Datasets:

ytzi

/

the-stack-dedup-python-scored-filtered

like 0

d9b0c3d32e07c56a0732f0fca454740538a940fe

py

Python

setup.py

Kaslanarian/PythonSVM

715eeef2a245736167addf45a6aee8b40b54d0c7

2021-09-25T01:00:37.000Z

2021-09-27T12:13:24.000Z

setup.py

Kaslanarian/PythonSVM

715eeef2a245736167addf45a6aee8b40b54d0c7

2021-09-17T12:08:14.000Z

2021-09-17T12:08:14.000Z

setup.py

Kaslanarian/PythonSVM

715eeef2a245736167addf45a6aee8b40b54d0c7

import setuptools #enables develop setuptools.setup( name='pysvm', version='0.1', description='PySVM : A NumPy implementation of SVM based on SMO algorithm', author_email="191300064@smail.nju.edu.cn", packages=['pysvm'], license='MIT License', long_description=open('README.md', encoding='utf-8').read(), install_requires=[ #自动安装依赖 'numpy', 'sklearn' ], url='https://github.com/Kaslanarian/PySVM', )

d9b0df7f5ef294a68858d836af143c289d120187

py

Python

Object_detection_image.py

hiperus0988/pyao

72c56975a3d45aa033bdf7650b5369d59240395f

2021-06-09T22:17:57.000Z

2021-06-09T22:17:57.000Z

Object_detection_image.py

hiperus0988/pyao

72c56975a3d45aa033bdf7650b5369d59240395f

Object_detection_image.py

hiperus0988/pyao

72c56975a3d45aa033bdf7650b5369d59240395f

######## Image Object Detection Using Tensorflow-trained Classifier ######### # # Author: Evan Juras # Date: 1/15/18 # Description: # This program uses a TensorFlow-trained classifier to perform object detection. # It loads the classifier uses it to perform object detection on an image. # It draws boxes and scores around the objects of interest in the image. ## Some of the code is copied from Google's example at ## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb ## and some is copied from Dat Tran's example at ## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py ## but I changed it to make it more understandable to me. # Import packages import os import cv2 import numpy as np import tensorflow as tf import sys # This is needed since the notebook is stored in the object_detection folder. sys.path.append("..") # Import utilites from utils import label_map_util from utils import visualization_utils as vis_util # Name of the directory containing the object detection module we're using MODEL_NAME = 'inference_graph' IMAGE_NAME = 'test1.jpg' # Grab path to current working directory CWD_PATH = os.getcwd() # Path to frozen detection graph .pb file, which contains the model that is used # for object detection. PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb') # Path to label map file PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt') # Path to image PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME) # Number of classes the object detector can identify NUM_CLASSES = 6 # Load the label map. # Label maps map indices to category names, so that when our convolution # network predicts `5`, we know that this corresponds to `king`. # Here we use internal utility functions, but anything that returns a # dictionary mapping integers to appropriate string labels would be fine label_map = label_map_util.load_labelmap(PATH_TO_LABELS) categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) category_index = label_map_util.create_category_index(categories) # Load the Tensorflow model into memory. detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) # Define input and output tensors (i.e. data) for the object detection classifier # Input tensor is the image image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # Output tensors are the detection boxes, scores, and classes # Each box represents a part of the image where a particular object was detected detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0') # Each score represents level of confidence for each of the objects. # The score is shown on the result image, together with the class label. detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') # Number of objects detected num_detections = detection_graph.get_tensor_by_name('num_detections:0') # Load image using OpenCV and # expand image dimensions to have shape: [1, None, None, 3] # i.e. a single-column array, where each item in the column has the pixel RGB value image = cv2.imread(PATH_TO_IMAGE) image_expanded = np.expand_dims(image, axis=0) # Perform the actual detection by running the model with the image as input (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_expanded}) # Draw the results of the detection (aka 'visulaize the results') vis_util.visualize_boxes_and_labels_on_image_array( image, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8, min_score_thresh=0.60) # All the results have been drawn on image. Now display the image. cv2.imshow('Object detector', image) # Press any key to close the image cv2.waitKey(0) # Clean up cv2.destroyAllWindows()

d9b62ab258f0b51ef25d431f8fa66de9acd438a7

py

Python

setup.py

giggslam/python-messengerbot-sdk

4a6fadf96fe3425da9abc4726fbb84db6d84f7b5

2019-03-05T08:33:34.000Z

2021-12-13T01:52:47.000Z

setup.py

giggslam/python-messengerbot-sdk

4a6fadf96fe3425da9abc4726fbb84db6d84f7b5

setup.py

giggslam/python-messengerbot-sdk

4a6fadf96fe3425da9abc4726fbb84db6d84f7b5

2019-03-07T07:58:02.000Z

2020-12-18T10:08:47.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # 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 re import sys from setuptools import setup from setuptools.command.test import test as TestCommand __version__ = '' with open('facebookbot/__about__.py', 'r') as fd: reg = re.compile(r'__version__ = [\'"]([^\'"]*)[\'"]') for line in fd: m = reg.match(line) if m: __version__ = m.group(1) break def _requirements(): with open('requirements.txt', 'r') as fd: return [name.strip() for name in fd.readlines()] with open('README.rst', 'r') as fd: long_description = fd.read() setup( name="fbsdk", version=__version__, author="Sam Chang", author_email="t0915290092@gmail.com", maintainer="Sam Chang", maintainer_email="t0915290092@gmail.com", url="https://github.com/boompieman/fbsdk", description="Facebook Messaging API SDK for Python", long_description=long_description, license='Apache License 2.0', packages=[ "facebookbot", "facebookbot.models" ], install_requires=_requirements(), classifiers=[ "Development Status :: 5 - Production/Stable", "License :: OSI Approved :: Apache Software License", "Intended Audience :: Developers", "Programming Language :: Python :: 3", "Topic :: Software Development" ] )

d9b8347698a1fe18b6d9ec66f6bfbfa77f2567be

py

Python

using_paramiko.py

allupramodreddy/cisco_py

5488b56d9324011860b78998e694dcce6da5e3d1

using_paramiko.py

allupramodreddy/cisco_py

5488b56d9324011860b78998e694dcce6da5e3d1

using_paramiko.py

allupramodreddy/cisco_py

5488b56d9324011860b78998e694dcce6da5e3d1

#!/usr/local/bin/python3 import paramiko,time #using as SSH Client client = paramiko.SSHClient() # check dir(client) to find available options. # auto adjust host key verification with yes or no client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # time for connecting to remote Cisco IOS """ Manually taking input addr = input('Provide IP address to connect to: ') user = input('Username: ') pwd = getpass.getpass('Password: ')""" # Taking input from files f1 = open("devices.txt","r") f2 = open("commands.txt","r") for line in f1: client = paramiko.SSHClient() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) data = line.split(" ") # print(data) addr = data[0] user = data[1] pwd = data[2] f3 = open(addr+".txt","w+") # print(addr +" "+ user +" " +pwd) client.connect(addr,username=user,password=pwd,allow_agent=False,look_for_keys=False) # we have to ask for Shell device_access = client.invoke_shell() for line in f2: device_access.send(line) time.sleep(1) output = device_access.recv(55000).decode('ascii') f3.write(output) """ THIS CODE IS FOR SINGLE COMMAND, FOR MULTIPLE COMMANDS CODE BELOW # send command to the device device_access.send("ter len 0\nshow run \n") time.sleep(2) # receive output from the device, convert it to byte-like format and print it print(device_access.recv(550000).decode('ascii')) # We can print the same to a file too with open("csr1000v.txt","w") as f: f.write(device_access.recv(550000).decode('ascii'))"""

d9b86cc42aaff67200ff3f4f5f6d27121835fd8c

py

Python

old/.history/a_20201125192943.py

pscly/bisai1

e619186cec5053a8e02bd59e48fc3ad3af47d19a

old/.history/a_20201125192943.py

pscly/bisai1

e619186cec5053a8e02bd59e48fc3ad3af47d19a

old/.history/a_20201125192943.py

pscly/bisai1

e619186cec5053a8e02bd59e48fc3ad3af47d19a

# for n in range(400,500): # i = n // 100 # j = n // 10 % 10 # k = n % 10 # if n == i ** 3 + j ** 3 + k ** 3: # print(n) # 第一道题(16) # input("请输入(第一次):") # s1 = input("请输入(第二次):") # l1 = s1.split(' ') # l2 = [] # for i in l1: # if i.isdigit(): # l2.append(int(i)) # for i in l2: # if not (i % 6): # print(i, end=" ") # 第二道题(17) out_l1 = [] def bian_int_list(l1): re_l1 = [] # 返回出去的列表 for i in l1: re_l1.append(i) def jisuan(str_num): he1 = 0 global out_l1 for i in l1(): he1 += int(i)**2 if he1 > int(str_num): out_l1.append(str_num) return None while 1: in_1 = input("请输入数值:") nums_l1 = in_1.split(' ')

d9c69927875c451378bcb7d50069e903036beefa

py

Python

bathymetry_blink/bathymetry_blink.py

poster515/BlinkyTape_Python

edc2f7e43fbf07dbfdeba60da7acb7ae7a3707d0

2015-02-14T11:37:21.000Z

2021-05-10T17:24:16.000Z

bathymetry_blink/bathymetry_blink.py

poster515/BlinkyTape_Python

edc2f7e43fbf07dbfdeba60da7acb7ae7a3707d0

2015-02-14T17:33:24.000Z

2021-10-05T20:32:19.000Z

bathymetry_blink/bathymetry_blink.py

poster515/BlinkyTape_Python

edc2f7e43fbf07dbfdeba60da7acb7ae7a3707d0

2015-01-24T23:36:54.000Z

2021-10-02T23:40:08.000Z

""" This script will modulate the blinky lights using the following algorithm: 1) uses user-provided location to obtain row of pixel data from bathy image 2) samples a 'number of LEDs' number of pixels from that row 3) shifts the sampled row data to center it at the location specified by user 4) displays resulting pixels on Blinky Tape 5) shifts next row by a given latitude, also specified by user 6) sleeps for user-specified period of time Uses the following arguments: -l/--location: tuple Location of the user in tuple(lat, lon). This represents the center of the LED strip. Defaults to (0, 0) -u/--update-interval: int Update interval of the script, in minutes. Defaults to 10. -p/--port: str Serial port of the BlinkyLight (e.g., 'ttyAMA0', 'COM3'). Defaults to 'COM5'. -d/--delta_latitude: int Vertical change in latitude every update rate. May be 0, but this will result in a never-changing LEDs. -i/--image: str Name of the PNG image that contains the color coded pathymetric data. The file current named mapserv.png was obtained using the following API: https://www.gebco.net/data_and_products/gebco_web_services/web_map_service/mapserv?request=getmap&service=wms&BBOX=-90,-180,90,180&format=image/png&height=600&width=1200&crs=EPSG:4326&layers=GEBCO_LATEST_SUB_ICE_TOPO&version=1.3.0 In lieu of providing command line arguments, you may alternatively edit the defaults in bath_config.json. NOTE: runs via: runfile('/BlinkyTape_Python/bathymetry_blink/bathymetry_blink.py', wdir='/BlinkyTape_Python/') (C) 2021 Joseph Post (https://joeycodes.dev) MIT Licensed """ import optparse import json from blinkytape import BlinkyTape from time import sleep from PIL import Image import numpy as np import sys MAX_ERRORS = 3 num_errors = 0 # Obtain default parameters with open("./bathymetry_blink/bathy_config.json") as f: config = json.load(f) # Default Blinky Tape port on Raspberry Pi is /dev/ttyACM0 parser = optparse.OptionParser() parser.add_option("-p", "--port", dest="portname", help="serial port (ex: /dev/ttyACM0)", default=config["port"]) parser.add_option("-l", "--location", dest="location", help="Location of the center of the LED strip (ex: 70,-110)", default=config["location"]) parser.add_option("-u", "--update-rate", dest="update_rate", help="How often to update elevation profile (mins) (ex: 5)", default=config["update_rate"]) parser.add_option("-d", "--delta-latitude", dest="delta_latitude", help="Change in latitude during update (ex: 5)", default=config["delta_latitude"]) parser.add_option("-n", "--num-leds", dest="num_leds", help="Number of LEDs in strip (ex: 60)", default=config["num_leds"]) parser.add_option("-i", "--image", dest="image_name", help="Name of the map/bathymetry image (ex: ./mapserv.png)", default=config["image"]) (options, args) = parser.parse_args() if args: print("Unknown parameters: " + args) # grab the values provided by user (or defaults) port = options.portname loc = options.location rate = options.update_rate delta = options.delta_latitude n_leds = options.num_leds i_name = options.image_name # Some visual indication that it works, for headless setups (green tape) bt = BlinkyTape(port, n_leds) bt.displayColor(0, 100, 0) bt.show() sleep(2) while True: try: # first, load image im = Image.open(i_name) # Can be many different formats. cols, rows = im.size a = np.asarray(im) # of shape (rows, cols, channels) # map loc latitude to 0-based index latitude_index = min(rows - 1, max(0, (int)(((loc[0] - -90) / (90 - -90)) * (rows - 0) + 0))) longitude_index = min(cols - 1, max(0, (int)(((loc[1] - -180) / (180 - -180)) * (cols - 0) + 0))) # update the location of the next row of elevation data to take loc[0] += delta loc[0] = ((loc[0] + 90) % 180) - 90 # wraps to next pole if overflow print("Lat index: " + str(latitude_index)) print("Lon index: " + str(longitude_index)) print("Next latitude: " + str(loc[0])) # grab the applicable pixel indices indices = [(int)(x*(cols/n_leds)) for x in range(n_leds)] # sample that row of pixel data output_pixels = np.take(a[latitude_index], indices, axis=0) # rotate the row to center around the specified longitude output_pixels = np.roll(output_pixels, longitude_index, axis=0) # send all pixel data to bt for pixel in output_pixels: print("Sending r: {}, g: {}, b: {}".format(*pixel)) bt.sendPixel(*pixel) # finally, show the image bt.show() # delete variables for memory management del a del im # Tape resets to stored pattern after a few seconds of inactivity sleep(rate * 60) # Wait specified number of minutes # sleep(10) # Wait specified number of minutes except KeyboardInterrupt: print("Keyboard interrupt, ending program.") sys.exit() except RuntimeError as e: print("Encountered runtime error: " + e.args[0]) # flush any incomplete data bt.show() num_errors += 1 if num_errors > MAX_ERRORS: sys.exit("Error count exceeds that allowed.")

d9d317f8ac0c3d87ca7347265d7a9836b41ed098

py

Python

gci-vci-serverless/src/helpers/vp_saves_helpers.py

ClinGen/gene-and-variant-curation-tools

30f21d8f03d8b5c180c1ce3cb8401b5abc660080

2021-09-17T20:39:07.000Z

2021-09-17T20:39:07.000Z

gci-vci-serverless/src/helpers/vp_saves_helpers.py

ClinGen/gene-and-variant-curation-tools

30f21d8f03d8b5c180c1ce3cb8401b5abc660080

2021-08-29T17:24:26.000Z

2022-03-25T17:24:31.000Z

gci-vci-serverless/src/helpers/vp_saves_helpers.py

ClinGen/gene-and-variant-curation-tools

30f21d8f03d8b5c180c1ce3cb8401b5abc660080

import datetime import uuid import simplejson as json from src.db.s3_client import Client as S3Client from decimal import Decimal def get_from_archive(archive_key): ''' Download a VP Save from S3. :param str archive_key: The vp_save data's location (S3 bucket and file path). This value is required. ''' if archive_key is None or '/' not in archive_key: raise ValueError() bucket, key = archive_key.split('/', 1) s3_client = S3Client() try: archive_object = json.loads(s3_client.get_object(bucket, key)['Body'].read(),parse_float=Decimal) except Exception as e: print('ERROR: Error downloading ' + key + ' from ' + bucket + ' bucket. ERROR\n%s' %e) raise return archive_object def build(vp_save={}): ''' Builds and returns a valid vp_save object. Builds a new vp_save object by creating default values for required fields and combines any of the given attributes. ''' vp_save['PK'] = str(uuid.uuid4()) # Set timestamps (for new data) now = datetime.datetime.now().isoformat() vp_save['date_created'] = now vp_save['last_modified'] = now vp_save['item_type'] = 'vp_save' return vp_save def archive(bucket, vp_save_pk, save_data): ''' Archives a vp save data to S3. Uploads the save data object as a JSON file to S3. The location of the archive depends on the bucket and the primary key of the save data. If the upload fails, an exception is raised. If successful, returns the archive location. :param str bucket: The name of the S3 bucket for the archive. This value is required. :param str vp_save_pk: The vp_save PK to use as the name of the JSON file. This value is required. :param obj save_data: The save data object to archive. This value is required. ''' if bucket is None or len(bucket) <= 0: raise ValueError() if vp_save_pk is None or len(vp_save_pk) <= 0: raise ValueError() if not save_data: raise ValueError() archive_file = __archive_key(save_data) + '/' + vp_save_pk + '.json' # Upload curation data to S3 archive bucket. s3_client = S3Client() try: s3_client.put_object( bytes(json.dumps(save_data).encode('UTF-8')), bucket, archive_file ) except Exception as e: print('ERROR: Error uploading ' + archive_file + ' to ' + bucket + ' bucket. ERROR\n%s' %e) raise archive_key_comps = [bucket, archive_file] return '/'.join(archive_key_comps) def __archive_key(save_data): return save_data['PK']

d9d368d362ab070d71b3363fe0fb20728ec9660d

py

Python

src/entity/002_createRdf.py

toyo-bunko/paper_app

f988e05cf83711d98c5ed735c0fd74fcf11e0f05

2021-02-28T15:38:37.000Z

2021-02-28T15:38:37.000Z

src/entity/002_createRdf.py

toyo-bunko/paper_app

f988e05cf83711d98c5ed735c0fd74fcf11e0f05

src/entity/002_createRdf.py

toyo-bunko/paper_app

f988e05cf83711d98c5ed735c0fd74fcf11e0f05

import shutil import os import json import glob import yaml import sys import urllib import ssl import csv import time import requests import json import csv from rdflib import URIRef, BNode, Literal, Graph from rdflib.namespace import RDF, RDFS, FOAF, XSD from rdflib import Namespace all = Graph() with open("data/dict.json") as f: ln_map = json.load(f) st_path = "../data/index.json" with open(st_path) as f: result = json.load(f) uris = [] for obj in result: fields = ["spatial", "agential"] for field in fields: values = obj[field] for value in values: uri = "chname:"+value if field == "spatial": uri = "place:"+value if uri not in uris: uris.append(uri) for uri in uris: print(uri) tmp = uri.split(":") prefix = tmp[0] suffix = tmp[1] ln = suffix ln_org = "" if ln in ln_map: ln_org = ln ln = ln_map[ln] if len(ln) > 20: continue # ln = obj["uri"].split(":")[1] ''' wiki_path = "data/wikidata/"+ln+".json" wiki = {} if os.path.exists(wiki_path): with open(wiki_path) as f: wiki = json.load(f) # sameAs stmt = (subject, URIRef("http://www.w3.org/2002/07/owl#sameAs"), URIRef(wiki_url)) all.add(stmt) obj = wiki["entities"][wiki_url.split("/")[-1]] # description if "descriptions" in obj and "ja" in obj["descriptions"]: stmt = (subject, URIRef("http://schema.org/description"), Literal(obj["descriptions"]["ja"]["value"], lang="ja")) all.add(stmt) # label if "labels" in obj and "ja" in obj["labels"]: stmt = (subject, RDFS.label, Literal(obj["labels"]["ja"]["value"])) all.add(stmt) ln = wiki_url.split("/")[-1] ''' db_path = "data/dbpedia_ja/"+ln+".json" wiki_path = "data/wikidata/"+ln+".json" db = {} wiki = {} if os.path.exists(db_path): with open(db_path) as f: db = json.load(f) if os.path.exists(wiki_path): with open(wiki_path) as f: wiki = json.load(f) db_uri = "http://ja.dbpedia.org/resource/"+ln if db_uri not in db: print("not" , db_uri) continue # ###### subject = URIRef("https://shibusawa-dlab.github.io/lab1/api/"+prefix+"/"+ln) if prefix == "chname": stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Agent")) all.add(stmt) elif prefix == "time": stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Time")) all.add(stmt) elif prefix == "place": stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Place")) all.add(stmt) elif prefix == "event": stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Event")) all.add(stmt) elif prefix == "org": stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Organization")) all.add(stmt) elif prefix == "keyword": stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Keyword")) all.add(stmt) elif prefix == "type": stmt = (subject, RDF.type, URIRef("https://jpsearch.go.jp/term/type/Type")) all.add(stmt) # ###### obj = db[db_uri] stmt = (subject, URIRef("http://www.w3.org/2002/07/owl#sameAs"), URIRef(db_uri)) all.add(stmt) if "http://dbpedia.org/ontology/thumbnail" in obj: stmt = (subject, URIRef("http://schema.org/image"), URIRef(obj["http://dbpedia.org/ontology/thumbnail"][0]["value"])) all.add(stmt) if "http://www.w3.org/2000/01/rdf-schema#label" in obj: labels = obj["http://www.w3.org/2000/01/rdf-schema#label"] for label in labels: if label["lang"] == "ja": stmt = (subject, RDFS.label, Literal(label["value"])) all.add(stmt) if "http://www.w3.org/2000/01/rdf-schema#comment" in obj: labels = obj["http://www.w3.org/2000/01/rdf-schema#comment"] for label in labels: stmt = (subject, URIRef("http://schema.org/description"), Literal(label["value"], lang=label["lang"])) all.add(stmt) if "http://www.w3.org/2002/07/owl#sameAs" in obj: labels = obj["http://www.w3.org/2002/07/owl#sameAs"] for label in labels: value = label["value"] if "http://dbpedia.org" in value or "http://ja.dbpedia.org" in value or "www.wikidata.org" in value: stmt = (subject, URIRef("http://www.w3.org/2002/07/owl#sameAs"), URIRef(value)) all.add(stmt) # 位置情報 ''' if "point" in obj and prefix == "place": value = obj["point"]["value"].split(" ") # addGeo関数 geoUri = addGeo({ "lat" : float(value[0]), "long": float(value[1]) }) stmt = (subject, URIRef("http://schema.org/geo"), geoUri) if suffix not in places: places[suffix] = { "lat" : float(value[0]), "long": float(value[1]) } all.add(stmt) ''' # 正規化前 if ln_org != "" and ln != ln_org: stmt = (subject, URIRef("http://schema.org/name"), Literal(ln_org)) all.add(stmt) path = "data/all.json" all.serialize(destination=path, format='json-ld') all.serialize(destination=path.replace(".json", ".rdf"), format='pretty-xml')

