vshare/state/snapshot/container/%s/lun/*" % self.container) if ret_dict: for vol_id in ret_dict.values(): snaps = vip.get_node_values( "/vshare/state/snapshot/container/%s/lun/%s/snap/*" % (self.container, vol_id)) self.lun_tracker.update_from_snapshot_ids(snaps.values()) def check_for_setup_error(self): """Returns an error if prerequisites aren't met.""" vip = self.vmem_vip.basic if len(self.container) == 0: raise InvalidBackendConfig(reason=_('container is missing')) if not self._is_supported_vmos_version(self.vmem_vip.version): msg = _('VMOS version is not supported') raise InvalidBackendConfig(reason=msg) bn1 = ("/vshare/state/local/container/%s/threshold/usedspace" "/threshold_hard_val" % self.container) bn2 = ("/vshare/state/local/container/%s/threshold/provision" "/threshold_hard_val" % self.container) ret_dict = vip.get_node_values([bn1, bn2]) for node in ret_dict: # The infrastructure does not support space reclamation so # ensure it is disabled. When used space exceeds the hard # limit, snapshot space reclamation begins. Default is 0 # => no space reclamation. # if node.endswith('/usedspace/threshold_hard_val'): if ret_dict[node] != 0: msg = _('space reclamation threshold is enabled') raise InvalidBackendConfig(reason=msg) # The infrastructure does not support overprovisioning so # ensure it is disabled. When provisioned space exceeds # the hard limit, further provisioning is stopped. # Default is 100 => provisioned space equals usable space. # elif node.endswith('/provision/threshold_hard_val'): if ret_dict[node] != 100: msg = _('provisioned space threshold not equal to ' 'usable space') raise InvalidBackendConfig(reason=msg) def create_volume(self, volume): """Creates a volume.""" self._create_lun(volume) def delete_volume(self, volume): """Deletes a volume.""" self._delete_lun(volume) def create_snapshot(self, snapshot): """Creates a snapshot from an existing volume.""" self._create_lun_snapshot(snapshot) def delete_snapshot(self, snapshot): """Deletes a snapshot.""" self._delete_lun_snapshot(snapshot) def create_volume_from_snapshot(self, volume, snapshot): """Creates a volume from a snapshot.""" snapshot['size'] = snapshot['volume']['size'] self._create_lun(volume) self.copy_volume_data(self.context, snapshot, volume) def create_cloned_volume(self, volume, src_vref): """Creates a full clone of the specified volume.""" self._create_lun(volume) self.copy_volume_data(self.context, src_vref, volume) def extend_volume(self, volume, new_size): """Extend an existing volume's size. The equivalent CLI command is "lun resize container <container_name> name <lun_name> size <gb>" Arguments: volume -- volume object provided by the Manager new_size -- new (increased) size in GB to be applied """ v = self.vmem_vip LOG.info(_("Extending lun %(id)s, from %(size)s to %(new_size)s GB") % {'id': volume['id'], 'size': volume['size'], 'new_size': new_size}) try: self._send_cmd(v.lun.resize_lun, 'Success', self.container, volume['id'], new_size) except Exception: LOG.exception(_("LUN extend failed!")) raise @utils.synchronized('vmem-lun') def _create_lun(self, volume): """Creates a new lun. The equivalent CLI command is "lun create container <container_name> name <lun_name> size <gb>" Arguments: volume -- volume object provided by the Manager """ lun_type = '0' v = self.vmem_vip LOG.info(_("Creating lun %(name)s, %(size)s GB") % volume) if self.config.use_thin_luns: lun_type = '1' # using the defaults for fields: quantity, nozero, # readonly, startnum, blksize, naca, alua, preferredport # try: self._send_cmd(v.lun.create_lun, 'LUN create: success!', self.container, volume['id'], volume['size'], 1, '0', lun_type, 'w', 1, 512, False, False, None) except ViolinBackendErrExists: LOG.info(_("Lun %s already exists, continuing"), volume['id']) except Exception: LOG.warn(_("Lun create failed!")) raise @utils.synchronized('vmem-lun') def _delete_lun(self, volume): """Deletes a lun. The equivalent CLI command is "no lun create container <container_name> name <lun_name>" Arguments: volume -- volume object provided by the Manager """ v = self.vmem_vip success_msgs = ['lun deletion started', ''] LOG.info(_("Deleting lun %s"), volume['id']) try: self._send_cmd(v.lun.bulk_delete_luns, success_msgs, self.container, volume['id']) except ViolinBackendErrNotFound: LOG.info(_("Lun %s already deleted, continuing"), volume['id']) except ViolinBackendErrExists: LOG.warn(_("Lun %s has dependent snapshots, skipping"), volume['id']) raise exception.VolumeIsBusy(volume_name=volume['id']) except Exception: LOG.exception(_("Lun delete failed!")) raise self.lun_tracker.free_lun_id_for_volume(volume) @utils.synchronized('vmem-snap') def _create_lun_snapshot(self, snapshot): """Creates a new snapshot for a lun. The equivalent CLI command is "snapshot create container <container> lun <volume_name> name <snapshot_name>" Arguments: snapshot -- snapshot object provided by the Manager """ v = self.vmem_vip LOG.info(_("Creating snapshot %s"), snapshot['id']) try: self._send_cmd(v.snapshot.create_lun_snapshot, 'Snapshot create: success!', self.container, snapshot['volume_id'], snapshot['id']) except ViolinBackendErrExists: LOG.info(_("Snapshot %s already exists, continuing"), snapshot['id']) except Exception: LOG.exception(_("LUN snapshot create failed!")) raise @utils.synchronized('vmem-snap') def _delete_lun_snapshot(self, snapshot): """Deletes an existing snapshot for a lun. The equivalent CLI command is "no snapshot create container <container> lun <volume_name> name <snapshot_name>" Arguments: snapshot -- snapshot object provided by the Manager """ v = self.vmem_vip LOG.info(_("Deleting snapshot %s"), snapshot['id']) try: self._send_cmd(v.snapshot.delete_lun_snapshot, 'Snapshot delete: success!', self.container, snapshot['volume_id'], snapshot['id']) except ViolinBackendErrNotFound: LOG.info(_("Snapshot %s already deleted, continuing"), snapshot['id']) except Exception: LOG.exception(_("LUN snapshot delete failed!")) raise self.lun_tracker.free_lun_id_for_snapshot(snapshot) def _send_cmd(self, request_func, success_msgs, *args): """Run an XG request function, and retry until the request returns a success message, a failure message, or the global request timeout is hit. This wrapper is meant to deal with backend requests that can fail for any variety of reasons, for instance, when the system is already busy handling other LUN requests. It is also smart enough to give up if clustering is down (eg no HA available), there is no space left, or other "fatal" errors are returned (see _fatal_error_code() for a list of all known error conditions). Arguments: request_func -- XG api method to call success_msgs -- Success messages expected from the backend *args -- argument array to be passed to the request_func Returns: The response dict from the last XG call. """ resp = {} start = time.time() done = False if isinstance(success_msgs, basestring): success_msgs = [success_msgs] while not done: if time.time() - start >= self.request_timeout: raise RequestRetryTimeout(timeout=self.request_timeout) resp = request_func(*args) if not resp['message']: # XG requests will return None for a message if no message # string is passed int the raw response resp['message'] = '' for msg in success_msgs: if not resp['code'] and msg in resp['message']: done = True break self._fatal_error_code(resp) return resp def _send_cmd_and_verify(self, request_func, verify_func, request_success_msgs='', rargs=[], vargs=[]): """Run an XG request function, and verify success using an additional verify function. If the verification fails, then retry the request/verify cycle until both functions are successful, the request function returns a failure message, or the global request timeout is hit. This wrapper is meant to deal with backend requests that can fail for any variety of reasons, for instance, when the system is already busy handling other LUN requests. It is also smart enough to give up if clustering is down (eg no HA available), there is no space left, or other "fatal" errors are returned (see _fatal_error_code() for a list of all known error conditions). Arguments: request_func -- XG api method to call verify_func -- function to call to verify request was completed successfully (eg for export) request_success_msg -- Success message expected from the backend for the request_func *rargs -- argument array to be passed to the request_func *vargs -- argument array to be passed to the verify_func Returns: The response dict from the last XG call. """ resp = {} start = time.time() request_needed = True verify_needed = True if isinstance(request_success_msgs, basestring): request_success_msgs = [request_success_msgs] while request_needed or verify_needed: if time.time() - start >= self.request_timeout: raise RequestRetryTimeout(timeout=self.request_timeout) if request_needed: resp = request_func(*rargs) if not resp['message']: # XG requests will return None for a message if no message # string is passed int the raw response resp['message'] = '' for msg in request_success_msgs: if not resp['code'] and msg in resp['message']: # XG request func was completed request_needed = False break self._fatal_error_code(resp) elif verify_needed: success = verify_func(*vargs) if success: # XG verify func was completed verify_needed = False else: # try sending the request again request_needed = True return resp def _get_igroup(self, volume, connector): """Gets the igroup that should be used when configuring a volume. Arguments: volume -- volume object used to determine the igroup name Returns: igroup_name -- name of igroup (for configuring targets & initiators) """ v = self.vmem_vip # Use the connector's primary hostname and use that as the # name of the igroup. The name must follow syntax rules # required by the array: "must contain only alphanumeric # characters, dashes, and underscores. The first character # must be alphanumeric". # igroup_name = re.sub(r'[\W]', '_', connector['host']) # verify that the igroup has been created on the backend, and # if it doesn't exist, create it! # bn = "/vshare/config/igroup/%s" % igroup_name resp = v.basic.get_node_values(bn) if not len(resp): v.igroup.create_igroup(igroup_name) return igroup_name def _get_volume_type_extra_spec(self, volume, spec_key): """Parse data stored in a volume_type's extra_specs table. Code adapted from examples in cinder/volume/drivers/solidfire.py and cinder/openstack/common/scheduler/filters/capabilities_filter.py. Arguments: volume -- volume object containing volume_type to query spec_key -- the metadata key to search for Returns: spec_value -- string value associated with spec_key """ spec_value = None ctxt = context.get_admin_context() typeid = volume['volume_type_id'] if typeid: volume_type = volume_types.get_volume_type(ctxt, typeid) volume_specs = volume_type.get('extra_specs') for key, val in volume_specs.iteritems(): # Havana release altered extra_specs to require a # prefix on all non-host-capability related extra # specs, so that prefix is stripped here before # checking the key. # if ':' in key: scope = key.split(':') key = scope[1] if key == spec_key: spec_value = val break return spec_value def _wait_for_exportstate(self, volume_name, state=False): """Polls backend to verify volume's export configuration. XG sets/queries following a request to create or delete a lun export may fail on the backend if vshared is still processing the export action (or times out). We can check whether it is done by polling the export binding for a lun to ensure it is created or deleted. This function will try to verify the creation or removal of export state on both gateway nodes of the array every 5 seconds for up to 30 seconds. Arguments: volume_name -- name of volume to be polled state -- True to poll for existence, False for lack of Returns: True if the export state was correctly added or removed (depending on 'state' param) """ status = [False, False] mg_conns = [self.vmem_mga.basic, self.vmem_mgb.basic] success = False bn = "/vshare/config/export/container/%s/lun/%s" \ % (self.container, volume_name) for i in xrange(6): for node_id in xrange(2): if not status[node_id]: resp = mg_conns[node_id].get_node_values(bn) if state and len(resp.keys()): status[node_id] = True elif (not state) and (not len(resp.keys())): status[node_id] = True if status[0] and status[1]: success = True break else: time.sleep(5) return success def _is_supported_vmos_version(self, version_string): """Check that the version of VMOS running on the gateways is valid for use with the OpenStack drivers.""" for pattern in VMOS_SUPPORTED_VERSION_PATTERNS: if re.match(pattern, version_string): LOG.debug("Verified VMOS version %s is supported" % version_string) return True return False def _fatal_error_code(self, response): """Check the error code in a XG response for a fatal error, and returns an appropriate exception. Error codes extracted from vdmd_mgmt.c. Arguments: response -- a response dict result from an XG request """ # known non-fatal response codes # retry_codes = {1024: 'lun deletion in progress, try again later', 14032: 'lc_err_lock_busy'} if response['code'] == 14000: # lc_generic_error raise ViolinBackendErr(message=response['message']) elif response['code'] == 14002: # lc_err_assertion_failed raise ViolinBackendErr(message=response['message']) elif response['code'] == 14004: # lc_err_not_found raise ViolinBackendErrNotFound() elif response['code'] == 14005: # lc_err_exists raise ViolinBackendErrExists() elif response['code'] == 14008: # lc_err_unexpected_arg raise ViolinBackendErr(message=response['message']) elif response['code'] == 14014: # lc_err_io_error raise ViolinBackendErr(message=response['message']) elif response['code'] == 14016: # lc_err_io_closed raise ViolinBackendErr(message=response['message']) elif response['code'] == 14017: # lc_err_io_timeout raise ViolinBackendErr(message=response['message']) elif response['code'] == 14021: # lc_err_unexpected_case raise ViolinBackendErr(message=response['message']) elif response['code'] == 14025: # lc_err_no_fs_space raise ViolinBackendErr(message=response['message']) elif response['code'] == 14035: # lc_err_range raise ViolinBackendErr(message=response['message']) elif response['code'] == 14036: # lc_err_invalid_param raise ViolinBackendErr(message=response['message']) elif response['code'] == 14121: # lc_err_cancelled_err raise ViolinBackendErr(message=response['message']) elif response['code'] == 512: # Not enough free space in container (vdmd bug) raise ViolinBackendErr(message=response['message']) elif response['code'] == 1 and 'LUN ID conflict' \ in response['message']: # lun id conflict while attempting to export raise ViolinBackendErr(message=response['message']) class LunIdList(object): """Tracks available lun_ids for use when exporting a new lun for the first time. After instantiating a new LunIdList object, it should be updated (basically quiescing volumes/snapshot lun ID allocation between the array and the corresponding Openstack DB metadata). After that, the object can be queried to capture the next 'available' lun ID for use with exporting a new volume or snapshot. Only when the volume/snapshot is deleted entirely, the lun ID should be freed. Lun IDs are montonically increasing up to a max value of 16k, after which the selection will loop around to lun ID 1 and will continue to increment until an available ID is found. """ def __init__(self, db, *args, **kwargs): self.max_lun_id = 16000 self.lun_id_list = [0] * self.max_lun_id self.lun_id_list[0] = 1 self.prev_index = 1 self.free_index = 1 self.context = context.get_admin_context() self.db = db def update_from_volume_ids(self, id_list=[]): """Walk a list of volumes collected that the array knows about and check for any saved lun_id metadata for each of those volumes to fully sync the list. Note that the metadata keys are stored as strings. Arguments: id_list -- array containing names of volumes that exist on the backend (volume 'names' are UUIDs if they were made via the VMEM driver API) """ for item in id_list: try: metadata = self.db.volume_metadata_get(self.context, item) except exception.VolumeNotFound: LOG.warn(_("No db state for lun %s, skipping lun_id update"), item) else: if metadata and 'lun_id' in metadata: index = int(metadata['lun_id']) self.lun_id_list[index] = 1 LOG.debug("Set lun_id=%d for volume_id=%s" % (index, item)) self.update_free_index(index) def update_from_snapshot_ids(self, id_list=[]): """Walk a list of snapshots collected that the array knows about and check for any saved lun_id metadata for each of those snapshots to fully sync the list. Note that the metadata keys are stored as strings. Arguments: id_list -- array containing names of snapshots that exist on the backend (snapshot 'names' are UUIDs if they were made via the VMEM driver API) """ for item in id_list: try: metadata = self.db.snapshot_metadata_get(self.context, item) except exception.SnapshotNotFound: LOG.warn(_("No db state for snap %s, skipping lun_id update"), item) else: if metadata and 'lun_id' in metadata: index = int(metadata['lun_id']) self.lun_id_list[index] = 1 LOG.debug("Set lun_id=%d for snapshot_id=%s" % (index, item)) self.update_free_index(index) def get_lun_id_for_volume(self, volume): """Allocate a free a lun ID to a volume and create a lun_id tag in the volume's metadata. Arguments: volume -- the volume object to allocate a lun_id to """ metadata = self.db.volume_metadata_get(self.context, volume['id']) if not metadata or 'lun_id' not in metadata: metadata = {} metadata['lun_id'] = self.get_next_lun_id_str() self.db.volume_metadata_update(self.context, volume['id'], metadata, False) LOG.debug("Assigned lun_id %s to volume %s" % (metadata['lun_id'], volume['id'])) return metadata['lun_id'] def get_lun_id_for_snapshot(self, snapshot): """Allocate a free a lun ID to a snapshot and create a lun_id tag in the snapshot's metadata. Arguments: snapshot -- the snapshot object to allocate a lun_id to """ metadata = self.db.snapshot_metadata_get(self.context, snapshot['id']) if not metadata or 'lun_id' not in metadata: metadata = {} metadata['lun_id'] = self.get_next_lun_id_str() self.db.snapshot_metadata_update(self.context, snapshot['id'], metadata, False) LOG.debug("Assigned lun_id %s to volume %s" % (metadata['lun_id'], snapshot['id'])) return metadata['lun_id'] def free_lun_id_for_volume(self, volume): """Remove the lun_id tag saved in the volume's metadata and free the lun ID in the internal tracking array. Arguments: volume -- the volume object with a lun ID to be free'd """ metadata = self.db.volume_metadata_get(self.context, volume['id']) if metadata and 'lun_id' in metadata: self.free_lun_id_str(metadata['lun_id']) def free_lun_id_for_snapshot(self, snapshot): """Remove the lun_id tag saved in the snapshot's metadata and free the lun ID in the internal tracking array. Arguments: snapshot -- the snapshot object with a lun ID to be free'd """ metadata = self.db.snapshot_metadata_get(self.context, snapshot['id']) if metadata and 'lun_id' in metadata: self.free_lun_id_str(metadata['lun_id']) def get_next_lun_id_str(self): """Mark the next available lun_id as allocated and return it to the caller. Returns: next_id -- the lun ID that being allocated to the caller """ next_id = self.free_index self.lun_id_list[next_id] = 1 self.update_free_index() return str(next_id) def free_lun_id_str(self, value_str): """Mark a lun_id as now available, as if the lun was de-allocated. Arguments: value_str -- lun ID to free (in string format) """ value = int(value_str) self.lun_id_list[value] = 0 self.update_free_index() def update_free_index(self, index=None): """Update the free index, monotonically increasing, and looping back to 1 after the max lun ID value is hit. Arguments: index -- assume that all values below this number may be already allocated, so start searching at that value if it is higher than the free_index """ i = 0 count = 0 max_size = len(self.lun_id_list) if index and index > self.free_index: i = index + 1 else: i = self.free_index # avoid possibility of indexError if i >= max_size: i = 1 while self.lun_id_list[i] == 1 and count < max_size: count += 1 i += 1 if i >= max_size: i = 1 self.free_index = i if count == max_size: raise exception.Error("Cannot find free lun_id, giving up!")
