Datasets:

---

xlangai

/

the-stack-vault

like 0

Follow

XLang NLP Lab 48

f7153d3e5f303fac4afc1dc66b303035bd382d50

py

Python

doc/api/epydoc/build.py

swamper123/pymodbus

7dfac6f19c60d3aa50a168ff82db88204dfb3a30

doc/api/epydoc/build.py

swamper123/pymodbus

7dfac6f19c60d3aa50a168ff82db88204dfb3a30

2020-10-29T12:01:38.000Z

2022-03-21T02:39:59.000Z

doc/api/epydoc/build.py

swamper123/pymodbus

7dfac6f19c60d3aa50a168ff82db88204dfb3a30

#!/usr/bin/env python3 ''' Epydoc API Runner ------------------ Using pkg_resources, we attempt to see if epydoc is installed, if so, we use its cli program to compile the documents ''' try: import sys, os, shutil import pkg_resources pkg_resources.require("epydoc") from epydoc.cli import cli sys.argv = '''epydoc.py pymodbus --html --simple-term --quiet --include-log --graph=all --docformat=plaintext --debug --exclude=._ --exclude=tests --output=html/ '''.split() #bugs in trunk for --docformat=restructuredtext if not os.path.exists("./html"): os.mkdir("./html") print( "Building Epydoc API Documentation") cli() if os.path.exists('../../../build'): shutil.move("html", "../../../build/epydoc") except Exception as ex: import traceback,sys traceback.print_exc(file=sys.stdout) print( "Epydoc not avaliable...not building")

f71573d18019e66119ed0720c4b4edddc4c1a5eb

py

Python

atom/nucleus/python/test/test_order_reconcile_return_object.py

AbhiGupta03/SDK

f3a61aae7a847f07f0c22a154ca88dc378e9d25e

atom/nucleus/python/test/test_order_reconcile_return_object.py

AbhiGupta03/SDK

f3a61aae7a847f07f0c22a154ca88dc378e9d25e

atom/nucleus/python/test/test_order_reconcile_return_object.py

AbhiGupta03/SDK

f3a61aae7a847f07f0c22a154ca88dc378e9d25e

# coding: utf-8 """ Hydrogen Nucleus API The Hydrogen Nucleus API # noqa: E501 OpenAPI spec version: 1.9.5 Contact: info@hydrogenplatform.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import nucleus_api from nucleus_api.models.order_reconcile_return_object import OrderReconcileReturnObject # noqa: E501 from nucleus_api.rest import ApiException class TestOrderReconcileReturnObject(unittest.TestCase): """OrderReconcileReturnObject unit test stubs""" def setUp(self): pass def tearDown(self): pass def testOrderReconcileReturnObject(self): """Test OrderReconcileReturnObject""" # FIXME: construct object with mandatory attributes with example values # model = nucleus_api.models.order_reconcile_return_object.OrderReconcileReturnObject() # noqa: E501 pass if __name__ == '__main__': unittest.main()

f715b38795d175d33576ab05dea9a3fe41688f13

py

Python

samples/snippets/detect/label-products.py

glaswasser/python-vision

706c314a86b8f35c313bb3e907ae84317dca1a0b

samples/snippets/detect/label-products.py

glaswasser/python-vision

706c314a86b8f35c313bb3e907ae84317dca1a0b

samples/snippets/detect/label-products.py

glaswasser/python-vision

706c314a86b8f35c313bb3e907ae84317dca1a0b

from detect import (detect_logos, detect_text) import pandas as pd import re import os #from __future__ import print_function from google.cloud import vision images_path = "C:\\Users\\heinz\\Yagora GmbH\\Ievgen Kyrda - Crawler\\images\\foodnewsgermany_images/" file_names = os.listdir(os.path.dirname(images_path)) file_paths = [images_path + f for f in file_names] logos = [detect_logos(f) for f in file_paths] texts = [detect_text(f)[0].description for f in file_paths] # remove line break symbols texts = [x.replace("\n", ", ") for x in texts] contained = [] #contained[1] = "test" for i in range(len(logos)): # loop over future rows of df tmp = [] for j in logos[i]: # for every logo-row, check if in text if j.lower() in texts[i].lower(): tmp.append(logos[i]) else: tmp.append(None) contained.append(tmp) detect_df = pd.DataFrame( list(zip(file_names, texts, logos, contained, file_paths)), columns = ["files", "texts", "logos", "probable_brand", "file_path"] ) detect_df # other ideas: # if logo in existing logos, add logo from PIL import Image from io import BytesIO from IPython.display import HTML import base64 pd.set_option('display.max_colwidth', -1) def get_thumbnail(path): i = Image.open(path) i.thumbnail((150, 150), Image.LANCZOS) return i def image_base64(im): if isinstance(im, str): im = get_thumbnail(im) with BytesIO() as buffer: im.save(buffer, 'jpeg') return base64.b64encode(buffer.getvalue()).decode() def image_formatter(im): return f'<img src="data:image/jpeg;base64,{image_base64(im)}">' #dogs['file'] = dogs.id.map(lambda id: f'../input/train/{id}.jpg') detect_df['image'] = detect_df.file_path.map(lambda f: get_thumbnail(f)) HTML(detect_df.to_html(formatters={'image': image_formatter}, escape=False))

f715e2b4af325720c565d744e3e3558d6ec968b2

py

Python

bookworm/annotation/annotation_gui.py

mush42/bookworm

a4bdd89363137a89a1bed1e9e072de4fb55576fd

2019-07-19T22:12:15.000Z

2020-08-26T17:45:19.000Z

bookworm/annotation/annotation_gui.py

mush42/bookworm

a4bdd89363137a89a1bed1e9e072de4fb55576fd

2019-07-15T10:17:00.000Z

2020-07-26T11:22:53.000Z

bookworm/annotation/annotation_gui.py

mush42/bookworm

a4bdd89363137a89a1bed1e9e072de4fb55576fd

2019-09-03T13:13:31.000Z

2020-08-25T13:55:27.000Z

# coding: utf-8 import wx from enum import IntEnum from bookworm import speech from bookworm.gui.settings import SettingsPanel from bookworm.structured_text import TextRange from bookworm.logger import logger from .annotator import Bookmarker, NoteTaker, Quoter from .annotation_dialogs import ( BookmarksViewer, CommentsDialog, QuotesDialog, GenericAnnotationWithContentDialog, ) log = logger.getChild(__name__) class AnnotationSettingsPanel(SettingsPanel): config_section = "annotation" def addControls(self): # Translators: the title of a group of controls in the UIBox = self.make_static_box(_("Annotation")) wx.CheckBox( UIBox, -1, # Translators: the label of a checkbox _("Speak the bookmark when jumping"), name="annotation.speak_bookmarks_on_jumping", ) wx.CheckBox( UIBox, -1, # Translators: the label of a checkbox _("Select the bookmarked line when jumping"), name="annotation.select_bookmarked_line_on_jumping", ) wx.CheckBox( UIBox, -1, # Translators: the label of a checkbox _("Use visual styles to indicate annotations"), name="annotation.use_visuals", ) wx.CheckBox( UIBox, -1, # Translators: the label of a checkbox _("Use sounds to indicate the presence of comments"), name="annotation.play_sound_for_comments", ) class AnnotationsMenuIds(IntEnum): addBookmark = 241 addNamedBookmark = 242 addNote = 243 quoteSelection = 244 viewBookmarks = 245 class StatelessAnnotationsMenuIds(IntEnum): viewNotes = 246 viewQuotes = 247 ANNOTATIONS_KEYBOARD_SHORTCUTS = { AnnotationsMenuIds.addBookmark: "Ctrl-B", AnnotationsMenuIds.addNamedBookmark: "Ctrl-Shift-B", AnnotationsMenuIds.addNote: "Ctrl-M", AnnotationsMenuIds.quoteSelection: "Ctrl-H", } class AnnotationMenu(wx.Menu): """Annotation menu.""" def __init__(self, service): super().__init__() self.service = service self.view = service.view self.reader = service.reader # Add menu items self.Append( AnnotationsMenuIds.addBookmark, # Translators: the label of an item in the application menubar _("Add &Bookmark\tCtrl-B"), # Translators: the help text of an item in the application menubar _("Add a bookmark at the current position"), ) self.Append( AnnotationsMenuIds.addNamedBookmark, # Translators: the label of an item in the application menubar _("Add &Named Bookmark...\tCtrl-Shift-B"), # Translators: the help text of an item in the application menubar _("Add a named bookmark at the current position"), ) self.Append( AnnotationsMenuIds.addNote, # Translators: the label of an item in the application menubar _("Add Co&mment...\tCtrl-M"), # Translators: the help text of an item in the application menubar _("Add a comment at the current position"), ) self.Append( AnnotationsMenuIds.quoteSelection, # Translators: the label of an item in the application menubar _("&Highlight Selection\tCtrl-H"), # Translators: the help text of an item in the application menubar _("Highlight selected text and save it."), ) self.Append( AnnotationsMenuIds.viewBookmarks, # Translators: the label of an item in the application menubar _("Saved &Bookmarks..."), # Translators: the help text of an item in the application menubar _("View added bookmarks"), ) self.Append( StatelessAnnotationsMenuIds.viewNotes, # Translators: the label of an item in the application menubar _("Saved Co&mments..."), # Translators: the help text of an item in the application menubar _("View, edit, and remove comments."), ) self.Append( StatelessAnnotationsMenuIds.viewQuotes, # Translators: the label of an item in the application menubar _("Saved &Highlights..."), # Translators: the help text of an item in the application menubar _("View saved highlights."), ) # Translators: the label of an item in the application menubar # EventHandlers self.view.Bind( wx.EVT_MENU, self.onAddBookmark, id=AnnotationsMenuIds.addBookmark ) self.view.Bind( wx.EVT_MENU, self.onAddNamedBookmark, id=AnnotationsMenuIds.addNamedBookmark ) self.view.Bind(wx.EVT_MENU, self.onAddNote, id=AnnotationsMenuIds.addNote) self.view.Bind( wx.EVT_MENU, self.onQuoteSelection, id=AnnotationsMenuIds.quoteSelection ) self.view.Bind( wx.EVT_MENU, self.onViewBookmarks, id=AnnotationsMenuIds.viewBookmarks ) self.view.Bind( wx.EVT_MENU, self.onViewNotes, id=StatelessAnnotationsMenuIds.viewNotes ) self.view.Bind( wx.EVT_MENU, self.onViewQuotes, id=StatelessAnnotationsMenuIds.viewQuotes ) def _add_bookmark(self, name=""): bookmarker = Bookmarker(self.reader) insertionPoint = self.view.contentTextCtrl.GetInsertionPoint() __, __, current_lino = self.view.contentTextCtrl.PositionToXY(insertionPoint) count = 0 for bkm in bookmarker.get_for_page(self.reader.current_page): __, __, lino = self.view.contentTextCtrl.PositionToXY(bkm.position) if lino == current_lino: count += 1 bookmarker.delete(bkm.id) self.service.style_bookmark(self.view, bkm.position, enable=False) if count and not name: return speech.announce(_("Bookmark removed")) Bookmarker(self.reader).create(title=name, position=insertionPoint) # Translators: spoken message speech.announce(_("Bookmark Added")) self.service.style_bookmark(self.view, insertionPoint) def onAddBookmark(self, event): self._add_bookmark() def onAddNamedBookmark(self, event): bookmark_name = self.view.get_text_from_user( # Translators: title of a dialog _("Add Named Bookmark"), # Translators: label of a text entry _("Bookmark name:"), ) if bookmark_name: self._add_bookmark(bookmark_name) def onAddNote(self, event): _with_tags = wx.GetKeyState(wx.WXK_SHIFT) insertionPoint = self.view.contentTextCtrl.GetInsertionPoint() comment_text = self.view.get_text_from_user( # Translators: the title of a dialog to add a comment _("New Comment"), # Translators: the label of an edit field to enter a comment _("Comment:"), style=wx.OK | wx.CANCEL | wx.TE_MULTILINE | wx.CENTER, ) if not comment_text: return note = NoteTaker(self.reader).create( title="", content=comment_text, position=insertionPoint ) self.service.style_comment(self.view, insertionPoint) if _with_tags: # add tags tags_text = self.view.get_text_from_user( # Translators: title of a dialog _("Tag Comment"), # Translators: label of a text entry _("Tags:"), ) if tags_text: for tag in tags_text.split(): note.tags.append(tag.strip()) NoteTaker.model.session.commit() def onQuoteSelection(self, event): _with_tags = wx.GetKeyState(wx.WXK_SHIFT) quoter = Quoter(self.reader) selected_text = self.view.contentTextCtrl.GetStringSelection() if not selected_text: return speech.announce(_("No selection")) x, y = self.view.get_selection_range() for q in quoter.get_for_page(): q_range = TextRange(q.start_pos, q.end_pos) if (q_range.start == x) and (q_range.stop == y): quoter.delete(q.id) self.service.style_highlight(self.view, x, y, enable=False) # Translators: spoken message return speech.announce(_("Highlight removed")) elif (q.start_pos < x) and (q.end_pos > y): # Translators: spoken message speech.announce(_("Already highlighted")) return wx.Bell() if (x in q_range) or (y in q_range): if x not in q_range: q.start_pos = x q.session.commit() self.service.style_highlight(self.view, x, q_range.stop) return speech.announce(_("Highlight extended")) elif y not in q_range: q.end_pos = y q.session.commit() self.service.style_highlight(self.view, q_range.start, y) # Translators: spoken message return speech.announce(_("Highlight extended")) quote = quoter.create(title="", content=selected_text, start_pos=x, end_pos=y) # Translators: spoken message speech.announce(_("Selection highlighted")) self.service.style_highlight(self.view, x, y) if _with_tags: # add tags tags_text = self.view.get_text_from_user( # Translators: title of a dialog _("Tag Highlight"), # Translators: label of a text entry _("Tags:"), ) if tags_text: for tag in tags_text.split(): quote.tags.append(tag.strip()) Quoter.model.session.commit() def onViewBookmarks(self, event): with BookmarksViewer( parent=self.view, reader=self.reader, annotator=Bookmarker, # Translators: the title of a dialog to view bookmarks title=_("Bookmarks | {book}").format(book=self.reader.current_book.title), ) as dlg: dlg.ShowModal() def onViewNotes(self, event): Dialog = ( CommentsDialog if self.reader.ready else GenericAnnotationWithContentDialog ) with Dialog( parent=self.view, title=_("Comments"), reader=self.reader, annotator_cls=NoteTaker, can_edit=True, ) as dlg: dlg.ShowModal() def onViewQuotes(self, event): Dialog = ( QuotesDialog if self.reader.ready else GenericAnnotationWithContentDialog ) with Dialog( parent=self.view, title=_("Highlights"), reader=self.reader, annotator_cls=Quoter, ) as dlg: dlg.ShowModal()

f71617895efc3dfd23246121c700461891099a24

py

Python

docs/conf.py

EVEprosper/ProsperDatareader

31f0d77074c21222161774f4d653326925611167

docs/conf.py

EVEprosper/ProsperDatareader

31f0d77074c21222161774f4d653326925611167

2017-08-14T02:25:42.000Z

2018-11-16T19:15:52.000Z

docs/conf.py

EVEprosper/ProsperDatareader

31f0d77074c21222161774f4d653326925611167

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # ProsperDatareader documentation build configuration file, created by # sphinx-quickstart on Mon Jul 31 09:30:33 2017. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # ## vv TODO vv: autodocs ## import os import sys sys.path.insert(0, os.path.abspath('../prosper/datareader')) sys.path.insert(0, os.path.abspath('../prosper')) from _version import __version__ ## ^^ TODO ^^ ## import alabaster # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', #'sphinx.ext.napoleon', 'sphinx.ext.doctest', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', 'sphinx.ext.githubpages', 'sphinxcontrib.napoleon', 'alabaster', ] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The master toctree document. master_doc = 'index' # General information about the project. project = 'ProsperDatareader' copyright = '2017, John Purcell' author = 'John Purcell' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '0.9.0' # The full version, including alpha/beta/rc tags. release = '0.9.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme_path = [alabaster.get_path()] html_theme = 'alabaster' html_static_path = ['_static'] templates_path = ['templates'] html_show_sourcelink = False # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # html_theme_options = { 'logo': 'logo-colour-sm.png', 'description': 'Uniform Data Collection', 'description_font_style': 'italic', 'github_user': 'eveprosper', 'github_repo': 'prosperdatareader', 'github_banner': True, } html_favicon = "static/prosper.ico" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['static'] # Custom sidebar templates, must be a dictionary that maps document names # to template names. # # This is required for the alabaster theme # refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars html_sidebars = { 'index': [ 'about.html', 'patreon.html', 'globaltoc.html', 'searchbox.html', ], '**': [ 'about.html', 'patreon.html', 'globaltoc.html', 'searchbox.html' ] } #html_sidebars = { # '**': [ # 'about.html', # 'navigation.html', # 'relations.html', # needs 'show_related': True theme option to display # 'searchbox.html', # 'donate.html', # ] #} # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = 'ProsperDatareaderdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'ProsperDatareader.tex', 'ProsperDatareader Documentation', 'John Purcell', 'manual'), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'prosperdatareader', 'ProsperDatareader Documentation', [author], 1) ] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'ProsperDatareader', 'ProsperDatareader Documentation', author, 'ProsperDatareader', 'One line description of project.', 'Miscellaneous'), ]

f71669f5529851928377789218c8de2abda6eaf6

py

Python

测试/tensorflow_hello/2.practices_on_nlp.py

shayxu-ai/A-Repository-for-Machine-Learning

4b4cea15bb005d1c58f4395fde97cadf44fb0186

测试/tensorflow_hello/2.practices_on_nlp.py

shayxu-ai/A-Repository-for-Machine-Learning

4b4cea15bb005d1c58f4395fde97cadf44fb0186

测试/tensorflow_hello/2.practices_on_nlp.py

shayxu-ai/A-Repository-for-Machine-Learning

4b4cea15bb005d1c58f4395fde97cadf44fb0186

# -*- coding: utf-8 -*- # @Time: 2020/2/5,005 22:02 # @Last Update: 2020/2/5,005 22:02 # @Author: 徐缘 # @FileName: 2.practices_on_nlp.py # @Software: PyCharm from __future__ import absolute_import, division, print_function, unicode_literals # 导入一些熟悉的陌生人 # 绝对引入，精确除法，print，unicode类型字符串。都是为了适配python2，不加也罢 import numpy as np import tensorflow as tf from tensorflow.keras.layers import Dense, Flatten, Conv2D from tensorflow.keras import Model from tensorflow import keras import tensorflow_hub as hub # 模型库 import tensorflow_datasets as tfds # 数据|库 https://tensorflow.google.cn/datasets/api_docs/python/tfds?hl=en tfds.disable_progress_bar() def version(): """ 国际惯例，先看下版本 """ print("Eager mode: ", tf.executing_eagerly()) print("Hub version: ", hub.__version__) print("tfds version", tfds.__version__) print("GPU is", "available" if tf.config.experimental.list_physical_devices("GPU") else "NOT AVAILABLE") def tf_hub_hello(): """ 预训练word2vector(迁移学习) + 全连接层 loss: 0.329 accuracy: 0.858 我记得 cnn 文本分类可以有95%呢 """ train_data, validation_data, test_data = tfds.load( name="imdb_reviews", split=('train[:60%]', 'train[60%:]', 'test'), as_supervised=True) train_examples_batch, train_labels_batch = next(iter(train_data.batch(10))) print(train_examples_batch) print(train_labels_batch) embedding = "https://hub.tensorflow.google.cn/google/tf2-preview/gnews-swivel-20dim/1" hub_layer = hub.KerasLayer(embedding, input_shape=[], dtype=tf.string, trainable=True) print(hub_layer(train_examples_batch[:3])) model = tf.keras.Sequential() model.add(hub_layer) model.add(tf.keras.layers.Dense(16, activation='relu')) model.add(tf.keras.layers.Dense(1, activation='sigmoid')) # model.summary() model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) history = model.fit(train_data.shuffle(10000).batch(512), epochs=20, validation_data=validation_data.batch(512), verbose=1) results = model.evaluate(test_data.batch(512), verbose=2) for name, value in zip(model.metrics_names, results): print("%s: %.3f" % (name, value)) def preprocess_text(): """ """ (train_data, test_data), info = tfds.load( # Use the version pre-encoded with an ~8k vocabulary. 'imdb_reviews/subwords8k', # Return the train/test datasets as a tuple. split=(tfds.Split.TRAIN, tfds.Split.TEST), # Return (example, label) pairs from the dataset (instead of a dictionary). as_supervised=True, # Also return the `info` structure. with_info=True) encoder = info.features['text'].encoder print('Vocabulary size: {}'.format(encoder.vocab_size)) sample_string = 'Hello TensorFlow.' encoded_string = encoder.encode(sample_string) print('Encoded string is {}'.format(encoded_string)) original_string = encoder.decode(encoded_string) print('The original string: "{}"'.format(original_string)) assert original_string == sample_string for ts in encoded_string: print('{} ----> {}'.format(ts, encoder.decode([ts]))) for train_example, train_label in train_data.take(1): print('Encoded text:', train_example[:10].numpy()) print('Label:', train_label.numpy()) encoder.decode(train_example) BUFFER_SIZE = 1000 train_batches = ( train_data .shuffle(BUFFER_SIZE) .padded_batch(32, train_data.output_shapes)) test_batches = ( test_data .padded_batch(32, train_data.output_shapes)) for example_batch, label_batch in train_batches.take(2): print("Batch shape:", example_batch.shape) print("label shape:", label_batch.shape) model = keras.Sequential([ keras.layers.Embedding(encoder.vocab_size, 16), keras.layers.GlobalAveragePooling1D(), keras.layers.Dense(1, activation='sigmoid')]) model.summary() model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) history = model.fit(train_batches, epochs=10, validation_data=test_batches, validation_steps=30) loss, accuracy = model.evaluate(test_batches) print("Loss: ", loss) print("Accuracy: ", accuracy) history_dict = history.history history_dict.keys() import matplotlib.pyplot as plt acc = history_dict['accuracy'] val_acc = history_dict['val_accuracy'] loss = history_dict['loss'] val_loss = history_dict['val_loss'] epochs = range(1, len(acc) + 1) # "bo" is for "blue dot" plt.plot(epochs, loss, 'bo', label='Training loss') # b is for "solid blue line" plt.plot(epochs, val_loss, 'b', label='Validation loss') plt.title('Training and validation loss') plt.xlabel('Epochs') plt.ylabel('Loss') plt.legend() plt.show() plt.clf() # clear figure plt.plot(epochs, acc, 'bo', label='Training acc') plt.plot(epochs, val_acc, 'b', label='Validation acc') plt.title('Training and validation accuracy') plt.xlabel('Epochs') plt.ylabel('Accuracy') plt.legend(loc='lower right') plt.show() return if __name__ == '__main__': # version() preprocess_text()

f71684275afc20018793ca67d49712a2693a0850

py

Python

a3c_master_sewak.py

sebtac/MLxE

93baa6b7c9fd14e54abd7199e868fb828e9a7c52

2020-12-15T17:19:33.000Z

2020-12-15T17:19:33.000Z

a3c_master_sewak.py

sebtac/MLxE

93baa6b7c9fd14e54abd7199e868fb828e9a7c52

a3c_master_sewak.py

sebtac/MLxE

93baa6b7c9fd14e54abd7199e868fb828e9a7c52

""" A3C in Code - Centralized/ Gobal Network Parameter Server/ Controller Based On: A3C Code as in the book Deep Reinforcement Learning, Chapter 12. Runtime: Python 3.6.5 Dependencies: numpy, matplotlib, tensorflow (/ tensorflow-gpu), gym DocStrings: GoogleStyle Author : Mohit Sewak (p20150023@goa-bits-pilani.ac.in) Inspired from: A3C implementation on TensorFLow official github repository (Tensorflow/models/research) ********************************************************************** Adjusted by Seabstian Taciak as part of develeopment of MLxE Architecture @author: sebtac @contact: https://www.linkedin.com/in/sebastian-taciak-5893861/ """ # SET BEFORE RUNNIG # AGENT TYPE # 0 - Sewak Base Agent (Fixed) # 1 - Sewak DNN Adjusted # 2 - Sewak "Task" Modified # 3 - Sewak ISTB (Iterative, Synchronous Thread Based) Agent_Type = 3 learning_rate = 0.0001 import multiprocessing cores = multiprocessing.cpu_count() # DEFAULT SETTING #cores = 1 # FOR DEBUGGING # GENERAL IMPORTS import sys sys.path.append(r'C:\Users\surface\Documents\Python\RL\MLxE\Mohit Sewak RL\Mohit12_A3C') import time import winsound import logging import os import numpy as np import matplotlib.pyplot as plt logging.basicConfig() logger = logging.getLogger() logger.setLevel(logging.DEBUG) # DEEP LEARING and ENVIRONEMENT RELATER IMPORTS import tensorflow as tf import tensorflow_addons as tfa # ST for DNN Adjustment import gym # CUSTOM SEWAK's MODULES with OPTIONAL SEBTAC ADJUSTMENTS from experience_replay_sewak import SimpleListBasedMemory if Agent_Type == 0: from actorcritic_model_sewak import ActorCriticModel as ACModel # For Sewak Fixed version from a3c_worker_sewak_base import A3C_Worker # the intial Sewak's implementation with fixes of the Policy_Loss Calcultion elif Agent_Type == 1: from actorcritic_model_sewak import ActorCriticModel_Dimond as ACModel from a3c_worker_sewak_DNN_Adjusted import A3C_Worker elif Agent_Type == 2: from actorcritic_model_sewak import ActorCriticModel_Dimond as ACModel from a3c_worker_sewak_Task_Modifications import A3C_Worker elif Agent_Type == 3: from actorcritic_model_sewak import ActorCriticModel_DoubleDimond as ACModel from a3c_worker_sewak_ISTB import A3C_Worker # SEWAK's Implementation Fix """ - Policy Loss Calcualtion - Using actual play in example generation (was random) """ # DNN Adjustments """ - Adding monotonic decrease in Learing Rate relative to the number of episodes run with: self.alpha_power = 0.998 self.alpha_limit = 0.000001 - Modifying the Model to: common_network_size=[128,256,128], policy_network_size=[64,128,64], value_network_size=[64,128,64] - Changing the Optimizer to RectifiedAdam -- requaires tensorflow_addons - Changing Gamma coeffcient to 0.97 """ # Task Specific Modifications """ - Modified state representation with addition of 5th parameter representing the squared distance of the cart from the center of the plane - Adverse Initial Position - Negative Reward: -10.0 (originally 0.0) - Monotonically Decreasing Discount Factor (Gamma Coefficent) - Goal Specific Reward for cart being close to center of the pland and the pole being close to vertical """ class A3C_Master(): """A3C Master Centralized Master class of A3C used for hosting the global network parameters and spawning the agents. Args: env_name (str): Name of a valid gym environment model_dir (str): Directory for saving the model during training, and loading the same while playing learning_rate (float): The learning rate (alpha) for the optimizer Examples: agent = A3C_Master() agent.train() agent.play() """ def __init__(self, Agent_Type=Agent_Type, env_name='CartPole-v0', model_dir="models", learning_rate=learning_rate): #ST 0.001 for Fixed, 0.0001 otherwise self.env_name = env_name self.model_dir = model_dir self.alpha = learning_rate if not os.path.exists(model_dir): os.makedirs(model_dir) self.env = gym.make(self.env_name) self.action_size = self.env.action_space.n if Agent_Type <= 1: self.state_size = self.env.observation_space.shape[0] # For None TaH imlementations elif Agent_Type == 2: self.state_size = self.env.observation_space.shape[0] + 1 # ST for TaH implementation elif Agent_Type == 3: self.state_size = self.env.observation_space.shape[0] + 1 # ST for TaH implementation if Agent_Type == 0: self.optimizer = tf.keras.optimizers.Adam(self.alpha) else: self.optimizer = tfa.optimizers.RectifiedAdam(self.alpha) # ST DNN Adjustment logger.debug("StateSize:{}, ActionSize:{}".format(self.state_size, self.action_size)) self.master_model = ACModel(self.action_size) self.master_model(tf.convert_to_tensor(np.random.random((1, self.state_size)), dtype=tf.float32)) def train(self, cores): """Train the A3C agent Main function to train the A3C agent after instantiation. This method uses the number of processor cores to spawns as many Workers. The workers are spawned as multiple parallel threads instead of multiple parallel processes. Being a threaded execution, the workers share memory and hence can write directly into the shared global variables. A more optimal, completely asynchronous implementation could be to spawn the workers as different processes using a task queue or multiprocessing. In case if this is adopted, then the shared variables need to made accessible in the distributed environment. """ a3c_workers = [A3C_Worker(self.master_model, self.optimizer, i, self.env_name, self.model_dir, workers_num = cores, learning_rate = learning_rate) for i in range(cores)] for i, worker in enumerate(a3c_workers): logger.info("Starting worker {}".format(i)) worker.start() [worker.join() for worker in a3c_workers] self.plot_training_statistics() def play(self): """Play the environment using a trained agent This function opens a (graphical) window that will play a trained agent. The function will try to retrieve the model saved in the model_dir with filename formatted to contain the associated env_name. If the model is not found, then the function will first call the train function to start the training. """ env = self.env.unwrapped state = env.reset() model = self.master_model model_path = os.path.join(self.model_dir, 'model_{}.h5'.format(self.env_name)) if not os.path.exists(model_path): logger.info('A3CMaster: No model found at {}, starting fresh training before playing!'.format(model_path)) self.train() logger.info('A3CMaster: Playing env, Loading model from: {}'.format(model_path)) print("Model Path:", model_path) #model.load_weights(model_path) done = False step_counter = 0 reward_sum = 0 try: while not done: env.render(mode='rgb_array') policy, value = model(tf.convert_to_tensor(state[None, :], dtype=tf.float32)) policy = tf.nn.softmax(policy) action = np.argmax(policy) state, reward, done, _ = env.step(action) reward_sum += reward logger.info("{}. Reward: {}, action: {}".format(step_counter, reward_sum, action)) step_counter += 1 except KeyboardInterrupt: print("Received Keyboard Interrupt. Shutting down.") finally: env.close() def plot_training_statistics(self, training_statistics=None): """Plot training statistics This function plot the training statistics like the steps, rewards, discounted_rewards, and loss in each of the training episode. """ training_statistics = A3C_Worker.global_shared_training_stats if training_statistics is None \ else training_statistics all_episodes = [] all_steps = [] all_rewards = [] all_discounted_rewards = [] all_losses = [] for stats in training_statistics: worker, episode, steps, reward, discounted_rewards, loss = stats all_episodes.append(episode) all_steps.append(steps) all_rewards.append(reward) all_discounted_rewards.append(discounted_rewards) all_losses.append(loss) self._make_double_axis_plot(all_episodes, all_steps, all_rewards) self._make_double_axis_plot(all_episodes,all_discounted_rewards,all_losses, label_y1="Discounted Reward", label_y2="Loss", color_y1="cyan", color_y2="black") np.savetxt('run.csv', all_steps, delimiter=',', fmt='%d') @staticmethod def _make_double_axis_plot(data_x, data_y1, data_y2, x_label='Episodes (e)', label_y1='Steps To Episode Completion', label_y2='Reward in each Episode', color_y1="red", color_y2="blue"): """Internal helper function for plotting dual axis plots """ fig, ax1 = plt.subplots() ax1.set_xlabel(x_label) ax1.set_ylabel(label_y1, color=color_y1) ax1.plot(data_x, data_y1, color=color_y1) ax2 = ax1.twinx() ax2.set_ylabel(label_y2, color=color_y2) ax2.plot(data_x, data_y2, color=color_y2) fig.tight_layout() plt.show() if __name__ == "__main__": """Main function for testing the A3C Master code's implementation """ agent = A3C_Master(Agent_Type=Agent_Type) agent.train(cores) #agent.play() for i in range(10): winsound.Beep(500,500)

f7168f6333b407972ae83a3eb73553f078b2cd44

py

Python

src/sage/combinat/kazhdan_lusztig.py

saraedum/sage-renamed

d2da67b14da2ad766a5906425d60d43a3b3e1270

src/sage/combinat/kazhdan_lusztig.py

saraedum/sage-renamed

d2da67b14da2ad766a5906425d60d43a3b3e1270

src/sage/combinat/kazhdan_lusztig.py

saraedum/sage-renamed

d2da67b14da2ad766a5906425d60d43a3b3e1270

r""" Kazhdan-Lusztig Polynomials AUTHORS: - Daniel Bump (2008): initial version - Alan J.X. Guo (2014-03-18): ``R_tilde()`` method. """ #***************************************************************************** # Copyright (C) 2008 Daniel Bump <bump at match.stanford.edu> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # http://www.gnu.org/licenses/ #***************************************************************************** from __future__ import absolute_import, print_function, division from sage.rings.polynomial.polynomial_element import is_Polynomial from sage.misc.cachefunc import cached_method from sage.rings.polynomial.laurent_polynomial import LaurentPolynomial from sage.structure.sage_object import SageObject from sage.structure.unique_representation import UniqueRepresentation class KazhdanLusztigPolynomial(UniqueRepresentation, SageObject): """ A Kazhdan-Lusztig polynomial. INPUT: - ``W`` -- a Weyl Group - ``q`` -- an indeterminate OPTIONAL: - ``trace`` -- if ``True``, then this displays the trace: the intermediate results. This is instructive and fun. The parent of ``q`` may be a :class:`PolynomialRing` or a :class:`LaurentPolynomialRing`. REFERENCES: .. [KL79] \D. Kazhdan and G. Lusztig. *Representations of Coxeter groups and Hecke algebras*. Invent. Math. **53** (1979). no. 2, 165--184. :doi:`10.1007/BF01390031` :mathscinet:`MR0560412` .. [Dy93] \M. J. Dyer. *Hecke algebras and shellings of Bruhat intervals*. Compositio Mathematica, 1993, 89(1): 91-115. .. [BB05] \A. Bjorner, F. Brenti. *Combinatorics of Coxeter groups*. New York: Springer, 2005. EXAMPLES:: sage: W = WeylGroup("B3",prefix="s") sage: [s1,s2,s3] = W.simple_reflections() sage: R.<q> = LaurentPolynomialRing(QQ) sage: KL = KazhdanLusztigPolynomial(W,q) sage: KL.P(s2,s3*s2*s3*s1*s2) 1 + q A faster implementation (using the optional package Coxeter 3) is given by:: sage: W = CoxeterGroup(['B', 3], implementation='coxeter3') # optional - coxeter3 sage: W.kazhdan_lusztig_polynomial([2], [3,2,3,1,2]) # optional - coxeter3 q + 1 """ def __init__(self, W, q, trace=False): """ Initialize ``self``. EXAMPLES:: sage: W = WeylGroup("B3",prefix="s") sage: R.<q> = LaurentPolynomialRing(QQ) sage: KL = KazhdanLusztigPolynomial(W,q) sage: TestSuite(KL).run() """ self._coxeter_group = W self._q = q self._trace = trace self._one = W.one() self._base_ring = q.parent() if is_Polynomial(q): self._base_ring_type = "polynomial" elif isinstance(q, LaurentPolynomial): self._base_ring_type = "laurent" else: self._base_ring_type = "unknown" @cached_method def R(self, x, y): """ Return the Kazhdan-Lusztig `R` polynomial. INPUT: - ``x``, ``y`` -- elements of the underlying Coxeter group EXAMPLES:: sage: R.<q>=QQ[] sage: W = WeylGroup("A2", prefix="s") sage: [s1,s2]=W.simple_reflections() sage: KL = KazhdanLusztigPolynomial(W, q) sage: [KL.R(x,s2*s1) for x in [1,s1,s2,s1*s2]] [q^2 - 2*q + 1, q - 1, q - 1, 0] """ if x == 1: x = self._one if y == 1: y = self._one if x == y: return self._base_ring.one() if not x.bruhat_le(y): return self._base_ring.zero() if y.length() == 0: if x.length() == 0: return self._base_ring.one() else: return self._base_ring.zero() s = self._coxeter_group.simple_reflection(y.first_descent(side="left")) if (s*x).length() < x.length(): ret = self.R(s*x,s*y) if self._trace: print(" R(%s,%s)=%s" % (x, y, ret)) return ret else: ret = (self._q-1)*self.R(s*x,y)+self._q*self.R(s*x,s*y) if self._trace: print(" R(%s,%s)=%s" % (x, y, ret)) return ret @cached_method def R_tilde(self, x, y): r""" Return the Kazhdan-Lusztig `\tilde{R}` polynomial. Information about the `\tilde{R}` polynomials can be found in [Dy93]_ and [BB05]_. INPUT: - ``x``, ``y`` -- elements of the underlying Coxeter group EXAMPLES:: sage: R.<q> = QQ[] sage: W = WeylGroup("A2", prefix="s") sage: [s1,s2] = W.simple_reflections() sage: KL = KazhdanLusztigPolynomial(W, q) sage: [KL.R_tilde(x,s2*s1) for x in [1,s1,s2,s1*s2]] [q^2, q, q, 0] """ if x == 1: x = self._one if y == 1: y = self._one if not x.bruhat_le(y): return self._base_ring.zero() if x == y: return self._base_ring.one() s = self._coxeter_group.simple_reflection(y.first_descent(side="right")) if (x * s).length() < x.length(): ret = self.R_tilde(x * s, y * s) if self._trace: print(" R_tilde(%s,%s)=%s" % (x, y, ret)) return ret else: ret = self.R_tilde(x * s, y * s) + self._q * self.R_tilde(x, y * s) if self._trace: print(" R_tilde(%s,%s)=%s" % (x, y, ret)) return ret @cached_method def P(self, x, y): """ Return the Kazhdan-Lusztig `P` polynomial. If the rank is large, this runs slowly at first but speeds up as you do repeated calculations due to the caching. INPUT: - ``x``, ``y`` -- elements of the underlying Coxeter group .. SEEALSO:: :mod:`~sage.libs.coxeter3.coxeter_group.CoxeterGroup.kazhdan_lusztig_polynomial` for a faster implementation using Fokko Ducloux's Coxeter3 C++ library. EXAMPLES:: sage: R.<q> = QQ[] sage: W = WeylGroup("A3", prefix="s") sage: [s1,s2,s3] = W.simple_reflections() sage: KL = KazhdanLusztigPolynomial(W, q) sage: KL.P(s2,s2*s1*s3*s2) q + 1 """ if x == 1: x = self._one if y == 1: y = self._one if x == y: return self._base_ring.one() if not x.bruhat_le(y): return self._base_ring.zero() if y.length() == 0: if x.length() == 0: return self._base_ring.one() else: return self._base_ring.zero() p = sum(-self.R(x, t) * self.P(t, y) for t in self._coxeter_group.bruhat_interval(x, y) if t != x) tr = (y.length() - x.length() + 1) // 2 ret = p.truncate(tr) if self._trace: print(" P({},{})={}".format(x, y, ret)) return ret

f716cea90be05811860d24af0a9d540d7d2e2e6c

py

Python

code/distributeHI.py

modichirag/21cmhod

0807a7b0b880f4ba5bc7161b843d500ddcece5a7

code/distributeHI.py

modichirag/21cmhod

0807a7b0b880f4ba5bc7161b843d500ddcece5a7

code/distributeHI.py

modichirag/21cmhod

0807a7b0b880f4ba5bc7161b843d500ddcece5a7

import numpy as np import re, os from pmesh.pm import ParticleMesh from nbodykit.lab import BigFileCatalog, BigFileMesh, MultipleSpeciesCatalog, FFTPower from nbodykit import setup_logging from mpi4py import MPI import HImodels # enable logging, we have some clue what's going on. setup_logging('info') #Get model as parameter import argparse parser = argparse.ArgumentParser() parser.add_argument('-s', '--size', help='for small or big box', default='small') parser.add_argument('-m', '--model', help='model name to use') args = parser.parse_args() if args.model == None: print('Specify a model name') sys.exit() #print(args, args.model) model = args.model #'ModelD' boxsize = args.size # # #Global, fixed things scratchyf = '/global/cscratch1/sd/yfeng1/m3127/' scratchcm = '/global/cscratch1/sd/chmodi/m3127/H1mass/' project = '/project/projectdirs/m3127/H1mass/' cosmodef = {'omegam':0.309167, 'h':0.677, 'omegab':0.048} alist = [0.1429,0.1538,0.1667,0.1818,0.2000,0.2222,0.2500,0.2857,0.3333] #Parameters, box size, number of mesh cells, simulation, ... if boxsize == 'small': bs, nc, ncsim, sim, prefix = 256, 512, 2560, 'highres/%d-9100-fixed'%2560, 'highres' elif boxsize == 'big': bs, nc, ncsim, sim, prefix = 1024, 1024, 10240, 'highres/%d-9100-fixed'%10240, 'highres' else: print('Box size not understood, should be "big" or "small"') sys.exit() # It's useful to have my rank for printing... pm = ParticleMesh(BoxSize=bs, Nmesh=[nc, nc, nc]) rank = pm.comm.rank comm = pm.comm #Which model & configuration to use modeldict = {'ModelA':HImodels.ModelA, 'ModelB':HImodels.ModelB, 'ModelC':HImodels.ModelC} modedict = {'ModelA':'galaxies', 'ModelB':'galaxies', 'ModelC':'halos'} HImodel = modeldict[model] #HImodels.ModelB modelname = model mode = modedict[model] ofolder = '../data/outputs/' def distribution(aa, halocat, cencat, satcat, outfolder, mbins=None): '''Compute the fraction of HI in halos, centrals, satellites''' if rank==0: print('Calculating distribution') if mbins is None: mbins = np.logspace(9, 15, 100) hmass = halocat['Mass'].compute() htotal, hsize, h1total = [], [], [] for im in range(mbins.size-1): mask = (hmass >= mbins[im]) & (hmass < mbins[im+1]) rankweight = (hmass*mask).sum() htotal.append(comm.allreduce(rankweight)) rankweight = (mask).sum() hsize.append(comm.allreduce(rankweight)) h1bin = [] for cat in [halocat['HImass'], cencat['HImass'], cencat['HIsat']]: rankweight = (cat.compute()*mask).sum() h1bin.append(comm.allreduce(rankweight)) h1total.append(h1bin) # if rank==0: tosave = np.zeros((len(hsize), 5)) tosave[:, 1] = hsize tosave[:, 0] = htotal / (tosave[:, 1]) tosave[:, 2:] = h1total/ (tosave[:, 1].reshape(-1, 1)) tosave[np.isnan(tosave)] = 0 header = 'Halo Mass, Number Halos, HI halos, HI centrals, HI satellites' np.savetxt(outfolder + "HI_dist_{:6.4f}.txt".format(aa), tosave, fmt='%0.6e', header=header) if __name__=="__main__": if rank==0: print('Starting') suff='-m1_00p3mh-alpha-0p8-subvol' outfolder = ofolder + suff[1:] if bs == 1024: outfolder = outfolder + "-big" outfolder += "/%s/"%modelname if rank == 0: print(outfolder) #outfolder = ofolder + suff[1:] + "/%s/"%modelname try: os.makedirs(outfolder) except : pass for aa in alist: if rank == 0: print('\n ############## Redshift = %0.2f ############## \n'%(1/aa-1)) halocat = BigFileCatalog(scratchyf + sim+ '/fastpm_%0.4f//'%aa, dataset='LL-0.200') mp = halocat.attrs['MassTable'][1]*1e10## halocat['Mass'] = halocat['Length'].compute() * mp cencat = BigFileCatalog(scratchcm + sim+'/fastpm_%0.4f/cencat'%aa+suff) satcat = BigFileCatalog(scratchcm + sim+'/fastpm_%0.4f/satcat'%aa+suff) # HImodelz = HImodel(aa) halocat['HImass'], cencat['HImass'], satcat['HImass'] = HImodelz.assignHI(halocat, cencat, satcat) cencat['HIsat'] = HImodelz.getinsat(satcat['HImass'].compute(), satcat['GlobalID'].compute(), cencat.csize, cencat['Mass'].size, cencat.comm).local mbins = 10**np.arange(9, 15.1, 0.2) distribution(aa, halocat, cencat, satcat, outfolder, mbins=mbins)

f716fe49497e0fa092b298e9bf377c75b13a12bf

py

Python

servers/python/coweb/auth/public.py

opencoweb/coweb

7b3a87ee9eda735a859447d404ee16edde1c5671

2015-01-05T19:02:57.000Z

2021-11-19T02:48:09.000Z

servers/python/coweb/auth/public.py

xuelingxiao/coweb

7b3a87ee9eda735a859447d404ee16edde1c5671

2015-12-16T13:49:33.000Z

2019-06-17T13:38:50.000Z

servers/python/coweb/auth/public.py

xuelingxiao/coweb

7b3a87ee9eda735a859447d404ee16edde1c5671

2015-04-29T22:36:53.000Z

2021-11-18T03:24:29.000Z

''' Copyright (c) The Dojo Foundation 2011. All Rights Reserved. Copyright (c) IBM Corporation 2008, 2011. All Rights Reserved. ''' from .base import AuthBase class PublicAuth(AuthBase): cookieName = 'coweb.auth.public.username' _userId = 0 def requires_login(self): '''Does not require login. Usernames automatically generated.''' return False def requires_cookies(self): '''Uses tornado's secure cookies.''' return True def get_current_user(self, handler): ''' Generates a unique userXXX for this server instance and stores it in a secure cookie. ''' username = handler.get_secure_cookie(self.cookieName) if not username: # generate a random username and set it with a very short lifetime username = 'user%03d' % self._userId # yes, this might conflict between server restarts but it's dummy # public auth self._userId += 1 handler.set_secure_cookie(self.cookieName, username, expires_days=1) return username def clear_credentials(self, handler): '''Clears the authentication cookie.''' handler.clear_cookie(self.cookieName)

f716fffcedc3cbaba6d963cd4a7e2061ef83cc34

py

Python

mdeepctr/models/xdeepfm.py

TS-SE-GROUP/icme2019

7eefdb7de6a7ff3bec1721fafb822d80d80dbba3

2019-02-21T12:44:11.000Z

2022-03-30T11:42:33.000Z

mdeepctr/models/xdeepfm.py

rightnowandholdon/icme2019

fe9b31db7bf19b08d5e5d41a259f0a297eb21766

2019-04-11T13:14:46.000Z

2021-05-19T14:36:07.000Z

mdeepctr/models/xdeepfm.py

rightnowandholdon/icme2019

fe9b31db7bf19b08d5e5d41a259f0a297eb21766

2019-02-21T02:51:54.000Z

2021-12-10T02:04:28.000Z

# -*- coding:utf-8 -*- """ Author: Weichen Shen,wcshen1994@163.com Reference: [1] Lian J, Zhou X, Zhang F, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems[J]. arXiv preprint arXiv:1803.05170, 2018.(https://arxiv.org/pdf/1803.05170.pdf) """ import tensorflow as tf from ..input_embedding import preprocess_input_embedding from ..layers.core import PredictionLayer, MLP from ..layers.interaction import CIN from ..utils import check_feature_config_dict from ..layers.utils import concat_fun def xDeepFM(feature_dim_dict, embedding_size=8, hidden_size=(256, 256), cin_layer_size=(128, 128,), cin_split_half=True, cin_activation='relu', l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_deep=0, init_std=0.0001, seed=1024, keep_prob=1, activation='relu', final_activation='sigmoid', use_bn=False, output_dim=1,): """Instantiates the xDeepFM architecture. :param feature_dim_dict: dict,to indicate sparse field and dense field like {'sparse':{'field_1':4,'field_2':3,'field_3':2},'dense':['field_4','field_5']} :param embedding_size: positive integer,sparse feature embedding_size :param hidden_size: list,list of positive integer or empty list, the layer number and units in each layer of deep net :param cin_layer_size: list,list of positive integer or empty list, the feature maps in each hidden layer of Compressed Interaction Network :param cin_split_half: bool.if set to True, half of the feature maps in each hidden will connect to output unit :param cin_activation: activation function used on feature maps :param l2_reg_linear: float. L2 regularizer strength applied to linear part :param l2_reg_embedding: L2 regularizer strength applied to embedding vector :param l2_reg_deep: L2 regularizer strength applied to deep net :param init_std: float,to use as the initialize std of embedding vector :param seed: integer ,to use as random seed. :param keep_prob: float in (0,1]. keep_prob used in deep net :param activation: Activation function to use in deep net :param final_activation: str,output activation,usually ``'sigmoid'`` or ``'linear'`` :param use_bn: bool. Whether use BatchNormalization before activation or not.in deep net :return: A Keras model instance. """ check_feature_config_dict(feature_dim_dict) deep_emb_list, linear_logit, inputs_list = preprocess_input_embedding(feature_dim_dict, embedding_size, l2_reg_embedding, l2_reg_linear, init_std, seed, True) fm_input = concat_fun(deep_emb_list, axis=1) if len(cin_layer_size) > 0: exFM_out = CIN(cin_layer_size, cin_activation, cin_split_half, seed)(fm_input) exFM_logit = tf.keras.layers.Dense(1, activation=None,)(exFM_out) deep_input = tf.keras.layers.Flatten()(fm_input) output=[] for _ in range(output_dim): deep_out = MLP(hidden_size, activation, l2_reg_deep, keep_prob, use_bn, seed)(deep_input) deep_logit = tf.keras.layers.Dense( 1, use_bias=False, activation=None)(deep_out) if len(hidden_size) == 0 and len(cin_layer_size) == 0: # only linear final_logit = linear_logit elif len(hidden_size) == 0 and len(cin_layer_size) > 0: # linear + CIN final_logit = tf.keras.layers.add([linear_logit, exFM_logit]) elif len(hidden_size) > 0 and len(cin_layer_size) == 0: # linear +　Deep final_logit = tf.keras.layers.add([linear_logit, deep_logit]) elif len(hidden_size) > 0 and len(cin_layer_size) > 0: # linear + CIN + Deep final_logit = tf.keras.layers.add( [linear_logit, deep_logit, exFM_logit]) else: raise NotImplementedError output.append(PredictionLayer(final_activation)(final_logit)) model = tf.keras.models.Model(inputs=inputs_list, outputs=output) return model

f717181beb4c4046a045a44b6d0f4db857d85911

py

Python

forte/processors/stanfordnlp_processor.py

swapnull7/forte

737a72afd440d40c3826c3a7c5e4e44235c0f701

2021-01-01T12:07:27.000Z

2021-09-10T03:57:18.000Z

forte/processors/stanfordnlp_processor.py

swapnull7/forte

737a72afd440d40c3826c3a7c5e4e44235c0f701

forte/processors/stanfordnlp_processor.py

swapnull7/forte

737a72afd440d40c3826c3a7c5e4e44235c0f701

# Copyright 2019 The Forte Authors. 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. import logging from typing import List, Any, Dict import stanza from forte.common.configuration import Config from forte.common.resources import Resources from forte.data.data_pack import DataPack from forte.processors.base import PackProcessor from ft.onto.base_ontology import Token, Sentence, Dependency __all__ = [ "StandfordNLPProcessor", ] class StandfordNLPProcessor(PackProcessor): def __init__(self): super().__init__() self.nlp = None self.processors = set() def set_up(self): stanza.download(self.configs.lang, self.configs.dir) self.processors = set(self.configs.processors.split(',')) # pylint: disable=unused-argument def initialize(self, resources: Resources, configs: Config): super().initialize(resources, configs) self.set_up() self.nlp = stanza.Pipeline( lang=self.configs.lang, dir=self.configs.dir, use_gpu=self.configs.use_gpu, processors=self.configs.processors, ) @classmethod def default_configs(cls) -> Dict[str, Any]: """ This defines a basic config structure for StanfordNLP. :return: """ config = super().default_configs() config.update( { 'processors': 'tokenize,pos,lemma,depparse', 'lang': 'en', # Language code for the language to build the Pipeline 'use_gpu': False, 'dir': '.', }) return config def _process(self, input_pack: DataPack): doc = input_pack.text if len(doc) == 0: logging.warning("Find empty text in doc.") # sentence parsing sentences = self.nlp(doc).sentences # Iterating through stanfordnlp sentence objects for sentence in sentences: Sentence(input_pack, sentence.tokens[0].start_char, sentence.tokens[-1].end_char) tokens: List[Token] = [] if "tokenize" in self.processors: # Iterating through stanfordnlp word objects for word in sentence.words: misc = word.misc.split('|') t_start = -1 t_end = -1 for m in misc: k, v = m.split('=') if k == 'start_char': t_start = int(v) elif k == 'end_char': t_end = int(v) if t_start < 0 or t_end < 0: raise ValueError( "Cannot determine word start or end for " "stanfordnlp.") token = Token(input_pack, t_start, t_end) if "pos" in self.processors: token.pos = word.pos token.ud_xpos = word.xpos if "lemma" in self.processors: token.lemma = word.lemma tokens.append(token) # For each sentence, get the dependency relations among tokens if "depparse" in self.processors: # Iterating through token entries in current sentence for token, word in zip(tokens, sentence.words): child = token # current token parent = tokens[word.head - 1] # Head token relation_entry = Dependency(input_pack, parent, child) relation_entry.rel_type = word.deprel

f71722c909f61d25a111b3cedd33fb84627eb888

py

Python

msp430backend/msp430_ws/server_protocol.py

zlalanne/msp430-webcontrol

a4e8f84942c3e16fa447907d2cfff4587013d6b3

msp430backend/msp430_ws/server_protocol.py

zlalanne/msp430-webcontrol

a4e8f84942c3e16fa447907d2cfff4587013d6b3

msp430backend/msp430_ws/server_protocol.py

zlalanne/msp430-webcontrol

a4e8f84942c3e16fa447907d2cfff4587013d6b3

import requests import json import sys import re from twisted.internet.error import ConnectionDone from twisted.internet import protocol, threads, reactor from twisted.protocols.basic import LineReceiver from twisted.python import log from autobahn.websocket import WebSocketServerFactory, WebSocketServerProtocol, HttpException import settings import common_protocol import buffer import msp430_data.interface import msp430_data.utility class WebServerProtocol(LineReceiver): def __init__(self): self.client = None def lineReceived(self, line): line = line.strip() if self.client is None: if self.debug: log.msg("WebServerProtocol.lineReceived - No Client type") # TODO: This is an untested way to kill the connection. Need # to test. self.transport.loseConnection() else: self.client.dataReceived(line) def connectionLost(self, reason=ConnectionDone): if self.client is None: if self.debug: log.msg("WebServerProtocol.connectionClose - No Client type") return self.client.connectionLost(reason) def connectionMade(self): if self.transport.getPeer().host == settings.SITE_SERVER: self.client = WebServerClient(self) else: self.client = MSP430Client(self) self.client.connectionMade() def register_msp430(self): """This sends a http request to the django appliaction. This effictevely enters the MSP430 into the database of the django application. It then tells the websocket server to alert the user that a new MSP430 has come online""" # Need to send MAC and IP of MSP430 payload = {} payload['mac'] = self.client.mac payload['ip'] = self.client.protocol.transport.getPeer().host payload['iface'] = self.client.iface data = {'json': payload} try: headers = {'Content-type': 'application/json', 'Accept': 'text/plain'} response = requests.post("http://%s/tcp_comm/register/" % settings.SITE_SERVER_ADDRESS, data=json.dumps(data), headers=headers) except: pass # TODO: Need to validate response # Notify Browsers reactor.callFromThread(self.factory.ws_factory.register_msp430_wsite, self) def disconnect_msp430(self): payload = {} payload['mac'] = self.client.mac data = {'json':payload} try: headers = {'Content-type': 'application/json', 'Accept': 'text/plain'} response = requests.post("http://%s/tcp_comm/disconnect/" % settings.SITE_SERVER_ADDRESS, data=json.dumps(data), headers=headers) except: pass # Notify Browsers reactor.callFromThread(self.factory.ws_factory.disconnect_msp430_wsite, self) class WebServerClient(common_protocol.ProtocolState): def __init__(self, protocol): common_protocol.ProtocolState.__init__(self) self.protocol = protocol def connectionMade(self): pass def connectionLost(self, reason=ConnectionDone): pass def dataReceived(self, data): data = data.strip() try: msp430s = json.loads(data)["json"] log.msg(msp430s) for msp430 in msp430s: if self.protocol.factory.ws_factory.debug: log.msg('WebServerClient - Recieved config for MSP430 %s' % msp430['mac']) except: if self.protocol.factory.ws_factory.debug: log.msg('WebServerClient - Error parsing msp430 configs %s' % sys.exc_info()) return 'error' # Delegate the reqest to the WS factory self.protocol.factory.ws_factory.config_msp430(msp430) class ServerState(common_protocol.State): def __init__(self, client): self.client = client def activated(self): if self.client.protocol.debug: log.msg("%s.activated()" % self.__class__.__name__) def deactivated(self): if self.client.protocol.debug: log.msg("%s.deactivated()" % self.__class__.__name__) class WebSocketClient(common_protocol.ProtocolState): def __init__(self, protocol): common_protocol.ProtocolState.__init__(self) self.protocol = protocol def onMessage(self, msg): try: state = self.state_stack.pop_wr() except IndexError: if self.protocol.factory.debug: log.msg("%s.onMessage - Received a message in an unknown state, ignored", self.__class__.__name__) state.onMessage(msg) def onClose(self, wasClean, code, reason): pass def onOpen(self): pass class UserClient(WebSocketClient): """User client related protocol""" def __init__(self, protocol): WebSocketClient.__init__(self, protocol) self.associated_msp430 = None self.streaming_buffer_read = None self.streaming_buffer_write = None self.ackcount = 0 self.paused = True def register_to_msp430(self, msp430_mac): # Notify factory we want to unregister if registered first self.ackcount = 0 if self.associated_msp430 is not None: self.protocol.factory.unregister_user_to_msp430(self, self.associated_msp430) msp430 = self.protocol.factory.get_msp430(msp430_mac) if msp430: self.streaming_buffer_read = buffer.UpdateDict() self.streaming_buffer_write = buffer.UpdateDict() self.associated_msp430 = msp430 self.protocol.factory.register_user_to_msp430(self, self.associated_msp430) # begin streaming self.resume_streaming() def resume_streaming(self): self.paused = False self.copy_and_send() def pause_streaming(self): self.paused = True def copy_and_send(self): if self.ackcount <= -10 or self.paused: return # copy buffers self.protocol.factory.copy_msp430_buffers(self.associated_msp430, self.streaming_buffer_read, self.streaming_buffer_write) if len(self.streaming_buffer_read) > 0 or len(self.streaming_buffer_write) > 0: msg = {'cmd':common_protocol.ServerCommands.WRITE_DATA} msg['read'] = self.streaming_buffer_read msg['write'] = self.streaming_buffer_write self.ackcount -= 1 self.protocol.sendMessage(json.dumps(msg)) # keep polling until we run out of data reactor.callLater(0, self.copy_and_send) else: # when there's new data resume will be called self.pause_streaming() def unregister_to_msp430(self): self.pause_streaming() if self.associated_msp430 is not None: self.associated_msp430 = None def notifyMSP430State(self, msp430, state): if state == 'config': if self.associated_msp430 is not msp430: return msg = {'cmd':common_protocol.ServerCommands.MSP430_STATE_CHANGE, 'msp430_mac':msp430.client.mac, 'msp430_state':state} self.protocol.sendMessage(json.dumps(msg)) def onMessage(self, msg): try: msg = json.loads(msg) except: if self.protocol.debug: log.msg('UserState.onMessage - JSON error, dropping') self.protocol.failConnection() if msg['cmd'] == common_protocol.UserClientCommands.CONNECT_MSP430: mac = msg['msp430_mac'] self.register_to_msp430(mac) elif msg['cmd'] == common_protocol.UserClientCommands.ACK_DATA: ackcount = msg['ack_count'] self.ackcount += ackcount if self.ackcount > -10: self.copy_and_send() elif msg['cmd'] == common_protocol.UserClientCommands.WRITE_DATA: port = msg['iface_port'] value = msg['value'] if self.associated_msp430 is not None: self.associated_msp430.write_interface_data(port, value) def onClose(self, wasClean, code, reason): self.protocol.factory.disconnect_user(self) def onOpen(self): self.protocol.factory.register_user(self) class TCPClient(common_protocol.ProtocolState): def __init__(self, protocol): common_protocol.ProtocolState.__init__(self) self.protocol = protocol def connectionMade(self): pass def connectionLost(self, reason=ConnectionDone): pass def dataReceived(self, data): try: state = self.state_stack.pop_wr() except IndexError: if self.protocol.factory.debug: log.msg("%s.onMessage - Received a message in an unknown state, ignored", self.__class__.__name__) state.dataReceived(data) class MSP430Client(TCPClient): def __init__(self, protocol): TCPClient.__init__(self, protocol) def connectionMade(self): self.push_state(MSP430RegisterState(self)) def connectionLost(self, reason=ConnectionDone): # If we're registered remove ourselves from active client list if hasattr(self, 'mac'): self.protocol.factory.ws_factory.disconnect_msp430(self) pass def copy_buffers(self, read_buffer, write_buffer): try: state = self.current_state() except IndexError: # MSP430 has no states return False if isinstance(state, MSP430StreamState): for key, value in state.read_data_buffer_eq.iteritems(): read_buffer[key] = value for key, value in state.write_data_buffer_eq.iteritems(): write_buffer[key] = value return True return False def pause_streaming(self): try: state = self.current_state() except IndexError: # MSP430 has no states return False if isinstance(state, MSP430StreamState): state.pause_streaming() return True return False def resume_streaming(self): try: state = self.current_state() except IndexError: # MSP430 has no states return False if isinstance(state, MSP430StreamState): state.resume_streaming() return True return False def write_interface_data(self, key, data): try: state = self.current_state() except IndexError: # MSP430 has no states return False if isinstance(state, MSP430StreamState): state.write_interface_data(key, data) return True return False def config_io(self, reads, writes): """ read/writes are lsts of dicts with the following: 'ch_port': integer or boolean (check cls req) 'equation': empty, or python style math 'cls_name': class name as string, ex) 'ADC' Returns True/False for success """ # check the state of the MSP430 client try: state = self.current_state() except IndexError: # MSP430 has no states return False if isinstance(state, MSP430ConfigState): # ready to be configured # MSP430 was waiting for config pass elif isinstance(state, MSP430StreamState): # MSP430 is being re-configured state.drop_to_config(reads, writes) # config has to be delegated return True else: # MSP430 can't be put into a config state, fail return False state = self.current_state() # delegate the job to the config state return state.config_io(reads, writes) """MSP430 client related protocol and states""" class MSP430RegisterState(ServerState): def __init__(self, client): super(MSP430RegisterState, self).__init__(client) self.registered = False self.re_message_count = 0 def dataReceived(self, data): if self.re_message_count == 0 and not self.registered: # MSP430 is requesting to register # TODO: Add some MSP430 authentication here if data == common_protocol.ServerCommands.REGISTER: self.re_message_count += 1 if self.client.protocol.debug: log.msg("MSP430Client.dataReceived - Registration Request") self.client.protocol.sendLine(common_protocol.ServerCommands.ACK) else: self.client.protocol.sendLine(common_protocol.ServerCommands.NACK) elif self.re_message_count == 1 and not self.registered: self.client.protocol.sendLine(common_protocol.ServerCommands.ACK) def interface_desc(ifaces): # List of classes that resemble I/O. Creating a struct based on # their names, docstring, choices and I/O type to send to django # application. ret = [] for cls in ifaces: name = cls.__name__ desc = msp430_data.utility.trim(cls.__doc__) choices = [] for choice_pin, choice_desc in cls.IO_CHOICES: choice = {} choice['s'] = choice_pin choice['d'] = choice_desc choices.append(choice) ret.append({'name':name, 'desc':desc, 'choices':choices, 'io_type':cls.IO_TYPE}) return ret self.client.iface = {} self.client.interfaces = msp430_data.interface.get_interface_desc() for key in self.client.interfaces.iterkeys(): self.client.iface[key] = interface_desc(self.client.interfaces[key]) self.client.mac = data self.registered = True self.re_message_count = 0 if self.client.protocol.debug: log.msg("MSP430Client.dataReceived - Successful Registration") self.client.push_state(MSP430ConfigState(self.client)) # Add to dictionary of clients in the WS factory self.client.protocol.factory.ws_factory.register_msp430(self.client) else: # TODO: Something went wrong return class MSP430ConfigState(ServerState): """In this state, the MSP430 is waiting to be configured. Server is not required to configure the MSP430 immediately. """ def __init__(self, client): super(MSP430ConfigState, self).__init__(client) def dataReceived(self, data): if data == common_protocol.MSP430ClientCommands.CONFIG_OK: log.msg('MSP430ConfigState - MSP430 was configured') self.client.push_state(MSP430StreamState(self.client, reads=self.config_reads, writes=self.config_writes, interfaces=self.config_interfaces, mapping=self.config_mapping )) elif data == common_protocol.MSP430ClientCommands.CONFIG_FAIL: if self.client.protocol.debug: log.msg('MSP430ConfigState - MSP430 failed to configure') # TODO: Notify web server def config_io(self, reads, writes): """ read/writes are lsts of dicts with the following: 'ch_port': integer or boolean (check cls req) 'equation': empty, or python style math 'cls_name': class name as string, ex) 'ADC' Returns True/False for success """ self.display_reads = reads self.display_writes = writes # Format IO to store on the server def format_io(io_collection): # Duplicate equations allowed, duplicate instances not allowed instanced_io_dict = {} for io in io_collection: cls_str = io['cls_name'] ch_port = io['ch_port'] equation = io['equation'] key = 'cls:%s, port:%s' % (cls_str, ch_port) if key not in instanced_io_dict: io_new_dict = {'cls_name':cls_str, 'ch_port':ch_port} io_new_dict['equations'] = [equation] instanced_io_dict[key] = io_new_dict else: # we can have more then one equation per instance existing_instance = instanced_io_dict[key] equations = existing_instance['equations'] if equation not in equations: equations.append(equation) return instanced_io_dict # Format IO to give to the msp430 def format_io_msp430(io_collection): config_mapping = {} msp430_configs = {} i = 0 for key, io in io_collection: cls = getattr(msp430_data.interface, io['cls_name']) msp430_configs[str(i)] = {'pin': io['ch_port'], 'opcode': cls.IO_OPCODE} config_mapping[str(i)] = io i += 1 return msp430_configs, config_mapping self.config_reads = format_io(reads) self.config_writes = format_io(writes) self.config_interfaces, self.config_mapping = format_io_msp430(self.config_reads.items() + self.config_writes.items()) if self.client.protocol.debug: log.msg('MSP430ConfigState - Pushing configs to remote MSP430') msg = {'cmd':common_protocol.ServerCommands.CONFIG, 'payload':self.config_interfaces} self.client.protocol.sendLine(json.dumps(msg)) class MSP430StreamState(ServerState): """ In this state the MSP430 has been configured and is streaming data""" def __init__(self, client, reads, writes, interfaces, mapping): super(MSP430StreamState, self).__init__(client) # Read/Write configs is used to communicate with the web # Interface config is used to communicate to the msp430 self.config_reads = reads self.config_writes = writes self.config_interfaces = interfaces self.config_mapping = mapping # Buffers for storing the evaluated data self.write_data_buffer_eq = {} self.read_data_buffer_eq = {} self.datamsgcount_ack = 0 def evaluate_eq(self, eq, value): if eq != '': # TODO: fix security x = value new_value = eval(eq) else: new_value = value return new_value def deactivated(self): super(MSP430StreamState, self).deactivated() self.client.protocol.factory.ws_factory.notify_clients_msp430_state_change(self.client.protocol, state='drop_stream') def activated(self): super(MSP430StreamState, self).activated() self.client.protocol.factory.ws_factory.notify_clients_msp430_state_change(self.client.protocol, state='stream') def dataReceived(self, data): try: data = json.loads(data) except ValueError: log.msg("MSP430StreamState.dataReceived - Problem with JSON structure") log.msg(data) return if data['cmd'] == common_protocol.MSP430ClientCommands.DROP_TO_CONFIG_OK: # Order here is important, pop first! self.client.pop_state() self.client.current_state().config_io(self.delegate_config_reads, self.delegate_config_writes) if data['cmd'] == common_protocol.MSP430ClientCommands.DATA: self.datamsgcount_ack += 1 interfaces = data['interfaces'] for key, value in interfaces.iteritems(): interface = self.config_mapping[key] cls_name = interface["cls_name"] pin = interface["ch_port"] cls = getattr(msp430_data.interface, cls_name) # Convert from raw data string to correct data type new_value = cls.parse_input(value) # Evaluate equation if msp430_data.interface.IWrite in cls.__bases__: for eq in interface["equations"]: new_key = "cls:{}, port:{}, eq:{}".format(cls_name, int(pin), eq) self.write_data_buffer_eq[new_key] = {"calculated" : self.evaluate_eq(eq, new_value), "real": new_value} elif msp430_data.interface.IRead in cls.__bases__: for eq in interface["equations"]: new_key = "cls:{}, port:{}, eq:{}".format(cls_name, int(pin), eq) self.read_data_buffer_eq[new_key] = self.evaluate_eq(eq, new_value) # Notify factory to update listening clients if self.datamsgcount_ack >= 5: data = {'cmd':common_protocol.ServerCommands.ACK_DATA, 'count':self.datamsgcount_ack} self.client.protocol.sendLine(json.dumps(data, sort_keys=True)) self.datamsgcount_ack = 0 # Notify factory of new data event self.client.protocol.factory.ws_factory.msp430_new_data_event(self.client) def resume_streaming(self): # Starting to stream again, reset the ack count self.datamsgcount_ack = 0 msg = {'cmd':common_protocol.ServerCommands.RESUME_STREAMING} self.client.protocol.sendLine(json.dumps(msg)) def pause_streaming(self): msg = {'cmd':common_protocol.ServerCommands.PAUSE_STREAMING} self.client.protocol.sendLine(json.dumps(msg)) def write_interface_data(self, key, value): # Get the class and port from the key match = re.match(r'cls:([A-Za-z0-9_]+),\ port:(\d+)', key) try: cls_name = match.group(1) pin = match.group(2) except: # TODO: add correct exception return cls = getattr(msp430_data.interface, cls_name) payload = {'opcode': cls.IO_OPCODE, 'pin' : pin, 'value' : str(value)} msg = {'cmd':common_protocol.ServerCommands.WRITE_DATA, 'payload': payload} self.client.protocol.sendLine(json.dumps(msg)) def drop_to_config(self, reads, writes): # Drop remote MSP430 to config state msg = {'cmd':common_protocol.ServerCommands.DROP_TO_CONFIG} self.client.protocol.sendLine(json.dumps(msg)) self.delegate_config_reads = reads self.delegate_config_writes = writes class MSP430ServerProtocol(WebSocketServerProtocol): """Base server protocol, instantiates child protocols""" def __init__(self): self.client = None def onConnect(self, connectionRequest): """Connection to WebSocket Protocol""" def user(headers): if self.debug: log.msg("MSP430ServerProtocol.onConnect - User connected") return UserClient(self) # MSP430 won't connect via WebSocket so only option is the client paths = { '/':user, } if connectionRequest.path not in paths: raise HttpException(httpstatus.HTTP_STATUS_CODE_NOT_FOUND[0], httpstatus.HTTP_STATUS_CODE_NOT_FOUND[1]) self.client = paths[connectionRequest.path](connectionRequest.headers) def onMessage(self, msg, binary): """Message received from client""" if self.client is None: if self.debug: log.msg("MSP430ServerProtocol.onMessage - No Client type") self.failConnection() self.client.onMessage(msg) def onOpen(self): WebSocketServerProtocol.onOpen(self) if self.client is not None: self.client.onOpen() def onClose(self, wasClean, code, reason): """Connect closed, cleanup""" # base logs WebSocketServerProtocol.onClose(self, wasClean, code, reason) if self.client is None: if self.debug: log.msg("MSP430ServerProtocol.onClose - No Client type") return self.client.onClose(wasClean, code, reason) class MSP430SocketServerFactory(WebSocketServerFactory): """Manages every MSP430 connected to the server.""" def __init__(self, *args, **kwargs): WebSocketServerFactory.__init__(self, *args, **kwargs) # safari self.allowHixie76 = True # Identify MSP430's by their macs # Identify user by peerstr self.msp430_clients = {} self.user_client = {} # Key MSP430 mac, value list of user clients self.msp430_clients_registered_users = {} def register_user_to_msp430(self, client, msp430): if len(self.msp430_clients_registered_users[msp430.mac]) == 0: # MSP430 wasn't streaming, start streaming! msp430.resume_streaming() if client not in self.msp430_clients_registered_users[msp430.mac]: self.msp430_clients_registered_users[msp430.mac].append(client) if self.debug: log.msg('MSP430SocketServerFactory.register_user_to_msp430 msp430:%s user:%s' % (msp430.mac, client.protocol.peerstr)) def unregister_user_to_msp430(self, client, msp430): client.unregister_to_msp430() if msp430 is None: return if msp430.mac in self.msp430_clients_registered_users: if client in self.msp430_clients_registered_users[msp430.mac]: self.msp430_clients_registered_users[msp430.mac].remove(client) if self.debug: log.msg('msp430SocketServerFactory.unregister_user_to_msp430 msp430:%s user:%s' % (msp430.mac, client.protocol.peerstr)) if msp430.mac not in self.msp430_clients_registered_users or len(self.msp430_clients_registered_users[msp430.mac]) == 0: # Pause streaming msp430.pause_streaming() def msp430_new_data_event(self, msp430): # resume streaming on any MSP430s waiting for new data for client in self.msp430_clients_registered_users[msp430.mac]: client.resume_streaming() def copy_msp430_buffers(self, msp430, read_buffer, write_buffer): msp430.copy_buffers(read_buffer, write_buffer) def get_msp430(self, msp430_mac): if msp430_mac in self.msp430_clients: return self.msp430_clients[msp430_mac] return None def notify_clients_msp430_state_change(self, msp430, state='offline'): for peerstr, user in self.user_client.iteritems(): user.notifyMSP430State(msp430, state) def register_user(self, user): if user.protocol.peerstr not in self.user_client: self.user_client[user.protocol.peerstr] = user if self.debug: log.msg('MSP430SocketServerFactory.register_user %s' % user.protocol.peerstr) def disconnect_user(self, user): if self.debug: log.msg('MSP430SocketServerFactory.disconnect_user %s' % user.protocol.peerstr) del self.user_client[user.protocol.peerstr] self.unregister_user_to_msp430(user, user.associated_msp430) def register_msp430(self, msp430): # This is called when the MSP430 has been authenticated with the WS server # register on the site server msp430.protocol.register_msp430() # register locally to the factory self.msp430_clients[msp430.mac] = msp430 self.msp430_clients_registered_users[msp430.mac] = [] if self.debug: log.msg("MSP430SocketServerFactory.register_msp430 - %s registered, %d msp430" % (msp430.mac, len(self.msp430_clients))) def register_msp430_wsite(self, msp430): """Called after MSP430 has been registed to the website""" self.notify_clients_msp430_state_change(msp430, state='online') def disconnect_msp430(self, msp430): if hasattr(msp430, 'mac'): if self.debug: log.msg("MSP430SocketServerFactory.disconnect_msp430 - %s msp430 disconnected" % (msp430.mac,)) reactor.callInThread(msp430.protocol.disconnect_msp430) try: del self.msp430_clients[msp430.mac] del self.msp430_clients_registered_users[msp430.mac] except KeyError: log.msg(self.msp430_clients) def disconnect_msp430_wsite(self, msp430): """Called after MSP430 has been disconnected from web server""" self.notify_clients_msp430_state_change(msp430, state='offline') def config_msp430(self, configs): """ Not thread safe configs: dict with the following keys: 'read': lst of port configs 'write: lst of port configs 'mac': '00:00:...' port config dict with the following keys: 'ch_port': integer or boolean (check cls req) 'equation': empty, or python style math 'cls_name': class name as string, ex) 'ADC' Return: True/False for success """ # Check if MSP430 is actually an active client mac = configs['mac'] if mac not in self.msp430_clients: return False msp430_client = self.msp430_clients[mac] return msp430_client.config_io(reads=configs['read'], writes=configs['write'])

f717282762e91799ab097381b849786ec18cef78

py

Python

tensorbay/opendataset/SegTrack2/__init__.py

Hoteryoung/tensorbay-python-sdk

53c34dd529c20ec69b34ddd348b5c8e74f4094d0

tensorbay/opendataset/SegTrack2/__init__.py

Hoteryoung/tensorbay-python-sdk

53c34dd529c20ec69b34ddd348b5c8e74f4094d0

tensorbay/opendataset/SegTrack2/__init__.py

Hoteryoung/tensorbay-python-sdk

53c34dd529c20ec69b34ddd348b5c8e74f4094d0

#!/usr/bin/env python3 # # Copytright 2021 Graviti. Licensed under MIT License. # # pylint: disable=invalid-name """Dataloader of the SegTrack2 dataset.""" from .loader import SegTrack2 __all__ = ["SegTrack2"]

f7172919cb32b1bce840769389a548df78d2e786

py

Python

tempest/api/compute/admin/test_fixed_ips_negative.py

midokura/tempest

b0ec1d280f057d5d9c2eda081bcbda7e381ecb3b

tempest/api/compute/admin/test_fixed_ips_negative.py

midokura/tempest

b0ec1d280f057d5d9c2eda081bcbda7e381ecb3b

tempest/api/compute/admin/test_fixed_ips_negative.py

midokura/tempest

b0ec1d280f057d5d9c2eda081bcbda7e381ecb3b

# Copyright 2013 NEC Corporation. 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. from tempest_lib import exceptions as lib_exc from tempest.api.compute import base from tempest import config from tempest import test CONF = config.CONF class FixedIPsNegativeTestJson(base.BaseV2ComputeAdminTest): @classmethod def resource_setup(cls): super(FixedIPsNegativeTestJson, cls).resource_setup() if CONF.service_available.neutron: msg = ("%s skipped as neutron is available" % cls.__name__) raise cls.skipException(msg) cls.client = cls.os_adm.fixed_ips_client cls.non_admin_client = cls.fixed_ips_client server = cls.create_test_server(wait_until='ACTIVE') server = cls.servers_client.get_server(server['id']) for ip_set in server['addresses']: for ip in server['addresses'][ip_set]: if ip['OS-EXT-IPS:type'] == 'fixed': cls.ip = ip['addr'] break if cls.ip: break @test.attr(type=['negative', 'gate']) @test.services('network') def test_list_fixed_ip_details_with_non_admin_user(self): self.assertRaises(lib_exc.Unauthorized, self.non_admin_client.get_fixed_ip_details, self.ip) @test.attr(type=['negative', 'gate']) @test.services('network') def test_set_reserve_with_non_admin_user(self): body = {"reserve": "None"} self.assertRaises(lib_exc.Unauthorized, self.non_admin_client.reserve_fixed_ip, self.ip, body) @test.attr(type=['negative', 'gate']) @test.services('network') def test_set_unreserve_with_non_admin_user(self): body = {"unreserve": "None"} self.assertRaises(lib_exc.Unauthorized, self.non_admin_client.reserve_fixed_ip, self.ip, body) @test.attr(type=['negative', 'gate']) @test.services('network') def test_set_reserve_with_invalid_ip(self): # NOTE(maurosr): since this exercises the same code snippet, we do it # only for reserve action body = {"reserve": "None"} # NOTE(eliqiao): in Juno, the exception is NotFound, but in master, we # change the error code to BadRequest, both exceptions should be # accepted by tempest self.assertRaises((lib_exc.NotFound, lib_exc.BadRequest), self.client.reserve_fixed_ip, "my.invalid.ip", body) @test.attr(type=['negative', 'gate']) @test.services('network') def test_fixed_ip_with_invalid_action(self): body = {"invalid_action": "None"} self.assertRaises(lib_exc.BadRequest, self.client.reserve_fixed_ip, self.ip, body)