d9d80db949c5d5f415b809076411a2404da55e53

py

Python

sympy/combinatorics/testutil.py

ethankward/sympy

44664d9f625a1c68bc492006cfe1012cb0b49ee4

2019-05-18T22:36:49.000Z

2019-05-24T05:56:16.000Z

sympy/combinatorics/testutil.py

ethankward/sympy

44664d9f625a1c68bc492006cfe1012cb0b49ee4

2020-04-22T12:45:26.000Z

2020-04-22T12:45:26.000Z

sympy/combinatorics/testutil.py

ethankward/sympy

44664d9f625a1c68bc492006cfe1012cb0b49ee4

2021-02-16T16:40:49.000Z

2022-03-07T18:28:41.000Z

from sympy.combinatorics import Permutation from sympy.combinatorics.util import _distribute_gens_by_base rmul = Permutation.rmul def _cmp_perm_lists(first, second): """ Compare two lists of permutations as sets. This is used for testing purposes. Since the array form of a permutation is currently a list, Permutation is not hashable and cannot be put into a set. Examples ======== >>> from sympy.combinatorics.permutations import Permutation >>> from sympy.combinatorics.testutil import _cmp_perm_lists >>> a = Permutation([0, 2, 3, 4, 1]) >>> b = Permutation([1, 2, 0, 4, 3]) >>> c = Permutation([3, 4, 0, 1, 2]) >>> ls1 = [a, b, c] >>> ls2 = [b, c, a] >>> _cmp_perm_lists(ls1, ls2) True """ return {tuple(a) for a in first} == \ {tuple(a) for a in second} def _naive_list_centralizer(self, other, af=False): from sympy.combinatorics.perm_groups import PermutationGroup """ Return a list of elements for the centralizer of a subgroup/set/element. This is a brute force implementation that goes over all elements of the group and checks for membership in the centralizer. It is used to test ``.centralizer()`` from ``sympy.combinatorics.perm_groups``. Examples ======== >>> from sympy.combinatorics.testutil import _naive_list_centralizer >>> from sympy.combinatorics.named_groups import DihedralGroup >>> D = DihedralGroup(4) >>> _naive_list_centralizer(D, D) [Permutation([0, 1, 2, 3]), Permutation([2, 3, 0, 1])] See Also ======== sympy.combinatorics.perm_groups.centralizer """ from sympy.combinatorics.permutations import _af_commutes_with if hasattr(other, 'generators'): elements = list(self.generate_dimino(af=True)) gens = [x._array_form for x in other.generators] commutes_with_gens = lambda x: all(_af_commutes_with(x, gen) for gen in gens) centralizer_list = [] if not af: for element in elements: if commutes_with_gens(element): centralizer_list.append(Permutation._af_new(element)) else: for element in elements: if commutes_with_gens(element): centralizer_list.append(element) return centralizer_list elif hasattr(other, 'getitem'): return _naive_list_centralizer(self, PermutationGroup(other), af) elif hasattr(other, 'array_form'): return _naive_list_centralizer(self, PermutationGroup([other]), af) def _verify_bsgs(group, base, gens): """ Verify the correctness of a base and strong generating set. This is a naive implementation using the definition of a base and a strong generating set relative to it. There are other procedures for verifying a base and strong generating set, but this one will serve for more robust testing. Examples ======== >>> from sympy.combinatorics.named_groups import AlternatingGroup >>> from sympy.combinatorics.testutil import _verify_bsgs >>> A = AlternatingGroup(4) >>> A.schreier_sims() >>> _verify_bsgs(A, A.base, A.strong_gens) True See Also ======== sympy.combinatorics.perm_groups.PermutationGroup.schreier_sims """ from sympy.combinatorics.perm_groups import PermutationGroup strong_gens_distr = _distribute_gens_by_base(base, gens) current_stabilizer = group for i in range(len(base)): candidate = PermutationGroup(strong_gens_distr[i]) if current_stabilizer.order() != candidate.order(): return False current_stabilizer = current_stabilizer.stabilizer(base[i]) if current_stabilizer.order() != 1: return False return True def _verify_centralizer(group, arg, centr=None): """ Verify the centralizer of a group/set/element inside another group. This is used for testing ``.centralizer()`` from ``sympy.combinatorics.perm_groups`` Examples ======== >>> from sympy.combinatorics.named_groups import (SymmetricGroup, ... AlternatingGroup) >>> from sympy.combinatorics.perm_groups import PermutationGroup >>> from sympy.combinatorics.permutations import Permutation >>> from sympy.combinatorics.testutil import _verify_centralizer >>> S = SymmetricGroup(5) >>> A = AlternatingGroup(5) >>> centr = PermutationGroup([Permutation([0, 1, 2, 3, 4])]) >>> _verify_centralizer(S, A, centr) True See Also ======== _naive_list_centralizer, sympy.combinatorics.perm_groups.PermutationGroup.centralizer, _cmp_perm_lists """ if centr is None: centr = group.centralizer(arg) centr_list = list(centr.generate_dimino(af=True)) centr_list_naive = _naive_list_centralizer(group, arg, af=True) return _cmp_perm_lists(centr_list, centr_list_naive) def _verify_normal_closure(group, arg, closure=None): from sympy.combinatorics.perm_groups import PermutationGroup """ Verify the normal closure of a subgroup/subset/element in a group. This is used to test sympy.combinatorics.perm_groups.PermutationGroup.normal_closure Examples ======== >>> from sympy.combinatorics.named_groups import (SymmetricGroup, ... AlternatingGroup) >>> from sympy.combinatorics.testutil import _verify_normal_closure >>> S = SymmetricGroup(3) >>> A = AlternatingGroup(3) >>> _verify_normal_closure(S, A, closure=A) True See Also ======== sympy.combinatorics.perm_groups.PermutationGroup.normal_closure """ if closure is None: closure = group.normal_closure(arg) conjugates = set() if hasattr(arg, 'generators'): subgr_gens = arg.generators elif hasattr(arg, '__getitem__'): subgr_gens = arg elif hasattr(arg, 'array_form'): subgr_gens = [arg] for el in group.generate_dimino(): for gen in subgr_gens: conjugates.add(gen ^ el) naive_closure = PermutationGroup(list(conjugates)) return closure.is_subgroup(naive_closure) def canonicalize_naive(g, dummies, sym, *v): """ Canonicalize tensor formed by tensors of the different types g permutation representing the tensor dummies list of dummy indices msym symmetry of the metric v is a list of (base_i, gens_i, n_i, sym_i) for tensors of type `i` base_i, gens_i BSGS for tensors of this type n_i number ot tensors of type `i` sym_i symmetry under exchange of two component tensors of type `i` None no symmetry 0 commuting 1 anticommuting Return 0 if the tensor is zero, else return the array form of the permutation representing the canonical form of the tensor. Examples ======== >>> from sympy.combinatorics.testutil import canonicalize_naive >>> from sympy.combinatorics.tensor_can import get_symmetric_group_sgs >>> from sympy.combinatorics import Permutation, PermutationGroup >>> g = Permutation([1, 3, 2, 0, 4, 5]) >>> base2, gens2 = get_symmetric_group_sgs(2) >>> canonicalize_naive(g, [2, 3], 0, (base2, gens2, 2, 0)) [0, 2, 1, 3, 4, 5] """ from sympy.combinatorics.perm_groups import PermutationGroup from sympy.combinatorics.tensor_can import gens_products, dummy_sgs from sympy.combinatorics.permutations import Permutation, _af_rmul v1 = [] for i in range(len(v)): base_i, gens_i, n_i, sym_i = v[i] v1.append((base_i, gens_i, [[]]*n_i, sym_i)) size, sbase, sgens = gens_products(*v1) dgens = dummy_sgs(dummies, sym, size-2) if isinstance(sym, int): num_types = 1 dummies = [dummies] sym = [sym] else: num_types = len(sym) dgens = [] for i in range(num_types): dgens.extend(dummy_sgs(dummies[i], sym[i], size - 2)) S = PermutationGroup(sgens) D = PermutationGroup([Permutation(x) for x in dgens]) dlist = list(D.generate(af=True)) g = g.array_form st = set() for s in S.generate(af=True): h = _af_rmul(g, s) for d in dlist: q = tuple(_af_rmul(d, h)) st.add(q) a = list(st) a.sort() prev = (0,)*size for h in a: if h[:-2] == prev[:-2]: if h[-1] != prev[-1]: return 0 prev = h return list(a[0]) def graph_certificate(gr): """ Return a certificate for the graph gr adjacency list The graph is assumed to be unoriented and without external lines. Associate to each vertex of the graph a symmetric tensor with number of indices equal to the degree of the vertex; indices are contracted when they correspond to the same line of the graph. The canonical form of the tensor gives a certificate for the graph. This is not an efficient algorithm to get the certificate of a graph. Examples ======== >>> from sympy.combinatorics.testutil import graph_certificate >>> gr1 = {0:[1, 2, 3, 5], 1:[0, 2, 4], 2:[0, 1, 3, 4], 3:[0, 2, 4], 4:[1, 2, 3, 5], 5:[0, 4]} >>> gr2 = {0:[1, 5], 1:[0, 2, 3, 4], 2:[1, 3, 5], 3:[1, 2, 4, 5], 4:[1, 3, 5], 5:[0, 2, 3, 4]} >>> c1 = graph_certificate(gr1) >>> c2 = graph_certificate(gr2) >>> c1 [0, 2, 4, 6, 1, 8, 10, 12, 3, 14, 16, 18, 5, 9, 15, 7, 11, 17, 13, 19, 20, 21] >>> c1 == c2 True """ from sympy.combinatorics.permutations import _af_invert from sympy.combinatorics.tensor_can import get_symmetric_group_sgs, canonicalize items = list(gr.items()) items.sort(key=lambda x: len(x[1]), reverse=True) pvert = [x[0] for x in items] pvert = _af_invert(pvert) # the indices of the tensor are twice the number of lines of the graph num_indices = 0 for v, neigh in items: num_indices += len(neigh) # associate to each vertex its indices; for each line # between two vertices assign the # even index to the vertex which comes first in items, # the odd index to the other vertex vertices = [[] for i in items] i = 0 for v, neigh in items: for v2 in neigh: if pvert[v] < pvert[v2]: vertices[pvert[v]].append(i) vertices[pvert[v2]].append(i+1) i += 2 g = [] for v in vertices: g.extend(v) assert len(g) == num_indices g += [num_indices, num_indices + 1] size = num_indices + 2 assert sorted(g) == list(range(size)) g = Permutation(g) vlen = [0]*(len(vertices[0])+1) for neigh in vertices: vlen[len(neigh)] += 1 v = [] for i in range(len(vlen)): n = vlen[i] if n: base, gens = get_symmetric_group_sgs(i) v.append((base, gens, n, 0)) v.reverse() dummies = list(range(num_indices)) can = canonicalize(g, dummies, 0, *v) return can

d9d95781d1bacab44253ba285649d7b99ee1e33d

py

Python

src/vatic_checker/config.py

jonkeane/vatic-checker

fa8aec6946dcfd3f466b62f9c00d81bc43514b22

src/vatic_checker/config.py

jonkeane/vatic-checker

fa8aec6946dcfd3f466b62f9c00d81bc43514b22

src/vatic_checker/config.py

jonkeane/vatic-checker

fa8aec6946dcfd3f466b62f9c00d81bc43514b22

localhost = "http://localhost/" # your local host database = "mysql://root@localhost/vaticChecker" # server://user:pass@localhost/dbname min_training = 2 # the minimum number of training videos to be considered recaptcha_secret = "" # recaptcha secret for verification duplicate_annotations = False # Should the server allow for duplicate annotations? import os.path import sys sys.path.append(os.path.dirname(os.path.abspath(__file__))) # TODO: remove on server import os os.environ['PYTHON_EGG_CACHE'] = '/tmp/apache'

d9e551f94d290cc9b470d1fddfc0e91666dab7ba

py

Python

setup.py

zhanghang1989/notedown

b0fa1eac88d1cd7fa2261d6c454f82669e6f552b

setup.py

zhanghang1989/notedown

b0fa1eac88d1cd7fa2261d6c454f82669e6f552b

setup.py

zhanghang1989/notedown

b0fa1eac88d1cd7fa2261d6c454f82669e6f552b

from setuptools import setup # create __version__ exec(open('./_version.py').read()) setup( name="notedown", version=__version__, description="Convert markdown to IPython notebook.", author="Aaron O'Leary", author_email='dev@aaren.me', url='http://github.com/aaren/notedown', install_requires=['ipython', ], entry_points={ 'console_scripts': [ 'notedown = notedown:cli', ], } )

d9e5c18f6a37dd4a96dd21f7ddefb31b197848dd

py

Python

multithreaded_webcrawler.py

the-muses-ltd/Multithreaded-Webcrawler-Cassandra-

eee68faf3c6ecb548edd0e96ce445dcd366fb735

multithreaded_webcrawler.py

the-muses-ltd/Multithreaded-Webcrawler-Cassandra-

eee68faf3c6ecb548edd0e96ce445dcd366fb735

multithreaded_webcrawler.py

the-muses-ltd/Multithreaded-Webcrawler-Cassandra-

eee68faf3c6ecb548edd0e96ce445dcd366fb735

# This is a reusable webcraawler architecture that can be adapted to scrape any webstie. # RESULTS: # Roughly 24 seconds per thousand courses scraped for ThreadPoolExecutor vs 63s for unthreaded script. # This is a very basic implementation of multithreading in order to show the proof of concept, but is a good base to build off of. import requests from bs4 import BeautifulSoup import csv from concurrent.futures import ProcessPoolExecutor, as_completed, ThreadPoolExecutor import time import logging from mitopencourseware_crawler_worker import mit_crawler def courses_spider(max_pages): data_to_csv = [] #holds all data to send to csv print("Webcrawler workers have started, please wait while we finish crawling...") # remove max pages loop (unecessary) page = 1 while page <= max_pages: url = 'https://ocw.mit.edu/courses/' source_code = requests.get(url) plain_text = source_code.text soup = BeautifulSoup(plain_text, 'html.parser') # Multithread only the work: # Tuning is required to find the most efficient amount of workers in the thread pool. with ThreadPoolExecutor(max_workers=30) as executor: start = time.time() futures = [ executor.submit(work, link) for link in soup.findAll('h4', {'class': 'course_title'}, limit=100) ] data_to_csv = [] for result in as_completed(futures): data_to_csv.append(result.result()) end = time.time() print("Time Taken to complete: {:.6f}s".format(end-start)) print("Courses extracted: ", len(data_to_csv)) page += 1 export_to_csv(data_to_csv) def work(link): # replace this fucntion with the specific crawler you want to use: return mit_crawler(link) # Exports data to a formatted csv file, this will be replaced with multithreaded API calls to the Cassandra Prisma Database # or on the cloud in production, it will be sent to the S3 temporary database to be picked up by the AWS Lambda funtion which will push it to the Cassandra Database def export_to_csv(csv_data): with open('web_crawl_data.csv',mode='w') as csv_file: field_names = ['Title','URL extension','External Website Logo','URL(href)','Description','Course logo URL'] csv_writer = csv.DictWriter(csv_file, fieldnames=field_names)#delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writeheader() for course in csv_data: course_data = { 'Title':course[0], 'URL extension':course[1], 'External Website Logo':course[2], 'URL(href)':course[3], 'Description':course[4], 'Course logo URL':course[5], } csv_writer.writerow(course_data)

d9efa4ffda8cacd286187e29ce110d292c7a1e64

py

Python

clpy/sparse/util.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

2018-06-07T07:43:10.000Z

2021-10-30T21:06:32.000Z

clpy/sparse/util.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

2018-06-07T08:35:03.000Z

2021-03-31T03:14:32.000Z

clpy/sparse/util.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

2018-06-19T11:07:53.000Z

2021-05-13T20:57:04.000Z

import clpy import clpy.sparse.base _preamble_atomic_add = ''' #if __CUDA_ARCH__ < 600 __device__ double atomicAdd(double* address, double val) { unsigned long long* address_as_ull = (unsigned long long*)address; unsigned long long old = *address_as_ull, assumed; do { assumed = old; old = atomicCAS(address_as_ull, assumed, __double_as_longlong(val + __longlong_as_double(assumed))); } while (assumed != old); return __longlong_as_double(old); } #endif ''' def isintlike(x): try: return bool(int(x) == x) except (TypeError, ValueError): return False def isscalarlike(x): return clpy.isscalar(x) or (clpy.sparse.base.isdense(x) and x.ndim == 0) def isshape(x): if not isinstance(x, tuple) or len(x) != 2: return False m, n = x return isintlike(m) and isintlike(n)

d9f9cd4e7a0b73e79eb71d2bdbfa755d69a9cc9d

py

Python

examples/first_char_last_column.py

clarkfitzg/sta141c

129704ba0952a4b80f9b093dcfa49f49f37b052d

2019-01-08T20:10:11.000Z

2021-11-26T12:18:58.000Z

examples/first_char_last_column.py

timilchene/sta141c-winter19

129704ba0952a4b80f9b093dcfa49f49f37b052d

2017-06-25T05:35:24.000Z

2017-06-25T05:35:24.000Z

examples/first_char_last_column.py

timilchene/sta141c-winter19

129704ba0952a4b80f9b093dcfa49f49f37b052d

2019-01-08T20:02:15.000Z

2021-12-16T23:27:56.000Z

#!/usr/bin/env python3 """ For the last column, print only the first character. Usage: $ printf "100,200\n0,\n" | python3 first_char_last_column.py Should print "100,2\n0," """ import csv from sys import stdin, stdout def main(): reader = csv.reader(stdin) writer = csv.writer(stdout) for row in reader: try: row[-1] = row[-1][0] except IndexError: # Python: Better to ask forgiveness than permission # Alternative: Look before you leap pass writer.writerow(row) if __name__ == "__main__": main()

8a045d9a56c4a8715b77c0b2cd2d5ff977fa98ed

py

Python

conf/feature_config.py

pupuwudi/nlp_xiaojiang

182ac4522b6012a52de6e1d0db7e6a47cb716e5b

conf/feature_config.py

pupuwudi/nlp_xiaojiang

182ac4522b6012a52de6e1d0db7e6a47cb716e5b

conf/feature_config.py

pupuwudi/nlp_xiaojiang

182ac4522b6012a52de6e1d0db7e6a47cb716e5b

2021-01-18T10:07:20.000Z

2022-01-12T10:09:47.000Z

# -*- coding: UTF-8 -*- # !/usr/bin/python # @time :2019/5/10 9:13 # @author :Mo # @function :path of FeatureProject import pathlib import sys import os # base dir projectdir = str(pathlib.Path(os.path.abspath(__file__)).parent.parent) sys.path.append(projectdir) # path of BERT model model_dir = projectdir + '/Data/chinese_L-12_H-768_A-12' config_name = model_dir + '/bert_config.json' ckpt_name = model_dir + '/bert_model.ckpt' vocab_file = model_dir + '/vocab.txt' # gpu使用率 gpu_memory_fraction = 0.32 # 默认取倒数第二层的输出值作为句向量 layer_indexes = [-2] # 序列的最大程度 max_seq_len = 32

8a1292fe9e365e4f3b12243aeeeb62b3fcd34222

py

Python

MIT/600.1x - Introduction to Computer Science and Programming Using Python/Unit 4/Problem Set 4/get_word_score.py

henriqueumeda/-Python-study

28e93a377afa4732037a29eb74d4bc7c9e24b62f

MIT/600.1x - Introduction to Computer Science and Programming Using Python/Unit 4/Problem Set 4/get_word_score.py

henriqueumeda/-Python-study

28e93a377afa4732037a29eb74d4bc7c9e24b62f

MIT/600.1x - Introduction to Computer Science and Programming Using Python/Unit 4/Problem Set 4/get_word_score.py

henriqueumeda/-Python-study

28e93a377afa4732037a29eb74d4bc7c9e24b62f

SCRABBLE_LETTER_VALUES = { 'a': 1, 'b': 3, 'c': 3, 'd': 2, 'e': 1, 'f': 4, 'g': 2, 'h': 4, 'i': 1, 'j': 8, 'k': 5, 'l': 1, 'm': 3, 'n': 1, 'o': 1, 'p': 3, 'q': 10, 'r': 1, 's': 1, 't': 1, 'u': 1, 'v': 4, 'w': 4, 'x': 8, 'y': 4, 'z': 10 } def getWordScore(word, n): """ Returns the score for a word. Assumes the word is a valid word. The score for a word is the sum of the points for letters in the word, multiplied by the length of the word, PLUS 50 points if all n letters are used on the first turn. Letters are scored as in Scrabble; A is worth 1, B is worth 3, C is worth 3, D is worth 2, E is worth 1, and so on (see SCRABBLE_LETTER_VALUES) word: string (lowercase letters) n: integer (HAND_SIZE; i.e., hand size required for additional points) returns: int >= 0 """ total_points = 0 for letter in word: total_points += SCRABBLE_LETTER_VALUES[letter] total_points *= len(word) if len(word) == n: total_points += 50 return total_points print(getWordScore('waybill', 7))