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design-patterns/structural/interpreter.py
verthais/exercise-python
0
109653
<filename>design-patterns/structural/interpreter.py from abc import abstractmethod class AbstractExpression(): @abstractmethod def interpret(self): pass class NonterminalExpression(AbstractExpression): def __init__(self, expression): self._expression = expression def interpret(self): print("Non-terminal expression being interpreted ...") self._expression.interpret() class TerminalExpression(AbstractExpression): def interpret(self): print("Terminal expression being interpreted ...") def main(): ast = NonterminalExpression(NonterminalExpression(TerminalExpression())) ast.interpret() if __name__ == "__main__": main()
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lib/Protocol/NVRDriverCLIProtocol.py
multi-service-fabric/element-manager
0
46317
#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename: NVRDriverCLIProtocol.py import traceback import re import GlobalModule from EmCommonLog import decorater_log from CgwshDriverCLIProtocol import CgwshDriverCLIProtocol class NVRDriverCLIProtocol(CgwshDriverCLIProtocol): ''' Class for processing NVR driver protocol(CLI) ''' @decorater_log def __init__(self, error_recv_message=[], connected_recv_message="~"): super(NVRDriverCLIProtocol, self).__init__(error_recv_message, connected_recv_message) @decorater_log def _exit_configuration_mode(self): ''' Release of configuration mode is forced. ''' re_txt_save_conf = "Save new configuration \? \(Y/N\)" send_message = [("quit", "{0}|{1}".format(">", re_txt_save_conf))] GlobalModule.EM_LOGGER.debug( "start exit command :\n%s" % (send_message,)) try: output = self._exec_interactive(send_message, self.error_recv_message) if re.search(re_txt_save_conf, output): self._send_command_no_save() except Exception as ex: GlobalModule.EM_LOGGER.debug("Error exit command:%s", ex) GlobalModule.EM_LOGGER.debug("Traceback:%s", traceback.format_exc()) else: GlobalModule.EM_LOGGER.debug("Success configuration exit") self._is_mode_configuration = False @decorater_log def _send_command_no_save(self): ''' Save new configuration ? n is set as reponse to question(Y/N). Return value: Received message ''' GlobalModule.EM_LOGGER.debug( "Send n for 'Save new configuration ? (Y/N)'") shell_obj = self._ssh_shell send_message = "n" receive_keyword = ">" shell_obj.send(send_message) return self._recv_message(shell_obj, receive_keyword)
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csvjoin/__main__.py
walkerever/csv-join
0
1614680
<reponame>walkerever/csv-join<gh_stars>0 from . import csvjoin_main if __name__ == "__main__" : csvjoin_main()
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pyvttbl/tests/test_stats_ttest_paired.py
yk/pyvttbl
0
58264
# Copyright (c) 2011, <NAME> [see LICENSE.txt] # This software is funded in part by NIH Grant P20 RR016454. import unittest import warnings import os import math from random import shuffle, random from collections import Counter,OrderedDict from dictset import DictSet,_rep_generator from math import isnan, isinf, floor import numpy as np from pprint import pprint as pp from pyvttbl import PyvtTbl from pyvttbl import DataFrame from pyvttbl.plotting import * from pyvttbl.stats import * from pyvttbl.misc.support import * class Test_ttest_paired(unittest.TestCase): def test0(self): """paired ttest""" R=Ttest([('t', -1.4106912317171967), ('p2tail', 0.19601578492449323), ('p1tail', 0.09800789246224662), ('n1', 9), ('n2', 9), ('r', 0.10182008678393427), ('df', 8), ('mu1', 4.555555555555555), ('mu2', 7.888888888888889), ('var1', 6.777777777777778), ('var2', 47.111111111111114), ('tc2tail', 1.8595480375228424), ('tc1tail', 2.3060041350333704), ('cohen_d', 0.47023041057239895), ('delta', 1.410691231717197), ('power1tail', 0.36186192660269623), ('power2tail', 0.23741605057147952)], paired=True, aname='A', bname='B', type='t-Test: Paired Two Sample for means') A=[3,4, 5,8,9, 1,2,4, 5] B=[6,19,3,2,14,4,5,17,1] D=Ttest() D.run(A,B,paired=True) ## print(D) for k in list(R.keys()): self.assertTrue(D[k],R[k]) def test01(self): """paired ttest""" R="""t-Test: Paired Two Sample for means A B ========================================= Mean 4.556 7.889 Variance 6.778 47.111 Observations 9 9 Pearson Correlation 0.102 df 8 t Stat -1.411 alpha 0.050 P(T<=t) one-tail 0.098 t Critical one-tail 2.306 P(T<=t) two-tail 0.196 t Critical two-tail 1.860 P(T<=t) two-tail 0.196 Effect size dz 0.470 delta 1.411 Observed power one-tail 0.362 Observed power two-tail 0.237 """ A=[3,4, 5,8,9, 1,2,4, 5] B=[6,19,3,2,14,4,5,17,1] D=Ttest() D.run(A,B,paired=True) self.assertEqual(str(D),R) def test4(self): R="""t-Test: Paired Two Sample for means PRE POST ============================================= Mean 87.250 87.083 Variance 1207.659 1166.629 Observations 12 12 Pearson Correlation 0.995 df 11 t Stat 0.163 alpha 0.050 P(T<=t) one-tail 0.437 t Critical one-tail 2.201 P(T<=t) two-tail 0.873 t Critical two-tail 1.796 P(T<=t) two-tail 0.873 Effect size dz 0.047 delta 0.163 Observed power one-tail 0.068 Observed power two-tail 0.035 """ df = DataFrame() df.read_tbl('data/example2_prepost.csv') D = df.ttest('PRE','POST',paired=True) self.assertEqual(str(D),R) def test__repr__(self): R=Ttest([('t', 2.310889197854228), ('p2tail', 0.026382412254338405), ('p1tail', 0.013191206127169203), ('n1', 21), ('n2', 23), ('df', 37.855400659439084), ('mu1', 51.476190476190474), ('mu2', 41.52173913043478), ('var1', 121.16190476190475), ('var2', 294.0790513833993), ('tc2tail', 1.6861153650443554), ('tc1tail', 2.0246481352107009), ('cohen_d', 0.6908475708680588), ('delta', 2.1846518399376538), ('power1tail', 0.6916337616595899), ('power2tail', 0.56712772561445368)], equal_variance=False, aname='A', bname='B', type='t-Test: Two-Sample Assuming Unequal Variances') A=[24,61,59,46,43,44,52,43,58,67,62,57,71,49,54,43,53,57,49,56,33] B=[42,33,46,37,43,41,10,42,55,19,17,55,26,54,60,28,62,20,53,48,37,85,42] D=Ttest() D.run(A,B,equal_variance=False) for key in list(R.keys()): self.assertAlmostEqual(D[key],R[key]) def suite(): return unittest.TestSuite(( unittest.makeSuite(Test_ttest_paired) )) if __name__ == "__main__": # run tests runner = unittest.TextTestRunner() runner.run(suite())
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gensim/gensim/test/svd_error.py
Abas-Khan/thesis
3
134302
#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2011 <NAME> <<EMAIL>> """USAGE: %(program)s MATRIX.mm [CLIP_DOCS] [CLIP_TERMS] Check truncated SVD error for the algo in gensim, using a given corpus. This script runs the decomposition with several internal parameters (number of requested factors, iterative chunk size) and reports error for each parameter combination. The number of input documents is clipped to the first CLIP_DOCS. Similarly, only the first CLIP_TERMS are considered (features with id >= CLIP_TERMS are ignored, effectively restricting the vocabulary size). If you don't specify them, the entire matrix will be used. Example: ./svd_error.py ~/gensim/results/wiki_en_v10k.mm.bz2 100000 10000 """ from __future__ import print_function, with_statement import logging import os import sys import time import bz2 import itertools import numpy as np import scipy.linalg import gensim try: from sparsesvd import sparsesvd except ImportError: # no SVDLIBC: install with `easy_install sparsesvd` if you want SVDLIBC results as well sparsesvd = None sparsesvd = None # don't use SVDLIBC FACTORS = [300] # which num_topics to try CHUNKSIZE = [10000, 1000] # which chunksize to try POWER_ITERS = [0, 1, 2, 4, 6] # extra power iterations for the randomized algo # when reporting reconstruction error, also report spectral norm error? (very slow) COMPUTE_NORM2 = False def norm2(a): """Spectral norm ("norm 2") of a symmetric matrix `a`.""" if COMPUTE_NORM2: logging.info("computing spectral norm of a %s matrix", str(a.shape)) return scipy.linalg.eigvalsh(a).max() # much faster than np.linalg.norm(2) else: return np.nan def rmse(diff): return np.sqrt(1.0 * np.multiply(diff, diff).sum() / diff.size) def print_error(name, aat, u, s, ideal_nf, ideal_n2): err = -np.dot(u, np.dot(np.diag(s), u.T)) err += aat nf, n2 = np.linalg.norm(err), norm2(err) print( '%s error: norm_frobenius=%f (/ideal=%g), norm2=%f (/ideal=%g), RMSE=%g' % (name, nf, nf / ideal_nf, n2, n2 / ideal_n2, rmse(err)) ) sys.stdout.flush() class ClippedCorpus(object): def __init__(self, corpus, max_docs, max_terms): self.corpus = corpus self.max_docs, self.max_terms = max_docs, max_terms def __iter__(self): for doc in itertools.islice(self.corpus, self.max_docs): yield [(f, w) for f, w in doc if f < self.max_terms] if __name__ == '__main__': logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) logging.info("running %s", " ".join(sys.argv)) program = os.path.basename(sys.argv[0]) # do we have enough cmd line arguments? if len(sys.argv) < 2: print(globals()["__doc__"] % locals()) sys.exit(1) fname = sys.argv[1] if fname.endswith('bz2'): mm = gensim.corpora.MmCorpus(bz2.BZ2File(fname)) else: mm = gensim.corpora.MmCorpus(fname) # extra cmd parameters = use a subcorpus (fewer docs, smaller vocab) if len(sys.argv) > 2: n = int(sys.argv[2]) else: n = mm.num_docs if len(sys.argv) > 3: m = int(sys.argv[3]) else: m = mm.num_terms logging.info("using %i documents and %i features", n, m) corpus = ClippedCorpus(mm, n, m) id2word = gensim.utils.FakeDict(m) logging.info("computing corpus * corpus^T") # eigenvalues of this matrix are singular values of `corpus`, squared aat = np.zeros((m, m), dtype=np.float64) for chunk in gensim.utils.grouper(corpus, chunksize=5000): num_nnz = sum(len(doc) for doc in chunk) chunk = gensim.matutils.corpus2csc(chunk, num_nnz=num_nnz, num_terms=m, num_docs=len(chunk), dtype=np.float32) chunk = chunk * chunk.T chunk = chunk.toarray() aat += chunk del chunk logging.info("computing full decomposition of corpus * corpus^t") aat = aat.astype(np.float32) spectrum_s, spectrum_u = scipy.linalg.eigh(aat) spectrum_s = spectrum_s[::-1] # re-order to descending eigenvalue order spectrum_u = spectrum_u.T[::-1].T np.save(fname + '.spectrum.npy', spectrum_s) for factors in FACTORS: err = -np.dot(spectrum_u[:, :factors], np.dot(np.diag(spectrum_s[:factors]), spectrum_u[:, :factors].T)) err += aat ideal_fro = np.linalg.norm(err) del err ideal_n2 = spectrum_s[factors + 1] print('*' * 40, "%i factors, ideal error norm_frobenius=%f, norm_2=%f" % (factors, ideal_fro, ideal_n2)) print("*" * 30, end="") print_error("baseline", aat, np.zeros((m, factors)), np.zeros((factors)), ideal_fro, ideal_n2) if sparsesvd: logging.info("computing SVDLIBC SVD for %i factors", factors) taken = time.time() corpus_ram = gensim.matutils.corpus2csc(corpus, num_terms=m) ut, s, vt = sparsesvd(corpus_ram, factors) taken = time.time() - taken del corpus_ram del vt u, s = ut.T.astype(np.float32), s.astype(np.float32)**2 # convert singular values to eigenvalues del ut print("SVDLIBC SVD for %i factors took %s s (spectrum %f .. %f)" % (factors, taken, s[0], s[-1])) print_error("SVDLIBC", aat, u, s, ideal_fro, ideal_n2) del u for power_iters in POWER_ITERS: for chunksize in CHUNKSIZE: logging.info( "computing incremental SVD for %i factors, %i power iterations, chunksize %i", factors, power_iters, chunksize ) taken = time.time() gensim.models.lsimodel.P2_EXTRA_ITERS = power_iters model = gensim.models.LsiModel( corpus, id2word=id2word, num_topics=factors, chunksize=chunksize, power_iters=power_iters ) taken = time.time() - taken u, s = model.projection.u.astype(np.float32), model.projection.s.astype(np.float32)**2 del model print( "incremental SVD for %i factors, %i power iterations, " "chunksize %i took %s s (spectrum %f .. %f)" % (factors, power_iters, chunksize, taken, s[0], s[-1]) ) print_error('incremental SVD', aat, u, s, ideal_fro, ideal_n2) del u logging.info("computing multipass SVD for %i factors, %i power iterations", factors, power_iters) taken = time.time() model = gensim.models.LsiModel( corpus, id2word=id2word, num_topics=factors, chunksize=2000, onepass=False, power_iters=power_iters ) taken = time.time() - taken u, s = model.projection.u.astype(np.float32), model.projection.s.astype(np.float32)**2 del model print( "multipass SVD for %i factors, " "%i power iterations took %s s (spectrum %f .. %f)" % (factors, power_iters, taken, s[0], s[-1]) ) print_error('multipass SVD', aat, u, s, ideal_fro, ideal_n2) del u logging.info("finished running %s", program)
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foodgram/migrations/0008_auto_20210303_2022.py
valkhmyrov/foodgram-project
0
42209
<reponame>valkhmyrov/foodgram-project # Generated by Django 3.1.6 on 2021-03-03 20:22 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('foodgram', '0007_recipe_pub_date'), ] operations = [ migrations.AlterModelOptions( name='recipe', options={'ordering': ['-pub_date']}, ), migrations.AlterModelOptions( name='tag', options={}, ), migrations.CreateModel( name='Follow', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('author', models.ForeignKey(help_text='Публицист', on_delete=django.db.models.deletion.CASCADE, related_name='following', to=settings.AUTH_USER_MODEL)), ('user', models.ForeignKey(help_text='Подписчик', on_delete=django.db.models.deletion.CASCADE, related_name='follower', to=settings.AUTH_USER_MODEL)), ], options={ 'ordering': ['user', 'author'], }, ), migrations.AddConstraint( model_name='follow', constraint=models.UniqueConstraint(fields=('user', 'author'), name='unique together'), ), ]
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tests/ut/python/parallel/test_onehot.py
ythlml/mindspore
7
66407
# Copyright 2019 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import mindspore as ms import mindspore.nn as nn from mindspore import Tensor from mindspore import context from mindspore.common.api import _executor from mindspore.ops import composite as C from mindspore.ops import operations as P from mindspore.ops.operations.comm_ops import _VirtualDataset context.set_context(mode=context.GRAPH_MODE) class NetWithLoss(nn.Cell): def __init__(self, network, strategy3, strategy4, axis): super(NetWithLoss, self).__init__() self.virtual_dataset = _VirtualDataset() self.one_hot = P.OneHot(axis=axis).set_strategy(strategy3) self.on_value = Tensor(2.0, ms.float32) self.off_value = Tensor(1.0, ms.float32) self.loss = P.SoftmaxCrossEntropyWithLogits().set_strategy(strategy4) self.network = network def construct(self, x, y, b): b_virtual = self.virtual_dataset(b) predict = self.network(x, y) label = self.one_hot(b_virtual, 64, self.on_value, self.off_value) return self.loss(predict, label)[0] class GradWrap(nn.Cell): def __init__(self, network): super(GradWrap, self).__init__() self.network = network def construct(self, x, y, b): return C.grad_all(self.network)(x, y, b) class Net(nn.Cell): def __init__(self, strategy1, strategy2): super().__init__() self.matmul = P.MatMul().set_strategy(strategy1) self.gelu = P.Gelu().set_strategy(strategy2) def construct(self, x, y): out = self.matmul(x, y) out = self.gelu(out) return out def compile_graph(strategy1, strategy2, strategy3, strategy4, auto=False, onthot_axis=-1): net = GradWrap(NetWithLoss(Net(strategy1, strategy2), strategy3, strategy4, axis=onthot_axis)) net.set_auto_parallel() if auto: context.set_auto_parallel_context(parallel_mode="auto_parallel") else: context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") x = Tensor(np.ones([64, 32]), dtype=ms.float32) y = Tensor(np.ones([32, 64]), dtype=ms.float32) b = Tensor(np.ones([64]), dtype=ms.int32) _executor.compile(net, x, y, b) def test_onehot_model_parallel(): context.set_auto_parallel_context(device_num=16, global_rank=0) strategy1 = ((2, 4), (4, 2)) strategy2 = ((2, 8),) strategy3 = ((1, 16), (), ()) strategy4 = ((16, 1), (16, 1)) compile_graph(strategy1, strategy2, strategy3, strategy4) def test_onehot_batch_parallel(): context.set_auto_parallel_context(device_num=16, global_rank=0) strategy1 = ((2, 4), (4, 2)) strategy2 = ((2, 8),) strategy3 = ((16, 1), (), ()) strategy4 = ((16, 1), (16, 1)) compile_graph(strategy1, strategy2, strategy3, strategy4) def test_onehot_batch_parallel_invalid_strategy(): context.set_auto_parallel_context(device_num=16, global_rank=0) strategy1 = ((2, 4), (4, 2)) strategy2 = ((2, 8),) strategy3 = ((16,), (), ()) strategy4 = ((16, 1), (16, 1)) try: compile_graph(strategy1, strategy2, strategy3, strategy4) except: pass def test_onehot_repeated_calculation(): context.set_auto_parallel_context(device_num=16, global_rank=0) strategy1 = ((2, 4), (4, 2)) strategy2 = ((2, 8),) strategy3 = ((4, 1), (), ()) strategy4 = ((16, 1), (16, 1)) compile_graph(strategy1, strategy2, strategy3, strategy4) def test_onehot_auto(): context.set_auto_parallel_context(device_num=16, global_rank=0) strategy1 = None strategy2 = None strategy3 = None strategy4 = None compile_graph(strategy1, strategy2, strategy3, strategy4, auto=True) def test_onehot_batch_parallel_axis0(): context.set_auto_parallel_context(device_num=16, global_rank=0) strategy1 = ((2, 4), (4, 2)) strategy2 = ((2, 8),) strategy3 = ((16, 1), (), ()) strategy4 = ((16, 1), (16, 1)) compile_graph(strategy1, strategy2, strategy3, strategy4, onthot_axis=0) # auto parallel for onehot axis equal to 0 has not been supported yet def test_onehot_batch_parallel_invalid_strategy_axis0(): context.set_auto_parallel_context(device_num=16, global_rank=0) strategy1 = ((2, 4), (4, 2)) strategy2 = ((2, 8),) strategy3 = None strategy4 = ((16, 1), (16, 1)) try: compile_graph(strategy1, strategy2, strategy3, strategy4, onthot_axis=0) except: pass def test_onehot_repeated_calculation_axis0(): context.set_auto_parallel_context(device_num=16, global_rank=0) strategy1 = ((2, 4), (4, 2)) strategy2 = ((2, 8),) strategy3 = ((4, 1), (), ()) strategy4 = ((16, 1), (16, 1)) compile_graph(strategy1, strategy2, strategy3, strategy4, onthot_axis=0) def test_onehot_auto_axis0(): context.set_auto_parallel_context(device_num=16, global_rank=14) strategy1 = None strategy2 = None strategy3 = None strategy4 = None compile_graph(strategy1, strategy2, strategy3, strategy4, auto=True, onthot_axis=0)
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forest_lite/server/lib/palette.py
uk-gov-mirror/MetOffice.forest-lite
6
15001
import bokeh.palettes def all_palettes(): """List of palette definitions""" for name in bokeh.palettes.all_palettes: for number in bokeh.palettes.all_palettes[name]: yield { "name": name, "number": number, "palette": bokeh.palettes.all_palettes[name][number] }
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src/features/build_features.py
helldragger/MetaWatch
1
193330
#%% import cv2; from pathlib import Path from dotenv import find_dotenv, load_dotenv # not used in this stub but often useful for finding various files #project_dir = Path(__file__).resolve().parents[2] # find .env automagically by walking up directories until it's found, then # load up the .env entries as environment variables load_dotenv(find_dotenv()) import numpy as np import cv2 as cv from matplotlib import pyplot as plt import seaborn as sns import pandas as pd from scipy import ndimage from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib import colors import seaborn as sns img = cv2.imread("C:\\Users\\Helldragger\\Documents\\projects\\MetaWatch\\MetaWatch\\src\\features\\original.jpg"); # we cut the image to keep only the interesting part: the overlay. #%% bgdModel = np.zeros((1,65),np.float64) fgdModel = np.zeros((1,65),np.float64) newmask = cv.imread('C:\\Users\\Helldragger\\Documents\\projects\\MetaWatch\\MetaWatch\\src\\features\\overlaymaskheroes.png',0) # wherever it is marked white (sure foreground), change mask=1 # wherever it is marked black (sure background), change mask=0 mask[newmask == 0] = 0 mask[newmask == 255] = 1 mask, bgdModel, fgdModel = cv.grabCut(img,mask,None,bgdModel,fgdModel,5,cv.GC_INIT_WITH_MASK) mask = np.where((mask==2)|(mask==0),0,1).astype('uint8') img2 = img*mask[:,:,np.newaxis] #%% plt.imshow(img),plt.colorbar(),plt.show() plt.imshow(img2),plt.colorbar(),plt.show() cv2.imshow("cut", img2); cv2.imshow("original", img); cv2.waitKey(0) croppedImg = img2[125:210, 35:1892]; plt.imshow(croppedImg),plt.colorbar(),plt.show() cv2.imshow("cropped", croppedImg); cv2.waitKey(0) #%% # read data about a single hero src1_mask = cv2.imread('C:\\Users\\Helldragger\\Documents\\projects\\MetaWatch\\MetaWatch\\src\\features\\maskheroA1.png',0); src1_mask = cv2.cvtColor(src1_mask,cv2.COLOR_GRAY2RGB) masked_image = cv2.bitwise_and(img, src1_mask) cv2.imshow("hero maked", masked_image); cv2.waitKey(0) #%% #cropping the hhero image y,x = masked_image[:,:,1].nonzero() # get the nonzero alpha coordinates minx = np.min(x) miny = np.min(y) maxx = np.max(x) maxy = np.max(y) cropImg = masked_image[miny:maxy, minx:maxx] #cv2.imwrite("cropped.png", cropImg) cv2.imshow("cropped", cropImg) cv2.waitKey(0) #%% # here we load various health bars and try to quantify their content: health, shield, armor, death symbol. imgs = {}; srcs = [ "frame377_hero_A1_health", "frame377_hero_A2_health", "frame377_hero_A3_health", "frame377_hero_A4_health", "frame377_hero_A5_health", "frame377_hero_A6_health", "frame377_hero_B1_health", "frame377_hero_B2_health", "frame377_hero_B3_health", "frame377_hero_B4_health", "frame377_hero_B5_health", "frame377_hero_B6_health", ] for src in srcs: imgs[src] = cv2.imread('C:\\Users\\Helldragger\\Documents\\projects\\MetaWatch\\MetaWatch\\src\\data\\tests\\'+src+'.jpg',-1); #src1_mask = cv2.imread('C:\\Users\\Helldragger\\Documents\\projects\\MetaWatch\\MetaWatch\\src\\features\\maskheroA1.png',0); #%% # here we create an histogram for each image # idea: counting the amount of health using the percentage of specific colours in the health zone. img = imgs["frame377_hero_B1_health"]; img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB ) img = cv2.cvtColor(img, cv2.COLOR_RGB2HLS ) channels = ('H','L','S') colors = ("B", 'G', 'R') for i,chann in enumerate(channels): histr = cv2.calcHist(img, [i], None, [256], [1, 256], True, False) histr /= max(histr) plt.plot(histr,color = colors[i]) plt.xlim([0,256]) plt.show() plt.imshow(img),plt.colorbar(),plt.show() #for img in imgs.values(): #%% # Reducing the amount of colors to 5 colors: reducedimg = imgs["frame377_hero_B1_health"]; #cv2.cvtColor(reducedimg, cv2.COLOR_HLS2BGR ) plt.imshow(reducedimg),plt.colorbar(),plt.show() reducedimg = reducedimg // 64 reducedimg = reducedimg * 64 plt.imshow(reducedimg),plt.colorbar(),plt.show() #%% img = imgs["frame377_hero_B1_health"]; img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB ) pixel_colors = img.reshape((np.shape(img)[0]*np.shape(img)[1], 3)) norm = colors.Normalize(vmin=-1.,vmax=1.) norm.autoscale(pixel_colors) pixel_colors = norm(pixel_colors).tolist() hlsImg = cv2.cvtColor(img, cv2.COLOR_RGB2HLS) h, l, s = cv2.split(hlsImg); #plt.imshow(img),plt.colorbar() fig = plt.figure() ax1 = fig.add_subplot(2, 1, 1, projection="3d") ax1.scatter(h.flatten(), l.flatten(), s.flatten(), facecolors=pixel_colors, marker=".") ax1.set_xlabel("Hue") ax1.set_ylabel("Luminosity") ax1.set_zlabel("Saturation") ax2 = fig.add_subplot(2, 1, 2) ax2.imshow(img) plt.show() #%% # trying to filter out noise colors: img = imgs["frame377_hero_B1_health"]; img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB ) hlsImg = cv2.cvtColor(img, cv2.COLOR_RGB2HLS) h, l, s = cv2.split(hlsImg); fig = plt.figure() ax = fig.add_subplot(4, 1, 1) ax.imshow(img) ax.set_label("pre filtering"); h, l, s = cv2.split(hlsImg); ax = fig.add_subplot(4, 1, 2) ax.imshow(h) ax.set_label("Hue"); ax = fig.add_subplot(4, 1, 3) ax.imshow(l) ax.set_label("Luminance"); ax = fig.add_subplot(4, 1, 4) ax.imshow(s) ax.set_label("Saturation"); plt.show() #%% def getRGBImg(name): return cv2.cvtColor(imgs[name], cv2.COLOR_BGR2RGB) def showHealthDetected(img): hlsImg = cv2.cvtColor(img, cv2.COLOR_RGB2HLS) hlsImg[hlsImg[:,:,1] < 100] = 0 #hlsImg[:,:,0] = 126 h, l, s = cv2.split(hlsImg); fig = plt.figure() ax = fig.add_subplot(5, 1, 1) ax.imshow(img) ax = fig.add_subplot(5, 1, 2) ax.imshow(h) ax.set_label("Hue"); ax = fig.add_subplot(5, 1, 3) ax.imshow(l) ax.set_label("Luminance"); ax = fig.add_subplot(5, 1, 4) ax.imshow(s) ax.set_label("Saturation"); resultimg = cv2.cvtColor(hlsImg, cv2.COLOR_HLS2RGB) ax = fig.add_subplot(5, 1, 5) ax.imshow(resultimg) plt.show() return resultimg analyzedImgs = {} for imgSrc in imgs.keys(): analyzedImgs[imgSrc] = showHealthDetected(getRGBImg(imgSrc)) #%% # recenter every img. # calculate the histogram of pixels > 0 per line. def showLineHistograms(img): img[img > 0] = 1 Ysize = len(img[:,0,0]) hist = img.sum(axis=1) fig = plt.figure() plt.plot(hist) plt.show() hist = np.zeros((22)); observations = np.array([]); for imgSrc in imgs.keys(): img = cv.cvtColor(analyzedImgs[imgSrc], cv2.COLOR_RGB2GRAY) imgSum = img.sum(axis=1); hist[: len(imgSum)] += imgSum observations = np.concatenate((observations, np.where(imgSum > 0)[0]) ); hist /= max(hist) fig = plt.figure() plt.plot(hist) plt.show() sns.distplot(observations) plt.show() #%% # here we try to detect the correct amount of notches on any image # the image is passed through a gradient filter to show only the variations. # this gradient filter is then def detectLabels(img): return ndimage.measurements.label(imgGrad) def calculateLabellingErrorRate(gradImg, expected): return detectLabels(gradImg)[1] - expected; notchesExpected = { "frame377_hero_A1_health":23, "frame377_hero_A2_health":24, "frame377_hero_A3_health":8, "frame377_hero_A4_health":10, "frame377_hero_A5_health":2, "frame377_hero_A6_health":6, "frame377_hero_B1_health":24, "frame377_hero_B2_health":8, "frame377_hero_B3_health":10, "frame377_hero_B4_health":8, "frame377_hero_B5_health":1, "frame377_hero_B6_health":8, } gradientThreshold = 30 bin_amount = abs(min(256, 256)) gradientThresholds = np.linspace(0,256,bin_amount) // 1 #%% errors = np.zeros(shape=(len(notchesExpected), bin_amount)) j = 0 for lowGradientThreshold in gradientThresholds: i = 0 for src in imgs.keys(): expected = notchesExpected[src]; img = imgs[src]; img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB ) imgGrad = ndimage.morphology.morphological_gradient(img[:,:,0], size=(1,2)) imgGrad[imgGrad<lowGradientThreshold] = 0 labels, count = ndimage.measurements.label(imgGrad) errors[i, j] = abs(calculateLabellingErrorRate(img, expected)) i += 1 j+=1 sns.heatmap(errors) plt.plot() plt.figure() errorsSummed = errors.sum(axis=0); errorsSummed sns.lineplot(data=errorsSummed) plt.plot() #plot.figure() bestThreshold = np.where(errorsSummed == min(errorsSummed))[0][-1]; print("best high pass gradient threshold: ",bestThreshold, "\n\terror count:", errorsSummed[bestThreshold]) # low pass gradient gave a best score of 60 errors, not enough. combining both systems gave a best score of 38. we will keep the simple high pass. #%% # best system, simple high filter, with a threshold of 52 or around 50 #%% img = imgs["frame377_hero_B1_health"]; img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB ) plt.imshow(img),plt.colorbar(),plt.show() imgGrad = ndimage.morphology.morphological_gradient(img[:,:,0], size=(1,2)) plt.imshow(imgGrad),plt.colorbar(),plt.show() imgGrad[imgGrad<30] = 0 plt.imshow(imgGrad),plt.colorbar(),plt.show() label, num_features = ndimage.measurements.label(imgGrad) print("###RGB mode on gradient") print(num_features, " features detected / 24.") HLSImg = cv.cvtColor(analyzedImgs[imgSrc], cv.COLOR_RGB2HLS) label, num_features = ndimage.measurements.label(HLSImg[:,:,0]) print("###HLSImg using