f7173966ea1c7e65d2e1a2dd36186f075d2562fc

py

Python

examples/Cliner/CliNER/code/feature_extraction/umls_dir/create_sqliteDB.py

swapnull7/forte

737a72afd440d40c3826c3a7c5e4e44235c0f701

examples/Cliner/CliNER/code/feature_extraction/umls_dir/create_sqliteDB.py

swapnull7/forte

737a72afd440d40c3826c3a7c5e4e44235c0f701

examples/Cliner/CliNER/code/feature_extraction/umls_dir/create_sqliteDB.py

swapnull7/forte

737a72afd440d40c3826c3a7c5e4e44235c0f701

# database.py creates a .db file for performing umls searches. import atexit import os import sqlite3 import sys from read_config import enabled_modules features_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) if features_dir not in sys.path: sys.path.append(features_dir) # find where umls tables are located enabled = enabled_modules() umls_tables = enabled['UMLS'] # set to True when create_db() is succesful success = False db_path = None conn = None MRSTY_TABLE_FILE = None MRCON_TABLE_FILE = None MRREL_TABLE_FILE = None LRABR_TABLE_FILE = None # this ensure files are closed properly and umls.db is removed if not succesful @atexit.register def umls_db_cleanup(): # pylint: disable=global-statement global success global conn global db_path global MRSTY_TABLE_FILE global MRCON_TABLE_FILE global MRREL_TABLE_FILE global LRABR_TABLE_FILE if conn is not None: conn.close() if MRSTY_TABLE_FILE is not None: MRSTY_TABLE_FILE.close() if MRCON_TABLE_FILE is not None: MRCON_TABLE_FILE.close() if MRREL_TABLE_FILE is not None: MRREL_TABLE_FILE.close() if LRABR_TABLE_FILE is not None: LRABR_TABLE_FILE.close() if success is False: # remove umls.db, it is junk now if db_path is not None: os.remove(db_path) def create_db(): # pylint: disable=global-statement global success global conn global db_path global MRSTY_TABLE_FILE global MRCON_TABLE_FILE global MRREL_TABLE_FILE global LRABR_TABLE_FILE print("\ncreating umls.db") # connect to the .db file we are creating. db_path = os.path.join(umls_tables, 'umls.db') conn = sqlite3.connect(db_path) conn.text_factory = str print("opening files") # load data in files. try: mrsty_path = os.path.join(umls_tables, 'MRSTY.RRF') MRSTY_TABLE_FILE = open(mrsty_path, "r") except IOError: print("\nNo file to use for creating MRSTY.RRF table\n") sys.exit() try: mrcon_path = os.path.join(umls_tables, 'MRCONSO.RRF') MRCON_TABLE_FILE = open(mrcon_path, "r") except IOError: print("\nNo file to use for creating MRCONSO.RRF table\n") sys.exit() try: mrrel_path = os.path.join(umls_tables, 'MRREL.RRF') MRREL_TABLE_FILE = open(mrrel_path, "r") except IOError: print("\nNo file to use for creating MRREL.RRF table\n") sys.exit() try: lrabr_path = os.path.join(umls_tables, 'LRABR') LRABR_TABLE_FILE = open(lrabr_path, "r") except IOError: print("\nNo file to use for creating LRABR table\n") sys.exit() print("creating tables") c = conn.cursor() # create tables. c.execute("CREATE TABLE MRSTY( CUI, TUI, STN, STY, ATUI, CVF ) ;") c.execute( "CREATE TABLE MRCON( CUI, LAT, TS, LUI, STT, SUI, ISPREF, AUI, SAUI, \ SCUI, SDUI, SAB, TTY, CODE, STR, SRL, SUPPRESS, CVF ) ;") c.execute( "CREATE TABLE MRREL( CUI1, AUI1, STYPE1, REL, CUI2, AUI2, STYPE2, \ RELA, RUI, SRUI, SAB, SL, RG, DIR, SUPPRESS, CVF );") c.execute("CREATE TABLE LRABR( EUI1, ABR, TYPE, EUI2, STR);") print("inserting data into MRSTY table") for line in MRSTY_TABLE_FILE: line = line.strip('\n') line = line.split('|') # end will always be empty str line.pop() assert len(line) == 6 c.execute("INSERT INTO MRSTY( CUI, TUI, STN, STY, ATUI, CVF ) \ values( ?, ?, ?, ?, ?, ?)", tuple(line)) print("inserting data into MRCON table") for line in MRCON_TABLE_FILE: line = line.strip('\n') line = line.split('|') # end will always be empty str line.pop() assert len(line) == 18 c.execute( "INSERT INTO MRCON( CUI, LAT, TS, LUI, STT, SUI, ISPREF, AUI, \ SAUI, SCUI, SDUI, SAB, TTY, CODE, STR, SRL, SUPPRESS, CVF ) \ values ( ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);", tuple(line)) print("inserting data into MRREL table") for line in MRREL_TABLE_FILE: line = line.strip('\n') line = line.split('|') # end will always be empty str line.pop() assert len(line) == 16 c.execute( "INSERT INTO MRREL( CUI1, AUI1, STYPE1, REL, CUI2, AUI2, STYPE2, \ RELA, RUI, SRUI, SAB, SL, RG, DIR, SUPPRESS, CVF ) \ values( ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ? )", tuple(line)) print("inserting into LRABR table") for line in LRABR_TABLE_FILE: line = line.strip('\n') line = line.split('|') line.pop() assert len(line) == 5 c.execute("INSERT INTO LRABR( EUI1, ABR, TYPE, EUI2, STR) \ values( ?, ?, ?, ?,?)", tuple(line)) print("creating indices") # create indices for faster queries c.execute("CREATE INDEX mrsty_cui_map ON MRSTY(CUI)") c.execute("CREATE INDEX mrcon_str_map ON MRCON(STR)") c.execute("CREATE INDEX mrcon_cui_map ON MRCON(CUI)") c.execute("CREATE INDEX mrrel_cui2_map ON MRREL( CUI2 )") c.execute("CREATE INDEX mrrel_cui1_map on MRREL( CUI1 ) ") c.execute("CREATE INDEX mrrel_rel_map on MRREL( REL )") c.execute("CREATE INDEX lrabr_abr_map on LRABR(ABR)") c.execute("CREATE INDEX lrabr_str_map on LRABR(STR)") # save changes to .db conn.commit() success = True print("\nsqlite database created") if __name__ == "__main__": create_db()

f717576ebe1b232b2fdba0695ea262b2ae5063cc

py

Python

src/base/base_train.py

MohamedAli1995/Cifar-100-Classifier

924704a81ce13062825a88b90b80e8ac2ba45d63

2019-05-12T16:11:20.000Z

2020-04-10T22:39:57.000Z

src/base/base_train.py

MohamedAli1995/Cifar-100-Classifier

924704a81ce13062825a88b90b80e8ac2ba45d63

src/base/base_train.py

MohamedAli1995/Cifar-100-Classifier

924704a81ce13062825a88b90b80e8ac2ba45d63

import tensorflow as tf class BaseTrain: """Standard base_train-class for easy multiple-inheritance. It is responsible for defining the functions to be implemented with any child. Attributes: sess: Tensorflow session to use. model: Model to be trained. data: Data_loader object to interact with dataset. config: Config object to store data related to training, testing and validation. logger: Logger object to use tensorboard. """ def __init__(self, sess, model, data, config, logger): self.model = model self.config = config self.sess = sess self.data = data self.logger = logger if not self.config.pretrain: # If not pretrain then initialize variables. self.init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) self.sess.run(self.init) def train(self): """Train the model for the number of epochs in config.num_epochs. Calls validate_epoch if config.use_val is set to true and per config.val_per_epoch. Returns: """ for cur_epoch in range(self.model.cur_epoch_tensor.eval(self.sess), self.config.num_epochs + 1, 1): self.data.prepare_new_epoch_data() self.train_epoch() if self.config.use_val and ( cur_epoch % self.config.val_per_epoch == 0 or cur_epoch == self.config.num_epochs): self.validate_epoch() self.sess.run(self.model.increment_cur_epoch_tensor) def train_epoch(self): """Implements the logic of training_epoch: -Loop over the batches of the training data and call the train step for each. -Add any summaries you want using the summary """ raise NotImplemented def train_step(self): """Implements the logic of the train step: -Run the tensorflow session -Returns: Any of the metrics needs to be summarized. """ raise NotImplementedError def validate_epoch(self): """Implements the logic of validation_epoch: -Loop over the batches of the validation data and call the validate step for each. -Add any summaries you want using the summary """ raise NotImplemented def validate_step(self): """Implements the logic of the validate step: -Run the tensorflow session -Returns: Any of the metrics needs to be summarized. """ raise NotImplemented

f7177676b64b016a2006776e619b093446b0ff41

py

Python

test/language/choice_types/python/UInt64ParamChoiceTest.py

PeachOS/zserio

ea01f6906c125a6baab7e8ed865eeb08cd46c37c

2019-02-06T17:50:24.000Z

2019-11-20T16:51:34.000Z

test/language/choice_types/python/UInt64ParamChoiceTest.py

PeachOS/zserio

ea01f6906c125a6baab7e8ed865eeb08cd46c37c

2019-11-25T16:25:51.000Z

2019-11-25T18:09:39.000Z

test/language/choice_types/python/UInt64ParamChoiceTest.py

PeachOS/zserio

ea01f6906c125a6baab7e8ed865eeb08cd46c37c

import unittest import zserio from testutils import getZserioApi class UInt64ParamChoiceTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.api = getZserioApi(__file__, "choice_types.zs").uint64_param_choice def testSelectorConstructor(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertEqual(self.VARIANT_A_SELECTOR, uint64ParamChoice.getSelector()) def testFromReader(self): selector = self.VARIANT_B_SELECTOR value = 234 writer = zserio.BitStreamWriter() UInt64ParamChoiceTest._writeUInt64ParamChoiceToStream(writer, selector, value) reader = zserio.BitStreamReader(writer.getByteArray()) uint64ParamChoice = self.api.UInt64ParamChoice.fromReader(reader, selector) self.assertEqual(selector, uint64ParamChoice.getSelector()) self.assertEqual(value, uint64ParamChoice.getB()) def testEq(self): uint64ParamChoice1 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) uint64ParamChoice2 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertTrue(uint64ParamChoice1 == uint64ParamChoice2) value = 99 uint64ParamChoice1.setA(value) self.assertFalse(uint64ParamChoice1 == uint64ParamChoice2) uint64ParamChoice2.setA(value) self.assertTrue(uint64ParamChoice1 == uint64ParamChoice2) diffValue = value + 1 uint64ParamChoice2.setA(diffValue) self.assertFalse(uint64ParamChoice1 == uint64ParamChoice2) def testHash(self): uint64ParamChoice1 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) uint64ParamChoice2 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertEqual(hash(uint64ParamChoice1), hash(uint64ParamChoice2)) value = 99 uint64ParamChoice1.setA(value) self.assertTrue(hash(uint64ParamChoice1) != hash(uint64ParamChoice2)) uint64ParamChoice2.setA(value) self.assertEqual(hash(uint64ParamChoice1), hash(uint64ParamChoice2)) diffValue = value + 1 uint64ParamChoice2.setA(diffValue) self.assertTrue(hash(uint64ParamChoice1) != hash(uint64ParamChoice2)) def testGetSelector(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_C_SELECTOR) self.assertEqual(self.VARIANT_C_SELECTOR, uint64ParamChoice.getSelector()) def testGetSetA(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) value = 99 uint64ParamChoice.setA(value) self.assertEqual(value, uint64ParamChoice.getA()) def testGetSetB(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) value = 234 uint64ParamChoice.setB(value) self.assertEqual(value, uint64ParamChoice.getB()) def testGetSetC(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_C_SELECTOR) value = 23456 uint64ParamChoice.setC(value) self.assertEqual(value, uint64ParamChoice.getC()) def testBitSizeOf(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertEqual(8, uint64ParamChoice.bitSizeOf()) uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) self.assertEqual(16, uint64ParamChoice.bitSizeOf()) def testInitializeOffsets(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) bitPosition = 1 self.assertEqual(9, uint64ParamChoice.initializeOffsets(bitPosition)) uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) self.assertEqual(17, uint64ParamChoice.initializeOffsets(bitPosition)) def testReadWrite(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) byteValue = 99 uint64ParamChoice.setA(byteValue) writer = zserio.BitStreamWriter() uint64ParamChoice.write(writer) readUInt64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) reader = zserio.BitStreamReader(writer.getByteArray()) readUInt64ParamChoice.read(reader) self.assertEqual(byteValue, readUInt64ParamChoice.getA()) self.assertEqual(uint64ParamChoice, readUInt64ParamChoice) uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) shortValue = 234 uint64ParamChoice.setB(shortValue) writer = zserio.BitStreamWriter() uint64ParamChoice.write(writer) readUInt64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) reader = zserio.BitStreamReader(writer.getByteArray()) readUInt64ParamChoice.read(reader) self.assertEqual(shortValue, readUInt64ParamChoice.getB()) self.assertEqual(uint64ParamChoice, readUInt64ParamChoice) @staticmethod def _writeUInt64ParamChoiceToStream(writer, selector, value): if selector == 1: writer.writeSignedBits(value, 8) elif selector in (2, 3, 4): writer.writeSignedBits(value, 16) elif selector in (5, 6): pass else: writer.writeSignedBits(value, 32) VARIANT_A_SELECTOR = 1 VARIANT_B_SELECTOR = 2 VARIANT_C_SELECTOR = 7

f7178a5f0eaaff23bb2efef24ecd4ae204c5ee9e

py

Python

src/django_backend_api/manage.py

Adityaraj1711/django-backend-architecture

7f3c270af0cb5dd2ebc097c7436a4958cd48ff7c

2020-04-28T19:25:28.000Z

2021-07-04T17:24:35.000Z

src/django_backend_api/manage.py

Adityaraj1711/django-backend-architecture

7f3c270af0cb5dd2ebc097c7436a4958cd48ff7c

2020-08-05T22:40:37.000Z

2022-03-12T00:24:36.000Z

src/django_backend_api/manage.py

Adityaraj1711/django-backend-architecture

7f3c270af0cb5dd2ebc097c7436a4958cd48ff7c

2020-10-29T13:10:01.000Z

2021-11-22T01:55:14.000Z

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'django_backend_api.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

f717ba6db19fac8fe6c6bc3c5388211b577e2db4

py

Python

tests/unit/fake_data_root/kubernetes/var/lib/juju/agents/unit-containerd-2/charm/hooks/relations/untrusted-container-runtime/requires.py

KellenRenshaw/hotsos

e3fc51ab7f8af606a5846a3486a7fda23d761583

2021-10-01T19:46:14.000Z

2022-03-31T17:05:08.000Z

tests/unit/fake_data_root/kubernetes/var/lib/juju/agents/unit-containerd-2/charm/hooks/relations/untrusted-container-runtime/requires.py

KellenRenshaw/hotsos

e3fc51ab7f8af606a5846a3486a7fda23d761583

2021-10-01T18:18:17.000Z

2022-03-29T12:23:20.000Z

tests/unit/fake_data_root/kubernetes/var/lib/juju/agents/unit-containerd-2/charm/hooks/relations/untrusted-container-runtime/requires.py

KellenRenshaw/hotsos

e3fc51ab7f8af606a5846a3486a7fda23d761583

2021-09-29T14:47:54.000Z

2022-03-18T14:52:16.000Z

from charms.reactive import ( Endpoint, set_flag, clear_flag ) from charms.reactive import ( when, when_not ) class ContainerRuntimeRequires(Endpoint): @when('endpoint.{endpoint_name}.changed') def changed(self): set_flag(self.expand_name('endpoint.{endpoint_name}.available')) @when_not('endpoint.{endpoint_name}.joined') def broken(self): clear_flag(self.expand_name('endpoint.{endpoint_name}.available')) def set_config(self, name, binary_path): """ Set the configuration to be published. :param name: String name of runtime :param binary_path: String runtime executable :return: None """ for relation in self.relations: relation.to_publish.update({ 'name': name, 'binary_path': binary_path })

f717ba7e0a6e1a58c6c5756909c027da10c010bb

py

Python

read_exif.py

kiyoon/camera-tools

2e269141597dd27ec7d41e49285e01ef566cb54c

2021-11-14T23:30:47.000Z

2021-11-14T23:30:47.000Z

read_exif.py

kiyoon/camera-tools

2e269141597dd27ec7d41e49285e01ef566cb54c

read_exif.py

kiyoon/camera-tools

2e269141597dd27ec7d41e49285e01ef566cb54c

#!/usr/bin/env python3 import argparse class Formatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter): pass parser = argparse.ArgumentParser( description='''Read EXIF data Author: Kiyoon Kim (yoonkr33@gmail.com)''', formatter_class=Formatter) parser.add_argument('input_files', type=str, nargs='+', help='files to read metadata') args = parser.parse_args() import glob import os import exiftool import pprint if __name__ == "__main__": for origpath in args.input_files: for path in glob.glob(origpath): # glob: Windows wildcard support root, fname_ext = os.path.split(path) fname, fext = os.path.splitext(fname_ext) with exiftool.ExifTool() as et: metadata = et.get_metadata(path) print(path) pprint.pprint(metadata)

f717bdb31e80489aaefa7fbd595c263379b27f37

py

Python

src/hub/dataload/sources/chembl/chembl_upload.py

ravila4/mychem.info

9b63b5f0957b5e7b252ca8122734a363905036b3

src/hub/dataload/sources/chembl/chembl_upload.py

ravila4/mychem.info

9b63b5f0957b5e7b252ca8122734a363905036b3

src/hub/dataload/sources/chembl/chembl_upload.py

ravila4/mychem.info

9b63b5f0957b5e7b252ca8122734a363905036b3

""" Chembl uploader """ # pylint: disable=E0401, E0611 import os import glob import pymongo import biothings.hub.dataload.storage as storage from biothings.hub.dataload.uploader import ParallelizedSourceUploader from hub.dataload.uploader import BaseDrugUploader from hub.datatransform.keylookup import MyChemKeyLookup from .chembl_parser import load_data SRC_META = { "url": 'https://www.ebi.ac.uk/chembl/', "license_url" : "https://www.ebi.ac.uk/about/terms-of-use", "license_url_short" : "http://bit.ly/2KAUCAm" } class ChemblUploader(BaseDrugUploader, ParallelizedSourceUploader): """ ChemblUploader - upload the Chembl data source """ name = "chembl" storage_class = storage.RootKeyMergerStorage __metadata__ = {"src_meta" : SRC_META} MOLECULE_PATTERN = "molecule.*.json" keylookup = MyChemKeyLookup( [("inchikey", "chembl.inchi_key"), ("inchi", "chembl.inchi"), ("chembl", "chembl.molecule_chembl_id"), ("chebi", "chembl.chebi_par_id"), ("drugcentral", "chembl.xrefs.drugcentral.id"), ("drugname", "chembl.pref_name")], # TODO: handle duplicate keys from pubchem # - we use RootKeyMergerStorage, but the num. duplicates # - is too high (>10000) # ("pubchem", "chembl.xrefs.pubchem.sid"), copy_from_doc=True) def jobs(self): """ this will generate arguments for self.load.data() method, allowing parallelization """ json_files = glob.glob(os.path.join(self.data_folder, self.__class__.MOLECULE_PATTERN)) return [(f,) for f in json_files] def load_data(self, data_folder): """load data from an input file""" self.logger.info("Load data from '%s'" % data_folder) return self.keylookup(load_data, debug=True)(data_folder) def post_update_data(self, *args, **kwargs): """create indexes following an update""" # pylint: disable=W0613 """ for idxname in ["chembl.chebi_par_id", "chembl.inchi", "chembl.molecule_chembl_id"]: self.logger.info("Indexing '%s'" % idxname) # background=true or it'll lock the whole database... self.collection.create_index(idxname, background=True) """ for idxname in ["chembl.chebi_par_id", "chembl.molecule_chembl_id"]: self.logger.info("Indexing '%s'" % idxname) # background=true or it'll lock the whole database... self.collection.create_index(idxname, background=True) @classmethod def get_mapping(cls): """return mapping data""" mapping = { "chembl": { "properties": { "biotherapeutic": { "properties": { "helm_notation": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "description": { "type": "text" }, "biocomponents": { "properties": { "organism": { "type": "text" }, "tax_id": { "type": "integer" }, "sequence": { "type": "text" }, "component_id": { "type": "integer" }, "description": { "type": "text" }, "component_type": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", } } }, "molecule_chembl_id": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", 'copy_to': ['all'], } } }, "therapeutic_flag": { "type": "boolean" }, "usan_stem": { "type": "text" }, "molecule_chembl_id": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "molecule_properties": { "properties": { "heavy_atoms": { "type": "integer" }, "acd_most_bpka": { "type": "float" }, "mw_freebase": { "type": "float" }, "num_ro5_violations": { "type": "integer" }, "molecular_species": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "qed_weighted": { "type": "float" }, "ro3_pass": { "type": "boolean" }, "full_mwt": { "type": "float" }, "num_lipinski_ro5_violations": { "type": "integer" }, "rtb": { "type": "integer" }, "psa": { "type": "float" }, "alogp": { "type": "float" }, "hbd": { "type": "integer" }, "acd_most_apka": { "type": "float" }, "hbd_lipinski": { "type": "integer" }, "acd_logp": { "type": "float" }, "full_molformula": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "aromatic_rings": { "type": "integer" }, "hba_lipinski": { "type": "integer" }, "mw_monoisotopic": { "type": "float" }, "hba": { "type": "integer" }, "acd_logd": { "type": "float" } } }, "helm_notation": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "max_phase": { "type": "integer" }, "inorganic_flag": { "type": "integer" }, "usan_stem_definition": { "type": "text" }, "dosed_ingredient": { "type": "boolean" }, "chebi_par_id": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "withdrawn_reason": { "type": "text" }, "molecule_hierarchy": { "properties": { "parent_chembl_id": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "molecule_chembl_id": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", } } }, "prodrug": { "type": "integer" }, "withdrawn_flag": { "type": "boolean" }, "usan_year": { "type": "integer" }, "parenteral": { "type": "boolean" }, "black_box_warning": { "type": "integer" }, "polymer_flag": { "type": "boolean" }, "molecule_synonyms": { "properties": { "molecule_synonym": { "type": "text" }, "synonyms": { "type": "text" }, "syn_type": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", } } }, "atc_classifications": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "molecule_type": { "type": "text" }, "first_in_class": { "type": "integer" }, "inchi": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "structure_type": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "withdrawn_class": { "type": "text" }, "inchi_key": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "topical": { "type": "boolean" }, "oral": { "type": "boolean" }, "xrefs": { "properties": { "drugcentral": { "properties": { "id": { "type": "integer" }, "name": { "type": "text" } } }, "tg-gates": { "properties": { "id": { "type": "integer" }, "name": { "type": "text" } } }, "wikipedia": { "properties": { "url_stub": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", } } }, "dailymed": { "properties": { "name": { "type": "text" } } }, "pubchem": { "properties": { "sid": { "type": "integer" } } } } }, "chirality": { "type": "integer" }, "usan_substem": { "type": "text" }, "indication_class": { "type": "text" }, "withdrawn_country": { "type": "text" }, "withdrawn_year": { "type": "integer" }, "availability_type": { "type": "integer" }, "smiles": { "normalizer": "keyword_lowercase_normalizer", "type": "keyword", }, "natural_product": { "type": "integer" }, "pref_name": { "type": "text", "copy_to": ["all"] }, "first_approval": { "type": "integer" } } } } return mapping

f717c2aceaf306d0f7eae56f9df7f70d7fb7e56b

py

Python

usort/util.py

thatch/usort

2ca1ff63d6cfc79e76ea95b69d162f4579c3fa3c

usort/util.py

thatch/usort

2ca1ff63d6cfc79e76ea95b69d162f4579c3fa3c

usort/util.py

thatch/usort

2ca1ff63d6cfc79e76ea95b69d162f4579c3fa3c

# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from contextlib import contextmanager from contextvars import ContextVar from pathlib import Path from time import monotonic from typing import Callable, Generator, List, Optional, Tuple import libcst as cst TIMINGS: ContextVar[List[Tuple[str, float]]] = ContextVar("TIMINGS") @contextmanager def timed(msg: str) -> Generator[None, None, None]: """ Records the monotonic duration of the contained context, with a given description. Timings are stored for later use/printing with `print_timings()`. """ before = monotonic() yield after = monotonic() try: TIMINGS.get().append((msg, after - before)) except LookupError: pass @contextmanager def save_timings(to: List[Tuple[str, float]]) -> Generator[None, None, None]: token = TIMINGS.set([]) yield to.extend(TIMINGS.get()) TIMINGS.reset(token) def merge_timings(more: List[Tuple[str, float]]) -> None: TIMINGS.get().extend(more) def print_timings(fn: Callable[[str], None] = print) -> None: """ Print all stored timing values in microseconds. """ for msg, duration in TIMINGS.get(): fn(f"{msg + ':':50} {int(duration*1000000):7} µs") def try_parse(path: Path, data: Optional[bytes] = None) -> cst.Module: """ Attempts to parse the file with all syntax versions known by LibCST. If parsing fails on all supported grammar versions, then raises the parser error from the first/newest version attempted. """ if data is None: data = path.read_bytes() with timed(f"parsing {path}"): parse_error: Optional[cst.ParserSyntaxError] = None for version in cst.KNOWN_PYTHON_VERSION_STRINGS[::-1]: try: mod = cst.parse_module( data, cst.PartialParserConfig(python_version=version) ) return mod except cst.ParserSyntaxError as e: # keep the first error we see in case parsing fails on all versions if parse_error is None: parse_error = e # not caring about existing traceback here because it's not useful for parse # errors, and usort_path is already going to wrap it in a custom class raise parse_error or Exception("unknown parse failure")

f717c597ce99c986760a12c223c014567cf34f38

py

Python

ThirdParty/ZopeInterface/zope/interface/tests/test_registry.py

OpenGeoscience/VTK

a373e975b9284a022f43a062ebf5042bb17b4e44

2016-02-01T02:29:51.000Z

2020-09-04T17:19:24.000Z

ThirdParty/ZopeInterface/zope/interface/tests/test_registry.py

OpenGeoscience/VTK

a373e975b9284a022f43a062ebf5042bb17b4e44

2021-02-08T20:22:15.000Z

2022-03-11T23:19:41.000Z

ThirdParty/ZopeInterface/zope/interface/tests/test_registry.py

OpenGeoscience/VTK

a373e975b9284a022f43a062ebf5042bb17b4e44

2017-02-13T09:11:02.000Z

2021-06-29T11:22:18.000Z

############################################################################## # # Copyright (c) 2001, 2002, 2009 Zope Foundation and Contributors. # All Rights Reserved. # # This software is subject to the provisions of the Zope Public License, # Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution. # THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED # WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS # FOR A PARTICULAR PURPOSE. # ############################################################################## """Component Registry Tests""" import unittest class _SilencePy3Deprecations(unittest.TestCase): # silence deprecation warnings under py3 def failUnless(self, expr): # St00pid speling. return self.assertTrue(expr) def failIf(self, expr): # St00pid speling. return self.assertFalse(expr) class ComponentsTests(_SilencePy3Deprecations): def _getTargetClass(self): from zope.interface.registry import Components return Components def _makeOne(self, name='test', *args, **kw): return self._getTargetClass()(name, *args, **kw) def _wrapEvents(self): from zope.interface import registry _events = [] def _notify(*args, **kw): _events.append((args, kw)) _monkey = _Monkey(registry, notify=_notify) return _monkey, _events def test_ctor_no_bases(self): from zope.interface.adapter import AdapterRegistry comp = self._makeOne('testing') self.assertEqual(comp.__name__, 'testing') self.assertEqual(comp.__bases__, ()) self.failUnless(isinstance(comp.adapters, AdapterRegistry)) self.failUnless(isinstance(comp.utilities, AdapterRegistry)) self.assertEqual(comp.adapters.__bases__, ()) self.assertEqual(comp.utilities.__bases__, ()) self.assertEqual(comp._utility_registrations, {}) self.assertEqual(comp._adapter_registrations, {}) self.assertEqual(comp._subscription_registrations, []) self.assertEqual(comp._handler_registrations, []) def test_ctor_w_base(self): base = self._makeOne('base') comp = self._makeOne('testing', (base,)) self.assertEqual(comp.__name__, 'testing') self.assertEqual(comp.__bases__, (base,)) self.assertEqual(comp.adapters.__bases__, (base.adapters,)) self.assertEqual(comp.utilities.__bases__, (base.utilities,)) def test___repr__(self): comp = self._makeOne('testing') self.assertEqual(repr(comp), '<Components testing>') # test _init_registries / _init_registrations via only caller, __init__. def test_assign_to___bases__(self): base1 = self._makeOne('base1') base2 = self._makeOne('base2') comp = self._makeOne() comp.__bases__ = (base1, base2) self.assertEqual(comp.__bases__, (base1, base2)) self.assertEqual(comp.adapters.__bases__, (base1.adapters, base2.adapters)) self.assertEqual(comp.utilities.__bases__, (base1.utilities, base2.utilities)) def test_registerUtility_both_factory_and_component(self): def _factory(): pass _to_reg = object() comp = self._makeOne() self.assertRaises(TypeError, comp.registerUtility, component=_to_reg, factory=_factory) def test_registerUtility_w_component(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Registered from zope.interface.registry import UtilityRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _info = _u('info') _name = _u('name') _to_reg = object() comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerUtility(_to_reg, ifoo, _name, _info) self.failUnless(comp.utilities._adapters[0][ifoo][_name] is _to_reg) self.assertEqual(comp._utility_registrations[ifoo, _name], (_to_reg, _info, None)) self.assertEqual(comp.utilities._subscribers[0][ifoo][''], (_to_reg,)) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, UtilityRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.failUnless(event.object.name is _name) self.failUnless(event.object.component is _to_reg) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is None) def test_registerUtility_w_factory(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Registered from zope.interface.registry import UtilityRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _info = _u('info') _name = _u('name') _to_reg = object() def _factory(): return _to_reg comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerUtility(None, ifoo, _name, _info, factory=_factory) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, UtilityRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.failUnless(event.object.name is _name) self.failUnless(event.object.component is _to_reg) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is _factory) def test_registerUtility_no_provided_available(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass class Foo(object): pass ifoo = IFoo('IFoo') _info = _u('info') _name = _u('name') _to_reg = Foo() comp = self._makeOne() self.assertRaises(TypeError, comp.registerUtility, _to_reg, None, _name, _info) def test_registerUtility_wo_provided(self): from zope.interface.declarations import directlyProvides from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Registered from zope.interface.registry import UtilityRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass class Foo(object): pass ifoo = IFoo('IFoo') _info = _u('info') _name = _u('name') _to_reg = Foo() directlyProvides(_to_reg, ifoo) comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerUtility(_to_reg, None, _name, _info) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, UtilityRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.failUnless(event.object.name is _name) self.failUnless(event.object.component is _to_reg) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is None) def test_registerUtility_duplicates_existing_reg(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _info = _u('info') _name = _u('name') _to_reg = object() comp = self._makeOne() comp.registerUtility(_to_reg, ifoo, _name, _info) _monkey, _events = self._wrapEvents() with _monkey: comp.registerUtility(_to_reg, ifoo, _name, _info) self.assertEqual(len(_events), 0) def test_registerUtility_replaces_existing_reg(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.interfaces import Registered from zope.interface.registry import UtilityRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _info = _u('info') _name = _u('name') _before, _after = object(), object() comp = self._makeOne() comp.registerUtility(_before, ifoo, _name, _info) _monkey, _events = self._wrapEvents() with _monkey: comp.registerUtility(_after, ifoo, _name, _info) self.assertEqual(len(_events), 2) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, UtilityRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.failUnless(event.object.name is _name) self.failUnless(event.object.component is _before) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is None) args, kw = _events[1] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, UtilityRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.failUnless(event.object.name is _name) self.failUnless(event.object.component is _after) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is None) def test_registerUtility_w_existing_subscr(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _info = _u('info') _name1 = _u('name1') _name2 = _u('name2') _to_reg = object() comp = self._makeOne() comp.registerUtility(_to_reg, ifoo, _name1, _info) _monkey, _events = self._wrapEvents() with _monkey: comp.registerUtility(_to_reg, ifoo, _name2, _info) self.assertEqual(comp.utilities._subscribers[0][ifoo][''], (_to_reg,)) def test_registerUtility_wo_event(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _info = _u('info') _name = _u('name') _to_reg = object() comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerUtility(_to_reg, ifoo, _name, _info, False) self.assertEqual(len(_events), 0) def test_unregisterUtility_neither_factory_nor_component_nor_provided(self): comp = self._makeOne() self.assertRaises(TypeError, comp.unregisterUtility, component=None, provided=None, factory=None) def test_unregisterUtility_both_factory_and_component(self): def _factory(): pass _to_reg = object() comp = self._makeOne() self.assertRaises(TypeError, comp.unregisterUtility, component=_to_reg, factory=_factory) def test_unregisterUtility_w_component_miss(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _name = _u('name') _to_reg = object() comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterUtility(_to_reg, ifoo, _name) self.failIf(unreg) self.failIf(_events) def test_unregisterUtility_w_component(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import UtilityRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _name = _u('name') _to_reg = object() comp = self._makeOne() comp.registerUtility(_to_reg, ifoo, _name) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterUtility(_to_reg, ifoo, _name) self.failUnless(unreg) self.failIf(comp.utilities._adapters) # all erased self.failIf((ifoo, _name) in comp._utility_registrations) self.failIf(comp.utilities._subscribers) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, UtilityRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.failUnless(event.object.name is _name) self.failUnless(event.object.component is _to_reg) self.failUnless(event.object.factory is None) def test_unregisterUtility_w_factory(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import UtilityRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _info = _u('info') _name = _u('name') _to_reg = object() def _factory(): return _to_reg comp = self._makeOne() comp.registerUtility(None, ifoo, _name, _info, factory=_factory) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterUtility(None, ifoo, _name, factory=_factory) self.failUnless(unreg) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, UtilityRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.failUnless(event.object.name is _name) self.failUnless(event.object.component is _to_reg) self.failUnless(event.object.factory is _factory) def test_unregisterUtility_wo_explicit_provided(self): from zope.interface.declarations import directlyProvides from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import UtilityRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass class Foo(object): pass ifoo = IFoo('IFoo') _info = _u('info') _name = _u('name') _to_reg = Foo() directlyProvides(_to_reg, ifoo) comp = self._makeOne() comp.registerUtility(_to_reg, ifoo, _name, _info) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterUtility(_to_reg, None, _name) self.failUnless(unreg) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, UtilityRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.failUnless(event.object.name is _name) self.failUnless(event.object.component is _to_reg) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is None) def test_unregisterUtility_wo_component_or_factory(self): from zope.interface.declarations import directlyProvides from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import UtilityRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass class Foo(object): pass ifoo = IFoo('IFoo') _info = _u('info') _name = _u('name') _to_reg = Foo() directlyProvides(_to_reg, ifoo) comp = self._makeOne() comp.registerUtility(_to_reg, ifoo, _name, _info) _monkey, _events = self._wrapEvents() with _monkey: # Just pass the interface / name unreg = comp.unregisterUtility(provided=ifoo, name=_name) self.failUnless(unreg) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, UtilityRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.failUnless(event.object.name is _name) self.failUnless(event.object.component is _to_reg) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is None) def test_unregisterUtility_w_existing_subscr(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _info = _u('info') _name1 = _u('name1') _name2 = _u('name2') _to_reg = object() comp = self._makeOne() comp.registerUtility(_to_reg, ifoo, _name1, _info) comp.registerUtility(_to_reg, ifoo, _name2, _info) _monkey, _events = self._wrapEvents() with _monkey: comp.unregisterUtility(_to_reg, ifoo, _name2) self.assertEqual(comp.utilities._subscribers[0][ifoo][''], (_to_reg,)) def test_registeredUtilities_empty(self): comp = self._makeOne() self.assertEqual(list(comp.registeredUtilities()), []) def test_registeredUtilities_notempty(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u from zope.interface.registry import UtilityRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _info = _u('info') _name1 = _u('name1') _name2 = _u('name2') _to_reg = object() comp = self._makeOne() comp.registerUtility(_to_reg, ifoo, _name1, _info) comp.registerUtility(_to_reg, ifoo, _name2, _info) reg = sorted(comp.registeredUtilities(), key=lambda r: r.name) self.assertEqual(len(reg), 2) self.failUnless(isinstance(reg[0], UtilityRegistration)) self.failUnless(reg[0].registry is comp) self.failUnless(reg[0].provided is ifoo) self.failUnless(reg[0].name is _name1) self.failUnless(reg[0].component is _to_reg) self.failUnless(reg[0].info is _info) self.failUnless(reg[0].factory is None) self.failUnless(isinstance(reg[1], UtilityRegistration)) self.failUnless(reg[1].registry is comp) self.failUnless(reg[1].provided is ifoo) self.failUnless(reg[1].name is _name2) self.failUnless(reg[1].component is _to_reg) self.failUnless(reg[1].info is _info) self.failUnless(reg[1].factory is None) def test_queryUtility_miss_no_default(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() self.failUnless(comp.queryUtility(ifoo) is None) def test_queryUtility_miss_w_default(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() _default = object() self.failUnless(comp.queryUtility(ifoo, default=_default) is _default) def test_queryUtility_hit(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _to_reg = object() comp = self._makeOne() comp.registerUtility(_to_reg, ifoo) self.failUnless(comp.queryUtility(ifoo) is _to_reg) def test_getUtility_miss(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import ComponentLookupError class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() self.assertRaises(ComponentLookupError, comp.getUtility, ifoo) def test_getUtility_hit(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _to_reg = object() comp = self._makeOne() comp.registerUtility(_to_reg, ifoo) self.failUnless(comp.getUtility(ifoo) is _to_reg) def test_getUtilitiesFor_miss(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() self.assertEqual(list(comp.getUtilitiesFor(ifoo)), []) def test_getUtilitiesFor_hit(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _name1 = _u('name1') _name2 = _u('name2') _to_reg = object() comp = self._makeOne() comp.registerUtility(_to_reg, ifoo, name=_name1) comp.registerUtility(_to_reg, ifoo, name=_name2) self.assertEqual(sorted(comp.getUtilitiesFor(ifoo)), [(_name1, _to_reg), (_name2, _to_reg)]) def test_getAllUtilitiesRegisteredFor_miss(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() self.assertEqual(list(comp.getAllUtilitiesRegisteredFor(ifoo)), []) def test_getAllUtilitiesRegisteredFor_hit(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _name1 = _u('name1') _name2 = _u('name2') _to_reg = object() comp = self._makeOne() comp.registerUtility(_to_reg, ifoo, name=_name1) comp.registerUtility(_to_reg, ifoo, name=_name2) self.assertEqual(list(comp.getAllUtilitiesRegisteredFor(ifoo)), [_to_reg]) def test_registerAdapter_w_explicit_provided_and_required(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Registered from zope.interface.registry import AdapterRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _info = _u('info') _name = _u('name') _to_reg = object() def _factory(context): return _to_reg comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerAdapter(_factory, (ibar,), ifoo, _name, _info) self.failUnless(comp.adapters._adapters[1][ibar][ifoo][_name] is _factory) self.assertEqual(comp._adapter_registrations[(ibar,), ifoo, _name], (_factory, _info)) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, AdapterRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.failUnless(event.object.name is _name) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is _factory) def test_registerAdapter_no_provided_available(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _info = _u('info') _name = _u('name') _to_reg = object() class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() self.assertRaises(TypeError, comp.registerAdapter, _Factory, (ibar,), name=_name, info=_info) def test_registerAdapter_wo_explicit_provided(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer from zope.interface.interfaces import Registered from zope.interface.registry import AdapterRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _info = _u('info') _name = _u('name') _to_reg = object() @implementer(ifoo) class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerAdapter(_Factory, (ibar,), name=_name, info=_info) self.failUnless(comp.adapters._adapters[1][ibar][ifoo][_name] is _Factory) self.assertEqual(comp._adapter_registrations[(ibar,), ifoo, _name], (_Factory, _info)) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, AdapterRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.failUnless(event.object.name is _name) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is _Factory) def test_registerAdapter_no_required_available(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _info = _u('info') _name = _u('name') class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() self.assertRaises(TypeError, comp.registerAdapter, _Factory, provided=ifoo, name=_name, info=_info) def test_registerAdapter_w_invalid_required(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _info = _u('info') _name = _u('name') class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() self.assertRaises(TypeError, comp.registerAdapter, _Factory, ibar, provided=ifoo, name=_name, info=_info) def test_registerAdapter_w_required_containing_None(self): from zope.interface.declarations import InterfaceClass from zope.interface.interface import Interface from zope.interface.interfaces import Registered from zope.interface.registry import AdapterRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _info = _u('info') _name = _u('name') class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerAdapter(_Factory, [None], provided=ifoo, name=_name, info=_info) self.failUnless(comp.adapters._adapters[1][Interface][ifoo][_name] is _Factory) self.assertEqual(comp._adapter_registrations[(Interface,), ifoo, _name], (_Factory, _info)) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, AdapterRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (Interface,)) self.failUnless(event.object.name is _name) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is _Factory) def test_registerAdapter_w_required_containing_class(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer from zope.interface.declarations import implementedBy from zope.interface.interfaces import Registered from zope.interface.registry import AdapterRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _info = _u('info') _name = _u('name') class _Factory(object): def __init__(self, context): self._context = context @implementer(ibar) class _Context(object): pass _ctx_impl = implementedBy(_Context) comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerAdapter(_Factory, [_Context], provided=ifoo, name=_name, info=_info) self.failUnless(comp.adapters._adapters[1][_ctx_impl][ifoo][_name] is _Factory) self.assertEqual(comp._adapter_registrations[(_ctx_impl,), ifoo, _name], (_Factory, _info)) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, AdapterRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (_ctx_impl,)) self.failUnless(event.object.name is _name) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is _Factory) def test_registerAdapter_w_required_containing_junk(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _info = _u('info') _name = _u('name') class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() self.assertRaises(TypeError, comp.registerAdapter, _Factory, [object()], provided=ifoo, name=_name, info=_info) def test_registerAdapter_wo_explicit_required(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Registered from zope.interface.registry import AdapterRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _info = _u('info') _name = _u('name') class _Factory(object): __component_adapts__ = (ibar,) def __init__(self, context): self._context = context comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerAdapter(_Factory, provided=ifoo, name=_name, info=_info) self.failUnless(comp.adapters._adapters[1][ibar][ifoo][_name] is _Factory) self.assertEqual(comp._adapter_registrations[(ibar,), ifoo, _name], (_Factory, _info)) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, AdapterRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.failUnless(event.object.name is _name) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is _Factory) def test_registerAdapter_wo_event(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _info = _u('info') _name = _u('name') _to_reg = object() def _factory(context): return _to_reg comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerAdapter(_factory, (ibar,), ifoo, _name, _info, event=False) self.assertEqual(len(_events), 0) def test_unregisterAdapter_neither_factory_nor_provided(self): comp = self._makeOne() self.assertRaises(TypeError, comp.unregisterAdapter, factory=None, provided=None) def test_unregisterAdapter_neither_factory_nor_required(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() self.assertRaises(TypeError, comp.unregisterAdapter, factory=None, provided=ifoo, required=None) def test_unregisterAdapter_miss(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterAdapter(_Factory, (ibar,), ifoo) self.failIf(unreg) def test_unregisterAdapter_hit_w_explicit_provided_and_required(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import AdapterRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() comp.registerAdapter(_Factory, (ibar,), ifoo) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterAdapter(_Factory, (ibar,), ifoo) self.failUnless(unreg) self.failIf(comp.adapters._adapters) self.failIf(comp._adapter_registrations) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, AdapterRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.assertEqual(event.object.name, '') self.assertEqual(event.object.info, '') self.failUnless(event.object.factory is _Factory) def test_unregisterAdapter_wo_explicit_provided(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer from zope.interface.interfaces import Unregistered from zope.interface.registry import AdapterRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') @implementer(ifoo) class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() comp.registerAdapter(_Factory, (ibar,), ifoo) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterAdapter(_Factory, (ibar,)) self.failUnless(unreg) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, AdapterRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.assertEqual(event.object.name, '') self.assertEqual(event.object.info, '') self.failUnless(event.object.factory is _Factory) def test_unregisterAdapter_wo_explicit_required(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import AdapterRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Factory(object): __component_adapts__ = (ibar,) def __init__(self, context): self._context = context comp = self._makeOne() comp.registerAdapter(_Factory, (ibar,), ifoo) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterAdapter(_Factory, provided=ifoo) self.failUnless(unreg) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, AdapterRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.assertEqual(event.object.name, '') self.assertEqual(event.object.info, '') self.failUnless(event.object.factory is _Factory) def test_registeredAdapters_empty(self): comp = self._makeOne() self.assertEqual(list(comp.registeredAdapters()), []) def test_registeredAdapters_notempty(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u from zope.interface.registry import AdapterRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IFoo') _info = _u('info') _name1 = _u('name1') _name2 = _u('name2') class _Factory(object): def __init__(self, context): pass comp = self._makeOne() comp.registerAdapter(_Factory, (ibar,), ifoo, _name1, _info) comp.registerAdapter(_Factory, (ibar,), ifoo, _name2, _info) reg = sorted(comp.registeredAdapters(), key=lambda r: r.name) self.assertEqual(len(reg), 2) self.failUnless(isinstance(reg[0], AdapterRegistration)) self.failUnless(reg[0].registry is comp) self.failUnless(reg[0].provided is ifoo) self.assertEqual(reg[0].required, (ibar,)) self.failUnless(reg[0].name is _name1) self.failUnless(reg[0].info is _info) self.failUnless(reg[0].factory is _Factory) self.failUnless(isinstance(reg[1], AdapterRegistration)) self.failUnless(reg[1].registry is comp) self.failUnless(reg[1].provided is ifoo) self.assertEqual(reg[1].required, (ibar,)) self.failUnless(reg[1].name is _name2) self.failUnless(reg[1].info is _info) self.failUnless(reg[1].factory is _Factory) def test_queryAdapter_miss_no_default(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() _context = object() self.failUnless(comp.queryAdapter(_context, ifoo) is None) def test_queryAdapter_miss_w_default(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() _context = object() _default = object() self.failUnless( comp.queryAdapter(_context, ifoo, default=_default) is _default) def test_queryAdapter_hit(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Factory(object): def __init__(self, context): self.context = context @implementer(ibar) class _Context(object): pass _context = _Context() comp = self._makeOne() comp.registerAdapter(_Factory, (ibar,), ifoo) adapter = comp.queryAdapter(_context, ifoo) self.failUnless(isinstance(adapter, _Factory)) self.failUnless(adapter.context is _context) def test_getAdapter_miss(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer from zope.interface.interfaces import ComponentLookupError class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') @implementer(ibar) class _Context(object): pass _context = _Context() comp = self._makeOne() self.assertRaises(ComponentLookupError, comp.getAdapter, _context, ifoo) def test_getAdapter_hit(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Factory(object): def __init__(self, context): self.context = context @implementer(ibar) class _Context(object): pass _context = _Context() comp = self._makeOne() comp.registerAdapter(_Factory, (ibar,), ifoo) adapter = comp.getAdapter(_context, ifoo) self.failUnless(isinstance(adapter, _Factory)) self.failUnless(adapter.context is _context) def test_queryMultiAdapter_miss(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') ibaz = IFoo('IBaz') @implementer(ibar) class _Context1(object): pass @implementer(ibaz) class _Context2(object): pass _context1 = _Context1() _context2 = _Context2() comp = self._makeOne() self.assertEqual(comp.queryMultiAdapter((_context1, _context2), ifoo), None) def test_queryMultiAdapter_miss_w_default(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') ibaz = IFoo('IBaz') @implementer(ibar) class _Context1(object): pass @implementer(ibaz) class _Context2(object): pass _context1 = _Context1() _context2 = _Context2() _default = object() comp = self._makeOne() self.failUnless( comp.queryMultiAdapter((_context1, _context2), ifoo, default=_default) is _default) def test_queryMultiAdapter_hit(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') ibaz = IFoo('IBaz') @implementer(ibar) class _Context1(object): pass @implementer(ibaz) class _Context2(object): pass _context1 = _Context1() _context2 = _Context2() class _Factory(object): def __init__(self, context1, context2): self.context = context1, context2 comp = self._makeOne() comp.registerAdapter(_Factory, (ibar, ibaz), ifoo) adapter = comp.queryMultiAdapter((_context1, _context2), ifoo) self.failUnless(isinstance(adapter, _Factory)) self.assertEqual(adapter.context, (_context1, _context2)) def test_getMultiAdapter_miss(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer from zope.interface.interfaces import ComponentLookupError class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') ibaz = IFoo('IBaz') @implementer(ibar) class _Context1(object): pass @implementer(ibaz) class _Context2(object): pass _context1 = _Context1() _context2 = _Context2() comp = self._makeOne() self.assertRaises(ComponentLookupError, comp.getMultiAdapter, (_context1, _context2), ifoo) def test_getMultiAdapter_hit(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') ibaz = IFoo('IBaz') @implementer(ibar) class _Context1(object): pass @implementer(ibaz) class _Context2(object): pass _context1 = _Context1() _context2 = _Context2() class _Factory(object): def __init__(self, context1, context2): self.context = context1, context2 comp = self._makeOne() comp.registerAdapter(_Factory, (ibar, ibaz), ifoo) adapter = comp.getMultiAdapter((_context1, _context2), ifoo) self.failUnless(isinstance(adapter, _Factory)) self.assertEqual(adapter.context, (_context1, _context2)) def test_getAdapters_empty(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') ibaz = IFoo('IBaz') @implementer(ibar) class _Context1(object): pass @implementer(ibaz) class _Context2(object): pass _context1 = _Context1() _context2 = _Context2() comp = self._makeOne() self.assertEqual( list(comp.getAdapters((_context1, _context2), ifoo)), []) def test_getAdapters_non_empty(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') ibaz = IFoo('IBaz') @implementer(ibar) class _Context1(object): pass @implementer(ibaz) class _Context2(object): pass _context1 = _Context1() _context2 = _Context2() class _Factory1(object): def __init__(self, context1, context2): self.context = context1, context2 class _Factory2(object): def __init__(self, context1, context2): self.context = context1, context2 _name1 = _u('name1') _name2 = _u('name2') comp = self._makeOne() comp.registerAdapter(_Factory1, (ibar, ibaz), ifoo, name=_name1) comp.registerAdapter(_Factory2, (ibar, ibaz), ifoo, name=_name2) found = sorted(comp.getAdapters((_context1, _context2), ifoo)) self.assertEqual(len(found), 2) self.assertEqual(found[0][0], _name1) self.failUnless(isinstance(found[0][1], _Factory1)) self.assertEqual(found[1][0], _name2) self.failUnless(isinstance(found[1][1], _Factory2)) def test_registerSubscriptionAdapter_w_nonblank_name(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _name = _u('name') _info = _u('info') _to_reg = object() def _factory(context): return _to_reg comp = self._makeOne() self.assertRaises(TypeError, comp.registerSubscriptionAdapter, _factory, (ibar,), ifoo, _name, _info) def test_registerSubscriptionAdapter_w_explicit_provided_and_required(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Registered from zope.interface.registry import SubscriptionRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _blank = _u('') _info = _u('info') _to_reg = object() def _factory(context): return _to_reg comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerSubscriptionAdapter(_factory, (ibar,), ifoo, info=_info) reg = comp.adapters._subscribers[1][ibar][ifoo][_blank] self.assertEqual(len(reg), 1) self.failUnless(reg[0] is _factory) self.assertEqual(comp._subscription_registrations, [((ibar,), ifoo, _blank, _factory, _info)]) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, SubscriptionRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.assertEqual(event.object.name, _blank) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is _factory) def test_registerSubscriptionAdapter_wo_explicit_provided(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer from zope.interface.interfaces import Registered from zope.interface.registry import SubscriptionRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _info = _u('info') _blank = _u('') _to_reg = object() @implementer(ifoo) class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerSubscriptionAdapter(_Factory, (ibar,), info=_info) reg = comp.adapters._subscribers[1][ibar][ifoo][_blank] self.assertEqual(len(reg), 1) self.failUnless(reg[0] is _Factory) self.assertEqual(comp._subscription_registrations, [((ibar,), ifoo, _blank, _Factory, _info)]) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, SubscriptionRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.assertEqual(event.object.name, _blank) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is _Factory) def test_registerSubscriptionAdapter_wo_explicit_required(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Registered from zope.interface.registry import SubscriptionRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _info = _u('info') _blank = _u('') class _Factory(object): __component_adapts__ = (ibar,) def __init__(self, context): self._context = context comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerSubscriptionAdapter( _Factory, provided=ifoo, info=_info) reg = comp.adapters._subscribers[1][ibar][ifoo][_blank] self.assertEqual(len(reg), 1) self.failUnless(reg[0] is _Factory) self.assertEqual(comp._subscription_registrations, [((ibar,), ifoo, _blank, _Factory, _info)]) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, SubscriptionRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.assertEqual(event.object.name, _blank) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is _Factory) def test_registerSubscriptionAdapter_wo_event(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _blank = _u('') _info = _u('info') _to_reg = object() def _factory(context): return _to_reg comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerSubscriptionAdapter(_factory, (ibar,), ifoo, info=_info, event=False) self.assertEqual(len(_events), 0) def test_registeredSubscriptionAdapters_empty(self): comp = self._makeOne() self.assertEqual(list(comp.registeredSubscriptionAdapters()), []) def test_registeredSubscriptionAdapters_notempty(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u from zope.interface.registry import SubscriptionRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IFoo') _info = _u('info') _blank = _u('') class _Factory(object): def __init__(self, context): pass comp = self._makeOne() comp.registerSubscriptionAdapter(_Factory, (ibar,), ifoo, info=_info) comp.registerSubscriptionAdapter(_Factory, (ibar,), ifoo, info=_info) reg = list(comp.registeredSubscriptionAdapters()) self.assertEqual(len(reg), 2) self.failUnless(isinstance(reg[0], SubscriptionRegistration)) self.failUnless(reg[0].registry is comp) self.failUnless(reg[0].provided is ifoo) self.assertEqual(reg[0].required, (ibar,)) self.assertEqual(reg[0].name, _blank) self.failUnless(reg[0].info is _info) self.failUnless(reg[0].factory is _Factory) self.failUnless(isinstance(reg[1], SubscriptionRegistration)) self.failUnless(reg[1].registry is comp) self.failUnless(reg[1].provided is ifoo) self.assertEqual(reg[1].required, (ibar,)) self.assertEqual(reg[1].name, _blank) self.failUnless(reg[1].info is _info) self.failUnless(reg[1].factory is _Factory) def test_unregisterSubscriptionAdapter_w_nonblank_name(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') _nonblank = _u('nonblank') comp = self._makeOne() self.assertRaises(TypeError, comp.unregisterSubscriptionAdapter, required=ifoo, provided=ibar, name=_nonblank) def test_unregisterSubscriptionAdapter_neither_factory_nor_provided(self): comp = self._makeOne() self.assertRaises(TypeError, comp.unregisterSubscriptionAdapter, factory=None, provided=None) def test_unregisterSubscriptionAdapter_neither_factory_nor_required(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() self.assertRaises(TypeError, comp.unregisterSubscriptionAdapter, factory=None, provided=ifoo, required=None) def test_unregisterSubscriptionAdapter_miss(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterSubscriptionAdapter(_Factory, (ibar,), ifoo) self.failIf(unreg) self.failIf(_events) def test_unregisterSubscriptionAdapter_hit_wo_factory(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import SubscriptionRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() comp.registerSubscriptionAdapter(_Factory, (ibar,), ifoo) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterSubscriptionAdapter(None, (ibar,), ifoo) self.failUnless(unreg) self.failIf(comp.adapters._subscribers) self.failIf(comp._subscription_registrations) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, SubscriptionRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.assertEqual(event.object.name, '') self.assertEqual(event.object.info, '') self.failUnless(event.object.factory is None) def test_unregisterSubscriptionAdapter_hit_w_factory(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import SubscriptionRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() comp.registerSubscriptionAdapter(_Factory, (ibar,), ifoo) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterSubscriptionAdapter(_Factory, (ibar,), ifoo) self.failUnless(unreg) self.failIf(comp.adapters._subscribers) self.failIf(comp._subscription_registrations) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, SubscriptionRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.assertEqual(event.object.name, '') self.assertEqual(event.object.info, '') self.failUnless(event.object.factory is _Factory) def test_unregisterSubscriptionAdapter_wo_explicit_provided(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer from zope.interface.interfaces import Unregistered from zope.interface.registry import SubscriptionRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') @implementer(ifoo) class _Factory(object): def __init__(self, context): self._context = context comp = self._makeOne() comp.registerSubscriptionAdapter(_Factory, (ibar,), ifoo) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterSubscriptionAdapter(_Factory, (ibar,)) self.failUnless(unreg) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, SubscriptionRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.assertEqual(event.object.name, '') self.assertEqual(event.object.info, '') self.failUnless(event.object.factory is _Factory) def test_unregisterSubscriptionAdapter_wo_explicit_required(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import SubscriptionRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Factory(object): __component_adapts__ = (ibar,) def __init__(self, context): self._context = context comp = self._makeOne() comp.registerSubscriptionAdapter(_Factory, (ibar,), ifoo) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterSubscriptionAdapter(_Factory, provided=ifoo) self.failUnless(unreg) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, SubscriptionRegistration)) self.failUnless(event.object.registry is comp) self.failUnless(event.object.provided is ifoo) self.assertEqual(event.object.required, (ibar,)) self.assertEqual(event.object.name, '') self.assertEqual(event.object.info, '') self.failUnless(event.object.factory is _Factory) def test_subscribers_empty(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') comp = self._makeOne() @implementer(ibar) class Bar(object): pass bar = Bar() self.assertEqual(list(comp.subscribers((bar,), ifoo)), []) def test_subscribers_non_empty(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Factory(object): __component_adapts__ = (ibar,) def __init__(self, context): self._context = context class _Derived(_Factory): pass comp = self._makeOne() comp.registerSubscriptionAdapter(_Factory, (ibar,), ifoo) comp.registerSubscriptionAdapter(_Derived, (ibar,), ifoo) @implementer(ibar) class Bar(object): pass bar = Bar() subscribers = comp.subscribers((bar,), ifoo) def _klassname(x): return x.__class__.__name__ subscribers = sorted(subscribers, key=_klassname) self.assertEqual(len(subscribers), 2) self.failUnless(isinstance(subscribers[0], _Derived)) self.failUnless(isinstance(subscribers[1], _Factory)) def test_registerHandler_w_nonblank_name(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _nonblank = _u('nonblank') comp = self._makeOne() def _factory(context): pass self.assertRaises(TypeError, comp.registerHandler, _factory, required=ifoo, name=_nonblank) def test_registerHandler_w_explicit_required(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Registered from zope.interface.registry import HandlerRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _blank = _u('') _info = _u('info') _to_reg = object() def _factory(context): return _to_reg comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerHandler(_factory, (ifoo,), info=_info) reg = comp.adapters._subscribers[1][ifoo][None][_blank] self.assertEqual(len(reg), 1) self.failUnless(reg[0] is _factory) self.assertEqual(comp._handler_registrations, [((ifoo,), _blank, _factory, _info)]) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, HandlerRegistration)) self.failUnless(event.object.registry is comp) self.assertEqual(event.object.required, (ifoo,)) self.assertEqual(event.object.name, _blank) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is _factory) def test_registerHandler_wo_explicit_required(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Registered from zope.interface.registry import HandlerRegistration from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _info = _u('info') _blank = _u('') class _Factory(object): __component_adapts__ = (ifoo,) def __init__(self, context): self._context = context comp = self._makeOne() _monkey, _events = self._wrapEvents() with _monkey: comp.registerHandler(_Factory, info=_info) reg = comp.adapters._subscribers[1][ifoo][None][_blank] self.assertEqual(len(reg), 1) self.failUnless(reg[0] is _Factory) self.assertEqual(comp._handler_registrations, [((ifoo,), _blank, _Factory, _info)]) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Registered)) self.failUnless(isinstance(event.object, HandlerRegistration)) self.failUnless(event.object.registry is comp) self.assertEqual(event.object.required, (ifoo,)) self.assertEqual(event.object.name, _blank) self.failUnless(event.object.info is _info) self.failUnless(event.object.factory is _Factory) def test_registeredHandlers_empty(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass comp = self._makeOne() self.failIf(list(comp.registeredHandlers())) def test_registeredHandlers_non_empty(self): from zope.interface.declarations import InterfaceClass from zope.interface.registry import HandlerRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') def _factory1(context): pass def _factory2(context): pass comp = self._makeOne() comp.registerHandler(_factory1, (ifoo,)) comp.registerHandler(_factory2, (ifoo,)) def _factory_name(x): return x.factory.__code__.co_name subscribers = sorted(comp.registeredHandlers(), key=_factory_name) self.assertEqual(len(subscribers), 2) self.failUnless(isinstance(subscribers[0], HandlerRegistration)) self.assertEqual(subscribers[0].required, (ifoo,)) self.assertEqual(subscribers[0].name, '') self.assertEqual(subscribers[0].factory, _factory1) self.assertEqual(subscribers[0].info, '') self.failUnless(isinstance(subscribers[1], HandlerRegistration)) self.assertEqual(subscribers[1].required, (ifoo,)) self.assertEqual(subscribers[1].name, '') self.assertEqual(subscribers[1].factory, _factory2) self.assertEqual(subscribers[1].info, '') def test_unregisterHandler_w_nonblank_name(self): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _nonblank = _u('nonblank') comp = self._makeOne() self.assertRaises(TypeError, comp.unregisterHandler, required=(ifoo,), name=_nonblank) def test_unregisterHandler_neither_factory_nor_required(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() self.assertRaises(TypeError, comp.unregisterHandler) def test_unregisterHandler_miss(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() unreg = comp.unregisterHandler(required=(ifoo,)) self.failIf(unreg) def test_unregisterHandler_hit_w_factory_and_explicit_provided(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import HandlerRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() _to_reg = object() def _factory(context): return _to_reg comp = self._makeOne() comp.registerHandler(_factory, (ifoo,)) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterHandler(_factory, (ifoo,)) self.failUnless(unreg) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, HandlerRegistration)) self.failUnless(event.object.registry is comp) self.assertEqual(event.object.required, (ifoo,)) self.assertEqual(event.object.name, '') self.failUnless(event.object.factory is _factory) def test_unregisterHandler_hit_w_only_explicit_provided(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import HandlerRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() _to_reg = object() def _factory(context): return _to_reg comp = self._makeOne() comp.registerHandler(_factory, (ifoo,)) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterHandler(required=(ifoo,)) self.failUnless(unreg) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, HandlerRegistration)) self.failUnless(event.object.registry is comp) self.assertEqual(event.object.required, (ifoo,)) self.assertEqual(event.object.name, '') self.failUnless(event.object.factory is None) def test_unregisterHandler_wo_explicit_required(self): from zope.interface.declarations import InterfaceClass from zope.interface.interfaces import Unregistered from zope.interface.registry import HandlerRegistration class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') class _Factory(object): __component_adapts__ = (ifoo,) def __init__(self, context): self._context = context comp = self._makeOne() comp.registerHandler(_Factory) _monkey, _events = self._wrapEvents() with _monkey: unreg = comp.unregisterHandler(_Factory) self.failUnless(unreg) self.assertEqual(len(_events), 1) args, kw = _events[0] event, = args self.assertEqual(kw, {}) self.failUnless(isinstance(event, Unregistered)) self.failUnless(isinstance(event.object, HandlerRegistration)) self.failUnless(event.object.registry is comp) self.assertEqual(event.object.required, (ifoo,)) self.assertEqual(event.object.name, '') self.assertEqual(event.object.info, '') self.failUnless(event.object.factory is _Factory) def test_handle_empty(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') comp = self._makeOne() @implementer(ifoo) class Bar(object): pass bar = Bar() comp.handle((bar,)) # doesn't raise def test_handle_non_empty(self): from zope.interface.declarations import InterfaceClass from zope.interface.declarations import implementer class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') _called_1 = [] def _factory_1(context): _called_1.append(context) _called_2 = [] def _factory_2(context): _called_2.append(context) comp = self._makeOne() comp.registerHandler(_factory_1, (ifoo,)) comp.registerHandler(_factory_2, (ifoo,)) @implementer(ifoo) class Bar(object): pass bar = Bar() comp.handle(bar) self.assertEqual(_called_1, [bar]) self.assertEqual(_called_2, [bar]) # Test _getUtilityProvided, _getAdapterProvided, _getAdapterRequired via their # callers (Component.registerUtility, Component.registerAdapter). class UtilityRegistrationTests(_SilencePy3Deprecations): def _getTargetClass(self): from zope.interface.registry import UtilityRegistration return UtilityRegistration def _makeOne(self, component=None, factory=None): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') class _Registry(object): def __repr__(self): return '_REGISTRY' registry = _Registry() name = _u('name') doc = 'DOCSTRING' klass = self._getTargetClass() return (klass(registry, ifoo, name, component, doc, factory), registry, name, ) def test_class_conforms_to_IUtilityRegistration(self): from zope.interface.verify import verifyClass from zope.interface.interfaces import IUtilityRegistration verifyClass(IUtilityRegistration, self._getTargetClass()) def test_instance_conforms_to_IUtilityRegistration(self): from zope.interface.verify import verifyObject from zope.interface.interfaces import IUtilityRegistration ur, _, _ = self._makeOne() verifyObject(IUtilityRegistration, ur) def test___repr__(self): class _Component(object): __name__ = 'TEST' _component = _Component() ur, _registry, _name = self._makeOne(_component) self.assertEqual(repr(ur), "UtilityRegistration(_REGISTRY, IFoo, %r, TEST, None, 'DOCSTRING')" % (_name)) def test___repr___provided_wo_name(self): class _Component(object): def __repr__(self): return 'TEST' _component = _Component() ur, _registry, _name = self._makeOne(_component) ur.provided = object() self.assertEqual(repr(ur), "UtilityRegistration(_REGISTRY, None, %r, TEST, None, 'DOCSTRING')" % (_name)) def test___repr___component_wo_name(self): class _Component(object): def __repr__(self): return 'TEST' _component = _Component() ur, _registry, _name = self._makeOne(_component) ur.provided = object() self.assertEqual(repr(ur), "UtilityRegistration(_REGISTRY, None, %r, TEST, None, 'DOCSTRING')" % (_name)) def test___hash__(self): _component = object() ur, _registry, _name = self._makeOne(_component) self.assertEqual(ur.__hash__(), id(ur)) def test___eq___identity(self): _component = object() ur, _registry, _name = self._makeOne(_component) self.failUnless(ur == ur) def test___eq___hit(self): _component = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component) self.failUnless(ur == ur2) def test___eq___miss(self): _component = object() _component2 = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component2) self.failIf(ur == ur2) def test___ne___identity(self): _component = object() ur, _registry, _name = self._makeOne(_component) self.failIf(ur != ur) def test___ne___hit(self): _component = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component) self.failIf(ur != ur2) def test___ne___miss(self): _component = object() _component2 = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component2) self.failUnless(ur != ur2) def test___lt___identity(self): _component = object() ur, _registry, _name = self._makeOne(_component) self.failIf(ur < ur) def test___lt___hit(self): _component = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component) self.failIf(ur < ur2) def test___lt___miss(self): _component = object() _component2 = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component2) ur2.name = _name + '2' self.failUnless(ur < ur2) def test___le___identity(self): _component = object() ur, _registry, _name = self._makeOne(_component) self.failUnless(ur <= ur) def test___le___hit(self): _component = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component) self.failUnless(ur <= ur2) def test___le___miss(self): _component = object() _component2 = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component2) ur2.name = _name + '2' self.failUnless(ur <= ur2) def test___gt___identity(self): _component = object() ur, _registry, _name = self._makeOne(_component) self.failIf(ur > ur) def test___gt___hit(self): _component = object() _component2 = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component2) ur2.name = _name + '2' self.failUnless(ur2 > ur) def test___gt___miss(self): _component = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component) self.failIf(ur2 > ur) def test___ge___identity(self): _component = object() ur, _registry, _name = self._makeOne(_component) self.failUnless(ur >= ur) def test___ge___miss(self): _component = object() _component2 = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component2) ur2.name = _name + '2' self.failIf(ur >= ur2) def test___ge___hit(self): _component = object() ur, _registry, _name = self._makeOne(_component) ur2, _, _ = self._makeOne(_component) ur2.name = _name + '2' self.failUnless(ur2 >= ur) class AdapterRegistrationTests(_SilencePy3Deprecations): def _getTargetClass(self): from zope.interface.registry import AdapterRegistration return AdapterRegistration def _makeOne(self, component=None): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Registry(object): def __repr__(self): return '_REGISTRY' registry = _Registry() name = _u('name') doc = 'DOCSTRING' klass = self._getTargetClass() return (klass(registry, (ibar,), ifoo, name, component, doc), registry, name, ) def test_class_conforms_to_IAdapterRegistration(self): from zope.interface.verify import verifyClass from zope.interface.interfaces import IAdapterRegistration verifyClass(IAdapterRegistration, self._getTargetClass()) def test_instance_conforms_to_IAdapterRegistration(self): from zope.interface.verify import verifyObject from zope.interface.interfaces import IAdapterRegistration ar, _, _ = self._makeOne() verifyObject(IAdapterRegistration, ar) def test___repr__(self): class _Component(object): __name__ = 'TEST' _component = _Component() ar, _registry, _name = self._makeOne(_component) self.assertEqual(repr(ar), ("AdapterRegistration(_REGISTRY, [IBar], IFoo, %r, TEST, " + "'DOCSTRING')") % (_name)) def test___repr___provided_wo_name(self): class _Component(object): def __repr__(self): return 'TEST' _component = _Component() ar, _registry, _name = self._makeOne(_component) ar.provided = object() self.assertEqual(repr(ar), ("AdapterRegistration(_REGISTRY, [IBar], None, %r, TEST, " + "'DOCSTRING')") % (_name)) def test___repr___component_wo_name(self): class _Component(object): def __repr__(self): return 'TEST' _component = _Component() ar, _registry, _name = self._makeOne(_component) ar.provided = object() self.assertEqual(repr(ar), ("AdapterRegistration(_REGISTRY, [IBar], None, %r, TEST, " + "'DOCSTRING')") % (_name)) def test___hash__(self): _component = object() ar, _registry, _name = self._makeOne(_component) self.assertEqual(ar.__hash__(), id(ar)) def test___eq___identity(self): _component = object() ar, _registry, _name = self._makeOne(_component) self.failUnless(ar == ar) def test___eq___hit(self): _component = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component) self.failUnless(ar == ar2) def test___eq___miss(self): _component = object() _component2 = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component2) self.failIf(ar == ar2) def test___ne___identity(self): _component = object() ar, _registry, _name = self._makeOne(_component) self.failIf(ar != ar) def test___ne___miss(self): _component = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component) self.failIf(ar != ar2) def test___ne___hit_component(self): _component = object() _component2 = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component2) self.failUnless(ar != ar2) def test___ne___hit_provided(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ibaz = IFoo('IBaz') _component = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component) ar2.provided = ibaz self.failUnless(ar != ar2) def test___ne___hit_required(self): from zope.interface.declarations import InterfaceClass class IFoo(InterfaceClass): pass ibaz = IFoo('IBaz') _component = object() _component2 = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component2) ar2.required = (ibaz,) self.failUnless(ar != ar2) def test___lt___identity(self): _component = object() ar, _registry, _name = self._makeOne(_component) self.failIf(ar < ar) def test___lt___hit(self): _component = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component) self.failIf(ar < ar2) def test___lt___miss(self): _component = object() _component2 = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component2) ar2.name = _name + '2' self.failUnless(ar < ar2) def test___le___identity(self): _component = object() ar, _registry, _name = self._makeOne(_component) self.failUnless(ar <= ar) def test___le___hit(self): _component = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component) self.failUnless(ar <= ar2) def test___le___miss(self): _component = object() _component2 = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component2) ar2.name = _name + '2' self.failUnless(ar <= ar2) def test___gt___identity(self): _component = object() ar, _registry, _name = self._makeOne(_component) self.failIf(ar > ar) def test___gt___hit(self): _component = object() _component2 = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component2) ar2.name = _name + '2' self.failUnless(ar2 > ar) def test___gt___miss(self): _component = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component) self.failIf(ar2 > ar) def test___ge___identity(self): _component = object() ar, _registry, _name = self._makeOne(_component) self.failUnless(ar >= ar) def test___ge___miss(self): _component = object() _component2 = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component2) ar2.name = _name + '2' self.failIf(ar >= ar2) def test___ge___hit(self): _component = object() ar, _registry, _name = self._makeOne(_component) ar2, _, _ = self._makeOne(_component) ar2.name = _name + '2' self.failUnless(ar2 >= ar) class SubscriptionRegistrationTests(unittest.TestCase): def _getTargetClass(self): from zope.interface.registry import SubscriptionRegistration return SubscriptionRegistration def _makeOne(self, component=None): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') ibar = IFoo('IBar') class _Registry(object): def __repr__(self): return '_REGISTRY' registry = _Registry() name = _u('name') doc = 'DOCSTRING' klass = self._getTargetClass() return (klass(registry, (ibar,), ifoo, name, component, doc), registry, name, ) def test_class_conforms_to_ISubscriptionAdapterRegistration(self): from zope.interface.verify import verifyClass from zope.interface.interfaces import ISubscriptionAdapterRegistration verifyClass(ISubscriptionAdapterRegistration, self._getTargetClass()) def test_instance_conforms_to_ISubscriptionAdapterRegistration(self): from zope.interface.verify import verifyObject from zope.interface.interfaces import ISubscriptionAdapterRegistration sar, _, _ = self._makeOne() verifyObject(ISubscriptionAdapterRegistration, sar) class HandlerRegistrationTests(_SilencePy3Deprecations): def _getTargetClass(self): from zope.interface.registry import HandlerRegistration return HandlerRegistration def _makeOne(self, component=None): from zope.interface.declarations import InterfaceClass from zope.interface._compat import _u class IFoo(InterfaceClass): pass ifoo = IFoo('IFoo') class _Registry(object): def __repr__(self): return '_REGISTRY' registry = _Registry() name = _u('name') doc = 'DOCSTRING' klass = self._getTargetClass() return (klass(registry, (ifoo,), name, component, doc), registry, name, ) def test_class_conforms_to_IHandlerRegistration(self): from zope.interface.verify import verifyClass from zope.interface.interfaces import IHandlerRegistration verifyClass(IHandlerRegistration, self._getTargetClass()) def test_instance_conforms_to_IHandlerRegistration(self): from zope.interface.verify import verifyObject from zope.interface.interfaces import IHandlerRegistration hr, _, _ = self._makeOne() verifyObject(IHandlerRegistration, hr) def test_properties(self): def _factory(context): pass hr, _, _ = self._makeOne(_factory) self.failUnless(hr.handler is _factory) self.failUnless(hr.factory is hr.handler) self.failUnless(hr.provided is None) def test___repr___factory_w_name(self): class _Factory(object): __name__ = 'TEST' hr, _registry, _name = self._makeOne(_Factory()) self.assertEqual(repr(hr), ("HandlerRegistration(_REGISTRY, [IFoo], %r, TEST, " + "'DOCSTRING')") % (_name)) def test___repr___factory_wo_name(self): class _Factory(object): def __repr__(self): return 'TEST' hr, _registry, _name = self._makeOne(_Factory()) self.assertEqual(repr(hr), ("HandlerRegistration(_REGISTRY, [IFoo], %r, TEST, " + "'DOCSTRING')") % (_name)) class _Monkey(object): # context-manager for replacing module names in the scope of a test. def __init__(self, module, **kw): self.module = module self.to_restore = dict([(key, getattr(module, key)) for key in kw]) for key, value in kw.items(): setattr(module, key, value) def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): for key, value in self.to_restore.items(): setattr(self.module, key, value) def test_suite(): return unittest.TestSuite(( unittest.makeSuite(ComponentsTests), unittest.makeSuite(UtilityRegistrationTests), unittest.makeSuite(AdapterRegistrationTests), unittest.makeSuite(SubscriptionRegistrationTests), unittest.makeSuite(AdapterRegistrationTests), ))