8a15ab57e7398ab067062419a83d15fd9bf34d36

py

Python

ex062.py

noahbarros/Python-Exercises

fafda898473bc984280e201ed11d8ad76cc8624a

2021-07-13T21:41:00.000Z

2021-07-13T21:41:00.000Z

ex062.py

noahbarros/Python-Exercises

fafda898473bc984280e201ed11d8ad76cc8624a

ex062.py

noahbarros/Python-Exercises

fafda898473bc984280e201ed11d8ad76cc8624a

primeiro = int(input('Digite o priemiro termo da PA: ')) razão = int(input('Digite a razão da PA: ')) termo = primeiro cont = 1 total = 0 mais = 10 while mais != 0: total += mais while cont <= total: print(f'{termo} ', end='') termo += razão cont += 1 print('Pausa') mais = int(input('Quantos termos você quer usar a mais? ')) print(f'a progressão foi finalizada com {total} termos mostrados')

8a19876a956cc7df8eee4ce39d6fc5531c4cfc7c

py

Python

src/api/datamanage/pro/lifecycle/data_trace/data_set_create.py

Chromico/bk-base

be822d9bbee544a958bed4831348185a75604791

2021-06-30T06:20:23.000Z

2022-03-22T03:05:49.000Z

src/api/datamanage/pro/lifecycle/data_trace/data_set_create.py

Chromico/bk-base

be822d9bbee544a958bed4831348185a75604791

2021-06-30T06:21:16.000Z

2022-03-29T07:36:13.000Z

src/api/datamanage/pro/lifecycle/data_trace/data_set_create.py

Chromico/bk-base

be822d9bbee544a958bed4831348185a75604791

2021-06-30T06:21:26.000Z

2022-03-29T12:42:26.000Z

# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making BK-BASE 蓝鲸基础平台 available. Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved. BK-BASE 蓝鲸基础平台 is licensed under the MIT License. License for BK-BASE 蓝鲸基础平台: -------------------------------------------------------------------- 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. """ from copy import deepcopy from datamanage.pro import exceptions as dm_pro_errors from datamanage.utils.api import MetaApi from datamanage.pro.utils.time import utc_to_local, str_to_datetime from datamanage.pro.lifecycle.models_dict import ( DATASET_CREATE_MAPPINGS, DATASET_CREATE_EVENT_INFO_DICT, DataTraceShowType, ComplexSearchBackendType, DataTraceFinishStatus, ) def get_dataset_create_info(dataset_id, dataset_type): """获取数据足迹中和数据创建相关信息 :param dataset_id: 数据id :param dataset_type: 数据类型 :return: 数据创建相关信息 :rtype: list """ # 1)从dgraph中获取数据创建相关信息 data_set_create_info_statement = """ { get_dataset_create_info(func: eq(%s, "%s")){created_by created_at} } """ % ( DATASET_CREATE_MAPPINGS[dataset_type]['data_set_pk'], dataset_id, ) query_result = MetaApi.complex_search( {"backend_type": ComplexSearchBackendType.DGRAPH.value, "statement": data_set_create_info_statement}, raw=True ) create_info_ret = query_result['data']['data']['get_dataset_create_info'] if not (isinstance(create_info_ret, list) and create_info_ret): raise dm_pro_errors.GetDataSetCreateInfoError(message_kv={'dataset_id': dataset_id}) # 2)得到格式化创建信息 create_trace_dict = deepcopy(DATASET_CREATE_EVENT_INFO_DICT) create_trace_dict.update( { "sub_type": dataset_type, "sub_type_alias": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], "description": DATASET_CREATE_MAPPINGS[dataset_type]['data_set_create_alias'], "created_at": utc_to_local(create_info_ret[0]['created_at']), "created_by": create_info_ret[0]['created_by'], "show_type": DataTraceShowType.DISPLAY.value, "datetime": str_to_datetime(utc_to_local(create_info_ret[0]['created_at'])), "status": DataTraceFinishStatus.STATUS, "status_alias": DataTraceFinishStatus.STATUS_ALIAS, } ) return [create_trace_dict]

8a20fc9b93bd3fc7e19c79190d5875b049bc7526

py

Python

build/lib/FinMesh/usgov/__init__.py

johnjdailey/FinMesh

64048b02bfec1a24de840877b38e82f4fa813d22

2020-08-14T16:09:54.000Z

2020-08-14T16:09:54.000Z

build/lib/FinMesh/usgov/__init__.py

johnjdailey/FinMesh

64048b02bfec1a24de840877b38e82f4fa813d22

build/lib/FinMesh/usgov/__init__.py

johnjdailey/FinMesh

64048b02bfec1a24de840877b38e82f4fa813d22

import os import requests import xmltodict import csv import json # # # # # # # # # # # FRED DATA BELOW # # # # # # # # # # # FRED_BASE_URL = 'https://api.stlouisfed.org/fred/' GEOFRED_BASE_URL = 'https://api.stlouisfed.org/geofred/' def append_fred_token(url): token = os.getenv('FRED_TOKEN') return f'{url}&api_key={token}' FRED_SERIES_OBS_URL = FRED_BASE_URL + 'series/observations?' def fred_series(series, file_type=None, realtime_start=None, realtime_end=None, limit=None, offset=None, sort_order=None, observation_start=None, observation_end=None, units=None, frequency=None, aggregation_method=None, output_type=None, vintage_dates=None): ## Returns time series historical data for the requested FRED data. url = FRED_SERIES_OBS_URL + f'series_id={series}' if file_type: url += f'&file_type={file_type}' if realtime_start: url += f'&realtime_start={realtime_start}' if realtime_end: url += f'&realtime_end={realtime_end}' if limit: url += f'&limit={limit}' if offset: url += f'&offset={offset}' if sort_order: url += f'&sort_order={sort_order}' if observation_start: url += f'&observation_start={observation_start}' if observation_end: url += f'&observation_end={observation_end}' if units: url += f'&units={units}' if frequency: url += f'&frequency={frequency}' if aggregation_method: url += f'&aggregation_method={aggregation_method}' if output_type: url += f'&output_type={output_type}' if vintage_dates: url += f'&vintage_dates={vintage_dates}' url = append_fred_token(url) result = requests.get(url) return result.text GEOFRED_SERIES_META_URL = GEOFRED_BASE_URL + 'series/group?' def geofred_series_meta(series_id, file_type=None): ## Returns meta data for the requested FRED data. url = GEOFRED_SERIES_META_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' url = append_fred_token(url) result = requests.get(url) return result.text GEOFRED_REGIONAL_SERIES_URL = GEOFRED_BASE_URL + 'series/data?' def geofred_regional_series(series_id, file_type=None, date=None, start_date=None): ## Returns the historical, geographically organized time series data for the requested FRED data. url = GEOFRED_REGIONAL_SERIES_URL + f'series_id={series_id}' if file_type: url += f'&file_type={file_type}' if date: url += f'&date={date}' if start_date: url += f'&start_date={start_date}' url = append_fred_token(url) result = requests.get(url) return result.text # # # # # # # # # # # # # # # # # GOVERNMENT YIELD CURVE DATA # # # # # # # # # # # # # # # # # GOV_YIELD_URL = 'https://data.treasury.gov/feed.svc/DailyTreasuryYieldCurveRateData?$filter=month(NEW_DATE)%20eq%204%20and%20year(NEW_DATE)%20eq%202019' def get_yield(): ## Returns government treasury bond yields. Organized in Python dictionary format by bond length. # Formatting of XML to Python Dict curve = requests.get(GOV_YIELD_URL) parse_curve = xmltodict.parse(curve.content) # This is based around retrieving the n last dates or average of n days. feed = parse_curve['feed'] entry = feed['entry'] last_entry = len(entry)-1 content = entry[last_entry]['content']['m:properties'] # Dict that contains the whole yield curve so there is no need to bring in each rate. yield_curve_values = { 'date' : entry[last_entry]['content']['m:properties']['d:NEW_DATE']['#text'], '1month' : float(content['d:BC_1MONTH']['#text']), '2month' : float(content['d:BC_2MONTH']['#text']), '3month' : float(content['d:BC_3MONTH']['#text']), '6month' : float(content['d:BC_6MONTH']['#text']), '1year' : float(content['d:BC_1YEAR']['#text']), '2year' : float(content['d:BC_2YEAR']['#text']), '3year' : float(content['d:BC_3YEAR']['#text']), '5year' : float(content['d:BC_5YEAR']['#text']), '10year' : float(content['d:BC_10YEAR']['#text']), '20year' : float(content['d:BC_20YEAR']['#text']), '30year' : float(content['d:BC_30YEAR']['#text']), } return yield_curve_values

8a29eefe067ae42942e4915562e64419af3d1cde

py

Python

scripts_python3/exchange/deleteExchange.py

bcvsolutions/winrm-ad-connector

9b45dae78d3ba24fe6b00e090f8763d3162e1570

scripts_python3/exchange/deleteExchange.py

bcvsolutions/winrm-ad-connector

9b45dae78d3ba24fe6b00e090f8763d3162e1570

2020-05-27T07:15:28.000Z

2020-12-17T05:22:54.000Z

scripts_python3/exchange/deleteExchange.py

bcvsolutions/winrm-ad-connector

9b45dae78d3ba24fe6b00e090f8763d3162e1570

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # All params from IdM is stored in environment and you can get them by os.environ["paramName"] import sys, os # this is needed for importing file winrm_wrapper from parent dir sys.path.append(os.path.join(os.path.dirname(__file__), '..')) import winrm_wrapper import codecs uid = os.environ["__UID__"] winrm_wrapper.writeLog("Delete start for " + uid) # Load PS script from file and replace params winrm_wrapper.writeLog("loading script") f = codecs.open(os.environ["script"], encoding='utf-8', mode='r') command = f.read() command = command.replace("$uid", uid) # Call wrapper winrm_wrapper.executeScript(os.environ["endpoint"], os.environ["authentication"], os.environ["user"], os.environ["password"], os.environ["caTrustPath"], os.environ["ignoreCaValidation"], command, uid) winrm_wrapper.writeLog("Delete end for " + uid) print("__UID__=" + uid) sys.exit()

8a2f400a7655554fbc57b5f622cd3afad8069e45

py

Python

gcp-python-fn/main.py

FuriKuri/faas-playground

52618e21064e327d2874d2b73cfe5fb247d3dd6e

2019-05-07T13:15:16.000Z

2019-05-07T13:15:16.000Z

gcp-python-fn/main.py

FuriKuri/faas-playground

52618e21064e327d2874d2b73cfe5fb247d3dd6e

gcp-python-fn/main.py

FuriKuri/faas-playground

52618e21064e327d2874d2b73cfe5fb247d3dd6e

def hello_world(request): request_json = request.get_json() name = 'World' if request_json and 'name' in request_json: name = request_json['name'] headers = { 'Access-Control-Allow-Origin': 'https://furikuri.net', 'Access-Control-Allow-Methods': 'GET, POST', 'Access-Control-Allow-Headers': 'Content-Type' } return ('Hello ' + name + '! From GCP + Python', 200, headers)

8a30c3ee79ce2efcb14fdc2c9e26c3ab71e499c1

py

Python

tests/test_i18n.py

vthriller/flask-kajiki

eadaa0aa45d23507066758b9e74091bddbc943c4

tests/test_i18n.py

vthriller/flask-kajiki

eadaa0aa45d23507066758b9e74091bddbc943c4

tests/test_i18n.py

vthriller/flask-kajiki

eadaa0aa45d23507066758b9e74091bddbc943c4

from kajiki import i18n from flask import request from flask_kajiki import render_template # N. B. settting i18n.gettext would affect tests from all modules, # so we test for request path that only functions from this module could set def gettext(s): if request.path == '/test_i18n': return s.upper() return s i18n.gettext = gettext def test_does_translations(app): """Callback interface is able to inject Translator filter""" with app.test_request_context(path='/test_i18n'): rendered = render_template('i18n.html') # TODO DOCTYPE; see also render_args expected = '<p>HELLO!</p>' assert rendered == expected

8a3543c746387ad12029585c2e306e26ec984737

py

Python

Deep_Q_Network/DQN_for_FrozenLake_Discrete_Domain.py

quangnguyendang/Reinforcement_Learning

2551ce95068561c553500838ee6b976f001ba667

Deep_Q_Network/DQN_for_FrozenLake_Discrete_Domain.py

quangnguyendang/Reinforcement_Learning

2551ce95068561c553500838ee6b976f001ba667

Deep_Q_Network/DQN_for_FrozenLake_Discrete_Domain.py

quangnguyendang/Reinforcement_Learning

2551ce95068561c553500838ee6b976f001ba667

# Credit to https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0 import gym import tensorflow as tf import numpy as np import matplotlib.pyplot as plt env = gym.make('FrozenLake-v0') # NEURAL NETWORK IMPLEMENTATION tf.reset_default_graph() # Feature vector for current state representation input1 = tf.placeholder(shape=[1, env.observation_space.n], dtype=tf.float32) # tf.Variable(<initial-value>, name=<optional-name>) # tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None) # Weighting W vector in range 0 - 0.01 (like the way Andrew Ng did with *0.01 W = tf.Variable(tf.random_uniform([env.observation_space.n, env.action_space.n], 0, 0.01)) # Qout with shape [1, env.action_space.n] - Action state value for Q[s, a] with every a available at a state Qout = tf.matmul(input1, W) # Greedy action at a state predict = tf.argmax(Qout, axis=1) # Feature vector for next state representation nextQ = tf.placeholder(shape=[1, env.action_space.n], dtype=tf.float32) # Entropy loss loss = tf.reduce_sum(tf.square(Qout - nextQ)) trainer = tf.train.GradientDescentOptimizer(learning_rate=0.1) updateModel = trainer.minimize(loss) # TRAIN THE NETWORK init = tf.global_variables_initializer() # Set learning parameters y = 0.99 e = 0.1 number_episodes = 2000 # List to store total rewards and steps per episode jList = [] rList = [] with tf.Session() as sess: sess.run(init) for i in range(number_episodes): print("Episode #{} is running!".format(i)) # First state s = env.reset() rAll = 0 d = False j = 0 # Q network while j < 200: # or While not d: j += 1 # Choose action by epsilon (e) greedy # print("s = ", s," --> Identity s:s+1: ", np.identity(env.observation_space.n)[s:s+1]) # s = 0 --> Identity s: s + 1: [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]] # s = 1 --> Identity s: s + 1: [[0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]] # Identity [s:s+1] is a one-hot vector # Therefore W is the actual Q value a, allQ = sess.run([predict, Qout], feed_dict={input1: np.identity(env.observation_space.n)[s:s+1]}) if np.random.rand(1) < e: a[0] = env.action_space.sample() s1, r, d, _ = env.step(a[0]) # Obtain next state Q value by feeding the new state throughout the network Q1 = sess.run(Qout, feed_dict={input1: np.identity(env.observation_space.n)[s1:s1+1]}) maxQ1 = np.max(Q1) targetQ = allQ targetQ[0, a[0]] = r + y * maxQ1 # Train our network using target and predicted Q values _, W1 = sess.run([updateModel, W], feed_dict={input1: np.identity(env.observation_space.n)[s:s+1], nextQ: targetQ}) rAll += r s = s1 if d: e = 1./((i/50) + 10) break jList.append(j) rList.append(rAll) env.close() plt.figure() plt.plot(rList, label="Return - Q Learning") plt.show() plt.figure() plt.plot(jList, label="Steps - Q Learning") plt.show() # ------------------------------------------------------------------------- # TABULAR IMPLEMENTATION # # # Set learning parameters # lr = 0.8 # y = 0.95 # number_episodes = 20000 # # # Initial table with all zeros # Q = np.zeros([env.observation_space.n, env.action_space.n]) # # # List of reward and steps per episode # rList = [] # for i in range (number_episodes): # print("Episode #{} is running!".format(i)) # s = env.reset() # rAll = 0 # d = False # j = 0 # while j < 99: # j += 1 # # Choose an action by greedily (with noise) picking from Q table # # Because of the noise, it is epsilon-greedy with epsilon decreasing over time # a = np.argmax(Q[s, :] + np.random.rand(1, env.action_space.n)*(1./(i + 1))) # s1, r, d, _ = env.step(a) # # env.render() # # # Update Q table with new knowledge # Q[s, a] = Q[s, a] + lr * (r + y * np.max(Q[s1, :]) - Q[s, a]) # rAll += r # s = s1 # if d: # break # rList.append(rAll)