Hue") print(num_features, " features detected / 24.") label, num_features = ndimage.measurements.label(HLSImg[:,:,1]) print("###HLSImg using Luminosity") print(num_features, " features detected / 24.") label, num_features = ndimage.measurements.label(HLSImg[:,:,2]) print("###HLSImg using Saturation") print(num_features, " features detected / 24.") #%% # here we try to differentiate notches types. notchesTypesExpected = { "frame377_hero_A1_health":{"white":20,"yellow":3, "blue":0, "red":0}, "frame377_hero_A2_health":{"white":17,"yellow":7, "blue":0, "red":0}, "frame377_hero_A3_health":{"white":8,"yellow":0, "blue":0, "red":0}, "frame377_hero_A4_health":{"white":8,"yellow":2, "blue":0, "red":0}, "frame377_hero_A5_health":{"white":2,"yellow":0, "blue":0, "red":0}, "frame377_hero_A6_health":{"white":2,"yellow":0, "blue":4, "red":0}, "frame377_hero_B1_health":{"white":20,"yellow":4, "blue":0, "red":0}, "frame377_hero_B2_health":{"white":8,"yellow":0, "blue":0, "red":0}, "frame377_hero_B3_health":{"white":8,"yellow":2, "blue":0, "red":0}, "frame377_hero_B4_health":{"white":8,"yellow":0, "blue":0, "red":0}, "frame377_hero_B5_health":{"white":0,"yellow":0, "blue":0, "red":1}, "frame377_hero_B6_health":{"white":8,"yellow":0, "blue":0, "red":0}, } #%% img = imgs["frame377_hero_A6_health"]; img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB ) originalimg = img #plt.imshow(img),plt.colorbar(),plt.show() imgHLS = cv2.cvtColor(img, cv2.COLOR_RGB2HLS ) #plt.imshow(imgHLS[:,:,1]),plt.colorbar(),plt.show() #imgHLS[:,:,1] = ndimage.grey_erosion(imgHLS[:,:,1], size=(1,2)); imgGrad = ndimage.morphology.morphological_gradient(imgHLS[:,:,1], size=(1,4)) #plt.imshow(imgGrad),plt.colorbar(),plt.show() imgGrad = ndimage.grey_erosion(imgGrad, size=(2,2)); #plt.imshow(imgGrad),plt.colorbar(),plt.show() imgGrad = ndimage.grey_dilation(imgGrad, size=(2,2)); #plt.imshow(imgGrad),plt.colorbar(),plt.show() imgGrad[imgGrad<52] = 0 #plt.imshow(imgGrad),plt.colorbar(),plt.show() imgHLS[:,:,1] = imgGrad; imgHLS[imgHLS[:,:,1] == 0] = 0 img = cv2.cvtColor(imgHLS, cv2.COLOR_HLS2RGB ) #plt.imshow(img),plt.colorbar(),plt.show() labels, count = ndimage.label(imgGrad) #plt.imshow(labels),plt.colorbar(),plt.show() #detect colors #plt.imshow(imgHLS[:,:,0]),plt.colorbar(),plt.show() #plt.imshow(imgHLS[:,:,1]),plt.colorbar(),plt.show() #plt.imshow(imgHLS[:,:,2]),plt.colorbar(),plt.show() colors = {"white":0,"yellow":0, "blue":0, "red":0} errors = np.zeros((len(notchesTypesExpected.keys()), 4)) i = 0 for imgKey in notchesTypesExpected.keys(): colors = {"white":0,"yellow":0, "blue":0, "red":0} expectedColors = notchesTypesExpected[imgKey] img = imgs[imgKey]; img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB ) imgGrad = ndimage.morphology.morphological_gradient(img[:,:,0], size=(1,2)) imgGrad[imgGrad<52] = 0 labels, count = ndimage.measurements.label(imgGrad) for label in range(1, count+1): labelMeanRGB = np.array(img[labels==label, :].mean(axis=0)) best_dist = -1 best_color = "" for color in COLOR_VALUES.keys(): curr_dist = np.sqrt(np.sum((COLOR_VALUES[color] - labelMeanRGB) ** 2)) if best_dist == -1 or curr_dist < best_dist: best_dist = curr_dist best_color = color print(i,": ",labelMeanRGB," => ", best_color) colors[best_color] += 1 # error detection j=0 for color in colors.keys(): errors[i, j] += abs(expectedColors[color] - colors[color]) print(j,"=",color) j+=1 i+=1 sns.heatmap(data=errors) print("total errors:", errors.sum(axis=0)) #%% #objective: find the best color codes to diminish errors in their case. #%% for i in range(1, count+1): labelRGB = originalimg[labels==i, :] labelMeanRGB = np.array(labelRGB.mean(axis=0)) best_dist = -1 best_color = "" print("cluster ",i) for color in COLOR_VALUES.keys(): curr_dist = np.sqrt(np.sum((COLOR_VALUES[color] - labelMeanRGB) ** 2)) print(color," => ",curr_dist) if best_dist == -1 or curr_dist < best_dist: best_dist = curr_dist best_color = color colors[best_color] += 1 print(colors) #detectedimg = np.zeros(originalimg.shape) #detectedimg = originalimg[labels != 0] #plt.imshow(detectedimg),plt.colorbar(),plt.show() #showHealthDetected(img) #%% labelHLS = imgHLS[labels==1, :] labelMeanHLS = np.array(labelHLS.mean(axis=0)) labelMeanHLS[1] = labelMeanHLS[1]/256 plt.imshow(labelHLS),plt.colorbar(),plt.show() plt.imshow([labelMeanHLS]),plt.colorbar(),plt.show() distToRed = abs(labelMeanHLS[0] - RED_HUE) distToYellow = abs(labelMeanHLS[0] - ORANGE_HUE) distToBlue = abs(labelMeanHLS[0] - BLUE_HUE) distToWhite = abs(labelMeanHLS[1] - 100) #%% labelRGB = originalimg[labels==1] #plt.imshow(labelRGB),plt.colorbar(),plt.show() labelMeanRGB = np.array([labelRGB.mean(axis=0)]) labelMeanRGB = labelMeanRGB / 255 #plt.imshow(labelMeanRGB),plt.colorbar(),plt.show() plt.imshow([labelMeanRGB]),plt.colorbar(),plt.show() #%% # # reading the current results. results = pd.read_csv("C:\\Users\\Helldragger\\Documents\\projects\\MetaWatch\\MetaWatch\\data\\temp\\2.csv") # barlett, kinda clean (4-6 anomalies) # bohman, blackmanharris, nuttall = same # rolling window: # [2.s] 120 clean( 1 anomaly) # [1.5] 90 semi clean (3 anomalies). # [1.s] 60 semi clean ( 4 - 6 death anomalies) # [.5s] 30 ugly (10+) res2 = results.groupby(['team', "hero"])["health", "armor", "shield", "death"].rolling(120).mean().reset_index()#.unstack(['team', "hero"]) res2["frame"] = res2["level_2"] // 12 res2.loc[res2.death > 0, "death"] = 1 res2 = res2.drop("level_2", axis=1) res3 = pd.melt(res2, ['team', "hero", "frame", "death"]) #sns.relplot(x="frame", y="value", hue='variable', col="team", kind="line", data=res3, row="hero") plt.style.use("seaborn-colorblind") fig, axes = plt.subplots(6,2, figsize=(1920,1080), dpi=400) i = 0 for team in res2.team.unique(): j = 0 for hero in res2.hero.unique(): frames = res2.loc[(res2.team==team) & (res2.hero == hero), "frame"] health = res2.loc[(res2.team==team) & (res2.hero == hero), "health"] shield = res2.loc[(res2.team==team) & (res2.hero == hero), "shield"] armor = res2.loc[(res2.team==team) & (res2.hero == hero), "armor"] axes[j,i].stackplot(frames, health, armor, shield,cmap=plt.get_cmap("Accent")) j+=1 i+=1 #plt.title('Recorded Game Statistics') plt.show() fig, axes = plt.subplots(6,2) i = 0 for team in res2.team.unique(): j = 0 for hero in res2.hero.unique(): current_data = res2.loc[(res2.team==team) & (res2.hero == hero)] frames = current_data.frame daed_frames = (current_data.health < 25) & (current_data.armor < 25) & (current_data.death == 1) axes[j,i].stackplot(frames,daed_frames, cmap=plt.get_cmap("Accent")) j+=1 i+=1 #%% # merging class DeathInterval: def __init__(self, start:int, previous=None, next=None): self.end = self.start = start self.previous = previous self.next = next def span(self): return self.end - self.start fig, axes = plt.subplots(6,2) i = 0 for team in res2.team.unique(): j = 0 for hero in res2.hero.unique(): current_data = res2.loc[(res2.team==team) & (res2.hero == hero)] frames = current_data.frame # daed_frames = (current_data.health < 25) & (current_data.armor < 25) & (current_data.death == 1) spawned_frames = (current_data.health >= 25) axes[j,i].stackplot(frames,spawned_frames) for frame in daed_frames: pass # TODO merge small intervals with little distance together, in order to clean the death reports. j+=1 i+=1 #%% # reading ultimate values import pytesseract def ocr_core(filename): """ This function will handle the core OCR processing of images. """ text = pytesseract.image_to_string(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY ), lang="Koverwatch") # We'll use Pillow's Image class to open the image and pytesseract to detect the string in the image return text #%% import pytesseract path = "C:/Users/Helldragger/Documents/projects/MetaWatch/MetaWatch/src/features/OCRTEST.png" img = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2GRAY ) plt.imshow(img) plt.show() srcTri = np.array( [[0, 0], [img.shape[1] - 1, 0], [0, img.shape[0] - 1]] ).astype(np.float32) dstTri = np.array( [[0, 0], [img.shape[1] - 1, 0], [img.shape[1]*0.08, img.shape[0] - 1]] ).astype(np.float32) warp_mat = cv.getAffineTransform(srcTri, dstTri) img = cv.warpAffine(img, warp_mat, (img.shape[1], img.shape[0])) plt.imshow(img) plt.show() thresh = 200 maxValue = 255 img = cv2.threshold(img, thresh, maxValue, cv2.THRESH_BINARY )[1] plt.imshow(img) plt.show() text = pytesseract.image_to_string(img, config="digits", lang="Koverwatch") print("img text: '{}'".format(text)) #%% import pytesseract import numpy as np import cv2 from matplotlib import pyplot as plt import seaborn as sns def convertUltimateImgToText(img): plt.imshow(img); plt.show(); img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY ) plt.imshow(img); plt.show(); srcTri = np.array( [[0, 0], [img.shape[1] - 1, 0], [0, img.shape[0] - 1]] ).astype(np.float32) dstTri = np.array( [[0, 0], [img.shape[1] - 1, 0], [img.shape[1]*0.19, img.shape[0] - 1]] ).astype(np.float32) warp_mat = cv2.getAffineTransform(srcTri, dstTri) img = cv2.warpAffine(img, warp_mat, (img.shape[1], img.shape[0])) plt.imshow(img); plt.show(); thresh = 200 maxValue = 255 img = cv2.threshold(img, thresh, maxValue, cv2.THRESH_BINARY )[1] plt.imshow(img); plt.show(); text = pytesseract.image_to_string(img, config="digits", lang="Koverwatch") print("img ult text: '{}'".format(text)) return text def test_UltimateParsing(testSources): # test format: COLOR_expectedValue.png expected = {} testedColors = set(); testedValues = set(); colorToIndex = {} valueToIndex = {} colorLabels = [] valueLabels = [] for src in testSources: color, value = src.split("_"); expected[src] = {"value":value, "color":color}; if color not in testedColors: colorToIndex[color] = len(testedColors) testedColors.add(color) colorLabels.append(color) if value not in testedValues: valueToIndex[value] = len(testedValues) testedValues.add(value) valueLabels.append(value) imgs = {} errors = np.zeros((len(testedValues), len(testedColors))) i = 0 legendPrinted = False for src in expected.keys(): ultimateImg = cv2.imread('src/data/tests/Ultimate/'+src+'.png',-1); ultimateImg = cv2.cvtColor(ultimateImg, cv2.COLOR_BGR2RGB ) assert ultimateImg is not None value = convertUltimateImgToText(ultimateImg) if value != expected[src]["value"]: errors[valueToIndex[expected[src]["value"]], colorToIndex[expected[src]["color"]]] += 1 sns.heatmap(data=errors, xticklabels=colorLabels, yticklabels=valueLabels)#.get_figure()#.savefig("src/data/tests/Ultimate/Tests_ErrorHeatmap.png") plt.show() totalErrors = errors.sum(axis=0).sum() print("total color errors:", errors.sum(axis=0)) print("total values errors:", errors.sum(axis=1)) print("total errors:", errors.sum(axis=0).sum()) ultimateAcc = 1 - (totalErrors / len(testSources)) print("Ultimate detection accuracy: ", ultimateAcc) #assert ultimateAcc >= 0.90 # 10% d'erreur FULL_TEST_SOURCES = [ "BLUE_0", "BLUE_1", "BLUE_14", "BLUE_15", "BLUE_16", "BLUE_24", "BLUE_25", "GREEN_13", "GREEN_16", "GREEN_21", "GREEN_22", "GREEN_39", "GREEN_42", "GREY_0", "GREY_11", "GREY_13", "GREY_34", "ORANGE_0", "RED_10", "RED_26", "WHITE_0", "WHITE_1", "WHITE_8", "WHITE_21", "WHITE_24", "WHITE_34" ] ACTUAL_TEST_SOURCES = [ "BLUE_0", "BLUE_1", "BLUE_14", "BLUE_15", "BLUE_16", "BLUE_24", "BLUE_25", ] test_UltimateParsing(ACTUAL_TEST_SOURCES) #%% from os import listdir from os.path import isfile, join imgDir = "src/data/tests/Ultimate/" onlyfiles = [f for f in listdir(imgDir) if isfile(join(imgDir, f))] imgHeatmap = None; lastImg = None; for fileSrc in onlyfiles: img = cv.imread(imgDir+"/"+fileSrc) img = cv.cvtColor(img, cv.COLOR_BGR2GRAY); if imgHeatmap is None: imgHeatmap=np.zeros(img.shape) lastImg = img if img.shape != imgHeatmap.shape: continue else: imgHeatmap = imgHeatmap + img sns.heatmap(imgHeatmap); plt.show(); thresholedHeatmap = imgHeatmap thresholedHeatmap[thresholedHeatmap> 110000] = 0 sns.heatmap(thresholedHeatmap); plt.show(); #%% plt.show(); cv.imshow("heatmap",imgHeatmap) cv.waitKey(0)
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venv/lib/python3.6/site-packages/ansible_collections/netbox/netbox/plugins/doc_fragments/common.py
usegalaxy-no/usegalaxy
38
189193
# -*- coding: utf-8 -*- # Copyright: (c) 2021, <NAME> (@devon-mar) # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type class ModuleDocFragment(object): DOCUMENTATION = r""" --- options: netbox_url: description: - The URL of the NetBox instance. - Must be accessible by the Ansible control host. required: true type: str netbox_token: description: - The NetBox API token. required: true type: str state: description: - The state of the object. choices: - present - absent default: present type: str query_params: description: - This can be used to override the specified values in ALLOWED_QUERY_PARAMS that are defined - in plugins/module_utils/netbox_utils.py and provides control to users on what may make - an object unique in their environment. required: false type: list elements: str validate_certs: description: - If C(no), SSL certificates will not be validated. - This should only be used on personally controlled sites using a self-signed certificates. default: true type: raw cert: description: - Certificate path required: false type: raw """
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utils/saver.py
dawnchen123/VS-Net
55
116899
import os.path as osp import shutil import torch from collections import OrderedDict import json class Saver(object): def __init__(self, cfg): self.cfg = cfg self.checkpoint_dir = cfg["checkpoint_dir"] self.export_dir = cfg["export_dir"] def save_checkpoint(self, state, is_best, filename="checkpoint.pth.tar", save_model=True): """Saves checkpoint to disk""" filename = osp.join(self.checkpoint_dir, filename) if save_model: torch.save(state, filename) if is_best: best_pred = state["best_pred"] with open(osp.join(self.export_dir, "best_pred.txt"), "w") as f: json.dump(best_pred, f) if save_model: shutil.copyfile(filename, osp.join(self.export_dir, "model_best.pth.tar"))
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tutorials/W0D1_PythonWorkshop1/solutions/W0D1_Tutorial1_Solution_93456241.py
eduardojdiniz/CompNeuro
2,294
23683
# Set random number generator np.random.seed(2020) # Initialize step_end, n, t_range, v and i step_end = int(t_max / dt) n = 50 t_range = np.linspace(0, t_max, num=step_end) v_n = el * np.ones([n, step_end]) i = i_mean * (1 + 0.1 * (t_max / dt)**(0.5) * (2 * np.random.random([n, step_end]) - 1)) # Loop for step_end - 1 steps for step in range(1, step_end): # Compute v_n v_n[:, step] = v_n[:, step - 1] + (dt / tau) * (el - v_n[:, step - 1] + r * i[:, step]) # Plot figure with plt.xkcd(): plt.figure() plt.title('Multiple realizations of $V_m$') plt.xlabel('time (s)') plt.ylabel('$V_m$ (V)') plt.plot(t_range, v_n.T, 'k', alpha=0.3) plt.show()
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models/utils.py
Jintao-Huang/EfficientNet_PyTorch
8
59146
<filename>models/utils.py try: from torch.hub import load_state_dict_from_url except ImportError: from torch.utils.model_zoo import load_url as load_state_dict_from_url import torch import torch.nn.functional as F import math def freeze_layers(model, layers): """冻结层""" for name, parameter in model.named_parameters(): for layer in layers: if layer in name: # 只要含有名字即可 parameter.requires_grad_(False) break else: parameter.requires_grad_(True) def model_info(model, img_size): img_size = img_size if isinstance(img_size, (tuple, list)) else (img_size, img_size) num_params = sum(x.numel() for x in model.parameters()) # number parameters num_grads = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients try: # FLOPS from thop import profile p = next(model.parameters()) x = torch.rand((1, 3, 32, 32), dtype=p.dtype, device=p.device) macs, num_params = profile(model, inputs=(x,), verbose=False) flops = 2 * macs flops_str = ", %.1f GFLOPS" % (flops * img_size[0] * img_size[1] / 32 / 32 / 1e9) # 640x640 GFLOPS except (ImportError, Exception): flops_str = "" print("Model Summary: %d layers, %d parameters, %d gradients%s" % (len(list(model.modules())), num_params, num_grads, flops_str)) def label_smoothing_cross_entropy(pred, target, smoothing: float = 0.1): """reference: https://github.com/seominseok0429/label-smoothing-visualization-pytorch :param pred: shape(N, In). 未过softmax :param target: shape(N,) :param smoothing: float :return: shape() """ pred = F.log_softmax(pred, dim=-1) ce_loss = F.nll_loss(pred, target) smooth_loss = -torch.mean(pred) return (1 - smoothing) * ce_loss + smoothing * smooth_loss def cosine_annealing_lr(epoch, T_max, min_lr, max_lr): return min_lr + (max_lr - min_lr) * (1 + math.cos(epoch / T_max * math.pi)) / 2
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src/tracker/main.py
ralphribeiro/lab
0
130005
<gh_stars>0 from datetime import datetime import matplotlib.pyplot as plt import pandas as pd import pvlib latitude = -23.313602 longitude = -46.221382 altitude = 655 tmz = 'America/Sao_Paulo' temperatura = 20 pressao = 94000 agora = datetime.now() dia = agora.today().day mes = agora.today().month ano = agora.today().year hora = agora.today().hour minuto = agora.today().minute segundo = agora.today().second data_inicio = datetime(ano, mes, dia, 0, 0, 0) data_fim = datetime(ano, mes, dia, 23, 59, 59) datas = pd.date_range(start=data_inicio, end=data_fim, freq='1Min', tz=tmz) posicao_sol = pvlib.solarposition.get_solarposition( datas, latitude, longitude, altitude, temperature=temperatura, pressure=pressao ) posicao_sol.plot() dt = pd.DatetimeIndex([agora.isoformat()]) posicao_sol_atual = pvlib.solarposition.get_solarposition( dt, latitude, longitude, altitude, temperature=temperatura, pressure=pressao, tz=tmz) plt.scatter(posicao_sol_atual.index.values[0], posicao_sol_atual['elevation'][0], linewidths=20, c='y') plt.show()
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dl_papers/common/data/batch_iterator.py
4Catalyzer/dl-papers
2
162980
import sys import threading from tqdm import tqdm import numpy as np import pandas as pd import six from six.moves.queue import Queue from ..utils import if_none __all__ = ('BatchIterator',) # ----------------------------------------------------------------------------- DONE = object() # ----------------------------------------------------------------------------- class BufferedIterator(six.Iterator): def __init__(self, source, buffer_size=2): assert buffer_size >= 2, "minimum buffer size is 2" # The effective buffer size is one larger, because the generation # process will generate one extra element and block until there is room # in the buffer. self.buffer = Queue(maxsize=buffer_size - 1) def populate_buffer(): try: for item in source: self.buffer.put((None, item)) except: self.buffer.put((sys.exc_info(), None)) else: self.buffer.put(DONE) thread = threading.Thread(target=populate_buffer) thread.daemon = True thread.start() def __iter__(self): return self def __next__(self): value = self.buffer.get() if value is DONE: raise StopIteration() exc_info, data = value if exc_info: six.reraise(*exc_info) return data # ----------------------------------------------------------------------------- class BatchIterator(object): def __init__( self, batch_size, training_epoch_size=None, no_stub_batch=False, shuffle=None, seed=None, buffer_size=2, ): self.batch_size = batch_size self.training_epoch_size = training_epoch_size self.no_stub_batch = no_stub_batch self.shuffle = shuffle if seed is not None: self.random = np.random.RandomState(seed) else: self.random = np.random self.buffer_size = buffer_size def __call__(self, data, *args, **kwargs): if if_none(self.shuffle, kwargs.get('training', False)): shuffled_data = self.shuffle_data(data, *args) if args: data = shuffled_data[0] args = shuffled_data[1:] else: data = shuffled_data if self.training_epoch_size is not None: data = data[:self.training_epoch_size] args = tuple( arg[:self.training_epoch_size] if arg is not None else arg for arg in args, ) batches, epoch_size, batch_size = self.create_batches( data, *args, **kwargs ) if self.buffer_size: batches = BufferedIterator(batches, buffer_size=self.buffer_size) # Don't wrap the batches with tqdm until after buffering, to avoid # displaying a progress bar whilst eagerly generating batches. return self.tqdm(batches, epoch_size, batch_size) def shuffle_data(self, *args): state = self.random.get_state() shuffled_data = tuple( self.shuffle_array(array, state) for array in args, ) if len(shuffled_data) == 1: return shuffled_data[0] else: return shuffled_data def shuffle_array(self, array, state): if array is None: return None self.random.set_state(state) if isinstance(array, pd.DataFrame): # Can't use sample because it's not consistent behavior for numpy # arrays. return array.iloc[self.random.permutation(len(array))] elif hasattr(array, 'shuffle'): # Handle e.g. DeferredArray, which has custom logic. return array.permutation(self.random) else: return self.random.permutation(array) def create_batches(self, data, *args, **kwargs): batch_size = self.batch_size if self.no_stub_batch: epoch_size = len(data) // batch_size * batch_size else: epoch_size = len(data) def batches(): for i in range(0, epoch_size, batch_size): batch_slice = slice(i, i + batch_size) x_batch = data[batch_slice] args_batch = tuple( arg[batch_slice] if arg is not None else arg for arg in args, ) yield self.transform(x_batch, *args_batch, **kwargs) return batches(), epoch_size, batch_size def transform(self, data, *args): return (data,) + args if args else data def tqdm(self, batches, epoch_size, batch_size): with tqdm( total=epoch_size, leave=False, disable=None, unit='ex', ) as pbar: for batch in batches: yield batch pbar.update(batch_size)
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backend/main_app/migrations/0005_rename_inn_user_company.py
RTUITLab/Energomach-Hack-2021-RealityGang
0
56199
<reponame>RTUITLab/Energomach-Hack-2021-RealityGang # Generated by Django 3.2 on 2021-05-22 10:50 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('main_app', '0004_auto_20210522_1349'), ] operations = [ migrations.RenameField( model_name='user', old_name='inn', new_name='company', ), ]
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f3dasm/abaqus/run/run_model.py
bessagroup/F3DASM
26
107957
<gh_stars>10-100 ''' Created on 2020-04-22 14:53:01 Last modified on 2020-09-30 14:37:32 Python 2.7.16 v0.1 @author: <NAME> (<EMAIL>) Main goal --------- Run a model sequentially. Notes ----- -abaqus cae is kept open during simulation time (appropriate if running time is low). ''' # imports # abaqus from abaqus import session # standard library import os import glob import pickle from collections import OrderedDict import time import traceback # local library from .utils import convert_dict_unicode_str from .stats import get_wait_time_from_log from ..modelling.model import BasicModel from ..modelling.model import WrapperModel from ...utils.file_handling import get_unique_file_by_ext from ...utils.utils import import_abstract_obj # object definition class RunModel(object): def __init__(self): ''' Notes ----- -assumes the same data is required to instantiate each model of the sequence. ''' # performance related self.init_time = time.time() self.time = OrderedDict([('total', None), ('running', OrderedDict()), ('waiting', OrderedDict()), ('post_processing', None)]) # read data self.filename, data = _read_data() self.pickle_dict = data # store variables self.variables = data['variables'] self.sim_info = data['sim_info'] self.keep_odb = data['keep_odb'] self.dump_py_objs = data['dump_py_objs'] # initialize variables self.models = OrderedDict() self.post_processing = OrderedDict() def execute(self): # instantiate models self._instantiate_models() # run models self._run_models() # post-processing start_time = time.time() self._perform_post_processing() self.time['post_processing'] = time.time() - start_time # dump results self._dump_results() # delete unnecessary files self._clean_dir() def _instantiate_models(self): for i, (model_name, info) in enumerate(self.sim_info.items()): # abstract objects abstract_model = import_abstract_obj(info['abstract_model']) pp_fnc_loc = info.get('post_processing_fnc', None) post_processing_fnc = import_abstract_obj(pp_fnc_loc) if pp_fnc_loc is not None else None for key in ['abstract_model', 'post_processing_fnc']: info.pop(key, None) # get args args = self.variables.copy() args.update(info) # instantiate model if i: args.update({'previous_model': list(self.models.values())[i - 1]}) if issubclass(abstract_model, BasicModel): model = abstract_model(name=model_name, **args) else: model = WrapperModel(name=model_name, abstract_model=abstract_model, post_processing_fnc=post_processing_fnc, **args) self.models[model_name] = model def _run_models(self): for model in self.models.values(): start_time = time.time() # run and create model model.create_model() model.write_inp(submit=True) # store times wait_time = get_wait_time_from_log(model.job_info['name']) if not model.abort else 0. self.time['running'][model.name] = time.time() - start_time - wait_time self.time['waiting'][model.name] = wait_time def _perform_post_processing(self): for model_name, model in reversed(self.models.items()): # avoid doing again post-processing if model_name in self.post_processing.keys(): continue # do post-processing of current model if isinstance(model, WrapperModel) and not callable(model.post_processing_fnc) or model.abort: self.post_processing[model_name] = None else: odb_name = '%s.odb' % model.job_info['name'] odb = session.openOdb(name=odb_name) self.post_processing[model_name] = model.perform_post_processing(odb) odb.close() # save post-processing of previous model (if applicable) if model.previous_model_results is not None and model.previous_model.name not in self.post_processing.keys(): self.post_processing[model.previous_model.name] = model.previous_model_results self.post_processing = OrderedDict(reversed(list(self.post_processing.items()))) def _dump_results(self): # results readable outside abaqus self.pickle_dict['post-processing'] = self.post_processing # TODO: update success to be less permissive (e.g. subroutine location) self.pickle_dict['success'] = True self.time['total'] = time.time() - self.init_time self.pickle_dict['time'] = self.time with open(self.filename, 'wb') as file: pickle.dump(self.pickle_dict, file, protocol=2) # more complete results readable within abaqus if self.dump_py_objs: # prepare models to be dumped for model in self.models.values(): model.dump(create_file=False) # dump models with open('%s_abq' % self.filename, 'wb') as file: pickle.dump(self.models, file, protocol=2) def _clean_dir(self): # return if is to keep odb if self.keep_odb: return job_names = [model.job_name for model in self.models.values()] for name in job_names: for filename in glob.glob('%s*' % name): if not filename.endswith(('.pkl', '.pkl_abq')): try: os.remove(filename) except: pass def _read_data(): # get pickle filename filename = get_unique_file_by_ext(ext='.pkl') # read file with open(filename, 'rb') as file: data = convert_dict_unicode_str(pickle.load(file)) return filename, data if __name__ == '__main__': try: # to avoid to stop running due to one simulation error # create run model run_model = RunModel() # run models run_model.execute() except: # create error file with open('error.txt', 'w') as file: traceback.print_exc(file=file) # update success flag filename, data = _read_data() with open(filename, 'rb') as file: data = pickle.load(file) data['success'] = False with open(filename, 'wb') as file: data = pickle.dump(data, file, protocol=2)
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cogs/oekaki.py