f71806245033ff31b7f8e029e27f81e487b11834

py

Python

cadnano/views/pathview/tools/pathselection.py

sherwoodyao/cadnano2.5

ce6ff019b88ee7728de947bd86b35861cf57848d

2015-01-13T02:54:40.000Z

2022-03-27T14:25:51.000Z

cadnano/views/pathview/tools/pathselection.py

scholer/cadnano2.5

ce6ff019b88ee7728de947bd86b35861cf57848d

2015-01-01T06:26:34.000Z

2022-03-02T12:48:05.000Z

cadnano/views/pathview/tools/pathselection.py

scholer/cadnano2.5

ce6ff019b88ee7728de947bd86b35861cf57848d

2015-01-22T19:57:49.000Z

2022-03-27T14:27:53.000Z

# -*- coding: utf-8 -*- import logging from math import floor from PyQt5.QtCore import ( QPointF, QRectF, Qt ) from PyQt5.QtGui import ( QPainterPath, QKeyEvent, QMouseEvent ) from PyQt5.QtWidgets import ( QGraphicsItem, QGraphicsItemGroup, QGraphicsPathItem, QGraphicsSceneMouseEvent, ) from cadnano.gui.palette import getPenObj from cadnano.views.pathview import pathstyles as styles from cadnano.views.pathview import ( PathRootItemT, ) from cadnano.cntypes import ( Vec2T, DocT ) logging.basicConfig(format='%(asctime)s %(message)s', level=logging.DEBUG) logger = logging.getLogger(__name__) class SelectionItemGroup(QGraphicsItemGroup): """SelectionItemGroup Attributes: getR (TYPE): Description selectionbox (TYPE): SelectionBox translateR (TYPE): Description viewroot: Description """ def __init__(self, boxtype: QGraphicsItem, constraint: str, viewroot: PathRootItemT): """ Args: boxtype: :class:`EndpointHandleSelectionBox` or :class:`VirtualHelixHandleSelectionBox` instance constraint: ``x`` or ``y``. Default to ``y`` (up and down) viewroot: view root item and object parent """ super(SelectionItemGroup, self).__init__(viewroot) self.viewroot: PathRootItemT = viewroot self.setFiltersChildEvents(True) # LOOK at Qt Source for deprecated code to replace this behavior # self.setHandlesChildEvents(True) # commented out NC self.setFlag(QGraphicsItem.ItemIsSelectable) self.setFlag(QGraphicsItem.ItemIsFocusable) # for keyPressEvents self.setFlag(QGraphicsItem.ItemHasNoContents) self._rect = QRectF() self._PEN = getPenObj(styles.BLUE_STROKE, styles.PATH_SELECTBOX_STROKE_WIDTH) self.selectionbox = boxtype(self) self._drag_enable = False self._dragged = False self._r0 = 0 # save original mousedown self._r = 0 # latest position for moving # self._lastKid = 0 # this keeps track of mousePressEvents within the class # to aid in intellignetly removing items from the group self._added_to_press_list = False self._pending_to_add_dict = {} if constraint == 'y': self.getR = self.selectionbox.getY self.translateR = self.selectionbox.translateY else: self.getR = self.selectionbox.getX self.translateR = self.selectionbox.translateX self._normal_select = True self.setZValue(styles.ZPATHSELECTION) # end def # def paint(self, painter, option, widget): # painter.drawRect(self.boundingRect()) # # end def def pendToAdd(self, item): """ Args: item (TYPE): Description """ self._pending_to_add_dict[item] = True # end def def isPending(self, item): """ Args: item (TYPE): Description Returns: TYPE: Description """ return item in self._pending_to_add_dict # end def def document(self) -> DocT: """ Returns: :class:`Document` """ return self.viewroot.document() # end def def pendToRemove(self, item): """ Args: item (TYPE): Description """ if item in self._pending_to_add_dict: del self._pending_to_add_dict[item] # end def def setNormalSelect(self, bool_val: bool): """ Args: bool_val: Description """ self._normal_select = bool_val # end def def isNormalSelect(self) -> bool: """ Returns: is it normal select? """ return self._normal_select # end def def processPendingToAddList(self): """ Adds to the local selection and the document if required """ doc = self.document() p2add = self._pending_to_add_dict # logger.debug("processPendingToAddList") if len(p2add) > 0: plist = list(self._pending_to_add_dict.keys()) for item in plist: if p2add[item]: p2add[item] = False # logger.debug("just checking1", item, item.group(), item.parentItem()) self.addToGroup(item) item.modelSelect(doc) # end for # logger.debug('finished') self._pending_to_add_dict = {} doc.updateStrandSelection() # end def def selectionLock(self): """ Returns: TYPE: Description """ return self.viewroot.selectionLock() # end def def setSelectionLock(self, selection_group): """ Args: selection_group (TYPE): Description """ self.viewroot.setSelectionLock(selection_group) # end def def keyPressEvent(self, event: QKeyEvent): """ Must intercept invalid input events. Make changes here Args: event (TYPE): Description """ key = event.key() if key in [Qt.Key_Backspace, Qt.Key_Delete]: self.selectionbox.deleteSelection() self.clearSelection(False) return QGraphicsItemGroup.keyPressEvent(self, event) else: return QGraphicsItemGroup.keyPressEvent(self, event) # end def def mousePressEvent(self, event: QGraphicsSceneMouseEvent): """Handler for user mouse press. Args: event: Contains item, scene, and screen coordinates of the the event, and previous event. """ # self.show() if event.button() != Qt.LeftButton: return QGraphicsItemGroup.mousePressEvent(self, event) else: self._drag_enable = True # required to get the itemChanged event to work # correctly for this self.setSelected(True) # self.selectionbox.resetTransform() self.selectionbox.resetPosition() self.selectionbox.refreshPath() # self.selectionbox.resetTransform() self.selectionbox.resetPosition() self.selectionbox.show() # for some reason we need to skip the first mouseMoveEvent self._dragged = False if self._added_to_press_list is False: self._added_to_press_list = True self.scene().views()[0].addToPressList(self) return QGraphicsItemGroup.mousePressEvent(self, event) # end def def mouseMoveEvent(self, event: QGraphicsSceneMouseEvent): """ Args: event: Description """ if self._drag_enable is True: # map the item to the scene coordinates # to help keep coordinates uniform rf = self.getR(self.mapFromScene(QPointF(event.scenePos()))) # for some reason we need to skip the first mouseMoveEvent if self._dragged is False: self._dragged = True self._r0 = rf # end if else: delta = self.selectionbox.delta(rf, self._r0) self.translateR(delta) # logger.debug('mouse move path selectionbox', delta, rf, self._r0) # end else self._r = rf # end if else: QGraphicsItemGroup.mouseMoveEvent(self, event) # end else # end def def customMouseRelease(self, event: QMouseEvent): """ Args: event: Description """ self.selectionbox.setParentItem(self.viewroot) self.selectionbox.hide() self.selectionbox.resetTransform() self._drag_enable = False # now do stuff if not (self._r0 == 0 and self._r == 0): modifiers = event.modifiers() self.selectionbox.processSelectedItems(self._r0, self._r, modifiers) # end if self._r0 = 0 # reset self._r = 0 # reset self.setFocus() # needed to get keyPresses post a move self._added_to_press_list = False # end def def resetSelection(self): """Summary Returns: TYPE: Description """ self._pending_to_add_dict = {} self._added_to_press_list = False self.clearSelection(False) self.setSelectionLock(None) self.selectionbox.setParentItem(self.viewroot) self.setParentItem(self.viewroot) # end def def clearSelection(self, value): """value is for keyPressEvents Arguments: value (QVariant): resolves in Python as an integer """ if value == False: # noqa self.selectionbox.hide() self.selectionbox.resetPosition() self.removeSelectedItems() self.viewroot.setSelectionLock(None) self.clearFocus() # this is to disable delete keyPressEvents self.prepareGeometryChange() self._rect.setWidth(0) # self._rect = QRectF() # end if else: self.setFocus() # this is to get delete keyPressEvents self.update(self.boundingRect()) # end def def itemChange(self, change, value): """docstring for itemChange Arguments: change (GraphicsItemChange): see http://doc.qt.io/qt-5/qgraphicsitem.html#GraphicsItemChange-enum value (QVariant): resolves in Python as an integer """ # logger.debug("ps itemChange") if change == QGraphicsItem.ItemSelectedChange: # logger.debug("isc", value) if value == False: # noqa self.clearSelection(False) return False else: return True elif change == QGraphicsItem.ItemChildAddedChange: # logger.debug("icac") if self._added_to_press_list is False: # logger.debug("kid added") self.setFocus() # this is to get delete keyPressEvents self.selectionbox.boxParent() # self.setParentItem(self.selectionbox.boxParent()) self._added_to_press_list = True self.scene().views()[0].addToPressList(self) return return QGraphicsItemGroup.itemChange(self, change, value) # end def def removeChild(self, child): """ remove only the child and ask it to restore it's original parent Args: child (TYPE): Description """ doc = self.document() self.removeFromGroup(child) child.modelDeselect(doc) # end def def removeSelectedItems(self): """docstring for removeSelectedItems """ doc = self.document() for item in self.childItems(): self.removeFromGroup(item) item.modelDeselect(doc) # end for doc.updateStrandSelection() # end def def setBoundingRect(self, rect): """Summary Args: rect (TYPE): Description Returns: TYPE: Description """ self.prepareGeometryChange() self._rect = rect # end def def boundingRect(self): """Summary Returns: TYPE: Description """ return self._rect # end class class VirtualHelixHandleSelectionBox(QGraphicsPathItem): """ docstring for VirtualHelixHandleSelectionBox """ _HELIX_HEIGHT = styles.PATH_HELIX_HEIGHT + styles.PATH_HELIX_PADDING _RADIUS = styles.VIRTUALHELIXHANDLEITEM_RADIUS _PEN_WIDTH = styles.SELECTIONBOX_PEN_WIDTH _BOX_PEN = getPenObj(styles.BLUE_STROKE, _PEN_WIDTH) def __init__(self, item_group: SelectionItemGroup): """ The item_group.parentItem() is expected to be a partItem Args: item_group (TYPE): Description """ super(VirtualHelixHandleSelectionBox, self).__init__(item_group.parentItem()) self._item_group = item_group self._rect = item_group.boundingRect() self.hide() self.setPen(self._BOX_PEN) self.setZValue(styles.ZPATHSELECTION) self._bounds = None self._pos0 = QPointF() # end def def getY(self, pos): """Summary Args: pos (TYPE): Description Returns: TYPE: Description """ pos = self._item_group.mapToScene(QPointF(pos)) return pos.y() # end def def translateY(self, delta): """Summary Args: delta (TYPE): Description Returns: TYPE: Description """ self.setY(delta) # end def def refreshPath(self): """Summary Returns: TYPE: Description """ self.prepareGeometryChange() self.setPath(self.painterPath()) self._pos0 = self.pos() # end def def painterPath(self): """Summary Returns: TYPE: Description """ i_g = self._item_group # the childrenBoundingRect is necessary to get this to work rect = self.mapRectFromItem(i_g, i_g.childrenBoundingRect()) radius = self._RADIUS path = QPainterPath() path.addRoundedRect(rect, radius, radius) path.moveTo(rect.right(), rect.center().y()) path.lineTo(rect.right() + radius / 2, rect.center().y()) return path # end def def processSelectedItems(self, r_start, r_end, modifiers): """docstring for processSelectedItems Args: r_start (TYPE): Description r_end (TYPE): Description modifiers (TYPE): Description """ margin = styles.VIRTUALHELIXHANDLEITEM_RADIUS delta = (r_end - r_start) # r delta mid_height = (self.boundingRect().height()) / 2 - margin helix_height = self._HELIX_HEIGHT if abs(delta) < mid_height: # move is too short for reordering return if delta > 0: # moved down, delta is positive indexDelta = int((delta - mid_height) / helix_height) else: # moved up, delta is negative indexDelta = int((delta + mid_height) / helix_height) # sort on y to determine the extremes of the selection group items = sorted(self._item_group.childItems(), key=lambda vhhi: vhhi.y()) part_item = items[0].partItem() part_item.reorderHelices([item.idNum() for item in items], indexDelta) # part_item.reorderHelices(items[0].idNum(), # items[-1].idNum(), # indexDelta) part_item.updateStatusBar("") # end def def boxParent(self): """Summary Returns: TYPE: Description """ temp = self._item_group.childItems()[0].partItem() self.setParentItem(temp) return temp # end def def deleteSelection(self): """ Delete selection operates outside of the documents a virtual helices are not actually selected in the model """ vh_handle_items = self._item_group.childItems() u_s = self._item_group.document().undoStack() u_s.beginMacro("delete Virtual Helices") for vhhi in vh_handle_items: part = vhhi.part() part.removeVirtualHelix(vhhi.idNum()) u_s.endMacro() # end def def bounds(self): """Summary Returns: TYPE: Description """ return self._bounds # end def def delta(self, yf, y0): """Summary Args: yf (TYPE): Description y0 (TYPE): Description Returns: TYPE: Description """ return yf - y0 # end def def resetPosition(self): """Summary Returns: TYPE: Description """ self.setPos(self._pos0) # end def # end class class EndpointHandleSelectionBox(QGraphicsPathItem): """Summary """ _PEN_WIDTH = styles.SELECTIONBOX_PEN_WIDTH _BOX_PEN = getPenObj(styles.SELECTED_COLOR, _PEN_WIDTH) _BASE_WIDTH = styles.PATH_BASE_WIDTH def __init__(self, item_group: SelectionItemGroup): """The item_group.parentItem() is expected to be a partItem Args: item_group: Description """ super(EndpointHandleSelectionBox, self).__init__(item_group.parentItem()) self._item_group = item_group self._rect = item_group.boundingRect() self.hide() self.setPen(self._BOX_PEN) self.setZValue(styles.ZPATHSELECTION) self._bounds = (0, 0) self._pos0 = QPointF() # end def def getX(self, pos: QPointF) -> float: """ Args: pos: Description Returns: ``x`` position """ return pos.x() # end def def translateX(self, delta: float): """ Args: delta: Description """ children = self._item_group.childItems() if children: p_i = children[0].partItem() str = "+%d" % delta if delta >= 0 else "%d" % delta p_i.updateStatusBar(str) self.setX(self._BASE_WIDTH * delta) # end def def resetPosition(self): """ """ self.setPos(self._pos0) def delta(self, xf: float, x0: float) -> float: """ Args: xf: Description x0: Description Returns: change distance """ bound_l, bound_h = self._bounds delta = int(floor((xf - x0) / self._BASE_WIDTH)) if delta > 0 and delta > bound_h: delta = bound_h elif delta < 0 and abs(delta) > bound_l: delta = -bound_l return delta def refreshPath(self): """ """ temp_low, temp_high = self._item_group.viewroot.document().getSelectionBounds() self._bounds = (temp_low, temp_high) # logger.debug("rp:", self._bounds) self.prepareGeometryChange() self.setPath(self.painterPath()) self._pos0 = self.pos() # end def def painterPath(self) -> QPainterPath: """ Returns: :class:`QPainterPath` """ bw = self._BASE_WIDTH i_g = self._item_group # the childrenBoundingRect is necessary to get this to work rect_IG = i_g.childrenBoundingRect() rect = self.mapRectFromItem(i_g, rect_IG) if rect.width() < bw: rect.adjust(-bw / 4, 0, bw / 2, 0) path = QPainterPath() path.addRect(rect) self._item_group.setBoundingRect(rect_IG) # path.addRoundedRect(rect, radius, radius) # path.moveTo(rect.right(),\ # rect.center().y()) # path.lineTo(rect.right() + radius / 2,\ # rect.center().y()) return path # end def def processSelectedItems(self, r_start: float, r_end: float, modifiers): """ Args: r_start: Description r_end: Description modifiers (TYPE): Description """ delta = self.delta(r_end, r_start) # TODO reenable do_maximize????? # if modifiers & Qt.AltModifier: # do_maximize = True # else: # do_maximize = False self._item_group.viewroot.document().resizeSelection(delta) # end def def deleteSelection(self): """Summary Returns: TYPE: Description """ self._item_group.document().deleteStrandSelection() def boxParent(self) -> QGraphicsItem: """Get the parent :class:`ProxyParentItem` Returns: :class:`ProxyParentItem` """ temp = self._item_group.childItems()[0].partItem().proxy() self.setParentItem(temp) return temp # end def def bounds(self) -> Vec2T: """ Returns: the bounds """ return self._bounds # end def # end class

f7180a3e45377a91711d6c8fa67895d8d860641f

py

Python

mlprocessors/consolecapture.py

flatironinstitute/mountaintools

d5680599381e0810c4aa5b309b9ef9ec7f2d1b25

2019-11-07T14:09:02.000Z

2021-09-23T01:09:04.000Z

mountaintools/mlprocessors/consolecapture.py

flatironinstitute/spikeforest_old

d9470194dc906b949178b9c44d14aea57a1f6c27

2019-05-04T09:34:53.000Z

2019-06-23T07:05:58.000Z

mountaintools/mlprocessors/consolecapture.py

flatironinstitute/spikeforest_old

d9470194dc906b949178b9c44d14aea57a1f6c27

2021-09-23T01:07:21.000Z

2021-09-23T01:07:21.000Z

from typing import Any import sys import time import os import tempfile class Logger2(): def __init__(self, file1: Any, file2: Any): self.file1 = file1 self.file2 = file2 def write(self, data: str) -> None: self.file1.write(data) self.file2.write(data) def flush(self) -> None: self.file1.flush() self.file2.flush() class ConsoleCapture(): def __init__(self): self._console_out = '' self._tmp_fname = None self._file_handle = None self._time_start = None self._time_stop = None self._original_stdout = sys.stdout self._original_stderr = sys.stderr def start_capturing(self) -> None: self._tmp_fname = tempfile.mktemp(suffix='.txt') self._file_handle = open(self._tmp_fname, 'w') sys.stdout = Logger2(self._file_handle, self._original_stdout) sys.stderr = Logger2(self._file_handle, self._original_stderr) self._time_start = time.time() def stop_capturing(self) -> None: assert self._tmp_fname is not None self._time_stop = time.time() sys.stdout = self._original_stdout sys.stderr = self._original_stderr self._file_handle.close() with open(self._tmp_fname, 'r') as f: self._console_out = f.read() os.unlink(self._tmp_fname) def addToConsoleOut(self, txt: str) -> None: self._file_handle.write(txt) def runtimeInfo(self) -> dict: assert self._time_start is not None return dict( start_time=self._time_start - 0, end_time=self._time_stop - 0, elapsed_sec=self._time_stop - self._time_start ) def consoleOut(self) -> str: return self._console_out

f718393cecda836a590a6dc97b77a13ca4ce20f5

py

Python

cathpy/core/align.py

shouldsee/cathpy

5f7fa1322434b2d254f0158c5840f029b12dbafe

cathpy/core/align.py

shouldsee/cathpy

5f7fa1322434b2d254f0158c5840f029b12dbafe

cathpy/core/align.py

shouldsee/cathpy

5f7fa1322434b2d254f0158c5840f029b12dbafe

""" Manipulate protein sequences and alignments """ # core import io import gzip import logging import re import functools # pip import dendropy # local from cathpy.core import error as err from cathpy.core.tests import is_valid_domain_id from cathpy.core.models import AminoAcid, AminoAcids, Residue, Segment LOG = logging.getLogger(__name__) class Sequence(object): """Class to represent a protein sequence.""" re_gap_chars = r'[.\-]' has_warned_about_deprecated_sequence_headers = False def __init__(self, hdr: str, seq: str, *, meta=None, description=None): self._hdr = hdr self._seq = seq try: hdr_info = Sequence.split_hdr(hdr) except: raise err.GeneralError('caught error while parsing sequence header: '+hdr) self._id = hdr_info['id'] self.accession = hdr_info['accession'] self.description = description self.id_type = hdr_info['id_type'] self.id_ver = hdr_info['id_ver'] self.segs = hdr_info['segs'] self.meta = hdr_info['meta'] if meta: for key, val in meta.items(): self.meta[key] = val @property def uid(self): """Returns the unique id for this Sequence""" return self._id def set_uid(self, _id): """Sets the unique id of the current Sequence object""" self._id = _id @property def is_cath_domain(self): """Returns whether this Sequence is a CATH domain.""" return self.id_type == 'domain' def get_residues(self): """ Returns an array of Residue objects based on this sequence. Note: if segment information has been specified then this will be used to calculate the `seq_num` attribute. Raises: OutOfBoundsError: problem mapping segment info to sequence """ residues = [] segs = self.segs if not segs: segs = [Segment(1, len(self.seq_no_gaps))] current_seg_offset = 0 def next_seg(): nonlocal current_seg_offset if current_seg_offset < len(segs): seg = segs[current_seg_offset] current_seg_offset += 1 return seg else: return None # theoretical length according to segment info vs length according to sequence seg_length = 0 for seg in segs: seg_length += seg.stop - seg.start + 1 actual_length = len(self.seq_no_gaps) if seg_length != actual_length: # should this be a warning? (with 1-n numbering as fallback?) raise err.OutOfBoundsError( ('segment information {} suggests that the sequence ' 'length should be {}, but the sequence has {} (non-gap) characters: {}').format( repr(segs), seg_length, actual_length, self.seq)) current_seg = next_seg() seq_num = current_seg.start for offset, aa in enumerate(self.seq, 0): if current_seg and seq_num > current_seg.stop: current_seg = next_seg() if not current_seg: if not Sequence.is_gap(aa): raise err.OutOfBoundsError( ('unable to map segment ({}) to sequence: ' 'the final segment ends at {}, but the sequence has {} residues ' '(offset: {}, aa: {})').format( repr(current_seg), seq_num-1, len(self.seq_no_gaps), offset, aa )) else: seq_num = None else: seq_num = current_seg.start if Sequence.is_gap(aa): res = Residue(aa) else: res = Residue(aa, seq_num) seq_num += 1 residues.append(res) return residues def get_res_at_offset(self, offset): """Return the residue character at the given offset (includes gaps).""" try: res = self.seq[offset] except: raise err.SeqIOError(( "Error: failed to get residue at offset {} from sequence " "with length {}: '{}'").format(offset, self.length(), self.seq)) return res def get_res_at_seq_position(self, seq_pos): """Return the residue character at the given sequence position (ignores gaps).""" seq_nogap = re.sub(Sequence.re_gap_chars, '', self.seq) try: res = seq_nogap[seq_pos-1] except: raise err.SeqIOError(( "Error: failed to get residue at position {} from sequence with {} " "non-gap sequence positions: '{}'").format( seq_pos, len(seq_nogap), self.seq)) return res def get_seq_position_at_offset(self, offset): """Returns sequence position (ignoring gaps) of the given residue (may include gaps).""" seq_to_offset = self.seq[:offset+1] if re.match(seq_to_offset[-1], Sequence.re_gap_chars): raise err.GapError( "Cannot get sequence position at offset {} since this corresponds to a gap".format( offset)) seq_nogap = re.sub(Sequence.re_gap_chars, '', seq_to_offset) return len(seq_nogap) def get_offset_at_seq_position(self, seq_pos): """Return the offset (with gaps) of the given sequence position (ignores gaps).""" current_seq_pos = 0 for offset in range(len(self.seq)): if not re.match(Sequence.re_gap_chars, self.seq[offset]): current_seq_pos += 1 if current_seq_pos == seq_pos: return offset raise err.OutOfBoundsError("failed to find offset at sequence position {}".format(seq_pos)) def length(self): """Return the length of the sequence.""" return len(self.seq) @property def seq(self): """Return the amino acid sequence as a string.""" return self._seq @property def seq_no_gaps(self): """Return the amino acid sequence as a string (after removing all gaps).""" seq = re.sub(self.re_gap_chars, '', self._seq) return seq def set_sequence(self, seq): """Sets the AA residues for this Sequence.""" self._seq = seq def set_cluster_id(self, id_str): """Sets the cluster id for this Sequence.""" self.meta['CLUSTER_ID'] = id_str @property def cluster_id(self): """Returns the cluster id for this Sequence.""" return self.meta['CLUSTER_ID'] if 'CLUSTER_ID' in self.meta else None @classmethod def split_hdr(cls, hdr: str) -> dict: """ Splits a sequence header into meta information. Args: hdr (str): header string (eg `'domain|4_2_0|1cukA01/3-23_56-123'`) Returns: info (dict): header info :: { 'id': 'domain|4_2_0|1cukA01/3-23_56-123', 'accession': '1cukA01', 'id_type': 'domain', 'id_ver': '4_2_0', 'segs': [Segment(3, 23), Segment(56,123)], 'meta': {} } """ accession = None id_type = None id_ver = None segs = [] meta = {} if not hdr: raise err.ParamError('hdr seems to be empty') # split meta features (after whitespace) hdr_parts = hdr.split(maxsplit=1) id_with_segs_str = hdr_parts[0] meta_str = hdr_parts[1] if len(hdr_parts) > 1 else None # split id / segments id_with_segs_parts = id_with_segs_str.split('/', maxsplit=1) id_str = id_with_segs_parts[0] segs_str = id_with_segs_parts[1] if len(id_with_segs_parts) > 1 else None # split id into type, id, version id_parts = id_str.split('|') # 1cukA01/23-123 if len(id_parts) == 1: accession = id_parts[0] if is_valid_domain_id(accession): id_type = 'domain' # domain|1cukA01/23-123 if len(id_parts) == 2: id_type, accession = id_parts # cath|4_2_0|5lhzA00/886-963 # cath|current|5lhzA00/886-963 if len(id_parts) == 3: id_type, id_ver, accession = id_parts if is_valid_domain_id(id_ver): if not __class__.has_warned_about_deprecated_sequence_headers: LOG.warning( ("Warning: found an old sequence header with TYPE|ID|VERSION '%s'. " "Parsing this as TYPE|VERSION|ID for now, but this is a hack, and " "may be deprecated in future versions (fix structural cluster reps?)" "(ignoring all future occurrences in this runtime)"), id_parts) __class__.has_warned_about_deprecated_sequence_headers = True id_type, accession, id_ver = id_parts # segments if segs_str: for seg_str in segs_str.split('_'): (start, stop) = seg_str.split('-') seg = Segment(int(start), int(stop)) segs.append(seg) # features if meta_str: meta_parts = meta_str.split() for f in meta_parts.split('=', maxsplit=1): if len(f) == 2: meta[f[0]] = f[1] else: LOG.warning("failed to parse meta feature from string %s", meta_str) return({'accession': accession, 'id': id_with_segs_str, 'id_type': id_type, 'id_ver': id_ver, 'segs': segs, 'meta': meta}) def to_fasta(self, wrap_width=80): """Return a string for this Sequence in FASTA format.""" fasta_str = "" fasta_str += '>' + self.uid + '\n' if wrap_width: for line in Sequence._chunker(self.seq, wrap_width): fasta_str += line + '\n' else: fasta_str += self.seq + '\n' return fasta_str def to_pir(self, wrap_width=60, use_accession=False): """Return a string for this Sequence in PIR format.""" pir_str = "" pir_str += '>P1;{}\n'.format(self.uid if not use_accession else self.accession) desc = self.description or self.accession pir_str += desc + '\n' seq = self.seq + '*' if wrap_width: for line in Sequence._chunker(seq, wrap_width): pir_str += line + '\n' else: pir_str += seq + '\n' return pir_str def copy(self): """Provide a deep copy of this sequence.""" s = Sequence(self._hdr, self.seq, meta=self.meta) return s def insert_gap_at_offset(self, offset, gap_char="-"): """Insert a gap into the current sequence at a given offset.""" new_seq = self.seq[:offset] + gap_char + self.seq[offset:] self.set_sequence(new_seq) def set_gap_char_at_offset(self, offset, gap_char): """ Set the gap character at the given offset. If the residue at a given position is a gap, then override the gap char with the given character. """ residues = list(self.seq) if Sequence.is_gap(residues[offset]) and residues[offset] != gap_char: residues[offset] = gap_char self.set_sequence("".join(residues)) def lower_case_at_offset(self, start, stop=None): """Lower case the residues in the given sequence window.""" if stop is None: stop = start + 1 old_seq = self.seq new_seq = old_seq[:start] + old_seq[start:stop].lower() + old_seq[stop:] self.set_sequence(new_seq) def set_all_gap_chars(self, gap_char='-'): """Sets all gap characters.""" seqstr = re.sub(self.re_gap_chars, gap_char, self.seq) self.set_sequence(seqstr) def set_lower_case_to_gap(self, gap_char='-'): """Set all lower-case characters to gap.""" seqstr = re.sub(r'[a-z]', gap_char, self.seq) self.set_sequence(seqstr) def slice_seq(self, start, stop=None): """Return a slice of this sequence.""" return self.seq[start:stop] @staticmethod def _chunker(text_str, width): return (text_str[pos:pos + width] for pos in range(0, len(text_str), width)) @staticmethod def is_gap(res_char): """Test whether a character is considered a gap.""" return res_char in ['-', '.'] @property def accession_and_seginfo(self): """Returns accession and segment info for this Sequence.""" segs_str = self.seginfo if segs_str: return self.accession + '/' + segs_str else: return self.accession @property def seginfo(self): """Returns the segment info for this Sequence.""" segs_str = '_'.join(['-'.join([str(s.start), str(s.stop)]) for s in self.segs]) return segs_str def apply_segments(self, segs): """ Returns a subset of the current sequence, chopped by the segments. Args: segs ([]): [Segment] or [[START, STOP], ...] Returns: seq (:class:`Sequence`): sequence object """ if self.segs: raise Exception("cannot apply segments as Sequence already has segments defined") seq = self.seq acc = self.accession startstops = [(seg[0], seg[1]) for seg in segs] seq_range = '_'.join(['{}-{}'.format(ss[0],ss[1]) for ss in startstops]) seq_parts = [seq[ss[0]-1:ss[1]] for ss in startstops] subseq = Sequence(hdr="{}/{}".format(acc, seq_range), seq="".join(seq_parts)) return subseq def __str__(self): """Represents this Sequence as a string.""" return '{:<30} {}'.format(self.uid, self.seq) def __len__(self): return len(self.seq) class Correspondence(object): """ Provides a mapping between ATOM and SEQRES residues. A correspondence is a type of alignment that provides the equivalences between the residues in the protein sequence (eg ``SEQRES`` records) and the residues actually observed in the structure (eg ``ATOM`` records). Within CATH, this is most commonly initialised from a GCF file: :: aln = Correspondence.from_gcf('/path/to/<uid>.gcf') TODO: allow this to be created from PDBe API endpoint. """ GCF_GAP_CHAR = '*' FASTA_GAP_CHAR = '-' def __init__(self, uid=None, *, hdr=None, residues=None,): """Create a new Correspondence object.""" self._uid = uid self._hdr = hdr self.residues = residues if residues else [] super().__init__() @property def uid(self): """Returns the unique id of the current Correspondence object.""" return self._uid @classmethod def from_gcf(cls, gcf_io): """Create a new Correspondence object from a GCF io / filename / string. This provides a correspondence between SEQRES and ATOM records for a given protein structure. Example format: :: >gi|void|ref1 A 1 5 A K 2 6 K G 3 7 G H 4 8 H P 5 9 P G 6 10 G P 7 10A P K 8 10B K A 9 11 A P 10 * * G 11 * * ... """ if isinstance(gcf_io, str): if gcf_io[0] == '>': gcf_io = io.StringIO(gcf_io) else: gcf_io = open(gcf_io) try: hdr = gcf_io.readline().strip() hdr = hdr[1:] # remove '>' uid = hdr.split('|')[-1] except AttributeError: # make a potentially confusing error slightly less so raise err.SeqIOError( "encountered an error trying to readline() on GCF io ({})".format(gcf_io)) line_no = 1 residues = [] for line in gcf_io: line_no += 1 try: seqres_aa, seqres_num, pdb_label, pdb_aa = line.split() if pdb_aa is not seqres_aa and pdb_aa is not Correspondence.GCF_GAP_CHAR: LOG.warning("pdb_aa '%s' does not match seqres_aa '%s' (line: %s)", pdb_aa, seqres_aa, line_no) except: raise err.SeqIOError("Error: failed to parse GCF '{}' ({}:{})".format( line, str(gcf_io), line_no)) if pdb_label is Correspondence.GCF_GAP_CHAR: pdb_label = None pdb_aa = None res = Residue(seqres_aa, int(seqres_num), pdb_label, pdb_aa=pdb_aa) residues.extend([res]) gcf_io.close() corr = Correspondence(uid=uid, hdr=hdr, residues=residues) return corr @property def seqres_length(self) -> int: """Returns the number of `SEQRES` residues""" return len(self.residues) @property def atom_length(self) -> int: """Returns the number of `ATOM` residues""" atom_residues = [res for res in self.residues if res.pdb_label is not None] return len(atom_residues) def get_res_at_offset(self, offset: int) -> Residue: """Returns the :class:`Residue` at the given offset (zero-based)""" return self.residues[offset] def get_res_by_seq_num(self, seq_num: int) -> Residue: """Return the :class:`Residue` with the given sequence number""" res = next((res for res in self.residues if res.seq_num == seq_num), None) return res def get_res_by_pdb_label(self, pdb_label: str) -> Residue: """Returns the :class:`Residue` that matches `pdb_label`""" res = next((res for res in self.residues if res.pdb_label == pdb_label), None) return res def get_res_by_atom_pos(self, pos: int) -> Residue: """Returns Residue corresponding to position in the ATOM sequence (ignores gaps).""" assert isinstance(pos, int) assert pos >= 1 atom_residues = [res for res in self.residues if res.pdb_label is not None] res = atom_residues[pos-1] return res def get_res_offset_by_atom_pos(self, pos: int) -> Residue: """ Returns offset of Residue at given position in the ATOM sequence (ignoring gaps). Raises: :class:`cathpy.error.OutOfBoundsError` """ assert isinstance(pos, int) assert pos >= 1 atom_pos = 0 for offset, res in enumerate(self.residues): if res.pdb_label is not None: atom_pos += 1 # LOG.debug("pos({}) -> res: offset: {}, res: {}, atom_pos: {}".format( # pos, offset, repr(res), atom_pos)) if atom_pos == pos: return offset atom_residues = [res for res in self.residues if res.pdb_label is not None] raise err.OutOfBoundsError( "failed to find residue in atom pos {}, last atom residue is {} (position {})".format( pos, repr(atom_residues[-1]), atom_pos)) @property def first_residue(self) -> Residue: """Returns the first residue in the correspondence.""" return self.get_res_at_offset(0) @property def last_residue(self) -> Residue: """Returns the last residue in the correspondence.""" return self.get_res_at_offset(-1) @property def atom_sequence(self) -> Sequence: """Returns a Sequence corresponding to the ATOM records.""" _id = "atom|{}".format(self.uid) res = [res.pdb_aa if res.pdb_label else Correspondence.FASTA_GAP_CHAR for res in self.residues] return Sequence(_id, "".join(res)) @property def seqres_sequence(self) -> Sequence: """Returns a Sequence corresponding to the SEQRES records.""" _id = "seqres|{}".format(self.uid) res = [res.aa for res in self.residues] return Sequence(_id, "".join(res)) def apply_seqres_segments(self, segs): """Returns a new correspondence from just the residues within the segments.""" current_seg_offset = 0 def next_seg(): nonlocal current_seg_offset # LOG.debug("apply_seqres_segments.next_seg: current={} segs={}".format( # current_seg_offset, repr(segs) )) if current_seg_offset < len(segs): seg = segs[current_seg_offset] current_seg_offset += 1 return seg current_seg = next_seg() selected_residues = [] for res in self.residues: # LOG.debug('apply_seqres.res: [{}] {}-{} seq_num={}'.format( # current_seg_offset, current_seg.start, current_seg.stop, # res.seq_num)) if res.seq_num >= current_seg.start and res.seq_num <= current_seg.stop: selected_residues.append(res) elif res.seq_num < current_seg.start: pass elif res.seq_num > current_seg.stop: current_seg = next_seg() if not current_seg: break else: raise err.SeqIOError("unexpected error - shouldn't be able to reach this code") corr = __class__(uid=self.uid, hdr=self._hdr, residues=selected_residues) return corr def to_gcf(self) -> str: """Renders the current object as a GCF string. Example format: :: >gi|void|ref1 A 1 5 A K 2 6 K G 3 7 G H 4 8 H P 5 9 P G 6 10 G P 7 10A P K 8 10B K A 9 11 A P 10 * * G 11 * * ... """ hdr = self._hdr if self._hdr else self.uid gcf_str = '>' + hdr + '\n' for res in self.residues: if res.pdb_label: pdb_label = '{}{}'.format(res.pdb_residue_num, res.pdb_insert_code if res.pdb_insert_code else ' ') vals = [res.aa, res.seq_num, pdb_label, res.pdb_aa] else: vals = [res.aa, res.seq_num, '* ', '*'] gcf_str += '{} {:>3} {:>4} {}\n'.format(*vals) return gcf_str def to_sequences(self) -> [Sequence]: """Returns the Correspondence as a list of `Sequence` objects""" seqs = (self.seqres_sequence, self.atom_sequence) return seqs def to_fasta(self, **kwargs) -> str: """Returns the Correspondence as a string (FASTA format).""" seqs = self.to_sequences() return seqs[0].to_fasta(**kwargs) + seqs[1].to_fasta(**kwargs) def to_aln(self): """Returns the Correspondence as an Align object.""" seqs = self.to_sequences() return Align(seqs=seqs) def __str__(self): return self.to_fasta() def __repr__(self): return self.to_fasta() class AlignMetaSummary(object): def __init__(self, *, seq_count, ec_term_counts=None, go_term_counts=None, cath_domain_count=0, dops_score=None, organism_newick=None): self.seq_count = seq_count self.ec_term_counts = ec_term_counts self.go_term_counts = go_term_counts self.cath_domain_count = cath_domain_count self.dops_score = dops_score self.organism_newick = organism_newick class Align(object): """ Object representing a protein sequence alignment. The only required field is `sequences`, otherwise all fields are optional and are mainly here to satisfy the named fields in `STOCKHOLM` alignment format. Args: seqs ([:class:`Sequence`]): aligned sequences (required) uid (str): unique identifier for this alignment accession (str): accession for this alignment author (str): person responsible for creating this alignment cath_version (str | :class:`CathVersion`): CATH version dops_score (float): sequence diversity score (0 low, 100 high) description (str): description to associate with this alignment aln_type (str): type of alignment (eg cluster type) min_bitscore (float): minimum bitscore for sequences in this alignment tree_nhx (str): store the tree (NHX format) tree_id (str): identifier of the tree """ REF_GAP_CHAR = '-' MERGE_GAP_CHAR = '.' STO_META_TO_ATTR = [ # required ('ID', '_uid'), ('AC', 'accession'), ('DE', 'description'), ('AU', 'author'), ('SE', 'meta.source_seed'), ('SS', 'meta.source_structure'), ('BM', 'meta.build_method'), ('SM', 'meta.search_method'), ('GA', 'meta.gathering_threshold'), ('TC', 'meta.trusted_cutoff'), ('NC', 'meta.noise_cutoff'), ('AC', 'accession'), ('TP', 'aln_type'), ('TC', 'min_bitscore'), ('SQ', None), # optional ('DC', 'meta.db_comment'), ('DR', { 'CATH': 'cath_version', 'DOPS': 'dops_score', 'INTERPRO': 'interpro', }), ('RC', 'meta.ref_comment'), ('RN', 'meta.ref_number'), ('RM', 'meta.ref_medline'), ('RT', 'meta.ref_title'), ('RA', 'meta.ref_author'), ('RL', 'meta.ref_location'), ('PI', 'meta.prev_id'), ('KW', 'meta.keywords'), ('CC', 'meta.comment'), ('NE', 'meta.pfam_accession'), ('NL', 'meta.seq_location'), ('WK', 'meta.wikipedia_link'), ('CL', 'meta.pfam_clan'), ('MB', 'meta.pfam_clan_membership'), # trees ('NH', 'tree_nhx'), ('TN', 'tree_id'), ] def __init__(self, seqs=None, *, uid=None, accession=None, author=None, cath_version=None, dops_score=None, description=None, aln_type=None, min_bitscore=None, tree_nhx=None, tree_id=None): self.meta = {} # per file meta data self.seq_meta = {} # consensus sequence-based meta data self.__seq_ids = set() self._uid = uid self.accession = accession self.author = author self.description = description self.cath_version = cath_version self.dops_score = dops_score self.accession = accession self.aln_type = aln_type self.min_bitscore = min_bitscore self.tree_nhx = tree_nhx self.tree_id = tree_id self.seqs = seqs if seqs else [] self.__aln_positions = 0 self._merge_counter = 0 @property def uid(self): """Returns the id of this Align object.""" return self._uid def set_uid(self, uid): """Sets the id of this Align object.""" self._uid = uid def _next_merge_id(self): self._merge_counter += 1 return self._merge_counter @property def sequences(self): """Provides access to the Sequence objects in the alignment.""" return self.seqs @property def aln_positions(self): """Returns the number of alignment positions.""" return self.__aln_positions @aln_positions.setter def aln_positions(self, value): self.__aln_positions = value @property def count_sequences(self): """Returns the number of sequences in the alignment.""" return len(self.seqs) @property def total_gap_positions(self): """Returns the total number of gaps in the alignment.""" total_gaps = 0 for s in self.seqs: total_gaps += s.seq.count(self.REF_GAP_CHAR) total_gaps += s.seq.count(self.MERGE_GAP_CHAR) return total_gaps @property def total_positions(self): """Returns the total number of positions in the alignment.""" return self.count_sequences * self.aln_positions def find_first_seq_by_accession(self, acc): """Returns the first Sequence with the given accession.""" seqs_with_acc = [seq for seq in self.seqs if seq.accession == acc] return seqs_with_acc[0] def find_seq_by_id(self, _id): """Returns the Sequence corresponding to the provided id.""" seqs_with_id = [seq for seq in self.seqs if seq.uid == _id] if len(seqs_with_id) > 1: raise err.SeqIOError("Found more than one ({}) sequence matching id '{}'".format( len(seqs_with_id), _id)) if not seqs_with_id: # ie empty list raise err.NoMatchesError('failed to find sequence with id {} in alignment'.format(_id)) return seqs_with_id[0] def find_seq_by_accession(self, acc): """Returns the Sequence corresponding to the provided id.""" seqs_with_acc = [seq for seq in self.seqs if seq.accession == acc] if len(seqs_with_acc) > 1: raise err.TooManyMatchesError( "Found more than one ({}) sequence matching accession '{}'".format( len(seqs_with_acc), acc),) if len(seqs_with_acc) == 0: raise err.NoMatchesError( 'failed to find sequence with accession {} in alignment'.format(acc)) return seqs_with_acc[0] def get_seq_at_offset(self, offset): """Returns the Sequence at the given offset (zero-based).""" return self.seqs[offset] @classmethod def from_fasta(cls, fasta_io): """Initialises an alignment object from a FASTA file / string / io""" aln = Align() aln.read_sequences_from_fasta(fasta_io) return aln @classmethod def from_pir(cls, pir_io): """Initialises an alignment object from a PIR file / string / io""" aln = Align() aln.read_sequences_from_pir(pir_io) return aln @staticmethod def _get_io_from_file_or_string(file_or_string): filename = str(file_or_string) if isinstance(file_or_string, str): filename = '<string>' if file_or_string[0] in ('>', '#'): # fasta or stockholm _io = io.StringIO(file_or_string) elif file_or_string.endswith('.gz'): _io = gzip.open(file_or_string, 'rt') else: _io = open(file_or_string, 'rt') elif isinstance(file_or_string, io.IOBase): _io = file_or_string else: _io = file_or_string LOG.warning("unexpected io type: %s", repr(file_or_string)) return _io, filename @classmethod def from_stockholm(cls, sto_io, *, nowarnings=False): """Initialises an alignment object from a STOCKHOLM file / string / io""" sto_io, sto_filename = cls._get_io_from_file_or_string(sto_io) aln = cls() sto_header = sto_io.readline() assert sto_header.startswith('# STOCKHOLM 1.0') aln_meta = {} aln_seq_meta = {} seq_meta_by_id = {} seq_aa_by_id = {} aln_meta_unrecognised_features = {} gc_meta_to_attr = {meta: attr for (meta, attr) in cls.STO_META_TO_ATTR} line_count = 0 for line in sto_io: line_count += 1 line = line.strip() if line.startswith('#=GF'): try: _, feature, per_file_ann = line.split(None, 2) except ValueError: if not nowarnings: LOG.warning('ignoring GF record with incorrect columns (%s:%s "%s")', sto_filename, line_count, line) except: raise err.ParseError('failed to parse line {} "{}"'.format( line_count, line)) if feature not in gc_meta_to_attr: raise err.ParseError( 'encountered unexpected GF tag {} in line {} "{}" (known tags: {})'.format( feature, line_count, line, repr(gc_meta_to_attr))) attr = gc_meta_to_attr[feature] if type(attr) is dict: key, val = re.compile(r'[;:]\s+').split(per_file_ann, maxsplit=1) per_file_ann = val if key in attr: attr = attr[key] else: LOG.warning('encountered unexpected GF tag %s->%s in line %s "%s" (known tags: %s)', feature, key, line_count, line, repr(attr)) if feature not in aln_meta_unrecognised_features: aln_meta_unrecognised_features[feature] = [] aln_meta_unrecognised_features[feature].extend([per_file_ann]) attr = None if attr: if attr.startswith('meta.'): attr = attr[len('meta.'):] aln_meta[attr] = per_file_ann else: LOG.debug('setting aln attr "%s" to "%s"', attr, per_file_ann) setattr(aln, attr, per_file_ann) elif line.startswith('#=GC'): try: _, feature, per_col_ann = line.split(None, 2) aln_seq_meta[feature] = per_col_ann except ValueError: if not nowarnings: LOG.warning('ignoring GC record with incorrect columns (%s:%s "%s")', sto_filename, line_count, line) except: raise err.ParseError('failed to parse line {} "{}"'.format( line_count, line)) elif line.startswith('#=GS'): try: _, seq_id, feature, per_seq_ann = line.split(None, 3) if feature == 'DR': dr_type, per_seq_ann = per_seq_ann.split(None, 1) dr_type = dr_type.rstrip(';') feature = feature + '_' + dr_type if seq_id not in seq_meta_by_id: seq_meta_by_id[seq_id] = {} seq_meta_by_id[seq_id][feature] = per_seq_ann except ValueError: if not nowarnings: LOG.warning('ignoring GS record with incorrect columns (%s:%s "%s")', sto_filename, line_count, line) except: raise err.ParseError('failed to parse line {} "{}"'.format( line_count, line)) elif line.startswith('#=GR'): _, seq_id, feature, per_res_ann = line.split(None, 3) seq_meta_by_id[seq_id][feature] = per_res_ann elif line.startswith('//'): pass else: seq_id, seq_aa = line.split() if seq_id not in seq_aa_by_id: seq_aa_by_id[seq_id] = '' seq_aa_by_id[seq_id] += seq_aa for seq_id, seq_aa in seq_aa_by_id.items(): seq_meta = seq_meta_by_id[seq_id] if seq_id in seq_meta_by_id else {} seq = Sequence(seq_id, seq_aa, meta=seq_meta) aln.add_sequence(seq) for key, val in aln_meta.items(): aln.meta[key] = val for key, val in aln_seq_meta.items(): aln.seq_meta[key] = val sto_io.close() return aln def read_sequences_from_fasta(self, fasta_io): """Parses aligned sequences from FASTA (str, file, io) and adds them to the current Align object. Returns the number of sequences that are added.""" fasta_io, fasta_filename = __class__._get_io_from_file_or_string(fasta_io) re_seqstr = re.compile(r'^[a-zA-Z.\-]+$') seq_added = 0 current_hdr = None current_seq = '' line_count = 0 for line in fasta_io: line_count += 1 line = line.rstrip() if line == "": break if line[0] == '>': if current_seq: seq = Sequence(current_hdr, current_seq) self.add_sequence(seq) current_seq = '' seq_added += 1 current_hdr = line[1:] else: if not re_seqstr.match(line): raise err.SeqIOError( ('encountered an error parsing FASTA: ' 'string "{}" does not look like a sequence ({}:{})').format( line, fasta_filename, line_count)) if not current_hdr: raise err.SeqIOError( ('encountered an error parsing FASTA: ' 'found sequence "{}" without a header ({}:{})').format( line, fasta_filename, line_count)) current_seq += str(line) fasta_io.close() if current_seq: seq = Sequence(current_hdr, current_seq) self.add_sequence(seq) seq_added += 1 return seq_added def read_sequences_from_pir(self, pir_io): """Parse aligned sequences from PIR (str, file, io) and adds them to the current Align object. Returns the number of sequences that are added.""" pir_io, pir_filename = __class__._get_io_from_file_or_string(pir_io) re_seqstr = re.compile(r'^[a-zA-Z.\-]+\*?$') seq_added = 0 current_hdr = None current_desc = None current_seq = '' line_count = 0 for line in pir_io: line_count += 1 line = line.rstrip() if line == "": continue if line[0] == '>': # following line is description as free text if current_seq: current_seq = current_seq.replace("*", "") seq = Sequence(current_hdr, current_seq, description=current_desc) self.add_sequence(seq) current_seq = '' seq_added += 1 seq_type, current_hdr = line[1:].split(';') line = next(pir_io).rstrip() current_desc = line else: if not re_seqstr.match(line): raise err.SeqIOError( ('encountered an error parsing PIR: ' 'string "{}" does not look like a sequence ({}:{})').format( line, pir_filename, line_count)) if not current_hdr: raise err.SeqIOError( ('encountered an error parsing PIR: ' 'found sequence "{}" without a header ({}:{})').format( line, pir_filename, line_count)) current_seq += str(line) pir_io.close() if current_seq: current_seq = current_seq.replace("*", "") seq = Sequence(current_hdr, current_seq, description=current_desc) self.add_sequence(seq) seq_added += 1 return seq_added def _reindex_seq_ids(self): self.__seq_ids = set() for seq in self.seqs: self.__seq_ids.add(seq.uid) def add_sequence(self, seq:Sequence, *, offset:int=None): """ Add a sequence to this alignment. Args: offset (int): the index in the list where the sequence should be added (default: append) """ if not offset: offset = len(self.sequences) if seq.uid in self.__seq_ids: raise err.SeqIOError(( "Error: cannot add a sequence with id {}, " "since this alignment already has a sequence with that id. [{}]").format( seq.uid, ",".join(self.__seq_ids))) if self.aln_positions: if self.aln_positions != seq.length(): raise err.SeqIOError(( "Error: cannot add a sequence (id:{}) " "with {} positions to an alignment with {} positions.").format( seq.uid, seq.length(), self.aln_positions)) else: self.__aln_positions = seq.length() self.seqs.insert(offset, seq) self.__seq_ids.add(seq.uid) return seq def subset(self, ids, *, collapse_gaps=True): """ Returns a subset of the alignment containing just the sequence ids """ seqs = [self.find_seq_by_id(i) for i in ids] new_align = Align(seqs=seqs) if collapse_gaps: new_align = new_align.remove_alignment_gaps() return new_align def remove_sequence_by_id(self, seq_id: str): """Removes a sequence from the alignment.""" for idx, seq in enumerate(self.seqs): if seq.uid == seq_id: LOG.info("Removing sequence with '{}' from alignment".format(seq_id)) del self.seqs[idx] return seq raise err.NoMatchesError('failed to find sequence with id {}'.format(seq_id)) def remove_alignment_gaps(self): """Return a new alignment after removing alignment positions that contain a gap for all sequences.""" seqs = self.seqs seq_length = seqs[0].length() new_seq_strings = ["" for s in range(len(seqs))] for aln_offset in range(seq_length): total_gaps = 0 for seq in seqs: if seq.seq[aln_offset] == '-' or seq.seq[aln_offset] == '.': total_gaps += 1 if total_gaps < len(seqs): for seq_pos in range(len(seqs)): res = seqs[seq_pos].seq[aln_offset] # print( "seq[{}:{}] pos:{} res:{}".format( # aln_offset, seqs[seq_pos].uid, seq_pos, res) ) new_seq_strings[seq_pos] += res else: LOG.debug("Removing complete gap from alignment offset: %s", aln_offset) new_aln = Align() for seq_pos in range(len(new_seq_strings)): hdr = seqs[seq_pos]._hdr seq_str = new_seq_strings[seq_pos] seq = Sequence(hdr, seq_str) new_aln.add_sequence(seq) return new_aln def insert_gap_at_offset(self, offset, gap_char='-'): """Insert a gap char at the given offset (zero-based).""" self.__aln_positions += 1 for s in self.seqs: s.insert_gap_at_offset(offset, gap_char) def set_gap_char_at_offset(self, offset, gap_char): """Override the gap char for all sequences at a given offset.""" for s in self.seqs: s.set_gap_char_at_offset(offset, gap_char) def lower_case_at_offset(self, start, stop=None): """Lower case all the residues in the given alignment window.""" for s in self.seqs: s.lower_case_at_offset(start, stop) def slice_seqs(self, start, stop=None): """Return an array of Sequence objects from start to end.""" return [Sequence(s._hdr, s.slice_seq(start, stop)) for s in self.seqs] def merge_alignment(self, merge_aln, ref_seq_acc: str, ref_correspondence: Correspondence = None, *, cluster_label=None, merge_ref_id=False, self_ref_id=False): """ Merges aligned sequences into the current object via a reference sequence. Sequences in ``merge_aln`` are brought into the current alignment using the equivalences identified in reference sequence ``ref_seq_acc`` (which must exist in both the ``self`` and ``merge_aln``). This function was originally written to merge FunFam alignments according to structural equivalences identified by CORA (a multiple structural alignment tool). Moving between structure and sequence provides the added complication that sequences in the structural alignment (CORA) are based on ATOM records, whereas sequences in the merge alignment (FunFams) are based on SEQRES records. The ``ref_correspondence`` argument allows this mapping to be taken into account. Args: merge_aln (Align): An Align containing the reference sequence and any additional sequences to merge. ref_seq_acc (str): The accession that will be used to find the reference sequence in the current alignment and merge_aln ref_correspondence (Correspondence): An optional Correspondence object that provides a mapping between the reference sequence found in ``self`` (ATOM records) and reference sequence as it appears in ``merge_aln`` (SEQRES records). cluster_label (str): Provide a label to differentiate the sequences being merged (eg for groupsim calculations). A default label is provided if this is ``None``. self_ref_id (str): Specify the id to use when adding the ref sequence from the current alignment. merge_ref_id (str): Specify the id to use when adding the ref sequence from the merge alignment. By default this sequence is only inluded in the final alignment (as ``<id>_merge``) if a custom correspondence is provided. Returns: [Sequence]: Array of Sequences added to the current alignment. Raises: MergeCorrespondenceError: problem mapping reference sequence between alignment and correspondence """ merge_aln = merge_aln.copy() if not cluster_label: cluster_label = self._next_merge_id() for seq in merge_aln.seqs: seq.set_cluster_id(cluster_label) ref_seq_in_ref = self.find_seq_by_accession(ref_seq_acc) ref_seq_in_ref.set_cluster_id(cluster_label) ref_seq_in_merge = merge_aln.find_seq_by_accession(ref_seq_acc) if self_ref_id: ref_seq_in_ref.set_uid(self_ref_id) # if the merge_ref_id has been specified, or there is not a 1:1 correspondence # between reference sequence in the alignments, then the merged ref sequence # will be included in the final alignment. Otherwise it will be removed. if merge_ref_id: ref_seq_in_merge.set_uid(merge_ref_id) else: ref_seq_in_merge.accession += '_merge' ref_id = ref_seq_in_merge.accession_and_seginfo ref_seq_in_merge.set_uid(ref_id) del ref_id if ref_seq_in_ref.uid is ref_seq_in_merge.uid: raise err.DuplicateSequenceError(( 'sequence in ref alignment [{}] cannot have the same id as ' 'sequence in merge alignment [{}] (consider specifying self_ref_id' 'or merge_ref_id)').format(ref_seq_in_ref.uid, ref_seq_in_merge.uid)) self._reindex_seq_ids() if ref_correspondence or merge_ref_id: merge_id_to_remove = None else: merge_id_to_remove = ref_seq_in_merge.uid if ref_correspondence is None: # fake a 1:1 correspondence for internal use # ignore any residue that does not have a seq_num (ie gap) residues = [res for res in ref_seq_in_ref.get_residues() if res.seq_num] for r in residues: r.set_pdb_label(str(r.seq_num)) # LOG.debug("fake correspondence: residue={}".format(repr(r))) ref_correspondence = Correspondence(ref_seq_acc, residues=residues) # check: ref sequence (in self) must match the ATOM sequence in Correspondence ref_no_gaps = ref_seq_in_ref.seq_no_gaps corr_no_gaps = ref_correspondence.atom_sequence.seq_no_gaps if ref_no_gaps != corr_no_gaps: raise err.MergeCorrespondenceError( seq_id=ref_seq_acc, aln_type='current', seq_type='ATOM', ref_no_gaps=ref_no_gaps, corr_no_gaps=corr_no_gaps) # check: ref sequence (in merge) must match the SEQRES sequence in Correspondence ref_no_gaps = ref_seq_in_merge.seq_no_gaps corr_no_gaps = ref_correspondence.seqres_sequence.seq_no_gaps if ref_no_gaps != corr_no_gaps: raise err.MergeCorrespondenceError( seq_id=ref_seq_acc, aln_type='merge', seq_type='SEQRES', ref_no_gaps=ref_no_gaps, corr_no_gaps=corr_no_gaps) # clean up del ref_no_gaps del corr_no_gaps ref_aln_pos = 0 ref_corr_pos = 0 merge_aln_pos = 0 correspondence_length = ref_correspondence.seqres_length LOG.debug("ref_alignment.positions: {}".format(self.aln_positions)) LOG.debug("merge_alignment.positions: {}".format(merge_aln.aln_positions)) LOG.debug("ref_seq_in_ref: {}".format(str(ref_seq_in_ref))) LOG.debug("ref_seq_in_merge: {}".format(str(ref_seq_in_merge))) while True: if merge_aln_pos >= merge_aln.aln_positions \ and ref_aln_pos >= self.aln_positions \ and ref_corr_pos >= correspondence_length: break LOG.debug("REF %s/%s; CORRESPONDENCE %s/%s; MERGE %s/%s", ref_aln_pos, self.aln_positions, ref_corr_pos, correspondence_length, merge_aln_pos, merge_aln.aln_positions) # sort the gaps in the reference alignment if ref_aln_pos < self.aln_positions: for seq in self.slice_seqs(0, ref_aln_pos): LOG.debug( "{:<10} {}".format("REF", str(seq)) ) ref_res_in_ref = ref_seq_in_ref.get_res_at_offset(ref_aln_pos) LOG.debug("REF_POSITION {:>3} of {:>3} => '{}'".format( ref_aln_pos, self.aln_positions, ref_res_in_ref)) # insert all the gaps in the reference alignment into the merge sequences # keep doing this until we don't have any more gaps if Sequence.is_gap(ref_res_in_ref): LOG.debug(("GAP '{}' in ref sequence in REF alignment [{}], " "inserting gap '{}' at position [{}] in all merge sequences").format( ref_res_in_ref, ref_aln_pos, ref_res_in_ref, merge_aln_pos)) merge_aln.insert_gap_at_offset(merge_aln_pos, gap_char=ref_res_in_ref) # this is a gap: do NOT increment ref_corr_pos ref_aln_pos += 1 merge_aln_pos += 1 continue # sort the gaps in the merge alignment if merge_aln_pos < merge_aln.aln_positions: # for seq in merge_aln.slice_seqs(0, merge_aln_pos): # LOG.debug( "{:<10} {}".format("MERGE", str(seq)) ) ref_res_in_merge = ref_seq_in_merge.get_res_at_offset(merge_aln_pos) LOG.debug("MERGE_POSITION {:>3} of {:>3} => '{}'".format( ref_aln_pos, self.aln_positions, ref_res_in_ref)) # insert all the gaps in the merge alignment into the ref sequences # keep doing this until we don't have any more gaps if Sequence.is_gap(ref_res_in_merge): LOG.debug(("GAP '{}' in ref sequence in MERGE alignment [{}], " "inserting gap '{}' at position [{}] in all ref sequences").format( ref_res_in_merge, merge_aln_pos, Align.MERGE_GAP_CHAR, merge_aln_pos)) self.insert_gap_at_offset(ref_aln_pos, gap_char=Align.MERGE_GAP_CHAR) merge_aln.lower_case_at_offset(merge_aln_pos) merge_aln.set_gap_char_at_offset(merge_aln_pos, '.') #ref_corr_pos += 1 ref_aln_pos += 1 merge_aln_pos += 1 continue # if there are gaps in the correspondence then we add gaps to the ref sequence here if ref_corr_pos < correspondence_length: for seq in ref_correspondence.to_sequences(): seq = seq.slice_seq(0, ref_corr_pos) LOG.debug( "{:<10} {}".format("CORR", str(seq)) ) ref_res_in_corr = ref_correspondence.get_res_at_offset(ref_corr_pos) if ref_res_in_corr.pdb_label is None: LOG.debug(("GAP '{}' in ATOM records of correspondence [{}], " "inserting gap '{}' at position [{}] in ref sequences").format( '*', ref_corr_pos, Align.MERGE_GAP_CHAR, ref_aln_pos)) #merge_aln.insert_gap_at_offset(merge_aln_pos, gap_char=Align.MERGE_GAP_CHAR) self.insert_gap_at_offset(ref_aln_pos, gap_char=Align.MERGE_GAP_CHAR) merge_aln.lower_case_at_offset(merge_aln_pos) merge_aln.set_gap_char_at_offset(merge_aln_pos, '.') # IMPORTANT: do not increment merge_aln_pos ref_corr_pos += 1 ref_aln_pos += 1 merge_aln_pos += 1 continue ref_corr_pos += 1 ref_aln_pos += 1 merge_aln_pos += 1 LOG.info("FINISHED MERGE") # for seq in ref_correspondence.to_sequences(): # seq = seq.slice_seq(0, ref_corr_pos) # LOG.debug( "{:<10} {}".format("CORR", str(seq)) ) # for seq in self.seqs: # LOG.debug( "{:<10} {}".format("REF", str(seq)) ) # for seq in merge_aln.seqs: # LOG.debug( "{:<10} {}".format("MERGE", str(seq)) ) # add the merged sequences into this alignment for seq in merge_aln.seqs: self.add_sequence(seq) # for seq in self.seqs: # LOG.debug( "{:<10} {}".format("MERGED", str(seq)) ) # test the final, merged alignment # 1. get sequences that correspond to the input aln # 2. remove alignment positions where there's a gap in the reference sequence LOG.debug("Checking merge results for %s (%s) ...", ref_seq_acc, repr(ref_seq_in_merge._hdr)) for original_seq in merge_aln.seqs: # searching by accession is necessary for CATH domains (since the headers # in the structure-based alignment do not have segment information), # however uniprot accessions can appear multiple times so we need to use # the full id if original_seq.is_cath_domain: seq = self.find_seq_by_accession(original_seq.accession) else: seq = self.find_seq_by_id(original_seq.uid) # LOG.debug('Working on sequence: {}'.format(str(original_seq))) # this provides the residues in the merge alignment with seqres numbering ref_merge_residues = ref_seq_in_merge.get_residues() # the lookup lets us go from the seq numbering to the sequence offset ref_merge_seqnum_to_seqpos = {} for seq_pos, res in enumerate([res for res in ref_merge_residues if res.seq_num], 1): ref_merge_seqnum_to_seqpos[res.seq_num] = seq_pos if not seq: raise err.SeqIOError("failed to find sequence with id '{}' in merge aln".format(seq.uid)) for aln_offset in range(self.aln_positions): ref_res = ref_seq_in_ref.get_res_at_offset(aln_offset) merged_res_at_aln_offset = seq.get_res_at_offset(aln_offset) if ref_res == self.MERGE_GAP_CHAR: # everything else should be a '.' or a lowercase residue assert merged_res_at_aln_offset == '.' or re.match(r'[a-z]', merged_res_at_aln_offset) elif ref_res == self.REF_GAP_CHAR: # everything else should be a '-' or an uppercase residue assert merged_res_at_aln_offset == '-' or re.match(r'[A-Z]', merged_res_at_aln_offset) else: # find the sequence offset of this aln position in the ref sequence ref_seq_pos_in_ref = ref_seq_in_ref.get_seq_position_at_offset(aln_offset) # use the correspondence to find the equivalent reference residue in the merge alignment ref_corr_res = ref_correspondence.get_res_by_atom_pos(ref_seq_pos_in_ref) ref_seq_num_in_merge = ref_corr_res.seq_num if ref_seq_num_in_merge is None: raise err.GeneralError(('weird... found a residue without a seq_num in the correspondence record ' ' ref_seq_pos_in_ref: {}, res: {}, corr: {}').format( ref_seq_pos_in_ref, repr(ref_corr_res), repr(ref_correspondence))) if ref_seq_num_in_merge not in ref_merge_seqnum_to_seqpos: raise err.OutOfBoundsError(('failed to find seq_num {} ({}) in seqnum/seqpos ' 'lookup: {}\ncorrespondence (length: {})').format( ref_seq_num_in_merge, repr(ref_corr_res), ref_merge_seqnum_to_seqpos, ref_correspondence.seqres_length, )) # find out where this seq_num occurs in the merge sequence (account for segment numbering) ref_seq_pos_in_merge = ref_merge_seqnum_to_seqpos[ref_seq_num_in_merge] # find the aln offset for the equivalent position in the original merge alignment ref_merge_offset = ref_seq_in_merge.get_offset_at_seq_position(ref_seq_pos_in_merge) # LOG.debug("ref_seq_pos (ref): {}, ref_seq_pos (merge): {}, correspondence_res: {}, ref_merge_offset: {}".format( # ref_seq_pos_in_ref, ref_seq_pos_in_merge, repr(ref_corr_res), ref_merge_offset # )) # find the residue at the equivalent position in the merge alignment original_res = original_seq.get_res_at_offset(ref_merge_offset) if merged_res_at_aln_offset != original_res: raise err.MergeCheckError(("Expected the merged residue '{}' to " "match the original residue '{}' at alignment " "offset {} (sequence: '{}')\n\n" "CORR_ATOM: {}\n" "CORR_SEQRES: {}\n" "\n\n" "REF_SEQ_IN_REF: {}\n" "REF_SEQ_IN_MERGE: {}\n" "ORIGINAL_SEQ: {}\n" " {aln_pointer:>{merge_pos}}\n" "MERGED_SEQ: {}\n" " {aln_pointer:>{aln_pos}}\n" "(aln_offset={}, seq_pos(ref)={}, seq_num(merge)={}, seq_pos(merge)={}, ref_merge_offset={})" ).format( merged_res_at_aln_offset, original_res, aln_offset, seq.uid, ref_correspondence.atom_sequence, ref_correspondence.seqres_sequence, ref_seq_in_ref.seq, ref_seq_in_merge.seq, original_seq.seq, seq.seq, aln_offset, ref_seq_pos_in_ref, ref_seq_num_in_merge, ref_seq_pos_in_merge, ref_merge_offset, aln_pointer='^', aln_pos=(aln_offset+1), merge_pos=(ref_merge_offset+1) )) LOG.info("Finshed checking merge for {} ({})".format(ref_seq_acc, repr(ref_seq_in_merge._hdr))) # if we have not been given a correspondence then there's no point # adding the reference sequence from the reference alignment (since # there is a 1:1 mapping) if merge_id_to_remove: LOG.info("Removing reference sequence '%s' from alignment (because 'merge_ref_id' or 'ref_correspondence' is not set)", merge_id_to_remove) self.remove_sequence_by_id(merge_id_to_remove) seqs_by_cluster_id = {} for seq in self.seqs: if seq.cluster_id not in seqs_by_cluster_id: seqs_by_cluster_id[seq.cluster_id] = [] seqs_by_cluster_id[seq.cluster_id].extend([seq]) for cluster_id in seqs_by_cluster_id: seq_ids = ', '.join([s.uid for s in seqs_by_cluster_id[cluster_id]]) LOG.debug("Cluster %s: %s", cluster_id, seq_ids) return merge_aln.seqs def copy(self): """Return a deepcopy of this object.""" new_aln = Align() new_seqs = [s.copy() for s in self.seqs] new_aln.seqs = new_seqs new_aln.aln_positions = new_aln.seqs[0].length() return new_aln def to_fasta(self, wrap_width=80): """Returns the alignment as a string (FASTA format)""" fasta_str = '' for seq in self.seqs: fasta_str += seq.to_fasta(wrap_width=wrap_width) return fasta_str def to_pir(self, wrap_width=80): """Returns the alignment as a string (PIR format)""" pir_str = '' for seq in self.seqs: pir_str += seq.to_pir(wrap_width=wrap_width) return pir_str def write_fasta(self, fasta_file, wrap_width=80): """Write the alignment to a file in FASTA format.""" with open(fasta_file, 'w') as f: for seq in self.seqs: f.write(seq.to_fasta(wrap_width=wrap_width)) def write_pir(self, pir_file, wrap_width=80, *, use_accession=False): """Write the alignment to a file in PIR format.""" with open(pir_file, 'w') as f: for seq in self.seqs: f.write(seq.to_pir(wrap_width=wrap_width, use_accession=use_accession)) def add_scorecons(self): """Add scorecons annotation to this alignment.""" from cathpy.core.util import ScoreconsRunner scons = ScoreconsRunner() LOG.info("Calculating scorecons / DOPS ...") # output alignment to tmp fasta file scons_result = scons.run_alignment(self) self.dops_score = scons_result.dops self.seq_meta['scorecons'] = scons_result.to_string def add_groupsim(self): """Add groupsim annotation to this alignment.""" from cathpy.core.util import GroupsimRunner gs = GroupsimRunner() LOG.info("Calculating GroupSim ...") # output alignment to tmp fasta file gs_result = gs.run_alignment(self) self.seq_meta['groupsim'] = gs_result.to_string def write_sto(self, sto_file, *, meta=None): """Write the alignment to a file in STOCKHOLM format.""" # putting these here to separate the data from the formatting sto_format = '1.0' # allow meta keys to be provided in args, otherwise fill with the # appropriate alignment attributes aln_meta = {} if meta: for key, attr in self.STO_META_TO_ATTR: aln_meta[key] = meta.get(key, None) comment_pad = 0 for seq in self.seqs: comment_pad = max(comment_pad, len(seq.uid) + 1) seq_pad = comment_pad + 8 gc_pad = seq_pad - 5 # single data point about the file def _GF(f, key, val): f.write('#=GF {} {}\n'.format(key, val)) # single data point about each sequence def _GS(f, seq_id, key, val): if key.startswith('DR_'): val = "{}; {}".format(key[3:], val) key = 'DR' f.write('#=GS {:<{comment_pad}} {} {}\n'.format(seq_id, key, val, comment_pad=comment_pad)) # positional data about the file def _GC(f, key, per_pos_str): f.write('#=GC {:<{gc_pad}} {}\n'.format(key, per_pos_str, gc_pad=gc_pad)) # positional data about each sequence def _GR(f, seq_id, key, per_pos_str): f.write('#=GR {:<{comment_pad}} {} {}\n'.format(seq_id, key, per_pos_str, comment_pad=comment_pad)) def _SEQ(f, seq): f.write('{:<{seq_pad}} {}\n'.format(seq.uid, seq.seq, seq_pad=seq_pad)) def _START(f): f.write('# STOCKHOLM {}\n'.format(sto_format)) def _END(f): f.write('//\n') with open(sto_file, 'w') as f: _START(f) _GF(f, 'ID', aln_meta.get('ID', self.uid)) _GF(f, 'DE', aln_meta.get('DE', self.description)) _GF(f, 'AC', aln_meta.get('AC', self.accession)) _GF(f, 'TP', aln_meta.get('TP', self.aln_type)) if self.cath_version: _GF(f, 'DR', 'CATH: ' + self.cath_version) if self.dops_score: _GF(f, 'DR', 'DOPS: {:.3f}'.format(float(self.dops_score))) for key, val in sorted(self.meta.items()): _GF(f, key, val) for seq in self.seqs: for key, val in seq.meta.items(): _GS(f, seq.uid, key, val) if self.min_bitscore: _GF(f, 'TC', self.min_bitscore) _GF(f, 'SQ', self.count_sequences) for seq in self.seqs: _SEQ(f, seq) for key, val in sorted(self.seq_meta.items()): _GC(f, key, val) _END(f) def get_meta_summary(self): """ Returns summary of information about meta data This makes some assumptions about the formatting of certain `GS DR` records in stockholm files. """ uniq_go_counts = {} uniq_ec_counts = {} cath_domain_count = 0 nodes_by_id = {} tree = dendropy.Tree() nodes_by_id['ROOT'] = tree.seed_node all_taxon_terms = set() for seq in self.seqs: go_terms = [] ec_terms = [] org_terms = [] if seq.is_cath_domain: cath_domain_count += 1 if 'DR_GO' in seq.meta: go_terms = list(filter(None, [s.strip() for s in seq.meta['DR_GO'].split(';')])) if 'DR_EC' in seq.meta: ec_terms = list(filter(None, [s.strip() for s in seq.meta['DR_EC'].split(';')])) if 'DR_ORG' in seq.meta: org_terms = list(filter(None, [s.strip() for s in seq.meta['DR_ORG'].split(';')])) for go_term in go_terms: if go_term not in uniq_go_counts: uniq_go_counts[go_term] = 0 uniq_go_counts[go_term] += 1 for ec_term in ec_terms: if ec_term not in uniq_ec_counts: uniq_ec_counts[ec_term] = 0 uniq_ec_counts[ec_term] += 1 for org_term in org_terms: all_taxon_terms.add(org_term) for idx in range(len(org_terms)-1, 0, -1): org_term = org_terms[idx] parent_org_term = org_terms[idx-1] if idx > 1 else 'ROOT' node_id = '/'.join(org_terms[:idx]) if node_id not in nodes_by_id: nodes_by_id[node_id] = dendropy.Node(label=org_term) node = nodes_by_id[node_id] parent_node_id = '/'.join(org_terms[:idx-1]) if idx > 1 else 'ROOT' if parent_node_id not in nodes_by_id: nodes_by_id[parent_node_id] = dendropy.Node(label=parent_org_term) parent_node = nodes_by_id[parent_node_id] parent_node.add_child(node) if not hasattr(node, 'sequence_count'): setattr(node, 'sequence_count', 0) if not hasattr(parent_node, 'sequence_count'): setattr(parent_node, 'sequence_count', 0) node.sequence_count += 1 taxon_namespace = dendropy.TaxonNamespace(all_taxon_terms) tree.taxon_namespace = taxon_namespace for node_id, node in nodes_by_id.items(): taxon_id = node_id.split('/')[-1] node.taxon = taxon_namespace.get_taxon(taxon_id) node.label = "{} ({})".format(node.label, node.sequence_count) tree.seed_node.label = "ROOT ({})".format(self.count_sequences) # LOG.info("tree:\n{}".format(tree.as_ascii_plot(show_internal_node_labels=True))) # LOG.info("newick: {}".format(tree.as_string(schema="newick"))) organism_newick = tree.as_string(schema="newick").strip() uniq_ec_counts = uniq_ec_counts if uniq_ec_counts else None uniq_go_counts = uniq_go_counts if uniq_go_counts else None return AlignMetaSummary( ec_term_counts=uniq_ec_counts, go_term_counts=uniq_go_counts, cath_domain_count=cath_domain_count, seq_count=self.count_sequences, dops_score=float(self.dops_score), organism_newick=organism_newick, ) def __str__(self): return "\n".join([str(seq) for seq in self.seqs])