8a3651a34d3b1893e6f70ebe64b9db39d329cd63

py

Python

testing/cross_language/util/supported_key_types.py

chanced/tink

9cc3a01ac0165b033ed51dc9d0812a98b4b6e305

testing/cross_language/util/supported_key_types.py

chanced/tink

9cc3a01ac0165b033ed51dc9d0812a98b4b6e305

testing/cross_language/util/supported_key_types.py

chanced/tink

9cc3a01ac0165b033ed51dc9d0812a98b4b6e305

2022-01-02T20:54:04.000Z

2022-01-02T20:54:04.000Z

# 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. """All KeyTypes and which languages support them.""" # Placeholder for import for type annotations from tink import aead from tink import daead from tink import hybrid from tink import mac from tink import prf from tink import signature from tink import streaming_aead from tink.proto import tink_pb2 # All languages supported by cross-language tests. ALL_LANGUAGES = ['cc', 'java', 'go', 'python'] # All KeyTypes (without the prefix 'type.googleapis.com/google.crypto.tink.') AEAD_KEY_TYPES = [ 'AesEaxKey', 'AesGcmKey', 'AesGcmSivKey', 'AesCtrHmacAeadKey', 'ChaCha20Poly1305Key', 'XChaCha20Poly1305Key', ] DAEAD_KEY_TYPES = ['AesSivKey'] STREAMING_AEAD_KEY_TYPES = [ 'AesCtrHmacStreamingKey', 'AesGcmHkdfStreamingKey', ] HYBRID_PRIVATE_KEY_TYPES = ['EciesAeadHkdfPrivateKey'] MAC_KEY_TYPES = [ 'AesCmacKey', 'HmacKey', ] SIGNATURE_KEY_TYPES = [ 'EcdsaPrivateKey', 'Ed25519PrivateKey', 'RsaSsaPkcs1PrivateKey', 'RsaSsaPssPrivateKey', ] PRF_KEY_TYPES = [ 'AesCmacPrfKey', 'HmacPrfKey', 'HkdfPrfKey', ] ALL_KEY_TYPES = ( AEAD_KEY_TYPES + DAEAD_KEY_TYPES + STREAMING_AEAD_KEY_TYPES + HYBRID_PRIVATE_KEY_TYPES + MAC_KEY_TYPES + SIGNATURE_KEY_TYPES + PRF_KEY_TYPES) # All languages that are supported by a KeyType SUPPORTED_LANGUAGES = { 'AesEaxKey': ['cc', 'java', 'python'], 'AesGcmKey': ['cc', 'java', 'go', 'python'], 'AesGcmSivKey': ['cc', 'python'], 'AesCtrHmacAeadKey': ['cc', 'java', 'go', 'python'], 'ChaCha20Poly1305Key': ['java', 'go'], 'XChaCha20Poly1305Key': ['cc', 'java', 'go', 'python'], 'AesSivKey': ['cc', 'java', 'go', 'python'], 'AesCtrHmacStreamingKey': ['cc', 'java', 'go', 'python'], 'AesGcmHkdfStreamingKey': ['cc', 'java', 'go', 'python'], 'EciesAeadHkdfPrivateKey': ['cc', 'java', 'go', 'python'], 'AesCmacKey': ['cc', 'java', 'go', 'python'], 'HmacKey': ['cc', 'java', 'go', 'python'], 'EcdsaPrivateKey': ['cc', 'java', 'go', 'python'], 'Ed25519PrivateKey': ['cc', 'java', 'go', 'python'], 'RsaSsaPkcs1PrivateKey': ['cc', 'java', 'python'], 'RsaSsaPssPrivateKey': ['cc', 'java', 'python'], 'AesCmacPrfKey': ['cc', 'java', 'go', 'python'], 'HmacPrfKey': ['cc', 'java', 'go', 'python'], 'HkdfPrfKey': ['cc', 'java', 'go', 'python'], } KEY_TYPE_FROM_URL = { 'type.googleapis.com/google.crypto.tink.' + key_type: key_type for key_type in ALL_KEY_TYPES} # For each KeyType, a list of all KeyTemplate Names that must be supported. KEY_TEMPLATE_NAMES = { 'AesEaxKey': ['AES128_EAX', 'AES256_EAX'], 'AesGcmKey': ['AES128_GCM', 'AES256_GCM'], 'AesGcmSivKey': ['AES128_GCM_SIV', 'AES256_GCM_SIV'], 'AesCtrHmacAeadKey': ['AES128_CTR_HMAC_SHA256', 'AES256_CTR_HMAC_SHA256'], 'ChaCha20Poly1305Key': ['CHACHA20_POLY1305'], 'XChaCha20Poly1305Key': ['XCHACHA20_POLY1305'], 'AesSivKey': ['AES256_SIV'], 'AesCtrHmacStreamingKey': [ 'AES128_CTR_HMAC_SHA256_4KB', 'AES256_CTR_HMAC_SHA256_4KB', ], 'AesGcmHkdfStreamingKey': [ 'AES128_GCM_HKDF_4KB', 'AES256_GCM_HKDF_4KB', 'AES256_GCM_HKDF_1MB', ], 'EciesAeadHkdfPrivateKey': [ 'ECIES_P256_HKDF_HMAC_SHA256_AES128_GCM', 'ECIES_P256_HKDF_HMAC_SHA256_AES128_CTR_HMAC_SHA256' ], 'AesCmacKey': ['AES_CMAC'], 'HmacKey': [ 'HMAC_SHA256_128BITTAG', 'HMAC_SHA256_256BITTAG', 'HMAC_SHA512_256BITTAG', 'HMAC_SHA512_512BITTAG' ], 'EcdsaPrivateKey': [ 'ECDSA_P256', 'ECDSA_P384', 'ECDSA_P384_SHA384', 'ECDSA_P521', 'ECDSA_P256_IEEE_P1363', 'ECDSA_P384_IEEE_P1363', 'ECDSA_P384_SHA384_IEEE_P1363', 'ECDSA_P521_IEEE_P1363' ], 'Ed25519PrivateKey': ['ED25519'], 'RsaSsaPkcs1PrivateKey': [ 'RSA_SSA_PKCS1_3072_SHA256_F4', 'RSA_SSA_PKCS1_4096_SHA512_F4' ], 'RsaSsaPssPrivateKey': [ 'RSA_SSA_PSS_3072_SHA256_SHA256_32_F4', 'RSA_SSA_PSS_4096_SHA512_SHA512_64_F4' ], 'AesCmacPrfKey': ['AES_CMAC_PRF'], 'HmacPrfKey': ['HMAC_PRF_SHA256', 'HMAC_PRF_SHA512'], 'HkdfPrfKey': ['HKDF_PRF_SHA256'], } # KeyTemplate (as Protobuf) for each KeyTemplate name. KEY_TEMPLATE = { 'AES128_EAX': aead.aead_key_templates.AES128_EAX, 'AES256_EAX': aead.aead_key_templates.AES256_EAX, 'AES128_GCM': aead.aead_key_templates.AES128_GCM, 'AES256_GCM': aead.aead_key_templates.AES256_GCM, 'AES128_GCM_SIV': aead.aead_key_templates.AES128_GCM_SIV, 'AES256_GCM_SIV': aead.aead_key_templates.AES256_GCM_SIV, 'AES128_CTR_HMAC_SHA256': aead.aead_key_templates.AES128_CTR_HMAC_SHA256, 'AES256_CTR_HMAC_SHA256': aead.aead_key_templates.AES256_CTR_HMAC_SHA256, 'CHACHA20_POLY1305': tink_pb2.KeyTemplate( type_url=('type.googleapis.com/google.crypto.tink.' + 'ChaCha20Poly1305Key'), output_prefix_type=tink_pb2.TINK), 'XCHACHA20_POLY1305': aead.aead_key_templates.XCHACHA20_POLY1305, 'AES256_SIV': daead.deterministic_aead_key_templates.AES256_SIV, 'AES128_CTR_HMAC_SHA256_4KB': streaming_aead.streaming_aead_key_templates.AES128_CTR_HMAC_SHA256_4KB, 'AES256_CTR_HMAC_SHA256_4KB': streaming_aead.streaming_aead_key_templates.AES256_CTR_HMAC_SHA256_4KB, 'AES128_GCM_HKDF_4KB': streaming_aead.streaming_aead_key_templates.AES128_GCM_HKDF_4KB, 'AES256_GCM_HKDF_4KB': streaming_aead.streaming_aead_key_templates.AES256_GCM_HKDF_4KB, 'AES256_GCM_HKDF_1MB': streaming_aead.streaming_aead_key_templates.AES256_GCM_HKDF_1MB, 'ECIES_P256_HKDF_HMAC_SHA256_AES128_GCM': hybrid.hybrid_key_templates.ECIES_P256_HKDF_HMAC_SHA256_AES128_GCM, 'ECIES_P256_HKDF_HMAC_SHA256_AES128_CTR_HMAC_SHA256': hybrid.hybrid_key_templates .ECIES_P256_HKDF_HMAC_SHA256_AES128_CTR_HMAC_SHA256, 'AES_CMAC': mac.mac_key_templates.AES_CMAC, 'HMAC_SHA256_128BITTAG': mac.mac_key_templates.HMAC_SHA256_128BITTAG, 'HMAC_SHA256_256BITTAG': mac.mac_key_templates.HMAC_SHA256_256BITTAG, 'HMAC_SHA512_256BITTAG': mac.mac_key_templates.HMAC_SHA512_256BITTAG, 'HMAC_SHA512_512BITTAG': mac.mac_key_templates.HMAC_SHA512_512BITTAG, 'ECDSA_P256': signature.signature_key_templates.ECDSA_P256, 'ECDSA_P384': signature.signature_key_templates.ECDSA_P384, 'ECDSA_P384_SHA384': signature.signature_key_templates.ECDSA_P384_SHA384, 'ECDSA_P521': signature.signature_key_templates.ECDSA_P521, 'ECDSA_P256_IEEE_P1363': signature.signature_key_templates.ECDSA_P256_IEEE_P1363, 'ECDSA_P384_IEEE_P1363': signature.signature_key_templates.ECDSA_P384_IEEE_P1363, 'ECDSA_P384_SHA384_IEEE_P1363': signature.signature_key_templates.ECDSA_P384_SHA384_IEEE_P1363, 'ECDSA_P521_IEEE_P1363': signature.signature_key_templates.ECDSA_P521_IEEE_P1363, 'ED25519': signature.signature_key_templates.ED25519, 'RSA_SSA_PKCS1_3072_SHA256_F4': signature.signature_key_templates.RSA_SSA_PKCS1_3072_SHA256_F4, 'RSA_SSA_PKCS1_4096_SHA512_F4': signature.signature_key_templates.RSA_SSA_PKCS1_4096_SHA512_F4, 'RSA_SSA_PSS_3072_SHA256_SHA256_32_F4': signature.signature_key_templates.RSA_SSA_PSS_3072_SHA256_SHA256_32_F4, 'RSA_SSA_PSS_4096_SHA512_SHA512_64_F4': signature.signature_key_templates.RSA_SSA_PSS_4096_SHA512_SHA512_64_F4, 'AES_CMAC_PRF': prf.prf_key_templates.AES_CMAC, 'HMAC_PRF_SHA256': prf.prf_key_templates.HMAC_SHA256, 'HMAC_PRF_SHA512': prf.prf_key_templates.HMAC_SHA512, 'HKDF_PRF_SHA256': prf.prf_key_templates.HKDF_SHA256, } SUPPORTED_LANGUAGES_BY_TEMPLATE_NAME = { name: SUPPORTED_LANGUAGES[KEY_TYPE_FROM_URL[template.type_url]] for name, template in KEY_TEMPLATE.items() }

8a43f4805ca2bfbefacf005fd91befea7f1c3e71

py

Python

gen-cfg.py

magetron/secure-flow-prototype

c683939620fec889f882ea095d2b27e3e4bb98fe

gen-cfg.py

magetron/secure-flow-prototype

c683939620fec889f882ea095d2b27e3e4bb98fe

gen-cfg.py

magetron/secure-flow-prototype

c683939620fec889f882ea095d2b27e3e4bb98fe

from staticfg import CFGBuilder userCfg = CFGBuilder().build_from_file('user.py', './auction/user.py') bidCfg = CFGBuilder().build_from_file('bid.py', './auction/bid.py') auctionCfg = CFGBuilder().build_from_file('auction.py','./auction/auction.py') #auctionEventCfg = CFGBuilder().build_from_file('auction_event.py','./auction/auction_event.py') bidCfg.build_visual('bidCfg', 'pdf') auctionCfg.build_visual('auctionCfg', 'pdf') #auctionEventCfg.build_visual('auctionEventCfg.pdf', 'pdf')

8a4ccded7f4f9f9be895e48e8a31955a7046241e

py

Python

dddppp/settings.py

tysonclugg/dddppp

22f52d671ca71c2df8d6ac566a1626e5f05b3159

dddppp/settings.py

tysonclugg/dddppp

22f52d671ca71c2df8d6ac566a1626e5f05b3159

dddppp/settings.py

tysonclugg/dddppp

22f52d671ca71c2df8d6ac566a1626e5f05b3159

""" Django settings for dddppp project. Generated by 'django-admin startproject' using Django 1.8.2. For more information on this file, see https://docs.djangoproject.com/en/1.8/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.8/ref/settings/ """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os import pkg_resources import pwd PROJECT_NAME = 'dddppp' # Enforce a valid POSIX environment # Get missing environment variables via call to pwd.getpwuid(...) _PW_CACHE = None _PW_MAP = { 'LOGNAME': 'pw_name', 'USER': 'pw_name', 'USERNAME': 'pw_name', 'UID': 'pw_uid', 'GID': 'pw_gid', 'HOME': 'pw_dir', 'SHELL': 'pw_shell', } for _missing_env in set(_PW_MAP).difference(os.environ): if _PW_CACHE is None: _PW_CACHE = pwd.getpwuid(os.getuid()) os.environ[_missing_env] = str(getattr(_PW_CACHE, _PW_MAP[_missing_env])) del _PW_CACHE, _PW_MAP, pwd BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.8/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'nfd_lvt=&k#h#$a^_l09j#5%s=mg+0aw=@t84ry$&rps43c33+' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [ 'localhost', ] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'dddp', 'dddp.server', 'dddp.accounts', 'dddppp.slides', ] for (requirement, pth) in [ ('django-extensions', 'django_extensions'), ]: try: pkg_resources.get_distribution(requirement) except ( pkg_resources.DistributionNotFound, pkg_resources.VersionConflict, ): continue INSTALLED_APPS.append(pth) MIDDLEWARE_CLASSES = [ 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', #'django.middleware.security.SecurityMiddleware', ] ROOT_URLCONF = 'dddppp.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'dddppp.wsgi.application' # Database # https://docs.djangoproject.com/en/1.8/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': os.environ.get('PGDATABASE', PROJECT_NAME), 'USER': os.environ.get('PGUSER', os.environ['LOGNAME']), 'PASSWORD': os.environ.get('DJANGO_DATABASE_PASSWORD', ''), 'HOST': os.environ.get('PGHOST', ''), 'PORT': os.environ.get('PGPORT', ''), } } # Internationalization # https://docs.djangoproject.com/en/1.8/topics/i18n/ LANGUAGE_CODE = 'en-au' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.8/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'static') STATICFILES_STORAGE = 'whitenoise.django.GzipManifestStaticFilesStorage' # django-secure # see: https://github.com/carljm/django-secure/ for more options SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https') #SECURE_SSL_REDIRECT = True SECURE_CONTENT_TYPE_NOSNIFF = True SECURE_FRAME_DENY = True SESSION_COOKIE_SECURE = True SESSION_COOKIE_HTTPONLY = True DDDPPP_CONTENT_TYPES = [] PROJ_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))

8a4fee7da31280c4ead726e734baac5bb3fc023e

py

Python

setup.py

dantas/wifi

e9cd6df7d3411f1532843999f6c33f45369c3fe4

2019-04-29T14:57:45.000Z

2019-04-29T14:57:45.000Z

setup.py

dantas/wifi

e9cd6df7d3411f1532843999f6c33f45369c3fe4

setup.py

dantas/wifi

e9cd6df7d3411f1532843999f6c33f45369c3fe4

#!/usr/bin/env python from setuptools import setup import os __doc__ = """ Command line tool and library wrappers around iwlist and /etc/network/interfaces. """ def read(fname): return open(os.path.join(os.path.dirname(__file__), fname)).read() install_requires = [ 'setuptools', 'pbkdf2', ] try: import argparse except: install_requires.append('argparse') version = '1.0.0' setup( name='wifi', version=version, author='Rocky Meza, Gavin Wahl', author_email='rockymeza@gmail.com', description=__doc__, long_description=read('README.rst'), packages=['wifi'], scripts=['bin/wifi'], test_suite='tests', platforms=["Debian"], license='BSD', install_requires=install_requires, classifiers=[ "License :: OSI Approved :: BSD License", "Topic :: System :: Networking", "Operating System :: POSIX :: Linux", "Environment :: Console", "Programming Language :: Python", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.3", ], data_files=[ ('/etc/bash_completion.d/', ['extras/wifi-completion.bash']), ] )

8a50f54c898793f1acb00252a2b2f5ed4e326667

py

Python

setup.py

skojaku/fastnode2vec

bb65f68469f00f489fa6744d35b8756200b4e285

2020-04-21T18:58:47.000Z

2022-03-26T22:41:45.000Z

setup.py

skojaku/fastnode2vec

bb65f68469f00f489fa6744d35b8756200b4e285

2020-04-21T22:37:17.000Z

2022-03-31T22:36:03.000Z

setup.py

skojaku/fastnode2vec

bb65f68469f00f489fa6744d35b8756200b4e285

2020-07-30T01:41:59.000Z

2022-01-19T10:13:01.000Z

#!/usr/bin/env python3 import os from setuptools import setup def read(fname): return open(os.path.join(os.path.dirname(__file__), fname)).read() setup( name="fastnode2vec", version="0.0.5", author="Louis Abraham", license="MIT", author_email="louis.abraham@yahoo.fr", description="Fast implementation of node2vec", long_description=read("README.md"), long_description_content_type="text/markdown", url="https://github.com/louisabraham/fastnode2vec", packages=["fastnode2vec"], install_requires=["numpy", "numba", "gensim", "click", "tqdm"], python_requires=">=3.6", entry_points={"console_scripts": ["fastnode2vec = fastnode2vec.cli:node2vec"]}, classifiers=["Topic :: Scientific/Engineering :: Artificial Intelligence"], )

8a54334c8ec0d2c98a16bb220c95973a631adeb1

py

Python

unit_13/26-Data_Structures/4_Merge_Sort_and_Linked_Lists/3_linked_list_merge_sort.py

duliodenis/python_master_degree

3ab76838ce2fc1606f28e988a3273dd27122a621

2019-03-14T01:39:32.000Z

2022-02-03T00:36:43.000Z

unit_13/26-Data_Structures/4_Merge_Sort_and_Linked_Lists/3_linked_list_merge_sort.py

duliodenis/python_master_degree

3ab76838ce2fc1606f28e988a3273dd27122a621

2020-04-10T01:01:16.000Z

2020-04-10T01:01:16.000Z

unit_13/26-Data_Structures/4_Merge_Sort_and_Linked_Lists/3_linked_list_merge_sort.py

duliodenis/python_master_degree

3ab76838ce2fc1606f28e988a3273dd27122a621

2019-01-02T20:46:05.000Z

2020-07-08T22:47:48.000Z

# # Data Structures: Linked List Merge Sort: The Conquer Step # Python Techdegree # # Created by Dulio Denis on 3/24/19. # Copyright (c) 2019 ddApps. All rights reserved. # ------------------------------------------------ from linked_list import Node, LinkedList def merge_sort(linked_list): ''' Sorts a linked list in ascending order. - Recuresively divide the linked list into sublists containing a single node - Repeatedly merge the sublists to produce sorted swublists until one remains Returns a sorted linked list. Runs in O(kn log n) time. ''' if linked_list.size() == 1: return linked_list elif linked_list.is_empty(): return linked_list left_half, right_half = split(linked_list) left = merge_sort(left_half) right = merge_sort(right_half) return merge(left, right) def split(linked_list): ''' Divide the unsorted list at the midpoint into sublists. Takes O(k log n) quasilinear time. ''' if linked_list == None or linked_list.head == None: left_half = linked_list right_half = None return left_half, right_half else: # non-empty linked lists size = linked_list.size() midpoint = size // 2 mid_node = linked_list.node_at_index(midpoint-1) left_half = linked_list right_half = LinkedList() right_half = mid_node.next_node mid_node.next_node = None return left_half, right_half def merge(left, right): ''' Merges two linked lists, sorting by data in nodes. Returns a new, merged list. Runs in O(n) linear time. ''' # Create a new linked list that contains nodes from # merging left and right merged = LinkedList() # Add a fake head that is discarded later to simplify code merged.add(0) # Set current to the head of the linked list current = merged.head # Obtain head nodes for left and right linked lists left_head = left.head right_head = right.head # Iterate over left and right until we reach the tail node # of either while left_head or right_head: # If the head node of the left is None, we're past the tail # Add the node from right to merged linkned list if left_head is None: current.next_node = right_head # Call next on right to set loop condition to False right_head = right_head.next_node # If the head node of right is None, we're past the tail # Add the tail node from left to merged linked list elif right_head is None: current.next_node = left_head # Call next on left to set loop condition to False left_head = left_head.next_node else: # Not at either tail node # Obtain node data to perform comparison operations left_data = left_head.data right_data = right_head.data # If data on left is less than right, set current to left node if left_data < right_data: current.next_node = left_head # Move left head to next node left_head = left_head.next_node # If data on left is greater than right, set current to right node else: current.next_node = right_head # Move right head to next node right_head = right_head.next_node # Move current to next node current = current.next_node # Discard fake head and set first merged node as head head = merged.head.next_node merged.head = head return merged l = LinkedList() l.add(10) l.add(2) l.add(44) l.add(15) l.add(200) print(l) sorted_linked_list = merge_sort(l) print(sorted_linked_list)

8a5438fd129b5b6996b6b2555c75bb6bb382b7d5

py

Python

nearpy/examples/example2.py

samyoo78/NearPy

1b534b864d320d875508e95cd2b76b6d8c07a90b

2015-01-02T21:45:28.000Z

2022-03-02T11:04:27.000Z

nearpy/examples/example2.py

samyoo78/NearPy

1b534b864d320d875508e95cd2b76b6d8c07a90b

2015-02-06T09:47:46.000Z

2021-09-26T01:45:26.000Z

nearpy/examples/example2.py

samyoo78/NearPy

1b534b864d320d875508e95cd2b76b6d8c07a90b

2015-01-07T04:45:41.000Z

2021-11-25T17:46:07.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2013 Ole Krause-Sparmann # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import numpy import scipy import unittest import time from nearpy import Engine from nearpy.distances import CosineDistance from nearpy.hashes import RandomBinaryProjections, HashPermutations, HashPermutationMapper def example2(): # Dimension of feature space DIM = 100 # Number of data points (dont do too much because of exact search) POINTS = 20000 ########################################################## print('Performing indexing with HashPermutations...') t0 = time.time() # Create permutations meta-hash permutations = HashPermutations('permut') # Create binary hash as child hash rbp_perm = RandomBinaryProjections('rbp_perm', 14) rbp_conf = {'num_permutation':50,'beam_size':10,'num_neighbour':100} # Add rbp as child hash of permutations hash permutations.add_child_hash(rbp_perm, rbp_conf) # Create engine engine_perm = Engine(DIM, lshashes=[permutations], distance=CosineDistance()) # First index some random vectors matrix = numpy.zeros((POINTS,DIM)) for i in range(POINTS): v = numpy.random.randn(DIM) matrix[i] = v engine_perm.store_vector(v) # Then update permuted index permutations.build_permuted_index() t1 = time.time() print('Indexing took %f seconds' % (t1-t0)) # Get random query vector query = numpy.random.randn(DIM) # Do random query on engine 3 print('\nNeighbour distances with HashPermutations:') print(' -> Candidate count is %d' % engine_perm.candidate_count(query)) results = engine_perm.neighbours(query) dists = [x[2] for x in results] print(dists) # Real neighbours print('\nReal neighbour distances:') query = query.reshape((DIM)) dists = CosineDistance().distance(matrix, query) dists = dists.reshape((-1,)) dists = sorted(dists) print(dists[:10]) ########################################################## print('\nPerforming indexing with HashPermutationMapper...') t0 = time.time() # Create permutations meta-hash permutations2 = HashPermutationMapper('permut2') # Create binary hash as child hash rbp_perm2 = RandomBinaryProjections('rbp_perm2', 14) # Add rbp as child hash of permutations hash permutations2.add_child_hash(rbp_perm2) # Create engine engine_perm2 = Engine(DIM, lshashes=[permutations2], distance=CosineDistance()) # First index some random vectors matrix = numpy.zeros((POINTS,DIM)) for i in range(POINTS): v = numpy.random.randn(DIM) matrix[i] = v engine_perm2.store_vector(v) t1 = time.time() print('Indexing took %f seconds' % (t1-t0)) # Get random query vector query = numpy.random.randn(DIM) # Do random query on engine 4 print('\nNeighbour distances with HashPermutationMapper:') print(' -> Candidate count is %d' % engine_perm2.candidate_count(query)) results = engine_perm2.neighbours(query) dists = [x[2] for x in results] print(dists) # Real neighbours print('\nReal neighbour distances:') query = query.reshape((DIM)) dists = CosineDistance().distance(matrix,query) dists = dists.reshape((-1,)) dists = sorted(dists) print(dists[:10]) ########################################################## print('\nPerforming indexing with multiple binary hashes...') t0 = time.time() hashes = [] for k in range(20): hashes.append(RandomBinaryProjections('rbp_%d' % k, 10)) # Create engine engine_rbps = Engine(DIM, lshashes=hashes, distance=CosineDistance()) # First index some random vectors matrix = numpy.zeros((POINTS,DIM)) for i in range(POINTS): v = numpy.random.randn(DIM) matrix[i] = v engine_rbps.store_vector(v) t1 = time.time() print('Indexing took %f seconds' % (t1-t0)) # Get random query vector query = numpy.random.randn(DIM) # Do random query on engine 4 print('\nNeighbour distances with multiple binary hashes:') print(' -> Candidate count is %d' % engine_rbps.candidate_count(query)) results = engine_rbps.neighbours(query) dists = [x[2] for x in results] print(dists) # Real neighbours print('\nReal neighbour distances:') query = query.reshape((DIM)) dists = CosineDistance().distance(matrix,query) dists = dists.reshape((-1,)) dists = sorted(dists) print(dists[:10]) ##########################################################