Zenrac/CS-Pound
1
102041
import discord from discord.ext import commands from chickensmoothie import _get_web_data class Oekaki: def __init__(self, bot): self.bot = bot @commands.command() @commands.guild_only() async def oekaki(self, ctx, link: str = ''): data = await _get_web_data(link) # Get Oekaki data if data[0]: # If data is valid base_link = 'http://www.chickensmoothie.com/Forum/' oekaki_title = data[1].xpath('//h3[@class="first"]/a/text()')[0] # Title of drawing image = 'https://www.chickensmoothie.com' + data[1].xpath('//li[@class="ok-topic-head-image large"]/img/@src')[0] # Image of drawing user_icon = base_link[:-1] + data[1].xpath('//dl[@class="postprofile"]')[0].xpath('dt/a/img/@src')[0][1:] # The profile picture of the artist warning_text = 'Reminder!! Copying another person\'s art without permission to reproduce their work is a form of art-theft!' # General warning text regarding Oekaki art if data[1].xpath('//table[@class="ok-drawing-info"]/tr')[0].xpath('td')[1].xpath('a/text()')[0] == 'Click to view': # If drawing is based off another drawing artist_links = data[1].xpath('//table[@class="ok-drawing-info"]/tr')[1].xpath('td')[1].xpath('a/@href') # Drawing information titles artist_values = data[1].xpath('//table[@class="ok-drawing-info"]/tr')[1].xpath('td')[1].xpath('a/text()') # Drawing information values else: # If drawing is not based off another drawing artist_links = data[1].xpath('//table[@class="ok-drawing-info"]/tr')[0].xpath('td')[1].xpath('a/@href') # Drawing information titles artist_values = data[1].xpath('//table[@class="ok-drawing-info"]/tr')[0].xpath('td')[1].xpath('a/text()') # Drawing information values artist_text = '[' + artist_values[0] + '](' + base_link + artist_links[0][1:] + ') [' + artist_values[1] + '(' + base_link + artist_links[1][1:] + ')]' # [Artist Name](Link to Artist) [gallery](Link to Artist gallery) | Formats to Artist Name [gallery] embed = discord.Embed(title=oekaki_title, colour=0x4ba139, url=link) # Create embed embed.add_field(name='Artist', value=artist_text) # Add Artist field embed.set_footer(text=warning_text, icon_url="https://vignette.wikia.nocookie.net/pufflescp/images/6/68/Red_Warning_Triangle.png/revision/latest?cb=20160718024653&format=original") # Add warning text to footer embed.set_image(url=image) # Add drawing to embed embed.set_thumbnail(url=user_icon) # Set thumbnail as user profile picture await ctx.send(embed=embed) # Send embed else: # If data is not valid await ctx.send(embed=data[1]) # Send embed def setup(bot): bot.add_cog(Oekaki(bot))
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desafios/desafio#17.py
thiagocanabarro/PythonProjects
0
187048
#Faça um programa que leia o comprimento do cateto oposto e do cateto adjacente de um triângulo retângulo, calcule e mostre o comprimento da hipotenusa. from math import hypot co = float(input("Digite o valor do cateto oposto: ")) ca = float(input("Digite o valor do cateto adjacente: ")) hi = hypot (co,ca) print("O valor da hipotenusa é: {:.2f}".format(hi))
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deepspeech_pytorch/testing.py
Chudbrochil/deepspeech.pytorch-2.1
13
18503
<reponame>Chudbrochil/deepspeech.pytorch-2.1 import hydra import torch from tqdm import tqdm from deepspeech_pytorch.configs.inference_config import EvalConfig from deepspeech_pytorch.decoder import GreedyDecoder from deepspeech_pytorch.loader.data_loader import SpectrogramDataset, AudioDataLoader from deepspeech_pytorch.utils import load_model, load_decoder @torch.no_grad() def evaluate(cfg: EvalConfig): device = torch.device("cuda" if cfg.model.cuda else "cpu") model = load_model(device=device, model_path=cfg.model.model_path, use_half=cfg.model.use_half) decoder = load_decoder(labels=model.labels, cfg=cfg.lm) target_decoder = GreedyDecoder(model.labels, blank_index=model.labels.index('_')) test_dataset = SpectrogramDataset(audio_conf=model.audio_conf, manifest_filepath=hydra.utils.to_absolute_path(cfg.test_manifest), labels=model.labels, normalize=True) test_loader = AudioDataLoader(test_dataset, batch_size=cfg.batch_size, num_workers=cfg.num_workers) wer, cer, output_data = run_evaluation(test_loader=test_loader, device=device, model=model, decoder=decoder, target_decoder=target_decoder, save_output=cfg.save_output, verbose=cfg.verbose, use_half=cfg.model.use_half) print('Test Summary \t' 'Average WER {wer:.3f}\t' 'Average CER {cer:.3f}\t'.format(wer=wer, cer=cer)) if cfg.save_output: torch.save(output_data, hydra.utils.to_absolute_path(cfg.save_output)) @torch.no_grad() def run_evaluation(test_loader, device, model, decoder, target_decoder, save_output=None, verbose=False, use_half=False): model.eval() total_cer, total_wer, num_tokens, num_chars = 0, 0, 0, 0 output_data = [] for i, (data) in tqdm(enumerate(test_loader), total=len(test_loader)): inputs, targets, input_percentages, target_sizes = data input_sizes = input_percentages.mul_(int(inputs.size(3))).int() inputs = inputs.to(device) if use_half: inputs = inputs.half() # unflatten targets split_targets = [] offset = 0 for size in target_sizes: split_targets.append(targets[offset:offset + size]) offset += size out, output_sizes = model(inputs, input_sizes) decoded_output, _ = decoder.decode(out, output_sizes) target_strings = target_decoder.convert_to_strings(split_targets) if save_output is not None: # add output to data array, and continue output_data.append((out.cpu(), output_sizes, target_strings)) for x in range(len(target_strings)): transcript, reference = decoded_output[x][0], target_strings[x][0] wer_inst = decoder.wer(transcript, reference) cer_inst = decoder.cer(transcript, reference) total_wer += wer_inst total_cer += cer_inst num_tokens += len(reference.split()) num_chars += len(reference.replace(' ', '')) if verbose: print("Ref:", reference.lower()) print("Hyp:", transcript.lower()) print("WER:", float(wer_inst) / len(reference.split()), "CER:", float(cer_inst) / len(reference.replace(' ', '')), "\n") wer = float(total_wer) / num_tokens cer = float(total_cer) / num_chars return wer * 100, cer * 100, output_data
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scripts/obix/get_obix_history_config.py
rmay-intwine/volttron
1
78473
# Copyright (c) 2018, Battelle Memorial Institute # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR # ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # The views and conclusions contained in the software and documentation are those # of the authors and should not be interpreted as representing official policies, # either expressed or implied, of the FreeBSD Project. # # This material was prepared as an account of work sponsored by an # agency of the United States Government. Neither the United States # Government nor the United States Department of Energy, nor Battelle, # nor any of their employees, nor any jurisdiction or organization # that has cooperated in the development of these materials, makes # any warranty, express or implied, or assumes any legal liability # or responsibility for the accuracy, completeness, or usefulness or # any information, apparatus, product, software, or process disclosed, # or represents that its use would not infringe privately owned rights. # # Reference herein to any specific commercial product, process, or # service by trade name, trademark, manufacturer, or otherwise does # not necessarily constitute or imply its endorsement, recommendation, # r favoring by the United States Government or any agency thereof, # or Battelle Memorial Institute. The views and opinions of authors # expressed herein do not necessarily state or reflect those of the # United States Government or any agency thereof. # # PACIFIC NORTHWEST NATIONAL LABORATORY # operated by BATTELLE for the UNITED STATES DEPARTMENT OF ENERGY # under Contract DE-AC05-76RL01830 from xml.dom.minidom import parseString import requests import argparse import getpass import csv import sys import json parser = argparse.ArgumentParser(description='Create Obix driver configurations for site.') parser.add_argument('url', default="", help='Url of the exports on the site') parser.add_argument('csvfile', nargs='?', type=argparse.FileType('w'), default=sys.stdout, help="Output file for register CSV configuration.") parser.add_argument('devicefile', nargs='?', type=argparse.FileType('w'), default=sys.stdout, help="Output file for device configuration.") parser.add_argument('-u', '--username', default="", help='Username for site log in.') parser.add_argument('-p', '--password', default="", help='Password for site log in.') parser.add_argument('-d', '--device-name', default="", help="Default device name for 'Device Name' column.") args = parser.parse_args() username=args.username password=<PASSWORD> url=args.url device_name = args.device_name while not username: username = raw_input("Username: ") while not password: password = getpass.getpass("Password: ") if not url.endswith("/"): url += '/' csv_file = csv.DictWriter(args.csvfile, ["Device Name", "Volttron Point Name", "Obix Name"]) csv_file.writeheader() result = requests.get(url, auth=(username, password)) document = parseString(result.text) elements = document.getElementsByTagName("ref") def get_csv_row(element): result = {} name = element.getAttribute("name").replace("$20", " ").replace("$2d", "-") result["Volttron Point Name"] = result["Obix Name"] = name result["Device Name"] = device_name return result for e in elements: row = get_csv_row(e) csv_file.writerow(row) config = { "url": url, "username": username, "password": password, # Interval to query interface for updates in minutes. # History points are only published if new data is available # config points are gathered and published at this interval. "check_interval": 15, # Path prefix for all publishes "path_prefix": "devices/obix/history/", "register_config": "config://obix_h.csv" } json.dump(config, args.devicefile, indent=4)
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overwatch/base/dataTransfer.py
ostr00000/OVERWATCH
11
109175
#!/usr/bin/env python """ Handle and move files from the receiver(s) to Overwatch sites and EOS. This simple module is responsible for moving data which is provided by the receiver to other sites. It will retry a few times if sites are unavailable. We take a simple approach of determine which files to transfer, and then moving them to the appropriate locations. We could try to use something like ``watchdog`` to do something more clever when a file changes. However, we want to batch transfer files to take advantage of ``rsync``, so such an approach would require much more complicated bookkeeping (for example, what happens if a file shows up when transferring data, etc). The much simpler approach that we use solves our problem just as well, but is also much easier to write and maintain. .. codeauthor:: <NAME> <<EMAIL>>, Yale University """ # Python 2/3 support from __future__ import print_function from future.utils import iteritems from future.utils import itervalues # General import os import math import time import shutil import subprocess import tempfile import functools import ROOT # Logging import logging logger = logging.getLogger(__name__) # Config from . import config (parameters, filesRead) = config.readConfig(config.configurationType.processing) def retry(tries, delay = 3, backoff = 2): """ Retries a function or method until it returns ``True`` or runs out of retries. Retries are performed at a specific delay with an additional back off term. The retries go as .. math:: t = delay * backoff^(nTry) where :math:`nTry` is the trial that we are on. Note that 2 retries corresponds to three function calls - the original call, and then up to two retries if the calls fail. Original decorator from the `Python wiki <https://wiki.python.org/moin/PythonDecoratorLibrary#Retry>`__, and using some additional improvements `here <https://www.saltycrane.com/blog/2009/11/trying-out-retry-decorator-python/>`__, and some ideas `here <https://www.calazan.com/retry-decorator-for-python-3/>`__. Args: tries (int): Number of times to retry. delay (float): Delay in seconds for the initial retry. Default: 3. backoff (float): Amount to multiply the delay by between each retry. See the formula above. Returns: bool: True if the function succeeded. """ # Argument validation if backoff <= 1: raise ValueError("Backoff must be greater than 1.") tries = math.floor(tries) if tries < 0: raise ValueError("Tries must be 0 or greater.") if delay <= 0: raise ValueError("Delay must be greater than 0.") def deco_retry(f): @functools.wraps(f) def f_retry(*args, **kwargs): # Make mutable mtries, mdelay = tries, delay # First attempt at calling the function rv = f(*args, **kwargs) while mtries > 0: # If we ever get a return value of `True`, we are done. if rv is True: return True # Setup for the next attempt and wait before the next attempt mtries -= 1 time.sleep(mdelay) mdelay *= backoff # Try again rv = f(*args, **kwargs) # Ran out of tries. Return failure. return False # true decorator -> decorated function return f_retry # @retry(arg[, ...]) -> true decorator return deco_retry def determineFilesToMove(directory): """ Determine the files which are available to be moved or otherwise transferred. Since there could be additional directories which we want to ignore, we want to use avoid using ``os.walk()``, which will include subdirectories. Instead, we use a simpler solution with ``os.listdir()`` and verify that we include only files. Note: These files are required to be ROOT files by requring that they end with the ``.root`` extension. Note: We explicitly have to select the filenames on ``endswith(".root")`` rather than just ``"root" in f`` because ROOT creates temporary files of the form ``.desiredFilename.root.randomString`` where ``randomString`` is usually 6 characters long. The files disappear almost immediately (and the desired filename shows up). This is presumably to ensure that writes are atomic when writing new objects. Thus, we want to avoid these temporary files. And we can do that be selecting that it ends with ".root". The temporary ROOT files (detailed in the comments) failed with the following error: .. code-block:: none rsync: stat "data/.EMChistos_294832_C_2018_10_22_9_53_55.root.izUVHA" failed: No such file or directory (2) rsync: rename "data/.EMChistos_294832_C_2018_10_22_9_53_55.root.izUVHA" -> "EMChistos_294832_C_2018_10_22_9_53_55.root": No such file or directory (2) rsync error: some files/attrs were not transferred (see previous errors) (code 23) at main.c(1196) [sender=3.1.2] Thanks to the retry capabilities, it will immediately retry, so this shouldn't cause a major problem on the transfer side. However, it will cause problems on the receiver side because the malformed filenames won't be handled properly by Overwatch. Args: directory (str): Path to the directory where the files are stored. Returns: list: List of the files available to be moved. Note that just the filenames are stored, so it's the callers responsibility to include the directory when using the filename. """ # NOTE: See the information above about why we explicitly select on ``endswith("root")``. return [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f)) and f.endswith(".root")] @retry(tries = parameters["dataTransferRetries"]) def rsyncFilesFromFilelist(directory, destination, filelistFilename, transferredFilenames): """ Transfer files via rsync based on a list of filenames in a given file. Note: This must be a separate function which returns a bool to work properly with the retry wrapper. Consequently, we return any transferred filenames by reference via ``transferredFilenames``. Args: directory (str): Path to the directory where the files are stored locally. destination (str): Path to the remote directory where the files are stored. Since the files are being transferred with rsync via ssh, this path should be of the form ``user@host:/dir/path``. filelistFilename (str): Filename of the file which contains the list of files to transfer. transferredFilenames (list): List of filenames which were transfer. Used to return this information because we have to return ``True`` or ``False`` with the retry wrapper. Returns: bool: True if the files were transferred successfully. """ # Check destination value. If we are testing, it doesn't need to have the ":", but for normal operation, # it usually will. if ":" not in destination: logger.warning("Expected, but did not find, \":\" in the destination path \"{destination}\".".format(destination = destination)) # Needed so that rsync will transfer to that directory! if not destination.endswith("/"): destination = destination + "/" # Define the rsync command itself. rsync = [ "rsync", # -l preserves symlinks. # -t preserves timestamps. # -h presents human readable information. # -v is verbose. (Not currently included). "-lth", # This outputs just the name of the file that is transferred. r"--out-format=%n", # Files from is relative to the remote path. "--files-from={name}".format(name = filelistFilename), # Source directory, # Destination destination, ] logger.debug("Args: {rsync}".format(rsync = rsync)) try: result = subprocess.check_output(args = rsync, universal_newlines = True) except subprocess.CalledProcessError: # The call failed for some reason. We want to handle it. logger.debug("rsync call failed!") return False # NOTE: Getting to this point is equivalent to having a return code of 0. # Change: "hello\nworld\n" -> ["hello", "world"] parsedResult = result.strip("\n").split("\n") # Extend so we don't entirely reassign the reference. transferredFilenames.extend(parsedResult) logger.debug("Result: {}".format(result)) logger.debug("Result parsed: {}".format(parsedResult)) return True def copyFilesToOverwatchSites(directory, destination, filenames): """ Copy the given files to the Overwatch deployment sites. The Overwatch sites and where the files should be stored at those sites is determined in the configuration. Retries should usually not be necessary here, but are included as an additional assurance. Args: directory (str): Path to the directory where the files are stored locally. destination (str): Path to the remote directory where the files are stored. Since the files are being transferred with rsync via ssh, this path should be of the form ``user@host:/dir/path``. filenames (list): Paths to files to copy to each Overwatch site. Returns: list: Filenames for all of the files which **failed**. """ # First write the filenames out to a temp file so we can pass them to rsync. with tempfile.NamedTemporaryFile() as f: # Need encode because the file is written as bytes. f.write("\n".join(filenames).encode()) # Move back to the front so it can be read. f.seek(0) # Perform the actual files transfer. transferredFilenames = [] success = rsyncFilesFromFilelist(directory = directory, destination = destination, filelistFilename = f.name, transferredFilenames = transferredFilenames) logger.debug("transferredFilenames: {}".format(transferredFilenames)) # We want to return the files that _failed_, so if the files were transferred, # we return an empty list. Otherwise, we return the files that were not transferred. failedFilenames = list(set(filenames) - set(transferredFilenames)) return [] if success else failedFilenames @retry(tries = parameters["dataTransferRetries"]) def copyFileToEOSWithRoot(directory, destination, filename): """ Copy a given file to EOS using ROOT capabilities. We include the possibility to show the ROOT ``cp`` progress bar if we are in debug mode. Args: directory (str): Path to the directory where the files are stored locally. destination (str): Directory on EOS to which the file should be copied. filename (str): Local filename of the string to be copied. This will be used for setting the path where it will be copied. Returns: bool: True if the file was copied successfully """ source = os.path.join(directory, filename) destination = os.path.join(destination, filename) # We only want to see such information if we are debugging. Otherwise, it will just clog up the logs. showProgressBar = parameters["debug"] logger.debug("Copying file from {source} to {destination}".format(source = source, destination = destination)) return ROOT.TFile.Cp(source, destination, showProgressBar) def copyFilesToEOS(directory, destination, filenames): """ Copy the given filenames to EOS. Files which failed are returned so that these files can be saved and the admin can be alerted to take additional actions. Args: directory (str): Path to the directory where the files are stored locally. destination (str): Directory on EOS to which the file should be copied. filenames (list): Files to copy to EOS. Returns: list: Filenames for all of the files which **failed**. """ failedFilenames = [] for f in filenames: # This function will automatically retry. res = copyFileToEOSWithRoot(directory = directory, destination = destination, filename = f) # Store the failed files so we can notify the admin that something went wrong. if res is False: failedFilenames.append(f) return failedFilenames def storeFailedFiles(siteName, filenames): """ Store failed files in a safe place for later transfer. This function should be called for each site. Each site maintains a different directory as different files could fail for different sites. Args: siteName (str): Name of the site for which the files failed to transfer. filenames (list): Filenames which failed to transfer. Returns: None. """ # Create the storage location if necessary and copy the files to it. storagePath = os.path.join(parameters["receiverDataTempStorage"], siteName) if not os.path.exists(storagePath): os.makedirs(storagePath) for f in filenames: try: shutil.copy2(os.path.join(parameters["receiverData"], f), os.path.join(storagePath, f)) except shutil.Error as e: # Log the exception (so it will get logged and sent via mail when appropriate), and then # re-raise it so it doesn't get lost. logger.critical("Error in copying the failed transfer files. Error: {e}".format(e = e)) # raise with no arguments re-raises the last exception. raise # Sanity check that the files were successfully copied. # There can be other files in the directory, so we just need to be certain that the filenames # that we are handling here are actually copied. assert set(filenames).issubset(os.listdir(storagePath)) # By logging, it will be sent to the admins when appropriate. # To ensure that we don't get overwhelmed by messages which only vary by the filename used, we # only include the sitename in the error. However, by printing the information at the info level, # it will be included via sentry, so we'll still have information the filenames which failed. logger.info("Files failed to copy for site {siteName}. Filenames: {filenames}".format(siteName = siteName, filenames = filenames)) logger.error("Files failed to copy for site {siteName}".format(siteName = siteName)) def processReceivedFiles(): """ Main driver function for receiver file processing and moving. This function relies on the values of "receiverData", "receiverDataTempStorage", "dataTransferLocations". Note: Configuration is controlled via the Overwatch YAML configuration system. In particular, the options relevant here are defined in the base module. Args: None. Returns: tuple: (successfullyTransferred, failedFilenames) where successfullyTransferred (list) is the filenames of the files which were successfully transferred, and failedFilenames (dict) is the filenames which failed to be transferred, with the keys as the site names and the values as the filenames. """ # These are just raw filenames. filenamesToTransfer = determineFilesToMove(directory = parameters["receiverData"]) if not filenamesToTransfer: logger.info("No new files found. Returning.") return None, None logger.info("Transfering data to sites: {sites}".format(sites = ", ".join(parameters["dataTransferLocations"]))) failedFilenames = {} for siteName, destination in iteritems(parameters["dataTransferLocations"]): transferFunc = copyFilesToOverwatchSites if "EOS" in siteName.upper(): transferFunc = copyFilesToEOS # Perform the actual transfer for each configured location. # We need to keep track of which files failed to transfer to which sites. failedFilenames[siteName] = transferFunc(directory = parameters["receiverData"], destination = destination, filenames = filenamesToTransfer) # Handle filenames which haven't been deleted. # We only need to do this if there are some which failed. # We also keep track of which failed for logging. totalFailedFilenames = set() if any(itervalues(failedFilenames)): # Copy to a safe temporary location for storage until they can be dealt with. # We make a copy and store them separately because the same file could have failed for multiple # transfers. However, we shouldn't lose much in storage because these aren't intended to stay a # long time. for siteName, filenames in iteritems(failedFilenames): totalFailedFilenames.update(filenames) # Only attempt to store the failed files if some files actually failed. if filenames: storeFailedFiles(siteName = siteName, filenames = filenames) # Log which filenames were transferred successfully. # We will eventually want to return a list instead of a set, so we just convert it here. successfullyTransferred = list(set(filenamesToTransfer) - totalFailedFilenames) logger.info("Fully successfully transferred: {successfullyTransferred}".format(successfullyTransferred = successfullyTransferred)) # Now we can safely remove all files, because any that have failed have already been copied. # Protect from data loss when debugging. if parameters["debug"] is False: for f in filenamesToTransfer: os.remove(os.path.join(parameters["receiverData"], f)) else: logger.debug("Files to remove: {filenamesToTransfer}".format(filenamesToTransfer = filenamesToTransfer)) return (successfullyTransferred, failedFilenames)
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raylab/utils/types.py
angelolovatto/raylab
29
150037
"""Collection of type annotations.""" from typing import Callable, Dict, Tuple, Union from torch import Tensor DynamicsFn = Callable[[Tensor, Tensor], Tuple[Tensor, Tensor]] RewardFn = Callable[[Tensor, Tensor, Tensor], Tensor] StatDict = Dict[str, Union[float, int]] TerminationFn = Callable[[Tensor, Tensor, Tensor], Tensor]
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gsa_module/samples/lhs_opt.py
damar-wicaksono/gsa-module
4
188975
# -*- coding: utf-8 -*- """lhs_opt.py: Module to generate design matrix from an optimized Latin Hypercube design """ import numpy as np from . import lhs __author__ = "<NAME>" def create_ese(n: int, d: int, seed: int, max_outer: int, obj_function: str="w2_discrepancy", threshold_init: float=0, num_exchanges: int=0, max_inner: int = 0, improving_params: list = [0.1, 0.8], exploring_params: list = [0.1, 0.8, 0.9, 0.7]) -> np.ndarray: """Generate an optimized LHS using Enhanced Stochastic Evolutionary Alg. The default parameters of the optimization can be overridden, if necessary. :param n: the number of samples :param d: the number of dimension :param seed: the random seed number :param max_outer: the maximum number of outer iterations :param obj_function: the objective function to optimize :param threshold_init: the initial threshold :param num_exchanges: the number of candidates in perturbation step :param max_inner: the maximum number of inner iterations :param improving_params: the 2 parameters used in improve process (a) the cut-off value to decrease the threshold (b) the multiplier to decrease or increase the threshold :param exploring_params: the 4 parameters used in explore process (a) the cut-off value of acceptance, start increasing the threshold (b) the cut-off value of acceptance, start decreasing the threshold (c) the cooling multiplier for the threshold (d) the warming multiplier for the threshold """ from .opt_alg.stochastic_evolutionary import optimize # If dimension is less than 2, abort optimization if d < 2: raise ValueError("Dimension less than 2, optimization irrelevant!") if seed is not None: np.random.seed(seed) # Create initial LHD sample dm = lhs.create(n, d, seed=seed) # Optimize the LHD sample dm_opt = optimize(dm, obj_function, threshold_init, num_exchanges, max_inner, max_outer, improving_params, exploring_params) return dm_opt.dm_best
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python3/distortion_correct_aksk_demo.py