f71854cf216fd9c15655470c36650db459061d05

py

Python

KSFD/ksfdtimeseries.py

leonavery/KSFD

090e388df13a2674676cbaa53171f2a87291ba9b

KSFD/ksfdtimeseries.py

leonavery/KSFD

090e388df13a2674676cbaa53171f2a87291ba9b

KSFD/ksfdtimeseries.py

leonavery/KSFD

090e388df13a2674676cbaa53171f2a87291ba9b

""" MPI-aware read and write PETSc Vec to HDF5 The goal of this module is to save snapshots of a PETSc Vec to HDF5 files, and obviously to read them again later. The obvious way to do this is parallel HDF5. Unfortunately, distributions of HDF5 and h5py may be built without support for parallel operation. (In particular, the conda-forge version doesn't have it.) This is accomplished through the following kludge: When a KSFD.TimeSeries is created with name tsname and argument mpiok True, the runtime envirnoment is checked to find out if parallel HDF5 is enabled (using h5py.getconfig().mpi). If so, the data are stored in an HDF5 file named '{name}MPI.h5'.format(name=tsname). Note: there is a serious problem with parallel HDF5: variable length records can't be written. If you try, you get this exception: OSError: Can't write data (Parallel IO does not support writing VL datatypes yet) Since that makes parallel HDF5 a nonstarter for my purposes, mpiok defaults to False. You won't get parallel MPI unless you specifically ask for it, and then dealing with the lack of VL records is your problem. If not, each process stores the data it owns in a file named '{name}s{size}r{rank}.h5'.format(name=tsname, size=comm.size, rank=comm.rank) where comm is the MPI communicator. If run sequentially the data will all be stored in a file called '{name}s1r0.h5'. It is intended that the *MPI.h5 file created using parallele HDF5 and the *s1r0.h5 file created when running sequentially and parallel HDF5 is not available will be the same. The same procedure is used for finding the filename when opening in read/write mode ('r+' or 'a'). When opening a TimeSeries for read (mode 'r') TimeSeries checks (in order) for the *s<size>r<rank>.h5 file, then the *MPI.h5 file ,and finally a *s1r0.h5 file, and opens the first it finds. In this case the retrieve methods will only return the components of the vector owned by the local process. Finally, I will write a simple script to merge all the files of *s<size>r<rank>.h5 series into a single *MPI.h5 file. In this way an MPi process group of any size will be able to retrieve data written by a process group of any size. """ import h5py, os, re, gc, time import traceback as tb import numpy as np import petsc4py from mpi4py import MPI # # These imports are placed inside a try/except so that this script can # be executed standalone to check for syntax errors. # try: from .ksfddebug import log from .ksfdgrid import Grid except ImportError: from ksfddebug import log from ksfdgrid import Grid def logSERIES(*args, **kwargs): log(*args, system='SERIES', **kwargs) class KSFDTimeSeries: """ Base class for TimeSeries KSFDTimeSeries is intended as an abstract base class for reading and writing time series from KSFD solutions to HDF5 files. It is not formally defined as an ABC: you can instantiate it if you really wish, but it is not designed to make that a useful thing to do. """ def __init__( self, basename, size=1, rank=0, mpiok=False, mode='r+', retries=0, retry_interval=60 ): """ Required parameter: basename: the prefix of the filename. Optional keyword parameters: size=1: Number of MPI processes. This typically corresponds to comm.size for an MPI communicator comm. rank=0: Number of the MPI process that created this file. Typically comm.rank. mpiok=True: Whether parallel HDF5 should be used to store to store all the data from all MPI processes in a single file. mode='r+': The file mode for opening the h5py.File. retries=0. If nonzero, retry faile dopens this many times. retry_interval=60: time (in secodns) between successive retries. Note: the open will block while waiting for a successful retry. size, rank, and mpiok are used mostly to figure out what filename to use. They need not correspond to the actual current MPU configuration. For instance, they may correspond to the config when the time series was created. """ self.get_filename(basename, size, rank, mpiok, mode) self.retries = retries self.retry_interval = retry_interval self._size = size self._rank = rank self._mode = mode self._tsf = self.open_with_retry() _ = self.info # make sure '/info' exists self.try_to_set('size', self.size) self.try_to_set('rank', self.rank) if 'times' in self.tsf: self.ts = np.array(self.tsf['times'][()]) try: self.ks = np.array(self.tsf['ks'][()]) except KeyError: self.ks = np.arange(len(self.ts)) self.order = np.array(self.tsf['order'][()]) else: self.ts = np.array([], dtype=float) self.ks = np.array([], dtype=int) self.order = np.array([], dtype=int) self.lastk = self.ks.size - 1 self.sorted = False self.tsf.flush() def parse_filename(filename): """ filename is a name like 'bases2r1.h5'. parse_filename returns (basename, size, rank, mpi) (('base', 2, 1, False) for the example). For a filename like 'tests/test1mpi.h5', returns ('base', 1, 0, True). """ mpipat = '(.*)MPI\.h5' nompi_pat = '(.*)s(\d+)r(\d+)\.h5' res = re.fullmatch(mpipat, filename) if res: return (res[1], 1, 0, True) res = re.fullmatch(nompi_pat, filename) if res: return (res[1], res[2], res[3], False) raise ValueError( "Couldn't parse filename {fname}".format(fname=filename) ) def set_grid(self, grid): self._grid = grid self._dim = grid.dim self._dof = grid.dof if self.rank_owns_file: self._ranges = grid.ranges # if ( # 'ranges' in self.tsf and # not np.all(self.tsf['ranges'][()] == self.ranges) # ): # raise ValueError( # "data ranges {filerange} in {file} doesn't " + # "match grid range {gridrange}".format( # filerange=str(self.tsf['ranges'][()]), # file=self.filename, # gridrange=str(grid.ranges) # ) # ) self.myslice = (slice(0, None),)*(self.dim + 1) else: self._ranges = tuple((0, np) for np in grid.nps) # # Slice of the global array belonging to this process: self.myslice = (slice(0, None),) + tuple( slice(*r) for r in grid.ranges ) self.try_to_set('ranges', self.ranges) def get_filename(self, basename, size=1, rank=0, mpiok=True, mode='r+'): """ Get name of file to be opened by this process self.filename is set to the name of the HDF5 file to be opened. This is also returned as the function value. In addition, the following flags are set: self.creating: True if creating a new file. self.rank_owns_file: True if the file will be exclusively owned by this process. """ self.usempi = mpiok and h5py.get_config().mpi name_nompi = '{name}s{size}r{rank}.h5'.format( name=basename, size=size, rank=rank ) name_mpi = '{name}MPI.h5'.format(name=basename) name_seq = '{name}s1r0.h5'.format(name=basename) self.driver = None if self.usempi and os.path.isfile(name_mpi): self.creating = mode[0] == 'w' or mode[0] == 'x' self.rank_owns_file = size == 1 self.filename = name_mpi elif self.usempi and (mode[0] == 'w' or mode[0] == 'x'): self.creating = True self.rank_owns_file = size == 1 self.filename = name_mpi elif os.path.isfile(name_nompi): self.creating = mode[0] == 'w' or mode[0] == 'x' self.rank_owns_file = True self.filename = name_nompi elif (mode == 'r' or mode == 'a') and os.path.isfile(name_seq): self.creating = False self.rank_owns_file = size == 1 self.filename = name_seq # Allow reading from MPi file even if we're not using MPI: elif (mode == 'r' or mode == 'a') and os.path.isfile(name_mpi): self.creating = False self.rank_owns_file = size == 1 self.filename = name_mpi else: self.creating = mode != 'r' self.rank_owns_file = not self.usempi self.filename = name_mpi if self.usempi else name_nompi if self.creating and not self.rank_owns_file and self.usempi: self.driver = 'mpio' if self.creating: os.makedirs(os.path.dirname(self.filename), exist_ok=True) logSERIES('self.filename', self.filename) logSERIES('self.creating', self.creating) logSERIES('self.rank_owns_file', self.rank_owns_file) logSERIES('self.driver', self.driver) logSERIES('self.usempi', self.usempi) return self.filename def open(self, filename, usempi, mode): if mode in ['w', 'w-', 'x', 'a']: dirname = os.path.dirname(os.path.abspath(filename)) try: os.makedirs(dirname, exist_ok=True) except FileExistsError: pass def grid_save(self): grid = self.grid attrs = ['dim', 'dof', 'nps', 'bounds', 'spacing', 'order', 'stencil_width', 'stencil_type', 'boundary_type', 'globalSshape', 'globalVshape', 'globalCshape', 'Slshape', 'Vlshape', 'ranges', 'Clshape', 'Cashape', 'coordsNoGhosts', 'coordsWithGhosts', ] for a in attrs: self.try_to_set('/grid/' + a, getattr(grid, a)) def grid_read(self): """Reads grid params from open file, returns dict""" ggroup = self.tsf['grid'] gd = {} attrs = ['dim', 'dof', 'nps', 'bounds', 'spacing', 'order', 'stencil_width', 'stencil_type', 'boundary_type', 'globalSshape', 'globalVshape', 'globalCshape', 'Slshape', 'Vlshape', 'ranges', 'Clshape', 'Cashape', 'coordsNoGhosts', 'coordsWithGhosts', ] for a in attrs: try: val = ggroup[a][()] if a.endswith('shape'): gd[a] = tuple(val) elif np.isscalar(val): gd[a] = val.item() else: gd[a] = val except KeyError: gd[a] = None gd['width'] = gd['bounds'][0] gd['height'] = gd['bounds'][1] if gd['dim'] > 1 else 1.0 gd['depth'] = gd['bounds'][2] if gd['dim'] > 2 else 1.0 gd['nx'] = gd['nps'][0] gd['ny'] = gd['nps'][1] if gd['dim'] > 1 else 8 gd['nz'] = gd['nps'][2] if gd['dim'] > 2 else 8 return gd def grid_load(self, gd=None): """Reads grid params from open file and creates new Grid.""" if gd is None: gd = self.grid_read() grid = Grid( dim=gd['dim'], width=gd['width'], height=gd['height'], depth=gd['depth'], nx=gd['nx'], ny=gd['ny'], nz=gd['nz'], dof=gd['dof'], order=gd['order'], stencil_width=gd['stencil_width'], stencil_type=gd['stencil_type'], boundary_type=gd['boundary_type'] ) self.set_grid(grid) # # info is a place for caller to store stuff @property def info(self): """Place for caller to store extra stuff""" if not hasattr(self, '_info') or not self._info: self._info = self.tsf.require_group('/info') return self._info @property def tsFile(self): """The open h5File object""" return self._tsf @property def tsf(self): return self._tsf @property def size(self): return self._size @property def rank(self): return self._rank @property def mode(self): return self._mode @property def ranges(self): return self._ranges @property def comm(self): return self._comm @property def grid(self): return self._grid @property def dim(self): return self._dim @property def dof(self): return self._dof def try_to_set(self, key, val): """Try to set self.tsf[key] to val, but ignore exceptions""" if (self.mode == 'r'): return try: del self.tsf[key] except KeyError: pass try: self.tsf[key] = val except ValueError: pass def _sort(self): if getattr(self, 'sorted', False): return ts = getattr(self, 'ts', np.array([])) self.try_to_set('times', ts) self.order = ts.argsort() self.try_to_set('order', self.order) self.sts = ts self.sts.sort() ks = getattr(self, 'ks', []) lastk = getattr(self, 'lastk', -1) self.try_to_set('ks', ks) self.try_to_set('lastk', lastk) self.sorted = True def flush(self): self._sort() self.tsf.flush() def temp_close(self): """ temp_close closes the HDF5 file in which the TimeSeries is stored without destroying associated information. The file can be reopened with little loss of time. temp_close and reopen are intended for use during long solutions. If there is a crash during solution, a temp-closed TimeSeries will be left in a valid state for later use. """ self._sort() self.tsf.close() def open_with_retry( self, fname=None, mode=None, driver=None, comm=None ): if fname is None: fname = self.filename if mode is None: mode = self.mode if driver is None: driver = self.driver if comm is None: comm = self.comm if isinstance(comm, petsc4py.PETSc.Comm): comm = comm.tompi4py() logSERIES('fname, mode, driver, comm', fname, mode, driver, comm) try: if driver == 'mpio': logSERIES('trying 4-argument open') comm.Barrier() logSERIES('comm.rank, comm.size', comm.rank, comm.size) tsf = h5py.File(fname, mode=mode, driver=driver, comm=comm) else: logSERIES('trying 3-argument open') tsf = h5py.File(fname, mode=mode, driver=driver) except OSError: retries_left = self.retries if retries_left <= 0: logSERIES('open failed: re-raising exception') raise while retries_left > 0: logSERIES('reopen failed with OSError: {n} retries left'.format( n=retries_left )) logSERIES('tb.format_exc()', tb.format_exc()) time.sleep(self.retry_interval) try: if driver == 'mpio': logSERIES('trying 4-argument open') comm.Barrier() logSERIES('comm.rank, comm.size', comm.rank, comm.size) tsf = h5py.File(fname, mode=mode, driver=driver, comm=comm) else: logSERIES('trying 3-argument open') tsf = h5py.File(fname, mode=mode, driver=driver) failed = False except OSError: failed = True if retries_left <= 1: raise if not failed: break retries_left -= 1 return tsf def reopen(self): """ Reopen a temp_closed TimeSeries """ mode = self.mode if self.mode == 'r' else 'r+' self._tsf = self.open_with_retry(mode=mode) def close(self): if not hasattr(self, '_tsf') or not self._tsf: self.reopen() self._sort() self.tsf.close() del self._tsf gc.collect() # def __del__(self): # self.close() def store(self, data, t, k=None): if isinstance(data, petsc4py.PETSc.Vec): vals = data.array.reshape(self.grid.Vlshape, order='F') else: vals = data.reshape(self.grid.Vlshape, order='F') logSERIES('k, t', k, t) if k is None: k = self.lastk + 1 self.lastk = k self.ks = np.append(self.ks, k) self.ts = np.append(self.ts, t) key = 'data' + str(k) try: dset = self.tsf.create_dataset(key, self.grid.Vlshape, dtype=vals.dtype) except OSError: dset = self.tsf[key] # dset already exists Cvals = vals.copy(order='C') # h5py requires C order if self.rank_owns_file: dset.write_direct(Cvals) else: dset[self.myslice] = Cvals dset.attrs['k'] = k dset.attrs['t'] = t self.sorted = False self.tsf.flush() def store_slice(self, ranges, data, t, tol=1e-7): shape = (self.grid.dof,) + tuple( r[1] - r[0] for r in ranges ) slc = (slice(0, None),) + tuple( slice(*r) for r in ranges ) vals = data.reshape(shape, order='F') na, nb, ta, tb = self.find_time(t) logSERIES('na, nb, ta, tb', na, nb, ta, tb) if abs(t-ta) <= abs(tb-t): n, tn = na, ta else: n, tn = nb, tb if ( (not (t == 0.0 and tn == 0.0)) and ((self.sts.size <= n) or (abs(t-tn)/max(abs(t), abs(tn)) > tol)) ): # # New time point: append it to the lists # k = self.lastk + 1 self.lastk = k self.ks = np.append(self.ks, k) self.ts = np.append(self.ts, t) key = 'data' + str(k) dset = self.tsf.create_dataset(key, self.grid.Vlshape, dtype=vals.dtype) logSERIES('k, t', k, t) dset.attrs['k'] = k dset.attrs['t'] = t self.sorted = False else: k = n key = 'data' + str(k) dset = self.tsf[key] dset[slc] = vals self.tsf.flush() def times(self): self._sort() return self.ts def steps(self): self._sort() return self.ks def sorted_times(self): self._sort() return self.sts def sorted_steps(self): self._sort() return self.order def retrieve_by_number(self, k): key = 'data' + str(k) dset = self.tsf[key] if self.rank_owns_file: return np.array(dset) else: return np.array(dset)[self.myslice] def find_time(self, t): """ Find the time points closest to t Returns tuple (a, b, ta, tb) a and b are the numbers (ints) of the points flanking t. ta and tb (floats) are the corresponding times. If there is a time point exactly matchig nt, than a == b, ta == tb == t. """ self._sort() if self.sts.size == 0: return (0, 0, t - 1.0, t - 1.0) if (t <= self.sts[0]): a = 0 return (self.ks[a], self.ks[a], self.sts[a], self.sts[a]) elif (t >= self.sts[-1]): a = len(self.sts) - 1 return (self.ks[a], self.ks[a], self.sts[a], self.sts[a]) else: b = self.sts.searchsorted(t) nb = self.order[b] tb = self.sts[b] if (b >= len(self.order) - 1): return(b, b, self.sts[b], self.sts[b]) elif tb == t: return(b, b, tb, tb) a = b - 1 na = self.order[a] ta = self.sts[a] return (a, b, ta, tb) def retrieve_by_time(self, t): """ Retrieve a time point. Arguments: t: the time to be retrieved. """ na, nb, ta, tb = self.find_time(t) adata = self.retrieve_by_number(na) if na == nb: return adata bdata = self.retrieve_by_number(nb) data = ((t-ta)*bdata + (tb-t)*adata)/(tb-ta) return(data) class TimeSeries(KSFDTimeSeries): def __init__( self, basename, grid=None, comm=None, mpiok=False, mode='r+', retries=0, retry_interval=60 ): """ Open a KSFD.TimeSeries Required parameters: basename: the name of the TimeSeries. (This is a prefix of the names of the HDF5 files in which data are stored.) Optional parameters: grid: The KSFD.Grid on which the PETSc Vecs to be saved are defined. This must be supplied when creating a new TimeSeries. When opening an existig nseries, it will be read from the file if not supplied. comm: the MPI communicator. (If not supplied, grid.comm is used.) mpiok=False: whether it is Ok to use parallel HDF5. mode: the file mode (See h5py.h5File.) retries=0. If nonzero, retry faile dopens this many times. retry_interval=60: time (in secodns) between successive retries. Note: the open will block while waiting for a successful retry. """ if comm: self._comm = comm elif grid: self._comm = grid.comm else: self._comm = MPI.COMM_SELF self._mode = mode self._size = self.comm.size self._rank = self.comm.rank self.mpiok = mpiok super().__init__(basename, size=self.size, rank=self.rank, mpiok=mpiok, mode=mode, retries=retries, retry_interval=retry_interval) if (grid): self.set_grid(grid) self.grid_save() else: self.grid_load() class Gatherer(KSFDTimeSeries): """ Gatherer is a special-purpose iterator to allow a single sequential process to read the separate files written by a TimeSeries run under MPI. For instance, to reconstruct the global vector at the last time (assuming it fits in memory in a single process): gather = Gatherer(basename='base', size=4) grid = gather.grid lastk = gather.sorted_steps()[-1] vec = grid.Vdmda.createGlobalVec() vecarray = vec.array.reshape(grid.globalVshape, order='F') for series in gather: vec = grid.Vdmda.createGlobalVec() rank = series.rank vecarray[series.slice] = series.retrieve_by_number(lastk) <do something with vec...> This gatherer would iterate through files bases4r0.h5, bases4r1.h5, bases4r2.h5, and bases4r3.h5. Note that with every iteration it closes the last file and opens the next. Thus, if you want to iterate over all times, it is more efficient to nest the loops like this: for series in gather: for t in series.times(): <do something for this file at this time) than the other way. (The other way would be more intuitive, but my expectation is that this class will be used mostly to gather all TimeSeries files into a single file, which then can be processed efficiently as a TimeSeries.) """ def __init__( self, basename, size=None, retries=0, retry_interval=60 ): """ Required positional parameter basename: the prefix of the filenames for the TimeSeries being read. As a convenience, this can be a special filename that matches the regular expression '(.+)s(\d+)@.*' (That is a literal '@'. Then the basename is the (.+) and the size is the (\d+) following the 's' and preceding '@'. For example, "bases4@' or 'bases4@.h5' would both serve for a series with basename 'base' and size 4. Optional keyword parameter: size=None: This argument can be omitted only if the basename has the special @ filename format. Otherwise, it must be supplied. Gatherer is read-only (mode 'r'). """ self._comm = MPI.COMM_SELF self.retries = retries self.retry_interval = retry_interval gatherre = '(.+)s(\d+)@.*' fname_match = re.fullmatch(gatherre, basename) if fname_match: base = fname_match[1] size = int(fname_match[2]) else: base = basename size = size self.basename = base if not isinstance(size, int) or size <= 0: raise ValueError( 'size {size} is not a positive int' ) # # This opens the first file. We have to do that so as to read # and initialize things like grid, times, etc. # super().__init__( basename=base, size=size, rank=0, mpiok=False, mode='r', retries=retries, retry_interval=retry_interval ) self.set_ranges() # # Since we have to open the rank 0 file before startig # iteration, the following flag is used to determine whether # to open a new file when __iter__ is called # self.iter_started = False self.iter_stopped = False def set_ranges(self): self.rank_owns_file = True gd = self.grid_read() self.grid_load(gd) self._ranges = gd['ranges'] self._shape = (self.dof,) + tuple( r[1] - r[0] for r in self.ranges ) self._slice = (slice(0, None),) + tuple( slice(*r) for r in self.ranges ) @property def slice(self): return self._slice @property def shape(self): return self._shape def __iter__(self): return self def __next__(self): if self.iter_stopped: # # We previously exhausted the iteration. Restart it # self.tsf.close() self.__init__(self.basename, self.size, retries=self.retries, retry_interval=self.retry_interval ) elif self.iter_started: # # We're not just starting: move on to next file # self.tsf.close() self._rank = self.rank + 1 if self.rank >= self.size: self.iter_stopped = True raise StopIteration super().__init__( basename=self.basename, size=self.size, rank=self.rank, mpiok=False, mode='r', retries=self.retries, retry_interval=self.retry_interval ) self.set_ranges() self.iter_started = True self.iter_stopped = False return self

f71859cec54c2858e6e96dfaa122fa325313a2ed

py

Python

artellapipe/core/assetfile.py

ArtellaPipe/artellapipe

3400f6a55f124f639143fe01c559059eaba23b22

2019-10-28T05:18:30.000Z

2020-08-21T05:36:52.000Z

artellapipe/core/assetfile.py

tpoveda/artellapipe

3400f6a55f124f639143fe01c559059eaba23b22

2020-01-22T02:41:54.000Z

2020-03-17T10:49:12.000Z

artellapipe/core/assetfile.py

tpoveda/artellapipe

3400f6a55f124f639143fe01c559059eaba23b22

#! /usr/bin/env python # -*- coding: utf-8 -*- """ Module that contains implementations for asset files """ from __future__ import print_function, division, absolute_import __author__ = "Tomas Poveda" __license__ = "MIT" __maintainer__ = "Tomas Poveda" __email__ = "tpovedatd@gmail.com" import os import logging import tpDcc as tp from tpDcc.libs.python import osplatform, path as path_utils import artellapipe from artellapipe.core import defines, file LOGGER = logging.getLogger('artellapipe') class ArtellaAssetFile(file.ArtellaFile, object): def __init__(self, file_asset=None, file_path=None, file_name=None): self._asset = file_asset file_name = file_name or self._asset.get_name() if self._asset else None super(ArtellaAssetFile, self).__init__(file_name=file_name, file_path=file_path) @property def asset(self): """ Returns asset linked to this file type :return: ArtellaAssset """ return self._asset def has_valid_object(self): """ Implements base ArtellaFile has_valid_object function Returns whether valid object is attached to this file :return: bool """ return bool(self._asset) def get_template_dict(self, **kwargs): """ Returns dictionary with the template data for this file :param extension: str :return: dict """ template_dict = { 'project_id': self._project.id, 'project_id_number': self._project.id_number, 'asset_name': self._asset.get_name(), 'asset_type': self._asset.get_category(), 'file_extension': kwargs.get('extension', self.FILE_EXTENSIONS[0]) } return template_dict def get_project(self): """ Implements base ArtellaFile get_project function Returns project where this asset file belongs to :return: ArtellaProject """ return self._asset.project def get_file( self, status=defines.ArtellaFileStatus.WORKING, extension=None, fix_path=False, version=None, **kwargs): """ Implements base ArtellaFile get_file function Returns file of the attached object :param file_type: str :param status: str :param extension: str :param fix_path: bool :param version: str :return: str """ template_dict = self.get_template_dict() return self._asset.get_file( file_type=self.FILE_TYPE, status=status, extension=extension, fix_path=fix_path, version=version, extra_dict=template_dict) def get_path(self): """ Implements base ArtellaFile get_path function Returns path of the attached object :return: str """ return self._asset.get_path() def get_name(self): """ Returns name of the attached object :return: str """ return self._asset.get_name() def get_extension(self): """ Returns the extension of the aseet file :return: str """ return self.get_project().assets_library_file_types.get() def get_latest_published_versions(self): """ Implements base ArtellaFile get_path function Returns latest published version of file :return: str """ file_path = self.get_path() return artellapipe.AssetsMgr().get_latest_published_versions(file_path, file_type=self.FILE_TYPE) def get_file_paths(self, return_first=False, fix_path=True, **kwargs): if self.FILE_TYPE not in self._asset.FILES: LOGGER.warning( 'FileType "{}" is not a valid file for Assets of type "{}"'.format( self.FILE_TYPE, self._asset.FILE_TYPE)) return list() file_paths = super( ArtellaAssetFile, self).get_file_paths(return_first=return_first, fix_path=fix_path, **kwargs) if file_paths: return file_paths status = kwargs.get('status', defines.ArtellaFileStatus.PUBLISHED) if status == defines.ArtellaFileStatus.WORKING: file_path = self.get_working_path() else: file_path = self.get_latest_local_published_path() if not file_path: return None if return_first else file_paths if fix_path: file_path = artellapipe.FilesMgr().fix_path(file_path) if return_first: return file_path else: return [file_path] def _open_file(self, file_path): if file_path and os.path.isfile(file_path): if path_utils.clean_path(tp.Dcc.scene_name()) == path_utils.clean_path(file_path): return True tp.Dcc.open_file(file_path) return True elif file_path and os.path.isdir(file_path): osplatform.open_file(file_path) return True else: if file_path: folder_path = os.path.dirname(file_path) if os.path.isdir(folder_path): osplatform.open_file(folder_path) return True LOGGER.warning('Impossible to open file: "{}"'.format(file_path)) return False

f71860515aa0c48a7527206271305a67a617026e

py

Python

entropylab/instruments/tests/test_qcodes_dummy.py

IgorQM/entropy

8cbd3da356d8196e89eb9d810e643c80d6608481

entropylab/instruments/tests/test_qcodes_dummy.py

IgorQM/entropy

8cbd3da356d8196e89eb9d810e643c80d6608481

entropylab/instruments/tests/test_qcodes_dummy.py

IgorQM/entropy

8cbd3da356d8196e89eb9d810e643c80d6608481

from typing import Optional, Dict, Any import pytest @pytest.mark.skip() def test_qcodes_dummy(): from qcodes.instrument.base import InstrumentBase as qcodes_InstrumentBase from entropylab.instruments.qcodes_adapter import QcodesAdapter class MockQcodesDriver(qcodes_InstrumentBase): def __init__( self, name: str, metadata: Optional[Dict[Any, Any]] = None ) -> None: super().__init__(name, metadata) self.add_parameter("p") setter = lambda val: print(val) getter = lambda: 1 self.add_parameter("s", set_cmd=self.setter, get_cmd=self.getter) self.add_parameter("g", set_cmd=setter, get_cmd=getter) def setter(self, val): print(val) self.s = val def getter(self): return self.s def free_function(self): print("i'm free") class QcodesDummy(QcodesAdapter): def __init__(self): super().__init__(MockQcodesDriver, "QcodesDummy") def revert_to_snapshot(self, snapshot: str): pass dummy = QcodesDummy() print(dummy) dummy.connect() instance = dummy.get_instance() instance.set("s", "printed") instance.free_function() instance.set("g", "g") assert instance.get("s") == "printed" assert instance.get("g") == 1 dummy.teardown() @pytest.mark.skip() def test_qcodes_dummy_object(): # Importing in test so general pytest discovery wont enforce qcodes installation from qcodes.instrument.base import InstrumentBase as qcodes_InstrumentBase from entropylab.instruments.qcodes_adapter import QcodesAdapter class MockQcodesDriver(qcodes_InstrumentBase): def __init__( self, name: str, metadata: Optional[Dict[Any, Any]] = None ) -> None: super().__init__(name, metadata) self.add_parameter("p") setter = lambda val: print(val) getter = lambda: 1 self.add_parameter("s", set_cmd=self.setter, get_cmd=self.getter) self.add_parameter("g", set_cmd=setter, get_cmd=getter) def setter(self, val): print(val) self.s = val def getter(self): return self.s def free_function(self): print("i'm free") dummy = QcodesAdapter(MockQcodesDriver, "dummy_inst") dummy.connect() instance = dummy.get_instance() instance.set("s", "printed") instance.free_function() instance.set("g", "g") assert instance.get("s") == "printed" assert instance.get("g") == 1 dummy.teardown() @pytest.mark.skip() def test_qcodes_dummy_object_dynamic_spec(): # Importing in test so general pytest discovery wont enforce qcodes installation from qcodes.instrument.base import InstrumentBase as qcodes_InstrumentBase from entropylab.instruments.qcodes_adapter import QcodesAdapter class MockQcodesDriver(qcodes_InstrumentBase): def __init__( self, name: str, metadata: Optional[Dict[Any, Any]] = None ) -> None: super().__init__(name, metadata) self.add_parameter("p") setter = lambda val: print(val) getter = lambda: 1 self.add_parameter("s", set_cmd=self.setter, get_cmd=self.getter) self.add_parameter("g", set_cmd=setter, get_cmd=getter) def setter(self, val): print(val) self.s = val def getter(self): return self.s def free_function(self): print("i'm free") dummy = QcodesAdapter(MockQcodesDriver, "dummy_inst") driver_spec = dummy.get_dynamic_driver_specs() print(driver_spec) assert len(driver_spec.parameters) == 3 assert driver_spec.parameters[0].name == "p" assert driver_spec.parameters[1].name == "s" assert driver_spec.parameters[2].name == "g" assert len(driver_spec.functions) == 0 assert len(driver_spec.undeclared_functions) == 3 assert driver_spec.undeclared_functions[0].name == "free_function" @pytest.mark.skip() def test_qcodes_dummy_snapshot(): # Importing in test so general pytest discovery wont enforce qcodes installation from qcodes.instrument.base import InstrumentBase as qcodes_InstrumentBase from entropylab.instruments.qcodes_adapter import QcodesAdapter class MockQcodesDriver(qcodes_InstrumentBase): def __init__( self, name: str, metadata: Optional[Dict[Any, Any]] = None ) -> None: super().__init__(name, metadata) self.add_parameter("p") setter = lambda val: print(val) getter = lambda: 1 self.add_parameter("s", set_cmd=self.setter, get_cmd=self.getter) self.add_parameter("g", set_cmd=setter, get_cmd=getter) def setter(self, val): print(val) self.s = val def getter(self): return self.s def free_function(self): print("i'm free") dummy = QcodesAdapter(MockQcodesDriver, "dummy_inst") dummy.connect() snapshot = dummy.snapshot(True) print(snapshot) assert len(snapshot) > 0

f7188fdf67798b6b524e83b8873542b335c4a5b4

py

Python

acq4/devices/PatchStar/patchstar.py

tropp/ACQ4

792e05e99cedfc175593d200aeabecd6fa6304ce

acq4/devices/PatchStar/patchstar.py

tropp/ACQ4

792e05e99cedfc175593d200aeabecd6fa6304ce

acq4/devices/PatchStar/patchstar.py

tropp/ACQ4

792e05e99cedfc175593d200aeabecd6fa6304ce

# -*- coding: utf-8 -*- import time import numpy as np from PyQt4 import QtGui, QtCore from ..Stage import Stage, MoveFuture, StageInterface from acq4.drivers.PatchStar import PatchStar as PatchStarDriver from acq4.util.Mutex import Mutex from acq4.util.Thread import Thread from acq4.pyqtgraph import debug, ptime, SpinBox class PatchStar(Stage): """ A Scientifica PatchStar manipulator. port: <serial port> # eg. 'COM1' or '/dev/ttyACM0' """ def __init__(self, man, config, name): self.port = config.pop('port') self.scale = config.pop('scale', (1e-7, 1e-7, 1e-7)) self.dev = PatchStarDriver(self.port) self._lastMove = None man.sigAbortAll.connect(self.stop) Stage.__init__(self, man, config, name) # clear cached position for this device and re-read to generate an initial position update self._lastPos = None self.getPosition(refresh=True) self.setUserSpeed(3e-3) # Set scaling for each axis self.dev.send('UUX 6.4') self.dev.send('UUY 6.4') self.dev.send('UUZ 6.4') # makes 1 roe turn == 1 second movement for any speed self.dev.send('JS 200') # Set approach angle self.dev.send('ANGLE %f' % self.pitch) self.dev.send('APPROACH 0') # thread for polling position changes self.monitor = MonitorThread(self) self.monitor.start() def capabilities(self): """Return a structure describing the capabilities of this device""" if 'capabilities' in self.config: return self.config['capabilities'] else: return { 'getPos': (True, True, True), 'setPos': (True, True, True), 'limits': (False, False, False), } def stop(self): """Stop the manipulator immediately. """ with self.lock: self.dev.stop() if self._lastMove is not None: self._lastMove._stopped() self._lastMove = None def setUserSpeed(self, v): """Set the speed of the rotary controller (m/turn). """ self.userSpeed = v self.dev.setSpeed(v / self.scale[0]) def _getPosition(self): # Called by superclass when user requests position refresh with self.lock: pos = self.dev.getPos() pos = [pos[i] * self.scale[i] for i in (0, 1, 2)] if pos != self._lastPos: self._lastPos = pos emit = True else: emit = False if emit: # don't emit signal while locked self.posChanged(pos) return pos def targetPosition(self): with self.lock: if self._lastMove is None or self._lastMove.isDone(): return self.getPosition() else: return self._lastMove.targetPos def quit(self): self.monitor.stop() Stage.quit(self) def _move(self, abs, rel, speed, linear): with self.lock: if self._lastMove is not None and not self._lastMove.isDone(): self.stop() pos = self._toAbsolutePosition(abs, rel) self._lastMove = PatchStarMoveFuture(self, pos, speed, self.userSpeed) return self._lastMove def deviceInterface(self, win): return PatchStarGUI(self, win) class MonitorThread(Thread): """Thread to poll for manipulator position changes. """ def __init__(self, dev): self.dev = dev self.lock = Mutex(recursive=True) self.stopped = False self.interval = 0.3 Thread.__init__(self) def start(self): self.stopped = False Thread.start(self) def stop(self): with self.lock: self.stopped = True def setInterval(self, i): with self.lock: self.interval = i def run(self): minInterval = 100e-3 interval = minInterval lastPos = None while True: try: with self.lock: if self.stopped: break maxInterval = self.interval pos = self.dev._getPosition() # this causes sigPositionChanged to be emitted if pos != lastPos: # if there was a change, then loop more rapidly for a short time. interval = minInterval lastPos = pos else: interval = min(maxInterval, interval*2) time.sleep(interval) except: debug.printExc('Error in PatchStar monitor thread:') time.sleep(maxInterval) class PatchStarMoveFuture(MoveFuture): """Provides access to a move-in-progress on a PatchStar manipulator. """ def __init__(self, dev, pos, speed, userSpeed): MoveFuture.__init__(self, dev, pos, speed) self._interrupted = False self._errorMSg = None self._finished = False pos = (np.array(pos) / np.array(self.dev.scale)).astype(int) if speed == 'fast': speed = 1e-3 elif speed == 'slow': speed = 1e-6 with self.dev.dev.lock: self.dev.dev.moveTo(pos, speed / self.dev.scale[0]) # reset to user speed immediately after starting move # (the move itself will run with the previous speed) self.dev.dev.setSpeed(userSpeed / self.dev.scale[0]) def wasInterrupted(self): """Return True if the move was interrupted before completing. """ return self._interrupted def isDone(self): """Return True if the move is complete. """ return self._getStatus() != 0 def _getStatus(self): # check status of move unless we already know it is complete. # 0: still moving; 1: finished successfully; -1: finished unsuccessfully if self._finished: if self._interrupted: return -1 else: return 1 if self.dev.dev.isMoving(): # Still moving return 0 # did we reach target? pos = self.dev._getPosition() if ((np.array(pos) - np.array(self.targetPos))**2).sum()**0.5 < 1e-6: # reached target self._finished = True return 1 else: # missed self._finished = True self._interrupted = True self._errorMsg = "Move did not complete." return -1 def _stopped(self): # Called when the manipulator is stopped, possibly interrupting this move. status = self._getStatus() if status == 1: # finished; ignore stop return elif status == -1: self._errorMsg = "Move was interrupted before completion." elif status == 0: # not actually stopped! This should not happen. raise RuntimeError("Interrupted move but manipulator is still running!") else: raise Exception("Unknown status: %s" % status) def errorMessage(self): return self._errorMsg class PatchStarGUI(StageInterface): def __init__(self, dev, win): StageInterface.__init__(self, dev, win) # Insert patchstar-specific controls into GUI self.psGroup = QtGui.QGroupBox('PatchStar Rotary Controller') self.layout.addWidget(self.psGroup, self.nextRow, 0, 1, 2) self.nextRow += 1 self.psLayout = QtGui.QGridLayout() self.psGroup.setLayout(self.psLayout) self.speedLabel = QtGui.QLabel('Speed') self.speedSpin = SpinBox(value=self.dev.userSpeed, suffix='m/turn', siPrefix=True, dec=True, limits=[1e-6, 10e-3]) self.revXBtn = QtGui.QPushButton('Reverse X') self.revYBtn = QtGui.QPushButton('Reverse Y') self.revZBtn = QtGui.QPushButton('Reverse Z') self.psLayout.addWidget(self.speedLabel, 0, 0) self.psLayout.addWidget(self.speedSpin, 0, 1) self.psLayout.addWidget(self.revXBtn, 1, 1) self.psLayout.addWidget(self.revYBtn, 2, 1) self.psLayout.addWidget(self.revZBtn, 3, 1) self.revXBtn.clicked.connect(lambda: self.dev.dev.send('JDX')) self.revYBtn.clicked.connect(lambda: self.dev.dev.send('JDY')) self.revZBtn.clicked.connect(lambda: self.dev.dev.send('JDZ')) self.speedSpin.valueChanged.connect(lambda v: self.dev.setDefaultSpeed(v))

f71898c3ed083524faabeea56c687bae2ca86d8e

py

Python

src/python/pants/backend/python/rules/setup_py_util_test.py

mpopenko-exos/pants

47d27037c8b13291fc9023e56ddd1b1defdf1b8e

src/python/pants/backend/python/rules/setup_py_util_test.py

mpopenko-exos/pants

47d27037c8b13291fc9023e56ddd1b1defdf1b8e

2018-09-04T17:37:34.000Z

2018-09-04T19:42:58.000Z

src/python/pants/backend/python/rules/setup_py_util_test.py

mpopenko-exos/pants

47d27037c8b13291fc9023e56ddd1b1defdf1b8e

# Copyright 2019 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from pants.backend.python.rules.setup_py_util import distutils_repr testdata = { 'foo': 'bar', 'baz': { 'qux': [123, 456], 'quux': ('abc', b'xyz'), 'corge': {1, 2, 3} }, 'various_strings': [ "x'y", 'aaa\nbbb' ] } expected = """ { 'foo': 'bar', 'baz': { 'qux': [ 123, 456, ], 'quux': ( 'abc', 'xyz', ), 'corge': { 1, 2, 3, }, }, 'various_strings': [ 'x\\\'y', \"\"\"aaa\nbbb\"\"\", ], } """.strip() def test_distutils_repr(): assert expected == distutils_repr(testdata)

f718a3bfab465e69af8eecdfc4731de81a8437f6

py

Python

deletionwatcher.py

Floern/SmokeDetector

2818bbd23af15440836c61c4023d063264433c66

deletionwatcher.py

Floern/SmokeDetector

2818bbd23af15440836c61c4023d063264433c66

deletionwatcher.py

Floern/SmokeDetector

2818bbd23af15440836c61c4023d063264433c66

2018-10-11T13:41:49.000Z

2018-10-11T13:41:49.000Z

# coding=utf-8 import json import requests import time # noinspection PyPackageRequirements import websocket # noinspection PyPackageRequirements from bs4 import BeautifulSoup from threading import Thread from urllib.parse import urlparse import metasmoke from globalvars import GlobalVars import datahandling # noinspection PyClassHasNoInit,PyBroadException,PyMethodParameters class DeletionWatcher: @classmethod def update_site_id_list(self): soup = BeautifulSoup(requests.get("https://meta.stackexchange.com/topbar/site-switcher/site-list").text, "html.parser") site_id_dict = {} for site in soup.findAll("a", attrs={"data-id": True}): site_name = urlparse(site["href"]).netloc site_id = site["data-id"] site_id_dict[site_name] = site_id GlobalVars.site_id_dict = site_id_dict @classmethod def check_websocket_for_deletion(self, post_site_id, post_url, timeout): time_to_check = time.time() + timeout post_id = post_site_id[0] post_type = post_site_id[2] if post_type == "answer": question_id = str(datahandling.get_post_site_id_link(post_site_id)) if question_id is None: return else: question_id = post_id post_site = post_site_id[1] if post_site not in GlobalVars.site_id_dict: return site_id = GlobalVars.site_id_dict[post_site] ws = websocket.create_connection("wss://qa.sockets.stackexchange.com/") ws.send(site_id + "-question-" + question_id) while time.time() < time_to_check: ws.settimeout(time_to_check - time.time()) try: a = ws.recv() except websocket.WebSocketTimeoutException: t_metasmoke = Thread(name="metasmoke send deletion stats", target=metasmoke.Metasmoke.send_deletion_stats_for_post, args=(post_url, False)) t_metasmoke.start() return False if a is not None and a != "": try: action = json.loads(a)["action"] if action == "hb": ws.send("hb") continue else: d = json.loads(json.loads(a)["data"]) except: continue if d["a"] == "post-deleted" and str(d["qId"]) == question_id \ and ((post_type == "answer" and "aId" in d and str(d["aId"]) == post_id) or post_type == "question"): t_metasmoke = Thread(name="metasmoke send deletion stats", target=metasmoke.Metasmoke.send_deletion_stats_for_post, args=(post_url, True)) t_metasmoke.start() return True t_metasmoke = Thread(name="metasmoke send deletion stats", target=metasmoke.Metasmoke.send_deletion_stats_for_post, args=(post_url, False)) t_metasmoke.start() return False @classmethod def check_if_report_was_deleted(self, post_site_id, post_url, message): was_report_deleted = self.check_websocket_for_deletion(post_site_id, post_url, 1200) if was_report_deleted: try: message.delete() except: pass @classmethod def post_message_if_not_deleted(self, post_site_id, post_url, message_text, room): was_report_deleted = self.check_websocket_for_deletion(post_site_id, post_url, 300) if not was_report_deleted and not datahandling.is_false_positive(post_site_id[0:2]) and not \ datahandling.is_ignored_post(post_site_id[0:2]): room.send_message(message_text)

f718be3ab1e8857e704777f735842ba57cdcf3f2

py

Python

sdk/communication/azure-communication-chat/tests/test_chat_thread_client_async.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

2022-03-09T08:59:13.000Z

2022-03-09T08:59:13.000Z

sdk/communication/azure-communication-chat/tests/test_chat_thread_client_async.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

sdk/communication/azure-communication-chat/tests/test_chat_thread_client_async.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

2022-03-04T06:21:56.000Z

2022-03-04T06:21:56.000Z

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See LICENSE.txt in the project root for # license information. # ------------------------------------------------------------------------- from azure.core.credentials import AccessToken from datetime import datetime from msrest.serialization import TZ_UTC from azure.communication.chat.aio import ChatThreadClient from azure.communication.chat import ( ChatParticipant, ChatMessageType ) from azure.communication.chat._shared.models import( CommunicationUserIdentifier ) from unittest_helpers import mock_response from azure.core.exceptions import HttpResponseError from unittest.mock import Mock, patch import pytest import time import calendar def _convert_datetime_to_utc_int(input): return int(calendar.timegm(input.utctimetuple())) async def mock_get_token(): return AccessToken("some_token", _convert_datetime_to_utc_int(datetime.now().replace(tzinfo=TZ_UTC))) credential = Mock(get_token=mock_get_token) @pytest.mark.asyncio async def test_update_topic(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" raised = False async def mock_send(*_, **__): return mock_response(status_code=204) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) topic = "update topic" try: await chat_thread_client.update_topic(topic=topic) except: raised = True assert raised == False @pytest.mark.asyncio async def test_send_message(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id='1596823919339' raised = False async def mock_send(*_, **__): return mock_response(status_code=201, json_payload={"id": message_id}) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) create_message_result_id = None try: content='hello world' sender_display_name='sender name' metadata={ "tags": "tag" } create_message_result = await chat_thread_client.send_message( content, sender_display_name=sender_display_name, metadata=metadata) create_message_result_id = create_message_result.id except: raised = True assert raised == False assert create_message_result_id == message_id @pytest.mark.asyncio async def test_send_message_w_type(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id='1596823919339' raised = False message_str = "Hi I am Bob." create_message_result_id = None chat_message_types = [ChatMessageType.TEXT, ChatMessageType.HTML, "text", "html"] for chat_message_type in chat_message_types: async def mock_send(*_, **__): return mock_response(status_code=201, json_payload={ "id": message_id, "type": chat_message_type, "sequenceId": "3", "version": message_id, "content": { "message": message_str, "topic": "Lunch Chat thread", "participants": [ { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b", "displayName": "Bob", "shareHistoryTime": "2020-10-30T10:50:50Z" } ], "initiator": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b" }, "senderDisplayName": "Bob", "createdOn": "2021-01-27T01:37:33Z", "senderId": "8:acs:46849534-eb08-4ab7-bde7-c36928cd1547_00000007-e155-1f06-1db7-3a3a0d00004b" }) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) try: content='hello world' sender_display_name='sender name' create_message_result = await chat_thread_client.send_message( content, chat_message_type=chat_message_type, sender_display_name=sender_display_name) create_message_result_id = create_message_result.id except: raised = True assert raised == False assert create_message_result_id == message_id @pytest.mark.asyncio async def test_send_message_w_invalid_type_throws_error(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id='1596823919339' raised = False # the payload is irrelevant - it'll fail before async def mock_send(*_, **__): return mock_response(status_code=201, json_payload={"id": message_id}) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) create_message_result_id = None chat_message_types = [ChatMessageType.PARTICIPANT_ADDED, ChatMessageType.PARTICIPANT_REMOVED, ChatMessageType.TOPIC_UPDATED, "participant_added", "participant_removed", "topic_updated", "ChatMessageType.TEXT", "ChatMessageType.HTML", "ChatMessageType.PARTICIPANT_ADDED", "ChatMessageType.PARTICIPANT_REMOVED", "ChatMessageType.TOPIC_UPDATED"] for chat_message_type in chat_message_types: try: content='hello world' sender_display_name='sender name' create_message_result = await chat_thread_client.send_message( content, chat_message_type=chat_message_type, sender_display_name=sender_display_name) except: raised = True assert raised == True @pytest.mark.asyncio async def test_get_message(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id='1596823919339' raised = False message_str = "Hi I am Bob." async def mock_send(*_, **__): return mock_response(status_code=200, json_payload={ "id": message_id, "type": "text", "sequenceId": "3", "version": message_id, "content": { "message": message_str, "topic": "Lunch Chat thread", "participants": [ { "communicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}}, "displayName": "Bob", "shareHistoryTime": "2020-10-30T10:50:50Z" } ], "initiatorCommunicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}} }, "senderDisplayName": "Bob", "createdOn": "2021-01-27T01:37:33Z", "senderCommunicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}}, "deletedOn": "2021-01-27T01:37:33Z", "editedOn": "2021-01-27T01:37:33Z", "metadata": { "tags": "tag" } }) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) message = None try: message = await chat_thread_client.get_message(message_id) except: raised = True assert raised == False assert message.id == message_id assert message.type == ChatMessageType.TEXT assert message.content.message == message_str assert message.metadata["tags"] == "tag" assert len(message.content.participants) > 0 @pytest.mark.asyncio async def test_list_messages(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id='1596823919339' raised = False async def mock_send(*_, **__): return mock_response(status_code=200, json_payload={"value": [{ "id": message_id, "type": "text", "sequenceId": "3", "version": message_id, "content": { "message": "message_str", "topic": "Lunch Chat thread", "participants": [ { "communicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}}, "displayName": "Bob", "shareHistoryTime": "2020-10-30T10:50:50Z" } ], "initiatorCommunicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}} }, "senderDisplayName": "Bob", "createdOn": "2021-01-27T01:37:33Z", "senderCommunicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}}, "deletedOn": "2021-01-27T01:37:33Z", "editedOn": "2021-01-27T01:37:33Z" }]}) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) chat_messages = None try: chat_messages = chat_thread_client.list_messages(results_per_page=1) except: raised = True assert raised == False items = [] async for item in chat_messages: items.append(item) assert len(items) == 1 assert items[0].id == message_id @pytest.mark.asyncio async def test_list_messages_with_start_time(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" raised = False async def mock_send(*_, **__): return mock_response(status_code=200, json_payload={ "value": [ { "id": "message_id_1", "type": "text", "sequenceId": "3", "version": "message_id_1", "content": { "message": "message_str", "topic": "Lunch Chat thread", "participants": [ { "communicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}}, "displayName": "Bob", "shareHistoryTime": "2020-10-30T10:50:50Z" } ], "initiatorCommunicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}} }, "senderDisplayName": "Bob", "createdOn": "2021-01-27T01:37:33Z", "senderCommunicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}}, "deletedOn": "2021-01-27T01:37:33Z", "editedOn": "2021-01-27T01:37:33Z" }, { "id": "message_id_2", "type": "text", "sequenceId": "3", "version": "message_id_2", "content": { "message": "message_str", "topic": "Lunch Chat thread", "participants": [ { "communicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}}, "displayName": "Bob", "shareHistoryTime": "2020-10-30T10:50:50Z" } ], "initiatorCommunicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}} }, "senderDisplayName": "Bob", "createdOn": "2021-01-27T01:37:33Z", "senderCommunicationIdentifier": {"rawId": "string", "communicationUser": { "id": "8:acs:8540c0de-899f-5cce-acb5-3ec493af3800_0e59221d-0c1d-46ae-9544-c963ce56c10b"}}, "deletedOn": "2021-01-27T01:37:33Z", "editedOn": "2021-01-27T01:37:33Z" }]}) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) chat_messages = None try: chat_messages = chat_thread_client.list_messages( start_time=datetime(2020, 8, 17, 18, 0, 0) ) except: raised = True assert raised == False items = [] async for item in chat_messages: items.append(item) assert len(items) == 2 @pytest.mark.asyncio async def test_update_message_content(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id='1596823919339' raised = False async def mock_send(*_, **__): return mock_response(status_code=204) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) try: content = "updated message content" await chat_thread_client.update_message(message_id, content=content) except: raised = True assert raised == False @pytest.mark.asyncio async def test_update_message_metadata(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id='1596823919339' raised = False async def mock_send(*_, **__): return mock_response(status_code=204) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) try: metadata={ "tags": "tag" } await chat_thread_client.update_message(message_id, metadata=metadata) except: raised = True assert raised == False @pytest.mark.asyncio async def test_delete_message(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id='1596823919339' raised = False async def mock_send(*_, **__): return mock_response(status_code=204) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) try: await chat_thread_client.delete_message(message_id) except: raised = True assert raised == False @pytest.mark.asyncio async def test_list_participants(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" participant_id="8:acs:57b9bac9-df6c-4d39-a73b-26e944adf6ea_9b0110-08007f1041" raised = False async def mock_send(*_, **__): return mock_response(status_code=200, json_payload={"value": [ { "communicationIdentifier": { "rawId": participant_id, "communicationUser": { "id": participant_id } }, "displayName": "Bob", "shareHistoryTime": "2020-10-30T10:50:50Z" } ]}) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) chat_thread_participants = None try: chat_thread_participants = chat_thread_client.list_participants() except: raised = True assert raised == False items = [] async for item in chat_thread_participants: items.append(item) assert len(items) == 1 @pytest.mark.asyncio async def test_list_participants_with_results_per_page(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" participant_id_1 = "8:acs:9b665d53-8164-4923-ad5d-5e983b07d2e7_00000006-5399-552c-b274-5a3a0d0000dc" participant_id_2 = "8:acs:9b665d53-8164-4923-ad5d-5e983b07d2e7_00000006-9d32-35c9-557d-5a3a0d0002f1" raised = False async def mock_send(*_, **__): return mock_response(status_code=200, json_payload={ "value": [ { "communicationIdentifier": { "rawId": participant_id_1, "communicationUser": { "id": participant_id_1 } }, "displayName": "Bob", "shareHistoryTime": "2020-10-30T10:50:50Z" }, { "communicationIdentifier": { "rawId": participant_id_2, "communicationUser": { "id": participant_id_2 } }, "displayName": "Bob", "shareHistoryTime": "2020-10-30T10:50:50Z" } ]}) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) chat_thread_participants = None try: chat_thread_participants = chat_thread_client.list_participants(results_per_page=2) except: raised = True assert raised == False items = [] async for item in chat_thread_participants: items.append(item) assert len(items) == 2 @pytest.mark.asyncio async def test_add_participants(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" new_participant_id="8:acs:57b9bac9-df6c-4d39-a73b-26e944adf6ea_9b0110-08007f1041" raised = False async def mock_send(*_, **__): return mock_response(status_code=201) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) new_participant = ChatParticipant( identifier=CommunicationUserIdentifier(new_participant_id), display_name='name', share_history_time=datetime.utcnow()) participants = [new_participant] try: await chat_thread_client.add_participants(participants) except: raised = True assert raised == False @pytest.mark.asyncio async def test_add_participants_w_failed_participants_returns_nonempty_list(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" new_participant_id="8:acs:57b9bac9-df6c-4d39-a73b-26e944adf6ea_9b0110-08007f1041" raised = False error_message = "some error message" async def mock_send(*_, **__): return mock_response(status_code=201, json_payload={ "invalidParticipants": [ { "code": "string", "message": error_message, "target": new_participant_id, "details": [] } ] }) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) new_participant = ChatParticipant( identifier=CommunicationUserIdentifier(new_participant_id), display_name='name', share_history_time=datetime.utcnow()) participants = [new_participant] try: result = await chat_thread_client.add_participants(participants) except: raised = True assert raised == False assert len(result) == 1 failed_participant = result[0][0] communication_error = result[0][1] assert new_participant.identifier.properties['id'] == failed_participant.identifier.properties['id'] assert new_participant.display_name == failed_participant.display_name assert new_participant.share_history_time == failed_participant.share_history_time assert error_message == communication_error.message @pytest.mark.asyncio async def test_remove_participant(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" participant_id="8:acs:57b9bac9-df6c-4d39-a73b-26e944adf6ea_9b0110-08007f1041" raised = False async def mock_send(*_, **__): return mock_response(status_code=204) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) try: await chat_thread_client.remove_participant(identifier=CommunicationUserIdentifier(participant_id)) except: raised = True assert raised == False @pytest.mark.asyncio async def test_send_typing_notification(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" raised = False async def mock_send(*_, **__): return mock_response(status_code=200) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) try: await chat_thread_client.send_typing_notification() except: raised = True assert raised == False @pytest.mark.asyncio async def test_send_typing_notification_with_sender_display_name(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" raised = False async def mock_send(*_, **__): return mock_response(status_code=200) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) try: await chat_thread_client.send_typing_notification(sender_display_name="John") except: raised = True assert raised == False @pytest.mark.asyncio async def test_send_read_receipt(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id="1596823919339" raised = False async def mock_send(*_, **__): return mock_response(status_code=200) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) try: await chat_thread_client.send_read_receipt(message_id) except: raised = True assert raised == False @pytest.mark.asyncio async def test_list_read_receipts(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id="1596823919339" raised = False async def mock_send(*_, **__): return mock_response(status_code=200, json_payload={"value": [ { "chatMessageId": message_id, "senderCommunicationIdentifier": { "rawId": "string", "communicationUser": { "id": "string" } } } ]}) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) read_receipts = None try: read_receipts = chat_thread_client.list_read_receipts() except: raised = True assert raised == False items = [] async for item in read_receipts: items.append(item) assert len(items) == 1 @pytest.mark.asyncio async def test_list_read_receipts_with_results_per_page(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id_1 = "1596823919339" message_id_2 = "1596823919340" raised = False async def mock_send(*_, **__): return mock_response(status_code=200, json_payload={ "value": [ { "chatMessageId": message_id_1, "senderCommunicationIdentifier": { "rawId": "string", "communicationUser": { "id": "string" } } }, { "chatMessageId": message_id_2, "senderCommunicationIdentifier": { "rawId": "string", "communicationUser": { "id": "string" } } } ]}) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) read_receipts = None try: read_receipts = chat_thread_client.list_read_receipts(results_per_page=2) except: raised = True assert raised == False items = [] async for item in read_receipts: items.append(item) assert len(items) == 2 @pytest.mark.asyncio async def test_list_read_receipts_with_results_per_page_and_skip(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" message_id_1 = "1596823919339" raised = False async def mock_send(*_, **__): return mock_response(status_code=200, json_payload={ "value": [ { "chatMessageId": message_id_1, "senderCommunicationIdentifier": { "rawId": "string", "communicationUser": { "id": "string" } } } ]}) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) read_receipts = None try: read_receipts = chat_thread_client.list_read_receipts(results_per_page=1, skip=1) except: raised = True assert raised == False items = [] async for item in read_receipts: items.append(item) assert len(items) == 1 @pytest.mark.asyncio async def test_get_properties(): thread_id = "19:bcaebfba0d314c2aa3e920d38fa3df08@thread.v2" raised = False async def mock_send(*_, **__): return mock_response(status_code=200, json_payload={ "id": thread_id, "topic": "Lunch Chat thread", "createdOn": "2020-10-30T10:50:50Z", "deletedOn": "2020-10-30T10:50:50Z", "createdByCommunicationIdentifier": {"rawId": "string", "communicationUser": {"id": "string"}} }) chat_thread_client = ChatThreadClient("https://endpoint", credential, thread_id, transport=Mock(send=mock_send)) get_thread_result = None try: get_thread_result = await chat_thread_client.get_properties() except: raised = True assert raised == False assert get_thread_result.id == thread_id

f718d25705647be878e1d7696bde13ea0e8f11b5

py

Python

testproj/testproj/testapp/admin.py

Polyconseil/django-select2-rocks

0cc29af55cdd7bec7da773966bec0da84fa7aa6c

2015-09-03T09:01:46.000Z

2021-01-28T20:15:18.000Z

testproj/testproj/testapp/admin.py

Polyconseil/django-select2-rocks

0cc29af55cdd7bec7da773966bec0da84fa7aa6c

2015-06-04T14:48:20.000Z

2018-02-28T09:53:03.000Z

testproj/testproj/testapp/admin.py

Polyconseil/django-select2-rocks

0cc29af55cdd7bec7da773966bec0da84fa7aa6c

2015-04-05T14:20:10.000Z

2016-09-30T17:02:01.000Z

from django.contrib import admin from .models import Beach, SelectedBeach admin.site.register(Beach) admin.site.register(SelectedBeach)

f718ea668f086785a2ecdd071fd77637573089f4

py

Python

discretisedfield/field.py

minrk/discretisedfield

251584f8d976a7fafdff5402d16327489407c4dd

discretisedfield/field.py

minrk/discretisedfield

251584f8d976a7fafdff5402d16327489407c4dd

discretisedfield/field.py

minrk/discretisedfield

251584f8d976a7fafdff5402d16327489407c4dd

import pyvtk import struct import matplotlib import numpy as np import mpl_toolkits.axes_grid1 import discretisedfield as df import ubermagutil.typesystem as ts import discretisedfield.util as dfu import matplotlib.pyplot as plt @ts.typesystem(mesh=ts.Typed(expected_type=df.Mesh), dim=ts.Scalar(expected_type=int, unsigned=True, const=True), name=ts.Name(const=True)) class Field: """Finite difference field. This class defines a finite difference field and enables certain operations for its analysis and visualisation. The field is defined on a finite difference mesh (`discretisedfield.Mesh`). Parameters ---------- mesh : discretisedfield.Mesh Finite difference rectangular mesh. dim : int, optional Dimension of the field value. For instance, if `dim=3` the field is a three-dimensional vector field and for `dim=1` the field is a scalar field. Defaults to `dim=3`. value : array_like, callable, optional Please refer to the `value` property: :py:func:`~discretisedfield.Field.value`. Defaults to 0, meaning that if the value is not provided in the initialisation process, "zero-field" will be defined. norm : numbers.Real, callable, optional Please refer to the `norm` property: :py:func:`~discretisedfield.Field.norm`. Defaults to `None` (`norm=None` defines no norm). name : str, optional Field name (defaults to `'field'`). The field name must be a valid Python variable name string. More specifically, it must not contain spaces, or start with underscore or numeric character. Examples -------- 1. Creating a uniform three-dimensional vector field on a nano-sized thin film. >>> import discretisedfield as df ... >>> p1 = (-50e-9, -25e-9, 0) >>> p2 = (50e-9, 25e-9, 5e-9) >>> cell = (1e-9, 1e-9, 0.1e-9) >>> mesh = df.Mesh(p1=p1, p2=p2, cell=cell) ... >>> dim = 3 >>> value = (0, 0, 1) >>> field = df.Field(mesh=mesh, dim=dim, value=value) >>> field Field(mesh=...) 2. Creating a scalar field. >>> import discretisedfield as df ... >>> p1 = (-10, -10, -10) >>> p2 = (10, 10, 10) >>> n = (1, 1, 1) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> dim = 1 >>> value = 3.14 >>> field = df.Field(mesh=mesh, dim=dim, value=value) >>> field Field(mesh=...) .. seealso:: :py:func:`~discretisedfield.Mesh` """ def __init__(self, mesh, dim=3, value=0, norm=None, name='field'): self.mesh = mesh self.dim = dim self.value = value self.norm = norm self.name = name @property def value(self): """Field value representation. This propertry returns a representation of the field value if it exists. Otherwise, the `numpy.ndarray` containing all values from the field is returned. Parameters ---------- value : 0, array_like, callable For scalar fields (`dim=1`) `numbers.Real` values are allowed. In the case of vector fields, "array_like" (list, tuple, numpy.ndarray) value with length equal to `dim` should be used. Finally, the value can also be a callable (e.g. Python function or another field), which for every coordinate in the mesh returns a valid value. If `value=0`, all values in the field will be set to zero independent of the field dimension. Returns ------- array_like, callable, numbers.Real The value used (representation) for setting the field is returned. However, if the actual value of the field does not correspond to the initially used value anymore, a `numpy.ndarray` is returned containing all field values. Raises ------ ValueError If unsupported type is passed Examples -------- 1. Different ways of setting and getting the field value. >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (2, 2, 1) >>> cell = (1, 1, 1) >>> mesh = df.Mesh(p1=p1, p2=p2, cell=cell) >>> value = (0, 0, 1) >>> # if value is not specified, zero-field is defined >>> field = df.Field(mesh=mesh, dim=3) >>> field.value 0 >>> field.value = (0, 0, 1) >>> field.value (0, 0, 1) >>> # Setting the field value using a Python function (callable). >>> def value_function(pos): ... x, y, z = pos ... if x <= 1: ... return (0, 0, 1) ... else: ... return (0, 0, -1) >>> field.value = value_function >>> field.value <function value_function at ...> >>> # We now change the value of a single cell so that the >>> # representation used for initialising field is not valid >>> # anymore. >>> field.array[0, 0, 0, :] = (0, 0, 0) >>> field.value array(...) .. seealso:: :py:func:`~discretisedfield.Field.array` """ value_array = dfu.as_array(self.mesh, self.dim, self._value) if np.array_equal(self.array, value_array): return self._value else: return self.array @value.setter def value(self, val): self._value = val self.array = dfu.as_array(self.mesh, self.dim, val) @property def array(self): """Numpy array of a field value. `array` has shape of `(self.mesh.n[0], self.mesh.n[1], self.mesh.n[2], dim)`. Parameters ---------- array : numpy.ndarray Numpy array with dimensions `(self.mesh.n[0], self.mesh.n[1], self.mesh.n[2], dim)` Returns ------- numpy.ndarray Field values array. Raises ------ ValueError If setting the array with wrong type, shape, or value. Examples -------- 1. Accessing and setting the field array. >>> import discretisedfield as df >>> import numpy as np ... >>> p1 = (0, 0, 0) >>> p2 = (1, 1, 1) >>> cell = (0.5, 1, 1) >>> mesh = df.Mesh(p1=p1, p2=p2, cell=cell) >>> value = (0, 0, 1) >>> field = df.Field(mesh=mesh, dim=3, value=value) >>> field.array array(...) >>> field.array.shape (2, 1, 1, 3) >>> field.array = np.ones(field.array.shape) >>> field.array array(...) .. seealso:: :py:func:`~discretisedfield.Field.value` """ return self._array @array.setter def array(self, val): if isinstance(val, np.ndarray) and \ val.shape == self.mesh.n + (self.dim,): self._array = val else: msg = (f'Unsupported type(val)={type(val)} ' 'or invalid value dimensions.') raise ValueError(msg) @property def norm(self): """Norm of a field. This property computes the norm of the field and returns it as a `discretisedfield.Field` object with `dim=1`. Norm of a scalar field cannot be set and `ValueError` is raised. Parameters ---------- numbers.Real, numpy.ndarray Norm value Returns ------- discretisedfield.Field Scalar field with norm values. Raises ------ ValueError If setting the norm with wrong type, shape, or value. In addition, if the field is scalar (dim=1) or it contains zero vector values. Examples -------- 1. Manipulating the field norm >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (1, 1, 1) >>> cell = (1, 1, 1) >>> mesh = df.Mesh(p1=p1, p2=p2, cell=cell) >>> field = df.Field(mesh=mesh, dim=3, value=(0, 0, 1)) >>> field.norm Field(...) >>> field.norm = 2 >>> field.norm Field(...) >>> field.value = (1, 0, 0) >>> field.norm.array array([[[[1.]]]]) """ current_norm = np.linalg.norm(self.array, axis=-1)[..., None] return Field(self.mesh, dim=1, value=current_norm, name='norm') @norm.setter def norm(self, val): if val is not None: if self.dim == 1: msg = f'Cannot set norm for field with dim={self.dim}.' raise ValueError(msg) if not np.all(self.norm.array): msg = 'Cannot normalise field with zero values.' raise ValueError(msg) self.array /= self.norm.array # normalise to 1 self.array *= dfu.as_array(self.mesh, dim=1, val=val) @property def average(self): """Field average. It computes the average of the field over the entire volume of the mesh. Returns ------- tuple Field average tuple whose length equals to the field's dimension. Examples -------- 1. Computing the vector field average. >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (5, 5, 5) >>> cell = (1, 1, 1) >>> mesh = df.Mesh(p1=p1, p2=p2, cell=cell) >>> field1 = df.Field(mesh=mesh, dim=3, value=(0, 0, 1)) >>> field1.average (0.0, 0.0, 1.0) >>> field2 = df.Field(mesh=mesh, dim=1, value=55) >>> field2.average (55.0,) """ return tuple(self.array.mean(axis=(0, 1, 2))) def __repr__(self): """Field representation string. This method returns the string that can ideally be copied in another Python script so that exactly the same field object could be defined. However, this is usually not the case due to complex values used. Returns ------- str Field representation string. Example ------- 1. Getting field representation string. >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (2, 2, 1) >>> cell = (1, 1, 1) >>> mesh = df.Mesh(p1=p1, p2=p2, cell=cell) >>> field = df.Field(mesh, dim=1, value=1) >>> repr(field) "Field(mesh=...)" """ return (f'Field(mesh={repr(self.mesh)}, ' f'dim={self.dim}, name=\'{self.name}\')') def __call__(self, point): """Sample the field at `point`. It returns the value of the discreatisation cell `point` belongs to. It always returns a tuple, whose length is the same as the dimension of the field. Parameters ---------- point : (3,) array_like The mesh point coordinate :math:`(p_{x}, p_{y}, p_{z})`. Returns ------- tuple A tuple, whose length is the same as the dimension of the field. Example ------- 1. Sampling the field value >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (20, 20, 20) >>> n = (20, 20, 20) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, dim=3, value=(1, 3, 4)) >>> point = (10, 2, 3) >>> field(point) (1.0, 3.0, 4.0) """ value = self.array[self.mesh.point2index(point)] if self.dim > 1: value = tuple(value) return value def __getattr__(self, name): """Extracting the component of the vector field. If `'x'`, `'y'`, or `'z'` is accessed, a new scalar field of that component will be returned. This method is effective for vector fields with dimension 2 or 3. Returns ------- discretisedfield.Field Scalar field with vector field component values. Examples -------- 1. Accessing the vector field components. >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (2, 2, 2) >>> cell = (1, 1, 1) >>> mesh = df.Mesh(p1=p1, p2=p2, cell=cell) >>> field = df.Field(mesh=mesh, dim=3, value=(0, 0, 1)) >>> field.x Field(...) >>> field.y Field(...) >>> field.z Field(...) >>> field.z.dim 1 """ if name in list(dfu.axesdict.keys())[:self.dim] and 1 < self.dim <= 3: # Components x, y, and z make sense only for vector fields # with typical dimensions 2 and 3. component_array = self.array[..., dfu.axesdict[name]][..., None] fieldname = f'{self.name}-{name}'.format(self.name, name) return Field(mesh=self.mesh, dim=1, value=component_array, name=fieldname) else: msg = f'{type(self).__name__} object has no attribute {name}.' raise AttributeError(msg.format(type(self).__name__, name)) def __dir__(self): """Extension of the tab-completion list. Adds `'x'`, `'y'`, and `'z'`, depending on the dimension of the field, to the tab-completion list. This is effective in IPython or Jupyter notebook environment. """ if 1 < self.dim <= 3: extension = list(dfu.axesdict.keys())[:self.dim] else: extension = [] return list(self.__dict__.keys()) + extension def __iter__(self): """Generator yielding coordinates and values of all field cells. The discretisation cell coordinate corresponds to the cell centre point. Yields ------ tuple (2,) The first value is the mesh cell coordinates (`px`, `py`, `pz`), whereas the second one is the field value. Examples -------- 1. Iterating through the field coordinates and values >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (2, 2, 1) >>> cell = (1, 1, 1) >>> mesh = df.Mesh(p1=p1, p2=p2, cell=cell) >>> field = df.Field(mesh, dim=3, value=(0, 0, 1)) >>> for coord, value in field: ... print (coord, value) (0.5, 0.5, 0.5) (0.0, 0.0, 1.0) (1.5, 0.5, 0.5) (0.0, 0.0, 1.0) (0.5, 1.5, 0.5) (0.0, 0.0, 1.0) (1.5, 1.5, 0.5) (0.0, 0.0, 1.0) .. seealso:: :py:func:`~discretisedfield.Mesh.indices` """ for point in self.mesh.coordinates: yield point, self.__call__(point) def line(self, p1, p2, n=100): """Sampling the field along the line. Given two points :math:`p_{1}` and :math:`p_{2}`, :math:`n` position coordinates are generated and the corresponding field values. .. math:: \\mathbf{r}_{i} = i\\frac{\\mathbf{p}_{2} - \\mathbf{p}_{1}}{n-1} Parameters ---------- p1, p2 : (3,) array_like Two points between which the line is generated. n : int Number of points on the line. Yields ------ tuple The first element is the coordinate of the point on the line, whereas the second one is the value of the field. Raises ------ ValueError If `p1` or `p2` is outside the mesh domain. Examples -------- 1. Sampling the field along the line. >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (2, 2, 2) >>> cell = (1, 1, 1) >>> mesh = df.Mesh(p1=p1, p2=p2, cell=cell) >>> field = df.Field(mesh, dim=2, value=(0, 3)) >>> for coord, value in field.line(p1=(0, 0, 0), p2=(2, 0, 0), n=3): ... print(coord, value) (0.0, 0.0, 0.0) (0.0, 3.0) (1.0, 0.0, 0.0) (0.0, 3.0) (2.0, 0.0, 0.0) (0.0, 3.0) """ for point in self.mesh.line(p1=p1, p2=p2, n=n): yield point, self.__call__(point) def plane(self, *args, n=None, **kwargs): """Slices the field with a plane. If one of the axes (`'x'`, `'y'`, or `'z'`) is passed as a string, a plane perpendicular to that axis is generated which intersects the field at its centre. Alternatively, if a keyword argument is passed (e.g. `x=1`), a plane perpendicular to the x-axis and intersecting it at x=1 is generated. The number of points in two dimensions on the plane can be defined using `n` (e.g. `n=(10, 15)`). Using the generated plane, a new "two-dimensional" field is created and returned. Parameters ---------- n : tuple of length 2 The number of points on the plane in two dimensions Returns ------ discretisedfield.Field A field obtained as an intersection of mesh and the plane. Example ------- 1. Intersecting the field with a plane. >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (2, 2, 2) >>> cell = (1, 1, 1) >>> mesh = df.Mesh(p1=p1, p2=p2, cell=cell) >>> field = df.Field(mesh, dim=3) >>> field.plane(y=1) Field(mesh=...) """ plane_mesh = self.mesh.plane(*args, n=n, **kwargs) return self.__class__(plane_mesh, dim=self.dim, value=self) def write(self, filename, representation='txt', extend_scalar=False): """Write the field in .ovf, .omf, .ohf, or vtk format. If the extension of the `filename` is `.vtk`, a VTK file is written (:py:func:`~discretisedfield.Field._writevtk`). Otherwise, for `.ovf`, `.omf`, or `.ohf` extensions, an OOMMF file is written (:py:func:`~discretisedfield.Field._writeovf`). The representation (`bin4`, 'bin8', or 'txt') is passed using `representation` argument. Parameters ---------- filename : str Name of the file written. It depends on its extension the format it is going to be written as. representation : str In the case of OOMMF files (`.ovf`, `.omf`, or `.ohf`), representation can be specified (`bin4`, `bin8`, or `txt`). Defaults to 'txt'. extend_scalar : bool If True, a scalar field will be saved as a vector field. More precisely, if the value at a cell is 3, that cell will be saved as (3, 0, 0). This is valid only for the OVF file formats. Example ------- 1. Write an .omf file and delete it from the disk >>> import os >>> import discretisedfield as df ... >>> p1 = (0, 0, -5) >>> p2 = (5, 15, 15) >>> n = (5, 15, 20) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, value=(5, 6, 7)) >>> filename = 'mytestfile.omf' >>> field.write(filename) # write the file >>> os.remove(filename) # delete the file .. seealso:: :py:func:`~discretisedfield.Field.fromfile` """ if any([filename.endswith(ext) for ext in ['.omf', '.ovf', '.ohf']]): self._writeovf(filename, representation=representation, extend_scalar=extend_scalar) elif filename.endswith('.vtk'): self._writevtk(filename) else: msg = ('Allowed extensions for writing the field are ' '.omf, .ovf, .ohf, and .vtk.') raise ValueError(msg) def _writeovf(self, filename, representation='txt', extend_scalar=False): """Write the field in .ovf, .omf, or .ohf format. The extension of the `filename` should be `.ovf`, `.omf`, or `.ohf`. The representation (`bin4`, 'bin8', or 'txt') is passed using `representation` argument. Parameters ---------- filename : str Name of the file written. representation : str Representation of the file (`bin4`, `bin8`, or `txt`). Defaults to 'txt'. extend_scalar : bool If True, a scalar field will be saved as a vector field. More precisely, if the value at a cell is 3, that cell will be saved as (3, 0, 0). Example ------- 1. Write an .omf file and delete it from the disk >>> import os >>> import discretisedfield as df ... >>> p1 = (0, 0, -5) >>> p2 = (5, 15, 15) >>> n = (5, 15, 20) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, value=(5, 6, 7)) >>> filename = 'mytestfile.omf' >>> field._writeovf(filename) # write the file >>> os.remove(filename) # delete the file """ if extend_scalar and self.dim == 1: write_dim = 3 else: write_dim = self.dim header = ['OOMMF OVF 2.0', '', 'Segment count: 1', '', 'Begin: Segment', 'Begin: Header', '', 'Title: Field generated omf file', 'Desc: File generated by Field class', 'meshunit: m', 'meshtype: rectangular', f'xbase: {self.mesh.pmin[0] + self.mesh.cell[0]/2}', f'ybase: {self.mesh.pmin[1] + self.mesh.cell[1]/2}', f'zbase: {self.mesh.pmin[2] + self.mesh.cell[2]/2}', f'xnodes: {self.mesh.n[0]}', f'ynodes: {self.mesh.n[1]}', f'znodes: {self.mesh.n[2]}', f'xstepsize: {self.mesh.cell[0]}', f'ystepsize: {self.mesh.cell[1]}', f'zstepsize: {self.mesh.cell[2]}', f'xmin: {self.mesh.pmin[0]}', f'ymin: {self.mesh.pmin[1]}', f'zmin: {self.mesh.pmin[2]}', f'xmax: {self.mesh.pmax[0]}', f'ymax: {self.mesh.pmax[1]}', f'zmax: {self.mesh.pmax[2]}', f'valuedim: {write_dim}', f'valuelabels: {self.name}_x {self.name}_y {self.name}_z', 'valueunits: A/m A/m A/m', '', 'End: Header', ''] if representation == 'bin4': header.append('Begin: Data Binary 4') footer = ['End: Data Binary 4', 'End: Segment'] elif representation == 'bin8': header.append('Begin: Data Binary 8') footer = ['End: Data Binary 8', 'End: Segment'] elif representation == 'txt': header.append('Begin: Data Text') footer = ['End: Data Text', 'End: Segment'] # Write header lines to the ovf file. f = open(filename, 'w') f.write(''.join(map(lambda line: f'# {line}\n', header))) f.close() binary_reps = {'bin4': (1234567.0, 'f'), 'bin8': (123456789012345.0, 'd')} if representation in binary_reps: # Reopen the file with binary write, appending to the end # of the file. f = open(filename, 'ab') # Add the 8 bit binary check value that OOMMF uses. packarray = [binary_reps[representation][0]] # Write data to the ovf file. for i in self.mesh.indices: for vi in self.array[i]: packarray.append(vi) v_bin = struct.pack(binary_reps[representation][1]*len(packarray), *packarray) f.write(v_bin) f.close() else: # Reopen the file for txt representation, appending to the # file. f = open(filename, 'a') for i in self.mesh.indices: if self.dim == 3: v = [vi for vi in self.array[i]] elif self.dim == 1: if extend_scalar: v = [self.array[i][0], 0.0, 0.0] else: v = [self.array[i][0]] else: msg = (f'Cannot write dim={self.dim} field.') raise TypeError(msg) for vi in v: f.write(' ' + str(vi)) f.write('\n') f.close() # Write footer lines to OOMMF file. f = open(filename, 'a') f.write(''.join(map(lambda line: f'# {line}\n', footer))) f.close() def _writevtk(self, filename): """Write the field in the VTK format. The extension of the `filename` should be `.vtk`. Parameters ---------- filename : str Name of the file written. Example ------- 1. Write a .vtk file and delete it from the disk >>> import os >>> import discretisedfield as df ... >>> p1 = (0, 0, -5) >>> p2 = (5, 15, 15) >>> n = (5, 15, 20) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, value=(5, 6, 7)) >>> filename = 'mytestfile.vtk' >>> field._writevtk(filename) # write the file >>> os.remove(filename) # delete the file """ grid = [pmini + np.linspace(0, li, ni+1) for pmini, li, ni in zip(self.mesh.pmin, self.mesh.l, self.mesh.n)] structure = pyvtk.RectilinearGrid(*grid) vtkdata = pyvtk.VtkData(structure) vectors = [self.__call__(coord) for coord in self.mesh.coordinates] vtkdata.cell_data.append(pyvtk.Vectors(vectors, self.name)) for i, component in enumerate(dfu.axesdict.keys()): name = f'{self.name}_{component}' vtkdata.cell_data.append(pyvtk.Scalars(list(zip(*vectors))[i], name)) vtkdata.tofile(filename) @classmethod def fromfile(cls, filename, norm=None, name='field'): """Read the field from .ovf, .omf, or .ohf file. The extension of the `filename` should be `.ovf`, `.omf`, or `.ohf`. If the field should be normalised, `norm` argument can be passed. The `name` of the field defaults to `'field'`. This is a `classmethod` and should be called as `discretisedfield.Field.fromfile('myfile.omf')`. Parameters ---------- filename : str Name of the file to be read. norm : numbers.Real, numpy.ndarray, callable For details, refer to :py:func:`~discretisedfield.Field.value`. name : str Name of the field read. Returns ------- discretisedfield.Field Example ------- 1. Read a field from the .ovf file >>> import os >>> import discretisedfield as df ... >>> ovffile = os.path.join(os.path.dirname(__file__), ... 'tests', 'test_sample', ... 'mumax-output-linux.ovf') >>> field = df.Field.fromfile(ovffile) >>> field Field(mesh=...) .. seealso:: :py:func:`~discretisedfield.Field.write` """ mdatalist = ['xmin', 'ymin', 'zmin', 'xmax', 'ymax', 'zmax', 'xstepsize', 'ystepsize', 'zstepsize', 'valuedim'] mdatadict = dict() try: with open(filename, 'r', encoding='utf-8') as ovffile: f = ovffile.read() lines = f.split('\n') mdatalines = filter(lambda s: s.startswith('#'), lines) datalines = np.loadtxt(filter(lambda s: not s.startswith('#'), lines)) for line in mdatalines: for mdatum in mdatalist: if mdatum in line: mdatadict[mdatum] = float(line.split()[-1]) break except UnicodeDecodeError: with open(filename, 'rb') as ovffile: f = ovffile.read() lines = f.split(b'\n') mdatalines = filter(lambda s: s.startswith(bytes('#', 'utf-8')), lines) for line in mdatalines: for mdatum in mdatalist: if bytes(mdatum, 'utf-8') in line: mdatadict[mdatum] = float(line.split()[-1]) break header = b'# Begin: Data Binary ' data_start = f.find(header) header = f[data_start:data_start + len(header) + 1] data_start += len(b'# Begin: Data Binary 8') data_end = f.find(b'# End: Data Binary ') # ordered by length newlines = [b'\n\r', b'\r\n', b'\n'] for nl in newlines: if f.startswith(nl, data_start): data_start += len(nl) break if b'4' in header: formatstr = '@f' checkvalue = 1234567.0 elif b'8' in header: formatstr = '@d' checkvalue = 123456789012345.0 listdata = list(struct.iter_unpack(formatstr, f[data_start:data_end])) datalines = np.array(listdata) if datalines[0] != checkvalue: # These two lines cannot be accessed via # tests. Therefore, they are excluded from coverage. msg = 'Binary Data cannot be read.' # pragma: no cover raise AssertionError(msg) # pragma: no cover datalines = datalines[1:] # check value removal p1 = (mdatadict[key] for key in ['xmin', 'ymin', 'zmin']) p2 = (mdatadict[key] for key in ['xmax', 'ymax', 'zmax']) cell = (mdatadict[key] for key in ['xstepsize', 'ystepsize', 'zstepsize']) dim = int(mdatadict['valuedim']) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) field = df.Field(mesh, dim=dim, name=name) r_tuple = tuple(reversed(field.mesh.n)) + (int(mdatadict['valuedim']),) t_tuple = tuple(reversed(range(3))) + (3,) field.array = datalines.reshape(r_tuple).transpose(t_tuple) field.norm = norm # Normalise if norm is passed return field def mpl(self, figsize=None): """Plots a field plane using matplotlib. Before the field can be plotted, it must be sliced with a plane (e.g. `field.plane(`z`)`). Otherwise, ValueError is raised. For vector fields, this method plots both `quiver` (vector) and `imshow` (scalar) plots. The `imshow` plot represents the value of the out-of-plane vector component and the `quiver` plot is not coloured. On the other hand, only `imshow` is plotted for scalar fields. Where the norm of the field is zero, no vectors are shown and those `imshow` pixels are not coloured. In order to use this function inside Jupyter notebook `%matplotlib inline` must be activated after `discretisedfield` is imported. Parameters ---------- figsize : tuple, optional Length-2 tuple passed to the `matplotlib.figure` function. Raises ------ ValueError If the field has not been sliced with a plane. Example ------- >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (100, 100, 100) >>> n = (10, 10, 10) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, dim=3, value=(1, 2, 0)) >>> field.plane(z=50, n=(5, 5)).mpl() .. seealso:: :py:func:`~discretisedfield.Field.k3d_vectors` """ if not hasattr(self.mesh, 'info'): msg = ('Only sliced field can be plotted using mpl. ' 'For instance, field.plane(\'x\').mpl().') raise ValueError(msg) fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) planeaxis = dfu.raxesdict[self.mesh.info['planeaxis']] if self.dim > 1: # Vector field has both quiver and imshow plots. self.quiver(ax=ax, headwidth=5) scfield = getattr(self, planeaxis) coloredplot = scfield.imshow(ax=ax, norm_field=self.norm) else: # Scalar field has only imshow. scfield = self coloredplot = scfield.imshow(ax=ax, norm_field=None) # Add colorbar to imshow plot. cbar = self.colorbar(ax, coloredplot) # Add labels. ax.set_xlabel(dfu.raxesdict[self.mesh.info['axis1']]) ax.set_ylabel(dfu.raxesdict[self.mesh.info['axis2']]) if self.dim > 1: cbar.ax.set_ylabel(planeaxis + ' component') def imshow(self, ax, norm_field=None, **kwargs): """Plots a scalar field plane using `matplotlib.pyplot.imshow`. Before the field can be plotted, it must be sliced with a plane (e.g. `field.plane(`y`)`) and field must be of dimension 1 (scalar field). Otherwise, ValueError is raised. `imshow` adds the plot to `matplotlib.axes.Axes` passed via `ax` argument. If the scalar field plotted is extracted from a vector field, which has coordinates where the norm of the field is zero, the norm of that vector field can be passed using `norm_field` argument, so that pixels at those coordinates are not coloured. All other parameters accepted by `matplotlib.pyplot.imshow` can be passed. In order to use this function inside Jupyter notebook `%matplotlib inline` must be activated after `discretisedfield` is imported. Parameters ---------- ax : matplotlib.axes.Axes Axes object to which the scalar plot will be added. norm_field : discretisedfield.Field, optional A (scalar) norm field used for determining whether certain pixels should be coloured. Returns ------- matplotlib.image.AxesImage object Raises ------ ValueError If the field has not been sliced with a plane or its dimension is not 1. Example ------- >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (100, 100, 100) >>> n = (10, 10, 10) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, dim=1, value=2) >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> field.plane('y').imshow(ax=ax) <matplotlib.image.AxesImage object at ...> .. seealso:: :py:func:`~discretisedfield.Field.quiver` """ if not hasattr(self.mesh, 'info'): msg = ('Only sliced field can be plotted using imshow. ' 'For instance, field.plane(\'x\').imshow(ax=ax).') raise ValueError(msg) if self.dim > 1: msg = ('Only scalar (dim=1) fields can be plotted. Consider ' 'plotting one component, e.g. field.x.imshow(ax=ax) ' 'or norm field.norm.imshow(ax=ax).') raise ValueError(msg) points, values = list(zip(*list(self))) # If norm_field is passed, set values where norm=0 to np.nan, # so that they are not plotted. if norm_field is not None: values = list(values) # make values mutable for i, point in enumerate(points): if norm_field(point) == 0: values[i] = np.nan # "Unpack" values inside arrays. values = [v[0] if not np.isnan(v) else v for v in values] else: # "Unpack" values inside arrays. values = list(zip(*values)) points = list(zip(*points)) extent = [self.mesh.pmin[self.mesh.info['axis1']], self.mesh.pmax[self.mesh.info['axis1']], self.mesh.pmin[self.mesh.info['axis2']], self.mesh.pmax[self.mesh.info['axis2']]] n = (self.mesh.n[self.mesh.info['axis2']], self.mesh.n[self.mesh.info['axis1']]) imax = ax.imshow(np.array(values).reshape(n), origin='lower', extent=extent, **kwargs) return imax def quiver(self, ax=None, color_field=None, **kwargs): """Plots a vector field plane using `matplotlib.pyplot.quiver`. Before the field can be plotted, it must be sliced with a plane (e.g. `field.plane(`y`)`) and field must be of dimension 3 (vector field). Otherwise, ValueError is raised. `quiver` adds the plot to `matplotlib.axes.Axes` passed via `ax` argument. If there are coordinates where the norm of the field is zero, vectors are not plotted at those coordinates. By default, plot is not coloured, but by passing a `discretisedfield.Field` object of dimension 1 as `color_field`, quiver plot will be coloured based on the values from the field. All other parameters accepted by `matplotlib.pyplot.quiver` can be passed. In order to use this function inside Jupyter notebook `%matplotlib inline` must be activated after `discretisedfield` is imported. Parameters ---------- ax : matplotlib.axes.Axes Axes object to which the quiver plot will be added. color_field : discretisedfield.Field, optional A (scalar) field used for determining the colour of the quiver plot. Returns ------- matplotlib.quiver.Quiver object Raises ------ ValueError If the field has not been sliced with a plane or its dimension is not 3. Example ------- >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (100, 100, 100) >>> n = (10, 10, 10) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, dim=3, value=(1, 2, 0)) >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> field.plane(z=50).quiver(ax=ax, color_field=field.z) <matplotlib.quiver.Quiver object at ...> .. seealso:: :py:func:`~discretisedfield.Field.imshow` """ if not hasattr(self.mesh, 'info'): msg = ('Only sliced field can be plotted using quiver. ' 'For instance, field.plane(\'x\').quiver(ax=ax).') raise ValueError(msg) if self.dim != 3: msg = 'Only three-dimensional (dim=3) fields can be plotted.' raise ValueError(msg) points, values = list(zip(*list(self))) # Remove values where norm is 0 points, values = list(points), list(values) # make them mutable points = [p for p, v in zip(points, values) if not np.equal(v, 0).all()] values = [v for v in values if not np.equal(v, 0).all()] if color_field is not None: colors = [color_field(p) for p in points] colors = list(zip(*colors)) # "Unpack" values inside arrays. points, values = list(zip(*points)), list(zip(*values)) # Are there any vectors pointing out-of-plane? If yes, set the scale. if not any(values[self.mesh.info['axis1']] + values[self.mesh.info['axis2']]): kwargs['scale'] = 1 kwargs['pivot'] = 'mid' # arrow at the centre of the cell if color_field is None: # quiver plot is not coloured. qvax = ax.quiver(points[self.mesh.info['axis1']], points[self.mesh.info['axis2']], values[self.mesh.info['axis1']], values[self.mesh.info['axis2']], **kwargs) else: # quiver plot is coloured. qvax = ax.quiver(points[self.mesh.info['axis1']], points[self.mesh.info['axis2']], values[self.mesh.info['axis1']], values[self.mesh.info['axis2']], colors, **kwargs) return qvax def colorbar(self, ax, coloredplot, cax=None, **kwargs): """Adds a colorbar to the axes using `matplotlib.pyplot.colorbar`. Axes to which the colorbar should be added is passed via `ax` argument. If the colorbar axes are made before the method is called, they should be passed as `cax`. The plot to which the colorbar should correspond to is passed via `coloredplot`. All other parameters accepted by `matplotlib.pyplot.colorbar` can be passed. In order to use this function inside Jupyter notebook `%matplotlib inline` must be activated after `discretisedfield` is imported. Parameters ---------- ax : matplotlib.axes.Axes Axes object to which the colorbar will be added. coloredplot : matplotlib.quiver.Quiver, matplotlib.image.AxesImage A plot to which the colorbar should correspond cax : matplotlib.axes.Axes, optional Colorbar axes. Returns ------- matplotlib.colorbar.Colorbar Example ------- >>> import discretisedfield as df ... >>> p1 = (0, 0, 0) >>> p2 = (100, 100, 100) >>> n = (10, 10, 10) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, dim=3, value=(1, 2, 0)) >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> coloredplot = field.plane(z=50).quiver(ax=ax, color_field=field.z) >>> field.colorbar(ax=ax, coloredplot=coloredplot) <matplotlib.colorbar.Colorbar object at ...> """ if cax is None: divider = mpl_toolkits.axes_grid1.make_axes_locatable(ax) cax = divider.append_axes('right', size='5%', pad=0.1) cbar = plt.colorbar(coloredplot, cax=cax, **kwargs) return cbar def k3d_nonzero(self, color=dfu.colormap[0], plot=None, **kwargs): """Plots the voxels where the value of a scalar field is nonzero. All mesh cells where the value of the field is not zero will be marked using the same color. Only scalar fields can be plotted. Otherwise, ValueError is raised. Different colour of voxels can be passed in the RGB format using `color` parameter. This function is often used to look at the defined sample in the finite difference mesh, by inspecting its norm (`field.norm.k3d_nonzero`). If `plot` is passed as a `k3d.plot.Plot`, plot is added to it. Otherwise, a new k3d plot is created. All arguments allowed in `k3d.voxels()` can be passed. This function is to be called in Jupyter notebook. Parameters ---------- color : int/hex, optional Voxel color in hexadecimal format. plot : k3d.plot.Plot, optional If this argument is passed, plot is added to it. Otherwise, a new k3d plot is created. Example ------- >>> import discretisedfield as df ... >>> p1 = (-50, -50, -50) >>> p2 = (50, 50, 50) >>> n = (10, 10, 10) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, dim=3, value=(1, 2, 0)) >>> def normfun(pos): ... x, y, z = pos ... if x**2 + y**2 < 30**2: ... return 1 ... else: ... return 0 >>> field.norm = normfun >>> field.norm.k3d_nonzero() Plot(...) .. seealso:: :py:func:`~discretisedfield.Field.k3d_voxels` """ if self.dim > 1: msg = ('Only scalar (dim=1) fields can be plotted. Consider ' 'plotting one component, e.g. field.x.k3d_nonzero() ' 'or norm field.norm.k3d_nonzero().') raise ValueError(msg) plot_array = np.copy(self.array) # make a deep copy plot_array = np.squeeze(plot_array) # remove an empty dimension plot_array = np.swapaxes(plot_array, 0, 2) # k3d: arrays are (z, y, x) plot_array[plot_array != 0] = 1 # all cells have the same colour # In the case of nano-sized samples, fix the order of # magnitude of the plot extent to avoid freezing the k3d plot. if np.any(np.divide(self.mesh.cell, 1e-9) < 1e3): pmin = np.divide(self.mesh.pmin, 1e-9) pmax = np.divide(self.mesh.pmax, 1e-9) else: pmin = self.mesh.pmin pmax = self.mesh.pmax dfu.voxels(plot_array, pmin, pmax, colormap=color, plot=plot, **kwargs) def k3d_voxels(self, norm_field=None, plot=None, **kwargs): """Plots the scalar field as a coloured `k3d.voxels()` plot. At all mesh cells, a voxel will be plotted anc coloured according to its value. If the scalar field plotted is extracted from a vector field, which has coordinates where the norm of the field is zero, the norm of that vector field can be passed using `norm_field` argument, so that voxels at those coordinates are not showed. Only scalar fields can be plotted. Otherwise, ValueError is raised. If `plot` is passed as a `k3d.plot.Plot`, plot is added to it. Otherwise, a new k3d plot is created. All arguments allowed in `k3d.voxels()` can be passed. This function is to be called in Jupyter notebook. Parameters ---------- norm_field : discretisedfield.Field, optional A (scalar) norm field used for determining whether certain voxels should be plotted. plot : k3d.plot.Plot, optional If this argument is passed, plot is added to it. Otherwise, a new k3d plot is created. Example ------- >>> import discretisedfield as df ... >>> p1 = (-50, -50, -50) >>> p2 = (50, 50, 50) >>> n = (10, 10, 10) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, dim=3, value=(1, 2, 0)) >>> def normfun(pos): ... x, y, z = pos ... if x**2 + y**2 < 30**2: ... return 1 ... else: ... return 0 >>> field.norm = normfun >>> field.x.k3d_voxels(norm_field=field.norm) Plot(...) .. seealso:: :py:func:`~discretisedfield.Field.k3d_vectors` """ if self.dim > 1: msg = ('Only scalar (dim=1) fields can be plotted. Consider ' 'plotting one component, e.g. field.x.k3d_nonzero() ' 'or norm field.norm.k3d_nonzero().') raise ValueError(msg) plot_array = np.copy(self.array) # make a deep copy plot_array = plot_array[..., 0] # remove an empty dimension plot_array -= plot_array.min() # In the case of uniform fields, division by zero can be # encountered. if plot_array.max() != 0: plot_array /= plot_array.max() plot_array *= 254 plot_array += 1 plot_array = plot_array.round() plot_array = plot_array.astype(int) if norm_field is not None: for index in self.mesh.indices: if norm_field(self.mesh.index2point(index)) == 0: plot_array[index] = 0 plot_array = np.swapaxes(plot_array, 0, 2) # k3d: arrays are (z, y, x) cmap = matplotlib.cm.get_cmap('viridis', 256) colormap = [dfu.num2hexcolor(i, cmap) for i in range(cmap.N)] # In the case of nano-sized samples, fix the order of # magnitude of the plot extent to avoid freezing the k3d plot. if np.any(np.divide(self.mesh.cell, 1e-9) < 1e3): pmin = np.divide(self.mesh.pmin, 1e-9) pmax = np.divide(self.mesh.pmax, 1e-9) else: pmin = self.mesh.pmin pmax = self.mesh.pmax dfu.voxels(plot_array, pmin, pmax, colormap=colormap, plot=plot, **kwargs) def k3d_vectors(self, color_field=None, points=True, plot=None, **kwargs): """Plots the vector field as a `k3d.vectors()` plot. At all mesh cells, a vector will be plotted if its norm is not zero. Vectors can be coloured according to the values of the scalar field passed as `color_field`. Only vector fields can be plotted. Otherwise, ValueError is raised. Points at the discretisation cell centres can be added by setting `points=True`. If `plot` is passed as a `k3d.plot.Plot`, plot is added to it. Otherwise, a new k3d plot is created. All arguments allowed in `k3d.vectors()` can be passed. This function is to be called in Jupyter notebook. Parameters ---------- color_field : discretisedfield.Field, optional A (scalar) field used for determining the colours of vectors. points : bool, optional If `True`, points will be added to the discretisation cell centres. plot : k3d.plot.Plot, optional If this argument is passed, plot is added to it. Otherwise, a new k3d plot is created. Example ------- 1. Plotting an entire vector field. >>> import discretisedfield as df ... >>> p1 = (-50, -50, -50) >>> p2 = (50, 50, 50) >>> n = (10, 10, 10) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, dim=3, value=(1, 2, 0)) >>> field.k3d_vectors(color_field=field.x) Plot(...) 2. Plotting the slice of a vector field. >>> import discretisedfield as df ... >>> p1 = (-50, -50, -50) >>> p2 = (50, 50, 50) >>> n = (10, 10, 10) >>> mesh = df.Mesh(p1=p1, p2=p2, n=n) >>> field = df.Field(mesh, dim=3, value=(1, 2, 0)) >>> field.plane('x').k3d_vectors(color_field=field.x) Plot(...) .. seealso:: :py:func:`~discretisedfield.Field.k3d_voxels` """ if self.dim != 3: msg = 'Only three-dimensional (dim=3) fields can be plotted.' raise ValueError(msg) coordinates, vectors, color_values = [], [], [] norm = self.norm # assigned to be computed only once for coord, value in self: if norm(coord) > 0: coordinates.append(coord) vectors.append(value) if color_field is not None: color_values.append(color_field(coord)[0]) coordinates, vectors = np.array(coordinates), np.array(vectors) # In the case of nano-sized samples, fix the order of # magnitude of the coordinates to avoid freezing the k3d plot. if np.any(np.divide(self.mesh.cell, 1e-9) < 1e3): coordinates /= 1e-9 cell = np.divide(self.mesh.cell, 1e-9) else: cell = self.mesh.cell # Scale the vectors to correspond to the size of cells. vectors /= vectors.max() vectors *= 0.8*np.array(cell) # Middle of the arrow is at the cell centre. coordinates -= 0.5 * vectors if color_field is not None: color_values = np.array(color_values) color_values -= color_values.min() # In the case of uniform fields, division by zero can be # encountered. if color_values.max() != 0: color_values /= color_values.max() color_values *= 256 color_values = color_values.round() color_values = color_values.astype(int) cmap = matplotlib.cm.get_cmap('viridis', 256) colors = [] for c in color_values: color = dfu.num2hexcolor(c, cmap) colors.append((color, color)) else: colors = [] plot = dfu.vectors(coordinates, vectors, colors=colors, plot=plot, **kwargs) if points: dfu.points(coordinates + 0.5 * vectors, plot=plot)