8a60852354e6415290eaf2e5371028a21ee46376

py

Python

models/AI-Model-Zoo/VAI-1.3-Model-Zoo-Code/PyTorch/pt_personreid-res18_market1501_176_80_1.1G_1.3/code/core/data_manager.py

guochunhe/Vitis-AI

e86b6efae11f8703ee647e4a99004dc980b84989

2020-12-18T14:49:19.000Z

2020-12-18T14:49:19.000Z

models/AI-Model-Zoo/VAI-1.3-Model-Zoo-Code/PyTorch/pt_personreid-res50_market1501_256_128_5.4G_1.3/code/core/data_manager.py

guochunhe/Vitis-AI

e86b6efae11f8703ee647e4a99004dc980b84989

models/AI-Model-Zoo/VAI-1.3-Model-Zoo-Code/PyTorch/pt_personreid-res50_market1501_256_128_5.4G_1.3/code/core/data_manager.py

guochunhe/Vitis-AI

e86b6efae11f8703ee647e4a99004dc980b84989

# Copyright 2019 Xilinx Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function, absolute_import import glob import re from os import path as osp from .market1501 import Market1501 __factory = { 'market1501': Market1501 } def get_names(): return list(__factory.keys()) def init_dataset(name, *args, **kwargs): if name not in __factory.keys(): raise KeyError("Unknown datasets: {}".format(name)) return __factory[name](*args, **kwargs)

8a62e622419e3b5175ed6a324e076188b956be4c

py

Python

azure-devops/azext_devops/vstsCompressed/service_hooks/v4_0/models/__init__.py

vijayraavi/azure-devops-cli-extension

88f1420c5815cb09bea15b050f4c553e0f326dad

azure-devops/azext_devops/vstsCompressed/service_hooks/v4_0/models/__init__.py

vijayraavi/azure-devops-cli-extension

88f1420c5815cb09bea15b050f4c553e0f326dad

2020-04-27T07:45:19.000Z

2021-04-05T07:27:15.000Z

azure-devops/azext_devops/vstsCompressed/service_hooks/v4_0/models/__init__.py

vijayraavi/azure-devops-cli-extension

88f1420c5815cb09bea15b050f4c553e0f326dad

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # Generated file, DO NOT EDIT # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------------------------- from .models import Consumer from .models import ConsumerAction from .models import Event from .models import EventTypeDescriptor from .models import ExternalConfigurationDescriptor from .models import FormattedEventMessage from .models import IdentityRef from .models import InputDescriptor from .models import InputFilter from .models import InputFilterCondition from .models import InputValidation from .models import InputValue from .models import InputValues from .models import InputValuesError from .models import InputValuesQuery from .models import Notification from .models import NotificationDetails from .models import NotificationResultsSummaryDetail from .models import NotificationsQuery from .models import NotificationSummary from .models import Publisher from .models import PublisherEvent from .models import PublishersQuery from .models import ReferenceLinks from .models import ResourceContainer from .models import SessionToken from .models import Subscription from .models import SubscriptionsQuery from .models import VersionedResource __all__ = [ 'Consumer', 'ConsumerAction', 'Event', 'EventTypeDescriptor', 'ExternalConfigurationDescriptor', 'FormattedEventMessage', 'IdentityRef', 'InputDescriptor', 'InputFilter', 'InputFilterCondition', 'InputValidation', 'InputValue', 'InputValues', 'InputValuesError', 'InputValuesQuery', 'Notification', 'NotificationDetails', 'NotificationResultsSummaryDetail', 'NotificationsQuery', 'NotificationSummary', 'Publisher', 'PublisherEvent', 'PublishersQuery', 'ReferenceLinks', 'ResourceContainer', 'SessionToken', 'Subscription', 'SubscriptionsQuery', 'VersionedResource', ]

8a678b6dfe1f80688ee851169cd059181b03b309

py

Python

electrum/dnssec.py

Jesusown/electrum

0df05dd914c823acae1828cad3b20bdeb13150e9

2015-01-02T17:05:36.000Z

2022-03-29T07:28:29.000Z

electrum/dnssec.py

Jesusown/electrum

0df05dd914c823acae1828cad3b20bdeb13150e9

2015-01-01T21:20:25.000Z

2022-03-31T23:55:01.000Z

electrum/dnssec.py

Jesusown/electrum

0df05dd914c823acae1828cad3b20bdeb13150e9

2015-01-02T18:31:25.000Z

2022-03-28T15:35:03.000Z

#!/usr/bin/env python # # Electrum - lightweight Bitcoin client # Copyright (C) 2015 Thomas Voegtlin # # 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. # Check DNSSEC trust chain. # Todo: verify expiration dates # # Based on # http://backreference.org/2010/11/17/dnssec-verification-with-dig/ # https://github.com/rthalley/dnspython/blob/master/tests/test_dnssec.py import dns import dns.name import dns.query import dns.dnssec import dns.message import dns.resolver import dns.rdatatype import dns.rdtypes.ANY.NS import dns.rdtypes.ANY.CNAME import dns.rdtypes.ANY.DLV import dns.rdtypes.ANY.DNSKEY import dns.rdtypes.ANY.DS import dns.rdtypes.ANY.NSEC import dns.rdtypes.ANY.NSEC3 import dns.rdtypes.ANY.NSEC3PARAM import dns.rdtypes.ANY.RRSIG import dns.rdtypes.ANY.SOA import dns.rdtypes.ANY.TXT import dns.rdtypes.IN.A import dns.rdtypes.IN.AAAA from .logging import get_logger _logger = get_logger(__name__) # hard-coded trust anchors (root KSKs) trust_anchors = [ # KSK-2017: dns.rrset.from_text('.', 1 , 'IN', 'DNSKEY', '257 3 8 AwEAAaz/tAm8yTn4Mfeh5eyI96WSVexTBAvkMgJzkKTOiW1vkIbzxeF3+/4RgWOq7HrxRixHlFlExOLAJr5emLvN7SWXgnLh4+B5xQlNVz8Og8kvArMtNROxVQuCaSnIDdD5LKyWbRd2n9WGe2R8PzgCmr3EgVLrjyBxWezF0jLHwVN8efS3rCj/EWgvIWgb9tarpVUDK/b58Da+sqqls3eNbuv7pr+eoZG+SrDK6nWeL3c6H5Apxz7LjVc1uTIdsIXxuOLYA4/ilBmSVIzuDWfdRUfhHdY6+cn8HFRm+2hM8AnXGXws9555KrUB5qihylGa8subX2Nn6UwNR1AkUTV74bU='), # KSK-2010: dns.rrset.from_text('.', 15202, 'IN', 'DNSKEY', '257 3 8 AwEAAagAIKlVZrpC6Ia7gEzahOR+9W29euxhJhVVLOyQbSEW0O8gcCjF FVQUTf6v58fLjwBd0YI0EzrAcQqBGCzh/RStIoO8g0NfnfL2MTJRkxoX bfDaUeVPQuYEhg37NZWAJQ9VnMVDxP/VHL496M/QZxkjf5/Efucp2gaD X6RS6CXpoY68LsvPVjR0ZSwzz1apAzvN9dlzEheX7ICJBBtuA6G3LQpz W5hOA2hzCTMjJPJ8LbqF6dsV6DoBQzgul0sGIcGOYl7OyQdXfZ57relS Qageu+ipAdTTJ25AsRTAoub8ONGcLmqrAmRLKBP1dfwhYB4N7knNnulq QxA+Uk1ihz0='), ] def _check_query(ns, sub, _type, keys): q = dns.message.make_query(sub, _type, want_dnssec=True) response = dns.query.tcp(q, ns, timeout=5) assert response.rcode() == 0, 'No answer' answer = response.answer assert len(answer) != 0, ('No DNS record found', sub, _type) assert len(answer) != 1, ('No DNSSEC record found', sub, _type) if answer[0].rdtype == dns.rdatatype.RRSIG: rrsig, rrset = answer elif answer[1].rdtype == dns.rdatatype.RRSIG: rrset, rrsig = answer else: raise Exception('No signature set in record') if keys is None: keys = {dns.name.from_text(sub):rrset} dns.dnssec.validate(rrset, rrsig, keys) return rrset def _get_and_validate(ns, url, _type): # get trusted root key root_rrset = None for dnskey_rr in trust_anchors: try: # Check if there is a valid signature for the root dnskey root_rrset = _check_query(ns, '', dns.rdatatype.DNSKEY, {dns.name.root: dnskey_rr}) break except dns.dnssec.ValidationFailure: # It's OK as long as one key validates continue if not root_rrset: raise dns.dnssec.ValidationFailure('None of the trust anchors found in DNS') keys = {dns.name.root: root_rrset} # top-down verification parts = url.split('.') for i in range(len(parts), 0, -1): sub = '.'.join(parts[i-1:]) name = dns.name.from_text(sub) # If server is authoritative, don't fetch DNSKEY query = dns.message.make_query(sub, dns.rdatatype.NS) response = dns.query.udp(query, ns, 3) assert response.rcode() == dns.rcode.NOERROR, "query error" rrset = response.authority[0] if len(response.authority) > 0 else response.answer[0] rr = rrset[0] if rr.rdtype == dns.rdatatype.SOA: continue # get DNSKEY (self-signed) rrset = _check_query(ns, sub, dns.rdatatype.DNSKEY, None) # get DS (signed by parent) ds_rrset = _check_query(ns, sub, dns.rdatatype.DS, keys) # verify that a signed DS validates DNSKEY for ds in ds_rrset: for dnskey in rrset: htype = 'SHA256' if ds.digest_type == 2 else 'SHA1' good_ds = dns.dnssec.make_ds(name, dnskey, htype) if ds == good_ds: break else: continue break else: raise Exception("DS does not match DNSKEY") # set key for next iteration keys = {name: rrset} # get TXT record (signed by zone) rrset = _check_query(ns, url, _type, keys) return rrset def query(url, rtype): # 8.8.8.8 is Google's public DNS server nameservers = ['8.8.8.8'] ns = nameservers[0] try: out = _get_and_validate(ns, url, rtype) validated = True except Exception as e: _logger.info(f"DNSSEC error: {repr(e)}") out = dns.resolver.resolve(url, rtype) validated = False return out, validated