MeekoI/ais-sdk
0
5556
<filename>python3/distortion_correct_aksk_demo.py # -*- coding:utf-8 -*- from ais_sdk.utils import encode_to_base64 from ais_sdk.utils import decode_to_wave_file from ais_sdk.distortion_correct import distortion_correct_aksk from ais_sdk.utils import init_global_env import json if __name__ == '__main__': # # access moderation distortion correct.post data by ak,sk # app_key = '*************' app_secret = '************' init_global_env(region='cn-north-1') demo_data_url = 'https://ais-sample-data.obs.cn-north-1.myhuaweicloud.com/vat-invoice.jpg' #call interface use the url correction is true means do not correction result = distortion_correct_aksk(app_key, app_secret, "", demo_data_url, True) result_obj = json.loads(result) if result_obj['result']['data'] != '': decode_to_wave_file(result_obj['result']['data'], 'data/moderation-distortion-aksk-1.png') else: print(result) # call interface use the file result = distortion_correct_aksk(app_key, app_secret, encode_to_base64('data/moderation-distortion.jpg'), '', True) result_obj = json.loads(result) if result_obj['result']['data'] != '': decode_to_wave_file(result_obj['result']['data'], 'data/moderation-distortion-aksk-2.png') else: print(result)
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main.py
wynshiter/WhatIsYourName
3
58566
<filename>main.py # This is a sample Python script. import load_data import birthday_timeStamp import itertools class WhatIsYourName(): def __init__(self,surname:str,birth_day:str,Expectation:str): self.surname= surname self.birth_day = birth_day self.Expectation = Expectation self.DBSession = load_data.get_conn(load_data.STR_PATH_SQLITE, True) self.name_list = [] def get_my_name(self): bt = birthday_timeStamp.birthday_timeStamp(str_birthday_time=self.birth_day) bt_index_array = bt.get_birthday_time_random_id() #根据生日 随机生成的字序号 做笛卡尔积 name_bt_index_array = [x for x in itertools.product(bt_index_array, bt_index_array)] surname_index = load_data.get_word_id(self.DBSession,self.surname) name_result_bt_index_array = [[surname_index,x[0],x[1]] for x in name_bt_index_array] return name_result_bt_index_array def print_hi(self,prefix,name_index_array): # 以三个字为例 print(f'Hi, {prefix}\n') name = self.surname + \ load_data.get_word_info(self.DBSession,name_index_array[1],"Word")+ \ load_data.get_word_info(self.DBSession,name_index_array[2],"Word") self.name_list.append([self.surname,name,name_index_array]) print(name+"\n") if __name__ == '__main__': baby_name = WhatIsYourName(surname = "王",birth_day="2022-02-28 23:40:10",Expectation="") print(baby_name.get_my_name()) baby_name_array = baby_name.get_my_name() for name in baby_name_array: baby_name.print_hi('你的名字是：',name) print(baby_name.name_list)
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secretScript_sxsw.py
grapealope/secrets
1
1602759
import os, sys import json import csv import collections import random from pprint import pprint from playsound import playsound import datetime from six import string_types from boto3 import Session from botocore.exceptions import BotoCoreError, ClientError from contextlib import closing from pollySpeak import newSession, pollySpeech from concatMp3 import concatMp3 from nltk import tokenize from google.cloud import translate import utils from utils import createTimestampedDir from pollySpeak import newSession, pollySpeech, speakSecrets # Defaults (integrate this into main, and with keyword args) from params import * csvfile = '/Users/kalmar/Documents/code/secrets/secrets_data/secrets_sxsw_sn_curated.csv' datapath = '/Users/kalmar/Documents/code/secrets/secrets_data/secrets_sxsw.json' createSecretsJsonFromCSV(csvfile, datapath) secrets = [] with open(datapath) as data_file: secrets = json.load(data_file) voiceIds = ['Joanna', 'Kendra', 'Amy', 'Joey', 'Brian', 'Matthew', 'Nicole', 'Russell'] whisperFreq = 0.2 # What percentage of the secrets should be whispered? # attenuations_list = [0, 2.5, 5, 7.5, 10] # attenuations_list = [0, 2, 4, 6, 8] # attenuations_list = [0, 1, 2, 3, 4] attenuations_list = [0] random_min = 1000 random_max = 4000 secrets_per_file = 195 # Shuffle secrets random.shuffle(secrets) secrets[0:10] mp3path = createTimestampedDir(mp3path_base) speakSecrets(secrets, voiceIds, mp3path, whisperFreq=0.15, randVoice=True, mp3_padding='random', createLog=True, attenuation_list=attenuations_list) concatMp3(mp3path + '/', file_name='mergedSecrets-group-{}'.format(0), file_padding='random', random_min=1000, random_max=4000, verbose=True) for fdx in range(0,5): range_start = fdx * secrets_per_file range_stop = range_start + secrets_per_file print('{} range: {}-{}'.format(fdx, range_start, range_stop)) concatMp3(mp3path + '/', file_name='mergedSecrets-group-{}'.format(fdx), file_padding='random', random_min=1000, random_max=4000, range_start=range_start, range_stop=range_stop, verbose=True) # ------------------------------------------ # # Test voices mp3path = createTimestampedDir(mp3path_base) attenuations_list = [0] voiceIds = ['Joanna', 'Kendra', 'Amy', 'Joey', 'Brian'] voiceIds = ['Salli', 'Kimberly', 'Justin', 'Emma', 'Nicole', 'Russell', 'Matthew', 'Geraint', 'Celine', 'Mathieu', 'Chantal', 'Hans', 'Marlene', 'Vicki', 'Ricardo', 'Vitoria', 'Miguel', 'Penelope', 'Astrid', 'Maxim', 'Tatyana', 'Carla', 'Giorgio'] for voiceId in voiceIds: speakSecrets([secrets[1]], [voiceId], mp3path, whisperFreq=0, randVoice=False, outputFileIdx=voiceId, createLog=False, attenuation_list=attenuations_list) # ------------------------------------------ # # English voiceIds = ['Joanna', 'Kendra', 'Amy', 'Joey', 'Brian'] speakSecrets(english_secrets[0:70], voiceIds, createTimestampedDir(mp3path_base), randVoice=True) speakSecrets(english_secrets[70:140], voiceIds, createTimestampedDir(mp3path_base), randVoice=True) speakSecrets(english_secrets[140:210], voiceIds, createTimestampedDir(mp3path_base), randVoice=True) speakSecrets(english_secrets[210:280], voiceIds, createTimestampedDir(mp3path_base), randVoice=True) speakSecrets(english_secrets[280:], voiceIds, createTimestampedDir(mp3path_base), randVoice=True) speakSecrets(english_secrets[137:138], voiceIds, createTimestampedDir(mp3path_base), randVoice=True) speakSecrets(english_secrets[138:139], voiceIds, createTimestampedDir(mp3path_base), randVoice=True) speakSecrets(english_secrets[0:500], voiceIds, createTimestampedDir(mp3path_base), randVoice=True, verbose=True) # ------------------------------------------ # # Italian voiceIds = ['Carla', 'Giorgio'] speakSecrets(italian_secrets[0:50], voiceIds, createTimestampedDir(mp3path_base), randVoice=True, language='it', target_lang='it') speakSecrets(italian_secrets[50:100], voiceIds, createTimestampedDir(mp3path_base), randVoice=True, language='it', target_lang='it') speakSecrets(italian_secrets[100:], voiceIds, createTimestampedDir(mp3path_base), randVoice=True, language='it', target_lang='it') # ------------------------------------------ # # Fix timing mp3path = '/Users/kalmar/Documents/code/secrets/audio/2017-11-10-test' concatMp3(mp3path + '/', file_padding=45000) concatMp3(mp3path + '/', file_padding='random') mp3path = '/Users/kalmar/Documents/code/secrets/audio/2017-11-10-1' concatMp3(mp3path + '/', file_padding='random') mp3path = '/Users/kalmar/Documents/code/secrets/audio/2017-11-10-2' concatMp3(mp3path + '/', file_padding='random') mp3path = '/Users/kalmar/Documents/code/secrets/audio/2017-11-10-3' concatMp3(mp3path + '/', file_padding='random') mp3path = '/Users/kalmar/Documents/code/secrets/audio/2017-11-10-4' concatMp3(mp3path + '/', file_padding='random') mp3path = '/Users/kalmar/Documents/code/secrets/audio/2017-11-10-5' concatMp3(mp3path + '/', file_padding='random') mp3path = '/Users/kalmar/Documents/code/secrets/audio/2017-05-20-03-25' concatMp3(mp3path + '/', file_padding=mp3_padding) mp3path = '/Users/kalmar/Documents/code/secrets/audio/2017-05-20-03-43' concatMp3(mp3path + '/', file_padding=mp3_padding) mp3path = '/Users/kalmar/Documents/code/secrets/audio/2017-05-20-03-48' concatMp3(mp3path + '/', file_padding=mp3_padding) mp3path = '/Users/kalmar/Documents/code/secrets/audio/2017-05-20-03-52' concatMp3(mp3path + '/', file_padding=mp3_padding)
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main.py
liaoyongming/flaskapi
0
81601
#!/usr/bin/env python from flask_script import Manager, Shell from app import create_app,db from flask_migrate import Migrate,MigrateCommand app = create_app() if __name__ == '__main__': app.run(debug=True)
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djangoerp/core/templatetags/avatar.py
xarala221/django-erp
345
86580
#!/usr/bin/env python """This file is part of the django ERP project. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ __author__ = '<NAME> <<EMAIL>>' __copyright__ = 'Copyright (c) 2013-2015, django ERP Team' __version__ = '0.0.5' from hashlib import md5 from django import template from django.utils.html import format_html, mark_safe, escape register = template.Library() @register.simple_tag def avatar(email, size=32, default="mm", css_class="avatar image"): """Returns the gravatar image associated to the given email. More info: http://www.gravatar.com Example tag usage: {% avatar email_address 80 "http://.../my_default_image.jpg" [css_class] %} """ # Creates and returns the URL. h = "" if email: h = md5(email.encode('utf-8')).hexdigest() url = 'http://www.gravatar.com/avatar/%s?s=%s&r=g' % (h, escape(size)) # Adds a default image URL (if present). if default: url += "&d=%s" % escape(default) url = mark_safe(url) return format_html('<img class="{}" width="{}" height="{}" src="{}" />', css_class, size, size, url)
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PyFlow/Packages/DepthAI_Device/Nodes/ModelZoo/PedestrianDetectionAdas2Node.py
AsherVo/depthai-gui
46
198851
from pathlib import Path from PyFlow.Core.Common import * from PyFlow.Core.NodeBase import NodePinsSuggestionsHelper from common import DeviceNode class PedestrianDetectionAdas2Node(DeviceNode): def __init__(self, name): super(PedestrianDetectionAdas2Node, self).__init__(name) self.frame = self.createInputPin('frame', 'FramePin') self.out_tensor = self.createOutputPin('out_tensor', 'NeuralTensorPin') self.frame.enableOptions(PinOptions.AllowMultipleConnections) self.out_tensor.enableOptions(PinOptions.AllowMultipleConnections) @staticmethod def pinTypeHints(): helper = NodePinsSuggestionsHelper() helper.addInputDataType('FramePin') helper.addOutputDataType('NeuralTensorPin') helper.addInputStruct(StructureType.Multi) helper.addOutputStruct(StructureType.Multi) return helper @staticmethod def category(): return 'Model Zoo' @staticmethod def keywords(): return [] @staticmethod def description(): return "Description in rst format." def build_pipeline(self, pipeline): detection_nn = pipeline.createNeuralNetwork() detection_nn.setBlobPath(str(Path(str((Path(__file__).parent / Path('models/pedestrian-detection-adas-0002.blob')).resolve().absolute())).resolve().absolute())) self.connection_map["out_tensor"] = detection_nn.out self.connection_map["frame"] = detection_nn.input
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queries/staff.py
CaladBlogBaal/Victorique
0
187487
<filename>queries/staff.py def search_staff(): query = """ query ($id: Int, $search: String, $type: MediaType) { Media(search: $search, id: $id, type: $type) { id idMal type title { romaji english } staff { edges { node { primaryOccupations siteUrl image { large } name { full } } } } } } """ return query
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src/scenic/core/type_support.py
joel-mb/Scenic
0
139219
<reponame>joel-mb/Scenic """Support for checking Scenic types.""" import sys import inspect import numbers import typing from scenic.core.distributions import (Distribution, RejectionException, StarredDistribution, distributionFunction) from scenic.core.lazy_eval import (DelayedArgument, valueInContext, requiredProperties, needsLazyEvaluation, toDelayedArgument) from scenic.core.vectors import Vector from scenic.core.errors import RuntimeParseError, saveErrorLocation # Typing and coercion rules: # # coercible to a scalar: # instances of numbers.Real (by calling float()) # coercible to a heading: # anything coercible to a scalar # anything with a toHeading() method # coercible to a Vector: # tuples/lists of length 2 # anything with a toVector() method # coercible to an object of type T: # instances of T # # Finally, Distributions are coercible to T iff their valueType is. ## Basic types class Heading(float): """Dummy class used as a target for type coercions to headings.""" pass def underlyingType(thing): """What type this value ultimately evaluates to, if we can tell.""" if isinstance(thing, Distribution): return thing.valueType elif isinstance(thing, TypeChecker) and len(thing.types) == 1: return thing.types[0] else: return type(thing) def isA(thing, ty): """Does this evaluate to a member of the given Scenic type?""" return issubclass(underlyingType(thing), ty) def unifyingType(opts): # TODO improve? """Most specific type unifying the given types.""" types = [] for opt in opts: if isinstance(opt, StarredDistribution): ty = underlyingType(opt) typeargs = typing.get_args(ty) if typeargs == (): types.append(ty) else: for ty in typeargs: if ty is not Ellipsis: types.append(ty) else: types.append(underlyingType(opt)) if all(issubclass(ty, numbers.Real) for ty in types): return float mro = inspect.getmro(types[0]) for parent in mro: if all(issubclass(ty, parent) for ty in types): return parent raise RuntimeError(f'broken MRO for types {types}') ## Type coercions (for internal use -- see the type checking API below) def canCoerceType(typeA, typeB): """Can values of typeA be coerced into typeB?""" import scenic.syntax.veneer as veneer # TODO improve if typing.get_origin(typeA) is typing.Union: # only raise an error now if none of the possible types will work; # we'll do more careful checking at runtime return any(canCoerceType(ty, typeB) for ty in typing.get_args(typeA)) if typeB is float: return issubclass(typeA, numbers.Real) elif typeB is Heading: return canCoerceType(typeA, float) or hasattr(typeA, 'toHeading') elif typeB is Vector: return issubclass(typeA, (tuple, list)) or hasattr(typeA, 'toVector') elif typeB is veneer.Behavior: return issubclass(typeA, typeB) or typeA in (type, type(None)) else: return issubclass(typeA, typeB) def canCoerce(thing, ty): """Can this value be coerced into the given type?""" tt = underlyingType(thing) if canCoerceType(tt, ty): return True elif isinstance(thing, Distribution) and tt is object: return True # fall back on type-checking at runtime else: return False def coerce(thing, ty, error='wrong type'): """Coerce something into the given type.""" assert canCoerce(thing, ty), (thing, ty) import scenic.syntax.veneer as veneer # TODO improve? realType = ty if ty is float: coercer = coerceToFloat elif ty is Heading: coercer = coerceToHeading ty = numbers.Real realType = float elif ty is Vector: coercer = coerceToVector elif ty is veneer.Behavior: coercer = coerceToBehavior else: coercer = None if isinstance(thing, Distribution): vt = thing.valueType if typing.get_origin(vt) is typing.Union: possibleTypes = typing.get_args(vt) else: possibleTypes = (vt,) if all(issubclass(possible, ty) for possible in possibleTypes): return thing # no coercion necessary else: return TypecheckedDistribution(thing, realType, error, coercer=coercer) elif coercer: try: return coercer(thing) except CoercionFailure as e: raise RuntimeParseError(f'{error} ({e.args[0]})') from None else: return thing class CoercionFailure(Exception): pass def coerceToFloat(thing) -> float: return float(thing) def coerceToHeading(thing) -> float: if hasattr(thing, 'toHeading'): return thing.toHeading() return float(thing) def coerceToVector(thing) -> Vector: if isinstance(thing, (tuple, list)): l = len(thing) if l != 2: raise CoercionFailure('expected 2D vector, got ' f'{type(thing).__name__} of length {l}') return Vector(*thing) else: return thing.toVector() def coerceToBehavior(thing): import scenic.syntax.veneer as veneer # TODO improve if thing is None or isinstance(thing, veneer.Behavior): return thing else: assert issubclass(thing, veneer.Behavior) return thing() class TypecheckedDistribution(Distribution): def __init__(self, dist, ty, errorMessage, coercer=None): super().__init__(dist, valueType=ty) self.dist = dist self.errorMessage = errorMessage self.coercer = coercer self.loc = saveErrorLocation() def sampleGiven(self, value): val = value[self.dist] suffix = None if self.coercer: if canCoerceType(type(val), self.valueType): try: return self.coercer(val) except CoercionFailure as e: suffix = f' ({e.args[0]})' elif isinstance(val, self.valueType): return val if suffix is None: suffix = f' (expected {self.valueType.__name__}, got {type(val).__name__})' raise RuntimeParseError(self.errorMessage + suffix, self.loc) def conditionTo(self, value): self.dist.conditionTo(value) def __repr__(self): return f'TypecheckedDistribution({self.dist}, {self.valueType})' def coerceToAny(thing, types, error): """Coerce something into any of the given types, printing an error if impossible.""" for ty in types: if canCoerce(thing, ty): return coerce(thing, ty, error) from scenic.syntax.veneer import verbosePrint verbosePrint(f'Failed to coerce {thing} of type {underlyingType(thing)} to {types}', file=sys.stderr) raise RuntimeParseError(error) ## Top-level type checking/conversion API def toTypes(thing, types, typeError='wrong type'): """Convert something to any of the given types, printing an error if impossible.""" if needsLazyEvaluation(thing): # cannot check the type now; create proxy object to check type after evaluation return TypeChecker(thing, types, typeError) else: return coerceToAny(thing, types, typeError) def toType(thing, ty, typeError='wrong type'): """Convert something to a given type, printing an error if impossible.""" return toTypes(thing, (ty,), typeError) def toScalar(thing, typeError='non-scalar in scalar context'): """Convert something to a scalar, printing an error if impossible.""" return toType(thing, float, typeError) def toHeading(thing, typeError='non-heading in heading context'): """Convert something to a heading, printing an error if impossible.""" return toType(thing, Heading, typeError) def toVector(thing, typeError='non-vector in vector context'): """Convert something to a vector, printing an error if impossible.""" return toType(thing, Vector, typeError) def evaluateRequiringEqualTypes(func, thingA, thingB, typeError='type mismatch'): """Evaluate the func, assuming thingA and thingB have the same type. If func produces a lazy value, it should not have any required properties beyond those of thingA and thingB.""" if not needsLazyEvaluation(thingA) and not needsLazyEvaluation(thingB): if underlyingType(thingA) is not underlyingType(thingB): raise RuntimeParseError(typeError) return func() else: # cannot check the types now; create proxy object to check types after evaluation return TypeEqualityChecker(func, thingA, thingB, typeError) ## Proxy objects for lazy type checking class TypeChecker(DelayedArgument): """Checks that a given lazy value has one of a given list of types.""" def __init__(self, arg, types, error): def check(context): val = arg.evaluateIn(context) return coerceToAny(val, types, error) super().__init__(requiredProperties(arg), check) self.inner = arg self.types = types def __str__(self): return f'TypeChecker({self.inner},{self.types})' class TypeEqualityChecker(DelayedArgument): """Lazily evaluates a function, after checking that two lazy values have the same type.""" def __init__(self, func, checkA, checkB, error): props = requiredProperties(checkA) | requiredProperties(checkB) def check(context): ca = valueInContext(checkA, context) cb = valueInContext(checkB, context) if underlyingType(ca) is not underlyingType(cb): raise RuntimeParseError(error) return valueInContext(func(), context) super().__init__(props, check) self.inner = func self.checkA = checkA self.checkB = checkB def __str__(self): return f'TypeEqualityChecker({self.inner},{self.checkA},{self.checkB})'
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tests/pbraiders/database/processor/init/setup/config.py
pbraiders/pomponne-test-bdd
1
170935
<filename>tests/pbraiders/database/processor/init/setup/config.py<gh_stars>1-10 # coding=utf-8 """Insert default users in database""" from pbraiders.database.processor.abstract import AbstractProcessor class Config(AbstractProcessor): def execute(self, config: dict): print('Inserting config') self._pAdapter.execute(u"delete from `{d}`.`config`;") self._pAdapter.executemany( u"insert into `{d}`.`config` (`name`, `value`, `role`) VALUES (%(name)s, %(value)s, %(role)s);", config)
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mp/data/datasets/ds_mr_hippocampus_dryad.py
MECLabTUDA/ACS
5
142075
# ------------------------------------------------------------------------------ # Hippocampus segmentation published by Dryad # (https://datadryad.org/stash/dataset/doi:10.5061/dryad.gc72v) # ------------------------------------------------------------------------------ import os import SimpleITK as sitk import mp.data.datasets.dataset_utils as du from mp.data.datasets.dataset_segmentation import SegmentationDataset, SegmentationInstance from mp.paths import storage_data_path from mp.utils.load_restore import join_path import re import nibabel as nib import numpy as np import re class DryadHippocampus(SegmentationDataset): r"""Class for the segmentation of the HarP dataset, https://datadryad.org/stash/dataset/doi:10.5061/dryad.gc72v. """ def __init__(self, subset=None, hold_out_ixs=None, merge_labels=True): # Modality is either: "T1w" or "T2w" # Resolution is either: "Standard" or "Hires" # If you want to use different resolutions or modalities, please create another object with a different subset default = {"Modality": "T1w", "Resolution": "Standard"} if subset is not None: default.update(subset) subset = default else: subset = default # Hires T2w is not available assert not (subset["Resolution"] == "Standard" and subset["Modality"] == "T2w"), \ "Hires T2w not available for the Dryad Hippocampus dataset" if hold_out_ixs is None: hold_out_ixs = [] global_name = 'DryadHippocampus' name = du.get_dataset_name(global_name, subset) dataset_path = os.path.join(storage_data_path, global_name, "Merged Labels" if merge_labels else "Original", "".join([f"{key}[{subset[key]}]" for key in ["Modality", "Resolution"]]) ) original_data_path = du.get_original_data_path(global_name) # Copy the images if not done already if not os.path.isdir(dataset_path): _extract_images(original_data_path, dataset_path, merge_labels, subset) # Fetch all patient/study names study_names = set(file_name.split('.nii')[0].split('_gt')[0] for file_name in os.listdir(dataset_path)) # Build instances instances = [] for study_name in study_names: instances.append(SegmentationInstance( x_path=os.path.join(dataset_path, study_name + '.nii.gz'), y_path=os.path.join(dataset_path, study_name + '_gt.nii.gz'), name=study_name, group_id=None )) if merge_labels: label_names = ['background', 'hippocampus'] else: label_names = ['background', 'subiculum', 'CA1-3', 'CA4-DG'] super().__init__(instances, name=name, label_names=label_names, modality=subset["Modality"] + ' MRI', nr_channels=1, hold_out_ixs=hold_out_ixs) def _extract_images(source_path, target_path, merge_labels, subset): r"""Extracts images, merges mask labels (if specified) and saves the modified images. """ def bbox_3D(img): r = np.any(img, axis=(1, 2)) c = np.any(img, axis=(0, 2)) z = np.any(img, axis=(0, 1)) rmin, rmax = np.where(r)[0][[0, -1]] cmin, cmax = np.where(c)[0][[0, -1]] zmin, zmax = np.where(z)[0][[0, -1]] return rmin, rmax, cmin, cmax, zmin, zmax # Create directories os.makedirs(os.path.join(target_path)) # Patient folders s01, s02, ... for patient_folder in filter(lambda s: re.match(r"^s[0-9]+.*", s), os.listdir(source_path)): # Loading the image image_path = os.path.join(source_path, patient_folder, f"{patient_folder}_{subset['Modality'].lower()}_" f"{subset['Resolution'].lower()}_defaced_MNI.nii.gz") x = sitk.ReadImage(image_path) x = sitk.GetArrayFromImage(x) # For each MRI, there are 2 segmentation (left and right hippocampus) for side in ["L", "R"]: # Loading the label label_path = os.path.join(source_path, patient_folder, f"{patient_folder}_hippolabels_" f"{'hres' if subset['Resolution'] == 'Hires' else 't1w_standard'}" f"_{side}_MNI.nii.gz") y = sitk.ReadImage(label_path) y = sitk.GetArrayFromImage(y) # We need to recover the study name of the image name to construct the name of the segmentation files study_name = f"{patient_folder}_{side}" # Average label shape (T1w, standard): (37.0, 36.3, 26.7) # Average label shape (T1w, hires): (94.1, 92.1, 68.5) # Average label shape (T2w, hires): (94.1, 92.1, 68.5) assert x.shape == y.shape # Disclaimer: next part is ugly and not many checks are made # So we first compute the bounding box rmin, rmax, cmin, cmax, zmin, zmax = bbox_3D(y) # Compute the start idx for each dim dr = (rmax - rmin) // 4 dc = (cmax - cmin) // 4 dz = (zmax - zmin) // 4 # Reshaping y = y[rmin - dr: rmax + dr, cmin - dc: cmax + dc, zmin - dz: zmax + dz] if merge_labels: y[y > 1] = 1 x_cropped = x[rmin - dr: rmax + dr, cmin - dc: cmax + dc, zmin - dz: zmax + dz] # Save new images so they can be loaded directly sitk.WriteImage(sitk.GetImageFromArray(y), join_path([target_path, study_name + "_gt.nii.gz"])) sitk.WriteImage(sitk.GetImageFromArray(x_cropped), join_path([target_path, study_name + ".nii.gz"]))
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example_snippets/multimenus_snippets/Snippets/SciPy/Physical and mathematical constants/CODATA physical constants/H/helion-proton mass ratio.py
kuanpern/jupyterlab-snippets-multimenus
0
152959
<reponame>kuanpern/jupyterlab-snippets-multimenus constants.physical_constants["helion-proton mass ratio"]
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pubsub/synth.py
dgorelik/google-cloud-python
0
56403