f7190ba74292947809c2128ff0aaecac93157a21

py

Python

src/configs/model_id_opts.py

rgalhama/public_ICCM2021

6a528a26c649da0843b7acbc785aa99b80d29a74

src/configs/model_id_opts.py

rgalhama/public_ICCM2021

6a528a26c649da0843b7acbc785aa99b80d29a74

src/configs/model_id_opts.py

rgalhama/public_ICCM2021

6a528a26c649da0843b7acbc785aa99b80d29a74

""" Author : Raquel G. Alhama Desc: """ def strid_to_opts(strid): """ Given model id as string, extract parameter dictionary. Reverse of config_loader.opts2strid :param strid: :return: """ raise NotImplementedError #Method not finished parts = strid.split("_") param_keys=",".split("thr,win,dim,neg,dim,size,eig,neg,dyn,cds") #finish d={} for i,part in enumerate(parts): if part == 'post': pass elif part in param_keys: if i<len(parts) and not parts[i+1] not in param_keys: k=part v=parts[i+1] d[k]=v else: #key without value k=part v=1 d[k]=v else: #value pass return d # for p in parts:

f71916c16a3387a714ba74da62f20782e4f9fe3d

py

Python

core/views.py

ICFL-UP/Yrden

88c421f1b391e9a6943455b05b8f397e9023187b

core/views.py

ICFL-UP/Yrden

88c421f1b391e9a6943455b05b8f397e9023187b

2022-02-16T06:08:43.000Z

2022-02-16T06:08:55.000Z

core/views.py

ICFL-UP/Yrden

88c421f1b391e9a6943455b05b8f397e9023187b

import logging import os import json import shutil import threading from typing import Any, List from django.contrib.auth import login from django.forms.models import BaseModelForm from django.http.request import HttpRequest from django.http.response import HttpResponse from django.views.generic import ListView, DetailView, CreateView from django.core.paginator import Paginator, PageNotAnInteger, EmptyPage from django.urls import reverse_lazy from django.views.generic.edit import DeleteView from django.shortcuts import redirect, render from django.urls import reverse from django.utils import timezone from datetime import datetime from django.contrib.auth.mixins import LoginRequiredMixin from core.utils import build_zip_json, create_venv, extract_zip, get_python_choices, write_log from core.models import Plugin, PluginRun from core.forms import NewUserForm, PluginFormSet, PluginSourceForm from core.enums.log_type_enum import LogType logging.basicConfig(level=logging.DEBUG, format='[%(levelname)s] (%(threadName)-9s) %(message)s',) def register_request(request: HttpRequest): if request.method == "POST": form = NewUserForm(request.POST) if form.is_valid(): user = form.save() login(request, user) return redirect(reverse("core:index")) form = NewUserForm() return render(request=request, template_name="registration/register.html", context={"register_form":form}) class PluginIndexView(LoginRequiredMixin, ListView): model = Plugin template_name = 'core/index.html' context_object_name = 'plugins' paginate_by = 5 def get_context_data(self, **kwargs): context = super(PluginIndexView, self).get_context_data(**kwargs) plugins = self.get_queryset() page = self.request.GET.get('page') paginator = Paginator(plugins, self.paginate_by) try: plugins = paginator.page(page) except PageNotAnInteger: plugins = paginator.page(1) except EmptyPage: plugins = paginator.page(paginator.num_pages) context['plugins'] = plugins return context class PluginDetailView(LoginRequiredMixin, DetailView): model = Plugin template_name = 'core/plugin_detail.html' context_object_name = 'plugin' paginate_by = 5 def get_context_data(self, **kwargs): context = super(PluginDetailView, self).get_context_data(**kwargs) plugin_runs = PluginRun.objects.filter(plugin=self.kwargs['pk']) page = self.request.GET.get('page') paginator = Paginator(plugin_runs, self.paginate_by) try: plugin_runs = paginator.page(page) except PageNotAnInteger: plugin_runs = paginator.page(1) except EmptyPage: plugin_runs = paginator.page(paginator.num_pages) context['plugin_runs'] = plugin_runs return context class PluginCreateView(LoginRequiredMixin, CreateView): form_class = PluginSourceForm template_name = 'core/plugin_create_form.html' success_url = reverse_lazy('core:index') def get_context_data(self, **kwargs): context = super(PluginCreateView, self).get_context_data(**kwargs) context['plugin_formset'] = PluginFormSet() return context def post(self, request, *args, **kwargs): self.object = None form_class = self.get_form_class() form = self.get_form(form_class) plugin_formset = PluginFormSet(self.request.POST) if form.is_valid() and plugin_formset.is_valid(): return self.form_valid(form, plugin_formset, request.user) else: return self.form_invalid(form, plugin_formset) def form_valid(self, form: BaseModelForm, plugin_formset: PluginFormSet, user): # save PluginSource self.object = form.save(commit=False) self.object.source_dest = form.cleaned_data['source_dest'] self.object.source_hash = form.cleaned_data['source_hash'] self.object.upload_time = form.cleaned_data['upload_time'] self.object.upload_user = user self.object.save() build_hash_thread = threading.Thread( target=build_zip_json, args=(form.cleaned_data['plugin_zip_file'].file, self.object)) build_hash_thread.start() log_json: dict = { 'log_datetime': datetime.timestamp(timezone.now()), 'source_dest': self.object.source_dest, 'source_hash': self.object.source_hash, 'upload_time': self.object.upload_time.strftime("%m/%d/%Y, %H:%M:%S"), 'upload_user_username': self.object.upload_user.username, 'upload_user_email': self.object.upload_user.email, } write_log(LogType.CREATE, self.object, log_json) # save Plugin plugin: List[Plugin] = plugin_formset.save(commit=False) plugin[0].plugin_source = self.object plugin[0].python_version = plugin_formset.cleaned_data[0]['python_version'] plugin[0].plugin_dest = 'core' + os.sep + \ 'plugin' + os.sep + self.object.source_hash + '_' + \ str(datetime.timestamp(self.object.upload_time)) extract_zip_thread = threading.Thread(target=extract_zip, args=( form.cleaned_data['plugin_zip_file'], plugin[0].plugin_dest)) extract_zip_thread.start() plugin[0].save() extract_zip_thread.join() venv_thread = threading.Thread(target=create_venv, args=(plugin[0], )) venv_thread.start() return redirect(reverse("core:index")) def form_invalid(self, form, plugin_formset): return self.render_to_response( self.get_context_data(form=form, product_meta_formset=plugin_formset ) ) class PluginDeleteView(LoginRequiredMixin, DeleteView): model = Plugin template_name = 'core/plugin_delete.html' success_url = reverse_lazy('core:index') def delete(self, request: HttpRequest, *args: str, **kwargs: Any) -> HttpResponse: object: Plugin = self.get_object() user = request.user source_dest = object.plugin_source.source_dest shutil.rmtree(object.plugin_dest) deleted_time = timezone.now() deleted_dest = 'core' + os.sep + 'source' + os.sep + 'deleted_' + object.plugin_source.source_hash + \ '_' + str(datetime.timestamp(object.plugin_source.upload_time)) log_json: dict = { 'log_datetime': datetime.timestamp(deleted_time), 'source_dest': object.plugin_source.source_dest, 'source_hash': object.plugin_source.source_hash, 'upload_time': object.plugin_source.upload_time.strftime("%m/%d/%Y, %H:%M:%S"), 'upload_user_username': object.plugin_source.upload_user.username, 'upload_user_email': object.plugin_source.upload_user.email, 'source_file_hash': json.loads(object.plugin_source.source_file_hash), 'username': user.username, 'user_email': user.email, 'deleted_dest': deleted_dest } write_log(LogType.DELETE, object.plugin_source, log_json) shutil.move(source_dest, deleted_dest) object.plugin_source.source_hash = 'deleted_' + object.plugin_source.source_hash object.plugin_source.source_dest = deleted_dest object.plugin_source.save() return super().delete(request, *args, **kwargs)

f71918615f3a215dc0bc915794b798facde5f6a8

py

Python

qnarre/models/ibert_quant_modules.py

quantapix/qnarre.com

f51d5945c20ef8182c4aa11f1b407d064c190c70

qnarre/models/ibert_quant_modules.py

quantapix/qnarre.com

f51d5945c20ef8182c4aa11f1b407d064c190c70

qnarre/models/ibert_quant_modules.py

quantapix/qnarre.com

f51d5945c20ef8182c4aa11f1b407d064c190c70

import decimal import numpy as np import torch from torch import nn from torch.autograd import Function from ...utils import logging logger = logging.get_logger(__name__) class QuantEmbedding(qc.Module): def __init__( self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None, weight_bit=8, momentum=0.95, quant_mode=False, ): super().__init__() self.num_ = num_embeddings self.dim = embedding_dim self.padding_idx = padding_idx self.max_norm = max_norm self.norm_type = norm_type self.scale_grad_by_freq = scale_grad_by_freq self.sparse = sparse self.weight = nn.Parameter(torch.zeros([num_embeddings, embedding_dim])) self.register_buffer("weight_scaling_factor", torch.zeros(1)) self.register_buffer("weight_integer", torch.zeros_like(self.weight)) self.weight_bit = weight_bit self.momentum = momentum self.quant_mode = quant_mode self.percentile_mode = False self.weight_function = SymmetricQuantFunction.apply def forward(self, x, positions=None, incremental_state=None): if not self.quant_mode: return ( F.embedding( x, self.weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse, ), None, ) w = self.weight w_transform = w.data.detach() w_min = w_transform.min().expand(1) w_max = w_transform.max().expand(1) self.weight_scaling_factor = symmetric_linear_quantization_params( self.weight_bit, w_min, w_max, False ) self.weight_integer = self.weight_function( self.weight, self.weight_bit, self.percentile_mode, self.weight_scaling_factor ) emb_int = F.embedding( x, self.weight_integer, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse, ) return emb_int * self.weight_scaling_factor, self.weight_scaling_factor class QuantAct(qc.Module): def __init__( self, activation_bit, act_range_momentum=0.95, per_channel=False, channel_len=None, quant_mode=False, ): super().__init__() self.activation_bit = activation_bit self.act_range_momentum = act_range_momentum self.quant_mode = quant_mode self.per_channel = per_channel self.percentile = False self.act_function = SymmetricQuantFunction.apply if not self.per_channel: self.register_buffer("x_min", torch.zeros(1)) self.register_buffer("x_max", torch.zeros(1)) self.register_buffer("act_scaling_factor", torch.zeros(1)) self.x_min -= 1e-5 self.x_max += 1e-5 else: raise NotImplementedError("per-channel mode is not currently supported for activation.") def __repr__(self): return ( f"{self.__class__.__name__}(activation_bit={self.activation_bit}, " f"quant_mode: {self.activation_bit}, Act_min: {self.x_min.item():.2f}, " f"Act_max: {self.x_max.item():.2f})" ) def forward( self, x, pre_act_scaling_factor=None, identity=None, identity_scaling_factor=None, specified_min=None, specified_max=None, ): x_act = x if identity is None else identity + x # collect running stats if training if self.training: assert not self.percentile, "percentile mode is not currently supported for activation." assert ( not self.per_channel ), "per-channel mode is not currently supported for activation." x_min = x_act.data.min() x_max = x_act.data.max() assert ( x_max.isnan().sum() == 0 and x_min.isnan().sum() == 0 ), "NaN detected when computing min/max of the activation" # Initialization if self.x_min.min() > -1.1e-5 and self.x_max.max() < 1.1e-5: self.x_min = self.x_min + x_min self.x_max = self.x_max + x_max # exponential moving average (EMA) # use momentum to prevent the quantized values change greatly every iteration elif self.act_range_momentum == -1: self.x_min = torch.min(self.x_min, x_min) self.x_max = torch.max(self.x_max, x_max) else: self.x_min = self.x_min * self.act_range_momentum + x_min * ( 1 - self.act_range_momentum ) self.x_max = self.x_max * self.act_range_momentum + x_max * ( 1 - self.act_range_momentum ) if not self.quant_mode: return x_act, None x_min = self.x_min if specified_min is None else specified_min x_max = self.x_max if specified_max is None else specified_max self.act_scaling_factor = symmetric_linear_quantization_params( self.activation_bit, x_min, x_max, per_channel=self.per_channel ) if pre_act_scaling_factor is None: # this is for the input quantization quant_act_int = self.act_function( x, self.activation_bit, self.percentile, self.act_scaling_factor ) else: quant_act_int = FixedPointMul.apply( x, pre_act_scaling_factor, self.activation_bit, self.act_scaling_factor, identity, identity_scaling_factor, ) correct_output_scale = self.act_scaling_factor.view(-1) return quant_act_int * correct_output_scale, self.act_scaling_factor class QuantLinear(qc.Module): def __init__( self, in_features, out_features, bias=True, weight_bit=8, bias_bit=32, per_channel=False, quant_mode=False, ): super().__init__() self.in_features = in_features self.out_features = out_features self.weight = nn.Parameter(torch.zeros([out_features, in_features])) self.register_buffer("weight_integer", torch.zeros_like(self.weight)) self.register_buffer("fc_scaling_factor", torch.zeros(self.out_features)) if bias: self.bias = nn.Parameter(torch.zeros(out_features)) self.register_buffer("bias_integer", torch.zeros_like(self.bias)) self.weight_bit = weight_bit self.quant_mode = quant_mode self.per_channel = per_channel self.bias_bit = bias_bit self.quant_mode = quant_mode self.percentile_mode = False self.weight_function = SymmetricQuantFunction.apply def __repr__(self): s = super().__repr__() s = f"({s} weight_bit={self.weight_bit}, quant_mode={self.quant_mode})" return s def forward(self, x, prev_act_scaling_factor=None): if not self.quant_mode: return F.linear(x, weight=self.weight, bias=self.bias), None # assert that prev_act_scaling_factor is a scalar tensor assert prev_act_scaling_factor is not None and prev_act_scaling_factor.shape == (1,), ( "Input activation to the QuantLinear layer should be globally (non-channel-wise) quantized. " "Please add a QuantAct layer with `per_channel = True` before this QuantAct layer" ) w = self.weight w_transform = w.data.detach() if self.per_channel: w_min, _ = torch.min(w_transform, dim=1, out=None) w_max, _ = torch.max(w_transform, dim=1, out=None) else: w_min = w_transform.min().expand(1) w_max = w_transform.max().expand(1) self.fc_scaling_factor = symmetric_linear_quantization_params( self.weight_bit, w_min, w_max, self.per_channel ) self.weight_integer = self.weight_function( self.weight, self.weight_bit, self.percentile_mode, self.fc_scaling_factor ) bias_scaling_factor = self.fc_scaling_factor * prev_act_scaling_factor if self.bias is not None: self.bias_integer = self.weight_function( self.bias, self.bias_bit, False, bias_scaling_factor ) prev_act_scaling_factor = prev_act_scaling_factor.view(1, -1) x_int = x / prev_act_scaling_factor return ( F.linear(x_int, weight=self.weight_integer, bias=self.bias_integer) * bias_scaling_factor, bias_scaling_factor, ) class IntGELU(qc.Module): def __init__(self, quant_mode=True, force_dequant="none"): super().__init__() self.quant_mode = quant_mode if force_dequant in ["nonlinear", "gelu"]: logger.info("Force dequantize gelu") self.quant_mode = False if not self.quant_mode: self.activation_fn = nn.GELU() self.k = 1.4142 self.const = 14 # dummy integer constant self.coeff = [-0.2888, -1.769, 1] # a(x+b)**2 + c self.coeff[2] /= self.coeff[0] def int_erf(self, x_int, scaling_factor): b_int = torch.floor(self.coeff[1] / scaling_factor) c_int = torch.floor(self.coeff[2] / scaling_factor**2) sign = torch.sign(x_int) abs_int = torch.min(torch.abs(x_int), -b_int) y_int = sign * ((abs_int + b_int) ** 2 + c_int) scaling_factor = scaling_factor**2 * self.coeff[0] # avoid overflow y_int = floor_ste.apply(y_int / 2**self.const) scaling_factor = scaling_factor * 2**self.const return y_int, scaling_factor def forward(self, x, scaling_factor=None): if not self.quant_mode: return self.activation_fn(x), None x_int = x / scaling_factor sigmoid_int, sigmoid_scaling_factor = self.int_erf(x_int, scaling_factor / self.k) shift_int = 1.0 // sigmoid_scaling_factor x_int = x_int * (sigmoid_int + shift_int) scaling_factor = scaling_factor * sigmoid_scaling_factor / 2 return x_int * scaling_factor, scaling_factor class IntSoftmax(qc.Module): def __init__(self, output_bit, quant_mode=False, force_dequant="none"): super().__init__() self.output_bit = output_bit self.max_bit = 32 self.quant_mode = quant_mode if force_dequant in ["nonlinear", "softmax"]: logger.info("Force dequantize softmax") self.quant_mode = False self.act = QuantAct(16, quant_mode=self.quant_mode) self.x0 = -0.6931 # -ln2 self.const = 30 # dummy integer constant self.coef = [0.35815147, 0.96963238, 1.0] # ax**2 + bx + c self.coef[1] /= self.coef[0] self.coef[2] /= self.coef[0] def int_polynomial(self, x_int, scaling_factor): with torch.no_grad(): b_int = torch.floor(self.coef[1] / scaling_factor) c_int = torch.floor(self.coef[2] / scaling_factor**2) z = (x_int + b_int) * x_int + c_int scaling_factor = self.coef[0] * scaling_factor**2 return z, scaling_factor def int_exp(self, x_int, scaling_factor): with torch.no_grad(): x0_int = torch.floor(self.x0 / scaling_factor) x_int = torch.max(x_int, self.const * x0_int) q = floor_ste.apply(x_int / x0_int) r = x_int - x0_int * q exp_int, exp_scaling_factor = self.int_polynomial(r, scaling_factor) exp_int = torch.clamp(floor_ste.apply(exp_int * 2 ** (self.const - q)), min=0) scaling_factor = exp_scaling_factor / 2**self.const return exp_int, scaling_factor def forward(self, x, scaling_factor): if not self.quant_mode: return F.softmax(x, dim=-1), None x_int = x / scaling_factor x_int_max, _ = x_int.max(dim=-1, keepdim=True) x_int = x_int - x_int_max exp_int, exp_scaling_factor = self.int_exp(x_int, scaling_factor) # Avoid overflow exp, exp_scaling_factor = self.act(exp_int, exp_scaling_factor) exp_int = exp / exp_scaling_factor exp_int_sum = exp_int.sum(dim=-1, keepdim=True) factor = floor_ste.apply(2**self.max_bit / exp_int_sum) exp_int = floor_ste.apply(exp_int * factor / 2 ** (self.max_bit - self.output_bit)) scaling_factor = 1 / 2**self.output_bit return exp_int * scaling_factor, scaling_factor class IntLayerNorm(qc.Module): def __init__(self, normalized_shape, eps, output_bit=8, quant_mode=False, force_dequant="none"): super().__init__() self.normalized_shape = normalized_shape self.eps = eps self.weight = nn.Parameter(torch.zeros(normalized_shape)) self.bias = nn.Parameter(torch.zeros(normalized_shape)) self.quant_mode = quant_mode if force_dequant in ["nonlinear", "layernorm"]: logger.info("Force dequantize layernorm") self.quant_mode = False self.register_buffer("shift", torch.zeros(1)) self.output_bit = output_bit self.max_bit = 32 self.dim_sqrt = None self.activation = QuantAct(self.output_bit, quant_mode=self.quant_mode) def set_shift(self, y_int): with torch.no_grad(): y_sq_int = y_int**2 var_int = torch.sum(y_sq_int, axis=2, keepdim=True) shift = (torch.log2(torch.sqrt(var_int / 2**self.max_bit)).ceil()).max() shift_old = self.shift self.shift = torch.max(self.shift, shift) logger.info(f"Dynamic shift adjustment: {int(shift_old)} to {int(self.shift)}") def overflow_fallback(self, y_int): self.set_shift(y_int) # adjusts `self.shift` y_int_shifted = floor_ste.apply(y_int / 2**self.shift) y_sq_int = y_int_shifted**2 var_int = torch.sum(y_sq_int, axis=2, keepdim=True) return var_int def forward(self, x, scaling_factor=None): if not self.quant_mode: mean = x.mean(axis=2, keepdim=True) y = x - mean var = torch.mean(y**2, axis=2, keepdim=True) x = y / torch.sqrt(self.eps + var) x = x * self.weight + self.bias return x, None # compute sqrt of the feature dimension if it is the first run if self.dim_sqrt is None: n = torch.tensor(x.shape[2], dtype=torch.float) self.dim_sqrt = torch.sqrt(n).to(x.device) # Normalization: computes mean and variance(std) x_int = x / scaling_factor mean_int = round_ste.apply(x_int.mean(axis=2, keepdim=True)) y_int = x_int - mean_int y_int_shifted = floor_ste.apply(y_int / 2**self.shift) y_sq_int = y_int_shifted**2 var_int = torch.sum(y_sq_int, axis=2, keepdim=True) # overflow handling in training time if self.training: # if overflow is detected if var_int.max() >= 2**self.max_bit: var_int = self.overflow_fallback(y_int) assert var_int.max() < 2**self.max_bit + 0.1, ( "Error detected in overflow handling: " "`var_int` exceeds `self.max_bit` (the maximum possible bit width)" ) # To be replaced with integer-sqrt kernel that produces the same output std_int = floor_ste.apply(torch.sqrt(var_int)) * 2**self.shift factor = floor_ste.apply(2**31 / std_int) y_int = floor_ste.apply(y_int * factor / 2) scaling_factor = self.dim_sqrt / 2**30 # scaling and shifting bias = self.bias.data.detach() / (self.weight.data.detach()) bias_int = floor_ste.apply(bias / scaling_factor) y_int = y_int + bias_int scaling_factor = scaling_factor * self.weight x = y_int * scaling_factor return x, scaling_factor def get_percentile_min_max(input, lower_percentile, upper_percentile, output_tensor=False): input_length = input.shape[0] lower_index = round(input_length * (1 - lower_percentile * 0.01)) upper_index = round(input_length * upper_percentile * 0.01) upper_bound = torch.kthvalue(input, k=upper_index).values if lower_percentile == 0: lower_bound = upper_bound * 0 # lower_index += 1 else: lower_bound = -torch.kthvalue(-input, k=lower_index).values if not output_tensor: lower_bound = lower_bound.item() upper_bound = upper_bound.item() return lower_bound, upper_bound def linear_quantize(input, scale, zero_point, inplace=False): if len(input.shape) == 4: scale = scale.view(-1, 1, 1, 1) zero_point = zero_point.view(-1, 1, 1, 1) # reshape scale and zeropoint for linear weights elif len(input.shape) == 2: scale = scale.view(-1, 1) zero_point = zero_point.view(-1, 1) else: scale = scale.view(-1) zero_point = zero_point.view(-1) # quantized = float / scale + zero_point if inplace: input.mul_(1.0 / scale).add_(zero_point).round_() return input return torch.round(1.0 / scale * input + zero_point) def symmetric_linear_quantization_params( num_bits, saturation_min, saturation_max, per_channel=False ): with torch.no_grad(): n = 2 ** (num_bits - 1) - 1 if per_channel: scale, _ = torch.max( torch.stack([saturation_min.abs(), saturation_max.abs()], dim=1), dim=1 ) scale = torch.clamp(scale, min=1e-8) / n else: scale = max(saturation_min.abs(), saturation_max.abs()) scale = torch.clamp(scale, min=1e-8) / n return scale class SymmetricQuantFunction(Function): @staticmethod def forward(ctx, x, k, percentile_mode, scale): zero_point = torch.tensor(0.0).to(scale.device) n = 2 ** (k - 1) - 1 new_quant_x = linear_quantize(x, scale, zero_point, inplace=False) new_quant_x = torch.clamp(new_quant_x, -n, n - 1) ctx.scale = scale return new_quant_x @staticmethod def backward(ctx, grad_output): scale = ctx.scale if len(grad_output.shape) == 4: scale = scale.view(-1, 1, 1, 1) # reshape scale and zeropoint for linear weights elif len(grad_output.shape) == 2: scale = scale.view(-1, 1) else: scale = scale.view(-1) return grad_output.clone() / scale, None, None, None, None class floor_ste(Function): @staticmethod def forward(ctx, x): return torch.floor(x) @staticmethod def backward(ctx, grad_output): return grad_output.clone() class round_ste(Function): @staticmethod def forward(ctx, x): return torch.round(x) @staticmethod def backward(ctx, grad_output): return grad_output.clone() def batch_frexp(inputs, max_bit=31): shape_of_input = inputs.size() # trans the input to be a 1-d tensor inputs = inputs.view(-1) output_m, output_e = np.frexp(inputs.cpu().numpy()) tmp_m = [] for m in output_m: int_m_shifted = int( decimal.Decimal(m * (2**max_bit)).quantize( decimal.Decimal("1"), rounding=decimal.ROUND_HALF_UP ) ) tmp_m.append(int_m_shifted) output_m = np.array(tmp_m) output_e = float(max_bit) - output_e return ( torch.from_numpy(output_m).to(inputs.device).view(shape_of_input), torch.from_numpy(output_e).to(inputs.device).view(shape_of_input), ) class FixedPointMul(Function): @staticmethod def forward( ctx, pre_act, pre_act_scaling_factor, bit_num, z_scaling_factor, identity=None, identity_scaling_factor=None, ): if len(pre_act_scaling_factor.shape) == 3: reshape = lambda x: x # noqa: E731 else: reshape = lambda x: x.view(1, 1, -1) # noqa: E731 ctx.identity = identity n = 2 ** (bit_num - 1) - 1 with torch.no_grad(): pre_act_scaling_factor = reshape(pre_act_scaling_factor) if identity is not None: identity_scaling_factor = reshape(identity_scaling_factor) ctx.z_scaling_factor = z_scaling_factor z_int = torch.round(pre_act / pre_act_scaling_factor) _A = pre_act_scaling_factor.type(torch.double) _B = (z_scaling_factor.type(torch.float)).type(torch.double) new_scale = _A / _B new_scale = reshape(new_scale) m, e = batch_frexp(new_scale) output = z_int.type(torch.double) * m.type(torch.double) output = torch.round(output / (2.0**e)) if identity is not None: # needs addition of identity activation wx_int = torch.round(identity / identity_scaling_factor) _A = identity_scaling_factor.type(torch.double) _B = (z_scaling_factor.type(torch.float)).type(torch.double) new_scale = _A / _B new_scale = reshape(new_scale) m1, e1 = batch_frexp(new_scale) output1 = wx_int.type(torch.double) * m1.type(torch.double) output1 = torch.round(output1 / (2.0**e1)) output = output1 + output return torch.clamp(output.type(torch.float), -n - 1, n) @staticmethod def backward(ctx, grad_output): identity_grad = None if ctx.identity is not None: identity_grad = grad_output.clone() / ctx.z_scaling_factor return ( grad_output.clone() / ctx.z_scaling_factor, None, None, None, None, identity_grad, None, )

f7191a9344d5198ccde86f8f184716fe9107a381

py

Python

textacy/text_utils.py

tbsexton/textacy

964614213c7261f91f09c106334269388d45f790

textacy/text_utils.py

tbsexton/textacy

964614213c7261f91f09c106334269388d45f790

textacy/text_utils.py

tbsexton/textacy

964614213c7261f91f09c106334269388d45f790

""" Text Utils ---------- Set of small utility functions that take text strings as input. """ import logging import re from typing import Iterable, Optional, Set, Tuple from . import constants LOGGER = logging.getLogger(__name__) def is_acronym(token: str, exclude: Optional[Set[str]] = None) -> bool: """ Pass single token as a string, return True/False if is/is not valid acronym. Args: token: Single word to check for acronym-ness exclude: If technically valid but not actually good acronyms are known in advance, pass them in as a set of strings; matching tokens will return False. Returns: Whether or not ``token`` is an acronym. """ # exclude certain valid acronyms from consideration if exclude and token in exclude: return False # don't allow empty strings if not token: return False # don't allow spaces if " " in token: return False # 2-character acronyms can't have lower-case letters if len(token) == 2 and not token.isupper(): return False # acronyms can't be all digits if token.isdigit(): return False # acronyms must have at least one upper-case letter or start/end with a digit if not any(char.isupper() for char in token) and not ( token[0].isdigit() or token[-1].isdigit() ): return False # acronyms must have between 2 and 10 alphanumeric characters if not 2 <= sum(1 for char in token if char.isalnum()) <= 10: return False # only certain combinations of letters, digits, and '&/.-' allowed if not constants.RE_ACRONYM.match(token): return False return True def keyword_in_context( text: str, keyword: str, *, ignore_case: bool = True, window_width: int = 50, print_only: bool = True, ) -> Optional[Iterable[Tuple[str, str, str]]]: """ Search for ``keyword`` in ``text`` via regular expression, return or print strings spanning ``window_width`` characters before and after each occurrence of keyword. Args: text: Text in which to search for ``keyword``. keyword: Technically, any valid regular expression string should work, but usually this is a single word or short phrase: "spam", "spam and eggs"; to account for variations, use regex: "[Ss]pam (and|&) [Ee]ggs?" .. note:: If keyword contains special characters, be sure to escape them! ignore_case: If True, ignore letter case in ``keyword`` matching. window_width: Number of characters on either side of ``keyword`` to include as "context". print_only: If True, print out all results with nice formatting; if False, return all (pre, kw, post) matches as generator of raw strings. Yields: Next 3-tuple of prior context, the match itself, and posterior context. """ flags = re.IGNORECASE if ignore_case is True else 0 if print_only is True: for match in re.finditer(keyword, text, flags=flags): line = "{pre} {kw} {post}".format( pre=text[max(0, match.start() - window_width) : match.start()].rjust( window_width ), kw=match.group(), post=text[match.end() : match.end() + window_width].ljust(window_width), ) print(line) else: for match in re.finditer(keyword, text, flags=flags): yield ( text[max(0, match.start() - window_width) : match.start()], match.group(), text[match.end() : match.end() + window_width], ) KWIC = keyword_in_context """Alias of :func:`keyword_in_context <textacy.text_utils.keyword_in_context>`.""" def clean_terms(terms: Iterable[str]) -> Iterable[str]: """ Clean up a sequence of single- or multi-word strings: strip leading/trailing junk chars, handle dangling parens and odd hyphenation, etc. Args: terms: Sequence of terms such as "presidency", "epic failure", or "George W. Bush" that may be _unclean_ for whatever reason. Yields: Next term in `terms` but with the cruft cleaned up, excluding terms that were _entirely_ cruft Warning: Terms with (intentionally) unusual punctuation may get "cleaned" into a form that changes or obscures the original meaning of the term. """ # get rid of leading/trailing junk characters terms = (constants.RE_LEAD_TAIL_CRUFT_TERM.sub("", term) for term in terms) terms = (constants.RE_LEAD_HYPHEN_TERM.sub(r"\1", term) for term in terms) # handle dangling/backwards parens, don't allow '(' or ')' to appear without the other terms = ( "" if term.count(")") != term.count("(") or term.find(")") < term.find("(") else term if "(" not in term else constants.RE_DANGLING_PARENS_TERM.sub(r"\1\2\3", term) for term in terms ) # handle oddly separated hyphenated words terms = ( term if "-" not in term else constants.RE_NEG_DIGIT_TERM.sub( r"\1\2", constants.RE_WEIRD_HYPHEN_SPACE_TERM.sub(r"\1", term) ) for term in terms ) # handle oddly separated apostrophe'd words terms = ( constants.RE_WEIRD_APOSTR_SPACE_TERM.sub(r"\1\2", term) if "'" in term else term for term in terms ) # normalize whitespace terms = (constants.RE_NONBREAKING_SPACE.sub(" ", term).strip() for term in terms) for term in terms: if re.search(r"\w", term): yield term

f7192d36362e57de19098cfbb44d604a21beea70

py

Python

src/user/__init__.py

aleksandrgordienko/melissa-quiz

49b165acc9aae0ad84cf751cbeb4f6a27dd5ab0f

src/user/__init__.py

aleksandrgordienko/melissa-quiz

49b165acc9aae0ad84cf751cbeb4f6a27dd5ab0f

src/user/__init__.py

aleksandrgordienko/melissa-quiz

49b165acc9aae0ad84cf751cbeb4f6a27dd5ab0f

from user.user import User

f7194f875486f67d1eadb26fc5e87f6bfaed4596

py

Python

detect/image_detector.py

Prasad9/Detect-Flags-SSD

c0d662bde99ed8df33d72bd06d61d5eb869d31a5

2017-11-08T07:09:13.000Z

2022-03-28T07:09:47.000Z

detect/image_detector.py

Prasad9/Detect-Flags-SSD

c0d662bde99ed8df33d72bd06d61d5eb869d31a5

2018-03-08T04:30:19.000Z

2019-01-03T15:47:24.000Z

detect/image_detector.py

Prasad9/Detect-Flags-SSD

c0d662bde99ed8df33d72bd06d61d5eb869d31a5

2018-01-15T15:26:44.000Z

2021-08-18T08:02:51.000Z

from __future__ import print_function import mxnet as mx import numpy as np from timeit import default_timer as timer from dataset.iterator import DetTestImageIter import cv2 class ImageDetector(object): """ SSD detector which hold a detection network and wraps detection API Parameters: ---------- symbol : mx.Symbol detection network Symbol model_prefix : str name prefix of trained model epoch : int load epoch of trained model data_shape : int input data resize shape mean_pixels : tuple of float (mean_r, mean_g, mean_b) batch_size : int run detection with batch size ctx : mx.ctx device to use, if None, use mx.cpu() as default context """ def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \ classes, thresh = 0.6, plot_confidence = True, batch_size=1, ctx=None): self.ctx = ctx if self.ctx is None: self.ctx = mx.cpu() load_symbol, args, auxs = mx.model.load_checkpoint(model_prefix, epoch) if symbol is None: symbol = load_symbol self.mod = mx.mod.Module(symbol, label_names=None, context=ctx) self.data_shape = data_shape self.mod.bind(data_shapes=[('data', (batch_size, 3, data_shape, data_shape))]) self.mod.set_params(args, auxs) self.data_shape = data_shape self.mean_pixels = mean_pixels self.classes = classes self.colors = [] self.fill_random_colors_int() self.thresh = thresh self.plot_confidence = plot_confidence def fill_random_colors(self): import random for i in range(len(self.classes)): self.colors.append((random.random(), random.random(), random.random())) #print(self.colors) def fill_random_colors_int(self): import random for i in range(len(self.classes)): self.colors.append((random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))) #print(self.colors) def detect(self, det_iter, show_timer=False): """ detect all images in iterator Parameters: ---------- det_iter : DetIter iterator for all testing images show_timer : Boolean whether to print out detection exec time Returns: ---------- list of detection results """ num_images = det_iter._size result = [] detections = [] #if not isinstance(det_iter, mx.io.PrefetchingIter): # det_iter = mx.io.PrefetchingIter(det_iter) start = timer() for pred, _, _ in self.mod.iter_predict(det_iter): detections.append(pred[0].asnumpy()) time_elapsed = timer() - start if show_timer: print("Detection time for {} images: {:.4f} sec".format(num_images, time_elapsed)) for output in detections: for i in range(output.shape[0]): det = output[i, :, :] res = det[np.where(det[:, 0] >= 0)[0]] result.append(res) resized_img = det_iter.current_data() return result, resized_img def im_detect(self, img, show_timer=False): """ wrapper for detecting multiple images Parameters: ---------- im_list : list of str image path or list of image paths root_dir : str directory of input images, optional if image path already has full directory information extension : str image extension, eg. ".jpg", optional Returns: ---------- list of detection results in format [det0, det1...], det is in format np.array([id, score, xmin, ymin, xmax, ymax]...) """ im_list = [img] test_iter = DetTestImageIter(im_list, 1, self.data_shape, self.mean_pixels) return self.detect(test_iter, show_timer) def plot_rects(self, img, dets): img_shape = img.shape for i in range(dets.shape[0]): cls_id = int(dets[i, 0]) if cls_id >= 0: score = dets[i, 1] #print('Score is {}, class {}'.format(score, cls_id)) if score > self.thresh: xmin = int(dets[i, 2] * img_shape[1]) ymin = int(dets[i, 3] * img_shape[0]) xmax = int(dets[i, 4] * img_shape[1]) ymax = int(dets[i, 5] * img_shape[0]) cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 4) class_name = self.classes[cls_id] cv2.putText(img, class_name, (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4) #print('Class id = {}, Score = {}, Country = {}, rect = ({}, {}, {}, {})'.format(cls_id, score, class_name, xmin, ymin, xmax, ymax)) def detect_and_visualize_image(self, img, show_timer=False): """ wrapper for im_detect and visualize_detection Parameters: ---------- im_list : list of str or str image path or list of image paths root_dir : str or None directory of input images, optional if image path already has full directory information extension : str or None image extension, eg. ".jpg", optional Returns: ---------- """ dets, resized_img = self.im_detect(img, show_timer=show_timer) resized_img = resized_img.asnumpy() resized_img /= 255.0 for k, det in enumerate(dets): self.plot_rects(resized_img, det) return resized_img def scale_and_plot_rects(self, img, dets): img_shape = img.shape for i in range(dets.shape[0]): cls_id = int(dets[i, 0]) if cls_id >= 0: score = dets[i, 1] #print('Score is {}, class {}'.format(score, cls_id)) if score > self.thresh: xmin = int(dets[i, 2] * img_shape[1]) ymin = int(dets[i, 3] * img_shape[0]) xmax = int(dets[i, 4] * img_shape[1]) ymax = int(dets[i, 5] * img_shape[0]) cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 4) class_name = self.classes[cls_id] cv2.putText(img, class_name, (xmin, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 255), 3) if self.plot_confidence: score_color = (0, 255, 0) if score > 0.5 else (255, 0, 0) cv2.putText(img, '{:.3f}'.format(score), (xmax - 60, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 1, score_color, 1) def detect_and_layover_image(self, img, show_timer=False): """ wrapper for im_detect and visualize_detection Parameters: ---------- im_list : list of str or str image path or list of image paths root_dir : str or None directory of input images, optional if image path already has full directory information extension : str or None image extension, eg. ".jpg", optional Returns: ---------- """ dets, _ = self.im_detect(img, show_timer=show_timer) for k, det in enumerate(dets): self.scale_and_plot_rects(img, det) return img

f7194fe7656b09b6c529b0342d12157fb1da984f

py

Python

tests/apps/minimal2/application.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

tests/apps/minimal2/application.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

2016-11-01T18:42:34.000Z

2020-11-16T16:52:14.000Z

tests/apps/minimal2/application.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

2020-01-22T18:20:46.000Z

2020-01-22T18:20:46.000Z

from os import path from blazeutils import prependsitedir from blazeweb.application import WSGIApp from blazeweb.middleware import full_wsgi_stack from minimal2.config import settings as settingsmod from blazeweb.scripting import application_entry # make sure our base module gets put on the path try: import minimal2 # noqa except ImportError: prependsitedir(path.dirname(settingsmod.basedir), 'apps') def make_wsgi(profile='Default', use_session=True): app = WSGIApp(settingsmod, profile) if not use_session: app.settings.beaker.enabled = False return full_wsgi_stack(app) def script_entry(): application_entry(make_wsgi) if __name__ == '__main__': script_entry()

f71950d1cafe3ade67ae0b9180b0da8119152a85

py

Python

experiments/steven/disentanglement/pointmass/disentanglement_rig.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

experiments/steven/disentanglement/pointmass/disentanglement_rig.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

experiments/steven/disentanglement/pointmass/disentanglement_rig.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

import os.path as osp import torch.nn.functional as F import multiworld.envs.mujoco as mwmj import rlkit.misc.hyperparameter as hyp from rlkit.launchers.launcher_util import run_experiment from rlkit.launchers.experiments.disentanglement.launcher import \ disentangled_grill_her_twin_sac_experiment from rlkit.torch.vae.conv_vae import imsize48_default_architecture if __name__ == "__main__": variant = dict( env_id='Point2DEnv-Train-Axis-Eval-Everything-Images-v0', qf_kwargs=dict( hidden_sizes=[400, 300], ), policy_kwargs=dict( hidden_sizes=[400, 300], ), encoder_kwargs=dict( hidden_sizes=[400, 300], hidden_activation=F.tanh, ), twin_sac_trainer_kwargs=dict( reward_scale=1, discount=0.99, target_update_period=1, use_automatic_entropy_tuning=True, ), td3_trainer_kwargs=dict( tau=1e-3, ), max_path_length=100, algo_kwargs=dict( batch_size=256, num_epochs=50, num_eval_steps_per_epoch=1000, num_expl_steps_per_train_loop=1000, num_trains_per_train_loop=1000, min_num_steps_before_training=1000, ), replay_buffer_kwargs=dict( fraction_goals_rollout_goals=0.2, fraction_goals_env_goals=0.5, max_size=int(1e6), ob_keys_to_save=[ 'latent_observation', 'latent_desired_goal', 'latent_achieved_goal', 'state_achieved_goal', 'state_desired_goal', 'state_observation', ], goal_keys=['latent_desired_goal', 'state_desired_goal'], ), observation_key='latent_observation', desired_goal_key='latent_desired_goal', achieved_goal_key='latent_achieved_goal', vae_exploration_goal_sampling_mode='env', vae_evaluation_goal_sampling_mode='env', base_env_exploration_goal_sampling_mode='train', base_env_evaluation_goal_sampling_mode='test', vectorized=True, disentangled_qf_kwargs=dict( ), vae_wrapped_env_kwargs=dict( norm_order=1, reward_params=dict( type='vectorized_latent_distance', norm_order=1, ), ), use_vf_to_compute_policy=True, use_special_q_function=True, latent_dim=2, vae_n_vae_training_kwargs=dict( vae_class='spatialVAE', vae_kwargs=dict( input_channels=3, ), vae_trainer_kwargs=dict( lr=1e-3, beta=0, ), vae_train_epochs=50, num_image_examples=30000, vae_architecture=imsize48_default_architecture, ), # vae_path="logs/02-25-disentangle-images-relu/02-25-disentangle-images-relu_2020_02_25_12_59_17_id000--s4248/vae.pkl", save_video=True, save_video_kwargs=dict( save_video_period=10, imsize=48, ), ) search_space = { 'disentangled_qf_kwargs.encode_state': [True], } sweeper = hyp.DeterministicHyperparameterSweeper( search_space, default_parameters=variant, ) n_seeds = 1 mode = 'local' exp_prefix = '{}'.format( __file__.replace('/', '-').replace('_', '-').split('.')[0] ) n_seeds = 2 mode = 'local' exp_prefix = 'disentangle-extrapolate-vectorized-3' for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()): for _ in range(n_seeds): run_experiment( disentangled_grill_her_twin_sac_experiment, exp_prefix=exp_prefix, mode=mode, variant=variant, use_gpu=True, num_exps_per_instance=3, gcp_kwargs=dict( zone='us-east1-c', gpu_kwargs=dict( gpu_model='nvidia-tesla-k80', num_gpu=1, ) ), time_in_mins=int(2.5*24*60), )

f719631ce5568ca0573b1aff26b681add708c145

py

Python

lib/matplotlib/backends/qt_compat.py

pmarshwx/matplotlib

12be528dbf2114f7c25abf60de8100cb2d4494af

lib/matplotlib/backends/qt_compat.py

pmarshwx/matplotlib

12be528dbf2114f7c25abf60de8100cb2d4494af

lib/matplotlib/backends/qt_compat.py

pmarshwx/matplotlib

12be528dbf2114f7c25abf60de8100cb2d4494af

""" A Qt API selector that can be used to switch between PyQt and PySide. """ from __future__ import (absolute_import, division, print_function, unicode_literals) import six import os from matplotlib import rcParams, verbose # Available APIs. QT_API_PYQT = 'PyQt4' # API is not set here; Python 2.x default is V 1 QT_API_PYQTv2 = 'PyQt4v2' # forced to Version 2 API QT_API_PYSIDE = 'PySide' # only supports Version 2 API QT_API_PYQT5 = 'PyQt5' # use PyQt5 API; Version 2 with module shim ETS = dict(pyqt=(QT_API_PYQTv2, 4), pyside=(QT_API_PYSIDE, 4), pyqt5=(QT_API_PYQT5, 5)) # ETS is a dict of env variable to (QT_API, QT_MAJOR_VERSION) # If the ETS QT_API environment variable is set, use it, but only # if the varible if of the same major QT version. Note that # ETS requires the version 2 of PyQt4, which is not the platform # default for Python 2.x. QT_API_ENV = os.environ.get('QT_API') if rcParams['backend'] == 'Qt5Agg': QT_RC_MAJOR_VERSION = 5 else: QT_RC_MAJOR_VERSION = 4 QT_API = None if (QT_API_ENV is not None): try: QT_ENV_MAJOR_VERSION = ETS[QT_API_ENV][1] except KeyError: raise RuntimeError( ('Unrecognized environment variable %r, valid values are:' ' %r, %r or %r' % (QT_API_ENV, 'pyqt', 'pyside', 'pyqt5'))) if QT_ENV_MAJOR_VERSION == QT_RC_MAJOR_VERSION: # Only if backend and env qt major version are # compatible use the env variable. QT_API = ETS[QT_API_ENV][0] if QT_API is None: # No ETS environment or incompatible so use rcParams. if rcParams['backend'] == 'Qt5Agg': QT_API = rcParams['backend.qt5'] else: QT_API = rcParams['backend.qt4'] # We will define an appropriate wrapper for the differing versions # of file dialog. _getSaveFileName = None # Flag to check if sip could be imported _sip_imported = False # Now perform the imports. if QT_API in (QT_API_PYQT, QT_API_PYQTv2, QT_API_PYQT5): try: import sip _sip_imported = True except ImportError: # Try using PySide QT_API = QT_API_PYSIDE cond = ("Could not import sip; falling back on PySide\n" "in place of PyQt4 or PyQt5.\n") verbose.report(cond, 'helpful') if _sip_imported: if QT_API == QT_API_PYQTv2: if QT_API_ENV == 'pyqt': cond = ("Found 'QT_API=pyqt' environment variable. " "Setting PyQt4 API accordingly.\n") else: cond = "PyQt API v2 specified." try: sip.setapi('QString', 2) except: res = 'QString API v2 specification failed. Defaulting to v1.' verbose.report(cond + res, 'helpful') # condition has now been reported, no need to repeat it: cond = "" try: sip.setapi('QVariant', 2) except: res = 'QVariant API v2 specification failed. Defaulting to v1.' verbose.report(cond + res, 'helpful') if QT_API in [QT_API_PYQT, QT_API_PYQTv2]: # PyQt4 API from PyQt4 import QtCore, QtGui try: if sip.getapi("QString") > 1: # Use new getSaveFileNameAndFilter() _getSaveFileName = QtGui.QFileDialog.getSaveFileNameAndFilter else: # Use old getSaveFileName() def _getSaveFileName(*args, **kwargs): return (QtGui.QFileDialog.getSaveFileName(*args, **kwargs), None) except (AttributeError, KeyError): # call to getapi() can fail in older versions of sip def _getSaveFileName(*args, **kwargs): return QtGui.QFileDialog.getSaveFileName(*args, **kwargs), None else: # PyQt5 API from PyQt5 import QtCore, QtGui, QtWidgets _getSaveFileName = QtWidgets.QFileDialog.getSaveFileName # Alias PyQt-specific functions for PySide compatibility. QtCore.Signal = QtCore.pyqtSignal try: QtCore.Slot = QtCore.pyqtSlot except AttributeError: # Not a perfect match but works in simple cases QtCore.Slot = QtCore.pyqtSignature QtCore.Property = QtCore.pyqtProperty __version__ = QtCore.PYQT_VERSION_STR else: # try importing pyside try: from PySide import QtCore, QtGui, __version__, __version_info__ except ImportError: raise ImportError( "Matplotlib qt-based backends require an external PyQt4, PyQt5,\n" "or PySide package to be installed, but it was not found.") if __version_info__ < (1, 0, 3): raise ImportError( "Matplotlib backend_qt4 and backend_qt4agg require PySide >=1.0.3") _getSaveFileName = QtGui.QFileDialog.getSaveFileName # Apply shim to Qt4 APIs to make them look like Qt5 if QT_API in (QT_API_PYQT, QT_API_PYQTv2, QT_API_PYSIDE): '''Import all used QtGui objects into QtWidgets Here I've opted to simple copy QtGui into QtWidgets as that achieves the same result as copying over the objects, and will continue to work if other objects are used. ''' QtWidgets = QtGui

f7196801f8fa58470aa03ad73efa1012011af858

py

Python

sacla/scripts/backups/sacla3_Chip_Manager_v7BAK.py

beamline-i24/DiamondChips

02fb58a95ad2c1712c41b641eb5f197d688c54c3

sacla/scripts/backups/sacla3_Chip_Manager_v7BAK.py

beamline-i24/DiamondChips

02fb58a95ad2c1712c41b641eb5f197d688c54c3

sacla/scripts/backups/sacla3_Chip_Manager_v7BAK.py

beamline-i24/DiamondChips

02fb58a95ad2c1712c41b641eb5f197d688c54c3

#!/usr/bin/python import pv, os, re, sys import math, time, string import numpy as np from time import sleep from ca import caput, caget import logging as lg import sacla3_Chip_StartUp_v7 as startup import sacla3_Chip_Mapping_v7 as mapping lg.basicConfig(format='%(asctime)s %(levelname)s: \t%(message)s',level=lg.DEBUG, filename='SACLA3v7.log') ############################################## # MANAGER MANAGER MANAGER MANAGER MANAGER # # This version last edited 03Sep2017 by DAS # # Prep for SACLA3 # ############################################## def initialise(): lg.info('INITIALISED') lg.warning('INITIALISED') lg.debug('INITIALISED') caput(pv.me14e_stage_x + '.VMAX', 15) caput(pv.me14e_stage_y + '.VMAX', 15) caput(pv.me14e_stage_z + '.VMAX', 15) caput(pv.me14e_filter + '.VMAX', 15) caput(pv.me14e_stage_x + '.VELO', 15) caput(pv.me14e_stage_y + '.VELO', 15) caput(pv.me14e_stage_z + '.VELO', 15) caput(pv.me14e_filter + '.VELO', 15) caput(pv.me14e_stage_x + '.ACCL', 0.01) caput(pv.me14e_stage_y + '.ACCL', 0.01) caput(pv.me14e_stage_z + '.ACCL', 0.01) caput(pv.me14e_filter + '.ACCL', 0.01) caput(pv.me14e_stage_x + '.HLM', 30) caput(pv.me14e_stage_x + '.LLM', -30) caput(pv.me14e_stage_y + '.HLM', 30) caput(pv.me14e_stage_y + '.LLM', -30) caput(pv.me14e_stage_z + '.HLM', 5.1) caput(pv.me14e_stage_z + '.LLM', -4.1) caput(pv.me14e_filter + '.HLM', 0.1) caput(pv.me14e_filter + '.LLM', -45.0) caput('ME14E-MO-IOC-01:GP1', 0) caput('ME14E-MO-IOC-01:GP2', 0) print 'Clearing' for i in range(3, 100): pvar = 'ME14E-MO-IOC-01:GP' + str(i) val = caput(pvar, 1) sys.stdout.write('.') sys.stdout.flush() print '\nDONT FORGET TO DO THIS: export EPICS_CA_ADDR_LIST=172.23.190.255' print 'DONT FORGET TO DO THIS: export EPICS_CA_AUTO_ADDR_LIST=NO' print 'Initialisation Complete' def write_parameter_file(): print '\n\n', 10*'set', '\n' #param_path = '/dls_sw/i24/scripts/fastchips/parameter_files/' param_path = '/localhome/local/Documents/sacla/parameter_files/' param_fid = 'parameters.txt' print 'Writing Parameter File\n', param_path+param_fid lg.info('Writing Parameter File\n', param_path+param_fid) lg.info('CHIP_MANAGER\twrite_parameter_file:Writing') f = open(param_path + param_fid,'w') chip_name = caget(pv.me14e_chip_name) f.write('chip_name \t%s\n' %chip_name) print 'chip_name:', chip_name #f.write('path \t%s\n' %path) #print 'path:', path protein_name = caget(pv.me14e_filepath) f.write('protein_name \t%s\n' %protein_name) print 'protein_name:', protein_name n_exposures = caget(pv.me14e_gp3) f.write('n_exposures \t%s\n' %n_exposures) print 'n_exposures', n_exposures chip_type = caget(pv.me14e_gp1) #### Hack for sacla3 to bismuth chip type for oxford inner if str(chip_type) =='3': chip_type = '1' f.write('chip_type \t%s\n' %chip_type) print 'chip_type', chip_type map_type = caget(pv.me14e_gp2) f.write('map_type \t%s\n' %map_type) print 'map_type', map_type f.close() print '\n', 10*'set', '\n\n' def define_current_chip(chipid): load_stock_map('clear') """ Not sure what this is for: print 'Setting Mapping Type to Lite' caput(pv.me14e_gp2, 1) """ chip_type = caget(pv.me14e_gp1) print chip_type, chipid if chipid == 'toronto': caput(pv.me14e_gp1, 0) elif chipid == 'oxford': caput(pv.me14e_gp1, 1) elif chipid == 'hamburg': caput(pv.me14e_gp1, 2) elif chipid == 'hamburgfull': caput(pv.me14e_gp1, 2) elif chipid == 'bismuth1': caput(pv.me14e_gp1, 3) elif chipid == 'bismuth2': caput(pv.me14e_gp1, 4) elif chipid == 'regina': caput(pv.me14e_gp1, 5) #param_path = '/dls_sw/i24/scripts/fastchips/parameter_files/' param_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(param_path + chipid + '.pvar', 'r') for line in f.readlines(): s = line.rstrip('\n') print s if line.startswith('#'): continue caput(pv.me14e_pmac_str, s) print param_path + chipid + '.chip' print 10*'Done ' def save_screen_map(): #litemap_path = '/dls_sw/i24/scripts/fastchips/litemaps/' litemap_path = '/localhome/local/Documents/sacla/parameter_files/' print '\n\nSaving', litemap_path + 'currentchip.map' f = open(litemap_path + 'currentchip.map','w') print 'Printing only blocks with block_val == 1' for x in range(1, 82): block_str = 'ME14E-MO-IOC-01:GP%i' %(x+10) block_val = caget(block_str) if block_val == 1: print block_str, block_val line = '%02dstatus P3%02d1 \t%s\n' %(x, x, block_val) f.write(line) f.close() print 10*'Done ' return 0 def upload_parameters(chipid): if chipid == 'toronto': caput(pv.me14e_gp1, 0) width = 9 elif chipid == 'oxford': caput(pv.me14e_gp1, 1) width = 8 elif chipid == 'hamburg': caput(pv.me14e_gp1, 2) width = 3 elif chipid == 'bismuth1': caput(pv.me14e_gp1, 3) width = 1 elif chipid == 'bismuth2': caput(pv.me14e_gp1, 4) width = 7 elif chipid == 'regina': caput(pv.me14e_gp1, 5) width = 7 #litemap_path = '/dls_sw/i24/scripts/fastchips/litemaps/' litemap_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(litemap_path + 'currentchip.map','r') print 'chipid', chipid print width x = 1 for line in f.readlines()[:width**2]: cols = line.split( ) pvar = cols[1] value = cols[2] s = pvar +'='+ value if value != '1': s2 = pvar + ' ' sys.stdout.write(s2) else: sys.stdout.write(s+' ') sys.stdout.flush() if x == width: print x = 1 else: x += 1 caput(pv.me14e_pmac_str, s) sleep(0.02) print print 'Setting Mapping Type to Lite' caput(pv.me14e_gp2, 1) print 10*'Done ' def upload_full(): #fullmap_path = '/dls_sw/i24/scripts/fastchips/fullmaps/' fullmap_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(fullmap_path + 'currentchip.full', 'r').readlines() for x in range(len(f) / 2): pmac_list = [] for i in range(2): pmac_list.append(f.pop(0).rstrip('\n')) writeline = " ".join(pmac_list) print writeline caput(pv.me14e_pmac_str, writeline) sleep(0.02) print 10*'Done ' def load_stock_map(map_choice): print 'Please wait, adjusting lite map' # r33 = [19,18,17,26,31,32,33,24,25] r55 = [9,10,11,12,13,16,27,30,41,40,39,38,37,34,23,20] + r33 r77 = [7,6,5,4,3,2,1,14,15,28,29,42,43,44,45,46,47,48,49,36,35,22,21,8] + r55 # h33 = [3,2,1,6,7,8,9,4,5] x33 = [31,32,33,40,51,50,49,42,41] x55 = [25,24,23,22,21,34,39,52,57,58,59,60,61,48,43,30] + x33 x77 = [11,12,13,14,15,16,17,20,35,38,53,56,71,70,69,68,67,66,65,62,47,44,29,26] + x55 x99 = [9,8,7,6,5,4,3,2,1,18,19,36,37,54,55,72,73,74,75,76,77,78,79,80,81,64,63,46,45,28,27,10] + x77 x44 = [22,21,20,19,30,35,46,45,44,43,38,27,28,29,36,37] x49 = [x+1 for x in range(49)] x66 = [10,11,12,13,14,15,18,31,34,47,50,51,52,53,54,55,42,39,26,23] + x44 x88 = [8,7,6,5,4,3,2,1,16,17,32,33,48,49,64,63,62,61,60,59,58,57,56,41,40,25,24,9] + x66 map_dict = {} map_dict['clear']= [1] # map_dict['r33'] = r33 map_dict['r55'] = r55 map_dict['r77'] = r77 # map_dict['h33'] = h33 # map_dict['x33'] = x33 map_dict['x44'] = x44 map_dict['x49'] = x49 map_dict['x55'] = x55 map_dict['x66'] = x66 map_dict['x77'] = x77 map_dict['x88'] = x88 map_dict['x99'] = x99 print 'Clearing' for i in range(1, 82): pvar = 'ME14E-MO-IOC-01:GP' + str(i + 10) caput(pvar, 0) sys.stdout.write('.') sys.stdout.flush() print '\nmap cleared' print 'loading map_choice', map_choice for i in map_dict[map_choice]: pvar = 'ME14E-MO-IOC-01:GP' + str(i + 10) caput(pvar, 1) print 10*'Done ' def load_lite_map(): load_stock_map('clear') toronto_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03', 'A4':'04', 'A5':'05', 'A6':'06','A7':'07', 'A8':'08', 'A9':'09' ,'B1':'18', 'B2':'17', 'B3':'16', 'B4':'15', 'B5':'14', 'B6':'13','B7':'12', 'B8':'11', 'B9':'10' ,'C1':'19', 'C2':'20', 'C3':'21', 'C4':'22', 'C5':'23', 'C6':'24','C7':'25', 'C8':'26', 'C9':'27' ,'D1':'36', 'D2':'35', 'D3':'34', 'D4':'33', 'D5':'32', 'D6':'31','D7':'30', 'D8':'29', 'D9':'28' ,'E1':'37', 'E2':'38', 'E3':'39', 'E4':'40', 'E5':'41', 'E6':'42','E7':'43', 'E8':'44', 'E9':'45' ,'F1':'54', 'F2':'53', 'F3':'52', 'F4':'51', 'F5':'50', 'F6':'49','F7':'48', 'F8':'47', 'F9':'46' ,'G1':'55', 'G2':'56', 'G3':'57', 'G4':'58', 'G5':'59', 'G6':'60','G7':'61', 'G8':'62', 'G9':'63' ,'H1':'72', 'H2':'71', 'H3':'70', 'H4':'69', 'H5':'68', 'H6':'67','H7':'66', 'H8':'65', 'H9':'64' ,'I1':'73', 'I2':'74', 'I3':'75', 'I4':'76', 'I5':'77', 'I6':'78','I7':'79', 'I8':'80', 'I9':'81'} #Oxford_block_dict is wrong (columns and rows need to flip) added in script below to generate it automatically however kept this for backwards compatiability/reference oxford_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03', 'A4':'04', 'A5':'05', 'A6':'06','A7':'07', 'A8':'08' ,'B1':'16', 'B2':'15', 'B3':'14', 'B4':'13', 'B5':'12', 'B6':'11','B7':'10', 'B8':'09' ,'C1':'17', 'C2':'18', 'C3':'19', 'C4':'20', 'C5':'21', 'C6':'22','C7':'23', 'C8':'24' ,'D1':'32', 'D2':'31', 'D3':'30', 'D4':'29', 'D5':'28', 'D6':'27','D7':'26', 'D8':'25' ,'E1':'33', 'E2':'34', 'E3':'35', 'E4':'36', 'E5':'37', 'E6':'38','E7':'39', 'E8':'40' ,'F1':'48', 'F2':'47', 'F3':'46', 'F4':'45', 'F5':'44', 'F6':'43','F7':'42', 'F8':'41' ,'G1':'49', 'G2':'50', 'G3':'51', 'G4':'52', 'G5':'53', 'G6':'54','G7':'55', 'G8':'56' ,'H1':'64', 'H2':'63', 'H3':'62', 'H4':'61', 'H5':'60', 'H6':'59','H7':'58', 'H8':'57'} regina_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03', 'A4':'04', 'A5':'05', 'A6':'06','A7':'07' ,'B1':'14', 'B2':'13', 'B3':'12', 'B4':'11', 'B5':'10', 'B6':'09','B7':'08' ,'C1':'15', 'C2':'16', 'C3':'17', 'C4':'18', 'C5':'19', 'C6':'20','C7':'21' ,'D1':'28', 'D2':'27', 'D3':'26', 'D4':'25', 'D5':'24', 'D6':'23','D7':'22' ,'E1':'29', 'E2':'30', 'E3':'31', 'E4':'32', 'E5':'33', 'E6':'34','E7':'35' ,'F1':'42', 'F2':'41', 'F3':'40', 'F4':'39', 'F5':'38', 'F6':'37','F7':'36' ,'G1':'43', 'G2':'44', 'G3':'45', 'G4':'46', 'G5':'47', 'G6':'48','G7':'49'} hamburg_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03' ,'B1':'06', 'B2':'05', 'B3':'04' ,'C1':'07', 'C2':'08', 'C3':'09'} chip_type = caget(pv.me14e_gp1) if chip_type == 0: print 'Toronto Block Order' block_dict = toronto_block_dict elif chip_type == 1: print 'Oxford Block Order' #block_dict = oxford_block_dict rows = ['A','B','C','D','E','F','G','H'] columns = list(range(1,9)) btn_names = {} flip = True for x, column in enumerate(columns): for y,row in enumerate(rows): i=x*8+y if i%8 == 0 and flip == False: flip = True z = 8 - (y+1) elif i%8 == 0 and flip == True: flip = False z = y elif flip == False: z = y elif flip == True: z = 8 - (y+1) else: print('something is wrong with chip grid creation') break button_name = str(row)+str(column) lab_num = x*8+z label='%02.d'%(lab_num+1) btn_names[button_name] = label #print button_name, btn_names[button_name] block_dict = btn_names elif chip_type == 2: print 'Hamburg Block Order' block_dict = hamburg_block_dict elif chip_type == 5: print 'Regina Block Order' block_dict = regina_block_dict #litemap_path = '/dls_sw/i24/scripts/fastchips/litemaps/' litemap_path = '/localhome/local/Documents/sacla/parameter_files/' litemap_fid = str(caget(pv.me14e_gp5)) + '.lite' print 'opening', litemap_path + litemap_fid f = open(litemap_path + litemap_fid, 'r') print 'please wait, loading LITE map' for line in f.readlines(): entry = line.split() block_name = entry[0] yesno = entry[1] block_num = block_dict[block_name] pvar = 'ME14E-MO-IOC-01:GP' + str(int(block_num) + 10) print block_name, yesno, pvar caput(pvar, yesno) print 10*'Done ' def load_full_map(location ='SACLA'): if location == 'i24': chip_name, visit, sub_dir, n_exposures, chip_type, map_type = startup.scrape_parameter_file(location) else: chip_name, sub_dir, n_exposures, chip_type, map_type = startup.scrape_parameter_file(location) #fullmap_path = '/dls_sw/i24/scripts/fastchips/fullmaps/' fullmap_path = '/localhome/local/Documents/sacla/parameter_files/' fullmap_fid = fullmap_path + str(caget(pv.me14e_gp5)) + '.spec' print 'opening', fullmap_fid mapping.plot_file(fullmap_fid, chip_type) print '\n\n', 10*'PNG ' mapping.convert_chip_to_hex(full_map_fid, chip_type) os.system("cp %s %s" % (fullmap_fid[:-4]+'full', fullmap_path+'currentchip.full')) print 10*'Done ', '\n' def moveto(place): print 5 * (place + ' ') chip_type = caget(pv.me14e_gp1) print 'CHIP TYPE', chip_type if chip_type == 0: print 'Toronto Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, +18.975) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, +21.375) elif chip_type == 1: print 'Oxford Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, 25.40) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 25.40) elif chip_type == 2: print 'Hamburg Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': #caput(pv.me14e_stage_x, +17.16) caput(pv.me14e_stage_x, +24.968) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) #caput(pv.me14e_stage_y, -26.49) caput(pv.me14e_stage_y, +24.968) elif chip_type == 3: print 'Oxford Inner Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, 24.60) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 24.60) elif chip_type == 5: print 'Regina Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, +17.175) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, +17.175) else: print 'Unknown chip_type move' # Non Chip Specific Move if place == 'zero': caput(pv.me14e_pmac_str, '!x0y0z0') elif place == 'yag': caput(pv.me14e_stage_x, 1.0) caput(pv.me14e_stage_y, 1.0) caput(pv.me14e_stage_z, 1.0) elif place == 'load_position': print 'load position' caput(pv.me14e_filter, -25) caput(pv.me14e_stage_x, -25.0) caput(pv.me14e_stage_y, -25.0) caput(pv.me14e_stage_z, 0.0) caput(pv.me14e_pmac_str, 'M512=0 M511=1') #caput(pv.absb_mp_select, 'Robot') #caput(pv.ap1_mp_select, 'Robot') #caput(pv.blight_mp_select, 'Out') #caput(pv.det_z, 1480) elif place == 'collect_position': print 'collect position' caput(pv.me14e_filter, 25) caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) caput(pv.me14e_stage_z, 0.0) caput(pv.me14e_pmac_str, 'M512=0 M511=1') #caput(pv.absb_mp_select, 'Data Collection') #caput(pv.ap1_mp_select, 'In') #caput(pv.blight_mp_select, 'In') elif place == 'lightin': print 'light in' caput(pv.me14e_filter, 25) elif place == 'lightout': print 'light out' caput(pv.me14e_filter, -25) elif place == 'flipperin': ##### nb need M508=100 M509 =150 somewhere caput(pv.me14e_pmac_str, 'M512=0 M511=1') elif place == 'flipperout': caput(pv.me14e_pmac_str, ' M512=1 M511=1') def scrape_mtr_directions(): #param_path = '/dls_sw/i24/scripts/fastchips/parameter_files/' param_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(param_path + 'motor_direction.txt', 'r') mtr1_dir, mtr2_dir, mtr3_dir = 1,1,1 for line in f.readlines(): if line.startswith('mtr1'): mtr1_dir = float(int(line.split('=')[1])) elif line.startswith('mtr2'): mtr2_dir = float(int(line.split('=')[1])) elif line.startswith('mtr3'): mtr3_dir = float(int(line.split('=')[1])) else: continue f.close() return mtr1_dir, mtr2_dir, mtr3_dir def fiducial(point): scale = 10000.0 #param_path = '/dls_sw/i24/scripts/fastchips/parameter_files/' param_path = '/localhome/local/Documents/sacla/parameter_files/' mtr1_dir, mtr2_dir, mtr3_dir = scrape_mtr_directions() rbv_1 = caget(pv.me14e_stage_x + '.RBV') rbv_2 = caget(pv.me14e_stage_y + '.RBV') rbv_3 = caget(pv.me14e_stage_z + '.RBV') raw_1 = caget(pv.me14e_stage_x + '.RRBV') raw_2 = caget(pv.me14e_stage_y + '.RRBV') raw_3 = caget(pv.me14e_stage_z + '.RRBV') """ June 8th 2017 change from this to rbv f_x = (mtr1_dir*raw_1) / scale f_y = (mtr2_dir*raw_2) / scale f_z = (mtr3_dir*raw_3) / scale """ f_x = rbv_1 f_y = rbv_2 f_z = rbv_3 print '\nWriting Fiducial File', 20*('%s ' %point) print 'MTR\tRBV\tRAW\tDirect.\tf_value' print 'MTR1\t%1.4f\t%i\t%i\t%1.4f' % (rbv_1, raw_1, mtr1_dir, f_x) print 'MTR2\t%1.4f\t%i\t%i\t%1.4f' % (rbv_2, raw_2, mtr2_dir, f_y) print 'MTR3\t%1.4f\t%i\t%i\t%1.4f' % (rbv_3, raw_3, mtr3_dir, f_z) print 'Writing Fiducial File', 20*('%s ' %point) f = open(param_path + 'fiducial_%s.txt' %point, 'w') f.write('MTR\tRBV\tRAW\tCorr\tf_value\n') f.write('MTR1\t%1.4f\t%i\t%i\t%1.4f\n' % (rbv_1, raw_1, mtr1_dir, f_x)) f.write('MTR2\t%1.4f\t%i\t%i\t%1.4f\n' % (rbv_2, raw_2, mtr2_dir, f_y)) f.write('MTR3\t%1.4f\t%i\t%i\t%1.4f' % (rbv_3, raw_3, mtr3_dir, f_z)) f.close() print 10*'Done ' def scrape_mtr_fiducials(point): #param_path = '/dls_sw/i24/scripts/fastchips/parameter_files/' param_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(param_path+'fiducial_%i.txt' %point,'r') f_lines = f.readlines()[1:] f_x = float(f_lines[0].rsplit()[4]) f_y = float(f_lines[1].rsplit()[4]) f_z = float(f_lines[2].rsplit()[4]) f.close() return f_x, f_y, f_z def cs_maker(): chip_type = caget(pv.me14e_gp1) fiducial_dict = {} fiducial_dict[0] = [18.975, 21.375] fiducial_dict[1] = [25.400, 25.400] fiducial_dict[2] = [24.968, 24.968] fiducial_dict[3] = [24.600, 24.600] fiducial_dict[4] = [27.500, 27.500] fiducial_dict[5] = [17.175, 17.175] print chip_type, fiducial_dict[chip_type] mtr1_dir, mtr2_dir, mtr3_dir = scrape_mtr_directions() f1_x, f1_y, f1_z = scrape_mtr_fiducials(1) f2_x, f2_y, f2_z = scrape_mtr_fiducials(2) print 'AAAAAAAAAAAAAAAAABBBBBBBBBBBBBB' print 'mtr1 direction', mtr1_dir print 'mtr2 direction', mtr2_dir print 'mtr3 direction', mtr3_dir """ Theory Rx: rotation about X-axis, pitch Ry: rotation about Y-axis, yaw Rz: rotation about Z-axis, roll The order of rotation is Roll->Yaw->Pitch (Rx*Ry*Rz) Rx Ry Rz |1 0 0| | Cy 0 Sy| |Cz -Sz 0| | CyCz -CxSz Sy | |0 Cx -Sx|*| 0 1 0|*|Sz Cz 0| = | SxSyCz+CxSz -SxSySz+CxCz -SxCy| |0 Sx Cx| |-Sy 0 Cy| | 0 0 1| |-CxSyCz+SxSz CxSySz+SxCz CxCy| BELOW iS TEST TEST (CLOCKWISE) Rx Ry Rz |1 0 0| | Cy 0 -Sy| |Cz Sz 0| | CyCz CxSz -Sy | |0 Cx Sx|*| 0 1 0|*|-Sz Cz 0| = | SxSyCz-CxSz SxSySz+CxCz SxCy| |0 -Sx Cx| | Sy 0 Cy| | 0 0 1| | CxSyCz+SxSz CxSySz-SxCz CxCy| """ # Rotation Around Z # # If stages upsidedown (I24) change sign of Sz Sz1 = f1_y / fiducial_dict[chip_type][0] Sz2 = -1 * (f2_x / fiducial_dict[chip_type][1]) Sz = ((Sz1 + Sz2) / 2) Cz = np.sqrt((1 - Sz**2)) print 'Sz1 , %1.4f, %1.4f' % (Sz1, np.degrees(np.arcsin(Sz1))) print 'Sz2 , %1.4f, %1.4f' % (Sz2, np.degrees(np.arcsin(Sz2))) print 'Sz , %1.4f, %1.4f' % (Sz, np.degrees(np.arcsin(Sz))) print 'Cz , %1.4f, %1.4f\n' % (Cz, np.degrees(np.arccos(Cz))) # Rotation Around Y # Sy = f1_z / fiducial_dict[chip_type][0] Cy = np.sqrt((1 - Sy**2)) print 'Sy , %1.4f, %1.4f' % (Sy, np.degrees(np.arcsin(Sy))) print 'Cy , %1.4f, %1.4f\n' % (Cy, np.degrees(np.arccos(Cy))) # Rotation Around X # # If stages upsidedown (I24) change sign of Sx Sx = -1* f2_z / fiducial_dict[chip_type][1] Cx = np.sqrt((1 - Sx**2)) print 'Sx , %1.4f, %1.4f' % (Sx, np.degrees(np.arcsin(Sx))) print 'Cx , %1.4f, %1.4f\n' % (Cx, np.degrees(np.arccos(Cx))) # Crucifix 1: In normal orientation (sat on table facing away) # X=0.0000 , Y=0.0000, Z=0.0001000 (mm/cts for MRES and ERES) #scalex,scaley,scalez = 10010.0, 10000.0, 10000.0 # Crucifix 1: In beamline position (upside down facing away) # X=0.000099896 , Y=0.000099983, Z=0.0001000 (mm/cts for MRES and ERES) scalex, scaley, scalez = 10010.4, 10001.7, 10000.0 # Crucifix 2: In normal orientation (sat on table facing away) # X=0.0000999 , Y=0.00009996, Z=0.0001000 (mm/cts for MRES and ERES) #scalex,scaley,scalez = 10010.0, 10004.0, 10000.0 # Temple 1: In normal orientation (sat on table facing away) # X=0.0000 , Y=0.0000, Z=0.0001000 (mm/cts for MRES and ERES) #scalex,scaley,scalez = 10008.0, 10002.0, 10000.0 #minus signs added Aug17 in lab 30 preparing for sacla #added to y1factor x2factor x1factor = mtr1_dir * scalex * (Cy * Cz) y1factor = mtr2_dir * scaley * (-1. * Cx * Sz) z1factor = mtr3_dir * scalez * Sy x2factor = mtr1_dir * scalex * ((Sx*Sy*Cz) + (Cx*Sz)) y2factor = mtr2_dir * scaley * ((Cx*Cz) - (Sx*Sy*Sz)) z2factor = mtr3_dir * scalez * (-1. * Sx * Cy) x3factor = mtr1_dir * scalex * ((Sx*Sz) - (Cx*Sy*Cz)) y3factor = mtr2_dir * scaley * ((Cx*Sy*Sz) + (Sx*Cz)) z3factor = mtr3_dir * scalez * (Cx* Cy) """ Rx Ry Rz |1 0 0| | Cy 0 Sy| |Cz -Sz 0| | CyCz -CxSz Sy | |0 Cx -Sx|*| 0 1 0|*|Sz Cz 0| = | SxSyCz+CxSz -SxSySz+CxCz -SxCy| |0 Sx Cx| |-Sy 0 Cy| | 0 0 1| |-CxSyCz+SxSz CxSySz+SxCz CxCy| """ # skew is the difference between the Sz1 and Sz2 after rotation is taken out. # this should be measured in situ prior to expriment # In situ is measure by hand using opposite and adjacent RBV after calibration of # scale factors #print 10*'WARNING\n', '\nHave you calculated skew?\n\n', 10*'WARNING\n' # Crucifix 1 on table #skew = -0.187 # Crucifix 1 on beamline #skew = -0.1568 skew = 0.1863 # Crucifix 2 #skew = 0.060 # Temple 1 #skew = 0.02 print 'Skew being used is: %1.4f' %skew s1 = np.degrees(np.arcsin(Sz1)) s2 = np.degrees(np.arcsin(Sz2)) rot = np.degrees(np.arcsin((Sz1+Sz2) / 2)) calc_skew = ((s1-rot) - (s2-rot)) print 's1:%1.4f s2:%1.4f rot:%1.4f' %(s1, s2, rot) print 'Calculated rotation from current fiducials is: %1.4f' %rot print 'Calculated skew from current fiducials is: %1.4f' %calc_skew #skew = calc_skew sinD = np.sin((skew/2) * (np.pi/180)) cosD = np.cos((skew/2) * (np.pi/180)) new_x1factor = (x1factor * cosD) + (y1factor * sinD) new_y1factor = (x1factor * sinD) + (y1factor * cosD) new_x2factor = (x2factor * cosD) + (y2factor * sinD) new_y2factor = (x2factor * sinD) + (y2factor * cosD) cs1 = "#1->%+1.3fX%+1.3fY%+1.3fZ" % (new_x1factor, new_y1factor, z1factor) cs2 = "#2->%+1.3fX%+1.3fY%+1.3fZ" % (new_x2factor, new_y2factor, z2factor) cs3 = "#3->%+1.3fX%+1.3fY%+1.3fZ" % (x3factor, y3factor, z3factor) print '\n'.join([cs1, cs2, cs3]) print 'These should be 1. This is the sum of the squares of the factors divided by their scale' print np.sqrt(x1factor**2 + y1factor**2 + z1factor**2) / scalex print np.sqrt(x2factor**2 + y2factor**2 + z2factor**2) / scaley print np.sqrt(x3factor**2 + y3factor**2 + z3factor**2) / scalez print 'Long wait, please be patient' caput(pv.me14e_pmac_str, '!x0y0z0') sleep(2.5) caput(pv.me14e_pmac_str, '&2') caput(pv.me14e_pmac_str, cs1) caput(pv.me14e_pmac_str, cs2) caput(pv.me14e_pmac_str, cs3) caput(pv.me14e_pmac_str, '!x0y0z0') sleep(0.1) caput(pv.me14e_pmac_str, '#1hmz#2hmz#3hmz') sleep(0.1) print 5*'chip_type',type(chip_type) # NEXT THREE LINES COMMENTED OUT FOR CS TESTS 5 JUNE if str(chip_type) =='1': caput(pv.me14e_pmac_str, '!x0.4y0.4') sleep(0.1) caput(pv.me14e_pmac_str, '#1hmz#2hmz#3hmz') print 10*'CSDone ' else: caput(pv.me14e_pmac_str, '#1hmz#2hmz#3hmz') print 10*'CSDone ' def cs_reset(): cs1 = "#1->%+10000X%+0Y%+0Z" cs2 = "#2->%+0X%+10000Y%+0Z" cs3 = "#3->0X+0Y+10000Z" print '\n'.join([cs1, cs2, cs3]) caput(pv.me14e_pmac_str, '&2') sleep(0.5) caput(pv.me14e_pmac_str, cs1) sleep(0.5) caput(pv.me14e_pmac_str, cs2) sleep(0.5) caput(pv.me14e_pmac_str, cs3) print 10*'CSDone ' def main(args): if args[1] == 'initialise': initialise() elif args[1] == 'pvar_test': chipid = args[2] pvar_test(chipid) elif args[1] == 'moveto': moveto(args[2]) elif args[1] == 'fiducial': fiducial(args[2]) elif args[1] == 'cs_maker': cs_maker() elif args[1] == 'write_parameter_file': write_parameter_file() startup.run() elif args[1] == 'define_current_chip': chipid = args[2] define_current_chip(chipid) elif args[1] == 'load_stock_map': map_choice = args[2] load_stock_map(map_choice) elif args[1] == 'load_lite_map': load_lite_map() elif args[1] == 'load_full_map': load_full_map() elif args[1] == 'save_screen_map': save_screen_map() elif args[1] == 'upload_full': upload_full() elif args[1] == 'upload_parameters': chipid = args[2] upload_parameters(chipid) elif args[1] == 'cs_reset': cs_reset() else: print 'Unknown Command' if __name__ == '__main__': main(sys.argv)