8a681bd50a01e317584f76158f59adbe05396fb6

py

Python

specs/d3d11.py

ds-hwang/apitrace

b74347ebae0d033a013c4de3efb0e9165e9cea8f

2017-06-07T15:28:36.000Z

2017-06-07T15:28:36.000Z

specs/d3d11.py

jciehl/apitrace

0e01acc36de14e9ca7c0ced258767ffb99ac96ea

specs/d3d11.py

jciehl/apitrace

0e01acc36de14e9ca7c0ced258767ffb99ac96ea

2021-05-21T18:27:29.000Z

2021-05-21T18:27:29.000Z

########################################################################## # # Copyright 2012 Jose Fonseca # All Rights Reserved. # # 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. # ##########################################################################/ from dxgi import * from d3dcommon import * from d3d11sdklayers import * HRESULT = MAKE_HRESULT([ "D3D11_ERROR_FILE_NOT_FOUND", "D3D11_ERROR_TOO_MANY_UNIQUE_STATE_OBJECTS", "D3D11_ERROR_TOO_MANY_UNIQUE_VIEW_OBJECTS", "D3D11_ERROR_DEFERRED_CONTEXT_MAP_WITHOUT_INITIAL_DISCARD", "D3DERR_INVALIDCALL", "D3DERR_WASSTILLDRAWING", ]) ID3D11DepthStencilState = Interface("ID3D11DepthStencilState", ID3D11DeviceChild) ID3D11BlendState = Interface("ID3D11BlendState", ID3D11DeviceChild) ID3D11RasterizerState = Interface("ID3D11RasterizerState", ID3D11DeviceChild) ID3D11Resource = Interface("ID3D11Resource", ID3D11DeviceChild) ID3D11Buffer = Interface("ID3D11Buffer", ID3D11Resource) ID3D11Texture1D = Interface("ID3D11Texture1D", ID3D11Resource) ID3D11Texture2D = Interface("ID3D11Texture2D", ID3D11Resource) ID3D11Texture3D = Interface("ID3D11Texture3D", ID3D11Resource) ID3D11View = Interface("ID3D11View", ID3D11DeviceChild) ID3D11ShaderResourceView = Interface("ID3D11ShaderResourceView", ID3D11View) ID3D11RenderTargetView = Interface("ID3D11RenderTargetView", ID3D11View) ID3D11DepthStencilView = Interface("ID3D11DepthStencilView", ID3D11View) ID3D11UnorderedAccessView = Interface("ID3D11UnorderedAccessView", ID3D11View) ID3D11VertexShader = Interface("ID3D11VertexShader", ID3D11DeviceChild) ID3D11HullShader = Interface("ID3D11HullShader", ID3D11DeviceChild) ID3D11DomainShader = Interface("ID3D11DomainShader", ID3D11DeviceChild) ID3D11GeometryShader = Interface("ID3D11GeometryShader", ID3D11DeviceChild) ID3D11PixelShader = Interface("ID3D11PixelShader", ID3D11DeviceChild) ID3D11ComputeShader = Interface("ID3D11ComputeShader", ID3D11DeviceChild) ID3D11InputLayout = Interface("ID3D11InputLayout", ID3D11DeviceChild) ID3D11SamplerState = Interface("ID3D11SamplerState", ID3D11DeviceChild) ID3D11Asynchronous = Interface("ID3D11Asynchronous", ID3D11DeviceChild) ID3D11Query = Interface("ID3D11Query", ID3D11Asynchronous) ID3D11Predicate = Interface("ID3D11Predicate", ID3D11Query) ID3D11Counter = Interface("ID3D11Counter", ID3D11Asynchronous) ID3D11ClassInstance = Interface("ID3D11ClassInstance", ID3D11DeviceChild) ID3D11ClassLinkage = Interface("ID3D11ClassLinkage", ID3D11DeviceChild) ID3D11CommandList = Interface("ID3D11CommandList", ID3D11DeviceChild) ID3D11Device = Interface("ID3D11Device", IUnknown) D3D11_INPUT_CLASSIFICATION = Enum("D3D11_INPUT_CLASSIFICATION", [ "D3D11_INPUT_PER_VERTEX_DATA", "D3D11_INPUT_PER_INSTANCE_DATA", ]) D3D11_INPUT_ELEMENT_ALIGNED_BYTE_OFFSET = FakeEnum(UINT, [ "D3D11_APPEND_ALIGNED_ELEMENT", ]) D3D11_INPUT_ELEMENT_DESC = Struct("D3D11_INPUT_ELEMENT_DESC", [ (LPCSTR, "SemanticName"), (UINT, "SemanticIndex"), (DXGI_FORMAT, "Format"), (UINT, "InputSlot"), (D3D11_INPUT_ELEMENT_ALIGNED_BYTE_OFFSET, "AlignedByteOffset"), (D3D11_INPUT_CLASSIFICATION, "InputSlotClass"), (UINT, "InstanceDataStepRate"), ]) D3D11_FILL_MODE = Enum("D3D11_FILL_MODE", [ "D3D11_FILL_WIREFRAME", "D3D11_FILL_SOLID", ]) D3D11_PRIMITIVE_TOPOLOGY = Enum("D3D11_PRIMITIVE_TOPOLOGY", [ "D3D11_PRIMITIVE_TOPOLOGY_UNDEFINED", "D3D11_PRIMITIVE_TOPOLOGY_POINTLIST", "D3D11_PRIMITIVE_TOPOLOGY_LINELIST", "D3D11_PRIMITIVE_TOPOLOGY_LINESTRIP", "D3D11_PRIMITIVE_TOPOLOGY_TRIANGLELIST", "D3D11_PRIMITIVE_TOPOLOGY_TRIANGLESTRIP", "D3D11_PRIMITIVE_TOPOLOGY_LINELIST_ADJ", "D3D11_PRIMITIVE_TOPOLOGY_LINESTRIP_ADJ", "D3D11_PRIMITIVE_TOPOLOGY_TRIANGLELIST_ADJ", "D3D11_PRIMITIVE_TOPOLOGY_TRIANGLESTRIP_ADJ", "D3D11_PRIMITIVE_TOPOLOGY_1_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_2_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_3_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_4_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_5_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_6_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_7_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_8_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_9_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_10_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_11_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_12_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_13_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_14_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_15_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_16_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_17_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_18_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_19_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_20_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_21_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_22_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_23_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_24_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_25_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_26_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_27_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_28_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_29_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_30_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_31_CONTROL_POINT_PATCHLIST", "D3D11_PRIMITIVE_TOPOLOGY_32_CONTROL_POINT_PATCHLIST", ]) D3D11_PRIMITIVE = Enum("D3D11_PRIMITIVE", [ "D3D11_PRIMITIVE_UNDEFINED", "D3D11_PRIMITIVE_POINT", "D3D11_PRIMITIVE_LINE", "D3D11_PRIMITIVE_TRIANGLE", "D3D11_PRIMITIVE_LINE_ADJ", "D3D11_PRIMITIVE_TRIANGLE_ADJ", "D3D11_PRIMITIVE_1_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_2_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_3_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_4_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_5_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_6_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_7_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_8_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_9_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_10_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_11_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_12_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_13_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_14_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_15_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_16_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_17_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_18_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_19_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_20_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_21_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_22_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_23_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_24_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_25_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_26_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_27_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_28_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_29_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_30_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_31_CONTROL_POINT_PATCH", "D3D11_PRIMITIVE_32_CONTROL_POINT_PATCH", ]) D3D11_CULL_MODE = Enum("D3D11_CULL_MODE", [ "D3D11_CULL_NONE", "D3D11_CULL_FRONT", "D3D11_CULL_BACK", ]) D3D11_SO_DECLARATION_ENTRY = Struct("D3D11_SO_DECLARATION_ENTRY", [ (UINT, "Stream"), (LPCSTR, "SemanticName"), (UINT, "SemanticIndex"), (BYTE, "StartComponent"), (BYTE, "ComponentCount"), (BYTE, "OutputSlot"), ]) D3D11_VIEWPORT = Struct("D3D11_VIEWPORT", [ (FLOAT, "TopLeftX"), (FLOAT, "TopLeftY"), (FLOAT, "Width"), (FLOAT, "Height"), (FLOAT, "MinDepth"), (FLOAT, "MaxDepth"), ]) D3D11_RESOURCE_DIMENSION = Enum("D3D11_RESOURCE_DIMENSION", [ "D3D11_RESOURCE_DIMENSION_UNKNOWN", "D3D11_RESOURCE_DIMENSION_BUFFER", "D3D11_RESOURCE_DIMENSION_TEXTURE1D", "D3D11_RESOURCE_DIMENSION_TEXTURE2D", "D3D11_RESOURCE_DIMENSION_TEXTURE3D", ]) D3D11_SRV_DIMENSION = Enum("D3D11_SRV_DIMENSION", [ "D3D11_SRV_DIMENSION_UNKNOWN", "D3D11_SRV_DIMENSION_BUFFER", "D3D11_SRV_DIMENSION_TEXTURE1D", "D3D11_SRV_DIMENSION_TEXTURE1DARRAY", "D3D11_SRV_DIMENSION_TEXTURE2D", "D3D11_SRV_DIMENSION_TEXTURE2DARRAY", "D3D11_SRV_DIMENSION_TEXTURE2DMS", "D3D11_SRV_DIMENSION_TEXTURE2DMSARRAY", "D3D11_SRV_DIMENSION_TEXTURE3D", "D3D11_SRV_DIMENSION_TEXTURECUBE", "D3D11_SRV_DIMENSION_TEXTURECUBEARRAY", "D3D11_SRV_DIMENSION_BUFFEREX", ]) D3D11_DSV_DIMENSION = Enum("D3D11_DSV_DIMENSION", [ "D3D11_DSV_DIMENSION_UNKNOWN", "D3D11_DSV_DIMENSION_TEXTURE1D", "D3D11_DSV_DIMENSION_TEXTURE1DARRAY", "D3D11_DSV_DIMENSION_TEXTURE2D", "D3D11_DSV_DIMENSION_TEXTURE2DARRAY", "D3D11_DSV_DIMENSION_TEXTURE2DMS", "D3D11_DSV_DIMENSION_TEXTURE2DMSARRAY", ]) D3D11_RTV_DIMENSION = Enum("D3D11_RTV_DIMENSION", [ "D3D11_RTV_DIMENSION_UNKNOWN", "D3D11_RTV_DIMENSION_BUFFER", "D3D11_RTV_DIMENSION_TEXTURE1D", "D3D11_RTV_DIMENSION_TEXTURE1DARRAY", "D3D11_RTV_DIMENSION_TEXTURE2D", "D3D11_RTV_DIMENSION_TEXTURE2DARRAY", "D3D11_RTV_DIMENSION_TEXTURE2DMS", "D3D11_RTV_DIMENSION_TEXTURE2DMSARRAY", "D3D11_RTV_DIMENSION_TEXTURE3D", ]) D3D11_UAV_DIMENSION = Enum("D3D11_UAV_DIMENSION", [ "D3D11_UAV_DIMENSION_UNKNOWN", "D3D11_UAV_DIMENSION_BUFFER", "D3D11_UAV_DIMENSION_TEXTURE1D", "D3D11_UAV_DIMENSION_TEXTURE1DARRAY", "D3D11_UAV_DIMENSION_TEXTURE2D", "D3D11_UAV_DIMENSION_TEXTURE2DARRAY", "D3D11_UAV_DIMENSION_TEXTURE3D", ]) D3D11_USAGE = Enum("D3D11_USAGE", [ "D3D11_USAGE_DEFAULT", "D3D11_USAGE_IMMUTABLE", "D3D11_USAGE_DYNAMIC", "D3D11_USAGE_STAGING", ]) D3D11_BIND_FLAG = Flags(UINT, [ "D3D11_BIND_VERTEX_BUFFER", "D3D11_BIND_INDEX_BUFFER", "D3D11_BIND_CONSTANT_BUFFER", "D3D11_BIND_SHADER_RESOURCE", "D3D11_BIND_STREAM_OUTPUT", "D3D11_BIND_RENDER_TARGET", "D3D11_BIND_DEPTH_STENCIL", "D3D11_BIND_UNORDERED_ACCESS", ]) D3D11_CPU_ACCESS_FLAG = Flags(UINT, [ "D3D11_CPU_ACCESS_WRITE", "D3D11_CPU_ACCESS_READ", ]) D3D11_RESOURCE_MISC_FLAG = Flags(UINT, [ "D3D11_RESOURCE_MISC_GENERATE_MIPS", "D3D11_RESOURCE_MISC_SHARED", "D3D11_RESOURCE_MISC_TEXTURECUBE", "D3D11_RESOURCE_MISC_DRAWINDIRECT_ARGS", "D3D11_RESOURCE_MISC_BUFFER_ALLOW_RAW_VIEWS", "D3D11_RESOURCE_MISC_BUFFER_STRUCTURED", "D3D11_RESOURCE_MISC_RESOURCE_CLAMP", "D3D11_RESOURCE_MISC_SHARED_KEYEDMUTEX", "D3D11_RESOURCE_MISC_GDI_COMPATIBLE", ]) D3D11_MAP = Enum("D3D11_MAP", [ "D3D11_MAP_READ", "D3D11_MAP_WRITE", "D3D11_MAP_READ_WRITE", "D3D11_MAP_WRITE_DISCARD", "D3D11_MAP_WRITE_NO_OVERWRITE", ]) D3D11_MAP_FLAG = Flags(UINT, [ "D3D11_MAP_FLAG_DO_NOT_WAIT", ]) D3D11_RAISE_FLAG = Flags(UINT, [ "D3D11_RAISE_FLAG_DRIVER_INTERNAL_ERROR", ]) D3D11_CLEAR_FLAG = Flags(UINT, [ "D3D11_CLEAR_DEPTH", "D3D11_CLEAR_STENCIL", ]) D3D11_RECT = Alias("D3D11_RECT", RECT) D3D11_BOX = Struct("D3D11_BOX", [ (UINT, "left"), (UINT, "top"), (UINT, "front"), (UINT, "right"), (UINT, "bottom"), (UINT, "back"), ]) ID3D11DeviceChild.methods += [ StdMethod(Void, "GetDevice", [Out(Pointer(ObjPointer(ID3D11Device)), "ppDevice")]), StdMethod(HRESULT, "GetPrivateData", [(REFGUID, "guid"), Out(Pointer(UINT), "pDataSize"), Out(OpaquePointer(Void), "pData")]), StdMethod(HRESULT, "SetPrivateData", [(REFGUID, "guid"), (UINT, "DataSize"), (OpaqueBlob(Const(Void), "DataSize"), "pData")]), StdMethod(HRESULT, "SetPrivateDataInterface", [(REFGUID, "guid"), (OpaquePointer(Const(IUnknown)), "pData")]), ] D3D11_COMPARISON_FUNC = Enum("D3D11_COMPARISON_FUNC", [ "D3D11_COMPARISON_NEVER", "D3D11_COMPARISON_LESS", "D3D11_COMPARISON_EQUAL", "D3D11_COMPARISON_LESS_EQUAL", "D3D11_COMPARISON_GREATER", "D3D11_COMPARISON_NOT_EQUAL", "D3D11_COMPARISON_GREATER_EQUAL", "D3D11_COMPARISON_ALWAYS", ]) D3D11_DEPTH_WRITE_MASK = Enum("D3D11_DEPTH_WRITE_MASK", [ "D3D11_DEPTH_WRITE_MASK_ZERO", "D3D11_DEPTH_WRITE_MASK_ALL", ]) D3D11_STENCIL_OP = Enum("D3D11_STENCIL_OP", [ "D3D11_STENCIL_OP_KEEP", "D3D11_STENCIL_OP_ZERO", "D3D11_STENCIL_OP_REPLACE", "D3D11_STENCIL_OP_INCR_SAT", "D3D11_STENCIL_OP_DECR_SAT", "D3D11_STENCIL_OP_INVERT", "D3D11_STENCIL_OP_INCR", "D3D11_STENCIL_OP_DECR", ]) D3D11_DEPTH_STENCILOP_DESC = Struct("D3D11_DEPTH_STENCILOP_DESC", [ (D3D11_STENCIL_OP, "StencilFailOp"), (D3D11_STENCIL_OP, "StencilDepthFailOp"), (D3D11_STENCIL_OP, "StencilPassOp"), (D3D11_COMPARISON_FUNC, "StencilFunc"), ]) D3D11_DEPTH_STENCIL_DESC = Struct("D3D11_DEPTH_STENCIL_DESC", [ (BOOL, "DepthEnable"), (D3D11_DEPTH_WRITE_MASK, "DepthWriteMask"), (D3D11_COMPARISON_FUNC, "DepthFunc"), (BOOL, "StencilEnable"), (UINT8, "StencilReadMask"), (UINT8, "StencilWriteMask"), (D3D11_DEPTH_STENCILOP_DESC, "FrontFace"), (D3D11_DEPTH_STENCILOP_DESC, "BackFace"), ]) ID3D11DepthStencilState.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_DEPTH_STENCIL_DESC), "pDesc")]), ] D3D11_BLEND = Enum("D3D11_BLEND", [ "D3D11_BLEND_ZERO", "D3D11_BLEND_ONE", "D3D11_BLEND_SRC_COLOR", "D3D11_BLEND_INV_SRC_COLOR", "D3D11_BLEND_SRC_ALPHA", "D3D11_BLEND_INV_SRC_ALPHA", "D3D11_BLEND_DEST_ALPHA", "D3D11_BLEND_INV_DEST_ALPHA", "D3D11_BLEND_DEST_COLOR", "D3D11_BLEND_INV_DEST_COLOR", "D3D11_BLEND_SRC_ALPHA_SAT", "D3D11_BLEND_BLEND_FACTOR", "D3D11_BLEND_INV_BLEND_FACTOR", "D3D11_BLEND_SRC1_COLOR", "D3D11_BLEND_INV_SRC1_COLOR", "D3D11_BLEND_SRC1_ALPHA", "D3D11_BLEND_INV_SRC1_ALPHA", ]) D3D11_BLEND_OP = Enum("D3D11_BLEND_OP", [ "D3D11_BLEND_OP_ADD", "D3D11_BLEND_OP_SUBTRACT", "D3D11_BLEND_OP_REV_SUBTRACT", "D3D11_BLEND_OP_MIN", "D3D11_BLEND_OP_MAX", ]) D3D11_COLOR_WRITE_ENABLE = Enum("D3D11_COLOR_WRITE_ENABLE", [ "D3D11_COLOR_WRITE_ENABLE_ALL", "D3D11_COLOR_WRITE_ENABLE_RED", "D3D11_COLOR_WRITE_ENABLE_GREEN", "D3D11_COLOR_WRITE_ENABLE_BLUE", "D3D11_COLOR_WRITE_ENABLE_ALPHA", ]) D3D11_RENDER_TARGET_BLEND_DESC = Struct("D3D11_RENDER_TARGET_BLEND_DESC", [ (BOOL, "BlendEnable"), (D3D11_BLEND, "SrcBlend"), (D3D11_BLEND, "DestBlend"), (D3D11_BLEND_OP, "BlendOp"), (D3D11_BLEND, "SrcBlendAlpha"), (D3D11_BLEND, "DestBlendAlpha"), (D3D11_BLEND_OP, "BlendOpAlpha"), (UINT8, "RenderTargetWriteMask"), ]) D3D11_BLEND_DESC = Struct("D3D11_BLEND_DESC", [ (BOOL, "AlphaToCoverageEnable"), (BOOL, "IndependentBlendEnable"), (Array(D3D11_RENDER_TARGET_BLEND_DESC, 8), "RenderTarget"), ]) ID3D11BlendState.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_BLEND_DESC), "pDesc")]), ] D3D11_RASTERIZER_DESC = Struct("D3D11_RASTERIZER_DESC", [ (D3D11_FILL_MODE, "FillMode"), (D3D11_CULL_MODE, "CullMode"), (BOOL, "FrontCounterClockwise"), (INT, "DepthBias"), (FLOAT, "DepthBiasClamp"), (FLOAT, "SlopeScaledDepthBias"), (BOOL, "DepthClipEnable"), (BOOL, "ScissorEnable"), (BOOL, "MultisampleEnable"), (BOOL, "AntialiasedLineEnable"), ]) ID3D11RasterizerState.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_RASTERIZER_DESC), "pDesc")]), ] D3D11_SUBRESOURCE_DATA = Struct("D3D11_SUBRESOURCE_DATA", [ (OpaquePointer(Const(Void)), "pSysMem"), (UINT, "SysMemPitch"), (UINT, "SysMemSlicePitch"), ]) D3D11_MAPPED_SUBRESOURCE = Struct("D3D11_MAPPED_SUBRESOURCE", [ (OpaquePointer(Void), "pData"), (UINT, "RowPitch"), (UINT, "DepthPitch"), ]) ID3D11Resource.methods += [ StdMethod(Void, "GetType", [Out(Pointer(D3D11_RESOURCE_DIMENSION), "pResourceDimension")]), StdMethod(Void, "SetEvictionPriority", [(UINT, "EvictionPriority")]), StdMethod(UINT, "GetEvictionPriority", []), ] D3D11_BUFFER_DESC = Struct("D3D11_BUFFER_DESC", [ (UINT, "ByteWidth"), (D3D11_USAGE, "Usage"), (D3D11_BIND_FLAG, "BindFlags"), (D3D11_CPU_ACCESS_FLAG, "CPUAccessFlags"), (D3D11_RESOURCE_MISC_FLAG, "MiscFlags"), (UINT, "StructureByteStride"), ]) ID3D11Buffer.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_BUFFER_DESC), "pDesc")]), ] D3D11_TEXTURE1D_DESC = Struct("D3D11_TEXTURE1D_DESC", [ (UINT, "Width"), (UINT, "MipLevels"), (UINT, "ArraySize"), (DXGI_FORMAT, "Format"), (D3D11_USAGE, "Usage"), (D3D11_BIND_FLAG, "BindFlags"), (D3D11_CPU_ACCESS_FLAG, "CPUAccessFlags"), (D3D11_RESOURCE_MISC_FLAG, "MiscFlags"), ]) ID3D11Texture1D.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_TEXTURE1D_DESC), "pDesc")]), ] D3D11_TEXTURE2D_DESC = Struct("D3D11_TEXTURE2D_DESC", [ (UINT, "Width"), (UINT, "Height"), (UINT, "MipLevels"), (UINT, "ArraySize"), (DXGI_FORMAT, "Format"), (DXGI_SAMPLE_DESC, "SampleDesc"), (D3D11_USAGE, "Usage"), (D3D11_BIND_FLAG, "BindFlags"), (D3D11_CPU_ACCESS_FLAG, "CPUAccessFlags"), (D3D11_RESOURCE_MISC_FLAG, "MiscFlags"), ]) ID3D11Texture2D.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_TEXTURE2D_DESC), "pDesc")]), ] D3D11_TEXTURE3D_DESC = Struct("D3D11_TEXTURE3D_DESC", [ (UINT, "Width"), (UINT, "Height"), (UINT, "Depth"), (UINT, "MipLevels"), (DXGI_FORMAT, "Format"), (D3D11_USAGE, "Usage"), (D3D11_BIND_FLAG, "BindFlags"), (D3D11_CPU_ACCESS_FLAG, "CPUAccessFlags"), (D3D11_RESOURCE_MISC_FLAG, "MiscFlags"), ]) ID3D11Texture3D.