<filename>pubsub/synth.py # Copyright 2018 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """This script is used to synthesize generated parts of this library.""" import re import textwrap import synthtool as s from synthtool import gcp gapic = gcp.GAPICGenerator() common = gcp.CommonTemplates() version = "v1" # ---------------------------------------------------------------------------- # Generate pubsub GAPIC layer # ---------------------------------------------------------------------------- library = gapic.py_library( "pubsub", version, config_path="/google/pubsub/artman_pubsub.yaml", include_protos=True, ) s.move( library, excludes=[ "docs/**/*", "nox.py", "README.rst", "setup.py", "google/cloud/pubsub_v1/__init__.py", "google/cloud/pubsub_v1/types.py", ], ) # Adjust tests to import the clients directly. s.replace( "tests/unit/gapic/v1/test_publisher_client_v1.py", "from google.cloud import pubsub_v1", "from google.cloud.pubsub_v1.gapic import publisher_client", ) s.replace( "tests/unit/gapic/v1/test_publisher_client_v1.py", " pubsub_v1", " publisher_client" ) s.replace( "tests/unit/gapic/v1/test_subscriber_client_v1.py", "from google.cloud import pubsub_v1", "from google.cloud.pubsub_v1.gapic import subscriber_client", ) s.replace( "tests/unit/gapic/v1/test_subscriber_client_v1.py", " pubsub_v1", " subscriber_client", ) # DEFAULT SCOPES are being used. so let's force them in. s.replace( "google/cloud/pubsub_v1/gapic/*er_client.py", "# The name of the interface for this client. This is the key used to", """# The scopes needed to make gRPC calls to all of the methods defined in # this service _DEFAULT_SCOPES = ( 'https://www.googleapis.com/auth/cloud-platform', 'https://www.googleapis.com/auth/pubsub', ) \g<0>""", ) s.replace( "google/cloud/pubsub_v1/gapic/publisher_client.py", "import google.api_core.gapic_v1.method\n", "\g<0>import google.api_core.path_template\n", ) # Doc strings are formatted poorly s.replace( "google/cloud/pubsub_v1/proto/pubsub_pb2.py", 'DESCRIPTOR = _MESSAGESTORAGEPOLICY,\n\s+__module__.*\n\s+,\n\s+__doc__ = """', "\g<0>A message storage policy.\n\n\n ", ) s.replace( "google/cloud/pubsub_v1/gapic/subscriber_client.py", "subscription \(str\): The subscription whose backlog .*\n(.*\n)+?" "\s+Format is .*", """subscription (str): The subscription whose backlog the snapshot retains. Specifically, the created snapshot is guaranteed to retain: \\ (a) The existing backlog on the subscription. More precisely, this is \\ defined as the messages in the subscription's backlog that are \\ unacknowledged upon the successful completion of the \\ `CreateSnapshot` request; as well as: \\ (b) Any messages published to the subscription's topic following the \\ successful completion of the CreateSnapshot request. \\ Format is ``projects/{project}/subscriptions/{sub}``.""", ) s.replace( "google/cloud/pubsub_v1/gapic/publisher_client.py", "import functools\n", "import collections\n" "from copy import deepcopy\n\g<0>" ) s.replace( "google/cloud/pubsub_v1/gapic/publisher_client.py", "import pkg_resources\n", "\g<0>import six\n" ) s.replace( "google/cloud/pubsub_v1/gapic/publisher_client.py", "class PublisherClient", """# TODO: remove conditional import after Python 2 support is dropped if six.PY3: from collections.abc import Mapping else: from collections import Mapping def _merge_dict(d1, d2): # Modifies d1 in-place to take values from d2 # if the nested keys from d2 are present in d1. # https://stackoverflow.com/a/10704003/4488789 for k, v2 in d2.items(): v1 = d1.get(k) # returns None if v1 has no such key if v1 is None: raise Exception("{} is not recognized by client_config".format(k)) if isinstance(v1, Mapping) and isinstance(v2, Mapping): _merge_dict(v1, v2) else: d1[k] = v2 return d1 \n\n\g<0>""" ) s.replace( "google/cloud/pubsub_v1/gapic/publisher_client.py", "client_config \(dict\): DEPRECATED.", "client_config (dict):" ) s.replace( "google/cloud/pubsub_v1/gapic/publisher_client.py", "# Raise deprecation warnings .*\n.*\n.*\n.*\n.*\n.*\n", """default_client_config = deepcopy(publisher_client_config.config) if client_config is None: client_config = default_client_config else: client_config = _merge_dict(default_client_config, client_config) """ ) # document FlowControl settings in Python 3.5+ s.replace( "google/cloud/pubsub_v1/types.py", "FlowControl.__new__.__defaults__ = \(.*?\)", textwrap.dedent("""\ \g<0> if sys.version_info >= (3, 5): FlowControl.__doc__ = ( "The settings for controlling the rate at which messages are pulled " "with an asynchronous subscription." ) FlowControl.max_bytes.__doc__ = ( "The maximum total size of received - but not yet processed - messages " "before pausing the message stream." ) FlowControl.max_messages.__doc__ = ( "The maximum number of received - but not yet processed - messages before " "pausing the message stream." ) FlowControl.resume_threshold.__doc__ = ( "The relative threshold of the ``max_bytes`` and ``max_messages`` limits " "below which to resume the message stream. Must be a positive number not " "greater than ``1.0``." ) FlowControl.max_requests.__doc__ = "Currently not in use." FlowControl.max_request_batch_size.__doc__ = ( "The maximum number of requests scheduled by callbacks to process and " "dispatch at a time." ) FlowControl.max_request_batch_latency.__doc__ = ( "The maximum amount of time in seconds to wait for additional request " "items before processing the next batch of requests." ) FlowControl.max_lease_duration.__doc__ = ( "The maximum amount of time in seconds to hold a lease on a message " "before dropping it from the lease management." ) """), flags=re.DOTALL, ) # ---------------------------------------------------------------------------- # Add templated files # ---------------------------------------------------------------------------- templated_files = gcp.CommonTemplates().py_library(unit_cov_level=97, cov_level=100) s.move(templated_files) s.shell.run(["nox", "-s", "blacken"], hide_output=False)
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cyclegan/task_launcher_faceswap.py
dingyanna/DepthNets
114
1607015
import numpy as np import torch import glob import os import pickle import argparse from torch.utils.data import DataLoader from torch.utils.data.dataset import (TensorDataset, ConcatDataset) from i2i.cyclegan import CycleGAN from util import (convert_to_rgb, H5Dataset, DatasetFromFolder) from torchvision import transforms from skimage.io import imsave, imread from skimage.transform import rescale, resize from importlib import import_module def get_face_swap_iterators(bs): """DepthNet + GT <-> frontal GT faces""" filename_vgg = "data/vgg/vgg.h5" filename_celeba = "data/celeba/celebA.h5" filename_celeba_swap = "data/celeba_faceswap/celeba_faceswap.h5" a_train = H5Dataset(filename_celeba_swap, 'imgs', train=True) vgg_side_train = H5Dataset('%s' % filename_vgg, 'src_GT', train=True) vgg_frontal_train = H5Dataset('%s' % filename_vgg, 'tg_GT', train=True) celeba_side_train = H5Dataset('%s' % filename_celeba, 'src_GT', train=True) celeba_frontal_train = H5Dataset('%s' % filename_celeba, 'tg_GT', train=True) b_train = ConcatDataset((vgg_side_train, vgg_frontal_train, celeba_side_train, celeba_frontal_train)) a_valid = H5Dataset(filename_celeba_swap, 'imgs', train=False) vgg_side_valid = H5Dataset('%s' % filename_vgg, 'src_GT', train=False) vgg_frontal_valid = H5Dataset('%s' % filename_vgg, 'tg_GT', train=False) celeba_side_valid = H5Dataset('%s' % filename_celeba, 'src_GT', train=False) celeba_frontal_valid = H5Dataset('%s' % filename_celeba, 'tg_GT', train=False) b_valid = ConcatDataset((vgg_side_valid, vgg_frontal_valid, celeba_side_valid, celeba_frontal_valid)) loader_train_a = DataLoader(a_train, batch_size=bs, shuffle=True) loader_train_b = DataLoader(b_train, batch_size=bs, shuffle=True) loader_valid_a = DataLoader(a_valid, batch_size=bs, shuffle=True) loader_valid_b = DataLoader(b_valid, batch_size=bs, shuffle=True) return loader_train_a, loader_train_b, loader_valid_a, loader_valid_b def image_dump_handler(out_folder, scale_factor=1.): def _fn(losses, inputs, outputs, kwargs): if kwargs['iter'] != 1: return A_real = inputs[0].data.cpu().numpy() B_real = inputs[1].data.cpu().numpy() atob, atob_btoa, btoa, btoa_atob = \ [elem.data.cpu().numpy() for elem in outputs.values()] outs_np = [A_real, atob, atob_btoa, B_real, btoa, btoa_atob] # determine # of channels n_channels = outs_np[0].shape[1] w, h = outs_np[0].shape[-1], outs_np[0].shape[-2] # possible that A_real.bs != B_real.bs bs = np.min([outs_np[0].shape[0], outs_np[3].shape[0]]) grid = np.zeros((h*bs, w*6, 3)) for j in range(bs): for i in range(6): n_channels = outs_np[i][j].shape[0] img_to_write = convert_to_rgb(outs_np[i][j], is_grayscale=False) grid[j*h:(j+1)*h, i*w:(i+1)*w, :] = img_to_write imsave(arr=rescale(grid, scale=scale_factor), fname="%s/%i_%s.png" % (out_folder, kwargs['epoch'], kwargs['mode'])) return _fn if __name__ == '__main__': from torchvision.utils import save_image def parse_args(): parser = argparse.ArgumentParser(description="") parser.add_argument('--name', type=str, default="my_experiment") parser.add_argument('--batch_size', type=int, default=32) parser.add_argument('--network', type=str, default=None) parser.add_argument('--mode', choices=['train', 'test', 'vis'], default='train') parser.add_argument('--epochs', type=int, default=1000) parser.add_argument('--loss', type=str, choices=['mse', 'bce'], default='mse') parser.add_argument('--lamb', type=float, default=10.0) parser.add_argument('--beta', type=float, default=0.0) parser.add_argument('--lr', type=float, default=2e-4) parser.add_argument('--beta1', type=float, default=0.5) parser.add_argument('--beta2', type=float, default=0.999) parser.add_argument('--resume', type=str, default=None) parser.add_argument('--save_path', type=str, default='./results') parser.add_argument('--model_save_path', type=str, default='./models') parser.add_argument('--cpu', action='store_true') args = parser.parse_args() return args args = parse_args() # Dynamically load in the selected generator # module. mod = import_module(args.network.replace("/", ".").\ replace(".py", "")) gen_atob_fn, disc_a_fn, gen_btoa_fn, disc_b_fn = mod.get_network() print("Loading iterators...") it_train_a, it_train_b, it_valid_a, it_valid_b = \ get_face_swap_iterators(args.batch_size) print("Loading CycleGAN...") name = args.name net = CycleGAN( gen_atob_fn=gen_atob_fn, disc_a_fn=disc_a_fn, gen_btoa_fn=gen_btoa_fn, disc_b_fn=disc_b_fn, loss=args.loss, lamb=args.lamb, beta=args.beta, opt_d_args={'lr': args.lr, 'betas': (args.beta1, args.beta2)}, opt_g_args={'lr': args.lr, 'betas': (args.beta1, args.beta2)}, handlers=[image_dump_handler("%s/%s" % (args.save_path, name))], use_cuda=False if args.cpu else True ) if args.resume is not None: if args.resume == 'auto': # autoresume model_dir = "%s/%s" % (args.model_save_path, name) # List all the pkl files. files = glob.glob("%s/*.pkl" % model_dir) # Make them absolute paths. files = [ os.path.abspath(key) for key in files ] if len(files) > 0: # Get creation time and use that. latest_model = max(files, key=os.path.getctime) print("Auto-resume mode found latest model: %s" % latest_model) net.load(latest_model) else: print("Loading model: %s" % args.resume) net.load(args.resume) if args.mode == "train": print("Training...") net.train( itr_a_train=it_train_a, itr_b_train=it_train_b, itr_a_valid=it_valid_a, itr_b_valid=it_valid_b, epochs=args.epochs, model_dir="%s/%s" % (args.model_save_path, name), result_dir="%s/%s" % (args.save_path, name) ) elif args.mode == "vis": print("Converting A -> B...") net.g_atob.eval() aa = iter(it_train_a).next()[0:1] bb = net.g_atob(aa) save_image(aa*0.5 + 0.5, "tmp/aa.png") save_image(bb*0.5 + 0.5, "tmp/bb.png") elif args.mode == 'test': print("Dropping into pdb...") import pdb pdb.set_trace()
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collector_module/opmon_collector/tests/test_database_manager.py
nordic-institute/X-Road-Metrics
2
195503
#!/usr/bin/env python3 """ Unit tests for database_manager.py """ # The MIT License # Copyright (c) 2021- Nordic Institute for Interoperability Solutions (NIIS) # Copyright (c) 2017-2020 Estonian Information System Authority (RIA) # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import pytest import os import pathlib import time import mongomock import pymongo from opmon_collector.database_manager import DatabaseManager from opmon_collector.settings import OpmonSettingsManager @pytest.fixture def basic_settings(): os.chdir(pathlib.Path(__file__).parent.absolute()) return OpmonSettingsManager().settings @pytest.fixture() def mock_db_client(mocker): client = { 'collector_state_DEFAULT': {'server_list': mocker.Mock()}, 'query_db_DEFAULT': {'raw_messages': mocker.Mock()} } mocker.patch('opmon_collector.database_manager.pymongo.MongoClient', return_value=client) return client def test_db_manager_init(basic_settings): mongo_settings = basic_settings['mongodb'] xroad_instance = basic_settings['xroad']['instance'] d = DatabaseManager(mongo_settings, xroad_instance, 'testlogmanager') assert d.mongo_uri == \ f"mongodb://{mongo_settings['user']}:{mongo_settings['password']}@{mongo_settings['host']}/auth_db" assert d.db_name == 'query_db_DEFAULT' assert d.db_collector_state == 'collector_state_DEFAULT' assert d.collector_id == 'collector_DEFAULT' assert d.logger_m == 'testlogmanager' @mongomock.patch(servers=(('defaultmongodb', 27017),)) def test_save_server_list_to_database(basic_settings, mocker): mongo_settings = basic_settings['mongodb'] xroad_instance = basic_settings['xroad']['instance'] d = DatabaseManager(mongo_settings, xroad_instance, mocker.Mock()) d.save_server_list_to_database([1, 2, 3]) server_list, timestamp = d.get_server_list_from_database() assert timestamp == pytest.approx(float(time.time()), abs=1) assert server_list == [1, 2, 3] @mongomock.patch(servers=(('defaultmongodb', 27017),)) def test_insert_data_to_raw_messages(basic_settings, mocker): mongo_settings = basic_settings['mongodb'] xroad_instance = basic_settings['xroad']['instance'] d = DatabaseManager(mongo_settings, xroad_instance, mocker.Mock()) test_data = [{'test': 1}, {'data': 2}] d.insert_data_to_raw_messages(test_data) client = pymongo.MongoClient(d.mongo_uri) items = list(client['query_db_DEFAULT']['raw_messages'].find()) for item in items: assert item['insertTime'] == pytest.approx(float(time.time()), abs=1) assert test_data == items assert test_data is not items @mongomock.patch(servers=(('defaultmongodb', 27017),)) def test_set_next_records_timestamp(basic_settings, mocker): mongo_settings = basic_settings['mongodb'] xroad_instance = basic_settings['xroad']['instance'] d = DatabaseManager(mongo_settings, xroad_instance, mocker.Mock()) # Verify that no timestamp is found for server 'test-server' in mock db collection = pymongo.MongoClient(d.mongo_uri)[d.db_collector_state]['collector_pointer'] document = collection.find_one({'server': 'test-server'}) assert document is None # Verify that a new timestamp can be added d.set_next_records_timestamp('test-server', 123) t = d.get_next_records_timestamp('test-server', 0) assert t == 123 # Verify that an existing timestamp can be changed d.set_next_records_timestamp('test-server', 456) t = d.get_next_records_timestamp('test-server', 0) assert t == 456 @mongomock.patch(servers=(('defaultmongodb', 27017),)) def test_get_next_records_timestamp_non_existent_key(basic_settings, mocker): mongo_settings = basic_settings['mongodb'] xroad_instance = basic_settings['xroad']['instance'] d = DatabaseManager(mongo_settings, xroad_instance, mocker.Mock()) t = d.get_next_records_timestamp('newkey', 0) assert t == pytest.approx(float(time.time()), 1)
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demo/Mesh1D_demo.py
bond-anton/BDMesh
0
90300
import numpy as np from BDMesh.Mesh1D import Mesh1D MyMesh = Mesh1D(0.0, 10.0, 0.0, 0.0) print(MyMesh) print(MyMesh.physical_boundary_1) MyMesh.physical_boundary_1 = 2 print(MyMesh.physical_boundary_1) MyMesh2 = Mesh1D(2.0, 10.0, 0.0, 0.0) print(MyMesh) print(MyMesh2) print(MyMesh == MyMesh2) print(MyMesh == 2) print(MyMesh.local_nodes) MyMesh.local_nodes = np.linspace(0.0, 1.0, num=11, endpoint=True, dtype=np.float64) print(MyMesh.local_nodes) print(MyMesh.num) print(MyMesh.physical_nodes)
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old_scripts/midi_lights.py
cohnt/midi-piano-lights
0
164456
import pygame.midi import board import neopixel n_pixels = 177 n_keys = 88.0 pixels = neopixel.NeoPixel(board.D18, n_pixels, brightness=1.0, auto_write=False, pixel_order=neopixel.GRB) min_key = 21 max_key = 108 def number_to_light(note_number): led_num = int((note_number - min_key) * (float(n_pixels) / float(n_keys))) led_num = (int(n_pixels)) - led_num if led_num >= n_pixels: return n_pixels elif led_num < 0: return 0 else: return led_num def number_to_note(number): notes = ['c', 'c#', 'd', 'd#', 'e', 'f', 'f#', 'g', 'g#', 'a', 'a#', 'b'] return notes[number%12] def readInput(input_device): while True: if input_device.poll(): event = input_device.read(1)[0] data = event[0] timestamp = event[1] note_number = data[1] velocity = data[2] if data[0] != 248: if velocity == 127: continue if velocity > 0: print (note_number, number_to_note(note_number), velocity, "ON") for i in range(number_to_light(note_number+1), number_to_light(note_number)): pixels[i] = (255, 0, 0) pixels.show() else: print (note_number, number_to_note(note_number), velocity, "OFF") for i in range(number_to_light(note_number+1), number_to_light(note_number)): pixels[i] = (0, 0, 0) pixels.show() if __name__ == '__main__': pygame.midi.init() my_input = pygame.midi.Input(3) #only in my case the id is 2 readInput(my_input)
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schevo/test/test_extent_name_override.py
Schevo/schevo
1
92780
<reponame>Schevo/schevo<filename>schevo/test/test_extent_name_override.py """Test for extent name overriding, which is useful for exporting entity class definitions from a schema.""" # Copyright (c) 2001-2009 ElevenCraft Inc. # See LICENSE for details. from schevo.test import CreatesSchema class BaseOverride(CreatesSchema): body = ''' class Base(E.Entity): name = f.string() class Base2(E.Base): _actual_name = "BaseTwo" class BaseThree(E.BaseTwo): pass ''' def test_extents_in_db(self): expected = ['Base', 'BaseThree', 'BaseTwo'] result = db.extent_names() assert result == expected # class TestOverride1(BaseOverride): # include = True # format = 1 class TestOverride2(BaseOverride): include = True format = 2
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scripts/calculate_rank.py
daniel-theis/multicore-test-harness
15
3181
<reponame>daniel-theis/multicore-test-harness ################################################################################ # Copyright (c) 2017 <NAME>, <NAME>, <NAME> # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all #copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. ################################################################################ import sys import json from pprint import pprint class CalculateRank(object): def __init__(self, input_file): self._input_file = input_file def get_rank(self): # Read the configuration in the JSON file with open(self._input_file) as data_file: experiments_object = json.load(data_file) # Sort all the configurations in a list dict_list = list() for experiment in experiments_object: ranked_list = experiments_object[experiment]["it"] od = list(sorted(ranked_list.values(), key=lambda x:x['q_value'], reverse=True)) dict_list.append(od) # for it in dict_list: # print() # print() # for i in range(len(it)): # print(it[i]['mapping']) # print(it[i]['q_value']) # For each environment. get the rank in the other experiments and store in 'rank' for it in dict_list[0]: environment = it['mapping'] rank_list = list() # Look it up for each victim(experiment) for it2 in dict_list: # Find its rank there for i in range(len(it2)): env = it2[i]['mapping'] if environment == env: rank_here = i break rank_list.append(rank_here) it['rank'] = rank_list # Identify the ones that are not Pareto optimal rank_list_bad = list() for it1 in dict_list[0]: for it2 in dict_list[0]: if len([i for i, j in zip(it1['rank'], it2['rank']) if i > j]) == len(it1['rank']): rank_list_bad.append(it1) # Put the Pareto Optimal in a list paretto_optimal = list() for it in dict_list[0]: if not (it in rank_list_bad): paretto_optimal.append(it) # If there are ties, try to break them at fewer comparisons if len(paretto_optimal) > 1: rank_list_bad = list() for it1 in paretto_optimal: for it2 in paretto_optimal: if len([i for i, j in zip(it1['rank'], it2['rank']) if i > j]) == len(it1['rank']) - 1: rank_list_bad.append(it1) # Put the tie broken ones in a list paretto_optimal_tie_break = list() for it in paretto_optimal: if not (it in rank_list_bad): paretto_optimal_tie_break.append(it) print("With no tie breaking") for i in range(len(paretto_optimal)): print(paretto_optimal[i]['mapping']) print("With tie breaking") for i in range(len(paretto_optimal_tie_break)): print(paretto_optimal_tie_break[i]['mapping']) else: print(paretto_optimal[0]['mapping']) print("There was no tie breaking") if __name__ == "__main__": if len(sys.argv) != 2: print("usage: " + sys.argv[0] + " <ranked_environments>.json\n") exit(1) rank = CalculateRank(sys.argv[1]) rank.get_rank()
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SEGM01.py
abphilip-codes/Codechef_Practice
2
106841
<reponame>abphilip-codes/Codechef_Practice # https://www.codechef.com/problems/SEGM01 for T in range(int(input())): print("NO") if(len([s for s in input().split('0') if s])!=1) else print("YES")
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instance/config.py
adriankiprono/pitches_project
0
47485
SECRET_KEY='<KEY>'
[ 1, 29871, 13, 1660, 22245, 29911, 29918, 10818, 2433, 29966, 10818, 16299, 2 ]
tests/test_refo.py
yimian/refo
2
95349
<reponame>yimian/refo # Copyright (c) 2012, Machinalis S.R.L. # # Author: <NAME> <<EMAIL>> # # This file is part of REfO and is distributed under the Modified BSD License. # You should have received a copy of license in the LICENSE.txt file. import unittest import refo from refo.match import Match, match as refomatch import re import math def isprime(x): if x < 2: return False top = int(math.sqrt(x)) for i in xrange(2, top + 1): if x % i == 0: return False return True def _seq2str(seq): xs = [] for x in seq: if isprime(x): xs.append("a") else: xs.append("b") return "".join(xs) def path_function(x): def f(xs): if x in xs: return x * x return None return f class TestRefoModule(unittest.TestCase): seq = xrange(10000) a = refo.Predicate(isprime) b = refo.Predicate(lambda x: not isprime(x)) x = refo.Predicate(path_function(1)) y = refo.Predicate(path_function(2)) z = refo.Predicate(path_function(3)) string = _seq2str(seq) def _eq_span_n_stuff(self, m, strm): assert (m and strm) or (not m and not strm) self.assertNotEqual(m, None) self.assertNotEqual(strm, None) self.assertEqual(m.span(), strm.span()) def _eq_list_n_stuff(self, xs, strxs): xs = [x.span() for x in xs] strxs = [x.span() for x in strxs] self.assert_(xs == strxs) def test_match1(self): regexptn = self.b + self.b + self.a + self.a + self.b strregex = re.compile("bbaab") m = refomatch(regexptn, self.seq) strm = strregex.match(self.string) self._eq_span_n_stuff(m, strm) def test_match2(self): # This regular expression is known to kill the python re module # because it exploits the fact that the implementation has exponential # worst case complexity. # Instead, this implementation has polinomial worst case complexity, # and therefore this test should finish in a reasonable time. N = 100 a = refo.Literal("a") strg = "a" * N regexptn = refo.Question(a) * N + a * N m = refomatch(regexptn, strg) self.assertNotEqual(m, None) def test_search1(self): regexptn = self.a + self.b + self.b + self.b + self.a strregex = re.compile("abbba") m = refo.search(regexptn, self.seq) strm = strregex.search(self.string) self._eq_span_n_stuff(m, strm) def test_search2(self): tab = self.a + self.b + self.a regexptn = tab + self.b * 3 + tab strregex = re.compile("ababbbaba") m = refo.search(regexptn, self.seq) strm = strregex.search(self.string) self._eq_span_n_stuff(m, strm) def test_search3(self): tab = self.a + self.b regexptn = tab + tab + refo.Plus(self.b) strregex = re.compile("ababb+") m = refo.search(regexptn, self.seq) strm = strregex.search(self.string) self._eq_span_n_stuff(m, strm) def test_search4(self): tab = self.a + self.b regexptn = tab * 2 + refo.Plus(self.b, greedy=False) strregex = re.compile("ababb+?") m = refo.search(regexptn, self.seq) strm = strregex.search(self.string) self._eq_span_n_stuff(m, strm) def test_search5(self): tab = self.a + self.b regexptn = tab * (2, 5) strregex = re.compile("(?:ab){2,5}") m = refo.search(regexptn, self.seq) strm = strregex.search(self.string) self._eq_span_n_stuff(m, strm) def test_finditer1(self): tab = self.a + self.b regexptn = tab * (2, None) strregex = re.compile("(?:ab){2,}") xs = list(refo.finditer(regexptn, self.seq)) strxs = list(strregex.finditer(self.string)) self._eq_list_n_stuff(xs, strxs) def test_finditer2(self): tab = self.a + self.b regexptn = tab * (2, None) + refo.Group(refo.Plus(self.b), "foobar") strregex = re.compile("(?:ab){2,}(b+)") xs = list(refo.finditer(regexptn, self.seq)) strxs = list(strregex.finditer(self.string)) xs = [x.group("foobar") for x in xs] strxs = [x.span(1) for x in strxs] self.assert_(xs == strxs) def test_match_path(self): seq = [[1, 2], # x and y [1], # x [1, 2, 3], # x, y and z [1, 2], # x and y [2, 3], # y and z [0, 4, 5], []] regexptn = refo.Star(self.y) + refo.Plus(self.x + self.z) m = refomatch(regexptn, seq, keep_path=True) self.assert_(isinstance(m, Match)) path = m.get_path() self.assertEqual([4, 1, 9, 1, 9], path) if __name__ == "__main__": unittest.main()
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tbase/common/optimizers.py
iminders/TradeBaselines
16
180162
import torch mapping = {} def register(name): def _thunk(func): mapping[name] = func return func return _thunk @register("rmsprop") def rmsprop(): return torch.optim.RMSprop @register("adam") def adam(): return torch.optim.Adam def get_optimizer_func(name): """ If you want to register your own optimizer function, you just need: Usage Example: ------------- from tbase.common.optimizers import register @register("your_reward_function_name") def your_optimizer_func(**kwargs): ... return optimizer_func """ if callable(name): return name elif name in mapping: return mapping[name] else: raise ValueError('Unknown optimizer_func: {}'.format(name))
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Incident-Response/Tools/grr/grr/test/grr_response_test/end_to_end_tests/tests/timeline.py
sn0b4ll/Incident-Playbook
1
25142