f71968c2bfbb4980fde3dad9d2991f5150aef9eb

py

Python

setup.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

setup.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

2016-11-01T18:42:34.000Z

2020-11-16T16:52:14.000Z

setup.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

2020-01-22T18:20:46.000Z

2020-01-22T18:20:46.000Z

import os try: from setuptools import setup, find_packages except ImportError: from ez_setup import use_setuptools use_setuptools() from setuptools import setup, find_packages # pip install -e .[develop] develop_requires = [ 'WebTest', 'ScriptTest', 'coverage', 'docutils', 'minimock', 'nose', ] cdir = os.path.abspath(os.path.dirname(__file__)) README = open(os.path.join(cdir, 'readme.rst')).read() CHANGELOG = open(os.path.join(cdir, 'changelog.rst')).read() VERSION = open(os.path.join(cdir, 'blazeweb', 'version.txt')).read().strip() required_packages = [ 'Beaker>=1.5', 'BlazeUtils>0.3.7', 'Blinker>=1.0', 'decorator>=3.0.1', 'FormEncode>=1.2', 'html2text>=2.35', 'jinja2>=2.5', 'markdown2>=1.0.1', 'Paste>=1.7', 'PasteScript>=1.7', 'WebHelpers2', 'Werkzeug>=1.0.0', ] try: import json del json except ImportError: required_packages.append('simplejson>=2.1.1') setup( name="BlazeWeb", version=VERSION, description="A light weight WSGI framework with a pluggable architecture", long_description='\n\n'.join((README, CHANGELOG)), author="Randy Syring", author_email="randy.syring@level12.io", url='http://pypi.python.org/pypi/BlazeWeb/', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Internet :: WWW/HTTP' ], license='BSD', packages=find_packages(exclude=['tests']), include_package_data=True, install_requires=required_packages, extras_require={'develop': develop_requires}, entry_points=""" [console_scripts] bw = blazeweb.scripting:blazeweb_entry [blazeweb.no_app_command] help=paste.script.help:HelpCommand project = blazeweb.commands:ProjectCommand jinja-convert = blazeweb.commands:JinjaConvertCommand [blazeweb.app_command] serve = blazeweb.commands:ServeCommand help = paste.script.help:HelpCommand testrun = blazeweb.commands:TestRunCommand tasks = blazeweb.commands:TasksCommand shell = blazeweb.commands:ShellCommand routes = blazeweb.commands:RoutesCommand static-copy = blazeweb.commands:StaticCopyCommand component-map = blazeweb.commands:ComponentMapCommand [blazeweb.blazeweb_project_template] minimal = blazeweb.paster_tpl:MinimalProjectTemplate bwproject = blazeweb.paster_tpl:ProjectTemplate [nose.plugins] blazeweb_initapp = blazeweb.nose_plugin:InitAppPlugin [pytest11] blazeweb_initapp = blazeweb.pytest_plugin """, zip_safe=False )

f719a60077cb4b23bbe3c54efafc1d30bc3f8163

py

Python

config.py

LongKt7/Face_Recognize_Pytorch

baa02e633d379abe1001c8b8acb942617177329c

2019-03-13T16:05:11.000Z

2019-03-13T16:05:11.000Z

config.py

LongKt7/Face_Recognize_Pytorch

baa02e633d379abe1001c8b8acb942617177329c

config.py

LongKt7/Face_Recognize_Pytorch

baa02e633d379abe1001c8b8acb942617177329c

2019-03-15T09:09:08.000Z

2019-03-15T09:09:08.000Z

from easydict import EasyDict as edict # from pathlib import Path import torch import os from torchvision import transforms as trans from utils.constants import * list_model = ['wget https://www.dropbox.com/s/akktsgxp0n8cwn2/model_mobilefacenet.pth?dl=0 -O model_mobilefacenet.pth', 'wget https://www.dropbox.com/s/kzo52d9neybjxsb/model_ir_se50.pth?dl=0 -O model_ir_se50.pth', 'wget https://www.dropbox.com/s/rxavczg9dlxy3a8/model_ir50.pth?dl=0 -O model_ir50.pth'] def get_config(mode = 'app', net_size = 'large', net_mode = 'ir_se', use_mtcnn = 1, threshold = 1.25): conf = edict() conf.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") conf.input_size = [112, 112] conf.face_limit = 5 conf.min_face_size = 30 conf.mode = mode conf.net_size = net_size if mode =='app': assert net_size in ['mobi', 'large', None], 'net_size should be mobi or large, please change in cogfig.py' conf.use_tensor = True conf.work_path = WORK_PATH conf.model_path = '%s/models'%WORK_PATH conf.log_path = '%s/log'%WORK_PATH conf.save_path = '%s/save'%WORK_PATH conf.facebank_path = '%s/Face_bank'%WORK_PATH conf.threshold = threshold if use_mtcnn: conf.use_mtcnn = True else: conf.use_mtcnn = False #when inference, at maximum detect 10 faces in one image, my laptop is slow conf.test_transform = trans.Compose([ trans.ToTensor(), trans.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) ]) if net_size == 'large': conf.use_mobilfacenet = False if net_mode == 'ir_se': conf.net_mode = 'ir_se' # or 'ir' conf.weight_path = '%s/weights/model_ir_se50.pth'%WORK_PATH conf.url = list_model[1] else: conf.net_mode = 'ir' # or 'ir' conf.weight_path = '%s/weights/model_ir50.pth'%WORK_PATH conf.url = list_model[2] if net_size =='mobi': conf.use_mobilfacenet = True conf.weight_path = '%s/weights/model_mobilefacenet.pth'%WORK_PATH conf.url = list_model[0] conf.video_source = 0 if mode =='training_eval': conf.lr = 1e-3 conf.milestones = [18,30,42] conf.momentum = 0.9 conf.pin_memory = True # conf.num_workers = 4 # when batchsize is 200 conf.num_workers = 3 conf.train_root = "/mnt/01D4A1D481139570/Dataset/Face/casia" conf.file_list = '/mnt/01D4A1D481139570/Dataset/Face/casia_train.txt' conf.batch_size = 4 conf.lfw_root = '/mnt/01D4A1D481139570/Dataset/Face/data/LFW/lfw_align_112' conf.lfw_file_list = '/mnt/01D4A1D481139570/Dataset/Face/data/LFW/pairs.txt' conf.agedb_root = '/mnt/01D4A1D481139570/Dataset/Face/data/AgeDB-30/agedb30_align_112' conf.agedb_file_list = '/mnt/01D4A1D481139570/Dataset/Face/data/AgeDB-30/agedb_30_pair.txt' conf.cfp_root = '/mnt/01D4A1D481139570/Dataset/Face/data/CFP-FP/CFP_FP_aligned_112' conf.cfp_file_list = '/mnt/01D4A1D481139570/Dataset/Face/data/CFP-FP/cfp_fp_pair.txt' return conf

f719a616152547d0300a25992cdb6dbefb41b0a6

py

Python

utils/tests/test_util.py

Splendon/examples

ed4a8a01857b6ddca49559141acf5d0986eb01e1

utils/tests/test_util.py

Splendon/examples

ed4a8a01857b6ddca49559141acf5d0986eb01e1

utils/tests/test_util.py

Splendon/examples

ed4a8a01857b6ddca49559141acf5d0986eb01e1

# Copyright 2019 Graphcore Ltd. from statistics import mean import numpy as np import os import re import subprocess import sys import time """Library of utility functions common between frameworks""" def parse_results_for_speed(output, iter_tolerance, speed_tolerance): """Look for <iter number> sec/itr. <speed number> {other stuff}""" found_a_result = False for line in output.split("\n"): matches = re.match(r"([\d.]+) +sec/itr. +([\d.]+)", line) if matches: found_a_result = True iterations, speed = matches.groups() iterations = float(iterations) speed = float(speed) _verify_model_numbers( iter_tolerance, iterations, speed_tolerance, speed, line ) if not found_a_result: raise AssertionError("No results detected in this run") def parse_results_for_accuracy(output, expected_accuracies, acc_tolerance): """Look for Accuracy=<accuracy>%""" accuracies = [] for line in output.split("\n"): if re.match(r" + Accuracy=+([\d.]+)%", line): accuracy = float(re.match(r" + Accuracy=+([\d.]+)%", line).groups()[0]) accuracies.append(accuracy) elif re.search(r"Validation accuracy", line): accuracy_str = re.search(r"accuracy:\s(.*)", line).group(1) accuracy = float(accuracy_str[:accuracy_str.rfind("%")]) accuracies.append(accuracy) if len(accuracies) == 0: raise AssertionError("No results detected in this run") elif len(accuracies) != len(expected_accuracies): raise AssertionError("Expected accuracies and parsed accuracies have" " different lengths") _verify_model_accuracies(accuracies, expected_accuracies, acc_tolerance) def _verify_model_numbers(iter_tolerance, iterations, speed_tolerance, speed, line): iter_error = "" speed_error = "" # Verify iteration speed if iterations > iter_tolerance[1]: iter_error = ("The time per iteration has regressed above" " the tolerance maximum: " + str(iter_tolerance[1])) elif iterations < iter_tolerance[0]: iter_error = ("Time taken to compete an iteration was " "suspiciously fast. Please verify the model" " is operating correctly and tune tolerances" " accordingly.") # Verify item processing speed if speed < speed_tolerance[0]: speed_error = ("The number of items processed per second" " has regressed below the tolerance: " + str(speed_tolerance[0])) elif speed > speed_tolerance[1]: speed_error = ("The number of items processed per second" " was suspiciously high. Please verify the" " model is behaving correctly and tune" " tolerances accordingly.") if iter_error and speed_error: sys.stderr.write("\n".join([line, iter_error, speed_error])) raise AssertionError("Timings out of tolerance range") elif iter_error or speed_error: sys.stderr.write(line) raise AssertionError(iter_error + speed_error) def _verify_model_accuracies(accuracies, expected_accuracy, acc_tolerance): """Asserts a list of accuracies is within a list of expected accuracies with a tolerance applied. Args: accuracies: A list of floats representing the accuracies (%) produced by the model at each step. expected_accuracy: A list of floats representing the expected accuracies (%) produced by the model at each step. acc_tolerance: A float representing a percentage tolerance applied on top of the expected accuracies that the accuracies produced by the model should sit within. Raises: Assertion Error: Accuracy produced by the model are not within the expected limits. """ for iter_num in range(len(accuracies)): exp_acc = expected_accuracy[iter_num] exp_acc_str = ( "{0} = {1} +- {2} = [{3:.{5}f}, {4:.{5}f}]".format( "Expected accuracy (%)".ljust(22), exp_acc, acc_tolerance, exp_acc - acc_tolerance, exp_acc + acc_tolerance, 2 ) ) acc = accuracies[iter_num] acc_str = "{} = {:.{}f}".format( "Accuracy (%)".ljust(22), acc, 2 ) full_acc_str = "{}\n{}".format(acc_str, exp_acc_str) if acc < exp_acc - acc_tolerance: raise AssertionError( "After iteration {}, the model is less accurate" " than expected.\n" "{}".format(iter_num + 1, full_acc_str) ) elif acc > exp_acc + acc_tolerance: raise AssertionError( "After iteration {}, the model is producing an accuracy" " that is suspiciously high and should be reviewed.\n" "{}".format(iter_num + 1, full_acc_str) ) def assert_result_equals_tensor_value(output, tensor): """Searches for a single tensor result in the first line of the output Searches the first line of the string output for a line with format '[array([3., 8.], dtype=float32)]' and asserts its equal to the numpy tensor argument Args: output: String containing the string representation of a numpy tensor tensor: numpy tensor representing the expected result Returns: None Raises: Assertion Error: Output is not in correct format Assertion Error: Output does not contain a string representation of a numpy array Assertion Error: Output numpy array does not equal the expected numpy array """ # TODO - np representation over multiple lines # TODO - large np array output # TODO - multiple dimension np output list_regex = r"^\[.*?\]$" np_array_str_regex = r"array\(.*?, dtype=.*?\)$" first_line = output.split("\n")[0] if not re.match(list_regex, first_line): raise AssertionError( "Result not in expected string format." " Expecting stringified list " " eg. [array([3., 8.], dtype=float32)]" ) contents = first_line[1:-1] if not re.match(np_array_str_regex, contents): raise AssertionError( "Expecting numpy representation " "array with dtype " "eg. array([3., 8.], dtype=float32)" ) assert contents == np.array_repr(tensor), ( "Output value {} does not " "equal expected value {}".format(np.array_repr(contents), tensor) ) def parse_results_for_ipus_used(output): """Finds the number of IPUs used in the model by looking for string with format ' On 2 IPUs.' in output""" shards_regex = r" On ([\d.]+) IPUs." for line in output.split("\n"): matches = re.match(shards_regex, line) if matches: shards = matches.group(1) return int(shards) raise AssertionError("Expecting line detailing IPU usage " "eg. ' On 2 IPUs.'") def assert_shards(output, expected_shards): """Verify the expected number of shards used were actually used""" actual_shards = parse_results_for_ipus_used(output) assert actual_shards == expected_shards def get_final_accuracy(output): """Find and return the accuracy reported in a test's output.""" result_regex = r"Accuracy=([\d.]+)\%" result_list = parse_results_with_regex(output, result_regex) result = result_list[0] return result[-1] def get_final_loss(output): """Find and return the loss reported in a test's output.""" result_regex = r"Loss=([\d.]+)" result_list = parse_results_with_regex(output, result_regex) result = result_list[0] return result[-1] def get_average_speeds(output): """Finds the average seconds/iteration and tokens/second Args: output: String representing the output of a test. Returns: A tuple where the first element is a float representing the average iterations per second and the second the average tokens processed per second """ result_regex = r"([\d.]+) +sec/itr. +([\d.]+)" results = parse_results_with_regex(output, result_regex) itr_sec_list = results[0] tokens_sec_list = results[1] return mean(itr_sec_list), mean(tokens_sec_list) def parse_results_with_regex(output, regex): """Find and returns the regex matching results in output Looks through the output line by line looking for a matching regex. The function assembles a list of lists where each parent list is the results for that position in the regex string and each item in the child lists represents an order of the results found in the output Args: output: String representing the output of a test. regex: Regex of result to find. Returns: A list of lists of floats. Parent list represents the result at each position in the regex. Child list contains results received in the order they were output. Raises: AssertionError: a line matching the regex could not be found in the output """ results = [] for line in output.split("\n"): matches = re.search(regex, line) if matches: number_of_results = matches.lastindex if results == []: results = [None] * number_of_results for match_index in range(0, number_of_results): result = float(matches.group(match_index + 1)) if results[match_index]: results[match_index].append(result) continue results[match_index] = [result] if results == []: raise AssertionError("Regex {} not found in result".format(regex)) return results def get_total_epochs(output): """Finds the number of epochs model has run through by looking for string with format 'Epoch #3' in the models raw output""" epochs = None for line in output.split("\n"): epoch_match = re.search(r"Epoch #([\d.]+)", line) if epoch_match: epochs = int(epoch_match.group(1)) if not epochs: raise AssertionError("Epochs not found in output, eg. " "Epoch #3") return epochs def assert_total_run_time(total_time, time_range): """Checks total run time is within the required range Args: total_time: float representing number of seconds the test took to run time_range: a tuple of floats where the first element is the minimum time the test should run in in seconds and the second the maximum Raises: AssertionError: if the total_time is not between the minimum time and maximum time """ minimum_time = time_range[0] maximum_time = time_range[1] assert total_time >= minimum_time assert total_time <= maximum_time def assert_final_accuracy(output, minimum, maximum): """Gets the final accuracy given a raw model output and checks its value is between the minimum and maximum Args: output: String representing the raw output of a model minimum: a float representing a percentage (between 0.0% and 100%) that is the minimum accuracy for the model after running maximum: a float representing a percentage (between 0.0% and 100%) that is the maximum accuracy for the model after running Raises: AssertionError: if the final accuracy is not between the maximum and minimum percentages """ accuracy = get_final_accuracy(output) assert accuracy >= minimum assert accuracy <= maximum def run_python_script_helper(cwd, script, **kwargs): """A function that given a path and python script name, runs the script with kwargs as the command line arguments Args: cwd: string representing the directory of the python script script: string representing the full name of the python script kwargs: dictionary of string key and values that form the command line arguments when the script is run. Returns: A string representing the raw output of the python script run Raises: AssertionError: if the final accuracy is not between the maximum and minimum percentages """ py_version = "python{}".format(sys.version_info[0]) cmd = [py_version, script] if kwargs: args = [ str(item) for sublist in kwargs.items() for item in sublist if item != "" ] cmd.extend(args) out = subprocess.check_output(cmd, cwd=cwd, universal_newlines=True) print(out) return out def run_test_helper(subprocess_function, total_run_time=None, total_run_time_tolerance=0.1, **kwargs): """Helper function for running tests Takes in testable parameters, runs the test and checks the relevant parameters against test results Args: subprocess_function: the function that runs a subprocess of the model in question total_run_time_range: tuple float representing the expected upper and lower bounds for the total time taken to run the test Returns: A String representing the raw output of the models subprocess Raises: AssertionError: If the accuracy, time taken etc. are not within the expected bounds """ start_time = time.time() out = subprocess_function(**kwargs) total_time = time.time() - start_time if total_run_time: total_run_time_range = range_from_tolerances( total_run_time, total_run_time_tolerance ) assert_total_run_time(total_time, total_run_time_range) return out def range_from_tolerances(value, tolerance): """Helper function that takes a value and applies the tolerance Args: value: a float representing the mean value to which the tolerance will be applied tolerance: a float representing a percentage (between 0.0 and 1.0) which is applied symmetrically across the value argument Returns: A tuple of floats, the first element representing the tolerance applied below the value (minimum) and the second above (maximum) """ return ( get_minimum_with_tolerance(value, tolerance), get_maximum_with_tolerance(value, tolerance), ) def get_minimum_with_tolerance(value, tolerance): """Helper function that takes a value and applies the tolerance below the value Args: value: a float representing the mean value to which the tolerance will be applied tolerance: a float representing a percentage (between 0.0 and 1.0) which is applied to the value argument Returns: A float representing the tolerance applied below the value (maximum) """ return value * (1 - tolerance) def get_maximum_with_tolerance(value, tolerance): """Helper function that takes a value and applies the tolerance above the value Args: value: a float representing the mean value to which the tolerance will be applied tolerance: a float representing a percentage (between 0.0 and 1.0) which is applied to the value argument Returns: A float representing the tolerance applied above the value (minimum) """ return value * (1 + tolerance) def check_data_exists(data_path, expected_files_list): """Helper function that checks the expected data exists in a directory Args: data_path: A string representing the directory of where the data is expected to be expected_files_list: a list of strings representing the expected file names in the data_path directory Returns: A boolean which represents whether the expected files are found in the data_path directory """ if os.path.exists(data_path): for filename in expected_files_list: if not os.path.isfile(os.path.join(data_path, filename)): return False return True return False

f719bd0e61d8fc8ee4756b2db46ad0dfa8dfa39d

py

Python

twisted/test/test_text.py

sxamit/twisted

30f6966329c857c3631c60aeb420d84d7828e01e

2017-08-07T14:52:02.000Z

2017-08-07T14:52:02.000Z

Lib/site-packages/twisted/test/test_text.py

adzhou/Python27

a7113b69d54a04cc780143241c2f1fe81939ad3a

Lib/site-packages/twisted/test/test_text.py

adzhou/Python27

a7113b69d54a04cc780143241c2f1fe81939ad3a

2018-11-07T12:52:07.000Z

2018-11-07T12:52:07.000Z

# Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. """ Tests for L{twisted.python.text}. """ from cStringIO import StringIO from twisted.trial import unittest from twisted.python import text sampleText = \ """Every attempt to employ mathematical methods in the study of chemical questions must be considered profoundly irrational and contrary to the spirit of chemistry ... If mathematical analysis should ever hold a prominent place in chemistry - an aberration which is happily almost impossible - it would occasion a rapid and widespread degeneration of that science. -- Auguste Comte, Philosophie Positive, Paris, 1838 """ class WrapTests(unittest.TestCase): """ Tests for L{text.greedyWrap}. """ def setUp(self): self.lineWidth = 72 self.sampleSplitText = sampleText.split() self.output = text.wordWrap(sampleText, self.lineWidth) def test_wordCount(self): """ Compare the number of words. """ words = [] for line in self.output: words.extend(line.split()) wordCount = len(words) sampleTextWordCount = len(self.sampleSplitText) self.assertEqual(wordCount, sampleTextWordCount) def test_wordMatch(self): """ Compare the lists of words. """ words = [] for line in self.output: words.extend(line.split()) # Using assertEqual here prints out some # rather too long lists. self.assertTrue(self.sampleSplitText == words) def test_lineLength(self): """ Check the length of the lines. """ failures = [] for line in self.output: if not len(line) <= self.lineWidth: failures.append(len(line)) if failures: self.fail("%d of %d lines were too long.\n" "%d < %s" % (len(failures), len(self.output), self.lineWidth, failures)) def test_doubleNewline(self): """ Allow paragraphs delimited by two \ns. """ sampleText = "et\n\nphone\nhome." result = text.wordWrap(sampleText, self.lineWidth) self.assertEqual(result, ["et", "", "phone home.", ""]) class LineTests(unittest.TestCase): """ Tests for L{isMultiline} and L{endsInNewline}. """ def test_isMultiline(self): """ L{text.isMultiline} returns C{True} if the string has a newline in it. """ s = 'This code\n "breaks."' m = text.isMultiline(s) self.assertTrue(m) s = 'This code does not "break."' m = text.isMultiline(s) self.assertFalse(m) def test_endsInNewline(self): """ L{text.endsInNewline} returns C{True} if the string ends in a newline. """ s = 'newline\n' m = text.endsInNewline(s) self.assertTrue(m) s = 'oldline' m = text.endsInNewline(s) self.assertFalse(m) class StringyStringTests(unittest.TestCase): """ Tests for L{text.stringyString}. """ def test_tuple(self): """ Tuple elements are displayed on separate lines. """ s = ('a', 'b') m = text.stringyString(s) self.assertEqual(m, '(a,\n b,)\n') def test_dict(self): """ Dicts elements are displayed using C{str()}. """ s = {'a': 0} m = text.stringyString(s) self.assertEqual(m, '{a: 0}') def test_list(self): """ List elements are displayed on separate lines using C{str()}. """ s = ['a', 'b'] m = text.stringyString(s) self.assertEqual(m, '[a,\n b,]\n') class SplitTests(unittest.TestCase): """ Tests for L{text.splitQuoted}. """ def test_oneWord(self): """ Splitting strings with one-word phrases. """ s = 'This code "works."' r = text.splitQuoted(s) self.assertEqual(['This', 'code', 'works.'], r) def test_multiWord(self): s = 'The "hairy monkey" likes pie.' r = text.splitQuoted(s) self.assertEqual(['The', 'hairy monkey', 'likes', 'pie.'], r) # Some of the many tests that would fail: #def test_preserveWhitespace(self): # phrase = '"MANY SPACES"' # s = 'With %s between.' % (phrase,) # r = text.splitQuoted(s) # self.assertEqual(['With', phrase, 'between.'], r) #def test_escapedSpace(self): # s = r"One\ Phrase" # r = text.splitQuoted(s) # self.assertEqual(["One Phrase"], r) class StrFileTests(unittest.TestCase): def setUp(self): self.io = StringIO("this is a test string") def tearDown(self): pass def test_1_f(self): self.assertEqual(False, text.strFile("x", self.io)) def test_1_1(self): self.assertEqual(True, text.strFile("t", self.io)) def test_1_2(self): self.assertEqual(True, text.strFile("h", self.io)) def test_1_3(self): self.assertEqual(True, text.strFile("i", self.io)) def test_1_4(self): self.assertEqual(True, text.strFile("s", self.io)) def test_1_5(self): self.assertEqual(True, text.strFile("n", self.io)) def test_1_6(self): self.assertEqual(True, text.strFile("g", self.io)) def test_3_1(self): self.assertEqual(True, text.strFile("thi", self.io)) def test_3_2(self): self.assertEqual(True, text.strFile("his", self.io)) def test_3_3(self): self.assertEqual(True, text.strFile("is ", self.io)) def test_3_4(self): self.assertEqual(True, text.strFile("ing", self.io)) def test_3_f(self): self.assertEqual(False, text.strFile("bla", self.io)) def test_large_1(self): self.assertEqual(True, text.strFile("this is a test", self.io)) def test_large_2(self): self.assertEqual(True, text.strFile("is a test string", self.io)) def test_large_f(self): self.assertEqual(False, text.strFile("ds jhfsa k fdas", self.io)) def test_overlarge_f(self): self.assertEqual(False, text.strFile("djhsakj dhsa fkhsa s,mdbnfsauiw bndasdf hreew", self.io)) def test_self(self): self.assertEqual(True, text.strFile("this is a test string", self.io)) def test_insensitive(self): self.assertEqual(True, text.strFile("ThIs is A test STRING", self.io, False))

f719f0bd0810de624991f194db2d5e2731bca1d7

py

Python

etcd/setup.py

dvanderveer/integrations-core

41dd9950296455457c9b7342584153678503d5aa

etcd/setup.py

dvanderveer/integrations-core

41dd9950296455457c9b7342584153678503d5aa

etcd/setup.py

dvanderveer/integrations-core

41dd9950296455457c9b7342584153678503d5aa

# Always prefer setuptools over distutils from setuptools import setup # To use a consistent encoding from codecs import open from os import path import re here = path.abspath(path.dirname(__file__)) # get the long description from the readme file with open(path.join(here, 'README.md'), encoding='utf-8') as f: long_description = f.read() runtime_reqs = ['datadog_checks_base'] with open(path.join(here, 'requirements.txt'), encoding='utf-8') as f: for line in f.readlines(): line = line.strip() if not line or line.startswith('--hash') or line[0] == '#': continue req = line.rpartition('#') if not len(req[1]): if '--hash=' in req[2]: tokens = req[2].split() if len(tokens) > 1: runtime_reqs.append(tokens[0]) elif ';' in req[2]: runtime_reqs.append(req[2]) else: runtime_reqs.append(req[0]) def read(*parts): with open(path.join(here, *parts), 'r') as fp: return fp.read() def find_version(*file_paths): version_file = read(*file_paths) version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M) if version_match: return version_match.group(1) raise RuntimeError("Unable to find version string.") # https://packaging.python.org/guides/single-sourcing-package-version/ version = find_version("datadog_checks", "etcd", "__init__.py") setup( name='datadog-etcd', version=version, description='The Etcd check', long_description=long_description, keywords='datadog agent etcd check', # The project's main homepage. url='https://github.com/DataDog/integrations-core', # Author details author='Datadog', author_email='packages@datadoghq.com', # License license='MIT', # See https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Intended Audience :: System Administrators', 'Topic :: System :: Monitoring', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', ], # The package we're going to ship packages=['datadog_checks.etcd'], # Run-time dependencies install_requires=list(set(runtime_reqs)), # Development dependencies, run with: # $ pip install -e .[dev] extras_require={ 'dev': [ 'check-manifest', 'datadog_agent_tk>=5.15', ], }, # Testing setup and dependencies tests_require=[ 'nose', 'coverage', 'datadog_agent_tk>=5.15', ], test_suite='nose.collector', # Extra files to ship with the wheel package package_data={b'datadog_checks.etcd': ['conf.yaml.example']}, include_package_data=True, )

f719f96e68fd7b17d73ed6b9460ebade8987ebf6

py

Python

parseepo/serialize.py

cverluise/parseEPO

be1171a0f8e6fcafa711fa291aebb1fc2260d5e6

parseepo/serialize.py

cverluise/parseEPO

be1171a0f8e6fcafa711fa291aebb1fc2260d5e6

2021-02-02T22:38:50.000Z

2021-08-23T20:41:10.000Z

parseepo/serialize.py

cverluise/parseEPO

be1171a0f8e6fcafa711fa291aebb1fc2260d5e6

import html2text import pandas as pd from wasabi import Printer from parseepo import validate from parseepo.exception import SingleAttrException from parseepo.utils import prepare_name h = html2text.HTML2Text() msg = Printer() NAMES = ["EP", "Num", "Ext", "publication_date", "language", "attr", "text"] NESTED_ATTR = ["TITLE", "CLAIM", "AMEND", "title", "claims", "amendment"] def format_patent_df( data: list, prepare_names: bool = False, handle_html: bool = False ): """ Return data as a prepared DataFrame from a list of rows Nb: Input is [publication_number[Row]]. E.g. [['EP','0700059 A1','1996-03-06','de','TITLE',' Elektroma...'], ['EP','0700059 A1','1996-03-06','en','TITLE',' Electroma...'], ... :param data: List[List] :param prepare_names: bool, True if you want to prepare names for BQ compatibility :param handle_html: bool, True if you want to handle html :return: pd.DataFrame publication_date language attr text publication_number 0 1996-03-06 ... ... ... EP-0700059-A1 1 1996-03-06 ... ... ... EP-0700059-A1 2 1996-03-06 ... ... ... EP-0700059-A1 3 1996-03-06 ... ... ... EP-0700059-A1 4 1996-03-06 ... ... ... EP-0700059-A1 5 1996-03-06 ... ... ... EP-0700059-A1 6 1996-03-06 ... ... ... EP-0700059-A1 """ df_ = pd.DataFrame(data, columns=NAMES) df_["publication_number"] = df_["EP"] + "-" + df_["Num"] + "-" + df_["Ext"] df_ = df_.drop(["EP", "Num", "Ext"], axis=1) if prepare_names: df_["attr"] = df_["attr"].apply(lambda x: prepare_name(x, True)) if handle_html: df_["text"] = df_["text"].apply(lambda x: h.handle(x)) return df_ def unnest_attr(patent_dict: dict, publication_number: str): """ Unnest flat attributes returned as nested by the batch aggregation operation in serialize_patent. Raises warning if expected flat attributes has multiple values. :param patent_dict: dict, returned by serialize_patent :param publication_number: str, e.g. 'EP-0600083-A1' :return: dict In: { ..., 'PDFEP': {'language': ['en'], 'text': ['https://data.epo.org/publication-server/...']}, } Out: {..., 'PDFEP': 'https://data.epo.org/publication-server/...',} """ attrs = list(filter(lambda x: x not in NESTED_ATTR, patent_dict.keys())) for attr in attrs: val = patent_dict[attr]["text"] try: validate.single_attr(val, attr, publication_number) except SingleAttrException: msg.warn( f"{publication_number}: {attr} has more than 1 value. Only the first value " f"was kept. Add {attr} to the list NESTED_ATTR to fix this behavior." ) patent_dict.update( { attr: { "text": patent_dict[attr]["text"][0], "language": patent_dict[attr]["language"][0], } } ) def serialize_patent_df(patent_df: pd.DataFrame): """ Return the serialized patent :param patent_df: pd.DataFrame, returned by format_patent_df :return: dict {'ABSTR': '<p id="pa01" num="0001">A device ...', 'CLAIM': {'language': ['en'], 'text': ['<claim id="c-en-0001" ...']}, 'DESCR': '<heading id="h0001">Field of ...', 'PDFEP': 'https://data.epo.org/publication-server/...', 'TITLE': {'language': ['de', 'en', 'fr'], 'text': ['VORRICHTUNG ZUM ...', 'DEVICE FOR CONVEYING ...', "DISPOSITIF D'ACHEMINEMENT ...']}, 'publication_date': '1994-06-08', 'publication_number': 'EP-0600083-A1'} """ publication_number = patent_df["publication_number"].values[0] publication_date = patent_df["publication_date"].values[0] out = ( patent_df.drop(["publication_number", "publication_date"], axis=1) .groupby("attr") .aggregate(list) .T.to_dict() ) unnest_attr(out, publication_number) out.update({"publication_number": publication_number}) out.update({"publication_date": publication_date}) return out def serialize_patent( data: list, prepare_names: bool = False, handle_html: bool = False ): """ Return the serialized patent :param data: List[List[str]], E.g. [['EP','0700059 A1','1996-03-06','de','TITLE',' Elektroma...'], ['EP','0700059 A1','1996-03-06','en','TITLE',' Electroma...'], :param prepare_names: bool, True if you want to prepare names for BQ compatibility :param handle_html: bool, True if you want to handle html :return: dict """ out = format_patent_df(data, prepare_names, handle_html) out = serialize_patent_df(out) return out

f71a0da9d68a3d4c9024e6fcb718688385715211

py

Python

buttonlist/src/buttonlist/__main__.py

pmfrank/beeware-tutorials

96274b0a735bd468e946111baf441a527ff0b0d5

2021-06-04T05:51:39.000Z

2021-06-04T05:51:39.000Z

buttonlist/src/buttonlist/__main__.py

pmfrank/beeware-tutorials

96274b0a735bd468e946111baf441a527ff0b0d5

buttonlist/src/buttonlist/__main__.py

pmfrank/beeware-tutorials

96274b0a735bd468e946111baf441a527ff0b0d5

from buttonlist.app import main if __name__ == '__main__': main().main_loop()

f71a1e9ab3b466d5a052c9eb0a36e082154d5dbc

py

Python

igibson/robots/jr2_robot.py

suresh-guttikonda/iGibson

a69e623058180146466cd52d4bb3c00d1facdacf

2020-04-02T11:12:09.000Z

2022-03-24T21:46:58.000Z

igibson/robots/jr2_robot.py

suresh-guttikonda/iGibson

a69e623058180146466cd52d4bb3c00d1facdacf

2020-04-07T21:01:05.000Z

2022-03-31T10:07:39.000Z

igibson/robots/jr2_robot.py

suresh-guttikonda/iGibson

a69e623058180146466cd52d4bb3c00d1facdacf

2020-04-09T23:22:17.000Z

2022-03-17T21:49:03.000Z

import gym import numpy as np from igibson.robots.robot_locomotor import LocomotorRobot class JR2(LocomotorRobot): """ JR2 robot (no arm) Reference: https://cvgl.stanford.edu/projects/jackrabbot/ Uses joint velocity control """ def __init__(self, config): self.config = config self.velocity = config.get("velocity", 1.0) LocomotorRobot.__init__( self, "jr2_urdf/jr2.urdf", action_dim=4, scale=config.get("robot_scale", 1.0), is_discrete=config.get("is_discrete", True), control="velocity", ) def set_up_continuous_action_space(self): """ Set up continuous action space """ self.action_space = gym.spaces.Box(shape=(self.action_dim,), low=-1.0, high=1.0, dtype=np.float32) self.action_high = self.velocity * np.ones([self.action_dim]) self.action_low = -self.action_high def set_up_discrete_action_space(self): """ Set up discrete action space """ self.action_list = [ [self.velocity, self.velocity, 0, self.velocity], [-self.velocity, -self.velocity, 0, -self.velocity], [self.velocity, -self.velocity, -self.velocity, 0], [-self.velocity, self.velocity, self.velocity, 0], [0, 0, 0, 0], ] self.action_space = gym.spaces.Discrete(len(self.action_list)) self.setup_keys_to_action() def setup_keys_to_action(self): self.keys_to_action = { (ord("w"),): 0, # forward (ord("s"),): 1, # backward (ord("d"),): 2, # turn right (ord("a"),): 3, # turn left (): 4, }

f71a1fb42d65587e922d09e984061b07a1aaed3f

py

Python

askci/plugins/pam_auth/__init__.py

hpsee/askci

ef1e2e75481b71db7fbe774cb81938055aa596d0

2019-11-21T09:04:36.000Z

2019-11-23T13:29:43.000Z

askci/plugins/pam_auth/__init__.py

hpsee/askci

ef1e2e75481b71db7fbe774cb81938055aa596d0

2019-11-21T20:28:23.000Z

2019-11-26T19:34:22.000Z

askci/plugins/pam_auth/__init__.py

hpsee/askci

ef1e2e75481b71db7fbe774cb81938055aa596d0

AUTHENTICATION_BACKENDS = ( "django_pam.auth.backends.PAMBackend", "django.contrib.auth.backends.ModelBackend", )

f71a3354afd52b38a1b508cdd629a00d472d8746

py

Python

tests/test_logger.py

agraubert/agutil

d9a568df01959ed985c9c8e77bdd501ac13bdbbf

2017-06-05T15:46:22.000Z

2019-05-22T21:26:54.000Z

tests/test_logger.py

agraubert/agutil

d9a568df01959ed985c9c8e77bdd501ac13bdbbf

2016-06-22T18:57:47.000Z

2022-02-14T10:50:27.000Z

tests/test_logger.py

agraubert/agutil

d9a568df01959ed985c9c8e77bdd501ac13bdbbf

import unittest import unittest.mock import os from py_compile import compile import sys import random import time import tempfile from filecmp import cmp def make_random_string(length=25, lower=0, upper=255): return "".join(chr(random.randint(lower,upper)) for i in range(length)) def tempname(): (handle, name) = tempfile.mkstemp() os.close(handle) return name class test(unittest.TestCase): @classmethod def setUpClass(cls): cls.script_path = os.path.join( os.path.dirname( os.path.dirname( os.path.abspath(__file__) ) ), "agutil", "src", "logger.py" ) cls.data_path = os.path.join( os.path.dirname(os.path.abspath(__file__)), 'data', 'logger' ) sys.path.append(os.path.dirname(os.path.dirname(cls.script_path))) random.seed() def test_compilation(self): compiled_path = compile(self.script_path) self.assertTrue(compiled_path) @unittest.skipIf(sys.platform.startswith('win'), "Tempfile cannot be used in this way on Windows") def test_basic_logging(self): import agutil.src.logger time_mock = unittest.mock.Mock(side_effect = lambda fmt, time=0:fmt) agutil.src.logger.time.strftime = time_mock output_file = tempname() log = agutil.src.logger.Logger(output_file, loglevel=agutil.src.logger.Logger.LOGLEVEL_DETAIL) log.log("Test message") log.log("More messages!", sender="me") log.log("OH NO! This one's an error!", "Foo", "ERROR") foo_bound = log.bindToSender("Foo") log.mute("Foo", "Bar") foo_bound("Message 1") foo_bound("Message 2") log.log("This should appear in the log, but not the dump", "Bar", "WARN") foo_bound("Message 3") log.unmute("Foo") log.log("I've been unmuted!", "Foo") log.log("This should be a warning", "Anyone", "BLORG") time.sleep(.2) log.addChannel("BLORG", 15) log.setChannelCollection("BLORG", True) log.log("This should be seen", "Anyone", "BLORG") log.setChannelCollection("WARN", False) log.setChannelCollection("WARN", True) time.sleep(.2) log.log("This should appear in the dump", "Bar", "WARN") time.sleep(.1) self.assertFalse(log.close()) self.assertTrue(cmp( output_file, os.path.join( self.data_path, 'logger_compare.txt' ) )) os.remove(output_file)

f71a3706a5e1e09a9b5ac6542d63281e2cb4bab7

py

Python

tests/test_platform_api.py

jain-aayush1123/here-location-services-python

11ad5ef8273b4f243c43bc00ebd470f725b980bc

2021-02-15T13:49:29.000Z

2022-03-29T10:34:43.000Z

tests/test_platform_api.py

jain-aayush1123/here-location-services-python

11ad5ef8273b4f243c43bc00ebd470f725b980bc

2021-02-27T18:40:46.000Z

2021-10-03T15:49:27.000Z

tests/test_platform_api.py

jain-aayush1123/here-location-services-python

11ad5ef8273b4f243c43bc00ebd470f725b980bc

2021-02-16T04:58:08.000Z

2022-02-21T20:51:55.000Z

# Copyright (C) 2019-2021 HERE Europe B.V. # SPDX-License-Identifier: Apache-2.0 """This module will test platform api module.""" import pytest from requests_oauthlib import OAuth1 from here_location_services.platform.apis.aaa_oauth2_api import AAAOauth2Api from here_location_services.platform.apis.api import Api as PlaformApi from here_location_services.utils import get_apikey from tests.conftest import get_mock_response LS_API_KEY = get_apikey() def test_api_headers_property(): api = PlaformApi(access_token="dummy") assert api.headers == {"Authorization": "Bearer dummy"} def test_mock_request_post(mocker): mocker.patch("requests.post", return_value=True) api = PlaformApi(access_token="dummy") resp = api.post("dummy_url", data={"foo": "bar"}) assert resp is True def test_mock_request_scoped_access_token_excception(mocker): reason = "This is mock reason" text = "This is mock text" mock_response = get_mock_response(500, reason, text) mocker.patch("here_location_services.platform.apis.api.Api.post", return_value=mock_response) aaa_api = AAAOauth2Api(base_url="dummy") oauth = OAuth1( "dummy_key", client_secret="dummy_secret", signature_method="HMAC-SHA256", ) with pytest.raises(Exception): aaa_api.request_scoped_access_token(oauth=oauth, data="dummy_data")