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_TEXTURE3D_DESC), "pDesc")]), ] D3D11_TEXTURECUBE_FACE = Enum("D3D11_TEXTURECUBE_FACE", [ "D3D11_TEXTURECUBE_FACE_POSITIVE_X", "D3D11_TEXTURECUBE_FACE_NEGATIVE_X", "D3D11_TEXTURECUBE_FACE_POSITIVE_Y", "D3D11_TEXTURECUBE_FACE_NEGATIVE_Y", "D3D11_TEXTURECUBE_FACE_POSITIVE_Z", "D3D11_TEXTURECUBE_FACE_NEGATIVE_Z", ]) ID3D11View.methods += [ StdMethod(Void, "GetResource", [Out(Pointer(ObjPointer(ID3D11Resource)), "ppResource")]), ] D3D11_BUFFER_SRV = Struct("D3D11_BUFFER_SRV", [ (Union(None, [(UINT, "FirstElement"), (UINT, "ElementOffset")]), None), (Union(None, [(UINT, "NumElements"), (UINT, "ElementWidth")]), None), ]) D3D11_BUFFEREX_SRV_FLAG = Flags(UINT, [ "D3D11_BUFFEREX_SRV_FLAG_RAW", ]) D3D11_BUFFEREX_SRV = Struct("D3D11_BUFFEREX_SRV", [ (UINT, "FirstElement"), (UINT, "NumElements"), (D3D11_BUFFEREX_SRV_FLAG, "Flags"), ]) D3D11_TEX1D_SRV = Struct("D3D11_TEX1D_SRV", [ (UINT, "MostDetailedMip"), (UINT, "MipLevels"), ]) D3D11_TEX1D_ARRAY_SRV = Struct("D3D11_TEX1D_ARRAY_SRV", [ (UINT, "MostDetailedMip"), (UINT, "MipLevels"), (UINT, "FirstArraySlice"), (UINT, "ArraySize"), ]) D3D11_TEX2D_SRV = Struct("D3D11_TEX2D_SRV", [ (UINT, "MostDetailedMip"), (UINT, "MipLevels"), ]) D3D11_TEX2D_ARRAY_SRV = Struct("D3D11_TEX2D_ARRAY_SRV", [ (UINT, "MostDetailedMip"), (UINT, "MipLevels"), (UINT, "FirstArraySlice"), (UINT, "ArraySize"), ]) D3D11_TEX3D_SRV = Struct("D3D11_TEX3D_SRV", [ (UINT, "MostDetailedMip"), (UINT, "MipLevels"), ]) D3D11_TEXCUBE_SRV = Struct("D3D11_TEXCUBE_SRV", [ (UINT, "MostDetailedMip"), (UINT, "MipLevels"), ]) D3D11_TEXCUBE_ARRAY_SRV = Struct("D3D11_TEXCUBE_ARRAY_SRV", [ (UINT, "MostDetailedMip"), (UINT, "MipLevels"), (UINT, "First2DArrayFace"), (UINT, "NumCubes"), ]) D3D11_TEX2DMS_SRV = Struct("D3D11_TEX2DMS_SRV", [ (UINT, "UnusedField_NothingToDefine"), ]) D3D11_TEX2DMS_ARRAY_SRV = Struct("D3D11_TEX2DMS_ARRAY_SRV", [ (UINT, "FirstArraySlice"), (UINT, "ArraySize"), ]) D3D11_SHADER_RESOURCE_VIEW_DESC = Struct("D3D11_SHADER_RESOURCE_VIEW_DESC", [ (DXGI_FORMAT, "Format"), (D3D11_SRV_DIMENSION, "ViewDimension"), (Union(None, [ (D3D11_BUFFER_SRV, "Buffer"), (D3D11_TEX1D_SRV, "Texture1D"), (D3D11_TEX1D_ARRAY_SRV, "Texture1DArray"), (D3D11_TEX2D_SRV, "Texture2D"), (D3D11_TEX2D_ARRAY_SRV, "Texture2DArray"), (D3D11_TEX2DMS_SRV, "Texture2DMS"), (D3D11_TEX2DMS_ARRAY_SRV, "Texture2DMSArray"), (D3D11_TEX3D_SRV, "Texture3D"), (D3D11_TEXCUBE_SRV, "TextureCube"), (D3D11_TEXCUBE_ARRAY_SRV, "TextureCubeArray"), (D3D11_BUFFEREX_SRV, "BufferEx"), ]), None), ]) ID3D11ShaderResourceView.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_SHADER_RESOURCE_VIEW_DESC), "pDesc")]), ] D3D11_BUFFER_RTV = Struct("D3D11_BUFFER_RTV", [ (Union(None, [(UINT, "FirstElement"), (UINT, "ElementOffset")]), None), (Union(None, [(UINT, "NumElements"), (UINT, "ElementWidth")]), None), ]) D3D11_TEX1D_RTV = Struct("D3D11_TEX1D_RTV", [ (UINT, "MipSlice"), ]) D3D11_TEX1D_ARRAY_RTV = Struct("D3D11_TEX1D_ARRAY_RTV", [ (UINT, "MipSlice"), (UINT, "FirstArraySlice"), (UINT, "ArraySize"), ]) D3D11_TEX2D_RTV = Struct("D3D11_TEX2D_RTV", [ (UINT, "MipSlice"), ]) D3D11_TEX2DMS_RTV = Struct("D3D11_TEX2DMS_RTV", [ (UINT, "UnusedField_NothingToDefine"), ]) D3D11_TEX2D_ARRAY_RTV = Struct("D3D11_TEX2D_ARRAY_RTV", [ (UINT, "MipSlice"), (UINT, "FirstArraySlice"), (UINT, "ArraySize"), ]) D3D11_TEX2DMS_ARRAY_RTV = Struct("D3D11_TEX2DMS_ARRAY_RTV", [ (UINT, "FirstArraySlice"), (UINT, "ArraySize"), ]) D3D11_TEX3D_RTV = Struct("D3D11_TEX3D_RTV", [ (UINT, "MipSlice"), (UINT, "FirstWSlice"), (UINT, "WSize"), ]) D3D11_RENDER_TARGET_VIEW_DESC = Struct("D3D11_RENDER_TARGET_VIEW_DESC", [ (DXGI_FORMAT, "Format"), (D3D11_RTV_DIMENSION, "ViewDimension"), (Union(None, [ (D3D11_BUFFER_RTV, "Buffer"), (D3D11_TEX1D_RTV, "Texture1D"), (D3D11_TEX1D_ARRAY_RTV, "Texture1DArray"), (D3D11_TEX2D_RTV, "Texture2D"), (D3D11_TEX2D_ARRAY_RTV, "Texture2DArray"), (D3D11_TEX2DMS_RTV, "Texture2DMS"), (D3D11_TEX2DMS_ARRAY_RTV, "Texture2DMSArray"), (D3D11_TEX3D_RTV, "Texture3D"), ]), None), ]) ID3D11RenderTargetView.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_RENDER_TARGET_VIEW_DESC), "pDesc")]), ] D3D11_TEX1D_DSV = Struct("D3D11_TEX1D_DSV", [ (UINT, "MipSlice"), ]) D3D11_TEX1D_ARRAY_DSV = Struct("D3D11_TEX1D_ARRAY_DSV", [ (UINT, "MipSlice"), (UINT, "FirstArraySlice"), (UINT, "ArraySize"), ]) D3D11_TEX2D_DSV = Struct("D3D11_TEX2D_DSV", [ (UINT, "MipSlice"), ]) D3D11_TEX2D_ARRAY_DSV = Struct("D3D11_TEX2D_ARRAY_DSV", [ (UINT, "MipSlice"), (UINT, "FirstArraySlice"), (UINT, "ArraySize"), ]) D3D11_TEX2DMS_DSV = Struct("D3D11_TEX2DMS_DSV", [ (UINT, "UnusedField_NothingToDefine"), ]) D3D11_TEX2DMS_ARRAY_DSV = Struct("D3D11_TEX2DMS_ARRAY_DSV", [ (UINT, "FirstArraySlice"), (UINT, "ArraySize"), ]) D3D11_DSV_FLAG = Flags(UINT, [ "D3D11_DSV_READ_ONLY_DEPTH", "D3D11_DSV_READ_ONLY_STENCIL", ]) D3D11_DEPTH_STENCIL_VIEW_DESC = Struct("D3D11_DEPTH_STENCIL_VIEW_DESC", [ (DXGI_FORMAT, "Format"), (D3D11_DSV_DIMENSION, "ViewDimension"), (D3D11_DSV_FLAG, "Flags"), (Union(None, [ (D3D11_TEX1D_DSV, "Texture1D"), (D3D11_TEX1D_ARRAY_DSV, "Texture1DArray"), (D3D11_TEX2D_DSV, "Texture2D"), (D3D11_TEX2D_ARRAY_DSV, "Texture2DArray"), (D3D11_TEX2DMS_DSV, "Texture2DMS"), (D3D11_TEX2DMS_ARRAY_DSV, "Texture2DMSArray"), ]), None), ]) ID3D11DepthStencilView.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_DEPTH_STENCIL_VIEW_DESC), "pDesc")]), ] D3D11_BUFFER_UAV_FLAG = Flags(UINT, [ "D3D11_BUFFER_UAV_FLAG_RAW", "D3D11_BUFFER_UAV_FLAG_APPEND", "D3D11_BUFFER_UAV_FLAG_COUNTER", ]) D3D11_BUFFER_UAV = Struct("D3D11_BUFFER_UAV", [ (UINT, "FirstElement"), (UINT, "NumElements"), (D3D11_BUFFER_UAV_FLAG, "Flags"), ]) D3D11_TEX1D_UAV = Struct("D3D11_TEX1D_UAV", [ (UINT, "MipSlice"), ]) D3D11_TEX1D_ARRAY_UAV = Struct("D3D11_TEX1D_ARRAY_UAV", [ (UINT, "MipSlice"), (UINT, "FirstArraySlice"), (UINT, "ArraySize"), ]) D3D11_TEX2D_UAV = Struct("D3D11_TEX2D_UAV", [ (UINT, "MipSlice"), ]) D3D11_TEX2D_ARRAY_UAV = Struct("D3D11_TEX2D_ARRAY_UAV", [ (UINT, "MipSlice"), (UINT, "FirstArraySlice"), (UINT, "ArraySize"), ]) D3D11_TEX3D_UAV = Struct("D3D11_TEX3D_UAV", [ (UINT, "MipSlice"), (UINT, "FirstWSlice"), (UINT, "WSize"), ]) D3D11_UNORDERED_ACCESS_VIEW_DESC = Struct("D3D11_UNORDERED_ACCESS_VIEW_DESC", [ (DXGI_FORMAT, "Format"), (D3D11_UAV_DIMENSION, "ViewDimension"), (Union(None, [ (D3D11_BUFFER_UAV, "Buffer"), (D3D11_TEX1D_UAV, "Texture1D"), (D3D11_TEX1D_ARRAY_UAV, "Texture1DArray"), (D3D11_TEX2D_UAV, "Texture2D"), (D3D11_TEX2D_ARRAY_UAV, "Texture2DArray"), (D3D11_TEX3D_UAV, "Texture3D"), ]), None), ]) ID3D11UnorderedAccessView.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_UNORDERED_ACCESS_VIEW_DESC), "pDesc")]), ] D3D11_FILTER = Enum("D3D11_FILTER", [ "D3D11_FILTER_MIN_MAG_MIP_POINT", "D3D11_FILTER_MIN_MAG_POINT_MIP_LINEAR", "D3D11_FILTER_MIN_POINT_MAG_LINEAR_MIP_POINT", "D3D11_FILTER_MIN_POINT_MAG_MIP_LINEAR", "D3D11_FILTER_MIN_LINEAR_MAG_MIP_POINT", "D3D11_FILTER_MIN_LINEAR_MAG_POINT_MIP_LINEAR", "D3D11_FILTER_MIN_MAG_LINEAR_MIP_POINT", "D3D11_FILTER_MIN_MAG_MIP_LINEAR", "D3D11_FILTER_ANISOTROPIC", "D3D11_FILTER_COMPARISON_MIN_MAG_MIP_POINT", "D3D11_FILTER_COMPARISON_MIN_MAG_POINT_MIP_LINEAR", "D3D11_FILTER_COMPARISON_MIN_POINT_MAG_LINEAR_MIP_POINT", "D3D11_FILTER_COMPARISON_MIN_POINT_MAG_MIP_LINEAR", "D3D11_FILTER_COMPARISON_MIN_LINEAR_MAG_MIP_POINT", "D3D11_FILTER_COMPARISON_MIN_LINEAR_MAG_POINT_MIP_LINEAR", "D3D11_FILTER_COMPARISON_MIN_MAG_LINEAR_MIP_POINT", "D3D11_FILTER_COMPARISON_MIN_MAG_MIP_LINEAR", "D3D11_FILTER_COMPARISON_ANISOTROPIC", ]) D3D11_FILTER_TYPE = Enum("D3D11_FILTER_TYPE", [ "D3D11_FILTER_TYPE_POINT", "D3D11_FILTER_TYPE_LINEAR", ]) D3D11_TEXTURE_ADDRESS_MODE = Enum("D3D11_TEXTURE_ADDRESS_MODE", [ "D3D11_TEXTURE_ADDRESS_WRAP", "D3D11_TEXTURE_ADDRESS_MIRROR", "D3D11_TEXTURE_ADDRESS_CLAMP", "D3D11_TEXTURE_ADDRESS_BORDER", "D3D11_TEXTURE_ADDRESS_MIRROR_ONCE", ]) D3D11_SAMPLER_DESC = Struct("D3D11_SAMPLER_DESC", [ (D3D11_FILTER, "Filter"), (D3D11_TEXTURE_ADDRESS_MODE, "AddressU"), (D3D11_TEXTURE_ADDRESS_MODE, "AddressV"), (D3D11_TEXTURE_ADDRESS_MODE, "AddressW"), (FLOAT, "MipLODBias"), (UINT, "MaxAnisotropy"), (D3D11_COMPARISON_FUNC, "ComparisonFunc"), (Array(FLOAT, 4), "BorderColor"), (FLOAT, "MinLOD"), (FLOAT, "MaxLOD"), ]) ID3D11SamplerState.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_SAMPLER_DESC), "pDesc")]), ] D3D11_FORMAT_SUPPORT = Flags(UINT, [ "D3D11_FORMAT_SUPPORT_BUFFER", "D3D11_FORMAT_SUPPORT_IA_VERTEX_BUFFER", "D3D11_FORMAT_SUPPORT_IA_INDEX_BUFFER", "D3D11_FORMAT_SUPPORT_SO_BUFFER", "D3D11_FORMAT_SUPPORT_TEXTURE1D", "D3D11_FORMAT_SUPPORT_TEXTURE2D", "D3D11_FORMAT_SUPPORT_TEXTURE3D", "D3D11_FORMAT_SUPPORT_TEXTURECUBE", "D3D11_FORMAT_SUPPORT_SHADER_LOAD", "D3D11_FORMAT_SUPPORT_SHADER_SAMPLE", "D3D11_FORMAT_SUPPORT_SHADER_SAMPLE_COMPARISON", "D3D11_FORMAT_SUPPORT_SHADER_SAMPLE_MONO_TEXT", "D3D11_FORMAT_SUPPORT_MIP", "D3D11_FORMAT_SUPPORT_MIP_AUTOGEN", "D3D11_FORMAT_SUPPORT_RENDER_TARGET", "D3D11_FORMAT_SUPPORT_BLENDABLE", "D3D11_FORMAT_SUPPORT_DEPTH_STENCIL", "D3D11_FORMAT_SUPPORT_CPU_LOCKABLE", "D3D11_FORMAT_SUPPORT_MULTISAMPLE_RESOLVE", "D3D11_FORMAT_SUPPORT_DISPLAY", "D3D11_FORMAT_SUPPORT_CAST_WITHIN_BIT_LAYOUT", "D3D11_FORMAT_SUPPORT_MULTISAMPLE_RENDERTARGET", "D3D11_FORMAT_SUPPORT_MULTISAMPLE_LOAD", "D3D11_FORMAT_SUPPORT_SHADER_GATHER", "D3D11_FORMAT_SUPPORT_BACK_BUFFER_CAST", "D3D11_FORMAT_SUPPORT_TYPED_UNORDERED_ACCESS_VIEW", "D3D11_FORMAT_SUPPORT_SHADER_GATHER_COMPARISON", ]) D3D11_FORMAT_SUPPORT2 = Enum("D3D11_FORMAT_SUPPORT2", [ "D3D11_FORMAT_SUPPORT2_UAV_ATOMIC_ADD", "D3D11_FORMAT_SUPPORT2_UAV_ATOMIC_BITWISE_OPS", "D3D11_FORMAT_SUPPORT2_UAV_ATOMIC_COMPARE_STORE_OR_COMPARE_EXCHANGE", "D3D11_FORMAT_SUPPORT2_UAV_ATOMIC_EXCHANGE", "D3D11_FORMAT_SUPPORT2_UAV_ATOMIC_SIGNED_MIN_OR_MAX", "D3D11_FORMAT_SUPPORT2_UAV_ATOMIC_UNSIGNED_MIN_OR_MAX", "D3D11_FORMAT_SUPPORT2_UAV_TYPED_LOAD", "D3D11_FORMAT_SUPPORT2_UAV_TYPED_STORE", ]) ID3D11Asynchronous.methods += [ StdMethod(UINT, "GetDataSize", []), ] D3D11_ASYNC_GETDATA_FLAG = Flags(UINT, [ "D3D11_ASYNC_GETDATA_DONOTFLUSH", ]) D3D11_QUERY = Enum("D3D11_QUERY", [ "D3D11_QUERY_EVENT", "D3D11_QUERY_OCCLUSION", "D3D11_QUERY_TIMESTAMP", "D3D11_QUERY_TIMESTAMP_DISJOINT", "D3D11_QUERY_PIPELINE_STATISTICS", "D3D11_QUERY_OCCLUSION_PREDICATE", "D3D11_QUERY_SO_STATISTICS", "D3D11_QUERY_SO_OVERFLOW_PREDICATE", "D3D11_QUERY_SO_STATISTICS_STREAM0", "D3D11_QUERY_SO_OVERFLOW_PREDICATE_STREAM0", "D3D11_QUERY_SO_STATISTICS_STREAM1", "D3D11_QUERY_SO_OVERFLOW_PREDICATE_STREAM1", "D3D11_QUERY_SO_STATISTICS_STREAM2", "D3D11_QUERY_SO_OVERFLOW_PREDICATE_STREAM2", "D3D11_QUERY_SO_STATISTICS_STREAM3", "D3D11_QUERY_SO_OVERFLOW_PREDICATE_STREAM3", ]) D3D11_QUERY_MISC_FLAG = Flags(UINT, [ "D3D11_QUERY_MISC_PREDICATEHINT", ]) D3D11_QUERY_DESC = Struct("D3D11_QUERY_DESC", [ (D3D11_QUERY, "Query"), (D3D11_QUERY_MISC_FLAG, "MiscFlags"), ]) ID3D11Query.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_QUERY_DESC), "pDesc")]), ] D3D11_QUERY_DATA_TIMESTAMP_DISJOINT = Struct("D3D11_QUERY_DATA_TIMESTAMP_DISJOINT", [ (UINT64, "Frequency"), (BOOL, "Disjoint"), ]) D3D11_QUERY_DATA_PIPELINE_STATISTICS = Struct("D3D11_QUERY_DATA_PIPELINE_STATISTICS", [ (UINT64, "IAVertices"), (UINT64, "IAPrimitives"), (UINT64, "VSInvocations"), (UINT64, "GSInvocations"), (UINT64, "GSPrimitives"), (UINT64, "CInvocations"), (UINT64, "CPrimitives"), (UINT64, "PSInvocations"), (UINT64, "HSInvocations"), (UINT64, "DSInvocations"), (UINT64, "CSInvocations"), ]) D3D11_QUERY_DATA_SO_STATISTICS = Struct("D3D11_QUERY_DATA_SO_STATISTICS", [ (UINT64, "NumPrimitivesWritten"), (UINT64, "PrimitivesStorageNeeded"), ]) D3D11_COUNTER = Enum("D3D11_COUNTER", [ "D3D11_COUNTER_DEVICE_DEPENDENT_0", ]) D3D11_COUNTER_TYPE = Enum("D3D11_COUNTER_TYPE", [ "D3D11_COUNTER_TYPE_FLOAT32", "D3D11_COUNTER_TYPE_UINT16", "D3D11_COUNTER_TYPE_UINT32", "D3D11_COUNTER_TYPE_UINT64", ]) D3D11_COUNTER_DESC = Struct("D3D11_COUNTER_DESC", [ (D3D11_COUNTER, "Counter"), (UINT, "MiscFlags"), ]) D3D11_COUNTER_INFO = Struct("D3D11_COUNTER_INFO", [ (D3D11_COUNTER, "LastDeviceDependentCounter"), (UINT, "NumSimultaneousCounters"), (UINT8, "NumDetectableParallelUnits"), ]) ID3D11Counter.methods += [ StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_COUNTER_DESC), "pDesc")]), ] D3D11_STANDARD_MULTISAMPLE_QUALITY_LEVELS = Enum("D3D11_STANDARD_MULTISAMPLE_QUALITY_LEVELS", [ "D3D11_STANDARD_MULTISAMPLE_PATTERN", "D3D11_CENTER_MULTISAMPLE_PATTERN", ]) D3D11_DEVICE_CONTEXT_TYPE = Enum("D3D11_DEVICE_CONTEXT_TYPE", [ "D3D11_DEVICE_CONTEXT_IMMEDIATE", "D3D11_DEVICE_CONTEXT_DEFERRED", ]) D3D11_CLASS_INSTANCE_DESC = Struct("D3D11_CLASS_INSTANCE_DESC", [ (UINT, "InstanceId"), (UINT, "InstanceIndex"), (UINT, "TypeId"), (UINT, "ConstantBuffer"), (UINT, "BaseConstantBufferOffset"), (UINT, "BaseTexture"), (UINT, "BaseSampler"), (BOOL, "Created"), ]) ID3D11ClassInstance.methods += [ StdMethod(Void, "GetClassLinkage", [Out(Pointer(ObjPointer(ID3D11ClassLinkage)), "ppLinkage")]), StdMethod(Void, "GetDesc", [Out(Pointer(D3D11_CLASS_INSTANCE_DESC), "pDesc")]), StdMethod(Void, "GetInstanceName", [Out(LPSTR, "pInstanceName"), Out(Pointer(SIZE_T), "pBufferLength")]), StdMethod(Void, "GetTypeName", [Out(LPSTR, "pTypeName"), Out(Pointer(SIZE_T), "pBufferLength")]), ] ID3D11ClassLinkage.methods += [ StdMethod(HRESULT, "GetClassInstance", [(LPCSTR, "pClassInstanceName"), (UINT, "InstanceIndex"), Out(Pointer(ObjPointer(ID3D11ClassInstance)), "ppInstance")]), StdMethod(HRESULT, "CreateClassInstance", [(LPCSTR, "pClassTypeName"), (UINT, "ConstantBufferOffset"), (UINT, "ConstantVectorOffset"), (UINT, "TextureOffset"), (UINT, "SamplerOffset"), Out(Pointer(ObjPointer(ID3D11ClassInstance)), "ppInstance")]), ] ID3D11CommandList.methods += [ StdMethod(UINT, "GetContextFlags", []), ] D3D11_FEATURE_DATA_THREADING = Struct("D3D11_FEATURE_DATA_THREADING", [ (BOOL, "DriverConcurrentCreates"), (BOOL, "DriverCommandLists"), ]) D3D11_FEATURE_DATA_DOUBLES = Struct("D3D11_FEATURE_DATA_DOUBLES", [ (BOOL, "DoublePrecisionFloatShaderOps"), ]) D3D11_FEATURE_DATA_FORMAT_SUPPORT = Struct("D3D11_FEATURE_DATA_FORMAT_SUPPORT", [ (DXGI_FORMAT, "InFormat"), (D3D11_FORMAT_SUPPORT, "OutFormatSupport"), ]) D3D11_FEATURE_DATA_FORMAT_SUPPORT2 = Struct("D3D11_FEATURE_DATA_FORMAT_SUPPORT2", [ (DXGI_FORMAT, "InFormat"), (D3D11_FORMAT_SUPPORT2, "OutFormatSupport2"), ]) D3D11_FEATURE_DATA_D3D10_X_HARDWARE_OPTIONS = Struct("D3D11_FEATURE_DATA_D3D10_X_HARDWARE_OPTIONS", [ (BOOL, "ComputeShaders_Plus_RawAndStructuredBuffers_Via_Shader_4_x"), ]) D3D11_FEATURE, D3D11_FEATURE_DATA = EnumPolymorphic("D3D11_FEATURE", "Feature", [ ("D3D11_FEATURE_THREADING", Pointer(D3D11_FEATURE_DATA_THREADING)), ("D3D11_FEATURE_DOUBLES", Pointer(D3D11_FEATURE_DATA_DOUBLES)), ("D3D11_FEATURE_FORMAT_SUPPORT", Pointer(D3D11_FEATURE_DATA_FORMAT_SUPPORT)), ("D3D11_FEATURE_FORMAT_SUPPORT2", Pointer(D3D11_FEATURE_DATA_FORMAT_SUPPORT2)), ("D3D11_FEATURE_D3D10_X_HARDWARE_OPTIONS", Pointer(D3D11_FEATURE_DATA_D3D10_X_HARDWARE_OPTIONS)), ], Blob(Void, "FeatureSupportDataSize"), False) ID3D11DeviceContext.methods += [ StdMethod(Void, "VSSetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(Const(ObjPointer(ID3D11Buffer)), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "PSSetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(Const(ObjPointer(ID3D11ShaderResourceView)), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "PSSetShader", [(ObjPointer(ID3D11PixelShader), "pPixelShader"), (Array(Const(ObjPointer(ID3D11ClassInstance)), "NumClassInstances"), "ppClassInstances"), (UINT, "NumClassInstances")]), StdMethod(Void, "PSSetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(Const(ObjPointer(ID3D11SamplerState)), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "VSSetShader", [(ObjPointer(ID3D11VertexShader), "pVertexShader"), (Array(Const(ObjPointer(ID3D11ClassInstance)), "NumClassInstances"), "ppClassInstances"), (UINT, "NumClassInstances")]), StdMethod(Void, "DrawIndexed", [(UINT, "IndexCount"), (UINT, "StartIndexLocation"), (INT, "BaseVertexLocation")]), StdMethod(Void, "Draw", [(UINT, "VertexCount"), (UINT, "StartVertexLocation")]), StdMethod(HRESULT, "Map", [(ObjPointer(ID3D11Resource), "pResource"), (UINT, "Subresource"), (D3D11_MAP, "MapType"), (D3D11_MAP_FLAG, "MapFlags"), Out(Pointer(D3D11_MAPPED_SUBRESOURCE), "pMappedResource")]), StdMethod(Void, "Unmap", [(ObjPointer(ID3D11Resource), "pResource"), (UINT, "Subresource")]), StdMethod(Void, "PSSetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(Const(ObjPointer(ID3D11Buffer)), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "IASetInputLayout", [(ObjPointer(ID3D11InputLayout), "pInputLayout")]), StdMethod(Void, "IASetVertexBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(Const(ObjPointer(ID3D11Buffer)), "NumBuffers"), "ppVertexBuffers"), (Pointer(Const(UINT)), "pStrides"), (Pointer(Const(UINT)), "pOffsets")]), StdMethod(Void, "IASetIndexBuffer", [(ObjPointer(ID3D11Buffer), "pIndexBuffer"), (DXGI_FORMAT, "Format"), (UINT, "Offset")]), StdMethod(Void, "DrawIndexedInstanced", [(UINT, "IndexCountPerInstance"), (UINT, "InstanceCount"), (UINT, "StartIndexLocation"), (INT, "BaseVertexLocation"), (UINT, "StartInstanceLocation")]), StdMethod(Void, "DrawInstanced", [(UINT, "VertexCountPerInstance"), (UINT, "InstanceCount"), (UINT, "StartVertexLocation"), (UINT, "StartInstanceLocation")]), StdMethod(Void, "GSSetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(Const(ObjPointer(ID3D11Buffer)), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "GSSetShader", [(ObjPointer(ID3D11GeometryShader), "pShader"), (Array(Const(ObjPointer(ID3D11ClassInstance)), "NumClassInstances"), "ppClassInstances"), (UINT, "NumClassInstances")]), StdMethod(Void, "IASetPrimitiveTopology", [(D3D11_PRIMITIVE_TOPOLOGY, "Topology")]), StdMethod(Void, "VSSetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(Const(ObjPointer(ID3D11ShaderResourceView)), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "VSSetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(Const(ObjPointer(ID3D11SamplerState)), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "Begin", [(ObjPointer(ID3D11Asynchronous), "pAsync")]), StdMethod(Void, "End", [(ObjPointer(ID3D11Asynchronous), "pAsync")]), StdMethod(HRESULT, "GetData", [(ObjPointer(ID3D11Asynchronous), "pAsync"), Out(OpaqueBlob(Void, "DataSize"), "pData"), (UINT, "DataSize"), (D3D11_ASYNC_GETDATA_FLAG, "GetDataFlags")]), StdMethod(Void, "SetPredication", [(ObjPointer(ID3D11Predicate), "pPredicate"), (BOOL, "PredicateValue")]), StdMethod(Void, "GSSetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(Const(ObjPointer(ID3D11ShaderResourceView)), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "GSSetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(Const(ObjPointer(ID3D11SamplerState)), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "OMSetRenderTargets", [(UINT, "NumViews"), (Array(Const(ObjPointer(ID3D11RenderTargetView)), "NumViews"), "ppRenderTargetViews"), (ObjPointer(ID3D11DepthStencilView), "pDepthStencilView")]), StdMethod(Void, "OMSetRenderTargetsAndUnorderedAccessViews", [(UINT, "NumRTVs"), (Array(Const(ObjPointer(ID3D11RenderTargetView)), "NumRTVs"), "ppRenderTargetViews"), (ObjPointer(ID3D11DepthStencilView), "pDepthStencilView"), (UINT, "UAVStartSlot"), (UINT, "NumUAVs"), (Array(Const(ObjPointer(ID3D11UnorderedAccessView)), "NumUAVs"), "ppUnorderedAccessViews"), (Pointer(Const(UINT)), "pUAVInitialCounts")]), StdMethod(Void, "OMSetBlendState", [(ObjPointer(ID3D11BlendState), "pBlendState"), (Array(Const(FLOAT), 