#!/usr/bin/env python # Lint as: python3 """E2E tests for the timeline flow.""" import csv import io from typing import Sequence from typing import Text from absl.testing import absltest from grr_response_core.lib import rdfvalue from grr_response_core.lib.util import temp from grr_response_proto.api import timeline_pb2 from grr_response_test.end_to_end_tests import test_base class TestTimelineLinux(test_base.EndToEndTest): """A class with Linux-specific timeline tests.""" platforms = [test_base.EndToEndTest.Platform.LINUX] def testUsrBin(self): args = self.grr_api.types.CreateFlowArgs("TimelineFlow") args.root = "/bin/".encode("utf-8") flow = self.RunFlowAndWait("TimelineFlow", args=args) with temp.AutoTempFilePath(suffix=".body") as temp_filepath: timeline_format = timeline_pb2.ApiGetCollectedTimelineArgs.Format.BODY body = flow.GetCollectedTimeline(timeline_format) body.WriteToFile(temp_filepath) with io.open(temp_filepath, mode="r", encoding="utf-8") as temp_filedesc: entries = list(csv.reader(temp_filedesc, delimiter="|")) paths = [entry[1] for entry in entries] self.assertIn("/bin/bash", paths) self.assertIn("/bin/cat", paths) self.assertIn("/bin/chmod", paths) self.assertIn("/bin/cp", paths) self.assertIn("/bin/rm", paths) self.assertIn("/bin/sleep", paths) for entry in entries: assertBodyEntrySanity(self, entry) class TestTimelineWindows(test_base.EndToEndTest): """A class with Windows-specific timeline tests.""" platforms = [test_base.EndToEndTest.Platform.WINDOWS] def testWindows(self): args = self.grr_api.types.CreateFlowArgs("TimelineFlow") args.root = "C:\\Windows".encode("utf-8") flow = self.RunFlowAndWait("TimelineFlow", args=args) with temp.AutoTempFilePath(suffix=".body") as temp_filepath: timeline_format = timeline_pb2.ApiGetCollectedTimelineArgs.Format.BODY body = flow.GetCollectedTimeline(timeline_format) body.WriteToFile(temp_filepath) with io.open(temp_filepath, mode="r", encoding="utf-8") as temp_filedesc: entries = list(csv.reader(temp_filedesc, delimiter="|")) paths = [entry[1].lower() for entry in entries] self.assertIn("C:\\Windows\\explorer.exe".lower(), paths) self.assertIn("C:\\Windows\\notepad.exe".lower(), paths) self.assertIn("C:\\Windows\\regedit.exe".lower(), paths) self.assertIn("C:\\Windows\\System32\\dwm.exe".lower(), paths) for entry in entries: assertBodyEntrySanity(self, entry) def testWindowsBackslashEscape(self): args = self.grr_api.types.CreateFlowArgs("TimelineFlow") args.root = "C:\\Windows".encode("utf-8") flow = self.RunFlowAndWait("TimelineFlow", args=args) with temp.AutoTempFilePath(suffix=".body") as temp_filepath: body = flow.GetCollectedTimelineBody(backslash_escape=True) body.WriteToFile(temp_filepath) with io.open(temp_filepath, mode="r", encoding="utf-8") as temp_filedesc: content = temp_filedesc.read().lower() self.assertIn("|C:\\\\Windows\\\\explorer.exe|".lower(), content) self.assertIn("|C:\\\\Windows\\\\notepad.exe|".lower(), content) self.assertIn("|C:\\\\Windows\\\\regedit.exe|".lower(), content) self.assertIn("|C:\\\\Windows\\\\System32\\\\dwm.exe|".lower(), content) def assertBodyEntrySanity( # pylint: disable=invalid-name test: absltest.TestCase, entry: Sequence[Text], ) -> None: """Asserts that given row of a body file is sane.""" # Size should be non-negative (some files might be empty, though). test.assertGreaterEqual(int(entry[6]), 0) # All timestamps should be positive. test.assertGreater(int(entry[7]), 0) test.assertGreater(int(entry[8]), 0) test.assertGreater(int(entry[9]), 0) # All timestamps should be older than now. now = rdfvalue.RDFDatetime.Now() test.assertLessEqual(int(entry[7]), now.AsSecondsSinceEpoch()) test.assertLessEqual(int(entry[8]), now.AsSecondsSinceEpoch()) test.assertLessEqual(int(entry[9]), now.AsSecondsSinceEpoch())
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sobchak/report.py
JorisHartog/sobchak
0
92880
import os import logging class Report(object): """Report A Report object generates a report in the form of a HTML-page of a list of hypervisors and information about how certain migrations improve the resource distribution. """ def __init__(self, inventory, template='template.html'): self._inventory = inventory self._migration_report = '' self._template = self._fetch_template(template) self.title = 'Migration report' def _fetch_template(self, filename): """_fetch_template Reads a template and returns the contents. """ try: with open(filename, 'r') as template: return template.read() except Exception as e: logging.error('Could not load %s: %s', filename, e) exit(1) def add_migrations(self, migrations): """add_migrations Adds the migrations to the report. """ def code_block(c): return '<pre><code>' + c + '</code></pre>' migration_list = '<br />'.join([str(m) for m in migrations]) self._migration_report = code_block(migration_list) def save(self, filename='report.html'): """save Save the report as a HTML-file. """ with open(filename, 'w+') as f: f.write(self.page) print('Report available: {}'.format(os.path.abspath(filename))) @property def body(self): """body Returns the HTML body of the report. """ def img_tag(i): return \ '<img width="25%" src="data:image/png;base64,{}"/>'.format(i) body = '<h1>{}</h1>'.format(self.title) body += '<h2>Hypervisor info</h2>' for hypervisor in self._inventory.hypervisors: body += img_tag(hypervisor.plot) body += '<h2>Migration list</h2>' body += self._migration_report return body @property def page(self): """page Returns the report as HTML. """ variables = { 'title': self.title, 'body': self.body } content = self._template for key, value in variables.items(): content = content.replace('{{'+key+'}}', value) return content
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tools/eval_proposal_hit_rate.py
h-zcc/ref-nms
19
49529
import argparse import pickle from utils.hit_rate_utils import NewHitRateEvaluator from utils.constants import EVAL_SPLITS_DICT from lib.refer import REFER def threshold_with_confidence(exp_to_proposals, conf): results = {} for exp_id, proposals in exp_to_proposals.items(): assert len(proposals) >= 1 sorted_proposals = sorted(proposals, key=lambda p: p['score'], reverse=True) thresh_proposals = [sorted_proposals[0]] for prop in sorted_proposals[1:]: if prop['score'] > conf: thresh_proposals.append(prop) else: break results[exp_id] = thresh_proposals return results def main(args): dataset_splitby = '{}_{}'.format(args.dataset, args.split_by) eval_splits = EVAL_SPLITS_DICT[dataset_splitby] # Load proposals proposal_path = 'cache/proposals_{}_{}_{}.pkl'.format(args.m, args.dataset, args.tid) print('loading {} proposals from {}...'.format(args.m, proposal_path)) with open(proposal_path, 'rb') as f: proposal_dict = pickle.load(f) # Load refer refer = REFER('data/refer', dataset=args.dataset, splitBy=args.split_by) # Evaluate hit rate print('Hit rate on {}\n'.format(dataset_splitby)) evaluator = NewHitRateEvaluator(refer, top_N=None, threshold=args.thresh) print('conf: {:.3f}'.format(args.conf)) for split in eval_splits: exp_to_proposals = proposal_dict[split] exp_to_proposals = threshold_with_confidence(exp_to_proposals, args.conf) proposal_per_ref, hit_rate = evaluator.eval_hit_rate(split, exp_to_proposals) print('[{:5s}] hit rate: {:.2f} @ {:.2f}'.format(split, hit_rate*100, proposal_per_ref)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--m', type=str, required=True) parser.add_argument('--dataset', default='refcoco') parser.add_argument('--split-by', default='unc') parser.add_argument('--tid', type=str, required=True) parser.add_argument('--thresh', type=float, default=0.5) parser.add_argument('--conf', type=float, required=True) main(parser.parse_args())
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setup.py
lrado1/delimag
0
176562
<reponame>lrado1/delimag from setuptools import setup setup(name = 'delimag', version = '0.01', description = 'Delimag is a tool to analyze Pandas DataFrame objects with multiselect records.', packages = ['pandas', 'delimag'], author = 'lrado1', zip_safe=False)
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04-Working-With-Dataframes/4.Exercise_ Distinct Articles.py
RodriGonca/DP-203-Data-Engineer
0
39568
# Databricks notebook source # MAGIC %md # MAGIC # Introduction to DataFrames Lab # MAGIC ## Distinct Articles # COMMAND ---------- # MAGIC %md # MAGIC ##![Spark Logo Tiny](https://files.training.databricks.com/images/105/logo_spark_tiny.png) Instructions # MAGIC # MAGIC In the cell provided below, write the code necessary to count the number of distinct articles in our data set. # MAGIC 0. Copy and paste all you like from the previous notebook. # MAGIC 0. Read in our parquet files. # MAGIC 0. Apply the necessary transformations. # MAGIC 0. Assign the count to the variable `totalArticles` # MAGIC 0. Run the last cell to verify that the data was loaded correctly. # MAGIC # MAGIC **Bonus** # MAGIC # MAGIC If you recall from the beginning of the previous notebook, the act of reading in our parquet files will trigger a job. # MAGIC 0. Define a schema that matches the data we are working with. # MAGIC 0. Update the read operation to use the schema. # COMMAND ---------- # MAGIC %md # MAGIC ##![Spark Logo Tiny](https://files.training.databricks.com/images/105/logo_spark_tiny.png) Getting Started # MAGIC # MAGIC Run the following cell to configure our "classroom." # COMMAND ---------- # MAGIC %run "./Includes/Classroom-Setup" # COMMAND ---------- # MAGIC %md # MAGIC ##![Spark Logo Tiny](https://files.training.databricks.com/images/105/logo_spark_tiny.png) Show Your Work # COMMAND ---------- (source, sasEntity, sasToken) = getAzureDataSource() spark.conf.set(sasEntity, sasToken) path = source + "/wikipedia/pagecounts/staging_parquet_en_only_clean/" # COMMAND ---------- # TODO # Replace <<FILL_IN>> with your code. df = (spark # Our SparkSession & Entry Point .read # Our DataFrameReader <<FILL_IN>> # Read in the parquet files <<FILL_IN>> # Reduce the columns to just the one <<FILL_IN>> # Produce a unique set of values ) totalArticles = df.<<FILL_IN>> # Identify the total number of records remaining. print("Distinct Articles: {0:,}".format(totalArticles)) # COMMAND ---------- # MAGIC %md # MAGIC ##![Spark Logo Tiny](https://files.training.databricks.com/images/105/logo_spark_tiny.png) Verify Your Work # MAGIC Run the following cell to verify that your `DataFrame` was created properly. # COMMAND ---------- expected = 1783138 assert totalArticles == expected, "Expected the total to be " + str(expected) + " but found " + str(totalArticles)
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core/tests/test_models.py
CezarPoeta/Fusion
0
49351
<filename>core/tests/test_models.py<gh_stars>0 import uuid from django.test import TestCase from model_mommy import mommy from core.models import get_file_path class GetFilePathTestCase(TestCase): def setUp(self): self.filename = f'{uuid.uuid4()}.png' def test_get_file_path(self): arquivo = get_file_path('Nome', 'teste.png') self.assertTrue(len(arquivo),len(self.filename)) class ServicoTestCase(TestCase): def setUp(self): self.servico = mommy.make('Servico') def test_str(self): self.assertEquals(str(self.servico),self.servico.servico) class CargoTestCase(TestCase): def setUp(self): self.cargo = mommy.make('Cargo') def test_str(self): self.assertEquals(str(self.cargo),self.cargo.cargo) class FuncionarioTestCase(TestCase): def setUp(self): self.funcionario = mommy.make('Funcionario') def test_str(self): self.assertEquals(str(self.funcionario),self.funcionario.nome) class CaracteristicaTestCase(TestCase): def setUp(self): self.caracteristica = mommy.make('Caracteristica') def test_str(self): self.assertEquals(str(self.caracteristica),self.caracteristica.nome)
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part-2/deploy/main.py
limxl31/NTUOSS-API-Development-Workshop
0
158382
<filename>part-2/deploy/main.py """ Introduction to API Development (Part 2) NTUOSS TGIFHacks #132 by <NAME> for NTU Open Source Society """ # Imports from fastapi import FastAPI, Depends, status from pydantic import BaseModel from typing import List, Optional from database import SessionLocal from schema import DBMember from sqlalchemy import desc, asc from sqlalchemy.orm import Session # Create a FastAPI Instance app = FastAPI() # Database Dependency # Create a session for a request. def get_db(): db = SessionLocal() try: yield db finally: db.close() # A Pydantic Member # Data validation and settings management using python type annotations. class Member(BaseModel): name: str school: str graduation_year: int # Allow ORM fetch class Config: orm_mode = True # Methods for interacting with the SQLite Database # ------------------------------------------------ def get_member(db: Session, member_id: int): return db.query(DBMember).where(DBMember.id == member_id).first() def get_members(db: Session, sort_by: str): if(sort_by == 'desc'): return db.query(DBMember).order_by(desc(DBMember.name)).all() elif(sort_by == 'asc'): return db.query(DBMember).order_by(asc(DBMember.name)).all() else: return db.query(DBMember).all() def create_member(db: Session, member: Member): db_member = DBMember(**member.dict()) db.add(db_member) db.commit() db.refresh(db_member) return db_member # ------------------------------------------------ # API Routes # ------------------------------------------------ @app.post('/members/', response_model=Member) def create_members_view(member: Member, db: Session = Depends(get_db)): db_member = create_member(db, member) return db_member @app.get('/members/', response_model=List[Member]) def get_members_view(db: Session = Depends(get_db), sort_by: Optional[str] = None): return get_members(db, sort_by) @app.get('/member/{member_id}') def get_member_view(member_id: int, db: Session = Depends(get_db)): return get_member(db, member_id) # ------------------------------------------------ # Health Check @app.get('/healthcheck', status_code=status.HTTP_200_OK) def perform_healthcheck(): return {'healthcheck': 'Everything OK!'}
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python/7kyu/list_filtering.py
Sigmanificient/codewars
3
84251
<gh_stars>1-10 """Kate url: https://www.codewars.com/kata/53dbd5315a3c69eed20002dd.""" from typing import List, Any def filter_list(collection: List[Any]) -> List[int]: return [x for x in collection if isinstance(x, int)]
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ssguan/ignitor/registry/model.py
samuelbaizg/ssguan
1
146814
<reponame>samuelbaizg/ssguan<gh_stars>1-10 # -*- coding: utf-8 -*- # Copyright 2015 www.suishouguan.com # # Licensed under the Private License (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://github.com/samuelbaizg/ssguan/blob/master/LICENSE # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ssguan.ignitor import IGNITOR_DOMAIN from ssguan.ignitor.orm import properti from ssguan.ignitor.orm.model import Model from ssguan.ignitor.orm.validator import UniqueValidator class Registry(Model): ROOT_KEY = 'ROOT' @classmethod def meta_domain(cls): return IGNITOR_DOMAIN item_key = properti.StringProperty(required=True, validator=[UniqueValidator('item_key')]) parent_key = properti.StringProperty(required=True, default=ROOT_KEY) item_value = properti.ObjectProperty(required=True, length=255) item_desc = properti.StringProperty(required=False, length=255) valid_flag = properti.BooleanProperty(required=True, default=False)
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sdk/eventhub/azure-eventhubs/azure/eventhub/common.py
pjquirk/azure-sdk-for-python
1
193453
# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- from __future__ import unicode_literals import datetime import calendar import json import six from uamqp import Message, BatchMessage from uamqp import types, constants, errors from uamqp.message import MessageHeader, MessageProperties _NO_RETRY_ERRORS = ( b"com.microsoft:argument-out-of-range", b"com.microsoft:entity-disabled", b"com.microsoft:auth-failed", b"com.microsoft:precondition-failed", b"com.microsoft:argument-error" ) def _error_handler(error): """ Called internally when an event has failed to send so we can parse the error to determine whether we should attempt to retry sending the event again. Returns the action to take according to error type. :param error: The error received in the send attempt. :type error: Exception :rtype: ~uamqp.errors.ErrorAction """ if error.condition == b'com.microsoft:server-busy': return errors.ErrorAction(retry=True, backoff=4) if error.condition == b'com.microsoft:timeout': return errors.ErrorAction(retry=True, backoff=2) if error.condition == b'com.microsoft:operation-cancelled': return errors.ErrorAction(retry=True) if error.condition == b"com.microsoft:container-close": return errors.ErrorAction(retry=True, backoff=4) if error.condition in _NO_RETRY_ERRORS: return errors.ErrorAction(retry=False) return errors.ErrorAction(retry=True) def parse_sas_token(sas_token): """Parse a SAS token into its components. :param sas_token: The SAS token. :type sas_token: str :rtype: dict[str, str] """ sas_data = {} token = sas_token.partition(' ')[2] fields = token.split('&') for field in fields: key, value = field.split('=', 1) sas_data[key.lower()] = value return sas_data class EventData(object): """ The EventData class is a holder of event content. Acts as a wrapper to an uamqp.message.Message object. Example: .. literalinclude:: ../examples/test_examples_eventhub.py :start-after: [START create_event_data] :end-before: [END create_event_data] :language: python :dedent: 4 :caption: Create instances of EventData """ PROP_SEQ_NUMBER = b"x-opt-sequence-number" PROP_OFFSET = b"x-opt-offset" PROP_PARTITION_KEY = b"x-opt-partition-key" PROP_TIMESTAMP = b"x-opt-enqueued-time" PROP_DEVICE_ID = b"iothub-connection-device-id" def __init__(self, body=None, batch=None, to_device=None, message=None): """ Initialize EventData. :param body: The data to send in a single message. :type body: str, bytes or list :param batch: A data generator to send batched messages. :type batch: Generator :param to_device: An IoT device to route to. :type to_device: str :param message: The received message. :type message: ~uamqp.message.Message """ self._partition_key = types.AMQPSymbol(EventData.PROP_PARTITION_KEY) self._annotations = {} self._app_properties = {} self.msg_properties = MessageProperties() if to_device: self.msg_properties.to = '/devices/{}/messages/devicebound'.format(to_device) if batch: self.message = BatchMessage(data=batch, multi_messages=True, properties=self.msg_properties) elif message: self.message = message self.msg_properties = message.properties self._annotations = message.annotations self._app_properties = message.application_properties else: if isinstance(body, list) and body: self.message = Message(body[0], properties=self.msg_properties) for more in body[1:]: self.message._body.append(more) # pylint: disable=protected-access elif body is None: raise ValueError("EventData cannot be None.") else: self.message = Message(body, properties=self.msg_properties) @property def sequence_number(self): """ The sequence number of the event data object. :rtype: int or long """ return self._annotations.get(EventData.PROP_SEQ_NUMBER, None) @property def offset(self): """ The offset of the event data object. :rtype: ~azure.eventhub.common.Offset """ try: return Offset(self._annotations[EventData.PROP_OFFSET].decode('UTF-8')) except (KeyError, AttributeError): return None @property def enqueued_time(self): """ The enqueued timestamp of the event data object. :rtype: datetime.datetime """ timestamp = self._annotations.get(EventData.PROP_TIMESTAMP, None) if timestamp: return datetime.datetime.utcfromtimestamp(float(timestamp)/1000) return None @property def device_id(self): """ The device ID of the event data object. This is only used for IoT Hub implementations. :rtype: bytes """ return self._annotations.get(EventData.PROP_DEVICE_ID, None) @property def partition_key(self): """ The partition key of the event data object. :rtype: bytes """ try: return self._annotations[self._partition_key] except KeyError: return self._annotations.get(EventData.PROP_PARTITION_KEY, None) @partition_key.setter def partition_key(self, value): """ Set the partition key of the event data object. :param value: The partition key to set. :type value: str or bytes """ annotations = dict(self._annotations) annotations[self._partition_key] = value header = MessageHeader() header.durable = True self.message.annotations = annotations self.message.header = header self._annotations = annotations @property def application_properties(self): """ Application defined properties on the message. :rtype: dict """ return self._app_properties @application_properties.setter def application_properties(self, value): """ Application defined properties on the message. :param value: The application properties for the EventData. :type value: dict """ self._app_properties = value properties = dict(self._app_properties) self.message.application_properties = properties @property def body(self): """ The body of the event data object. :rtype: bytes or Generator[bytes] """ try: return self.message.get_data() except TypeError: raise ValueError("Message data empty.") def body_as_str(self, encoding='UTF-8'): """ The body of the event data as a string if the data is of a compatible type. :param encoding: The encoding to use for decoding message data. Default is 'UTF-8' :rtype: str or unicode """ data = self.body try: return "".join(b.decode(encoding) for b in data) except TypeError: return six.text_type(data) except: # pylint: disable=bare-except pass try: return data.decode(encoding) except Exception as e: raise TypeError("Message data is not compatible with string type: {}".format(e)) def body_as_json(self, encoding='UTF-8'): """ The body of the event loaded as a JSON object is the data is compatible. :param encoding: The encoding to use for decoding message data. Default is 'UTF-8' :rtype: dict """ data_str = self.body_as_str(encoding=encoding) try: return json.loads(data_str) except Exception as e: raise TypeError("Event data is not compatible with JSON type: {}".format(e)) class Offset(object): """ The offset (position or timestamp) where a receiver starts. Examples: Beginning of the event stream: >>> offset = Offset("-1") End of the event stream: >>> offset = Offset("@latest") Events after the specified offset: >>> offset = Offset("12345") Events from the specified offset: >>> offset = Offset("12345", True) Events after a datetime: >>> offset = Offset(datetime.datetime.utcnow()) Events after a specific sequence number: >>> offset = Offset(1506968696002) """ def __init__(self, value, inclusive=False): """ Initialize Offset. :param value: The offset value. :type value: ~datetime.datetime or int or str :param inclusive: Whether to include the supplied value as the start point. :type inclusive: bool """ self.value = value self.inclusive = inclusive def selector(self): """ Creates a selector expression of the offset. :rtype: bytes """ operator = ">=" if self.inclusive else ">" if isinstance(self.value, datetime.datetime): timestamp = (calendar.timegm(self.value.utctimetuple()) * 1000) + (self.value.microsecond/1000) return ("amqp.annotation.x-opt-enqueued-time {} '{}'".format(operator, int(timestamp))).encode('utf-8') if isinstance(self.value, six.integer_types): return ("amqp.annotation.x-opt-sequence-number {} '{}'".format(operator, self.value)).encode('utf-8') return ("amqp.annotation.x-opt-offset {} '{}'".format(operator, self.value)).encode('utf-8') class EventHubError(Exception): """ Represents an error happened in the client. :ivar message: The error message. :vartype message: str :ivar error: The error condition, if available. :vartype error: str :ivar details: The error details, if included in the service response. :vartype details: dict[str, str] """ def __init__(self, message, details=None): self.error = None self.message = message self.details = details if isinstance(message, constants.MessageSendResult): self.message = "Message send failed with result: {}".format(message) if details and isinstance(details, Exception): try: condition = details.condition.value.decode('UTF-8') except AttributeError: condition = details.condition.decode('UTF-8') _, _, self.error = condition.partition(':') self.message += "\nError: {}".format(self.error) try: self._parse_error(details.description) for detail in self.details: self.message += "\n{}".format(detail) except: # pylint: disable=bare-except self.message += "\n{}".format(details) super(EventHubError, self).__init__(self.message) def _parse_error(self, error_list): details = [] self.message = error_list if isinstance(error_list, six.text_type) else error_list.decode('UTF-8') details_index = self.message.find(" Reference:") if details_index >= 0: details_msg = self.message[details_index + 1:] self.message = self.message[0:details_index] tracking_index = details_msg.index(", TrackingId:") system_index = details_msg.index(", SystemTracker:") timestamp_index = details_msg.index(", Timestamp:") details.append(details_msg[:tracking_index]) details.append(details_msg[tracking_index + 2: system_index]) details.append(details_msg[system_index + 2: timestamp_index]) details.append(details_msg[timestamp_index + 2:]) self.details = details
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src/kernel-graphql/riotapi/objects.py
pseudonym117/kernel-graphql
1
82003
from graphene import Boolean, Field, Int, List, ObjectType, String, Schema from riotwatcher import RiotWatcher, ApiError from .riot_graphene.Champion import ChampionInfo from .riot_graphene.League import ApexLeagueType, LeagueList, RankedQueue from .riot_graphene.LolStatus import ShardStatus from .riot_graphene.Match import Match from .riot_graphene.Spectator import FeaturedGames from .riot_graphene.Summoner import Summoner class Query(ObjectType): championRotation = Field(ChampionInfo, region=String()) featuredGames = Field(FeaturedGames, region=String()) league = Field( LeagueList, region=String(), tier=ApexLeagueType(required=False), queue=RankedQueue(required=False), leagueId=String(required=False), ) match = Field(Match, region=String(), matchId=String()) status = Field(ShardStatus, region=String()) summoner = Field( Summoner, region=String(), name=String(required=False), accountId=String(required=False), puuid=String(required=False), summonerId=String(required=False), ) def resolve_championRotation(self, info, region: str): watcher: RiotWatcher = info.context champs = watcher.champion.rotations(region) return ChampionInfo(region, champs) def resolve_featuredGames(self, info, region: str): watcher: RiotWatcher = info.context games = watcher.spectator.featured_games(region) return FeaturedGames(region, games) def resolve_league( self, info, region: str, tier: int = None, queue: int = None, leagueId: str = None, ): watcher: RiotWatcher = info.context if leagueId: leagues = watcher.league.by_id(region, leagueId) else: if not queue or not tier: raise ValueError("both queue and tier must be provided") queue = RankedQueue.str_from_val(queue) if tier == ApexLeagueType.CHALLENGER: leagues = watcher.league.challenger_by_queue(region, queue) elif tier == ApexLeagueType.GRANDMASTER: leagues = watcher.league.grandmaster_by_queue(region, queue) elif tier == ApexLeagueType.MASTER: leagues = watcher.league.masters_by_queue(region, queue) else: raise ValueError("invalid tier provided") return LeagueList(region, leagues) def resolve_match(self, info, region: str, matchId: str): watcher: RiotWatcher = info.context try: match = watcher.match.by_id(region, matchId) return Match(region, match) except ApiError as e: if e.response.status_code == 404: return None raise def resolve_status(self, info, region: str): watcher: RiotWatcher = info.context shard = watcher.lol_status.shard_data(region=region) return ShardStatus(region, shard) def resolve_summoner( self, info, region: str, name: str = None, accountId: str = None, puuid: str = None, summonerId: str = None, ): watcher: RiotWatcher = info.context if puuid: summoner = watcher.summoner.by_puuid(region, puuid) elif accountId: summoner = watcher.summoner.by_account(region, accountId) elif summonerId: summoner = watcher.summoner.by_id(region, summonerId) elif name: summoner = watcher.summoner.by_name(region, name) else: raise ValueError("some identified must be defined") return Summoner(region, summoner) schema = Schema(query=Query)
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setup.py
jparsai/fabric8-analytics-auth
0
1601359
"""Project setup file.""" from setuptools import setup def get_requirements(): """Parse all packages mentioned in the 'requirements.txt' file.""" with open('requirements.txt') as fd: return fd.read().splitlines() setup( name='fabric8a_auth', version='0.0.1', description='a pip-installable package example', license='Apache License 2.0', packages=['fabric8a_auth'], author='<NAME>', author_email='<EMAIL>', keywords=['fabric8-analytics'], url='https://github.com/fabric8-analytics/fabric8-analytics-auth', install_requires=get_requirements(), )