f71a703f2090876a8e79cf5a51d2bb5e3344842c

py

Python

spyke/sort.py

spyke/spyke

20934521de9c557924911cf6190690ac1c6f8e80

2015-06-01T03:31:00.000Z

2022-03-18T09:12:28.000Z

spyke/sort.py

spyke/spyke

20934521de9c557924911cf6190690ac1c6f8e80

2017-03-24T19:16:02.000Z

2021-01-27T14:34:30.000Z

spyke/sort.py

spyke/spyke

20934521de9c557924911cf6190690ac1c6f8e80

2015-07-10T15:28:08.000Z

2022-03-17T19:30:45.000Z

"""Spike sorting classes and window""" from __future__ import division from __future__ import print_function __authors__ = ['Martin Spacek', 'Reza Lotun'] import os import sys import time import datetime from copy import copy import operator import random import shutil import hashlib import multiprocessing as mp from PyQt4 import QtCore, QtGui from PyQt4.QtCore import Qt from PyQt4.QtGui import QAction, QIcon, QApplication import numpy as np import scipy import scipy.signal #from scipy.cluster.hierarchy import fclusterdata import pylab as pl import pyximport pyximport.install(build_in_temp=False, inplace=True) from . import util # .pyx file from . import core from .core import (WaveForm, Gaussian, MAXLONGLONG, R, toiter, intround, printflush, lstrip, rstrip, lrstrip, pad, td2days, SpykeToolWindow, NList, NSList, dist, USList, ClusterChange, SpikeSelectionSlider, lrrep2Darrstripis, rollwin2D) from .detect import DEBUG from .surf import EPOCH from .plot import SpikeSortPanel, CLUSTERCOLOURDICT, WHITE from .__version__ import __version__ #MAXCHANTOLERANCE = 100 # um NSLISTWIDTH = 70 # minimize nslist width, enough for 7 digit spike IDs PANELWIDTHPERCOLUMN = 120 # sort panel width per column of channels PANELHEIGHTPERROW = 50 # sort panel height per row of channels VSCROLLBARWIDTH = 14 # hack SORTWINDOWHEIGHT = 1035 # TODO: this should be set programmatically MINSORTWINDOWWIDTH = 566 MEANWAVEMAXSAMPLES = 2000 NPCSPERCHAN = 7 PCALIB = 'mdp' ICALIB = 'sklearn' DEFMINISI = 50 # default minimum ISI to check for on export, us MAXGROUPISI = 100000 # us (100 ms) MAXGROUPDT = 100000000 # us (100 s) class Sort(object): """A spike sorting session, in which you can detect spikes and sort them into Neurons. A .sort file is a single Python2-pickled Sort object. A .json file is a jsonpickle-pickled Sort object""" def __init__(self, detector=None, stream=None, tw=None): self.__version__ = __version__ self.fname = '' self.user = '' self.notes = '' self.detector = detector # this Sort's current Detector object self.tw = tw # time window (us) relative to spike time self.stream = stream self.probe = stream.probe # only one probe design per sort allowed self.converter = stream.converter self.neurons = {} self.clusters = {} # neurons with multidm params scaled for plotting self.norder = [] # stores order of neuron ids display in nlist self.npcsperchan = NPCSPERCHAN def get_nextnid(self): """nextnid is used to retrieve the next unique single unit ID""" nids = list(self.neurons) if len(nids) == 0: return 1 # single unit nids start at 1 else: return max(max(nids) + 1, 1) # at least 1 nextnid = property(get_nextnid) def get_nextmuid(self): """nextmuid is used to retrieve the next unique multiunit ID""" nids = list(self.neurons) if len(nids) == 0: return -1 # multiunit ids start at -1 else: return min(min(nids) - 1, -1) # at most -1 nextmuid = property(get_nextmuid) def get_good(self): """Return array of nids marked by user as 'good'""" good = [] for neuron in self.neurons.values(): try: if neuron.good: good.append(neuron.id) except AttributeError: # neuron is from older sort, no .good attrib neuron.good = False return np.asarray(good) def set_good(self, good): """Set good flag to True for nids in good, False otherwise""" nids = list(self.neurons) assert np.all([ nid in nids for nid in good ]) # make sure all nids in good exist notgood = np.setdiff1d(nids, good) for nid in notgood: neuron = self.neurons[nid] neuron.good = False for nid in good: neuron = self.neurons[nid] neuron.good = True good = property(get_good, set_good) def get_stream(self): try: return self._stream except AttributeError: # this is likely a brand new sort, has yet to be assigned a Stream return None def set_stream(self, stream=None): """Check stream type and name and probe type, and restore filtmeth, car, sampfreq and shcorrect to stream when binding/modifying stream to self""" oldstream = self.stream if stream != None and oldstream != None: # do stream types match? if type(stream) != type(oldstream): raise ValueError("Stream types don't match: %s, %s" % (type(oldstream), type(stream))) # do stream probe types match? if type(stream.probe) != type(oldstream.probe): raise ValueError("Stream probe types don't match: %s, %s" % (type(oldstream.probe), type(stream.probe))) # is one stream fname a superset of the other? if (stream.fname not in oldstream.fname) and (oldstream.fname not in stream.fname): raise ValueError("Stream file names are not supersets of each other: %s, %s" % (oldstream.fname, stream.fname)) else: print('Stream file names are similar enough to proceed: %s, %s' % (stream.fname, oldstream.fname)) try: stream.filtmeth = self.filtmeth stream.car = self.car stream.sampfreq = self.sampfreq stream.shcorrect = self.shcorrect except AttributeError: pass # one of the above aren't bound self._stream = stream # set it print('Bound stream %r to sort %r' % (stream.fname, self.fname)) # now that tres is known, calculate window timepoints wrt spike time: self.calc_twts_twi() stream = property(get_stream, set_stream) def calc_twts_twi(self): """Calculate temporal window timepoints wrt spike time, and the indices of these timepoints wrt spike time""" tres = self.tres tw = self.tw twts = np.arange(tw[0], tw[1], tres) twts += twts[0] % tres # get rid of mod, so twts go through zero self.twts = twts self.twi = intround(twts[0] / tres), intround(twts[-1] / tres) #info('twi = %s' % (self.twi,)) def update_tw(self, tw): """Update tw and everything that depends on it. Note that this shouldn't be called directly by the user. Call SpykeWindow.update_spiketw() instead""" oldtw = self.tw self.tw = tw self.calc_twts_twi() dtw = np.asarray(tw) - np.asarray(oldtw) # new minus old self.spikes['t0'] += dtw[0] self.spikes['t1'] += dtw[1] self.spikes['tis'] = self.spikes['tis'] - intround(dtw[0] / self.tres) # recalculate any existing templates: for neuron in self.neurons.values(): if neuron.wave.data != None: neuron.update_wave() print('WARNING: all spike waveforms need to be reloaded!') def get_tres(self): return self.stream.tres tres = property(get_tres) def __getstate__(self): """Get object state for pickling""" # copy it cuz we'll be making changes, this is fast because it's just a shallow copy d = self.__dict__.copy() # Spikes and wavedata arrays are (potentially) saved separately. # usids and PCs/ICs can be regenerated from the spikes array. for attr in ['spikes', 'wavedata', 'usids', 'X', 'Xhash']: # keep _stream during normal pickling for multiprocessing, but remove it # manually when pickling to sort file try: del d[attr] except KeyError: pass return d def get_nspikes(self): try: return len(self.spikes) except AttributeError: return 0 nspikes = property(get_nspikes) def update_usids(self): """Update usids, which is an array of indices of unsorted spikes""" nids = self.spikes['nid'] self.usids, = np.where(nids == 0) # 0 means unclustered def get_spikes_sortedby(self, attr='id'): """Return array of all spikes, sorted by attribute 'attr'""" vals = self.spikes[attr] spikes = self.spikes[vals.argsort()] return spikes def get_wave(self, sid): """Return WaveForm corresponding to spike sid""" spikes = self.spikes nchans = spikes['nchans'][sid] chans = spikes['chans'][sid, :nchans] t0 = spikes['t0'][sid] t1 = spikes['t1'][sid] wavedata = self.wavedata[sid, 0:nchans] ts = np.arange(t0, t1, self.tres) # build them up return WaveForm(data=wavedata, ts=ts, chans=chans, tres=self.tres) def get_maxchan_wavedata(self, sid=None, nid=None): """Return wavedata of maxchan of spike sid or neuron nid""" if sid != None: assert nid == None chani = self.spikes['chani'][sid] return self.wavedata[sid, chani] elif nid != None: assert sid == None neuron = self.neurons[nid] chani, = np.where(neuron.chans == neuron.chan) assert len(chani) == 1 chani = chani[0] # pull out of length 1 array return neuron.wave.data[chani] def get_mean_wave(self, sids, nid=None): """Return the mean and std waveform of spike waveforms in sids""" spikes = self.spikes nsids = len(sids) if nsids > MEANWAVEMAXSAMPLES: step = nsids // MEANWAVEMAXSAMPLES + 1 s = ("get_mean_wave() sampling every %d spikes instead of all %d" % (step, nsids)) if nid != None: s = "neuron %d: " % nid + s print(s) sids = sids[::step] nsids = len(sids) # update chanss = spikes['chans'][sids] nchanss = spikes['nchans'][sids] chanslist = [ chans[:nchans] for chans, nchans in zip(chanss, nchanss) ] # list of arrays chanpopulation = np.concatenate(chanslist) groupchans = np.unique(chanpopulation) # comes out sorted wavedata = self.wavedata[sids] if wavedata.ndim == 2: # should be 3, get only 2 if nsids == 1 wavedata.shape = 1, wavedata.shape[0], wavedata.shape[1] # give it a singleton 3rd dim nt = wavedata.shape[-1] maxnchans = len(groupchans) data = np.zeros((maxnchans, nt)) # all spikes have same nt, but not necessarily same nchans, keep track of # how many spikes contributed to each of the group's chans nspikes = np.zeros((maxnchans, 1), dtype=int) for chans, wd in zip(chanslist, wavedata): chanis = groupchans.searchsorted(chans) # each spike's chans is a subset of groupchans data[chanis] += wd[:len(chans)] # accumulate nspikes[chanis] += 1 # inc spike count for this spike's chans #t0 = time.time() data /= nspikes # normalize all data points appropriately, this is now the mean var = np.zeros((maxnchans, nt)) for chans, wd in zip(chanslist, wavedata): chanis = groupchans.searchsorted(chans) # each spike's chans is a subset of groupchans var[chanis] += (wd[:len(chans)] - data[chanis]) ** 2 # accumulate 2nd moment var /= nspikes # normalize all data points appropriately, this is now the variance std = np.sqrt(var) # keep only those chans that at least 1/2 the spikes contributed to bins = list(groupchans) + [np.inf] # concatenate rightmost bin edge hist, bins = np.histogram(chanpopulation, bins=bins) chans = groupchans[hist >= nsids/2] chanis = groupchans.searchsorted(chans) data = data[chanis] std = std[chanis] return WaveForm(data=data, std=std, chans=chans) def check_ISIs(self, nids='good'): """Check that interspike intervals of spikes in each nid never fall below DEFMINISI""" print('Checking inter-spike intervals') if nids == 'good': nids = self.good elif nids == 'all': nids = sorted(self.neurons) for nid in nids: neuron = self.neurons[nid] spikets = self.spikes['t'][neuron.sids] # should be a sorted copy assert spikets.flags['OWNDATA'] # safe to modify in place spikets.sort() # just in case it isn't perfectly sorted ndupl = (np.diff(spikets) < DEFMINISI).sum() if ndupl > 0: msg = ('n%d has %d duplicate spikes (given DEFMINISI=%d us).\n' 'Remove duplicate spikes with the ISI tool in the Verify tab' % (nid, ndupl, DEFMINISI)) raise RuntimeError(msg) def check_wavealign(self, nids='good', maxdti=1): """Check that each neurons's primary peak on the max chan is no more than +/- maxdti timepoints away from the t=0 alignment timepoint""" print('Checking neuron mean waveform alignment') if nids == 'good': nids = self.good elif nids == 'all': nids = sorted(self.neurons) nt = self.twi[1] - self.twi[0] + 1 # expected number of points of each chan's wavedata for nid in nids: neuron = self.neurons[nid] wd = self.get_maxchan_wavedata(nid=nid) assert len(wd) == nt # find biggest positive and negative peaks, check which comes first, ensure # the primary peak is within maxdti of t=0 alignment timepoint: ppeakis, _ = scipy.signal.find_peaks(wd) # positive peak indices npeakis, _ = scipy.signal.find_peaks(-wd) # negative peak indices pmaxi = ppeakis[wd[ppeakis].argmax()] # max positive peak index nmaxi = npeakis[wd[npeakis].argmin()] # max negative peak index if nmaxi < pmaxi: # usual case: -ve then +ve peak peak1i = nmaxi else: # less common: +ve then -ve peak, make sure +ve peak is worthy of alignment pmax, nmax = wd[pmaxi], wd[nmaxi] if pmax > abs(nmax): # +ve peak is bigger than -ve peak, align to +ve peak peak1i = pmaxi else: peak1i = nmaxi # default to -ve peak alignti = 0 - self.twi[0] # +ve dti = peak1i - alignti #print("n%d: dti=%d" % (nid, dti)) if abs(dti) > maxdti: peak1uV = self.converter.AD2uV(wd[peak1i]) peak1us = intround(self.tres*(peak1i-alignti)) msg = ('Primary peak (%+d uV @ t=%d us) of n%d is %+d timepoints away from ' 'the t=0 us alignment point. Shift it closer and try again' % (peak1uV, peak1us, nid, dti)) raise RuntimeError(msg) def check_wavepadding(self, nids='good', npad=2): """Check if any spikes are edge padded, presumably due to being shifted but not reloaded. For robustness, check for consistent signs of padding across all channels. An edge is considered padded if it does not change over npad datapoints""" print('Checking spike waveform padding') assert npad >= 2 # need at least 2 points to do a diff if nids == 'good': nids = self.good elif nids == 'all': nids = sorted(self.neurons) for nid in nids: neuron = self.neurons[nid] for sid in neuron.sids: wd = self.wavedata[sid] # multichannel waveform data # are left and right edges of wavedata identical for npad number of points? l, r = wd[:, :npad], wd[:, -npad:] # shape (nchans, npad) leftpadded = (np.diff(l, axis=1) == 0).all() rightpadded = (np.diff(r, axis=1) == 0).all() # handle case where spike is right after or right before a 0-padded # region of data due to gaps between experiments: if leftpadded: if (wd[:, 0] == 0).all(): leftpadded = False if rightpadded: if (wd[:, -1] == 0).all(): rightpadded = False if leftpadded or rightpadded: msg = ('n%d has s%d that looks like it has been padded.\n' 'leftpadded, rightpadded = %r, %r\n' 'Reload s%d or n%d or all spikes and try again' % (nid, sid, leftpadded, rightpadded, sid, nid)) raise RuntimeError(msg) def check_contiguous_nids(self): """Check that neuron IDs are contiguous (no gaps)""" print('Checking that neuron IDs are contiguous') nids = np.array(list(self.neurons)) nids = nids[nids > 0] # only consider +ve nids nids.sort() if (np.diff(nids) != 1).any(): raise RuntimeError('Neuron IDs are not contiguous, renumber all and try again') def exportptcsfiles(self, basepath, sortpath, user='', notes=''): """Export spike data to binary .ptcs files under basepath, one file per recording""" # First check to make sure various things are OK before exporting: self.check_ISIs() self.check_wavealign() self.check_wavepadding() self.check_contiguous_nids() spikes = self.spikes exportdt = str(datetime.datetime.now()) # get an export datetime stamp exportdt = exportdt.split('.')[0] # ditch the us if self.stream.is_multi(): # self.stream is a MultiStream streams = self.stream.streams else: # self.stream is a single Stream streams = [self.stream] print('Exporting "good" clusters to:') # do a separate export for each recording: # absolute start and stop times of all streams, rounded to nearest raw timepoint: tranges = self.stream.tranges t0 = tranges[0, 0] # absolute start time of first stream for stream, trange in zip(streams, tranges): abst0 = trange[0] # absolute start time of this stream relative to t0 # time delta between this stream and first stream, to nearest raw timepoint, us: dt = abst0 - t0 dt = intround(dt) # to nearest int us self.exportptcsfile(stream, basepath, dt, exportdt, sortpath, user=user, notes=notes) def exportptcsfile(self, stream, basepath, dt, exportdt, sortpath, user='', notes=''): """Export spike data of all "good" spikes to binary .ptcs file in basepath. Constrain to spikes in stream, and undo any time delta in spike times. dt is the integer time difference between start of stream and start of first stream in the track, rounded to the nearest us (spike times are stored as int64 us in .ptcs)""" # build up list of PTCSNeuronRecords that have spikes in this stream, # and tally their spikes nsamplebytes = 4 # float32 nrecs = [] nspikes = 0 # only export neurons marked as "good", could be single or multi unit: for nid in sorted(self.good): neuron = self.neurons[nid] spikets = self.spikes['t'][neuron.sids] # should be a sorted copy assert spikets.flags['OWNDATA'] # safe to modify in place spikets.sort() # just in case it isn't perfectly sorted spikets -= dt # export spike times relative to t=0 of this recording # only include spikes that occurred during this recording lo, hi = spikets.searchsorted([stream.t0, stream.t1]) spikets = spikets[lo:hi] if len(spikets) == 0: continue # don't save empty neurons nrec = PTCSNeuronRecord(neuron, spikets, nsamplebytes, descr='') nrecs.append(nrec) nspikes += len(spikets) nneurons = len(nrecs) # create the header and write everything to file: path = os.path.join(basepath, stream.srcfnameroot) try: os.mkdir(path) except OSError: pass # path already exists? fname = stream.srcfnameroot + '.ptcs' fullfname = os.path.join(path, fname) header = PTCSHeader(self, sortpath, stream, nneurons, nspikes, nsamplebytes, fullfname, exportdt, user=user, notes=notes) with open(fullfname, 'wb') as f: header.write(f) for nrec in nrecs: nrec.write(f) print(fullfname) def exportcsv(self, fname): """Export all "good" spikes to a .csv file with time (s), nid, and maxchan as the columns""" sids = [] #chans = [] for nid in sorted(self.good): neuron = self.neurons[nid] sids.append(neuron.sids) # the alternative is to export each spike's unit's channel: #chans.append(np.tile(neuron.chan, neuron.nspikes)) sids = np.hstack(sids) spikes = self.spikes[sids] tsecs = spikes['t'] / 1e6 # convert from us to s nids = spikes['nid'] chans = spikes['chan'] #chans = np.hstack(chans) data = np.column_stack([tsecs, nids, chans]) print('Exporting (tsec, nid, chan) of all spikes marked as "good" to %s' % fname) np.savetxt(fname, data, fmt='%.6f, %d, %d') def exporttschid(self, basepath): """Export int64 (timestamp, channel, neuron id) 3 tuples to binary file""" raise NotImplementedError('Needs to be redone to work with multiple streams') spikes = self.spikes[self.spikes['nid'] > 0] # don't export unsorted/multiunit spikes dt = str(datetime.datetime.now()) # get an export timestamp dt = dt.split('.')[0] # ditch the us dt = dt.replace(' ', '_') dt = dt.replace(':', '.') srffnameroot = srffnameroot.replace(' ', '_') tschidfname = dt + '_' + srffnameroot + '.tschid' tschid = np.empty((len(spikes), 3), dtype=np.int64) tschid[:, 0] = spikes['t'] tschid[:, 1] = spikes['chan'] tschid[:, 2] = spikes['nid'] tschid.tofile(os.path.join(path, tschidfname)) # save it print(tschidfname) def exportdin(self, basepath): """Export stimulus din(s) to binary .din file(s) in basepath""" if self.stream.is_multi(): # self.stream is a MultiStream streams = self.stream.streams else: # self.stream is a single Stream streams = [self.stream] dinfiledtype=[('TimeStamp', '<i8'), ('SVal', '<i8')] # pairs of int64s print('Exporting DIN(s) to:') for stream in streams: try: # neither of these attribs should exist for recordings with no stimuli: svrecs = stream.srff.digitalsvalrecords dsprecs = stream.srff.displayrecords except AttributeError: continue # no din to export for this stream if len(svrecs) == 0 or stream.srff.ndigitalsvalrecords == 0: raise ValueError("digitalsvalrecords are empty for stream %r. Attribute " "shouldn't exist" % stream.fname) path = os.path.join(basepath, stream.srcfnameroot) try: os.mkdir(path) except OSError: pass # path already exists? # upcast SVal field from uint16 to int64, creates a copy, # but it's not too expensive: svrecs = svrecs.astype(dinfiledtype) # convert to normal n x 2 int64 array svrecs = svrecs.view(np.int64).reshape(-1, 2) # Some old recordings (<= ptc15) contain multiple experiments. # To deal with this, iterate over stream.srff.displayrecords, export one .din # per displayrecord. Append experiment ID to each .din filename, if necessary. svrects = svrecs[:, 0] dsprects = [ dsprec.TimeStamp for dsprec in dsprecs ] svalrecis = svrects.searchsorted(dsprects) assert svalrecis[0] == 0 svalrecis = svalrecis[1:] # exclude the trivial 0 index # split sval records according to displayrecord timestamps: dins = np.split(svrecs, svalrecis) assert len(dins) == len(dsprecs) for eid, din in enumerate(dins): if eid == 0 and len(dins) == 1: eidstr = '' elif len(dins) < 10: eidstr = '.%d' % eid else: # include leading zero to maintain alphabetical fname order eidstr = '.%02d' % eid dinfname = stream.srcfnameroot + eidstr + '.din' fullfname = os.path.join(path, dinfname) din.tofile(fullfname) # save it print(fullfname) def exporttextheader(self, basepath): """Export stimulus text header(s) to .textheader file(s) in basepath""" if self.stream.is_multi(): # self.stream is a MultiStream streams = self.stream.streams else: # self.stream is a single Stream streams = [self.stream] print('Exporting text header(s) to:') for stream in streams: try: dsprecs = stream.srff.displayrecords except AttributeError: # no textheader to export for this stream continue if len(dsprecs) == 0: raise ValueError("displayrecords are empty for stream %r. Attribute " "shouldn't exist" % stream.fname) path = os.path.join(basepath, stream.srcfnameroot) try: os.mkdir(path) except OSError: pass # path already exists? # Some old recordings (<= ptc15) contain multiple experiments. # To deal with this, iterate over stream.srff.displayrecords, export one # .textheader per displayrecord. Append experiment ID to each .textheader # filename, if necessary. for eid, dsprec in enumerate(dsprecs): textheader = dsprec.Header.python_tbl if eid == 0 and len(dsprecs) == 1: eidstr = '' elif len(dsprecs) < 10: eidstr = '.%d' % eid else: # include leading zero to maintain alphabetical fname order eidstr = '.%02d' % eid textheaderfname = stream.srcfnameroot + eidstr + '.textheader' fullfname = os.path.join(path, textheaderfname) with open(fullfname, 'w') as f: f.write(textheader) # save it print(fullfname) def exportall(self, basepath, sortpath): """Export spike data, stimulus din and textheader to basepath""" self.exportptcsfiles(basepath, sortpath) self.exportdin(basepath) self.exporttextheader(basepath) def exportspikewaves(self, sids, selchans, tis, fname, format): """Export spike waveform data of selected sids, selchans and tis to binary .spikes.zip file or text .spikes.csv file""" nspikes = len(sids) chans, chanslist = self.get_common_chans(sids, selchans) nchans = len(chans) ti0, ti1 = tis nt = ti1 - ti0 # fill in 3D data array: dtype = self.wavedata.dtype data = np.zeros((nspikes, nchans, nt), dtype=dtype) for sii, sid in enumerate(sids): spikechans = chanslist[sii] spikechanis = spikechans.searchsorted(chans) data[sii] = self.wavedata[sid][spikechanis, ti0:ti1] if format == 'text': # flatten timepoints of all chans into columns data.shape = nspikes, nchans*nt stream = self.stream assert stream.kind == 'highpass' # should be the only type ever saved to self if format == 'binary': nids = self.spikes['nid'][sids] spiketimes = self.spikes['t'][sids] chanpos = stream.probe.siteloc_arr() uVperAD = stream.converter.AD2uV(1) # convert 1 AD unit to uV with open(fname, 'wb') as f: np.savez_compressed(f, data=data, sids=sids, nids=nids, spiketimes=spiketimes, chans=chans, tis=tis, chanpos=chanpos, uVperAD=uVperAD) elif format == 'text': np.savetxt(fname, data, fmt='%d', delimiter=',') # data should be int else: raise ValueError('Unknown format: %r' % format) print('Exported %d spikes on chans=%r and tis=%r to %s' % (nspikes, list(chans), list(tis), fname)) def get_param_matrix(self, kind=None, sids=None, tis=None, selchans=None, norm=False, dims=None, scale=True): """Organize dims parameters from sids into a data matrix, each column corresponding to a dim. To do PCA/ICA clustering on all spikes, one maxchan at a time, caller needs to call this multiple times, one for each set of maxchan unique spikes,""" spikes = self.spikes dtypefields = list(spikes.dtype.fields) if sids is None: sids = spikes['id'] # default to all spikes comps = [ dim for dim in dims if dim.startswith('c') and dim[-1].isdigit() ] rmserror = np.any([ dim == 'RMSerror' for dim in dims ]) ncomp = len(comps) hascomps = ncomp > 0 if hascomps: X = self.get_component_matrix(kind, sids, tis=tis, chans=selchans, minncomp=ncomp, norm=norm) if rmserror: rms = self.get_rms_error(sids, tis=tis, chans=selchans) data = [] for dim in dims: if dim in dtypefields: data.append( np.float32(spikes[dim][sids]) ) elif dim.startswith('c') and dim[-1].isdigit(): compid = int(lstrip(dim, 'c')) data.append( np.float32(X[:, compid]) ) elif dim == 'RMSerror': data.append( np.float32(rms) ) else: raise RuntimeError('Unknown dim %r' % dim) # np.column_stack returns a copy, not modifying the original array data = np.column_stack(data) if scale: # ensure 0 mean, and unit variance/stdev for dim, d in zip(dims, data.T): # d iterates over columns d -= d.mean() if dim in ['x0', 'y0'] and self.probe.ncols > 1: try: x0std # normalize spatial params by x0 std except NameError: x0std = spikes['x0'].std() if x0std != 0.0: d /= x0std #elif dim == 't': # the longer the recording in hours, the greater the # # scaling in time # trange = d.max() - d.min() # tscale = trange / (60*60*1e6) # d *= tscale / d.std() else: # normalize all other dims by their std dstd = d.std() if dstd != 0.0: d /= dstd return data def get_component_matrix(self, kind, sids, tis=None, chans=None, minncomp=None, norm=False): """Find set of chans common to all sids, and do PCA/ICA on those waveforms. Or, if chans are specified, limit PCA/ICA to them. Return component matrix with at least minncomp dimensions""" spikes = self.spikes nt = self.wavedata.shape[2] if tis is None: # use full waveform tis = np.asarray([0, nt]) #print('tis: %r' % (tis,)) ti0, ti1 = tis assert ti0 < ti1 <= nt nt = ti1 - ti0 chans, chanslist = self.get_common_chans(sids, chans) nchans = len(chans) nspikes = len(sids) if nspikes < 2: raise RuntimeError("Need at least 2 spikes for %s" % kind) if nchans == 0: raise RuntimeError("Spikes have no common chans for %s" % kind) # check if desired components have already been calculated (cache hit): Xhash = self.get_Xhash(kind, sids, tis, chans, self.npcsperchan, norm) self.Xhash = Xhash # save as key to most recent component matrix in self.X try: self.X except AttributeError: self.X = {} # init the dimension reduction cache attrib if Xhash in self.X: print('Cache hit, using cached %ss from tis=%r, chans=%r of %d spikes' % (kind[:-1], list(tis), list(chans), nspikes)) return self.X[Xhash] # no need to recalculate print('Cache miss, (re)calculating %ss' % kind[:-1]) # collect data between tis from chans from all spikes: print('Doing %s on tis=%r, chans=%r of %d spikes' % (kind, list(tis), list(chans), nspikes)) # MDP complains of roundoff errors with float32 for large covariance matrices data = np.zeros((nspikes, nchans, nt), dtype=np.float64) for sii, sid in enumerate(sids): spikechans = chanslist[sii] spikechanis = spikechans.searchsorted(chans) spikedata = self.wavedata[sid][spikechanis, ti0:ti1] if norm: # normalize by Vpp of chan with max Vpp: maxptp = spikedata.ptp(axis=1).max() if maxptp != 0: # prevent div by 0 spikedata = spikedata / maxptp data[sii] = spikedata print('Input shape for %s: %r' % (kind, data.shape)) t0 = time.time() data.shape = nspikes, nchans*nt # flatten timepoints of all chans into columns print('Reshaped input for %s: %r' % (kind, data.shape)) if kind == 'PCA': # principal components analysis if PCALIB == 'mdp': import mdp # delay as late as possible X = mdp.pca(data, output_dim=5, svd=False) # svd=False is default elif PCALIB == 'sklearn': # sklearn's PCA is about 8x slower than mdp.pca, I think because it # doesn't tap into scipy.linalg.eig compiled code. RandomizedPCA is faster # than PCA, but isn't deterministic, and is still 2-3x slower than mdp.pca from sklearn.decomposition import PCA pca = PCA(n_components=5) X = pca.fit_transform(data) # do both the fit and the transform else: raise ValueError('Invalid PCALIB %r' % PCALIB) if X.shape[1] < minncomp: raise RuntimeError("Can't satisfy minncomp=%d request" % minncomp) elif kind == 'sPCA': # sparse principal components analysis from sklearn.decomposition import SparsePCA n_components = 5 alpha = 1 # sparseness parameter n_jobs = mp.cpu_count() spca = SparsePCA(n_components=n_components, alpha=alpha, n_jobs=n_jobs) X = spca.fit_transform(data) # do both the fit and the transform elif kind == 'mbsPCA': # mini batch sparse principal components analysis from sklearn.decomposition import MiniBatchSparsePCA n_components = 5 alpha = 1 # sparseness parameter n_jobs = mp.cpu_count() mbspca = MiniBatchSparsePCA(n_components=n_components, alpha=alpha, n_jobs=n_jobs) X = mbspca.fit_transform(data) # do both the fit and the transform elif kind == 'NMF': # non-negative matrix factorization from sklearn.decomposition import NMF n_components = 5 init = None # 'random', 'nndsvd', 'nndsvda', 'nndsvdar', 'custom' nmf = NMF(n_components=n_components, init=init) X = nmf.fit_transform(data) # do both the fit and the transform elif kind == 'tSNE': # t-distributed stochastic neighbor embedding # limit number of PCs to feed into ICA, keep up to npcsperchan components per # chan on average: ncomp = min((self.npcsperchan*nchans, data.shape[1])) print('ncomp: %d' % ncomp) import mdp # delay as late as possible # do PCA first, to reduce dimensionality and speed up ICA: data = mdp.pca(data, output_dim=ncomp) from sklearn.manifold import TSNE n_components = 3 # not suited for any more than 3, according to the paper #init = 'random', 'pca' tsne = TSNE(n_components=n_components) X = tsne.fit_transform(data) # do both the fit and the transform elif kind == 'ICA': # independent components analysis # ensure nspikes >= ndims**2 for good ICA convergence maxncomp = intround(np.sqrt(nspikes)) if maxncomp < minncomp: raise RuntimeError("Can't satisfy minncomp=%d request" % minncomp) if data.shape[0] <= data.shape[1]: raise RuntimeError('Need more observations than dimensions for ICA') # limit number of PCs to feed into ICA, keep up to npcsperchan components per # chan on average: ncomp = min((self.npcsperchan*nchans, maxncomp, data.shape[1])) if ICALIB == 'mdp': import mdp # delay as late as possible # do PCA first, to reduce dimensionality and speed up ICA: print('ncomp: %d' % ncomp) data = mdp.pca(data, output_dim=ncomp) # nonlinearity g='pow3', ie x**3. tanh seems to separate better, # but is a bit slower. gaus seems to be slower still, and no better # than tanh, but these are just vague impressions. # defaults to whitened=False, ie assumes data isn't whitened node = mdp.nodes.FastICANode(g='pow3') X = node(data) pm = node.get_projmatrix() X = X[:, np.any(pm, axis=0)] # keep only the non zero columns elif ICALIB == 'sklearn': from sklearn.decomposition import FastICA # when whiten=True (default), FastICA preprocesses the data using PCA, and # n_components is the number of PCs that are kept before doing ICA. alg = 'parallel' # parallel or deflation, default is parallel fun = 'logcosh' # logcosh, exp, or cube, default is logcosh maxiter = 100 # default is 200 tol = 0.5 # default is 0.0001, seems need >~ 0.1 to exit faster ## TODO: make FastICA algorithm (parallel, deflation), nonlinearity (logcosh, ## exp, cube) and IC sort method (abs(kurtosis) vs. negentropy) GUI options print('ncomp=%d, alg=%r, fun=%r, maxiter=%d, tol=%g' % (ncomp, alg, fun, maxiter, tol)) fastica = FastICA(n_components=ncomp, algorithm=alg, whiten=True, fun=fun, fun_args=None, max_iter=maxiter, tol=tol, w_init=None, random_state=None) X = fastica.fit_transform(data) # do both the fit and the transform #pm = fastica.components_ print('fastica niters: %d' % (fastica.n_iter_)) else: raise ValueError('Invalid ICALIB %r' % ICALIB) if X.shape[1] < 3: raise RuntimeError('Need at least 3 columns') # Sort ICs by decreasing kurtosis or negentropy. For kurtosis, see Scholz2004 (or # rather, opposite to their approach, which picked ICs with most negative # kurtosis). For methods of estimating negentropy, see Hyvarinen1997. ''' # sort by abs(kurtosis) of each IC (column) k = scipy.stats.kurtosis(X, axis=0) ki = abs(k).argsort()[::-1] # decreasing order of abs(kurtosis) print('Sort by abs(kurtosis):') print(k[ki]) X = X[:, ki] # sort the ICs ''' # sort by negentropy of each IC (column), this seems to work better than kurtosis # at separating clusters of similar size: ne = core.negentropy(X, axis=0) assert (ne > 0).all() nei = ne.argsort()[::-1] # decreasing order of negentropy print('Sort by negentropy:') print(ne[nei]) X = X[:, nei] # sort the ICs ''' import pylab as pl pl.figure() pl.imshow(pm) pl.colorbar() pl.title('original projmatrix') pl.figure() pl.imshow(pm[:, ki]) pl.colorbar() pl.title('decreasing abs(kurtosis) projmatrix') pl.figure() pl.imshow(pm[:, nei]) pl.colorbar() pl.title('decreasing negentropy projmatrix') ''' else: raise ValueError('Unknown kind %r' % kind) print('Output shape for %s: %r' % (kind, X.shape)) self.X[Xhash] = X # cache for fast future retrieval print('%s took %.3f sec' % (kind, time.time()-t0)) unids = list(np.unique(spikes['nid'][sids])) # set of all nids that sids span for nid in unids: # don't update pos of junk cluster, if any, since it might not have any chans # common to all its spikes, and therefore can't have PCA/ICA done on it if nid != 0: self.clusters[nid].update_comppos(X, sids) return X def get_rms_error(self, sids, tis=None, chans=None): """Calculate RMS error of spike waveforms (all from the same cluster) relative to their cluster's mean waveform. Consider only selected tis and chans""" spikes = self.spikes nids = np.unique(spikes['nid'][sids]) nid = nids[0] if len(nids) > 1 or nid == 0: raise RuntimeError("Spikes must all belong to the same (non-junk) cluster for " "RMS error calculation") nt = self.wavedata.shape[2] if tis is None: # use full waveform tis = np.asarray([0, nt]) #print('tis: %r' % (tis,)) ti0, ti1 = tis assert ti0 < ti1 <= nt nt = ti1 - ti0 chans, chanslist = self.get_common_chans(sids, chans) nchans = len(chans) nspikes = len(sids) if nchans == 0: raise RuntimeError("Spikes have no common chans for RMS error") # collect data between tis from chans from all spikes: print('Getting RMS error on tis=%r, chans=%r of %d spikes' % (list(tis), list(chans), nspikes)) data = np.zeros((nspikes, nchans, nt), dtype=np.float64) for sii, sid in enumerate(sids): spikechans = chanslist[sii] spikechanis = spikechans.searchsorted(chans) data[sii] = self.wavedata[sid][spikechanis, ti0:ti1] # get cluster mean waveform between tis on chans: wave = self.neurons[nid].get_wave() chanis = wave.chans.searchsorted(chans) meandata = np.float64(wave.data[chanis, ti0:ti1]) # calculate RMS error between each spike and the cluster mean waveform: se = (data - meandata) ** 2 # squared error # take mean across timepoints and chans, but not across spikes: mse = se.mean(axis=2).mean(axis=1) # mean squared error return np.sqrt(mse) def get_common_chans(self, sids, chans=None): """Find channels common to all sids, and optionally to chans as well. Also, return chanslist, ie list of arrays of chans of sids""" spikes = self.spikes chanss = spikes['chans'][sids] nchanss = spikes['nchans'][sids] #t0 = time.time() chanslist = [ cs[:ncs] for cs, ncs in zip(chanss, nchanss) ] # list of arrays #print('Building chanslist took %.3f sec' % (time.time()-t0)) commonchans = util.intersect1d_uint8(chanslist) # find intersection if chans is not None and len(chans) > 0: # values in chans but not in commonchans: diffchans = np.setdiff1d(chans, commonchans) commonchans = np.intersect1d(chans, commonchans) # values in both if len(diffchans) > 0: print('WARNING: ignored chans %r not common to all spikes' % list(diffchans)) return commonchans, chanslist def get_Xhash(self, kind, sids, tis, chans, npcsperchan, norm): """Return MD5 hex digest of args, for uniquely identifying the matrix resulting from dimension reduction of spike data""" h = hashlib.md5() h.update(kind.encode()) h.update(sids) h.update(tis) h.update(chans) if kind == 'ICA': # consider npcsperchan only if doing ICA h.update(str(npcsperchan).encode()) h.update(str(norm).encode()) return h.hexdigest() def create_neuron(self, id=None, inserti=None): """Create and return a new Neuron with a unique ID""" if id == None: id = self.nextnid if id in self.neurons: raise RuntimeError('Neuron %d already exists' % id) id = int(id) # get rid of numpy ints neuron = Neuron(self, id) # add neuron to self self.neurons[neuron.id] = neuron if inserti == None: self.norder.append(neuron.id) else: self.norder.insert(inserti, neuron.id) return neuron def remove_neuron(self, id): try: del self.neurons[id] # may already be removed due to recursive call del self.clusters[id] self.norder.remove(id) except (KeyError, ValueError): pass def shift(self, sids, nt): """Shift sid waveforms by nt timepoints: -ve shifts waveforms left, +ve shifts right. For speed, pad waveforms with edge values at the appropriate end""" spikes = self.spikes wd = self.wavedata for sid in sids: # maybe there's a more efficient way than iterating over sids core.shiftpad(wd[sid], nt) # modifies wd in-place # update spike parameters: dt = intround(nt * self.tres) # amount of time to shift by, signed, in us # so we can later reload the wavedata accurately, shifting the waveform right and # padding it on its left requires decrementing the associated timepoints # (and vice versa) spikes['t'][sids] -= dt spikes['t0'][sids] -= dt spikes['t1'][sids] -= dt # might result in some out of bounds tis because the original peaks # have shifted off the ends. Opposite sign wrt timepoints above, referencing within # wavedata: spikes['tis'][sids] = spikes['tis'][sids] + nt # this in-place operation raises a TypeError in numpy 1.11.2, something related to # subtracting an int from an unsigned int: #spikes['tis'][sid] += nt # caller should treat all sids as dirty ''' # replaced by util.alignbest_cy(): def alignbest(self, sids, tis, chans): """Align all sids between tis on chans by best fit according to mean squared error. chans are assumed to be a subset of channels of sids. Return sids that were actually moved and therefore need to be marked as dirty""" spikes = self.spikes nspikes = len(sids) nchans = len(chans) wd = self.wavedata nt = wd.shape[2] # num timepoints in each waveform ti0, ti1 = tis subnt = ti1 - ti0 # num timepoints to slice from each waveform # TODO: make maxshift a f'n of interpolation factor maxshift = 2 # shift +/- this many timepoints subntdiv2 = subnt // 2 #print('subntdiv2 on either side of t=0: %d' % subntdiv2) if subntdiv2 < maxshift: raise ValueError("Selected waveform duration too short") #maxshiftus = maxshift * self.stream.tres # NOTE: in this case, it may be faster to keep shifts and sti0s and sti1s as lists # of ints instead of np int arrays, maybe because their values are faster to iterate # over or index with in python loops and lists: shifts = range(-maxshift, maxshift+1) # from -maxshift to maxshift, inclusive nshifts = len(shifts) sti0s = [ ti0+shifti for shifti in range(nshifts) ] # shifted ti0 values sti1s = [ ti1+shifti for shifti in range(nshifts) ] # shifted ti1 values sti0ssti1s = zip(sti0s, sti1s) print("Padding waveforms with up to +/- %d points of fake data" % maxshift) # not worth subsampling here while calculating meandata, since all this # stuff in this loop is needed in the shift loop below subsd = np.zeros((nspikes, nchans, subnt), dtype=wd.dtype) # subset of spike data spikechanis = np.zeros((nspikes, nchans), dtype=np.int64) t0 = time.time() for sidi, sid in enumerate(sids): spike = spikes[sid] nspikechans = spike['nchans'] spikechans = spike['chans'][:nspikechans] spikechanis[sidi] = spikechans.searchsorted(chans) subsd[sidi] = wd[sid, spikechanis[sidi], ti0:ti1] print('Mean prep loop for best shift took %.3f sec' % (time.time()-t0)) t0 = time.time() meandata = subsd.mean(axis=0) # float64 print('Mean for best shift took %.3f sec' % (time.time()-t0)) # choose best shifted waveform for each spike # widesd holds current spike data plus padding on either side # to allow for full width slicing for all time shifts: maxnchans = spikes['nchans'].max() # of all spikes in sort widesd = np.zeros((maxnchans, maxshift+nt+maxshift), dtype=wd.dtype) shiftedsubsd = subsd.copy() # init tempsubshifts = np.zeros((nshifts, nchans, subnt), dtype=wd.dtype) dirtysids = [] t0 = time.time() for sidi, sid in enumerate(sids): # for speed, instead of adding real data, pad start and end with fake values chanis = spikechanis[sidi] sd = wd[sid] # sid's spike data widesd[:, maxshift:-maxshift] = sd # 2D widesd[:, :maxshift] = sd[:, 0, None] # pad start with first point per chan widesd[:, -maxshift:] = sd[:, -1, None] # pad end with last point per chan wideshortsd = widesd[chanis] # sid's padded spike data on chanis, 2D # keep this inner loop as fast as possible: for shifti, (sti0, sti1) in enumerate(sti0ssti1s): tempsubshifts[shifti] = wideshortsd[:, sti0:sti1] # len: subnt errors = tempsubshifts - meandata # (nshifts, nchans, subnt) - (nchans, subnt) # get sum squared errors by taking sum across highest two dims - for purpose # of error comparison, don't need to take mean or square root. Also, order # of summation along axes doesn't matter, as long as it's done on the highest two: sserrors = (errors**2).sum(axis=2).sum(axis=1) # nshifts long bestshifti = sserrors.argmin() bestshift = shifts[bestshifti] if bestshift != 0: # no need to update sort.wavedata[sid] if there's no shift # update time values: dt = bestshift * self.tres # time to shift by, signed, in us spikes['t'][sid] += dt # should remain halfway between t0 and t1 spikes['t0'][sid] += dt spikes['t1'][sid] += dt # might result in some out of bounds tis because the original peaks # have shifted off the ends. Opposite sign, referencing within wavedata: spikes['tis'][sid] -= bestshift # update sort.wavedata wd[sid] = widesd[:, bestshifti:bestshifti+nt] shiftedsubsd[sidi] = tempsubshifts[bestshifti] dirtysids.append(sid) # mark sid as dirty print('Shifting loop took %.3f sec' % (time.time()-t0)) AD2uV = self.converter.AD2uV stdevbefore = AD2uV(subsd.std(axis=0).mean()) stdevafter = AD2uV(shiftedsubsd.std(axis=0).mean()) print('stdev went from %.3f to %.3f uV' % (stdevbefore, stdevafter)) return dirtysids ''' def alignminmax(self, sids, to): """Align sids by their min or max. Return those that were actually moved and therefore need to be marked as dirty""" if not self.stream.is_open(): raise RuntimeError("No open stream to reload spikes from") spikes = self.spikes V0s = spikes['V0'][sids] V1s = spikes['V1'][sids] Vss = np.column_stack((V0s, V1s)) alignis = spikes['aligni'][sids] b = np.column_stack((alignis==0, alignis==1)) # 2D boolean array if to == 'min': i = Vss[b] > 0 # indices into sids of spikes aligned to the max peak elif to == 'max': i = Vss[b] < 0 # indices into sids of spikes aligned to the min peak else: raise ValueError('Unknown to %r' % to) sids = sids[i] # sids that need realigning nspikes = len(sids) print("Realigning %d spikes" % nspikes) if nspikes == 0: # nothing to do return [] # no sids to mark as dirty multichantis = spikes['tis'][sids] # nspikes x nchans x 2 arr chanis = spikes['chani'][sids] # nspikes arr of max chanis # peak tis on max chan of each spike, convert from uint8 to int32 for safe math tis = np.int32(multichantis[np.arange(nspikes), chanis]) # nspikes x 2 arr # NOTE: tis aren't always in temporal order! dpeaktis = tis[:, 1] - tis[:, 0] # could be +ve or -ve dpeaks = spikes['dt'][sids] # stored as +ve # for each spike, decide whether to add or subtract dpeak to/from its temporal values ordered = dpeaktis > 0 # in temporal order reversed = dpeaktis < 0 # in reversed temporal order alignis = spikes['aligni'][sids] alignis0 = alignis == 0 alignis1 = alignis == 1 dpeaki = np.zeros(nspikes, dtype=int) # add dpeak to temporal values to align to later peak dpeaki[ordered & alignis0 | reversed & alignis1] = 1 # subtact dpeak from temporal values to align to earlier peak dpeaki[ordered & alignis1 | reversed & alignis0] = -1 # upcast aligni from 1 byte to an int before doing arithmetic on it: #dalignis = -np.int32(alignis)*2 + 1 dts = dpeaki * dpeaks dtis = -dpeaki * abs(dpeaktis) # shift values spikes['t'][sids] += dts spikes['t0'][sids] += dts spikes['t1'][sids] += dts spikes['tis'][sids] = spikes['tis'][sids] + dtis[:, None, None] # update wrt new t0i spikes['aligni'][sids[alignis0]] = 1 spikes['aligni'][sids[alignis1]] = 0 # update wavedata for each shifted spike self.reload_spikes(sids) return sids # mark all sids as dirty def choose_new_meanchans(self, sids): """Get mean waveform of all sids, then find the mean's chan with max Vpp, then choose det.maxnchansperspike channels around that maxchan. Return meanchans, furthestchan, and furthestchani""" print('Choosing new channel set for all selected spikes') det = self.detector meanwave = self.get_mean_wave(sids) # mean chan with max Vpp: maxchan = meanwave.chans[meanwave.data.ptp(axis=1).argmax()] maxchani = det.chans.searchsorted(maxchan) distances = det.dm.data[maxchani] # keep the maxnchansperspike closest chans to maxchan, including maxchan: chanis = distances.argsort()[:det.maxnchansperspike] meanchans = det.chans[chanis] meanchans.sort() # keep them sorted print('meanchans: %r' % list(meanchans)) furthestchan = det.chans[chanis[-1]] print('furthestchan: %d' % furthestchan) furthestchani = meanchans.searchsorted(furthestchan) # sanity checks: assert len(meanchans) == det.maxnchansperspike assert maxchan in meanchans return meanchans, furthestchan, furthestchani def reload_spikes(self, sids, usemeanchans=False): """Update wavedata of designated spikes from stream. Optionally fix incorrect time values from .sort 0.3 files. Optionally choose new set of channels for all sids based on the chans closest to the mean of the sids. It's the caller's responsibility to mark sids as dirty and trigger resaving of .wave file""" ## TODO: add findmaxchan=False and recenteronmaxchan=False kwargs nsids = len(sids) print('(Re)loading %d spikes' % nsids) stream = self.stream if not stream.is_open(): raise RuntimeError("No open stream to reload spikes from") spikes = self.spikes det = self.detector ver_lte_03 = float(self.__version__) <= 0.3 if ver_lte_03: print('Fixing potentially incorrect time values during spike reloading') nfixed = 0 treload = time.time() if usemeanchans: if ver_lte_03: raise RuntimeError("Best not to choose new chans from mean until after " "converting to .sort >= 0.4") meanchans, furthestchan, furthestchani = self.choose_new_meanchans(sids) nmeanchans = len(meanchans) # split up sids into groups efficient for loading from stream: ts = spikes[sids]['t'] # noncontig, not a copy # ensure they're in temporal order: if not (np.diff(ts) >= 0).all(): print("Selected sids aren't in temporal order, sorting by time...") tsis = ts.argsort() sids = sids[tsis] print("Done sorting sids by time") # break up spikes by ISIs >= MAXGROUPISI: splitis = np.where(np.diff(ts) >= MAXGROUPISI)[0] + 1 groups = np.split(sids, splitis) # limit each group of sids to no more than MAXGROUPDT: groupi = 0 while groupi < len(groups): group = groups[groupi] # group of sids all with ISIs < MAXGROUPISI ## TODO: not a copy: is this the optimal way to get the times in this case? relts = spikes[group]['t'] - spikes[group[0]]['t'] splitis = np.where(np.diff(relts // MAXGROUPDT) > 0)[0] + 1 nsubgroups = len(splitis) + 1 if nsubgroups > 1: # del original group, replace with subgroups del groups[groupi] subgroups = np.split(group, splitis) groups[groupi:groupi] = subgroups groupi += len(subgroups) else: groupi += 1 print('ngroups: %d' % len(groups)) # process each group: sidi = 0 # init sid index across all groups, used as status counter for groupi, group in enumerate(groups): printflush('<%d>' % groupi, end='') assert len(group) > 0 # otherwise something went wrong above t0 = spikes[group[0]]['t0'] t1 = spikes[group[-1]]['t1'] if ver_lte_03: # load a little extra, in case we need to reload misaligned first and/or # last spike in this group t0 -= 5000 # -5 ms t1 += 5000 # +5 ms """ Find union of chans of sids in this group, ask Stream for only those such that no unnecessary resampling takes place on unneeded chans. Note that this doesn't make a difference when CAR is enabled in the stream, because the full set of enabled chans have to be maintained in Stream.__call__ until the very end. Don't bother cutting out the correct nchans for each sid. At worst, chan 0 (the "empty" chans array value) will be unnecessarily added to unionchans, and we'll retrieve one extra chan when creating tempwave, which will then later be discarded: """ unionchans = np.unique(spikes['chans'][group]) if usemeanchans: # now that we have the original unionchans of this group, # update this group's spikes array entries with meanchans: spikes['nchans'][group] = nmeanchans # we're using the max num chans, so assign the full array: spikes['chans'][group] = meanchans # now update unionchans as well: unionchans = np.unique(np.hstack((unionchans, meanchans))) if 0 not in stream.chans: # if chan 0 is disabled in stream # remove 0 from unionchans, otherwise an error would be raised when # calling stream() unionchans = unionchans[unionchans != 0] # load and resample only what's needed for this group: tempwave = stream(t0, t1, unionchans) # slice out each spike's reloaded data from tempwave: for sid in group: # print status: if sidi % 10000 == 0: printflush(sidi, end='') elif sidi % 1000 == 0: printflush('.', end='') if usemeanchans: # already checked above that ver_lte_03 == False # this spike's chans have been set to meanchans, now # check that each spike's maxchan is in meanchans: chan = spikes[sid]['chan'] if chan not in meanchans: # replace furthest chan with spike's maxchan: print("spike %d: replacing furthestchan %d with spike's maxchan %d" % (sid, furthestchan, chan)) nchans = spikes[sid]['nchans'] chans = spikes[sid]['chans'][:nchans] # replace furthest chan with max chan, modifies spikes array in-place: chans[furthestchani] = chan # make sure chans remain sorted: chans.sort() # this isn't necessary, because all the above was in-place: #spikes['chans'][sid][:nchans] = chans spike = spikes[sid] nchans = spike['nchans'] chans = spike['chans'][:nchans] rd = tempwave[spike['t0']:spike['t1']][chans].data # reloaded data if ver_lte_03: # fix potentially incorrect spike tis result = self.reload_spike_ver_lte_03(sid, nchans, tempwave, rd) if result == None: sidi += 1 # inc status counter continue # rollwin2D won't work, skip to next sid else: rd, fixed = result if fixed: nfixed += 1 nt = rd.shape[1] self.wavedata[sid, :nchans, :nt] = rd # update wavedata sidi += 1 # inc status counter print() if ver_lte_03: print('Fixed time values of %d spikes' % nfixed) print('(Re)loaded %d spikes, took %.3f sec' % (len(sids), time.time()-treload)) def reload_spike_ver_lte_03(self, sid, nchans, tempwave, rd): """In sort.__version__ <= 0.3, t, t0, t1, and tis were not updated during alignbest() calls. To fix this, load new data with old potentially incorrect t0 and t1 values, and compare this new data to existing old data in wavedata array. Find where the non-repeating parts of the old data fits into the new, and calculate the correction needed to fix the time values. Finally, reload new data according to these corrected time values.""" #print('Reloading sid from ver_lte_03: %d' % sid) od = self.wavedata[sid, :nchans] # old data # indices that strip const values from left and right ends: lefti, righti = lrrep2Darrstripis(od) od = od[:, lefti:righti] # stripped old data # reloaded data rd uses old incorrect t0 and t1, but they should be # wide enough to encompass the non-repeating parts of the old data width = od.shape[1] # rolling window width if not width <= rd.shape[1]: print('') # newline print("WARNING: od.shape[1]=%d > rd.shape[1]=%d for sid %d" % (od.shape[1], rd.shape[1], sid)) #import pdb; pdb.set_trace() return odinndis = np.where((rollwin2D(rd, width) == od).all(axis=1).all(axis=1))[0] if len(odinndis) == 0: # no hits of old data in new dnt = 0 # reload data based on current timepoints elif len(odinndis) == 1: # exactly 1 hit of old data in new odinndi = odinndis[0] # pull it out dnt = odinndi - lefti # num timepoints to correct by, signed else: raise RuntimeError("Multiple hits of old data in new, don't know " "how to reload spike %d" % sid) newrd, fixed = rd, False if dnt != 0: dt = intround(dnt * self.tres) # time to correct by, signed, in us spikes['t'][sid] += dt # should remain halfway between t0 and t1 spikes['t0'][sid] += dt spikes['t1'][sid] += dt # might result in some out of bounds tis because the original peaks # have shifted off the ends. Use opposite sign because we're # referencing within wavedata: # in versions <= 0.3, 'tis' were named 'phasetis': spikes['phasetis'][sid] = spikes['phasetis'][sid] - dnt spike = spikes[sid] # reslice tempwave again now that t0 and t1 have changed newrd = tempwave[spike['t0']:spike['t1']][chans].data fixed = True #printflush('F', end='') return newrd, fixed def reload_spikes_and_templates(self, sids, usemeanchans=False): self.reload_spikes(sids, usemeanchans=usemeanchans) # update neuron templates: unids = np.unique(self.spikes['nid'][sids]) unids = unids[unids != 0] # exclude junk cluster, which doesn't have a neuron neurons = [ self.neurons[nid] for nid in unids ] for neuron in neurons: neuron.update_wave() # update affected mean waveforms def init_spike_alignment(self): """Set initial spike alignment points according to alignment points of each spike's neuron""" print('Setting initial spike alignment points') ntis, nalignis = {}, {} # tis and aligni derived from each neuron's mean waveform for neuron in self.neurons.values(): nwave = neuron.get_wave() # update and return mean waveform mintis = nwave.data.argmin(axis=1) maxtis = nwave.data.argmax(axis=1) ntis[neuron.id] = np.column_stack([mintis, maxtis]) # choose aligni with least variance: nalignis[neuron.id] = np.argmin([mintis.std(), maxtis.std()]) AD2uV = self.converter.AD2uV for s, wd in zip(self.spikes, self.wavedata): sid = s['id'] # print out progress on a regular basis: if sid % 100000 == 0: printflush(sid, end='') elif sid % 10000 == 0: printflush('.', end='') nid = s['nid'] #chan = s['chan'] nchans = s['nchans'] chans = s['chans'][:nchans] neuronchans = self.neurons[nid].wave.chans assert (chans == neuronchans).all() s['tis'][:nchans] = ntis[nid] # set according to its neuron, wrt t0i=0 s['aligni'] = nalignis[nid] # set according to its neuron maxchani = s['chani'] t0i, t1i = int(s['tis'][maxchani, 0]), int(s['tis'][maxchani, 1]) s['dt'] = abs(t1i - t0i) / self.sampfreq * 1e6 # us # note that V0 and V1 might not be of opposite sign, because tis are derived # from mean neuron waveform, not from each individual spike: s['V0'], s['V1'] = AD2uV(wd[maxchani, t0i]), wd[maxchani, t1i] # uV s['Vpp'] = abs(s['V1'] - s['V0']) # uV print() def spatially_localize_spikes(self, sortwin, method='fit'): """Assuming that wavedata have been extracted and neuron mean waveforms calculated, find tis and perform spatial localization of every spike in self""" det = self.detector weights2f = self.extractor.weights2spatial weights2spatialmean = self.extractor.weights2spatialmean f = self.extractor.f nreject = 0 # number spikes rejected during spatial localization print('Running spatial localization on all %d spikes' % self.nspikes) tstart = time.clock() ## TODO: chan this be multithreaded/processed? for s, wd in zip(self.spikes, self.wavedata): # Get Vpp at each inclchan's tis, use as spatial weights: # see core.rowtake() or util.rowtake_cy() for indexing explanation: sid = s['id'] # print out progress on a regular basis: if sid % 10000 == 0: printflush(sid, end='') elif sid % 1000 == 0: printflush('.', end='') chan = s['chan'] nchans = s['nchans'] chans = s['chans'][:nchans] maxchani = s['chani'] chanis = det.chans.searchsorted(chans) w = np.float32(wd[np.arange(s['nchans'])[:, None], s['tis'][:nchans]]) # nchans x 2 w = abs(w).sum(axis=1) # Vpp for each chan, measured at t0i and t1i x = det.siteloc[chanis, 0] # 1D array (row) y = det.siteloc[chanis, 1] if method == 'fit': # localize by fitting extractor.f function to wavedata params = weights2f(f, w, x, y, maxchani) elif method == 'mean': # set localization to Vpp-weighted spatial mean and 0 sigma: x0, y0 = weights2spatialmean(w, x, y) # a very ad-hoc guess for spatial sigma: sx = 2 * dist((x0, y0), self.probe.SiteLoc[chan]) params = x0, y0, sx, sx else: print('Unknown method %r' % method) if params == None: # presumably a non-localizable many-channel noise event #printflush('X', end='') # to indicate a rejected spike if DEBUG: spiket = intround(s['t']) # nearest us det.log("Reject spike %d at t=%d based on fit params" % (sid, spiket)) neuron = self.neurons[s['nid']] # remove from its neuron, add to unsorted list of spikes: sortwin.MoveSpikes2List(neuron, [sid], update=False) # manually set localization params to Vpp-weighted spatial mean and 0 sigma: x0, y0 = weights2spatialmean(w, x, y) # set sigma to 0 um, and then later round lockr up to 1 um so that only one # raster tick shows up for each rejected spike, reducing clutter params = x0, y0, 0, 0 nreject += 1 # Save spatial fit params, and "lockout" only the channels within lockrx*sx # of the fit spatial location of the spike, up to a max of inclr. "Lockout" # in this case only refers to which channels are highlighted with a raster tick # for each spike: s['x0'], s['y0'], s['sx'], s['sy'] = params x0, y0 = s['x0'], s['y0'] # lockout radius for this spike: lockr = min(det.lockrx*s['sx'], det.inclr) # in um lockr = max(lockr, 1) # at least 1 um, so at least the maxchan gets a tick # test y coords of chans in y array, ylockchaniis can be used to index # into x, y and chans: ylockchaniis, = np.where(np.abs(y - y0) <= lockr) # convert bool arr to int # test Euclid distance from x0, y0 for each ylockchani: lockchaniis = ylockchaniis.copy() for ylockchanii in ylockchaniis: if dist((x[ylockchanii], y[ylockchanii]), (x0, y0)) > lockr: # Euclidean distance is too great, remove ylockchanii from lockchaniis: lockchaniis = lockchaniis[lockchaniis != ylockchanii] lockchans = chans[lockchaniis] nlockchans = len(lockchans) s['lockchans'][:nlockchans], s['nlockchans'] = lockchans, nlockchans print('Spatial localization of spikes took %.3f s' % (time.clock() - tstart)) return nreject ''' def get_component_matrix(self, dims=None, weighting=None): """Convert spike param matrix into pca/ica data for clustering""" import mdp # can't delay this any longer X = self.get_param_matrix(dims=dims) if weighting == None: return X if weighting.lower() == 'ica': node = mdp.nodes.FastICANode() elif weighting.lower() == 'pca': node = mdp.nodes.PCANode() else: raise ValueError, 'unknown weighting %r' % weighting node.train(X) features = node.execute(X) # returns all available components #self.node = node #self.weighting = weighting #self.features = features return features def get_ids(self, cids, spikes): """Convert a list of cluster ids into 2 dicts: n2sids maps neuron IDs to spike IDs; s2nids maps spike IDs to neuron IDs""" cids = np.asarray(cids) cids = cids - cids.min() # make sure cluster IDs are 0-based uniquecids = set(cids) nclusters = len(uniquecids) # neuron ID to spike IDs (plural) mapping n2sids = dict(zip(uniquecids, [ [] for i in range(nclusters) ])) s2nids = {} # spike ID to neuron ID mapping for spike, nid in zip(spikes, cids): s2nids[spike['id']] = nid n2sids[nid].append(spike['id']) return n2sids, s2nids def write_spc_input(self): """Generate input data file to SPC""" X = self.get_component_matrix() # write to space-delimited .dat file. Each row is a spike, each column a param spykedir = os.path.dirname(__file__) dt = str(datetime.datetime.now()) dt = dt.split('.')[0] # ditch the us dt = dt.replace(' ', '_') dt = dt.replace(':', '.') self.spcdatfname = os.path.join(spykedir, 'spc', dt+'.dat') # not sure why spc adds the dg_01 part: self.spclabfname = os.path.join(spykedir, 'spc', dt+'.dg_01.lab') f = open(self.spcdatfname, 'w') for params in X: # write text data to file, one row at a time params.tofile(f, sep=' ', format='%.6f') f.write('\n') f.close() def parse_spc_lab_file(self, fname=None): """Parse output .lab file from SPC. Each row in the file is the assignment of each spin (datapoint) to a cluster, one row per temperature datapoint. First column is temperature run number (0-based). 2nd column is the temperature. All remaining columns correspond to the datapoints in the order presented in the input .dat file. Returns (Ts, cids)""" #spikes = self.get_spikes_sortedby('id') if fname == None: defaultDir = r"C:\Documents and Settings\Administrator\Desktop\Charlie\From" dlg = wx.FileDialog(None, message="Open SPC .lab file", defaultDir=defaultDir, defaultFile='', wildcard="All files (*.*)|*.*|.lab files (*.lab)|*.lab|", style=wx.OPEN) if dlg.ShowModal() == wx.ID_OK: fname = dlg.GetPath() dlg.Destroy() data = np.loadtxt(fname, dtype=np.float32) Ts = data[:, 1] # 2nd column cids = np.int32(data[:, 2:]) # 3rd column on print('Parsed %r' % fname) return Ts, cids def parse_charlies_output(self, fname=None): if fname == None: fname = (r'C:\Documents and Settings\Administrator\Desktop\Charlie\' 'From\2009-07-20\clustered_events_coiflet_T0.125.txt') nids = np.loadtxt(fname, dtype=int) # one neuron id per spike return nids def write_spc_app_input(self): """Generate input data file to spc_app""" spikes = self.get_spikes_sortedby('id') X = self.get_component_matrix() # write to tab-delimited data file. Each row is a param, each column a spike # (this is the transpose of X) # first row has labels "AFFX", "NAME", and then spike ids # first col has labels "AFFX", and then param names f = open(r'C:\home\mspacek\Desktop\Work\SPC\Weizmann\spc_app\spc_app_input.txt', 'w') f.write('AFFX\tNAME\t') for spike in spikes: f.write('s%d\t' % spike['id']) f.write('\n') for parami, param in enumerate(['Vpp', 'dt', 'x0', 'y0', 'sx', 'sy', 'theta']): f.write(param+'\t'+param+'\t') for val in X[:, parami]: f.write('%f\t' % val) f.write('\n') f.close() def hcluster(self, t=1.0): """Hierarchically cluster self.spikes TODO: consider doing multiple cluster runs. First, cluster by spatial location (x0, y0). Then split those clusters up by Vpp. Then those by spatial distrib (sy/sx, theta), then by temporal distrib (dt, s1, s2). This will ensure that the lousier params will only be considered after the best ones already have, and therefore that you start off with pretty good clusters that are then only slightly refined using the lousy params """ spikes = self.get_spikes_sortedby('id') X = self.get_component_matrix() print(X) # try 'weighted' or 'average' with 'mahalanobis' cids = fclusterdata(X, t=t, method='single', metric='euclidean') n2sids, s2nids = self.get_ids(cids, spikes) return n2sids def export2Charlie(self, fname='spike_data', onlymaxchan=False, nchans=3, npoints=32): """Export spike data to a text file, one spike per row. Columns are x0, y0, followed by most prominent npoints datapoints (1/4, 3/4 wrt spike time) of each nearest nchans. This is to give to Charlie to do WPD and SPC on""" if onlymaxchan: nchans = 1 assert np.log2(npoints) % 1 == 0, 'npoints is not a power of 2' # get ti - time index each spike is assumed to be centered on self.spikes[0].update_wave(self.stream) # make sure it has a wave ti = intround(self.spikes[0].wave.data.shape[-1] / 4) # 13 for 50 kHz, 6 for 25 kHz dims = self.nspikes, 2+nchans*npoints output = np.empty(dims, dtype=np.float32) dm = self.detector.dm chanis = np.arange(len(dm.data)) coords = np.asarray(dm.coords) xcoords = coords[:, 0] ycoords = coords[:, 1] sids = list(self.spikes) # self.spikes is a dict! sids.sort() for sid in sids: spike = self.spikes[sid] chani = spike.chani # max chani x0, y0 = spike.x0, spike.y0 if onlymaxchan: nearestchanis = np.asarray([chani]) else: # find closest chans to x0, y0 d2s = (xcoords - x0)**2 + (ycoords - y0)**2 # squared distances sortis = d2s.argsort() nearestchanis = chanis[sortis][0:nchans] # pick the first nchan nearest chans if chani not in nearestchanis: print("WARNING: max chani %d is not among the %d chanis nearest " "(x0, y0) = (%.1f, %.1f) for spike %d at t=%d" % (chani, nchans, x0, y0, sid, spike.t)) if spike.wave.data is None: spike.update_wave(self.stream) row = [x0, y0] for chani in nearestchanis: chan = dm.chans[chani] # dereference try: data = spike.wave[chan].data[0] # pull out singleton dimension except IndexError: # empty array data = np.zeros(data.shape[-1], data.dtype) row.extend(data[ti-npoints/4:ti+npoints*3/4]) output[sid] = row dt = str(datetime.datetime.now()) dt = dt.split('.')[0] # ditch the us dt = dt.replace(' ', '_') dt = dt.replace(':', '.') fname += '.' + dt + '.txt' np.savetxt(fname, output, fmt='%.1f', delimiter=' ') def match(self, templates=None, weighting='signal', sort=True): """Match templates to all .spikes with nearby maxchans, save error values to respective templates. Note: slowest step by far is loading in the wave data from disk. (First match is slow, subsequent ones are ~ 15X faster.) Unless something's done about that in advance, don't bother optimizing here much. Right now, once waves are loaded, performance is roughly 20000 matches/sec TODO: Nick's alternative to gaussian distance weighting: have two templates: a mean template, and an stdev template, and weight the error between each matched spike and the mean on each chan at each timepoint by the corresponding stdev value (divide the error by the stdev, so that timepoints with low stdev are more sensitive to error) TODO: looks like I still need to make things more nonlinear - errors at high signal values aren't penalized enough, while errors at small signal values are penalized too much. Try cubing both signals, then taking sum(err**2) DONE: maybe even better, instead of doing an elaborate cubing of signal, followed by a rather elaborate gaussian spatiotemporal weighting of errors, just take difference of signals, and weight the error according to the abs(template_signal) at each point in time and across chans. That way, error in parts of the signal far from zero are considered more important than deviance of perhaps similar absolute value for signal close to zero """ # None defaults to matching all templates: templates = templates or self.templates.values() sys.stdout.write('matching') t0 = time.time() nspikes = len(self.spikes) dm = self.detector.dm for template in templates: template.err = [] # overwrite any existing .err attrib tw = template.tw templatewave = template.wave[template.chans] # pull out template's enabled chans #stdev = template.get_stdev()[template.chans] # pull out template's enabled chans # replace any 0s with 1s - TODO: what's best way to avoid singularities?: #stdev[stdev == 0] = 1 # Gaussian weighting in space and/or time: weights = template.get_weights(weighting=weighting, sstdev=self.detector.slock/2, tstdev=self.detector.tlock/2) for spike in self.spikes.values(): # check if spike.maxchan is outside some minimum distance from template.maxchan if dm[template.maxchan, spike.maxchan] > MAXCHANTOLERANCE: # um continue # don't even bother if spike.wave.data is None or template.tw != TW: # make sure their data line up spike.update_wave(tw) # this slows things down a lot, but is necessary # slice template's enabled chans out of spike, calculate sum of # squared weighted error # first impression is that dividing by stdev makes separation worse, not better # low stdev means more sensitive to error: #err = (templatewave.data - spike.wave[template.chans].data) / stdev * weights # pull out template's enabled chans from spike: spikewave = spike.wave[template.chans] if weighting == 'signal': tsdata = np.asarray([templatewave.data, spikewave.data]) # take elementwise max of abs of template and spike data: weights = np.abs(tsdata).max(axis=0) err = (templatewave.data - spikewave.data) * weights # weighted error err = (err**2).sum(axis=None) # sum of squared weighted error template.err.append((spike.id, intround(err))) template.err = np.asarray(template.err, dtype=np.int64) if sort and len(template.err) != 0: i = template.err[:, 1].argsort() # row indices that sort by error template.err = template.err[i] sys.stdout.write('.') print('\nmatch took %.3f sec' % (time.time()-t0)) ''' class Neuron(object): """A collection of spikes that have been deemed somehow, whether manually or automatically, to have come from the same cell. A Neuron's waveform is the mean of its member spikes""" def __init__(self, sort, id=None): self.sort = sort self.id = id # neuron id self.wave = WaveForm() # init to empty waveform self.sids = np.array([], dtype=int) # indices of spikes that make up this neuron # relative reference timestamp, here for symmetry with fellow spike rec # (obj.t comes up sometimes): self.t = 0 self.plt = None # Plot currently holding self self.cluster = None self.good = False # user can mark this neuron as "good" if so desired #self.fname # not here, let's allow neurons to have spikes from different files? def get_chans(self): if self.wave.data is None: self.update_wave() return self.wave.chans # self.chans just refers to self.wave.chans chans = property(get_chans) def get_chan(self): if self.wave.data is None: self.update_wave() return self.wave.chans[self.wave.data.ptp(axis=1).argmax()] # chan with max Vpp chan = property(get_chan) def get_nspikes(self): return len(self.sids) nspikes = property(get_nspikes) def __getstate__(self): """Get object state for pickling""" d = self.__dict__.copy() # don't save any calculated PCs/ICs: #d.pop('X', None) #d.pop('Xhash', None) # don't save plot self is assigned to, since that'll change anyway on unpickle d['plt'] = None return d def get_wave(self): """Check for valid mean and std waveform before returning it""" # many neuron waveforms saved in old .sort files won't have a wave.std field: try: self.wave.std except AttributeError: return self.update_wave() if self.wave == None or self.wave.data is None or self.wave.std is None: return self.update_wave() else: return self.wave # return existing waveform def update_wave(self): """Update mean and std of self's waveform""" sort = self.sort spikes = sort.spikes if len(self.sids) == 0: # no member spikes, perhaps I should be deleted? raise RuntimeError("n%d has no spikes and its waveform can't be updated" % self.id) meanwave = sort.get_mean_wave(self.sids, nid=self.id) # update self's Waveform object self.wave.data = meanwave.data self.wave.std = meanwave.std self.wave.ts = sort.twts.copy() # meanwave has no .ts, copy for clean jsonpickle self.wave.chans = meanwave.chans self.wave.tres = sort.tres # meanwave has no .tres return self.wave def __sub__(self, other): """Return difference array between self and other neurons' waveforms on common channels""" selfwavedata, otherwavedata = self.getCommonWaveData(other.chan, other.chans, other.wave.data) return selfwavedata - otherwavedata def getCommonWaveData(self, otherchan, otherchans, otherwavedata): """Return waveform data common to self's chans and otherchans, while requiring that both include the other's maxchan""" chans = np.intersect1d(self.chans, otherchans, assume_unique=True) if len(chans) == 0: raise ValueError('No common chans') if self.chan not in chans or otherchan not in chans: raise ValueError("maxchans aren't part of common chans") selfchanis = self.chans.searchsorted(chans) otherchanis = otherchans.searchsorted(chans) return self.wave.data[selfchanis], otherwavedata[otherchanis] ''' def get_stdev(self): """Return 2D array of stddev of each timepoint of each chan of member spikes. Assumes self.update_wave has already been called""" data = [] # TODO: speed this up by pre-allocating memory and then filling in the array for spike in self.spikes: data.append(spike.wave.data) # collect spike's data stdev = np.asarray(data).std(axis=0) return stdev def get_weights(self, weighting=None, sstdev=None, tstdev=None): """Returns unity, spatial, temporal, or spatiotemporal Gaussian weights for self's enabled chans in self.wave.data, given spatial and temporal stdevs""" nchans = len(self.wave.chans) nt = len(self.wave.data[0]) # assume all chans have the same number of timepoints if weighting == None: weights = 1 elif weighting == 'spatial': weights = self.get_gaussian_spatial_weights(sstdev) # vector elif weighting == 'temporal': weights = self.get_gaussian_temporal_weights(tstdev) # vector elif weighting == 'spatiotemporal': sweights = self.get_gaussian_spatial_weights(sstdev) tweights = self.get_gaussian_temporal_weights(tstdev) weights = np.outer(sweights, tweights) # matrix, outer product of the two elif weighting == 'signal': weights = None # this is handled by caller #print('\nweights:\n%r' % weights) return weights def get_gaussian_spatial_weights(self, stdev): """Return a vector that weights self.chans according to a 2D gaussian centered on self.maxchan with standard deviation stdev in um""" g = Gaussian(mean=0, stdev=stdev) # distances between maxchan and all enabled chans: d = self.sort.detector.dm[self.maxchan, self.chans] weights = g[d] weights.shape = (-1, 1) # vertical vector with nchans rows, 1 column return weights def get_gaussian_temporal_weights(self, stdev): """Return a vector that weights timepoints in self's mean waveform by a gaussian centered on t=0, with standard deviation stdev in us""" g = Gaussian(mean=0, stdev=stdev) ts = self.wave.ts # template mean timepoints relative to t=0 spike time weights = g[ts] # horizontal vector with 1 row, nt timepoints return weights ''' class PTCSHeader(object): """ Polytrode clustered spikes file header: formatversion: int64 (currently version 3) ndescrbytes: uint64 (nbytes, keep as multiple of 8 for nice alignment) descr: ndescrbytes of ASCII text (padded with null bytes if needed for 8 byte alignment) nneurons: uint64 (number of neurons) nspikes: uint64 (total number of spikes) nsamplebytes: uint64 (number of bytes per template waveform sample) samplerate: uint64 (Hz) npttypebytes: uint64 (nbytes, keep as multiple of 8 for nice alignment) pttype: npttypebytes of ASCII text (padded with null bytes if needed for 8 byte alignment) nptchans: uint64 (total num chans in polytrode) chanpos: nptchans * 2 * float64 (array of (x, y) positions, in um, relative to top of polytrode, indexed by 0-based channel IDs) nsrcfnamebytes: uint64 (nbytes, keep as multiple of 8 for nice alignment) srcfname: nsrcfnamebytes of ASCII text (source file name, probably .srf, padded with null bytes if needed for 8 byte alignment) datetime: float64 (absolute datetime corresponding to t=0 us timestamp, stored as days since epoch: December 30, 1899 at 00:00) ndatetimestrbytes: uint64 datetimestr: ndatetimestrbytes of ASCII text (human readable string representation of datetime, preferrably ISO 8601, padded with null bytes if needed for 8 byte alignment) """ FORMATVERSION = 3 # overall .ptcs file format version, not header format version def __init__(self, sort, sortpath, stream, nneurons, nspikes, nsamplebytes, fullfname, exportdt, user='', notes=''): self.sort = sort self.stream = stream self.nneurons = nneurons self.nspikes = nspikes self.nsamplebytes = nsamplebytes homelessfullfname = lstrip(fullfname, os.path.expanduser('~')) sortfname = sort.fname sortfullfname = os.path.join(sortpath, sortfname) sortfmoddt = str(datetime.datetime.fromtimestamp(os.path.getmtime(sortfullfname))) sortfmoddt = sortfmoddt.split('.')[0] # ditch the us sortfsize = os.path.getsize(sortfullfname) # in bytes d = {'file_type': '.ptcs (polytrode clustered spikes) file', 'original_fname': homelessfullfname, 'export_time': exportdt, 'sort': {'fname': sortfname, 'path': sortpath, 'fmtime': sortfmoddt, 'fsize': sortfsize}, 'user': user, 'notes': notes} descr = str(d) self.descr = pad(descr, align=8) self.srcfname = pad(lstrip(stream.fname, '../'), align=8) self.pttype = pad(stream.probe.name, align=8) self.dt = stream.datetime self.dtstr = pad(self.dt.isoformat(), align=8) def write(self, f): s = self.sort np.int64(self.FORMATVERSION).tofile(f) # formatversion np.uint64(len(self.descr)).tofile(f) # ndescrbytes f.write(self.descr) # descr np.uint64(self.nneurons).tofile(f) # nneurons np.uint64(self.nspikes).tofile(f) # nspikes np.uint64(self.nsamplebytes).tofile(f) # nsamplebytes np.uint64(s.sampfreq).tofile(f) # samplerate np.uint64(len(self.pttype)).tofile(f) # npttypebytes f.write(self.pttype) # pttype np.uint64(s.stream.probe.nchans).tofile(f) # nptchans np.float64(s.stream.probe.siteloc_arr()).tofile(f) # chanpos np.uint64(len(self.srcfname)).tofile(f) # nsrcfnamebytes f.write(self.srcfname) # srcfname np.float64(td2days(self.dt - EPOCH)).tofile(f) # datetime (in days) np.uint64(len(self.dtstr)).tofile(f) # ndatetimestrbytes f.write(self.dtstr) class PTCSNeuronRecord(object): """ Polytrode clustered spikes file neuron record: nid: int64 (signed neuron id, could be -ve, could be non-contiguous with previous) ndescrbytes: uint64 (nbytes, keep as multiple of 8 for nice alignment, defaults to 0) descr: ndescrbytes of ASCII text (padded with null bytes if needed for 8 byte alignment) clusterscore: float64 xpos: float64 (um) ypos: float64 (um) sigma: float64 (um) (Gaussian spatial sigma) nchans: uint64 (num chans in template waveforms) chanids: nchans * uint64 (0 based IDs of channels in template waveforms) maxchanid: uint64 (0 based ID of max channel in template waveforms) nt: uint64 (num timepoints per template waveform channel) nwavedatabytes: uint64 (nbytes, keep as multiple of 8 for nice alignment) wavedata: nwavedatabytes of nsamplebytes sized floats (template waveform data, laid out as nchans * nt, in uV, padded with null bytes if needed for 8 byte alignment) nwavestdbytes: uint64 (nbytes, keep as multiple of 8 for nice alignment) wavestd: nwavestdbytes of nsamplebytes sized floats (template waveform standard deviation, laid out as nchans * nt, in uV, padded with null bytes if needed for 8 byte alignment) nspikes: uint64 (number of spikes in this neuron) spike timestamps: nspikes * uint64 (us, should be sorted) """ def __init__(self, neuron, spikets=None, nsamplebytes=None, descr=''): n = neuron AD2uV = n.sort.converter.AD2uV self.neuron = neuron self.spikets = spikets # constrained to stream range, may be < neuron.sids self.wavedtype = {2: np.float16, 4: np.float32, 8: np.float64}[nsamplebytes] if n.wave.data is None or n.wave.std is None: # some may have never been displayed n.update_wave() # wavedata and wavestd are nchans * nt * nsamplebytes long: self.wavedata = pad(self.wavedtype(AD2uV(n.wave.data)), align=8) self.wavestd = pad(self.wavedtype(AD2uV(n.wave.std)), align=8) self.descr = pad(descr, align=8) def write(self, f): n = self.neuron np.int64(n.id).tofile(f) # nid np.uint64(len(self.descr)).tofile(f) # ndescrbytes f.write(self.descr) # descr, bytes np.float64(np.nan).tofile(f) # clusterscore np.float64(n.cluster.pos['x0']).tofile(f) # xpos (um) np.float64(n.cluster.pos['y0']).tofile(f) # ypos (um) np.float64(n.cluster.pos['sx']).tofile(f) # sigma (um) np.uint64(len(n.wave.chans)).tofile(f) # nchans np.uint64(n.wave.chans).tofile(f) # chanids np.uint64(n.chan).tofile(f) # maxchanid np.uint64(len(n.wave.ts)).tofile(f) # nt np.uint64(self.wavedata.nbytes).tofile(f) # nwavedatabytes self.wavedata.tofile(f) # wavedata np.uint64(self.wavestd.nbytes).tofile(f) # nwavestdbytes self.wavestd.tofile(f) # wavestd np.uint64(len(self.spikets)).tofile(f) # nspikes np.uint64(self.spikets).tofile(f) # spike timestamps (us) class PanelScrollArea(QtGui.QScrollArea): """A scroll area for the spikesortpanel""" def keyPressEvent(self, event): key = event.key() # seems the ENTER key needs be handled to directly call plot, unlike in sortwin # where the event is passed on to be handled by the list widgets if key in [Qt.Key_Enter, Qt.Key_Return]: sortwin = self.topLevelWidget() sortwin.parent().ui.plotButton.click() else: QtGui.QScrollArea.keyPressEvent(self, event) # pass it on class SortWindow(SpykeToolWindow): """Sort window""" def __init__(self, parent, pos=None): SpykeToolWindow.__init__(self, parent, flags=QtCore.Qt.Tool) self.spykewindow = parent ncols = self.sort.probe.ncols nrows = self.sort.probe.nrows # try and allow the same amount of horizontal space per column for 2 and 3 col probes: if ncols <= 2: self.MAINSPLITTERPOS = 300 else: self.MAINSPLITTERPOS = 265 # move it more to the left # make horizontal sort slider use as little vertical space as possible self.VSPLITTERPOS = 1 panelwidth = PANELWIDTHPERCOLUMN * ncols panelheight = PANELHEIGHTPERROW * nrows width = max(self.MAINSPLITTERPOS + panelwidth + VSCROLLBARWIDTH, MINSORTWINDOWWIDTH) size = (width, SORTWINDOWHEIGHT) self.setWindowTitle('Sort Window') self.move(*pos) self.resize(*size) self._source = None # source cluster for comparison self.slider = SpikeSelectionSlider(Qt.Horizontal, self) self.slider.setInvertedControls(True) self.slider.setToolTip('Position of sliding spike selection time window') self.connect(self.slider, QtCore.SIGNAL('valueChanged(int)'), self.on_slider_valueChanged) self.connect(self.slider, QtCore.SIGNAL('sliderPressed()'), self.on_slider_sliderPressed) self.nlist = NList(self) self.nlist.setToolTip('Neuron list') self.nslist = NSList(self) self.nslist.setToolTip('Sorted spike list') self.uslist = USList(self) # should really be multicolumn tableview self.uslist.setToolTip('Unsorted spike list') tw = self.spykewindow.sort.tw self.panel = SpikeSortPanel(self, tw=tw) self.panel.setMinimumSize(QtCore.QSize(panelwidth, panelheight)) self.panelscrollarea = PanelScrollArea(self) self.panelscrollarea.setWidget(self.panel) self.panelscrollarea.setMinimumWidth(panelwidth + VSCROLLBARWIDTH) self.panelscrollarea.setWidgetResizable(True) # allows panel to size bigger than min self.vsplitter = QtGui.QSplitter(Qt.Vertical) self.vsplitter.addWidget(self.slider) self.vsplitter.addWidget(self.nlist) self.vsplitter.addWidget(self.nslist) self.vsplitter.addWidget(self.uslist) self.mainsplitter = QtGui.QSplitter(Qt.Horizontal) self.mainsplitter.addWidget(self.vsplitter) self.mainsplitter.addWidget(self.panelscrollarea) self.layout = QtGui.QVBoxLayout() self.layout.setContentsMargins(0, 0, 0, 0) self.layout.addWidget(self.mainsplitter) mainwidget = QtGui.QWidget(self) mainwidget.setLayout(self.layout) self.setCentralWidget(mainwidget) self.toolbar = self.setupToolbar() self.addToolBar(self.toolbar) def setupToolbar(self): toolbar = QtGui.QToolBar(self) toolbar.setObjectName('toolbar') toolbar.setFloatable(True) toolbar.setIconSize(QtCore.QSize(16, 16)) # like in main spyke window actionDelete = QAction(QIcon('res/edit-delete.svg'), 'Del', self) tt = ('<nobr><b>Del</b> &nbsp; Delete selected spikes or clusters</nobr>\n' '<nobr><b>CTRL+Del</b> &nbsp; Delete selected spikes</nobr>') actionDelete.setToolTip(tt) self.connect(actionDelete, QtCore.SIGNAL('triggered()'), self.on_actionDelete_triggered) toolbar.addAction(actionDelete) actionMergeClusters = QAction('M', self) tt = '<nobr><b>M</b> &nbsp; Merge clusters</nobr>' actionMergeClusters.setToolTip(tt) self.connect(actionMergeClusters, QtCore.SIGNAL('triggered()'), self.on_actionMergeClusters_triggered) toolbar.addAction(actionMergeClusters) #actionToggleClustersGood = QAction(QIcon('res/dialog-apply.svg'), 'G', self) actionToggleClustersGood = QAction('G', self) tt = '<nobr><b>G</b> &nbsp; Toggle clusters as "good"</nobr>' actionToggleClustersGood.setToolTip(tt) self.connect(actionToggleClustersGood, QtCore.SIGNAL('triggered()'), self.on_actionToggleClustersGood_triggered) toolbar.addAction(actionToggleClustersGood) actionSplit = QAction('+', self) tt = '<nobr><b>+</b> &nbsp; Split off selected spikes</nobr>' actionSplit.setToolTip(tt) self.connect(actionSplit, QtCore.SIGNAL('triggered()'), self.on_actionSplit_triggered) toolbar.addAction(actionSplit) actionLabelMultiunit = QAction('-', self) tt = '<nobr><b>-</b> &nbsp; Label clusters as multiunit</nobr>' actionLabelMultiunit.setToolTip(tt) self.connect(actionLabelMultiunit, QtCore.SIGNAL('triggered()'), self.on_actionLabelMultiunit_triggered) toolbar.addAction(actionLabelMultiunit) actionChanSplitClusters = QAction('/', self) tt = '<nobr><b>/</b> &nbsp; Split clusters by channels</nobr>' actionChanSplitClusters.setToolTip(tt) self.connect(actionChanSplitClusters, QtCore.SIGNAL('triggered()'), self.on_actionChanSplitClusters_triggered) toolbar.addAction(actionChanSplitClusters) actionDensitySplit = QAction('P', self) tt = ('<nobr><b>P</b> &nbsp; Split cluster pair by density along line between ' 'their centers</nobr>') actionDensitySplit.setToolTip(tt) self.connect(actionDensitySplit, QtCore.SIGNAL('triggered()'), self.on_actionDensitySplit_triggered) toolbar.addAction(actionDensitySplit) actionRandomSplit = QAction('\\', self) tt = ('<nobr><b>\\</b> &nbsp; Randomly split each selected cluster in half</nobr>') actionRandomSplit.setToolTip(tt) self.connect(actionRandomSplit, QtCore.SIGNAL('triggered()'), self.on_actionRandomSplit_triggered) toolbar.addAction(actionRandomSplit) #actionRenumber = QAction(QIcon('res/gtk-edit.svg'), '#', self) actionRenumber = QAction('#', self) tt = ('<nobr><b>#</b> &nbsp; Renumber all clusters in vertical spatial order</nobr>\n' '<nobr><b>CTRL+#</b> &nbsp; Renumber selected cluster</nobr>') actionRenumber.setToolTip(tt) self.connect(actionRenumber, QtCore.SIGNAL('triggered()'), self.on_actionRenumber_triggered) toolbar.addAction(actionRenumber) actionFind = QAction(QIcon('res/edit-find.svg'), 'Find', self) tt = ('<nobr><b>CTRL+F</b> &nbsp; Find spike in cluster plot</nobr>') actionFind.setToolTip(tt) self.connect(actionFind, QtCore.SIGNAL('triggered()'), self.on_actionFind_triggered) toolbar.addAction(actionFind) actionSelectRandomSpikes = QAction('R', self) tt = '<nobr><b>R</b> &nbsp; Select random sample of spikes of current clusters</nobr>' actionSelectRandomSpikes.setToolTip(tt) self.connect(actionSelectRandomSpikes, QtCore.SIGNAL('triggered()'), self.on_actionSelectRandomSpikes_triggered) toolbar.addAction(actionSelectRandomSpikes) actionToggleErrors = QAction('E', self) actionToggleErrors.setCheckable(True) actionToggleErrors.setChecked(self.panel.enable_fills) tt = '<nobr><b>CTRL+E</b> &nbsp; Toggle visibility of template error limits</nobr>' actionToggleErrors.setToolTip(tt) self.connect(actionToggleErrors, QtCore.SIGNAL('toggled(bool)'), self.on_actionToggleErrors_toggled) toolbar.addAction(actionToggleErrors) self.actionToggleErrors = actionToggleErrors nsamplesComboBox = QtGui.QComboBox(self) nsamplesComboBox.setToolTip('Number of spikes per cluster to randomly select') nsamplesComboBox.setFocusPolicy(Qt.NoFocus) nsamplesComboBox.addItems(['100', '50', '20', '10', '5', '1']) nsamplesComboBox.setCurrentIndex(2) toolbar.addWidget(nsamplesComboBox) self.connect(nsamplesComboBox, QtCore.SIGNAL('activated(int)'), self.on_actionSelectRandomSpikes_triggered) self.nsamplesComboBox = nsamplesComboBox gainComboBox = QtGui.QComboBox(self) gainComboBox.setToolTip('Waveform gain (default: 1.5)') gainComboBox.setFocusPolicy(Qt.NoFocus) gainComboBox.addItems(['4', '3.75', '3.5', '3.25', '3', '2.75', '2.5', '2.25', '2', '1.75', '1.5', '1.25', '1', '0.75', '0.5', '0.25']) gainComboBox.setCurrentIndex(3) toolbar.addWidget(gainComboBox) self.connect(gainComboBox, QtCore.SIGNAL('activated(int)'), self.on_gainComboBox_triggered) self.gainComboBox = gainComboBox #actionAlignMin = QAction(QIcon('res/go-bottom.svg'), 'Min', self) actionAlignMin = QAction('Min', self) actionAlignMin.setToolTip('Align selected spikes to min') self.connect(actionAlignMin, QtCore.SIGNAL('triggered()'), self.on_actionAlignMin_triggered) toolbar.addAction(actionAlignMin) #actionAlignMax = QAction(QIcon('res/go-top.svg'), 'Max', self) actionAlignMax = QAction('Max', self) actionAlignMax.setToolTip('Align selected spikes to max') self.connect(actionAlignMax, QtCore.SIGNAL('triggered()'), self.on_actionAlignMax_triggered) toolbar.addAction(actionAlignMax) #actionAlignBest = QAction(QIcon('res/emblem-OK.png'), 'Best', self) actionAlignBest = QAction('B', self) tt = '<nobr><b>B</b> &nbsp; Align selected spikes by best fit</nobr>' actionAlignBest.setToolTip(tt) self.connect(actionAlignBest, QtCore.SIGNAL('triggered()'), self.on_actionAlignBest_triggered) toolbar.addAction(actionAlignBest) actionShiftLeft = QAction('[', self) tt = ('<nobr><b>[</b> &nbsp; Shift selected spikes 2 points left</nobr>\n' '<nobr><b>CTRL+[</b> &nbsp; Shift selected spikes 1 point left</nobr>') actionShiftLeft.setToolTip(tt) self.connect(actionShiftLeft, QtCore.SIGNAL('triggered()'), self.on_actionShiftLeft_triggered) toolbar.addAction(actionShiftLeft) actionShiftRight = QAction(']', self) tt = ('<nobr><b>]</b> &nbsp; Shift selected spikes 2 points right</nobr>\n' '<nobr><b>CTRL+]</b> &nbsp; Shift selected spikes 1 point right</nobr>') actionShiftRight.setToolTip(tt) self.connect(actionShiftRight, QtCore.SIGNAL('triggered()'), self.on_actionShiftRight_triggered) toolbar.addAction(actionShiftRight) incltComboBox = QtGui.QComboBox(self) incltComboBox.setToolTip("Waveform duration (us) to include for component " "analysis,\nasymmetric around spike time") incltComboBox.setFocusPolicy(Qt.NoFocus) dtw = self.sort.tw[1] - self.sort.tw[0] # spike time window width incltstep = intround(dtw / 10) # evenly spaced inclt values incltvals = np.arange(dtw, 0, -incltstep) incltComboBox.addItems([ str(incltval) for incltval in incltvals ]) incltComboBox.setCurrentIndex(0) toolbar.addWidget(incltComboBox) self.connect(incltComboBox, QtCore.SIGNAL('activated(int)'), self.on_incltComboBox_triggered) self.incltComboBox = incltComboBox #incltunitsLabel = QtGui.QLabel('us', self) #toolbar.addWidget(incltunitsLabel) nPCsPerChanSpinBox = QtGui.QSpinBox(self) nPCsPerChanSpinBox.setToolTip("Number of PCs to use per channel to feed into ICA") nPCsPerChanSpinBox.setFocusPolicy(Qt.NoFocus) toolbar.addWidget(nPCsPerChanSpinBox) nPCsPerChanSpinBox.setMinimum(1) self.connect(nPCsPerChanSpinBox, QtCore.SIGNAL('valueChanged(int)'), self.on_nPCsPerChanSpinBox_valueChanged) nPCsPerChanSpinBox.setValue(self.sort.npcsperchan) self.nPCsPerChanSpinBox = nPCsPerChanSpinBox #actionFindPrevMostSimilar = QAction(QIcon('res/go-previous.svg'), '<', self) actionFindPrevMostSimilar = QAction('<', self) tt = '<nobr><b>&lt;</b> &nbsp; Find previous most similar cluster</nobr>' actionFindPrevMostSimilar.setToolTip(tt) self.connect(actionFindPrevMostSimilar, QtCore.SIGNAL('triggered()'), self.on_actionFindPrevMostSimilar_triggered) toolbar.addAction(actionFindPrevMostSimilar) #actionFindNextMostSimilar = QAction(QIcon('res/go-next.svg'), '>', self) actionFindNextMostSimilar = QAction('>', self) tt = '<nobr><b>&gt;</b> &nbsp; Find next most similar cluster</nobr>' actionFindNextMostSimilar.setToolTip(tt) self.connect(actionFindNextMostSimilar, QtCore.SIGNAL('triggered()'), self.on_actionFindNextMostSimilar_triggered) toolbar.addAction(actionFindNextMostSimilar) actionReloadSpikes = QAction(QIcon('res/view-refresh.svg'), 'Reload', self) tt = ('<nobr><b>F5</b> &nbsp; Reload waveforms of selected spikes. ' 'If none selected, reload all</nobr>\n' '<nobr><b>CTRL+F5</b> &nbsp; Use mean waveform to choose chans to reload</nobr>') actionReloadSpikes.setToolTip(tt) self.connect(actionReloadSpikes, QtCore.SIGNAL('triggered()'), self.on_actionReloadSpikes_triggered) toolbar.addAction(actionReloadSpikes) actionSave = QAction(QIcon('res/document-save.svg'), '&Save', self) actionSave.setToolTip('Save sort panel to file') self.connect(actionSave, QtCore.SIGNAL('triggered()'), self.on_actionSave_triggered) toolbar.addAction(actionSave) return toolbar def get_sort(self): return self.spykewindow.sort sort = property(get_sort) # make this a property for proper behaviour after unpickling def closeEvent(self, event): self.spykewindow.HideWindow('Sort') def mousePressEvent(self, event): """These are mostly passed on up from spyke list views and sort panel. Left clicks are (or should be) filtered out""" buttons = event.buttons() if buttons == QtCore.Qt.MiddleButton: #self.on_actionSelectRandomSpikes_triggered() self.spykewindow.ui.plotButton.click() # same as hitting ENTER in nslist elif buttons == QtCore.Qt.RightButton: self.clear() def keyPressEvent(self, event): """Alpha character keypresses are by default caught by the child lists for quickly scrolling down to and selecting list items. However, the appropriate alpha keypresses have been set in the child lists to be ignored, so they propagate up to here""" key = event.key() modifiers = event.modifiers() ctrl = modifiers & Qt.ControlModifier # ctrl is down spw = self.spykewindow if key == Qt.Key_A: # ignored in SpykeListViews spw.ui.plotButton.click() # same as hitting ENTER in nslist elif key == Qt.Key_X: # ignored in SpykeListViews spw.ui.plotXcorrsButton.click() elif key == Qt.Key_N: # ignored in SpykeListViews spw.ui.normButton.click() elif key == Qt.Key_Escape: # deselect all spikes and all clusters self.clear() elif key == Qt.Key_Delete: self.on_actionDelete_triggered() elif key == Qt.Key_M: # ignored in SpykeListViews self.on_actionMergeClusters_triggered() elif key == Qt.Key_G: # ignored in SpykeListViews self.on_actionToggleClustersGood_triggered() elif key == Qt.Key_Equal: # ignored in SpykeListViews self.on_actionSplit_triggered() elif key == Qt.Key_Minus: # ignored in SpykeListViews self.on_actionLabelMultiunit_triggered() elif key == Qt.Key_Slash: # ignored in SpykeListViews self.on_actionChanSplitClusters_triggered() elif key == Qt.Key_P: # ignored in SpykeListViews self.on_actionDensitySplit_triggered() elif key == Qt.Key_Backslash: # ignored in SpykeListViews self.on_actionRandomSplit_triggered() elif key == Qt.Key_NumberSign: # ignored in SpykeListViews self.on_actionRenumber_triggered() elif key == Qt.Key_F: # ignored in SpykeListViews if ctrl: self.FindSpike() else: self.FindCluster() elif key == Qt.Key_R: # ignored in SpykeListViews self.on_actionSelectRandomSpikes_triggered() elif key == Qt.Key_Space: # ignored in SpykeListViews if ctrl: SpykeToolWindow.keyPressEvent(self, event) # pass it on else: spw.on_clusterButton_clicked() elif key == Qt.Key_B: # ignored in SpykeListViews self.on_actionAlignBest_triggered() elif key == Qt.Key_BracketLeft: # ignored in SpykeListViews self.on_actionShiftLeft_triggered() elif key == Qt.Key_BracketRight: # ignored in SpykeListViews self.on_actionShiftRight_triggered() elif key == Qt.Key_Comma: # ignored in SpykeListViews self.on_actionFindPrevMostSimilar_triggered() elif key == Qt.Key_Period: # ignored in SpykeListViews self.on_actionFindNextMostSimilar_triggered() elif key == Qt.Key_F5: # ignored in SpykeListViews self.on_actionReloadSpikes_triggered() elif key == Qt.Key_E: # ignored in SpykeListViews if ctrl: self.actionToggleErrors.toggle() else: self.clear() # E is synonymous with ESC elif key == Qt.Key_C: # toggle between PCA and ICA, ignored in SpykeListViews c = str(spw.ui.componentAnalysisComboBox.currentText()) if c == 'PCA': index = spw.ui.componentAnalysisComboBox.findText('ICA') spw.ui.componentAnalysisComboBox.setCurrentIndex(index) elif c == 'ICA': index = spw.ui.componentAnalysisComboBox.findText('PCA') spw.ui.componentAnalysisComboBox.setCurrentIndex(index) spw.on_plotButton_clicked() elif key == Qt.Key_T: # toggle plotting against time, ignored in SpykeListViews z = str(spw.ui.zDimComboBox.currentText()) if z == 't': spw.on_c0c1c2Button_clicked() # plot in pure component analysis space else: spw.on_c0c1tButton_clicked() # plot against time elif key == Qt.Key_W: # toggle plotting against RMSError, ignored in SpykeListViews z = str(spw.ui.zDimComboBox.currentText()) if z == 'RMSerror': spw.on_c0c1c2Button_clicked() # plot in pure component analysis space else: spw.ui.zDimComboBox.setCurrentIndex(3) spw.on_plotButton_clicked() # plot against RMSError elif key in [Qt.Key_Enter, Qt.Key_Return]: # this is handled at a lower level by on_actionItem_triggered # in the various listview controls pass else: SpykeToolWindow.keyPressEvent(self, event) # pass it on def clear(self): """Clear selections in this order: unsorted spikes, sorted spikes, cluster automatically selected for comparison, cluster 0, clusters""" spw = self.spykewindow clusters = spw.GetClusters() if len(self.uslist.selectedIndexes()) > 0: self.uslist.clearSelection() elif self.nslist.nrowsSelected > 0: self.nslist.clearSelection() elif len(clusters) == 2 and self._source in clusters: clusters.remove(self._source) spw.SelectClusters(clusters, on=False) elif 0 in spw.GetClusterIDs(): for cluster in spw.GetClusters(): if cluster.id == 0: spw.SelectClusters([cluster], on=False) break else: self.nlist.clearSelection() # reset colours in cluster plot: gw = spw.windows['Cluster'].glWidget gw.colour() gw.updateGL() def on_actionDelete_triggered(self): """Delete explicity selected spikes, or clusters""" selsids = self.spykewindow.GetSpikes() # IDs of explicitly selected spikes nselsids = len(selsids) if (QApplication.instance().keyboardModifiers() & Qt.ControlModifier or nselsids > 0): self.delete_spikes() else: self.delete_clusters() def delete_clusters(self): """Del button press/click""" spw = self.spykewindow clusters = spw.GetClusters() s = self.sort spikes = s.spikes sids = [] for cluster in clusters: sids.append(cluster.neuron.sids) sids = np.concatenate(sids) # save some undo/redo stuff message = 'delete clusters %r' % [ c.id for c in clusters ] cc = ClusterChange(sids, spikes, message) cc.save_old(clusters, s.norder, s.good) # deselect and delete clusters spw.DelClusters(clusters) if len(s.clusters) > 0: # select cluster that replaces the first of the deleted clusters in norder selrows = [ cc.oldnorder.index(oldunid) for oldunid in cc.oldunids ] if len(selrows) > 0: selrow = selrows[0] nlist = spw.windows['Sort'].nlist nlist.selectRows(selrow) # TODO: this sets selection, but not focus #else: # first of deleted clusters was last in norder, don't select anything # save more undo/redo stuff newclusters = [] cc.save_new(newclusters, s.norder, s.good) spw.AddClusterChangeToStack(cc) print(cc.message) def delete_spikes(self): """CTRL+Del button press/click""" self.spykewindow.SplitSpikes(delete=True) def on_actionSplit_triggered(self): """+ button click. Split off selected clusters into their own cluster""" self.spykewindow.SplitSpikes(delete=False) def on_actionMergeClusters_triggered(self): """Merge button (M) click. Merge selected clusters. Easier to use than running gac() on selected clusters using a really big sigma to force them to all merge""" spw = self.spykewindow clusters = spw.GetClusters() s = self.sort spikes = s.spikes sids = [] # spikes to merge for cluster in clusters: sids.append(cluster.neuron.sids) # merge any selected usids as well sids.append(spw.GetUnsortedSpikes()) sids = np.concatenate(sids) if len(sids) == 0: return # save some undo/redo stuff message = 'merge clusters %r' % [ c.id for c in clusters ] cc = ClusterChange(sids, spikes, message) cc.save_old(clusters, s.norder, s.good) # decide on newnid and where to insert it into norder newnid = None # merge by default into a new highest numbered nid inserti = None # order new cluster by default to end of nlist if len(clusters) == 1: # keep same position of this one nid in norder, regardless of whether it's # single-unit, multiunit, or junk inserti = s.norder.index(clusters[0].id) elif len(clusters) > 1: oldunids = np.asarray(cc.oldunids) suids = oldunids[oldunids > 0] # selected single unit nids if len(suids) > 0: # merge into largest selected single unit nid: spikecounts = np.asarray([ s.neurons[suid].nspikes for suid in suids ]) newnid = suids[spikecounts.argmax()] inserti = s.norder.index(newnid) # correct for shift due to deletion of oldunids that precede newnid in norder: inserti -= sum([ s.norder.index(oldunid) < inserti for oldunid in oldunids]) # delete selected clusters and deselect selected usids spw.DelClusters(clusters, update=False) self.uslist.clearSelection() # create new cluster #t0 = time.time() newcluster = spw.CreateCluster(update=False, id=newnid, inserti=inserti) neuron = newcluster.neuron self.MoveSpikes2Neuron(sids, neuron, update=False) plotdims = spw.GetClusterPlotDims() newcluster.update_pos() # save more undo/redo stuff cc.save_new([newcluster], s.norder, s.good) spw.AddClusterChangeToStack(cc) # now do some final updates spw.UpdateClustersGUI() spw.ColourPoints(newcluster) #print('applying clusters to plot took %.3f sec' % (time.time()-t0)) # select newly created cluster spw.SelectClusters(newcluster) cc.message += ' into cluster %d' % newcluster.id print(cc.message) def on_actionToggleClustersGood_triggered(self): """'Good' button (G) click. Toggle 'good' flag of all selected clusters""" spw = self.spykewindow clusters = spw.GetClusters() cids = [] for cluster in clusters: cluster.neuron.good = not cluster.neuron.good cids.append(cluster.id) self.nlist.updateAll() # nlist item colouring will change as a result print("Toggled 'good' flag of clusters %r" % cids) def on_actionLabelMultiunit_triggered(self): """- button click. Label all selected clusters as multiunit by deleting them and creating new ones with -ve IDs""" spw = self.spykewindow clusters = spw.GetClusters() s = self.sort spikes = s.spikes # only relabel single unit clusters: clusters = [ cluster for cluster in clusters if cluster.id > 0 ] if len(clusters) == 0: return sids = [] for cluster in clusters: sids.append(cluster.neuron.sids) sids = np.concatenate(sids) # save some undo/redo stuff message = 'label as multiunit clusters %r' % [ c.id for c in clusters ] cc = ClusterChange(sids, spikes, message) cc.save_old(clusters, s.norder, s.good) # delete old clusters inserti = s.norder.index(clusters[0].id) # collect cluster sids before cluster deletion sidss = [ cluster.neuron.sids for cluster in clusters ] spw.DelClusters(clusters, update=False) # create new multiunit clusters newclusters = [] for sids in sidss: muid = s.get_nextmuid() newcluster = spw.CreateCluster(update=False, id=muid, inserti=inserti) neuron = newcluster.neuron self.MoveSpikes2Neuron(sids, neuron, update=False) newcluster.update_pos() newclusters.append(newcluster) inserti += 1 # select newly labelled multiunit clusters spw.SelectClusters(newclusters) # save more undo/redo stuff cc.save_new(newclusters, s.norder, s.good) spw.AddClusterChangeToStack(cc) print(cc.message) def on_actionChanSplitClusters_triggered(self): """Split by channels button (/) click""" ## TODO: make sure this works on .srf files! Why was chancombosplit being used? self.spykewindow.maxchansplit() #self.spykewindow.chancombosplit() def on_actionDensitySplit_triggered(self): """Split cluster pair by density along line between their centers""" self.spykewindow.densitysplit() def on_actionRandomSplit_triggered(self): """Randomly split each selected cluster in half""" self.spykewindow.randomsplit() def on_actionRenumber_triggered(self): if QApplication.instance().keyboardModifiers() & Qt.ControlModifier: self.renumber_selected_cluster() else: self.renumber_all_clusters() def renumber_selected_cluster(self): """Renumber a single selected cluster to whatever free ID the user wants, for colouring purposes""" spw = self.spykewindow s = self.sort spikes = s.spikes cluster = spw.GetCluster() # exactly one selected cluster oldid = cluster.id newid = max(s.norder) + 1 newid, ok = QtGui.QInputDialog.getInt(self, "Renumber cluster", "This will clear the undo/redo stack, and is not undoable.\n" "Enter new ID:", value=newid) if not ok: return if newid in s.norder: print("Choose a non-existing nid to renumber to") return # deselect cluster spw.SelectClusters(cluster, on=False) # rename to newid cluster.id = newid # this indirectly updates neuron.id # update cluster and neuron dicts, and spikes array s.clusters[newid] = cluster s.neurons[newid] = cluster.neuron sids = cluster.neuron.sids spikes['nid'][sids] = newid # remove duplicate oldid dict entries del s.clusters[oldid] del s.neurons[oldid] # replace oldid with newid in norder s.norder[s.norder.index(oldid)] = newid # update colour of any relevant points in cluster plot spw.ColourPoints(cluster) # reselect cluster spw.SelectClusters(cluster) # some cluster changes in stack may no longer be applicable, reset cchanges del spw.cchanges[:] spw.cci = -1 print('Renumbered neuron %d to %d' % (oldid, newid)) def renumber_all_clusters(self): """Renumber single unit clusters consecutively from 1, ordered by y position. Do the same for multiunit (-ve number) clusters, starting from -1. Sorting by y position makes user inspection of clusters more orderly, makes the presence of duplicate clusters more obvious, and allows for maximal spatial separation between clusters of the same colour, reducing colour conflicts""" val = QtGui.QMessageBox.question(self.panel, "Renumber all clusters", "Are you sure? This will clear the undo/redo stack, and is not undoable.", QtGui.QMessageBox.Yes, QtGui.QMessageBox.No) if val == QtGui.QMessageBox.No: return spw = self.spykewindow s = self.sort spikes = s.spikes # get spatially and numerically ordered lists of new ids oldids = np.asarray(s.norder) oldsuids = oldids[oldids > 0] oldmuids = oldids[oldids < 0] # this is a bit confusing: find indices that would sort old ids by y pos, but then # what you really want is to find the y pos *rank* of each old id, so you need to # take argsort again: newsuids = np.asarray([ s.clusters[cid].pos['y0'] for cid in oldsuids ]).argsort().argsort() + 1 newmuids = np.asarray([ s.clusters[cid].pos['y0'] for cid in oldmuids ]).argsort().argsort() + 1 newmuids = -newmuids # multiunit, followed by single unit, no 0 junk cluster. Can't seem to do it the other # way around as of Qt 4.7.2 - it seems QListViews don't like having a -ve value in # the last entry. Doing so causes all 2 digit values in the list to become blank, # suggests a spacing calculation bug. Reproduce by making last entry multiunit, # undoing then redoing. Actually, maybe the bug is it doesn't like having a number # in the last entry with fewer digits than the preceding entry. Only seems to be a # problem when setting self.setUniformItemSizes(True). newids = np.concatenate([newmuids, newsuids]) # test if np.all(oldids == newids): print('Nothing to renumber: cluster IDs already ordered in y0 and contiguous') return # update for replacing oldids with newids oldids = np.concatenate([oldmuids, oldsuids]) # deselect current selections selclusters = spw.GetClusters() oldselids = [ cluster.id for cluster in selclusters ] spw.SelectClusters(selclusters, on=False) # delete junk cluster, if it exists if 0 in s.clusters: s.remove_neuron(0) print('Deleted junk cluster 0') if 0 in oldselids: oldselids.remove(0) # replace old ids with new ids cw = spw.windows['Cluster'] oldclusters = s.clusters.copy() # no need to deepcopy, just copy refs, not clusters dims = spw.GetClusterPlotDims() for oldid, newid in zip(oldids, newids): newid = int(newid) # keep as Python int, not numpy int if oldid == newid: continue # no need to waste time removing and recreating this cluster # change all occurences of oldid to newid cluster = oldclusters[oldid] cluster.id = newid # this indirectly updates neuron.id # update cluster and neuron dicts s.clusters[newid] = cluster s.neurons[newid] = cluster.neuron sids = cluster.neuron.sids spikes['nid'][sids] = newid # remove any orphaned cluster ids for oldid in oldids: if oldid not in newids: del s.clusters[oldid] del s.neurons[oldid] # reset norder s.norder = [] s.norder.extend(sorted([ int(newid) for newid in newmuids ])[::-1]) s.norder.extend(sorted([ int(newid) for newid in newsuids ])) # now do some final updates spw.UpdateClustersGUI() spw.ColourPoints(s.clusters.values()) # reselect the previously selected (but now renumbered) clusters, # helps user keep track oldiis = [ list(oldids).index(oldselid) for oldselid in oldselids ] newselids = newids[oldiis] spw.SelectClusters([s.clusters[cid] for cid in newselids]) # all cluster changes in stack are no longer applicable, reset cchanges del spw.cchanges[:] spw.cci = -1 print('Renumbering complete') def on_actionFind_triggered(self): """Find current cluster or spike""" ctrl = QApplication.instance().keyboardModifiers() & Qt.ControlModifier if ctrl: self.FindSpike() else: self.FindCluster() def FindCluster(self): """Move focus to location of currently selected (single) cluster""" spw = self.spykewindow try: cluster = spw.GetCluster() except RuntimeError as err: print(err) return gw = spw.windows['Cluster'].glWidget dims = spw.GetClusterPlotDims() gw.focus = np.float32([ cluster.normpos[dim] for dim in dims ]) gw.panTo() # pan to new focus gw.updateGL() def FindSpike(self): """Move focus to location of currently selected (single) spike""" spw = self.spykewindow try: sid = spw.GetSpike() except RuntimeError as err: print(err) return gw = spw.windows['Cluster'].glWidget pointis = gw.sids.searchsorted(sid) gw.focus = gw.points[pointis] gw.panTo() # pan to new focus gw.updateGL() def on_actionSelectRandomSpikes_triggered(self): """Select random sample of spikes in current cluster(s), or random sample of unsorted spikes if no cluster(S) selected""" nsamples = int(self.nsamplesComboBox.currentText()) if len(self.nslist.neurons) > 0: slist = self.nslist else: slist = self.uslist slist.clearSelection() # emits selectionChanged signal, .reset() doesn't slist.selectRandom(nsamples) def on_gainComboBox_triggered(self): """Set gain of panel based on gainComboBox selection""" panel = self.panel panel.gain = float(self.gainComboBox.currentText()) panel.do_layout() # resets axes lims and recalcs panel.pos panel._update_scale() panel.draw_refs() panel.updateAllItems() def on_actionAlignMin_triggered(self): self.Align('min') def on_actionAlignMax_triggered(self): self.Align('max') def on_actionAlignBest_triggered(self): self.Align('best') def on_actionShiftLeft_triggered(self): if QApplication.instance().keyboardModifiers() & Qt.ControlModifier: nt = -1 else: nt = -2 self.Shift(nt) def on_actionShiftRight_triggered(self): if QApplication.instance().keyboardModifiers() & Qt.ControlModifier: nt = 1 else: nt = 2 self.Shift(nt) def on_incltComboBox_triggered(self): """Change length of chan selection lines, optionally trigger cluster replot""" self.panel.update_selvrefs() self.panel.draw_refs() #self.spykewindow.ui.plotButton.click() def get_inclt(self): """Return inclt value in incltComboBox""" return float(self.incltComboBox.currentText()) # us inclt = property(get_inclt) def get_tis(self): """Return tis (start and end timepoint indices) of duration inclt, asymmetric around t=0 spike time. Note that any changes to the code here should also be made in the timepoint selection display code in SortPanel.update_selvrefs()""" s = self.sort inclt = self.inclt # duration to include, asymmetric around t=0 spike time (us) tw = self.panel.tw dtw = tw[1] - tw[0] # spike time window width left = intround(abs(tw[0]) / dtw * inclt) # left fraction wrt t=0 spike time right = inclt - left # right fraction wrt t=0 spike time tis = s.twts.searchsorted([-left, right]) return tis tis = property(get_tis) def on_nPCsPerChanSpinBox_valueChanged(self, val): self.sort.npcsperchan = val def on_actionReloadSpikes_triggered(self): spw = self.spykewindow sids = spw.GetAllSpikes() sort = self.sort if len(sids) == 0: # if no spikes specified, reload all spikes sids = sort.spikes['id'] usemeanchans = False if QApplication.instance().keyboardModifiers() & Qt.ControlModifier: usemeanchans = True sort.reload_spikes_and_templates(sids, usemeanchans=usemeanchans) # add sids to the set of dirtysids to be resaved to .wave file: spw.update_dirtysids(sids) # auto-refresh all plots: self.panel.updateAllItems() def on_actionFindPrevMostSimilar_triggered(self): self.findMostSimilarCluster('previous') def on_actionFindNextMostSimilar_triggered(self): self.findMostSimilarCluster('next') def on_actionToggleErrors_toggled(self, checked): self.panel.showFills(checked) def on_slider_valueChanged(self, slideri): self.nslist.clearSelection() # emits selectionChanged signal, .reset() doesn't if self.nslist.model().sliding == False: self.nslist.model().sids.sort() # change from nid order to sid order self.nslist.updateAll() # update to reflect new ordering self.nslist.model().sliding = True nsamples = int(self.nsamplesComboBox.currentText()) rows = np.arange(slideri, slideri+nsamples) self.nslist.selectRows(rows) def on_slider_sliderPressed(self): """Make slider click (without movement) highlight the first nsamples or fewer spikes when slider is at 0 position""" slideri = self.slider.value() if slideri == 0: nsamples = int(self.nsamplesComboBox.currentText()) nsamples = min(nsamples, self.nslist.model().nspikes) rows = np.arange(nsamples) self.nslist.selectRows(rows) def update_slider(self): """Update slider limits and step sizes""" nsamples = int(self.nsamplesComboBox.currentText()) nsids = len(self.nslist.sids) ulim = max(nsids-nsamples, 1) # upper limit self.slider.setRange(0, ulim) self.slider.setSingleStep(1) self.slider.setPageStep(nsamples) def findMostSimilarCluster(self, which='next'): """If no chans selected, compare source to next or previous most similar cluster based on chans the two have in common, while requiring the two have each others' max chans in common. If chans have been selected, use them as a starting set of chans to compare on. Also, use only the timepoint range selected in incltComboBox""" try: source = self.getClusterComparisonSource() except RuntimeError as err: print(err) return destinations = list(self.sort.clusters.values()) destinations.remove(source) selchans = np.sort(self.panel.chans_selected) if len(selchans) > 0: srcchans = np.intersect1d(source.neuron.wave.chans, selchans) if len(srcchans) == 0: print("Source cluster doesn't overlap with selected chans") return else: srcchans = source.neuron.wave.chans if self.spykewindow.ui.normButton.isChecked(): print("NOTE: findMostSimilarCluster() doesn't currently take spike amplitude " "normalization into account. To see the true amplitudes used to compare " "neuron pairs, turn off normalization") errors = [] dests = [] t0i, t1i = self.tis # timepoint range selected in incltComboBox # try and compare source neuron waveform to all destination neuron waveforms for dest in destinations: if dest.neuron.wave.data is None: # hasn't been calculated yet dest.neuron.update_wave() dstchans = dest.neuron.wave.chans if len(selchans) > 0: if not set(selchans).issubset(dstchans): continue dstchans = selchans cmpchans = np.intersect1d(srcchans, dstchans) if len(cmpchans) == 0: # not comparable continue # ensure maxchan of both source and dest neuron are both in cmpchans if source.neuron.chan not in cmpchans or dest.neuron.chan not in cmpchans: continue srcwavedata = source.neuron.wave[cmpchans].data[:, t0i:t1i] dstwavedata = dest.neuron.wave[cmpchans].data[:, t0i:t1i] error = core.rms(srcwavedata - dstwavedata) errors.append(error) dests.append(dest) if len(errors) == 0: print("No sufficiently overlapping clusters on selected chans to compare to") return errors = np.asarray(errors) dests = np.asarray(dests) desterrsortis = errors.argsort() if which == 'next': self._cmpid += 1 elif which == 'previous': self._cmpid -= 1 else: raise ValueError('Unknown which: %r' % which) self._cmpid = max(self._cmpid, 0) self._cmpid = min(self._cmpid, len(dests)-1) dest = dests[desterrsortis][self._cmpid] self.spykewindow.SelectClusters(dest) desterr = errors[desterrsortis][self._cmpid] print('n%d to n%d rmserror: %.2f uV' % (source.id, dest.id, self.sort.converter.AD2uV(desterr))) def getClusterComparisonSource(self): selclusters = self.spykewindow.GetClusters() errmsg = 'unclear which cluster to use as source for comparison' if len(selclusters) == 1: source = selclusters[0] self._source = source self._cmpid = -1 # init/reset elif len(selclusters) == 2: source = self._source if source not in selclusters: raise RuntimeError(errmsg) # deselect old destination cluster: selclusters.remove(source) self.spykewindow.SelectClusters(selclusters, on=False) else: self._source = None # reset for tidiness raise RuntimeError(errmsg) return source def Shift(self, nt): """Shift selected sids by nt timepoints""" s = self.sort spikes = s.spikes spw = self.spykewindow sids = np.concatenate((spw.GetClusterSpikes(), spw.GetUnsortedSpikes())) self.sort.shift(sids, nt) print('Shifted %d spikes by %d timepoints' % (len(sids), nt)) unids = np.unique(spikes['nid'][sids]) neurons = [ s.neurons[nid] for nid in unids ] for neuron in neurons: neuron.update_wave() # update affected mean waveforms # add dirtysids to the set to be resaved to .wave file: spw.update_dirtysids(sids) # auto-refresh all plots self.panel.updateAllItems() def Align(self, to): """Align all implicitly selected spikes to min or max, or best fit on selected chans""" s = self.sort spikes = s.spikes spw = self.spykewindow sids = np.concatenate((spw.GetClusterSpikes(), spw.GetUnsortedSpikes())) if to == 'best': tis = self.tis # find which chans are common to all sids: commonchans = s.get_common_chans(sids)[0] # check selected chans selchans = spw.get_selchans(sids) for selchan in selchans: if selchan not in commonchans: print("Chan %d not common to all spikes, pick from %r" % (selchan, list(commonchans))) return print('Best fit aligning %d spikes between tis=%r on chans=%r' % (len(sids), list(tis), selchans)) # numpy implementation: #dirtysids = s.alignbest(sids, tis, selchans) # cython implementation: dirtysids = util.alignbest_cy(s, sids, tis, np.int64(selchans)) else: # to in ['min', 'max'] print('Aligning %d spikes to %s' % (len(sids), to)) dirtysids = s.alignminmax(sids, to) paligned = len(dirtysids) / len(sids) * 100 print('Aligned %d/%d (%.1f%%) spikes' % (len(dirtysids), len(sids), paligned)) unids = np.unique(spikes['nid'][dirtysids]) neurons = [ s.neurons[nid] for nid in unids ] for neuron in neurons: neuron.update_wave() # update affected mean waveforms # add dirtysids to the set to be resaved to .wave file: spw.update_dirtysids(dirtysids) # auto-refresh all plots: self.panel.updateAllItems() def RemoveNeuron(self, neuron, update=True): """Remove neuron and all its spikes from the GUI and the Sort""" self.MoveSpikes2List(neuron, neuron.sids, update=update) self.sort.remove_neuron(neuron.id) if update: self.nlist.updateAll() def MoveSpikes2Neuron(self, sids, neuron=None, update=True): """Assign spikes from sort.spikes to a neuron, and trigger eventual update of mean wave. If neuron is None, create a new one""" sids = toiter(sids) spikes = self.sort.spikes if neuron == None: neuron = self.sort.create_neuron() neuron.sids = np.union1d(neuron.sids, sids) # update spikes['nid'][sids] = neuron.id if update: self.sort.update_usids() self.uslist.updateAll() if neuron in self.nslist.neurons: self.nslist.neurons = self.nslist.neurons # trigger nslist refresh # TODO: selection doesn't seem to be working, always jumps to top of list #self.uslist.Select(row) # automatically select the new item at that position neuron.wave.data = None # trigger template mean update return neuron def MoveSpikes2List(self, neuron, sids, update=True): """Move spikes from a neuron back to the unsorted spike list control""" sids = toiter(sids) if len(sids) == 0: return # nothing to do spikes = self.sort.spikes neuron.sids = np.setdiff1d(neuron.sids, sids) # return what's in 1st arr and not in 2nd spikes['nid'][sids] = 0 # unbind neuron id of sids in spikes struct array if update: self.sort.update_usids() self.uslist.updateAll() # this only makes sense if the neuron is currently selected in the nlist: if neuron in self.nslist.neurons: self.nslist.neurons = self.nslist.neurons # this triggers a refresh neuron.wave.data = None # triggers an update when it's actually needed def PlotClusterHistogram(self, X, nids): """Plot histogram of given clusters along a single dimension. If two clusters are given, project them onto axis connecting their centers, and calculate separation indices between them. Otherwise, plot the distribution of all given clusters (up to a limit) along the first dimension in X.""" spw = self.spykewindow mplw = spw.OpenWindow('MPL') unids = np.unique(nids) # each unid corresponds to a cluster, except possibly unid 0 nclusters = len(unids) if nclusters == 0: mplw.ax.clear() mplw.figurecanvas.draw() print("No spikes selected") return elif nclusters > 5: # to prevent slowdowns, don't plot too many mplw.ax.clear() mplw.figurecanvas.draw() print("Too many clusters selected for cluster histogram") return elif nclusters == 2: calc_measures = True else: calc_measures = False projdimi = 0 ndims = X.shape[1] points = [] # list of projection of each cluster's points onto dimi for unid in unids: sidis, = np.where(nids == unid) # don't seem to need contig points for NDsepmetric, no need for copy: points.append(X[sidis]) #points.append(np.ascontiguousarray(X[sidis])) if calc_measures: t0 = time.time() NDsep = util.NDsepmetric(*points, Nmax=20000) print('NDsep calc took %.3f sec' % (time.time()-t0)) # centers of both clusters, use median: c0 = np.median(points[0], axis=0) # ndims vector c1 = np.median(points[1], axis=0) # line connecting the centers of the two clusters, wrt c0 line = c1-c0 line /= np.linalg.norm(line) # make it unit length #print('c0=%r, c1=%r, line=%r' % (c0, c1, line)) else: line = np.zeros(ndims) line[projdimi] = 1.0 # pick out just the one component c0 = 0.0 # set origin at 0 # calculate projection of each cluster's points onto line projs = [] for cpoints in points: projs.append(np.dot(cpoints-c0, line)) if calc_measures: d = np.median(projs[1]) - np.median(projs[0]) # measure whether centers are at least 3 of the bigger stdevs away from # each other: maxstd = max(projs[0].std(), projs[1].std()) if maxstd == 0: oneDsep = 0 # not sure if this is ideal else: oneDsep = d / (3 * maxstd) #print('std0=%f, std1=%f, d=%f' % (projs[0].std(), projs[1].std(), d)) proj = np.concatenate(projs) nbins = max(intround(np.sqrt(len(proj))), 2) # seems like a good heuristic #print('nbins = %d' % nbins) edges = np.histogram(proj, bins=nbins)[1] hists = [] for i in range(nclusters): hists.append(np.histogram(projs[i], bins=edges)[0]) hist = np.concatenate([hists]) # one cluster hist per row masses = np.asarray([ h.sum() for h in hist ]) sortedmassis = masses.argsort() # Take the fraction of area that the two distribs overlap. # At each bin, take min value of the two distribs. Add up all those min values, # and divide by the mass of the smaller distrib. if calc_measures: overlaparearatio = hist.min(axis=0).sum() / masses[sortedmassis[0]] djs = core.DJS(hists[0], hists[1]) # plotting: ledges = edges[:-1] # keep just the left edges, discard the last right edge assert len(ledges) == nbins binwidth = ledges[1] - ledges[0] # plot: a = mplw.ax a.clear() windowtitle = "clusters %r" % list(unids) print(windowtitle) mplw.setWindowTitle(windowtitle) if calc_measures: #title = ("sep index=%.3f, overlap area ratio=%.3f, DJS=%.3f, sqrt(DJS)=%.3f" # % (oneDsep, overlaparearatio, djs, np.sqrt(djs))) title = ("%dDsep=%.3f, 1Dsep=%.3f, OAR=%.3f, DJS=%.3f" % (ndims, NDsep, oneDsep, overlaparearatio, djs)) print(title) a.set_title(title) cs = [ CLUSTERCOLOURDICT[unid] for unid in unids ] for i, c in enumerate(cs): # due to white background, replace white clusters with black: if c == WHITE: cs[i] = 'black' # plot the smaller cluster last, to maximize visibility: for i in sortedmassis[::-1]: a.bar(ledges, hist[i], width=binwidth, color=cs[i], edgecolor=cs[i]) ## TODO: tight_layout call needs updating for MPL 2.2: #mplw.f.tight_layout(pad=0.3) # crop figure to contents mplw.figurecanvas.draw()