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tic_tac_toe/admin.py
ricardopereiraesilva/Tic_Tac_Toe_OO_Python_Django
1
154496
<filename>tic_tac_toe/admin.py from django.contrib import admin from .models import JVDjangoDBModel admin.site.register(JVDjangoDBModel)
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Parse_SOM.py
timletz/materials_datascience_uw_somlab
1
146077
import re import pandas as pd import numpy as np COLUMN_NAMES = [ 'Material family', 'Youngs modulus', 'Specific stiffness', 'Yield strength', 'Tensile strength', 'Specific strength', 'Elongation', 'Compressive strength', 'Flexural modulus', 'Flexural strength', 'Shear modulus', 'Bulk modulus', 'Poisson ratio', 'Shape factor', 'Hardness vickers', 'Elastic stored energy', 'Fatigue strength', 'Fracture toughness', 'Toughness', 'Ductility index', 'Melting point', 'Max service temp', 'Min service temp', 'Thermal conductivity', 'Specific heat capacity', 'Thermal expansion coefficient', 'Thermal shock resistance', 'Thermal distortion resistance', 'Latent heat of fusion', 'Electrical resistivity', 'Electrical conductivity', 'Galvanic potential', 'Mechanical loss coefficient', ] BAD_PROPERTIES = [ 'Elongation', # This is because there's over 600 bad properties in the dataset 'Hardness vickers', # not worth saving 'Specific stiffness', 'Specific strength', 'Shape factor', 'Elastic stored energy', 'Toughness', 'Ductility index', 'Thermal shock resistance', 'Thermal distortion resistance', # These properties are not actually real properties of the material 'Max service temp', 'Min service temp', 'Electrical resistivity', # This really messes with our data selection 'Galvanic potential', ] # These are materials with invalid values for one or more properties # This entry is here because there's materials with higher, but valid values # And I don't want to lose them BAD_MATERIALS = [ "PEEK/IM carbon fiber, UD prepreg, UD lay-up" ] def parsing_material_data(material_text, new_file): with open(material_text, "r") as stuff_to_write: with open(new_file, "w") as stuff_written: in_thermal_properties = False in_electrical_properties = False in_mechanical_properties = False in_impact_properties = False for line in stuff_to_write: if line.startswith("done"): stuff_written.write(line) elif "Mechanical properties" in line: in_mechanical_properties = True elif "Impact & fracture properties" in line: in_mechanical_properties = False in_impact_properties = True elif "Thermal properties" in line: in_thermal_properties = True in_impact_properties = False elif "Electrical properties" in line: in_thermal_properties = False in_electrical_properties = True elif "Magnetic properties" in line: in_electrical_properties = False in_mechanical_properties = False in_thermal_properties = False in_impact_properties = False elif "Mechanical loss coefficient" in line: stuff_written.write(line) elif line.startswith("Material family"): stuff_written.write(line) elif in_thermal_properties is True or in_electrical_properties is True or in_mechanical_properties is True or in_impact_properties is True: stuff_written.write(line) def parsing_refined_data(new_file): material_name = [] material_family = [] young_modulus_values = [] specific_stiffness_values = [] yield_strength_values = [] tensile_strength_values = [] specific_strength_values = [] elongation_values = [] compressive_strength_values = [] flexural_modulus_values = [] flexural_strength_values = [] shear_modulus_values = [] bulk_modulus_values = [] poisson_ratio_values = [] shape_factor_values = [] hardness_vickers_values = [] elastic_stored_energy_values = [] fatigue_strength_values = [] fracture_toughness_values = [] toughness_values = [] ductility_index_values = [] melting_values = [] max_service_temp_values = [] min_service_temp_values = [] therm_cond_values = [] spec_heat_cap_values = [] therm_expan_coeff_values = [] therm_shock_resist_values = [] therm_dist_resist_values = [] latent_heat_fusion_values = [] elec_resist_values = [] elec_cond_values = [] galvanic_potential_values = [] mech_loss_coeff_values = [] with open(new_file, "r") as sample_info: for line in sample_info: if line.startswith("done"): for line_item in range(2, len(line.split(' '))): material_name.append((line.split(' ')[line_item].strip('" ,\t\n'))) elif line.startswith("Material family"): for line_item in range(1, len(line.split(' ')) - 1): material_family.append(line.split(' ')[line_item].strip(' ",\t\n')) elif line.startswith("Young's modulus (10^6 psi)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 young_modulus_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': young_modulus_values.append("Null") else: young_modulus_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Specific stiffness (lbf.ft/lb)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 specific_stiffness_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': specific_stiffness_values.append("Null") else: specific_stiffness_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Yield strength (elastic limit) (ksi)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 yield_strength_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': yield_strength_values.append("Null") else: yield_strength_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Tensile strength (ksi)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 tensile_strength_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': tensile_strength_values.append("Null") else: tensile_strength_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Specific strength (lbf.ft/lb)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 specific_strength_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': specific_strength_values.append("Null") else: specific_strength_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Elongation (% strain)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 elongation_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': elongation_values.append("Null") else: elongation_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Compressive strength (ksi)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 compressive_strength_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': compressive_strength_values.append("Null") else: compressive_strength_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Flexural modulus (10^6 psi)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 flexural_modulus_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': flexural_modulus_values.append("Null") else: flexural_modulus_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Flexural strength (modulus of rupture) (ksi)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 flexural_strength_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': flexural_strength_values.append("Null") else: flexural_strength_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Shear modulus (10^6 psi)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 shear_modulus_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': shear_modulus_values.append("Null") else: shear_modulus_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Bulk modulus (10^6 psi)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 bulk_modulus_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': bulk_modulus_values.append("Null") else: bulk_modulus_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Poisson's ratio"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 poisson_ratio_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': poisson_ratio_values.append("Null") else: poisson_ratio_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Shape factor"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 shape_factor_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': shape_factor_values.append("Null") else: shape_factor_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Hardness - Vickers (HV)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 hardness_vickers_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': hardness_vickers_values.append("Null") else: hardness_vickers_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Elastic stored energy (springs) (ft.lbf/in^3)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 elastic_stored_energy_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': elastic_stored_energy_values.append("Null") else: elastic_stored_energy_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Fatigue strength at 10^7 cycles (ksi)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 fatigue_strength_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': fatigue_strength_values.append("Null") else: fatigue_strength_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Fracture toughness (ksi.in^0.5)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 fracture_toughness_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': fracture_toughness_values.append("Null") else: fracture_toughness_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Toughness (G) (ft.lbf/in^2)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 toughness_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': toughness_values.append("Null") else: toughness_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Ductility index (mil)"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 ductility_index_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': ductility_index_values.append("Null") else: ductility_index_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Melting"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 melting_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': melting_values.append("Null") else: melting_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Maximum service temperature"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 max_service_temp_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': max_service_temp_values.append("Null") else: max_service_temp_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Minimum service temperature"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 min_service_temp_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': min_service_temp_values.append("Null") else: min_service_temp_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Thermal conductivity"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 therm_cond_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': therm_cond_values.append("Null") else: therm_cond_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Specific heat capacity"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 spec_heat_cap_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': spec_heat_cap_values.append("Null") else: spec_heat_cap_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Thermal expansion coefficient"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 therm_expan_coeff_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': therm_expan_coeff_values.append("Null") else: therm_expan_coeff_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Thermal shock resistance"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 therm_shock_resist_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': therm_shock_resist_values.append("Null") else: therm_shock_resist_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Thermal distortion resistance"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 therm_dist_resist_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': therm_dist_resist_values.append("Null") else: therm_dist_resist_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Latent heat of fusion"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 latent_heat_fusion_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': latent_heat_fusion_values.append("Null") else: latent_heat_fusion_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Electrical resistivity"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 elec_resist_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': elec_resist_values.append("Null") else: elec_resist_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Electrical conductivity"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 elec_cond_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': elec_cond_values.append("Null") else: elec_cond_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Galvanic potential"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 galvanic_potential_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': galvanic_potential_values.append("Null") else: galvanic_potential_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) elif line.startswith("Mechanical loss coefficient"): for line_item in range(1, (len(line.split(",")) - 1)): if " - " in line.split(",")[line_item].strip(" "): left_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[0] right_of_dash = line.split(",")[line_item].strip(" ").split(" - ")[1] average_of_dash = (float(left_of_dash) + float(right_of_dash)) / 2 mech_loss_coeff_values.append(round(average_of_dash, 6)) elif line.split(",")[line_item] is '': mech_loss_coeff_values.append("Null") else: mech_loss_coeff_values.append(round(float(line.split(",")[line_item].strip(" ")), 6)) #Brinell Hardness, Dielectric Constant, Dielectric Strength, Dissipation Factor, excluded because lacking way too much data (>10 rows of materials non-existent) null_extender = ["Null"] * 18 material_name.extend(null_extender) material_family.extend(null_extender) return(material_name, material_family, young_modulus_values, specific_stiffness_values, yield_strength_values, tensile_strength_values, specific_strength_values, elongation_values, compressive_strength_values, flexural_modulus_values, flexural_strength_values, shear_modulus_values, bulk_modulus_values, poisson_ratio_values, shape_factor_values, hardness_vickers_values, elastic_stored_energy_values, fatigue_strength_values, fracture_toughness_values, toughness_values, ductility_index_values, melting_values, max_service_temp_values, min_service_temp_values, therm_cond_values, spec_heat_cap_values, therm_expan_coeff_values, therm_shock_resist_values, therm_dist_resist_values, latent_heat_fusion_values, elec_resist_values, elec_cond_values, galvanic_potential_values, mech_loss_coeff_values) def null_invalid_properties(frame: pd.DataFrame): # This drops elements in our dataset which have invalid values for any of the following properties # found to contain a salvagable amount of invalid data COLUMNS_TO_SCRUB = [ "Tensile strength", "Compressive strength", "Flexural modulus", "Flexural strength", "Bulk modulus", "Elastic stored energy", "Fatigue strength", "Fracture toughness", "Toughness", "Thermal expansion coefficient", ] for c in COLUMNS_TO_SCRUB: # They're all some kind of number between 40k and 50k # with bias towards repeating values frame[c].mask(frame[c].gt(40000), inplace=True) # Be gingerly with this column because there's larger entries with valid values frame["Thermal shock resistance"].mask(frame["Thermal shock resistance"].eq(47650), inplace=True) frame["Fracture toughness"].mask(frame["Fracture toughness"].gt(12000), inplace=True) def properties_dataframe_from_file(path: str): property_lists = parsing_refined_data(path) frame = pd.DataFrame(index=property_lists[0], data={ k: v for (k, v) in zip(COLUMN_NAMES, property_lists[1:]) }) frame.replace("Null", np.nan, inplace=True) null_invalid_properties(frame) return frame if __name__ == "__main__": parsing_material_data("C:/Users/Everet/Documents/AMP_Project/Pres_3/Updated_PCM.csv", "C:/Users/Everet/Documents/AMP_Project/Pres_3/new_TEM.csv") material_name, material_family, young_modulus_values, specific_stiffness_values, yield_strength_values, tensile_strength_values, specific_strength_values, elongation_values, compressive_strength_values, flexural_modulus_values, flexural_strength_values, shear_modulus_values, bulk_modulus_values, poisson_ratio_values, shape_factor_values, hardness_vickers_values, elastic_stored_energy_values, fatigue_strength_values, fracture_toughness_values, toughness_values, ductility_index_values, melting_values, max_service_temp_values, min_service_temp_values, therm_cond_values, spec_heat_cap_values, therm_expan_coeff_values, therm_shock_resist_values, therm_dist_resist_values, latent_heat_fusion_values, elec_resist_values, elec_cond_values, galvanic_potential_values, mech_loss_coeff_values = parsing_refined_data("C:/Users/Everet/Documents/AMP_Project/Pres_3/new_TEM.csv") print(len(material_name), len(material_family), len(young_modulus_values), len(specific_stiffness_values), len(yield_strength_values), len(tensile_strength_values)) print(material_name[0]) print(material_family[0]) print(material_family[-4]) print(set(material_family)) #print(len(specific_strength_values), len(elongation_values), len(compressive_strength_values), len(flexural_modulus_values), len(flexural_strength_values)) #print(len(shear_modulus_values), len(bulk_modulus_values), len(poisson_ratio_values), len(shape_factor_values), len(hardness_vickers_values)) #print(len(elastic_stored_energy_values), len(fatigue_strength_values), len(fracture_toughness_values), len(toughness_values), len(ductility_index_values)) #print(len(melting_values), len(max_service_temp_values), len(min_service_temp_values), len(therm_cond_values), len(spec_heat_cap_values)) #print(len(therm_expan_coeff_values), len(therm_shock_resist_values), len(therm_dist_resist_values), len(latent_heat_fusion_values), len(elec_resist_values)) #print(len(elec_cond_values), len(galvanic_potential_values), len(mech_loss_coeff_values)) #33 properties total
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demo/python/horizon.py
ebraminio/astronomy-fork
138
33562
#!/usr/bin/env python3 # # horizon.py - by <NAME> - 2019-12-18 # # Example Python program for Astronomy Engine: # https://github.com/cosinekitty/astronomy # # This is a more advanced example. It shows how to use coordinate # transforms and a binary search to find the two azimuths where the # ecliptic intersects with an observer's horizon at a given date and time. # # To execute, run the command: # # python3 horizon.py latitude longitude [yyyy-mm-ddThh:mm:ssZ] # import sys import astronomy from astro_demo_common import ParseArgs NUM_SAMPLES = 4 def ECLIPLON(i): return (360.0 * i) / NUM_SAMPLES def HorizontalCoords(ecliptic_longitude, time, rot_ecl_hor): eclip = astronomy.Spherical( 0.0, # being "on the ecliptic plane" means ecliptic latitude is zero. ecliptic_longitude, 1.0 # any positive distance value will work fine. ) # Convert ecliptic angular coordinates to ecliptic vector. ecl_vec = astronomy.VectorFromSphere(eclip, time) # Use the rotation matrix to convert ecliptic vector to horizontal vector. hor_vec = astronomy.RotateVector(rot_ecl_hor, ecl_vec) # Find horizontal angular coordinates, correcting for atmospheric refraction. return astronomy.HorizonFromVector(hor_vec, astronomy.Refraction.Normal) def Search(time, rot_ecl_hor, e1, e2): tolerance = 1.0e-6 # one-millionth of a degree is close enough! # Binary search: find the ecliptic longitude such that the horizontal altitude # ascends through a zero value. The caller must pass e1, e2 such that the altitudes # bound zero in ascending order. while True: e3 = (e1 + e2) / 2.0 h3 = HorizontalCoords(e3, time, rot_ecl_hor) if abs(e2-e1) < tolerance: return (e3, h3) if h3.lat < 0.0: e1 = e3 else: e2 = e3 def FindEclipticCrossings(observer, time): # The ecliptic is a celestial circle that describes the mean plane of # the Earth's orbit around the Sun. We use J2000 ecliptic coordinates, # meaning the x-axis is defined to where the plane of the Earth's # equator on January 1, 2000 at noon UTC intersects the ecliptic plane. # The positive x-axis points toward the March equinox. # Calculate a rotation matrix that converts J2000 ecliptic vectors # to horizontal vectors for this observer and time. rot = astronomy.Rotation_ECL_HOR(time, observer) # Sample several points around the ecliptic. # Remember the horizontal coordinates for each sample. hor = [HorizontalCoords(ECLIPLON(i), time, rot) for i in range(NUM_SAMPLES)] for i in range(NUM_SAMPLES): a1 = hor[i].lat a2 = hor[(i+1) % NUM_SAMPLES].lat e1 = ECLIPLON(i) e2 = ECLIPLON(i+1) if a1 * a2 <= 0.0: if a2 > a1: (ex, h) = Search(time, rot, e1, e2) else: (ex, h) = Search(time, rot, e2, e1) if h.lon > 0.0 and h.lon < 180.0: direction = 'ascends' else: direction = 'descends' print('Ecliptic longitude {:0.4f} {} through horizon az {:0.4f}, alt {:0.5g}'.format(ex, direction, h.lon, h.lat)) return 0 if __name__ == '__main__': observer, time = ParseArgs(sys.argv) sys.exit(FindEclipticCrossings(observer, time))
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src/jira_dashboard.py
riptano/argus
2
185210
# Copyright 2018 DataStax, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from configparser import RawConfigParser from subprocess import Popen from typing import Dict, Optional, TYPE_CHECKING from src.jira_view import JiraView from src.utils import (browser, get_input, is_yes, pause, pick_value, print_separator) from .display_filter import DisplayFilter if TYPE_CHECKING: from src.display_filter import Column from src.jira_manager import JiraManager class JiraDashboard: """ Contains multiple JiraViews linked together into a single display """ def __init__(self, name: str, jira_views: Dict[str, JiraView]) -> None: self.name = name self._jira_views = jira_views @classmethod def build(cls, jira_views: Dict[str, JiraView]) -> Optional['JiraDashboard']: """ Links 2 or more JiraViews together, combining their results for display """ if len(jira_views) <= 1: print('Need at least 2 JiraViews to create a dashboard. Please create more JiraViews first.') return None view_name_options = list(jira_views.keys()) dash_name = get_input('Name this dashboard:', lowered=False) view_name = pick_value('Include which view?', view_name_options, True, 'Cancel') if view_name is None: return None dash_views = {view_name: jira_views[view_name]} view_name_options.remove(view_name) view_name = pick_value('Second view?', view_name_options, False) # make mypy happy - doesn't realize False means we can't have None if view_name is None: return None dash_views[view_name] = jira_views[view_name] view_name_options.remove(view_name) while len(view_name_options) > 0: if is_yes('Add another?'): view_name = pick_value('What view?', view_name_options, True, '[q] Cancel') if view_name == 'q' or view_name is None: break dash_views[view_name] = jira_views[view_name] view_name_options.remove(view_name) else: break return JiraDashboard(dash_name, dash_views) def display_dashboard(self, jira_manager: 'JiraManager', jira_views: Dict[str, JiraView]) -> None: df = DisplayFilter.default() matching_issues = [] for jira_view in list(self._jira_views.values()): matching_issues.extend(list(jira_view.get_issues().values())) filters = {} # type: Dict[Column, str] while True: filtered_issues = df.display_and_return_sorted_issues(jira_manager, matching_issues, 1, filters) print_separator(60) prompt = 'Input [#] integer value to open ticket in browser, [f] to filter column by string, [c] to clear filters, [q] to quit' custom = get_input(prompt) if custom == 'q': return elif custom == 'f': column_name = pick_value('Filter against which column?', [column.name for column in df.included_columns], True) if column_name is None: continue to_match = get_input('Filter for what string?', False) column_list = [col for col in df.included_columns if col.name == column_name] assert len(column_list) == 1, 'Expected only 1 match with column name {}, got {}'.format(column_name, len(column_list)) filters[column_list[0]] = to_match elif custom == 'c': filters = {} elif custom.isdigit(): intval = int(custom) - 1 issue = filtered_issues[intval] # As we cache JiraProject data on a JiraConnection basis, we need to reach into the JiraView, to their # contained JiraConnections, and check for presence of the owning JiraProject for this issuekey # in order to determine our base url to open a browser to this issue. I'm not in love with this. base_url = 'unknown' for jira_view in list(jira_views.values()): jira_connection = jira_view.jira_connection for jira_project in jira_connection.cached_projects: if jira_project.owns_issue(issue): base_url = jira_connection.url if base_url == 'unknown': print('Failed to find JiraConnection for issuekey: {}. Something went wrong.'.format(issue.issue_key)) else: issue_url = '{}browse/{}'.format(base_url, issue) try: Popen([browser(), issue_url]) print('Opened {}. Press enter to continue.'.format(issue_url)) pause() except OSError as oe: print('Failed to open browser [{}]. Probably need to configure your environment or update from main menu. Exception: {}'.format(browser(), oe)) else: print('Oops... Unrecognized input. Please try again.') pause() def edit_dashboard(self, all_views: Dict[str, JiraView]) -> None: # Remove currently held from list of all for iteration for view_name in list(self._jira_views.keys()): all_views.pop(view_name) cmd = '' while cmd != 'q': cmd = get_input('[A]dd a view, [R]emove a view, [Q]uit:') if cmd == 'r': to_remove = pick_value('Remove which view?', list(self._jira_views.keys()), True, 'Cancel') if to_remove is None: continue all_views.update({to_remove: self._jira_views[to_remove]}) self._jira_views.pop(to_remove) elif cmd == 'a': to_add = pick_value('Add which view?', list(all_views.keys()), True, 'Cancel') if to_add is None: continue self._jira_views[to_add] = all_views[to_add] all_views.pop(to_add) def add_jira_view(self, jira_view: JiraView) -> None: self._jira_views[jira_view.name] = jira_view def remove_jira_view(self, jira_view_name: str) -> None: if jira_view_name in self._jira_views: del self._jira_views[jira_view_name] def contains_jira_view(self, jira_view_name: str) -> bool: return jira_view_name in self._jira_views def save_config(self, config_parser: RawConfigParser) -> None: config_parser.set('Dashboards', self.name, ','.join(list(self._jira_views.keys()))) def __str__(self) -> str: result = 'Name: {}'.format(self.name) for view in self._jira_views: result += ', View: {}'.format(view) return result
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encrypt.py
mtlynch/simple-encrypt
0
48136
<filename>encrypt.py #!/usr/bin/python3 import argparse import base64 from cryptography import fernet from cryptography.hazmat import backends from cryptography.hazmat.primitives import hashes from cryptography.hazmat.primitives.kdf import pbkdf2 import getpass import secrets import sys def _derive_key(password: bytes) -> bytes: kdf = pbkdf2.PBKDF2HMAC( algorithm=hashes.SHA256(), length=32, salt=b'', iterations=pow(10, 7), backend=backends.default_backend()) return base64.urlsafe_b64encode(kdf.derive(password)) def password_encrypt(message: bytes, password: str) -> bytes: key = _derive_key(password.encode()) return fernet.Fernet(key).encrypt(message) if __name__ == '__main__': message = sys.stdin.read() password = <PASSWORD>() ciphertext = password_encrypt(message.encode(), password) print(ciphertext.decode())
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test.py
schanezon/webapp
0
78729
from flask import Flask, redirect, abort, url_for app = Flask(__name__) app.debug = True @app.route('/') def index(): return redirect(url_for('login')) @app.route('/login') def login(): abort(401) this_is_never_executed() if __name__ == '__main__': app.run()
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