f71a7085403e8ce0a19e0672e598aeec15a4a023

py

Python

examples/show_debug.py

Matuiss2/python-sc2

dd93215d8b09b7ddacfd5c3cc4e9f43641d3f953

2019-01-23T19:11:53.000Z

2019-04-05T17:45:49.000Z

examples/show_debug.py

Matuiss2/python-sc2

dd93215d8b09b7ddacfd5c3cc4e9f43641d3f953

examples/show_debug.py

Matuiss2/python-sc2

dd93215d8b09b7ddacfd5c3cc4e9f43641d3f953

2019-04-24T13:31:20.000Z

2019-04-24T13:31:20.000Z

import sc2 from sc2 import run_game, maps, Race, Difficulty from sc2.player import Bot, Computer class MyBot(sc2.BotAI): async def on_step(self, iteration): for structure in self.structures: self._client.debug_text_world( "\n".join([ f"{structure.type_id.name}:{structure.type_id.value}", f"({structure.position.x:.2f},{structure.position.y:.2f})", f"{structure.build_progress:.2f}", ] + [repr(x) for x in structure.orders]), structure.position3d, color=(0, 255, 0), size=12, ) await self._client.send_debug() def main(): run_game(maps.get("Abyssal Reef LE"), [ Bot(Race.Terran, MyBot()), Computer(Race.Protoss, Difficulty.Medium) ], realtime=True) if __name__ == '__main__': main()

f71a71c02c39541a49fbe5ad95d204ca99999495

py

Python

migrations/versions/0076_add_intl_flag_to_provider.py

cds-snc/notifier-api

90b385ec49efbaee7e607516fc7d9f08991af813

2019-11-28T16:58:41.000Z

2022-01-28T21:11:16.000Z

migrations/versions/0076_add_intl_flag_to_provider.py

cds-snc/notification-api

b1c1064f291eb860b494c3fa65ac256ad70bf47c

2019-07-08T12:57:24.000Z

2022-03-08T18:53:40.000Z

migrations/versions/0076_add_intl_flag_to_provider.py

cds-snc/notifier-api

90b385ec49efbaee7e607516fc7d9f08991af813

2020-01-24T19:56:43.000Z

2022-01-27T21:36:53.000Z

"""empty message Revision ID: 0076_add_intl_flag_to_provider Revises: 0075_create_rates_table Create Date: 2017-04-25 09:44:13.194164 """ # revision identifiers, used by Alembic. revision = "0076_add_intl_flag_to_provider" down_revision = "0075_create_rates_table" import sqlalchemy as sa from alembic import op def upgrade(): op.add_column( "provider_details", sa.Column( "supports_international", sa.Boolean(), nullable=False, server_default=sa.false(), ), ) op.add_column( "provider_details_history", sa.Column( "supports_international", sa.Boolean(), nullable=False, server_default=sa.false(), ), ) op.execute("UPDATE provider_details SET supports_international=True WHERE identifier='mmg'") op.execute("UPDATE provider_details_history SET supports_international=True WHERE identifier='mmg'") def downgrade(): op.drop_column("provider_details_history", "supports_international") op.drop_column("provider_details", "supports_international")

f71aa357327a98795cb190e3909dda5f261e7b6a

py

Python

acore/classifier_cov_pow_toy_pvalue.py

zhao-david/ACORE-LFI

91de88b77f0be110e42ed91bbb7a50b7ca83319a

acore/classifier_cov_pow_toy_pvalue.py

zhao-david/ACORE-LFI

91de88b77f0be110e42ed91bbb7a50b7ca83319a

acore/classifier_cov_pow_toy_pvalue.py

zhao-david/ACORE-LFI

91de88b77f0be110e42ed91bbb7a50b7ca83319a

from warnings import simplefilter simplefilter(action='ignore', category=FutureWarning) import numpy as np import argparse import pandas as pd from tqdm.auto import tqdm from datetime import datetime from sklearn.metrics import log_loss import seaborn as sns import matplotlib.pyplot as plt from utils.functions import train_clf, compute_statistics_single_t0, clf_prob_value, compute_bayesfactor_single_t0, \ odds_ratio_loss, train_pvalue_clf from models.toy_poisson import ToyPoissonLoader from models.toy_gmm import ToyGMMLoader from models.toy_gamma import ToyGammaLoader from or_classifiers.toy_example_list import classifier_dict, classifier_dict_mlpcomp, classifier_pvalue_dict model_dict = { 'poisson': ToyPoissonLoader, 'gmm': ToyGMMLoader, 'gamma': ToyGammaLoader } def main(run, rep, b, b_prime, alpha, t0_val, sample_size_obs, test_statistic, mlp_comp=False, monte_carlo_samples=500, debug=False, seed=7, size_check=1000, verbose=False, marginal=False, size_marginal=1000, guided_sim=False, guided_sample=1000, empirical_marginal=True): # Changing values if debugging b = b if not debug else 100 b_prime = b_prime if not debug else 100 size_check = size_check if not debug else 100 rep = rep if not debug else 2 model_obj = model_dict[run](marginal=marginal, size_marginal=size_marginal, empirical_marginal=empirical_marginal) classifier_dict_run = classifier_dict_mlpcomp if mlp_comp else classifier_dict # Get the correct functions msnh_sampling_func = model_obj.sample_msnh_algo5 grid_param = model_obj.grid gen_obs_func = model_obj.sample_sim gen_sample_func = model_obj.generate_sample gen_param_fun = model_obj.sample_param_values t0_grid = model_obj.pred_grid tp_func = model_obj.compute_exact_prob # Creating sample to check entropy about np.random.seed(seed) sample_check = gen_sample_func(sample_size=size_check, marginal=marginal) theta_vec = sample_check[:, :model_obj.d] x_vec = sample_check[:, (model_obj.d + 1):] bern_vec = sample_check[:, model_obj.d] true_prob_vec = tp_func(theta_vec=theta_vec, x_vec=x_vec) entropy_est = -np.average([np.log(true_prob_vec[kk]) if el == 1 else np.log(1 - true_prob_vec[kk]) for kk, el in enumerate(bern_vec)]) # Loop over repetitions and classifiers # Each time we train the different classifiers, we build the intervals and we record # whether the point is in or not. out_val = [] out_cols = ['test_statistic', 'b_prime', 'b', 'classifier', 'classifier_pvalue', 'run', 'rep', 'sample_size_obs', 'cross_entropy_loss', 'cross_entropy_loss_pvalue', 't0_true_val', 'theta_0_current', 'on_true_t0', 'estimated_pvalue', 'in_confint', 'out_confint', 'size_CI', 'true_entropy', 'or_loss_value', 'monte_carlo_samples', 'guided_sim', 'empirical_marginal', 'guided_sample'] pbar = tqdm(total=rep, desc='Toy Example for Simulations, n=%s, b=%s' % (sample_size_obs, b)) rep_counter = 0 not_update_flag = False while rep_counter < rep: # Generates samples for each t0 values, so to be able to check both coverage and power x_obs = gen_obs_func(sample_size=sample_size_obs, true_param=t0_val) # Train the classifier for the odds clf_odds_fitted = {} clf_pvalue_fitted = {} for clf_name, clf_model in sorted(classifier_dict_run.items(), key=lambda x: x[0]): clf_odds = train_clf(sample_size=b, clf_model=clf_model, gen_function=gen_sample_func, clf_name=clf_name, nn_square_root=True) if verbose: print('----- %s Trained' % clf_name) if test_statistic == 'acore': tau_obs = np.array([ compute_statistics_single_t0( clf=clf_odds, obs_sample=x_obs, t0=theta_0, grid_param_t1=grid_param, d=model_obj.d, d_obs=model_obj.d_obs) for theta_0 in t0_grid]) elif test_statistic == 'avgacore': tau_obs = np.array([ compute_bayesfactor_single_t0( clf=clf_odds, obs_sample=x_obs, t0=theta_0, gen_param_fun=gen_param_fun, d=model_obj.d, d_obs=model_obj.d_obs, log_out=False) for theta_0 in t0_grid]) elif test_statistic == 'logavgacore': tau_obs = np.array([ compute_bayesfactor_single_t0( clf=clf_odds, obs_sample=x_obs, t0=theta_0, gen_param_fun=gen_param_fun, d=model_obj.d, d_obs=model_obj.d_obs, log_out=True) for theta_0 in t0_grid]) else: raise ValueError('The variable test_statistic needs to be either acore, avgacore, logavgacore.' ' Currently %s' % test_statistic) # Calculating cross-entropy est_prob_vec = clf_prob_value(clf=clf_odds, x_vec=x_vec, theta_vec=theta_vec, d=model_obj.d, d_obs=model_obj.d_obs) loss_value = log_loss(y_true=bern_vec, y_pred=est_prob_vec) # Calculating or loss or_loss_value = odds_ratio_loss(clf=clf_odds, x_vec=x_vec, theta_vec=theta_vec, bern_vec=bern_vec, d=1, d_obs=1) clf_odds_fitted[clf_name] = (tau_obs, loss_value, or_loss_value) # Train the P-value regression algorithm for confidence levels if guided_sim: # Commenting the above -- we now sample a set of thetas from the parameter (of size guided_sample) # budget, then resample them according to the odds values, fit a gaussian and then sample the # datasets from that. theta_mat_sample = gen_param_fun(sample_size=guided_sample) if test_statistic == 'acore': stats_sample = np.apply_along_axis(arr=theta_mat_sample.reshape(-1, 1), axis=1, func1d=lambda row: compute_statistics_single_t0( clf=clf_odds, obs_sample=x_obs, t0=row, grid_param_t1=grid_param, d=model_obj.d, d_obs=model_obj.d_obs )) elif test_statistic == 'avgacore': stats_sample = np.apply_along_axis(arr=theta_mat_sample.reshape(-1, 1), axis=1, func1d=lambda row: compute_bayesfactor_single_t0( clf=clf_odds, obs_sample=x_obs, t0=row, gen_param_fun=gen_param_fun, d=model_obj.d, d_obs=model_obj.d_obs, monte_carlo_samples=monte_carlo_samples )) elif test_statistic == 'logavgacore': stats_sample = np.apply_along_axis(arr=theta_mat_sample.reshape(-1, 1), axis=1, func1d=lambda row: compute_bayesfactor_single_t0( clf=clf_odds, obs_sample=x_obs, t0=row, gen_param_fun=gen_param_fun, d=model_obj.d, d_obs=model_obj.d_obs, monte_carlo_samples=monte_carlo_samples, log_out=True )) else: raise ValueError('The variable test_statistic needs to be either acore, avgacore, logavgacore.' ' Currently %s' % test_statistic) # If there are log-odds, then some of the values might be negative, so we need to exponentiate them # so to make sure that the large negative numbers are counted correctly (i.e. as very low probability, # not probabilities with large magnitudes). if test_statistic in ['acore', 'logavgacore']: stats_sample = np.exp(stats_sample) stats_sample = stats_sample/np.sum(stats_sample) theta_mat_gaussian_fit = np.random.choice(a=theta_mat_sample, p=stats_sample.reshape(-1, ), size=guided_sample) std_gaussian_fit = np.std(theta_mat_gaussian_fit) if np.std(theta_mat_gaussian_fit) == 0.0 else 1.0 theta_mat = np.clip( a=np.random.normal(size=b_prime, loc=np.mean(theta_mat_gaussian_fit), scale=std_gaussian_fit), a_min=model_obj.low_int, a_max=model_obj.high_int) sample_mat = np.apply_along_axis(arr=theta_mat.reshape(-1, 1), axis=1, func1d=lambda row: gen_obs_func(sample_size=sample_size_obs, true_param=row)) else: # Generate a matrix with values for both the sampled thetas as the actual samples theta_mat, sample_mat = msnh_sampling_func(b_prime=b_prime, sample_size=sample_size_obs) full_mat = np.hstack((theta_mat.reshape(-1, 1), sample_mat)) if test_statistic == 'acore': stats_mat_generated = np.apply_along_axis(arr=full_mat, axis=1, func1d=lambda row: compute_statistics_single_t0( clf=clf_odds, obs_sample=row[model_obj.d:], t0=row[:model_obj.d], grid_param_t1=grid_param, d=model_obj.d, d_obs=model_obj.d_obs )) stats_mat_observed = np.apply_along_axis(arr=full_mat, axis=1, func1d=lambda row: compute_statistics_single_t0( clf=clf_odds, obs_sample=x_obs, t0=row[:model_obj.d], grid_param_t1=grid_param, d=model_obj.d, d_obs=model_obj.d_obs )) elif test_statistic == 'avgacore': stats_mat_generated = np.apply_along_axis(arr=full_mat, axis=1, func1d=lambda row: compute_bayesfactor_single_t0( clf=clf_odds, obs_sample=row[model_obj.d:], t0=row[:model_obj.d], gen_param_fun=gen_param_fun, d=model_obj.d, d_obs=model_obj.d_obs, monte_carlo_samples=monte_carlo_samples )) stats_mat_observed = np.apply_along_axis(arr=full_mat, axis=1, func1d=lambda row: compute_bayesfactor_single_t0( clf=clf_odds, obs_sample=x_obs, t0=row[:model_obj.d], gen_param_fun=gen_param_fun, d=model_obj.d, d_obs=model_obj.d_obs, monte_carlo_samples=monte_carlo_samples )) elif test_statistic == 'logavgacore': stats_mat_generated = np.apply_along_axis(arr=full_mat, axis=1, func1d=lambda row: compute_bayesfactor_single_t0( clf=clf_odds, obs_sample=row[model_obj.d:], t0=row[:model_obj.d], gen_param_fun=gen_param_fun, d=model_obj.d, d_obs=model_obj.d_obs, monte_carlo_samples=monte_carlo_samples, log_out=True )) stats_mat_observed = np.apply_along_axis(arr=full_mat, axis=1, func1d=lambda row: compute_bayesfactor_single_t0( clf=clf_odds, obs_sample=x_obs, t0=row[:model_obj.d], gen_param_fun=gen_param_fun, d=model_obj.d, d_obs=model_obj.d_obs, monte_carlo_samples=monte_carlo_samples, log_out=True )) else: raise ValueError('The variable test_statistic needs to be either acore, avgacore, logavgacore.' ' Currently %s' % test_statistic) if np.any(np.isnan(stats_mat_generated)) or not np.all(np.isfinite(stats_mat_generated)) or \ np.any(np.isnan(stats_mat_observed)) or not np.all(np.isfinite(stats_mat_observed)): not_update_flag = True break # Comparing the two vectors of values clf_pvalue_fitted[clf_name] = {} indicator_vec = np.greater(stats_mat_observed, stats_mat_generated).astype(int) for clf_name_pvalue, clf_model_pvalue in sorted(classifier_pvalue_dict.items(), key=lambda x: x[0]): # If there the indicator_vec is either all 0 or all 1, do not fit a classifier or sklearn will throw # an error out. Just return the class. if sum(indicator_vec) <= 1 or sum(indicator_vec) >= len(indicator_vec) - 1: pval_pred = np.repeat(sum(indicator_vec) / len(indicator_vec), b_prime) loss_value_pval = np.nan else: clf_pvalue = train_pvalue_clf(clf_model=clf_model_pvalue, X=theta_mat.reshape(-1, model_obj.d), y=indicator_vec.reshape(-1, ), clf_name=clf_name_pvalue, nn_square_root=True) pval_pred = clf_pvalue.predict_proba(t0_grid.reshape(-1, model_obj.d))[:, 1] theta_mat_pred = clf_pvalue.predict_proba(theta_mat.reshape(-1, model_obj.d))[:, 1] loss_value_pval = log_loss(y_true=indicator_vec, y_pred=theta_mat_pred) clf_pvalue_fitted[clf_name][clf_name_pvalue] = (pval_pred, loss_value_pval) # If there were some problems in calculating the statistics, get out of the loop if not_update_flag: not_update_flag = False continue # At this point all it's left is to record for clf_name, (tau_obs_val, cross_ent_loss, or_loss_value) in clf_odds_fitted.items(): for clf_name_qr, (pvalue_val, pvalue_celoss_val) in clf_pvalue_fitted[clf_name].items(): size_temp = np.mean((pvalue_val > alpha).astype(int)) for kk, theta_0_current in enumerate(t0_grid): out_val.append([ test_statistic, b_prime, b, clf_name, clf_name_qr, run, rep_counter, sample_size_obs, cross_ent_loss, pvalue_celoss_val, t0_val, theta_0_current, int(t0_val == theta_0_current), pvalue_val[kk], int(pvalue_val[kk] > alpha), int(pvalue_val[kk] <= alpha), size_temp, entropy_est, or_loss_value, monte_carlo_samples, int(guided_sim), int(empirical_marginal), guided_sample ]) pbar.update(1) rep_counter += 1 # Saving the results out_df = pd.DataFrame.from_records(data=out_val, index=range(len(out_val)), columns=out_cols) out_dir = 'sims/classifier_cov_pow_toy/' out_filename = 'classifier_reps_cov_pow_toy_pvalues_%steststats_%s_%sB_%sBprime_%s_%srep_alpha%s_sampleobs%s_t0val%s%s_%s.csv' % ( test_statistic, 'mlp_comp' if mlp_comp else 'toyclassifiers', b, b_prime, run, rep, str(alpha).replace('.', '-'), sample_size_obs, str(t0_val).replace('.', '-'), '_empirmarg' if empirical_marginal else '', datetime.strftime(datetime.today(), '%Y-%m-%d-%H-%M') ) out_df.to_csv(out_dir + out_filename) # Print results cov_df = out_df[out_df['on_true_t0'] == 1][['classifier', 'classifier_pvalue', 'in_confint', 'cross_entropy_loss', 'cross_entropy_loss_pvalue', 'size_CI']] print(cov_df.groupby(['classifier', 'classifier_pvalue']).agg({'in_confint': [np.average], 'size_CI': [np.average, np.std], 'cross_entropy_loss': [np.average], 'cross_entropy_loss_pvalue': [np.average]})) # Power plots out_df['class_combo'] = out_df[['classifier', 'classifier_pvalue']].apply(lambda x: x[0] + '---' + x[1], axis = 1) plot_df = out_df[['class_combo', 'theta_0_current', 'out_confint']].groupby( ['class_combo', 'theta_0_current']).mean().reset_index() fig = plt.figure(figsize=(20, 10)) sns.lineplot(x='theta_0_current', y='out_confint', hue='class_combo', data=plot_df, palette='cubehelix') plt.legend(loc='best', fontsize=25) plt.xlabel(r'$\theta$', fontsize=25) plt.ylabel('Power', fontsize=25) plt.title("Power of Hypothesis Test, B=%s, B'=%s, n=%s, %s" % ( b, b_prime, sample_size_obs, run.title()), fontsize=25) out_dir = 'images/classifier_cov_pow_toy/' outfile_name = 'power_classifier_reps_pvalue_%steststats_%sB_%sBprime_%s_%srep_alpha%s_sampleobs%s_t0val%s_%s.pdf' % ( test_statistic, b, b_prime, run, rep, str(alpha).replace('.', '-'), sample_size_obs, str(t0_val).replace('.', '-'), datetime.strftime(datetime.today(), '%Y-%m-%d') ) plt.tight_layout() plt.savefig(out_dir + outfile_name) plt.close() if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--seed', action="store", type=int, default=7, help='Random State') parser.add_argument('--rep', action="store", type=int, default=10, help='Number of Repetitions for calculating the Pinball loss') parser.add_argument('--b', action="store", type=int, default=5000, help='Sample size to train the classifier for calculating odds') parser.add_argument('--b_prime', action="store", type=int, default=1000, help='Sample size to train the quantile regression algorithm') parser.add_argument('--marginal', action='store_true', default=False, help='Whether we are using a parametric approximation of the marginal or' 'the baseline reference G') parser.add_argument('--alpha', action="store", type=float, default=0.1, help='Statistical confidence level') parser.add_argument('--run', action="store", type=str, default='poisson', help='Problem to run') parser.add_argument('--debug', action='store_true', default=False, help='If true, a very small value for the sample sizes is fit to make sure the' 'file can run quickly for debugging purposes') parser.add_argument('--verbose', action='store_true', default=False, help='If true, logs are printed to the terminal') parser.add_argument('--sample_size_obs', action="store", type=int, default=10, help='Sample size of the actual observed data.') parser.add_argument('--t0_val', action="store", type=float, default=10.0, help='True parameter which generates the observed dataset') parser.add_argument('--size_marginal', action="store", type=int, default=1000, help='Sample size of the actual marginal distribution, if marginal is True.') parser.add_argument('--monte_carlo_samples', action="store", type=int, default=500, help='Sample size for the calculation of the avgacore and logavgacore statistic.') parser.add_argument('--test_statistic', action="store", type=str, default='acore', help='Test statistic to compute confidence intervals. Can be acore|avgacore|logavgacore') parser.add_argument('--mlp_comp', action='store_true', default=False, help='If true, we compare different MLP training algorithm.') parser.add_argument('--empirical_marginal', action='store_true', default=False, help='Whether we are sampling directly from the empirical marginal for G') parser.add_argument('--guided_sim', action='store_true', default=False, help='If true, we guided the sampling for the B prime in order to get meaningful results.') parser.add_argument('--guided_sample', action="store", type=int, default=2500, help='The sample size to be used for the guided simulation. Only used if guided_sim is True.') argument_parsed = parser.parse_args() # b_vec = [100, 500, 1000] # for b_val in b_vec: main( run=argument_parsed.run, rep=argument_parsed.rep, marginal=argument_parsed.marginal, b=argument_parsed.b, b_prime=argument_parsed.b_prime, alpha=argument_parsed.alpha, debug=argument_parsed.debug, sample_size_obs=argument_parsed.sample_size_obs, t0_val=argument_parsed.t0_val, seed=argument_parsed.seed, verbose=argument_parsed.verbose, size_marginal=argument_parsed.size_marginal, monte_carlo_samples=argument_parsed.monte_carlo_samples, test_statistic=argument_parsed.test_statistic, mlp_comp=argument_parsed.mlp_comp, empirical_marginal=argument_parsed.empirical_marginal, guided_sim=argument_parsed.guided_sim, guided_sample=argument_parsed.guided_sample )

f71aa8d7c382bafc56b06793ddb3976f1a195ca1

py

Python

StructVBERT/tasks/vqa.py

onlyrico/AliceMind

a6a070b1610e4c4bfe84ee6c4195b2bc4f725ded

2021-08-05T05:41:50.000Z

2021-08-05T05:41:50.000Z

StructVBERT/tasks/vqa.py

onlyrico/AliceMind

a6a070b1610e4c4bfe84ee6c4195b2bc4f725ded

StructVBERT/tasks/vqa.py

onlyrico/AliceMind

a6a070b1610e4c4bfe84ee6c4195b2bc4f725ded

2021-07-10T09:50:47.000Z

2021-07-10T09:50:47.000Z

# coding=utf-8 # Copyleft 2019 project LXRT. import os import collections import torch import torch.nn as nn import logging from torch.utils.data.dataloader import DataLoader from tqdm import tqdm from param import args from lxrt.qa_answer_table import load_lxmert_qa from tasks.vqa_model import VQAModel from tasks.vqa_data import VQADataset, VQATorchDataset, VQAEvaluator DataTuple = collections.namedtuple("DataTuple", 'dataset loader evaluator') logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO) logger = logging.getLogger(__name__) def get_data_tuple(splits: str, bs:int, shuffle=False, drop_last=False) -> DataTuple: dset = VQADataset(splits) tset = VQATorchDataset(dset) evaluator = VQAEvaluator(dset) data_loader = DataLoader( tset, batch_size=bs, shuffle=shuffle, num_workers=args.num_workers, drop_last=drop_last, pin_memory=True ) return DataTuple(dataset=dset, loader=data_loader, evaluator=evaluator) class WarmupOptimizer(object): def __init__(self, _lr_base, optimizer, _data_size, _batch_size): self.optimizer = optimizer self._step = 0 self._lr_base = _lr_base self._rate = 0 self._data_size = _data_size self._batch_size = _batch_size def step(self): self._step += 1 rate = self.rate() for p in self.optimizer.param_groups: p['lr'] = rate self._rate = rate self.optimizer.step() def zero_grad(self): self.optimizer.zero_grad() def rate(self, step=None): if step is None: step = self._step if step <= int(self._data_size / self._batch_size * 1): r = self._lr_base * 1/4. elif step <= int(self._data_size / self._batch_size * 2): r = self._lr_base * 2/4. elif step <= int(self._data_size / self._batch_size * 3): r = self._lr_base * 3/4. else: r = self._lr_base return r def adjust_learning_rate(optimizer, decay_rate): optimizer._lr_base *= decay_rate class VQA: def __init__(self): # Datasets self.train_tuple = get_data_tuple( args.train, bs=args.batch_size, shuffle=True, drop_last=True ) if args.valid != "": self.valid_tuple = get_data_tuple( args.valid, bs=256, # for large model shuffle=False, drop_last=False ) else: self.valid_tuple = None # Model self.model = VQAModel(self.train_tuple.dataset.num_answers) self._lr_decay_epoch_list = [8, 10] self._lr_decay_rate = 0.2 # Load pre-trained weights if args.load_lxmert is not None: self.model.lxrt_encoder.load(args.load_lxmert) if args.load_lxmert_qa is not None: load_lxmert_qa(args.load_lxmert_qa, self.model, label2ans=self.train_tuple.dataset.label2ans) if args.fix_language_bert: assert args.patial_load state_dict = torch.load(args.patial_load) for k in state_dict.copy(): if not k.startswith('bert.'): state_dict['bert.' + k.replace('gamma', 'weight').replace('beta', 'bias')] = state_dict.pop(k) # fix bert parameters for name, param in self.model.lxrt_encoder.model.named_parameters(): # if 'pooler' in name: # pooler not fixed # continue if name in state_dict: logger.info('fix param for: {}'.format(name)) param.requires_grad = False # GPU options self.model = self.model.cuda() # Loss and Optimizer self.bce_loss = nn.BCEWithLogitsLoss() if 'bert' in args.optim: batch_per_epoch = len(self.train_tuple.loader) t_total = int(batch_per_epoch * args.epochs) logger.info("BertAdam Total Iters: %d" % t_total) from lxrt.optimization import BertAdam self.optim = BertAdam(list(self.model.parameters()), lr=args.lr, warmup=0.1, t_total=t_total) elif 'adam' in args.optim: batch_per_epoch = len(self.train_tuple.loader) optim = args.optimizer(filter(lambda p: p.requires_grad, self.model.parameters()), lr=0, betas=(0.9, 0.98), eps=1e-9) self.optim = WarmupOptimizer(args.lr, optim, batch_per_epoch * args.batch_size, args.batch_size) else: self.optim = args.optimizer(self.model.parameters(), args.lr) if args.amp_type is not None: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this example.") self.model, self.optim = amp.initialize(self.model, self.optim, opt_level=args.amp_type) if args.multiGPU: self.model.lxrt_encoder.multi_gpu() # Output Directory self.output = args.output os.makedirs(self.output, exist_ok=True) def train(self, train_tuple, eval_tuple): dset, loader, evaluator = train_tuple iter_wrapper = (lambda x: tqdm(x, total=len(loader))) if args.tqdm else (lambda x: x) best_valid = 0. for epoch in range(args.epochs): quesid2ans = {} if 'adam' in args.optim and epoch in self._lr_decay_epoch_list: adjust_learning_rate(self.optim, self._lr_decay_rate) for i, (ques_id, feats, boxes, sent, target) in iter_wrapper(enumerate(loader)): self.model.train() self.optim.zero_grad() feats, boxes, target = feats.cuda(), boxes.cuda(), target.cuda() logit = self.model(feats, boxes, sent) assert logit.dim() == target.dim() == 2 loss = self.bce_loss(logit, target) loss = loss * logit.size(1) if args.multiGPU: loss = loss.mean() # mean() to average on multi-gpu. if args.amp_type is not None: from apex import amp with amp.scale_loss(loss, self.optim) as scaled_loss: scaled_loss.backward() else: loss.backward() nn.utils.clip_grad_norm_(self.model.parameters(), args.clip_norm) self.optim.step() score, label = logit.max(1) for qid, l in zip(ques_id, label.cpu().numpy()): ans = dset.label2ans[l] quesid2ans[qid.item()] = ans log_str = "\nEpoch %d: Train %0.2f\n" % (epoch, evaluator.evaluate(quesid2ans) * 100.) if self.valid_tuple is not None: # Do Validation valid_score = self.evaluate(eval_tuple) if valid_score > best_valid: best_valid = valid_score self.save("BEST") log_str += "Epoch %d: Valid %0.2f\n" % (epoch, valid_score * 100.) + \ "Epoch %d: Best %0.2f\n" % (epoch, best_valid * 100.) logger.info(log_str) with open(self.output + "/log.log", 'a') as f: f.write(log_str) f.flush() self.save("LAST") def predict(self, eval_tuple: DataTuple, dump=None): """ Predict the answers to questions in a data split. :param eval_tuple: The data tuple to be evaluated. :param dump: The path of saved file to dump results. :return: A dict of question_id to answer. """ self.model.eval() dset, loader, evaluator = eval_tuple quesid2ans = {} for i, datum_tuple in enumerate(loader): ques_id, feats, boxes, sent = datum_tuple[:4] # Avoid seeing ground truth with torch.no_grad(): feats, boxes = feats.cuda(), boxes.cuda() logit = self.model(feats, boxes, sent) if args.with_score: logit = nn.Softmax(dim=1)(logit) score, label = logit.max(1) if args.with_score: for qid, l, s in zip(ques_id, label.cpu().numpy(), score.cpu().numpy()): ans = dset.label2ans[l] quesid2ans[qid.item()] = (ans, str(s)) else: for qid, l in zip(ques_id, label.cpu().numpy()): ans = dset.label2ans[l] quesid2ans[qid.item()] = ans if dump is not None: evaluator.dump_result(quesid2ans, dump) return quesid2ans def evaluate(self, eval_tuple: DataTuple, dump=None): """Evaluate all data in data_tuple.""" quesid2ans = self.predict(eval_tuple, dump) return eval_tuple.evaluator.evaluate(quesid2ans) @staticmethod def oracle_score(data_tuple): dset, loader, evaluator = data_tuple quesid2ans = {} for i, (ques_id, feats, boxes, sent, target) in enumerate(loader): _, label = target.max(1) for qid, l in zip(ques_id, label.cpu().numpy()): ans = dset.label2ans[l] quesid2ans[qid.item()] = ans return evaluator.evaluate(quesid2ans) def save(self, name): torch.save(self.model.state_dict(), os.path.join(self.output, "%s.pth" % name)) def load(self, path): logger.info("Load model from %s" % path) state_dict = torch.load("%s.pth" % path) self.model.load_state_dict(state_dict) if __name__ == "__main__": # Build Class vqa = VQA() # Load VQA model weights if args.load is not None: vqa.load(args.load) # Test or Train if args.test is not None: args.fast = args.tiny = False # Always loading all data in test if 'test' in args.test: vqa.predict( get_data_tuple(args.test, bs=950, shuffle=False, drop_last=False), dump=os.path.join(args.output, 'test_predict.json') ) elif 'val' in args.test: # Since part of valididation data are used in pre-training/fine-tuning, # only validate on the minival set. result = vqa.evaluate( get_data_tuple('minival', bs=950, shuffle=False, drop_last=False), dump=os.path.join(args.output, 'minival_predict.json') ) logger.info(result) else: assert False, "No such test option for %s" % args.test else: # print('Splits in Train data:', vqa.train_tuple.dataset.splits) logger.info('Splits in Train data: {}'.format(vqa.train_tuple.dataset.splits)) if vqa.valid_tuple is not None: logger.info('Splits in Valid data: {}'.format(vqa.valid_tuple.dataset.splits)) logger.info("Valid Oracle: %0.2f" % (vqa.oracle_score(vqa.valid_tuple) * 100)) else: logger.info("DO NOT USE VALIDATION") vqa.train(vqa.train_tuple, vqa.valid_tuple)

f71aa988a5098b28bbada6d39c5173f2c7f1034c

py

Python

python/ctci/1_arrays_strings/6_Compression.py

othonreyes/code_problems

6e65b26120b0b9d6e5ac7342a4d964696b7bd5bf

python/ctci/1_arrays_strings/6_Compression.py

othonreyes/code_problems

6e65b26120b0b9d6e5ac7342a4d964696b7bd5bf

python/ctci/1_arrays_strings/6_Compression.py

othonreyes/code_problems

6e65b26120b0b9d6e5ac7342a4d964696b7bd5bf

# Create a function that implements a basic compression algorithm by counting the chars # thtat are present in a string, if the result string is longer than input # then return original input. # # Examples: # aaabcccccaaa: a3b1c5a3 # abcdef: abcdef # aaaaaaaaaaba: a10b1a1 ### Note: Don't use extra space import unittest from collections import Counter def compress2(s1): newStr = [] count = 0 for i in range(len(s1)): # Explanation # the i != 0 is used to deal with the first character. # we could have done but requirs extra code: # char = s1[0] # requires to check if the s1 is not empty # - or - # char = '' # requires to check if char != '' if i != 0 and s1[i] != s1[i-1]: newStr.append(s1[i-1] + str(count)) count = 0 count += 1 newStr.append(s1[-1] + str(count)) # we do this to deal with the last characters return min(s1, ''.join(newStr), key=len) def compress(s1): newStr = '' char = '' count = 0 for i in range(len(s1)): if char != s1[i]: if char != '': # we do this to deal with the initial case newStr += char + str(count) char = s1[i] count = 1 else: count += 1 newStr += char + str(count) # we do this to deal with the last characters if len(newStr) > len(s1): return s1 return newStr class Test(unittest.TestCase): valid = ( ('aaabcccccaaa', 'a3b1c5a3'), ('abcdef', 'abcdef'), ('aaaaaaaaaaba', 'a10b1a1') ) def test(self): for [input, expected] in self.valid: print(input,' vs ',expected) result = compress(input) self.assertEqual(result, expected) if __name__ == "__main__": unittest.main()

f71aad03581521af34e46f4263fc80abdb4a99c3

py

Python

asposewordscloud/models/requests/insert_list_online_request.py

aspose-words-cloud/aspose-words-cloud-python

65c7b55fa4aac69b60d41e7f54aed231df285479

2018-07-15T17:01:52.000Z

2018-11-29T06:15:33.000Z

asposewordscloud/models/requests/insert_list_online_request.py

aspose-words-cloud/aspose-words-cloud-python

65c7b55fa4aac69b60d41e7f54aed231df285479

2018-09-28T12:59:34.000Z

2019-10-08T08:42:59.000Z

asposewordscloud/models/requests/insert_list_online_request.py

aspose-words-cloud/aspose-words-cloud-python

65c7b55fa4aac69b60d41e7f54aed231df285479

2020-12-21T07:59:17.000Z

2022-02-16T21:41:25.000Z

# coding: utf-8 # ----------------------------------------------------------------------------------- # <copyright company="Aspose" file="insert_list_online_request.py"> # Copyright (c) 2021 Aspose.Words for Cloud # </copyright> # <summary> # 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. # </summary> # ----------------------------------------------------------------------------------- import json from six.moves.urllib.parse import quote from asposewordscloud import * from asposewordscloud.models import * from asposewordscloud.models.requests import * from asposewordscloud.models.responses import * class InsertListOnlineRequest(BaseRequestObject): """ Request model for insert_list_online operation. Initializes a new instance. :param document The document. :param list_insert List object. :param load_encoding Encoding that will be used to load an HTML (or TXT) document if the encoding is not specified in HTML. :param password Password for opening an encrypted document. :param dest_file_name Result path of the document after the operation. If this parameter is omitted then result of the operation will be saved as the source document. :param revision_author Initials of the author to use for revisions.If you set this parameter and then make some changes to the document programmatically, save the document and later open the document in MS Word you will see these changes as revisions. :param revision_date_time The date and time to use for revisions. """ def __init__(self, document, list_insert, load_encoding=None, password=None, dest_file_name=None, revision_author=None, revision_date_time=None): self.document = document self.list_insert = list_insert self.load_encoding = load_encoding self.password = password self.dest_file_name = dest_file_name self.revision_author = revision_author self.revision_date_time = revision_date_time def create_http_request(self, api_client): # verify the required parameter 'document' is set if self.document is None: raise ValueError("Missing the required parameter `document` when calling `insert_list_online`") # noqa: E501 # verify the required parameter 'list_insert' is set if self.list_insert is None: raise ValueError("Missing the required parameter `list_insert` when calling `insert_list_online`") # noqa: E501 path = '/v4.0/words/online/post/lists' path_params = {} # path parameters collection_formats = {} if path_params: path_params = api_client.sanitize_for_serialization(path_params) path_params = api_client.parameters_to_tuples(path_params, collection_formats) for k, v in path_params: # specified safe chars, encode everything path = path.replace( '{%s}' % k, quote(str(v), safe=api_client.configuration.safe_chars_for_path_param) ) # remove optional path parameters path = path.replace('//', '/') query_params = [] if self.load_encoding is not None: query_params.append(('loadEncoding', self.load_encoding)) # noqa: E501 if self.password is not None: query_params.append(('password', self.password)) # noqa: E501 if self.dest_file_name is not None: query_params.append(('destFileName', self.dest_file_name)) # noqa: E501 if self.revision_author is not None: query_params.append(('revisionAuthor', self.revision_author)) # noqa: E501 if self.revision_date_time is not None: query_params.append(('revisionDateTime', self.revision_date_time)) # noqa: E501 header_params = {} # HTTP header `Content-Type` header_params['Content-Type'] = api_client.select_header_content_type( # noqa: E501 ['multipart/form-data']) # noqa: E501 form_params = [] if self.document is not None: form_params.append(['document', self.document, 'file']) # noqa: E501 if self.list_insert is not None: form_params.append(['listInsert', self.list_insert.to_json(), 'string']) # noqa: E501 body_params = None return { "method": "PUT", "path": path, "query_params": query_params, "header_params": header_params, "form_params": form_params, "body": body_params, "collection_formats": collection_formats, "response_type": 'InsertListOnlineResponse' # noqa: E501 } def get_response_type(self): return 'InsertListOnlineResponse' # noqa: E501 def deserialize_response(self, api_client, response): multipart = self.getparts(response) return InsertListOnlineResponse( self.deserialize(json.loads(multipart[0].text), ListResponse, api_client), self.deserialize_file(multipart[1].content, multipart[1].headers, api_client))

f71ad0e03a1f64c0b8808cee586a271e9c91b997

py

Python

clif/testing/python/non_raising_test.py

wangxf123456/clif

9bff8a28f5d266d6ea4f4bb0dc1d9c9a0c9ee5b1

2017-04-18T04:14:04.000Z

2022-03-03T21:22:44.000Z

clif/testing/python/non_raising_test.py

wangxf123456/clif

9bff8a28f5d266d6ea4f4bb0dc1d9c9a0c9ee5b1

2017-05-02T23:51:29.000Z

2021-12-06T19:10:11.000Z

clif/testing/python/non_raising_test.py

wangxf123456/clif

9bff8a28f5d266d6ea4f4bb0dc1d9c9a0c9ee5b1

2017-04-26T06:15:30.000Z

2022-01-07T02:17:20.000Z

# Copyright 2021 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. from absl.testing import absltest from clif.testing.python import non_raising class NonRaisingTest(absltest.TestCase): def testPlain(self): num = non_raising.MakeTestNonRaisingPlain() self.assertEqual(num, 1) def testMarked(self): num = non_raising.MakeTestNonRaisingMarked() self.assertEqual(num, -1) if __name__ == '__main__': absltest.main()

f71b00645a1360df4f8b7496608b98342bb43f7f

py

Python

gym_acnportal/gym_acnsim/envs/tests/test_action_spaces.py

caltech-netlab/gym-acnportal

cacd2e4aa9159a3bf7f0b8e3db2dbb0832d76e46

gym_acnportal/gym_acnsim/envs/tests/test_action_spaces.py

caltech-netlab/gym-acnportal

cacd2e4aa9159a3bf7f0b8e3db2dbb0832d76e46

2021-04-28T14:43:32.000Z

2021-04-28T14:58:04.000Z

gym_acnportal/gym_acnsim/envs/tests/test_action_spaces.py

sunash/gym-acnportal

cacd2e4aa9159a3bf7f0b8e3db2dbb0832d76e46

2020-05-12T19:13:51.000Z

2020-05-12T19:13:51.000Z

# coding=utf-8 """ Tests for SimAction and action space functions. """ import unittest from typing import Callable, Dict, List, Any from unittest.mock import create_autospec import numpy as np from gym import Space from ..action_spaces import ( SimAction, single_charging_schedule, zero_centered_single_charging_schedule, ) from ...interfaces import GymTrainedInterface class TestSimAction(unittest.TestCase): # noinspection PyMissingOrEmptyDocstring @classmethod def setUpClass(cls) -> None: # The type here is Any as space_function is actually a Mock # object, but there's no Mock type in the typing library. cls.space_function: Any = create_autospec(lambda interface: Space()) cls.to_schedule: Callable[ [GymTrainedInterface, np.ndarray], Dict[str, List[float]] ] = lambda interface, array: {"a": [0]} cls.name: str = "stub_action" cls.sim_action: SimAction = SimAction( cls.space_function, cls.to_schedule, cls.name ) cls.interface: GymTrainedInterface = create_autospec(GymTrainedInterface) def test_correct_on_init_sim_action_name(self) -> None: self.assertEqual(self.sim_action.name, self.name) def test_get_space(self) -> None: self.sim_action.get_space(self.interface) self.space_function.assert_called_once() def test_get_schedule(self) -> None: array: np.ndarray = np.array([[1, 0], [0, 1]]) self.assertEqual( self.sim_action.get_schedule(self.interface, array), {"a": [0]} ) class TestSingleChargingSchedule(unittest.TestCase): # Some class variables are defined outside of setUpClass so that # the code inspector knows that inherited classes have these # attributes. max_rate: float = 16.0 min_rate: float = 0.0 negative_rate: float = -4.0 deadband_rate: float = 6.0 # noinspection PyMissingOrEmptyDocstring @classmethod def setUpClass(cls) -> None: cls.sim_action: SimAction = single_charging_schedule() cls.station_ids: List[str] = ["T1", "T2"] cls.offset: float = 0.5 def _interface_builder(interface: Any, min_rate: float) -> Any: interface.station_ids = cls.station_ids interface.max_pilot_signal = lambda station_id: cls.max_rate interface.min_pilot_signal = lambda station_id: ( min_rate if station_id == cls.station_ids[1] else cls.min_rate ) return interface cls.interface: Any = _interface_builder( create_autospec(GymTrainedInterface), cls.min_rate ) cls.interface_negative_min: Any = _interface_builder( create_autospec(GymTrainedInterface), cls.negative_rate ) cls.interface_deadband_min: Any = _interface_builder( create_autospec(GymTrainedInterface), cls.deadband_rate ) def test_correct_on_init_single_name(self) -> None: self.assertEqual(self.sim_action.name, "single schedule") def _test_space_function_helper( self, interface: GymTrainedInterface, min_rate: float, max_rate: float ) -> None: out_space: Space = self.sim_action.get_space(interface) self.assertEqual(out_space.shape, (len(self.station_ids),)) np.testing.assert_equal(out_space.low, 2 * [min_rate]) np.testing.assert_equal(out_space.high, 2 * [max_rate]) self.assertEqual(out_space.dtype, "float") def test_single_space_function(self) -> None: self._test_space_function_helper(self.interface, self.min_rate, self.max_rate) def test_single_space_function_negative_min(self) -> None: self._test_space_function_helper( self.interface_negative_min, self.negative_rate, self.max_rate ) def test_single_space_function_deadband_min(self) -> None: self._test_space_function_helper( self.interface_deadband_min, self.min_rate, self.max_rate ) def test_single_to_schedule(self) -> None: good_schedule: Dict[str, List[float]] = self.sim_action.get_schedule( self.interface, np.array( [self.min_rate + self.offset, (self.max_rate - self.min_rate) / 2] ), ) self.assertEqual( good_schedule, { self.station_ids[0]: [self.min_rate + self.offset], self.station_ids[1]: [(self.max_rate - self.min_rate) / 2], }, ) def test_single_to_bad_schedule(self) -> None: # The get_schedule function does not test if the input schedule # array is within the action space. bad_schedule: Dict[str, List[float]] = self.sim_action.get_schedule( self.interface, np.array([self.min_rate - self.offset, self.max_rate + self.offset]), ) self.assertEqual( bad_schedule, { self.station_ids[0]: [self.min_rate - self.offset], self.station_ids[1]: [self.max_rate + self.offset], }, ) def test_single_error_schedule(self) -> None: with self.assertRaises(TypeError): _ = self.sim_action.get_schedule( self.interface, np.array( [[self.min_rate - self.offset], [self.max_rate + self.offset]] ), ) class TestZeroCenteredSingleChargingSchedule(TestSingleChargingSchedule): # noinspection PyMissingOrEmptyDocstring @classmethod def setUpClass(cls) -> None: super().setUpClass() cls.sim_action: SimAction = zero_centered_single_charging_schedule() cls.shifted_max = cls.max_rate - (cls.max_rate + cls.min_rate) / 2 cls.shifted_minimums = [ cls.min_rate - (cls.max_rate + cls.min_rate) / 2, cls.negative_rate - (cls.max_rate + cls.negative_rate) / 2, cls.min_rate - (cls.max_rate + cls.deadband_rate) / 2, ] cls.negative_max_shift = cls.max_rate - (cls.max_rate + cls.negative_rate) / 2 def test_correct_on_init_single_name(self) -> None: self.assertEqual(self.sim_action.name, "zero-centered single schedule") def test_single_space_function(self) -> None: self._test_space_function_helper( self.interface, self.shifted_minimums[0], self.shifted_max ) def test_single_space_function_negative_min(self) -> None: self._test_space_function_helper( self.interface_negative_min, self.shifted_minimums[1], self.negative_max_shift, ) def test_single_space_function_deadband_min(self) -> None: self._test_space_function_helper( self.interface_deadband_min, self.shifted_minimums[2], self.shifted_max ) def test_single_to_bad_schedule(self) -> None: # The get_schedule function does not test if the input schedule # array is within the action space. bad_schedule: Dict[str, List[float]] = self.sim_action.get_schedule( self.interface, np.array([self.min_rate - self.offset, self.max_rate + self.offset]), ) self.assertEqual( bad_schedule, { self.station_ids[0]: [ self.min_rate - self.offset + (self.max_rate + self.min_rate) / 2 ], self.station_ids[1]: [ self.max_rate + self.offset + (self.max_rate + self.min_rate) / 2 ], }, ) def test_single_to_schedule(self) -> None: good_schedule: Dict[str, List[float]] = self.sim_action.get_schedule( self.interface, np.array( [ self.min_rate - (self.max_rate + self.min_rate) / 2, self.max_rate - (self.max_rate + self.min_rate) / 2, ] ), ) self.assertEqual( good_schedule, { self.station_ids[0]: [self.min_rate], self.station_ids[1]: [self.max_rate], }, ) if __name__ == "__main__": unittest.main()

f71b2aad75e30594e61025ad33be2a2c17932235

py

Python

reinvent_models/link_invent/networks/encoder_decoder.py

GT4SD/-reinvent_models

e1cf00d1b24fe5f39354e34829adc25460da84e2

reinvent_models/link_invent/networks/encoder_decoder.py

GT4SD/-reinvent_models

e1cf00d1b24fe5f39354e34829adc25460da84e2

2022-03-07T12:18:00.000Z

2022-03-07T12:18:00.000Z

reinvent_models/link_invent/networks/encoder_decoder.py

GT4SD/reinvent_models

e1cf00d1b24fe5f39354e34829adc25460da84e2

""" Implementation of a network using an Encoder-Decoder architecture. """ import torch.nn as tnn from torch import Tensor from reinvent_models.link_invent.networks.decoder import Decoder from reinvent_models.link_invent.networks.encoder import Encoder class EncoderDecoder(tnn.Module): """ An encoder-decoder that combines input with generated targets. """ def __init__(self, encoder_params: dict, decoder_params: dict): super(EncoderDecoder, self).__init__() self._encoder = Encoder(**encoder_params) self._decoder = Decoder(**decoder_params) def forward(self, encoder_seqs: Tensor, encoder_seq_lengths: Tensor, decoder_seqs: Tensor, decoder_seq_lengths: Tensor): """ Performs the forward pass. :param encoder_seqs: A tensor with the output sequences (batch, seq_d, dim). :param encoder_seq_lengths: A list with the length of each input sequence. :param decoder_seqs: A tensor with the encoded input input sequences (batch, seq_e, dim). :param decoder_seq_lengths: The lengths of the decoder sequences. :return : The output logits as a tensor (batch, seq_d, dim). """ encoder_padded_seqs, hidden_states = self.forward_encoder(encoder_seqs, encoder_seq_lengths) logits, _, _ = self.forward_decoder(decoder_seqs, decoder_seq_lengths, encoder_padded_seqs, hidden_states) return logits def forward_encoder(self, padded_seqs: Tensor, seq_lengths: Tensor): """ Does a forward pass only of the encoder. :param padded_seqs: The data to feed the encoder. :param seq_lengths: The length of each sequence in the batch. :return : Returns a tuple with (encoded_seqs, hidden_states) """ return self._encoder(padded_seqs, seq_lengths) def forward_decoder(self, padded_seqs: Tensor, seq_lengths: Tensor, encoder_padded_seqs: Tensor, hidden_states: Tensor): """ Does a forward pass only of the decoder. :param hidden_states: The hidden states from the encoder. :param padded_seqs: The data to feed to the decoder. :param seq_lengths: The length of each sequence in the batch. :return : Returns the logits and the hidden state for each element of the sequence passed. """ return self._decoder(padded_seqs, seq_lengths, encoder_padded_seqs, hidden_states) def get_params(self): """ Obtains the params for the network. :return : A dict with the params. """ return { "encoder_params": self._encoder.get_params(), "decoder_params": self._decoder.get_params() }

f71b2b58505f1a73cc43c49801a8cae13c3f8a26

py

Python

src/Application/PythonScriptModule/proto/state_2.py

antont/tundra

5c9b0a3957071f08ab425dff701cdbb34f9e1868

2018-04-02T15:38:10.000Z

2018-04-02T15:38:10.000Z

src/Application/PythonScriptModule/proto/state_2.py

antont/tundra

5c9b0a3957071f08ab425dff701cdbb34f9e1868

src/Application/PythonScriptModule/proto/state_2.py

antont/tundra

5c9b0a3957071f08ab425dff701cdbb34f9e1868

2021-09-04T12:37:34.000Z

2021-09-04T12:37:34.000Z

import state def change(): state.x = 2

f71b2ed9253b60e916abe7efa50cc6715f2d213c

py

Python

test/crawler/testICrawler.py

AutoDash/AutoDash

3924795a04159f80ea3b65b2172747babd15f35f

2020-02-12T01:24:46.000Z

2020-02-13T00:50:46.000Z

test/crawler/testICrawler.py

AutoDash/AutoDash

3924795a04159f80ea3b65b2172747babd15f35f

2020-02-20T10:20:56.000Z

2022-02-10T01:42:46.000Z

test/crawler/testICrawler.py

AutoDash/AutoDash

3924795a04159f80ea3b65b2172747babd15f35f

2020-02-22T02:47:19.000Z

2020-02-22T02:47:19.000Z

#!/usr/bin/env python3 import unittest from src.crawler.iCrawler import iCrawler, UndefinedDatabaseException from src.data.MetaDataItem import MetaDataItem from test.mock.MockDataAccessor import MockDataAccessor class MockCrawler(iCrawler): def __init__(self): super().__init__() def next_downloadable(self): return MetaDataItem( title="title", url="fake url 1", download_src="youtube") class TestICrawler(unittest.TestCase): def setUp(self): self.crawler = MockCrawler() self.database = MockDataAccessor() def test_compiles(self): self.assertEqual(True, True) def test_no_database(self): metadata = self.crawler.next_downloadable() try: self.crawler.check_new_url(metadata.url) self.assertTrue(False) except UndefinedDatabaseException: # Expected error pass def test_check_new_url(self): self.crawler.set_database(self.database) metadata = self.crawler.next_downloadable() self.assertTrue(self.crawler.check_new_url(metadata.url)) self.database.publish_new_metadata(metadata) self.assertFalse(self.crawler.check_new_url(metadata.url)) def test_run(self): self.crawler.set_database(self.database) metadata = self.crawler.run({}) self.database.publish_new_metadata(metadata) id_list = self.database.fetch_video_id_list() self.assertTrue(len(id_list) == 1) metadata = self.database.fetch_metadata(id_list[0]) # Get exact copy of the metadata item that was published copy_metadata = self.crawler.next_downloadable() # need to do this cause the times can be off copy_metadata.date_created = metadata.date_created copy_metadata.id = metadata.id #need to do this because otherwise cant compare self.assertEqual(metadata.to_json(), copy_metadata.to_json()) if __name__ == '__main__': unittest.main()

f71b315d6312d73a4f7581bd22785f23c8cb7785

py

Python

sprokit/tests/bindings/python/sprokit/pipeline/test-scheduler_registry.py

dstoup/kwiver

a3a36317b446baf0feb6274235ab1ac6b4329ead

2017-07-31T07:07:32.000Z

2017-07-31T07:07:32.000Z

sprokit/tests/bindings/python/sprokit/pipeline/test-scheduler_registry.py

dstoup/kwiver

a3a36317b446baf0feb6274235ab1ac6b4329ead

2021-03-19T15:39:43.000Z

2021-09-08T02:47:15.000Z

sprokit/tests/bindings/python/sprokit/pipeline/test-scheduler_registry.py

acidburn0zzz/kwiver

6e4205f1c46df04759c57c040f01cc804b27e00d

#!@PYTHON_EXECUTABLE@ #ckwg +28 # Copyright 2011-2013 by Kitware, Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # * 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. # # * Neither name of Kitware, Inc. nor the names of any contributors may be used # to endorse or promote products derived from this software without specific # prior written permission. # # 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 AUTHORS 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. def test_import(): try: from sprokit.pipeline import config import sprokit.pipeline.scheduler_factory except: test_error("Failed to import the scheduler_factory module") def test_create(): from sprokit.pipeline import config from sprokit.pipeline import scheduler_factory scheduler_factory.SchedulerType() ## scheduler_factory.SchedulerTypes() scheduler_factory.SchedulerDescription() scheduler_factory.SchedulerModule() def test_api_calls(): from sprokit.pipeline import config from sprokit.pipeline import modules from sprokit.pipeline import pipeline from sprokit.pipeline import scheduler_factory modules.load_known_modules() sched_type = 'thread_per_process' c = config.empty_config() p = pipeline.Pipeline() scheduler_factory.create_scheduler(sched_type, p) scheduler_factory.create_scheduler(sched_type, p, c) scheduler_factory.types() scheduler_factory.description(sched_type) scheduler_factory.default_type def example_scheduler(check_init): from sprokit.pipeline import scheduler class PythonExample(scheduler.PythonScheduler): def __init__(self, pipe, conf): scheduler.PythonScheduler.__init__(self, pipe, conf) self.ran_start = check_init self.ran_wait = check_init self.ran_stop = check_init self.ran_pause = check_init self.ran_resume = check_init def _start(self): self.ran_start = True def _wait(self): self.ran_wait = True def _stop(self): self.ran_stop = True def _pause(self): self.ran_pause = True def _resume(self): self.ran_resume = True def __del__(self): if not self.ran_start: test_error("start override was not called") if not self.ran_wait: test_error("wait override was not called") if not self.ran_stop: test_error("stop override was not called") if not self.ran_pause: test_error("pause override was not called") if not self.ran_resume: test_error("resume override was not called") return PythonExample def test_register(): from sprokit.pipeline import config from sprokit.pipeline import modules from sprokit.pipeline import pipeline from sprokit.pipeline import scheduler_factory modules.load_known_modules() sched_type = 'python_example' sched_desc = 'simple description' scheduler_factory.add_scheduler(sched_type, sched_desc, example_scheduler(True)) if not sched_desc == scheduler_factory.description(sched_type): test_error("Description was not preserved when registering") p = pipeline.Pipeline() try: s = scheduler_factory.create_scheduler(sched_type, p) if s is None: raise Exception() except: test_error("Could not create newly registered scheduler type") def test_wrapper_api(): from sprokit.pipeline import config from sprokit.pipeline import modules from sprokit.pipeline import pipeline from sprokit.pipeline import process_factory from sprokit.pipeline import scheduler_factory sched_type = 'python_example' sched_desc = 'simple description' modules.load_known_modules() scheduler_factory.add_scheduler(sched_type, sched_desc, example_scheduler(False)) p = pipeline.Pipeline() proc_type = 'orphan' proc_name = 'orphan' proc = process_factory.create_process(proc_type, proc_name) p.add_process(proc) def check_scheduler(s): if s is None: test_error("Got a 'None' scheduler") return s.start() s.pause() s.resume() s.stop() s.start() s.wait() del s p.reset() p.setup_pipeline() s = scheduler_factory.create_scheduler(sched_type, p) check_scheduler(s) if __name__ == '__main__': import os import sys if not len(sys.argv) == 4: test_error("Expected three arguments") sys.exit(1) testname = sys.argv[1] os.chdir(sys.argv[2]) sys.path.append(sys.argv[3]) from sprokit.test.test import * run_test(testname, find_tests(locals()))

f71b4bb600bb418ed1ef7e86a5615b6ad8bfabf3

py

Python

python/dgl/nn/mxnet/conv/agnnconv.py

jinghuix/dgl

fae26dd15caac92458a08ad34889086e1e333ddd

2020-07-24T19:26:51.000Z

2021-08-21T21:04:11.000Z

python/dgl/nn/mxnet/conv/agnnconv.py

jinghuix/dgl

fae26dd15caac92458a08ad34889086e1e333ddd

python/dgl/nn/mxnet/conv/agnnconv.py

jinghuix/dgl

fae26dd15caac92458a08ad34889086e1e333ddd

2021-03-09T12:42:46.000Z

2021-03-09T12:42:46.000Z

"""MXNet Module for Attention-based Graph Neural Network layer""" # pylint: disable= no-member, arguments-differ, invalid-name import mxnet as mx from mxnet.gluon import nn from .... import function as fn from ..softmax import edge_softmax from ..utils import normalize from ....utils import expand_as_pair class AGNNConv(nn.Block): r"""Attention-based Graph Neural Network layer from paper `Attention-based Graph Neural Network for Semi-Supervised Learning <https://arxiv.org/abs/1803.03735>`__. .. math:: H^{l+1} = P H^{l} where :math:`P` is computed as: .. math:: P_{ij} = \mathrm{softmax}_i ( \beta \cdot \cos(h_i^l, h_j^l)) Parameters ---------- init_beta : float, optional The :math:`\beta` in the formula. learn_beta : bool, optional If True, :math:`\beta` will be learnable parameter. """ def __init__(self, init_beta=1., learn_beta=True): super(AGNNConv, self).__init__() with self.name_scope(): self.beta = self.params.get('beta', shape=(1,), grad_req='write' if learn_beta else 'null', init=mx.init.Constant(init_beta)) def forward(self, graph, feat): r"""Compute AGNN Layer. Parameters ---------- graph : DGLGraph The graph. feat : mxnet.NDArray The input feature of shape :math:`(N, *)` :math:`N` is the number of nodes, and :math:`*` could be of any shape. If a pair of mxnet.NDArray is given, the pair must contain two tensors of shape :math:`(N_{in}, *)` and :math:`(N_{out}, *})`, the the :math:`*` in the later tensor must equal the previous one. Returns ------- mxnet.NDArray The output feature of shape :math:`(N, *)` where :math:`*` should be the same as input shape. """ with graph.local_scope(): feat_src, feat_dst = expand_as_pair(feat, graph) graph.srcdata['h'] = feat_src graph.srcdata['norm_h'] = normalize(feat_src, p=2, axis=-1) if isinstance(feat, tuple) or graph.is_block: graph.dstdata['norm_h'] = normalize(feat_dst, p=2, axis=-1) # compute cosine distance graph.apply_edges(fn.u_dot_v('norm_h', 'norm_h', 'cos')) cos = graph.edata.pop('cos') e = self.beta.data(feat_src.context) * cos graph.edata['p'] = edge_softmax(graph, e) graph.update_all(fn.u_mul_e('h', 'p', 'm'), fn.sum('m', 'h')) return graph.dstdata.pop('h')

f71b5dd3b2f1f6ba21eafc9f59670a50d9efc222

py

Python

sciencer/expanders/__init__.py

SciencerIO/sciencer-toolkit

f17c4a5dfb6cc5dbabefe03b13eb1e5345f7b1b9

2022-03-28T17:27:21.000Z

2022-03-29T22:27:15.000Z

sciencer/expanders/__init__.py

SciencerIO/sciencer-toolkit

f17c4a5dfb6cc5dbabefe03b13eb1e5345f7b1b9

sciencer/expanders/__init__.py

SciencerIO/sciencer-toolkit

f17c4a5dfb6cc5dbabefe03b13eb1e5345f7b1b9

2022-03-28T14:47:53.000Z

2022-03-28T14:47:53.000Z

"""Sciencer Expanders""" from .expander import Expander from .expand_by_authors import ExpandByAuthors from .expand_by_references import ExpandByReferences from .expand_by_citations import ExpandByCitations

f71b632bb314545ed7732ce47684f88d027b19e7

py

Python

xpring/proto/__init__.py

mvadari/xpring-py

b837420127d1c1e5051ed305ed4f19fe9910a4f6

2019-12-11T00:54:56.000Z

2021-03-11T19:44:44.000Z

xpring/proto/__init__.py

mvadari/xpring-py

b837420127d1c1e5051ed305ed4f19fe9910a4f6

xpring/proto/__init__.py

mvadari/xpring-py

b837420127d1c1e5051ed305ed4f19fe9910a4f6

2020-02-28T18:40:46.000Z

2022-02-28T23:01:09.000Z

# The rest of this package, but not this __init__.py, is generated by protoc.

f71b65b3b003148f57d2ed310d5f76f0d067c474

py

Python

violas_client/canoser/bool_t.py

violas-core/violas-client

e8798f7d081ac218b78b81fd7eb2f8da92631a16