4), "BlendFactor"), (UINT, "SampleMask")]), StdMethod(Void, "OMSetDepthStencilState", [(ObjPointer(ID3D11DepthStencilState), "pDepthStencilState"), (UINT, "StencilRef")]), StdMethod(Void, "SOSetTargets", [(UINT, "NumBuffers"), (Array(Const(ObjPointer(ID3D11Buffer)), "NumBuffers"), "ppSOTargets"), (Pointer(Const(UINT)), "pOffsets")]), StdMethod(Void, "DrawAuto", []), StdMethod(Void, "DrawIndexedInstancedIndirect", [(ObjPointer(ID3D11Buffer), "pBufferForArgs"), (UINT, "AlignedByteOffsetForArgs")]), StdMethod(Void, "DrawInstancedIndirect", [(ObjPointer(ID3D11Buffer), "pBufferForArgs"), (UINT, "AlignedByteOffsetForArgs")]), StdMethod(Void, "Dispatch", [(UINT, "ThreadGroupCountX"), (UINT, "ThreadGroupCountY"), (UINT, "ThreadGroupCountZ")]), StdMethod(Void, "DispatchIndirect", [(ObjPointer(ID3D11Buffer), "pBufferForArgs"), (UINT, "AlignedByteOffsetForArgs")]), StdMethod(Void, "RSSetState", [(ObjPointer(ID3D11RasterizerState), "pRasterizerState")]), StdMethod(Void, "RSSetViewports", [(UINT, "NumViewports"), (Array(Const(D3D11_VIEWPORT), "NumViewports"), "pViewports")]), StdMethod(Void, "RSSetScissorRects", [(UINT, "NumRects"), (Array(Const(D3D11_RECT), "NumRects"), "pRects")]), StdMethod(Void, "CopySubresourceRegion", [(ObjPointer(ID3D11Resource), "pDstResource"), (UINT, "DstSubresource"), (UINT, "DstX"), (UINT, "DstY"), (UINT, "DstZ"), (ObjPointer(ID3D11Resource), "pSrcResource"), (UINT, "SrcSubresource"), (Pointer(Const(D3D11_BOX)), "pSrcBox")]), StdMethod(Void, "CopyResource", [(ObjPointer(ID3D11Resource), "pDstResource"), (ObjPointer(ID3D11Resource), "pSrcResource")]), StdMethod(Void, "UpdateSubresource", [(ObjPointer(ID3D11Resource), "pDstResource"), (UINT, "DstSubresource"), (Pointer(Const(D3D11_BOX)), "pDstBox"), (OpaquePointer(Const(Void)), "pSrcData"), (UINT, "SrcRowPitch"), (UINT, "SrcDepthPitch")]), StdMethod(Void, "CopyStructureCount", [(ObjPointer(ID3D11Buffer), "pDstBuffer"), (UINT, "DstAlignedByteOffset"), (ObjPointer(ID3D11UnorderedAccessView), "pSrcView")]), StdMethod(Void, "ClearRenderTargetView", [(ObjPointer(ID3D11RenderTargetView), "pRenderTargetView"), (Array(Const(FLOAT), 4), "ColorRGBA")]), StdMethod(Void, "ClearUnorderedAccessViewUint", [(ObjPointer(ID3D11UnorderedAccessView), "pUnorderedAccessView"), (Array(Const(UINT), 4), "Values")]), StdMethod(Void, "ClearUnorderedAccessViewFloat", [(ObjPointer(ID3D11UnorderedAccessView), "pUnorderedAccessView"), (Array(Const(FLOAT), 4), "Values")]), StdMethod(Void, "ClearDepthStencilView", [(ObjPointer(ID3D11DepthStencilView), "pDepthStencilView"), (D3D11_CLEAR_FLAG, "ClearFlags"), (FLOAT, "Depth"), (UINT8, "Stencil")]), StdMethod(Void, "GenerateMips", [(ObjPointer(ID3D11ShaderResourceView), "pShaderResourceView")]), StdMethod(Void, "SetResourceMinLOD", [(ObjPointer(ID3D11Resource), "pResource"), (FLOAT, "MinLOD")]), StdMethod(FLOAT, "GetResourceMinLOD", [(ObjPointer(ID3D11Resource), "pResource")]), StdMethod(Void, "ResolveSubresource", [(ObjPointer(ID3D11Resource), "pDstResource"), (UINT, "DstSubresource"), (ObjPointer(ID3D11Resource), "pSrcResource"), (UINT, "SrcSubresource"), (DXGI_FORMAT, "Format")]), StdMethod(Void, "ExecuteCommandList", [(ObjPointer(ID3D11CommandList), "pCommandList"), (BOOL, "RestoreContextState")]), StdMethod(Void, "HSSetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(Const(ObjPointer(ID3D11ShaderResourceView)), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "HSSetShader", [(ObjPointer(ID3D11HullShader), "pHullShader"), (Array(Const(ObjPointer(ID3D11ClassInstance)), "NumClassInstances"), "ppClassInstances"), (UINT, "NumClassInstances")]), StdMethod(Void, "HSSetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(Const(ObjPointer(ID3D11SamplerState)), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "HSSetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(Const(ObjPointer(ID3D11Buffer)), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "DSSetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(Const(ObjPointer(ID3D11ShaderResourceView)), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "DSSetShader", [(ObjPointer(ID3D11DomainShader), "pDomainShader"), (Array(Const(ObjPointer(ID3D11ClassInstance)), "NumClassInstances"), "ppClassInstances"), (UINT, "NumClassInstances")]), StdMethod(Void, "DSSetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(Const(ObjPointer(ID3D11SamplerState)), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "DSSetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(Const(ObjPointer(ID3D11Buffer)), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "CSSetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(Const(ObjPointer(ID3D11ShaderResourceView)), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "CSSetUnorderedAccessViews", [(UINT, "StartSlot"), (UINT, "NumUAVs"), (Array(Const(ObjPointer(ID3D11UnorderedAccessView)), "NumUAVs"), "ppUnorderedAccessViews"), (Pointer(Const(UINT)), "pUAVInitialCounts")]), StdMethod(Void, "CSSetShader", [(ObjPointer(ID3D11ComputeShader), "pComputeShader"), (Array(Const(ObjPointer(ID3D11ClassInstance)), "NumClassInstances"), "ppClassInstances"), (UINT, "NumClassInstances")]), StdMethod(Void, "CSSetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(Const(ObjPointer(ID3D11SamplerState)), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "CSSetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(Const(ObjPointer(ID3D11Buffer)), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "VSGetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(ObjPointer(ID3D11Buffer), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "PSGetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(ObjPointer(ID3D11ShaderResourceView), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "PSGetShader", [Out(Pointer(ObjPointer(ID3D11PixelShader)), "ppPixelShader"), Out(Array(ObjPointer(ID3D11ClassInstance), "*pNumClassInstances"), "ppClassInstances"), Out(Pointer(UINT), "pNumClassInstances")]), StdMethod(Void, "PSGetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(ObjPointer(ID3D11SamplerState), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "VSGetShader", [Out(Pointer(ObjPointer(ID3D11VertexShader)), "ppVertexShader"), Out(Array(ObjPointer(ID3D11ClassInstance), "*pNumClassInstances"), "ppClassInstances"), Out(Pointer(UINT), "pNumClassInstances")]), StdMethod(Void, "PSGetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(ObjPointer(ID3D11Buffer), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "IAGetInputLayout", [Out(Pointer(ObjPointer(ID3D11InputLayout)), "ppInputLayout")]), StdMethod(Void, "IAGetVertexBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(ObjPointer(ID3D11Buffer), "NumBuffers"), "ppVertexBuffers"), Out(Pointer(UINT), "pStrides"), Out(Pointer(UINT), "pOffsets")]), StdMethod(Void, "IAGetIndexBuffer", [Out(Pointer(ObjPointer(ID3D11Buffer)), "pIndexBuffer"), Out(Pointer(DXGI_FORMAT), "Format"), Out(Pointer(UINT), "Offset")]), StdMethod(Void, "GSGetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(ObjPointer(ID3D11Buffer), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "GSGetShader", [Out(Pointer(ObjPointer(ID3D11GeometryShader)), "ppGeometryShader"), Out(Array(ObjPointer(ID3D11ClassInstance), "*pNumClassInstances"), "ppClassInstances"), Out(Pointer(UINT), "pNumClassInstances")]), StdMethod(Void, "IAGetPrimitiveTopology", [Out(Pointer(D3D11_PRIMITIVE_TOPOLOGY), "pTopology")]), StdMethod(Void, "VSGetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(ObjPointer(ID3D11ShaderResourceView), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "VSGetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(ObjPointer(ID3D11SamplerState), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "GetPredication", [Out(Pointer(ObjPointer(ID3D11Predicate)), "ppPredicate"), Out(Pointer(BOOL), "pPredicateValue")]), StdMethod(Void, "GSGetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(ObjPointer(ID3D11ShaderResourceView), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "GSGetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(ObjPointer(ID3D11SamplerState), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "OMGetRenderTargets", [(UINT, "NumViews"), (Array(ObjPointer(ID3D11RenderTargetView), "NumViews"), "ppRenderTargetViews"), Out(Pointer(ObjPointer(ID3D11DepthStencilView)), "ppDepthStencilView")]), StdMethod(Void, "OMGetRenderTargetsAndUnorderedAccessViews", [(UINT, "NumRTVs"), (Array(ObjPointer(ID3D11RenderTargetView), "NumRTVs"), "ppRenderTargetViews"), Out(Pointer(ObjPointer(ID3D11DepthStencilView)), "ppDepthStencilView"), (UINT, "UAVStartSlot"), (UINT, "NumUAVs"), (Array(ObjPointer(ID3D11UnorderedAccessView), "NumUAVs"), "ppUnorderedAccessViews")]), StdMethod(Void, "OMGetBlendState", [Out(Pointer(ObjPointer(ID3D11BlendState)), "ppBlendState"), Out(Array(FLOAT, 4), "BlendFactor"), Out(Pointer(UINT), "pSampleMask")]), StdMethod(Void, "OMGetDepthStencilState", [Out(Pointer(ObjPointer(ID3D11DepthStencilState)), "ppDepthStencilState"), Out(Pointer(UINT), "pStencilRef")]), StdMethod(Void, "SOGetTargets", [(UINT, "NumBuffers"), (Array(ObjPointer(ID3D11Buffer), "NumBuffers"), "ppSOTargets")]), StdMethod(Void, "RSGetState", [Out(Pointer(ObjPointer(ID3D11RasterizerState)), "ppRasterizerState")]), StdMethod(Void, "RSGetViewports", [Out(Pointer(UINT), "pNumViewports"), Out(Array(D3D11_VIEWPORT, "*pNumViewports"), "pViewports")]), StdMethod(Void, "RSGetScissorRects", [Out(Pointer(UINT), "pNumRects"), Out(Array(D3D11_RECT, "*pNumRects"), "pRects")]), StdMethod(Void, "HSGetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(ObjPointer(ID3D11ShaderResourceView), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "HSGetShader", [Out(Pointer(ObjPointer(ID3D11HullShader)), "ppHullShader"), Out(Array(ObjPointer(ID3D11ClassInstance), "*pNumClassInstances"), "ppClassInstances"), Out(Pointer(UINT), "pNumClassInstances")]), StdMethod(Void, "HSGetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(ObjPointer(ID3D11SamplerState), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "HSGetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(ObjPointer(ID3D11Buffer), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "DSGetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(ObjPointer(ID3D11ShaderResourceView), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "DSGetShader", [Out(Pointer(ObjPointer(ID3D11DomainShader)), "ppDomainShader"), Out(Array(ObjPointer(ID3D11ClassInstance), "*pNumClassInstances"), "ppClassInstances"), Out(Pointer(UINT), "pNumClassInstances")]), StdMethod(Void, "DSGetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(ObjPointer(ID3D11SamplerState), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "DSGetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(ObjPointer(ID3D11Buffer), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "CSGetShaderResources", [(UINT, "StartSlot"), (UINT, "NumViews"), (Array(ObjPointer(ID3D11ShaderResourceView), "NumViews"), "ppShaderResourceViews")]), StdMethod(Void, "CSGetUnorderedAccessViews", [(UINT, "StartSlot"), (UINT, "NumUAVs"), (Array(ObjPointer(ID3D11UnorderedAccessView), "NumUAVs"), "ppUnorderedAccessViews")]), StdMethod(Void, "CSGetShader", [Out(Pointer(ObjPointer(ID3D11ComputeShader)), "ppComputeShader"), Out(Array(ObjPointer(ID3D11ClassInstance), "*pNumClassInstances"), "ppClassInstances"), Out(Pointer(UINT), "pNumClassInstances")]), StdMethod(Void, "CSGetSamplers", [(UINT, "StartSlot"), (UINT, "NumSamplers"), (Array(ObjPointer(ID3D11SamplerState), "NumSamplers"), "ppSamplers")]), StdMethod(Void, "CSGetConstantBuffers", [(UINT, "StartSlot"), (UINT, "NumBuffers"), (Array(ObjPointer(ID3D11Buffer), "NumBuffers"), "ppConstantBuffers")]), StdMethod(Void, "ClearState", []), StdMethod(Void, "Flush", []), StdMethod(D3D11_DEVICE_CONTEXT_TYPE, "GetType", []), StdMethod(UINT, "GetContextFlags", []), StdMethod(HRESULT, "FinishCommandList", [(BOOL, "RestoreDeferredContextState"), Out(Pointer(ObjPointer(ID3D11CommandList)), "ppCommandList")]), ] D3D11_CREATE_DEVICE_FLAG = Flags(UINT, [ "D3D11_CREATE_DEVICE_SINGLETHREADED", "D3D11_CREATE_DEVICE_DEBUG", "D3D11_CREATE_DEVICE_SWITCH_TO_REF", "D3D11_CREATE_DEVICE_PREVENT_INTERNAL_THREADING_OPTIMIZATIONS", "D3D11_CREATE_DEVICE_BGRA_SUPPORT", ]) ID3D11Device.methods += [ StdMethod(HRESULT, "CreateBuffer", [(Pointer(Const(D3D11_BUFFER_DESC)), "pDesc"), (Pointer(Const(D3D11_SUBRESOURCE_DATA)), "pInitialData"), Out(Pointer(ObjPointer(ID3D11Buffer)), "ppBuffer")]), StdMethod(HRESULT, "CreateTexture1D", [(Pointer(Const(D3D11_TEXTURE1D_DESC)), "pDesc"), (Pointer(Const(D3D11_SUBRESOURCE_DATA)), "pInitialData"), Out(Pointer(ObjPointer(ID3D11Texture1D)), "ppTexture1D")]), StdMethod(HRESULT, "CreateTexture2D", [(Pointer(Const(D3D11_TEXTURE2D_DESC)), "pDesc"), (Pointer(Const(D3D11_SUBRESOURCE_DATA)), "pInitialData"), Out(Pointer(ObjPointer(ID3D11Texture2D)), "ppTexture2D")]), StdMethod(HRESULT, "CreateTexture3D", [(Pointer(Const(D3D11_TEXTURE3D_DESC)), "pDesc"), (Pointer(Const(D3D11_SUBRESOURCE_DATA)), "pInitialData"), Out(Pointer(ObjPointer(ID3D11Texture3D)), "ppTexture3D")]), StdMethod(HRESULT, "CreateShaderResourceView", [(ObjPointer(ID3D11Resource), "pResource"), (Pointer(Const(D3D11_SHADER_RESOURCE_VIEW_DESC)), "pDesc"), Out(Pointer(ObjPointer(ID3D11ShaderResourceView)), "ppSRView")]), StdMethod(HRESULT, "CreateUnorderedAccessView", [(ObjPointer(ID3D11Resource), "pResource"), (Pointer(Const(D3D11_UNORDERED_ACCESS_VIEW_DESC)), "pDesc"), Out(Pointer(ObjPointer(ID3D11UnorderedAccessView)), "ppUAView")]), StdMethod(HRESULT, "CreateRenderTargetView", [(ObjPointer(ID3D11Resource), "pResource"), (Pointer(Const(D3D11_RENDER_TARGET_VIEW_DESC)), "pDesc"), Out(Pointer(ObjPointer(ID3D11RenderTargetView)), "ppRTView")]), StdMethod(HRESULT, "CreateDepthStencilView", [(ObjPointer(ID3D11Resource), "pResource"), (Pointer(Const(D3D11_DEPTH_STENCIL_VIEW_DESC)), "pDesc"), Out(Pointer(ObjPointer(ID3D11DepthStencilView)), "ppDepthStencilView")]), StdMethod(HRESULT, "CreateInputLayout", [(Array(Const(D3D11_INPUT_ELEMENT_DESC), "NumElements"), "pInputElementDescs"), (UINT, "NumElements"), (Blob(Const(Void), "BytecodeLength"), "pShaderBytecodeWithInputSignature"), (SIZE_T, "BytecodeLength"), Out(Pointer(ObjPointer(ID3D11InputLayout)), "ppInputLayout")]), StdMethod(HRESULT, "CreateVertexShader", [(Blob(Const(Void), "BytecodeLength"), "pShaderBytecode"), (SIZE_T, "BytecodeLength"), (ObjPointer(ID3D11ClassLinkage), "pClassLinkage"), Out(Pointer(ObjPointer(ID3D11VertexShader)), "ppVertexShader")]), StdMethod(HRESULT, "CreateGeometryShader", [(Blob(Const(Void), "BytecodeLength"), "pShaderBytecode"), (SIZE_T, "BytecodeLength"), (ObjPointer(ID3D11ClassLinkage), "pClassLinkage"), Out(Pointer(ObjPointer(ID3D11GeometryShader)), "ppGeometryShader")]), StdMethod(HRESULT, "CreateGeometryShaderWithStreamOutput", [(Blob(Const(Void), "BytecodeLength"), "pShaderBytecode"), (SIZE_T, "BytecodeLength"), (Array(Const(D3D11_SO_DECLARATION_ENTRY), "NumEntries"), "pSODeclaration"), (UINT, "NumEntries"), (Array(Const(UINT), "NumStrides"), "pBufferStrides"), (UINT, "NumStrides"), (UINT, "RasterizedStream"), (ObjPointer(ID3D11ClassLinkage), "pClassLinkage"), Out(Pointer(ObjPointer(ID3D11GeometryShader)), "ppGeometryShader")]), StdMethod(HRESULT, "CreatePixelShader", [(Blob(Const(Void), "BytecodeLength"), "pShaderBytecode"), (SIZE_T, "BytecodeLength"), (ObjPointer(ID3D11ClassLinkage), "pClassLinkage"), Out(Pointer(ObjPointer(ID3D11PixelShader)), "ppPixelShader")]), StdMethod(HRESULT, "CreateHullShader", [(Blob(Const(Void), "BytecodeLength"), "pShaderBytecode"), (SIZE_T, "BytecodeLength"), (ObjPointer(ID3D11ClassLinkage), "pClassLinkage"), Out(Pointer(ObjPointer(ID3D11HullShader)), "ppHullShader")]), StdMethod(HRESULT, "CreateDomainShader", [(Blob(Const(Void), "BytecodeLength"), "pShaderBytecode"), (SIZE_T, "BytecodeLength"), (ObjPointer(ID3D11ClassLinkage), "pClassLinkage"), Out(Pointer(ObjPointer(ID3D11DomainShader)), "ppDomainShader")]), StdMethod(HRESULT, "CreateComputeShader", [(Blob(Const(Void), "BytecodeLength"), "pShaderBytecode"), (SIZE_T, "BytecodeLength"), (ObjPointer(ID3D11ClassLinkage), "pClassLinkage"), Out(Pointer(ObjPointer(ID3D11ComputeShader)), "ppComputeShader")]), StdMethod(HRESULT, "CreateClassLinkage", [Out(Pointer(ObjPointer(ID3D11ClassLinkage)), "ppLinkage")]), StdMethod(HRESULT, "CreateBlendState", [(Pointer(Const(D3D11_BLEND_DESC)), "pBlendStateDesc"), Out(Pointer(ObjPointer(ID3D11BlendState)), "ppBlendState")]), StdMethod(HRESULT, "CreateDepthStencilState", [(Pointer(Const(D3D11_DEPTH_STENCIL_DESC)), "pDepthStencilDesc"), Out(Pointer(ObjPointer(ID3D11DepthStencilState)), "ppDepthStencilState")]), StdMethod(HRESULT, "CreateRasterizerState", [(Pointer(Const(D3D11_RASTERIZER_DESC)), "pRasterizerDesc"), Out(Pointer(ObjPointer(ID3D11RasterizerState)), "ppRasterizerState")]), StdMethod(HRESULT, "CreateSamplerState", [(Pointer(Const(D3D11_SAMPLER_DESC)), "pSamplerDesc"), Out(Pointer(ObjPointer(ID3D11SamplerState)), "ppSamplerState")]), StdMethod(HRESULT, "CreateQuery", [(Pointer(Const(D3D11_QUERY_DESC)), "pQueryDesc"), Out(Pointer(ObjPointer(ID3D11Query)), "ppQuery")]), StdMethod(HRESULT, "CreatePredicate", [(Pointer(Const(D3D11_QUERY_DESC)), "pPredicateDesc"), Out(Pointer(ObjPointer(ID3D11Predicate)), "ppPredicate")]), StdMethod(HRESULT, "CreateCounter", [(Pointer(Const(D3D11_COUNTER_DESC)), "pCounterDesc"), Out(Pointer(ObjPointer(ID3D11Counter)), "ppCounter")]), StdMethod(HRESULT, "CreateDeferredContext", [(UINT, "ContextFlags"), Out(Pointer(ObjPointer(ID3D11DeviceContext)), "ppDeferredContext")]), StdMethod(HRESULT, "OpenSharedResource", [(HANDLE, "hResource"), (REFIID, "ReturnedInterface"), Out(Pointer(ObjPointer(Void)), "ppResource")]), StdMethod(HRESULT, "CheckFormatSupport", [(DXGI_FORMAT, "Format"), Out(Pointer(D3D11_FORMAT_SUPPORT), "pFormatSupport")]), StdMethod(HRESULT, "CheckMultisampleQualityLevels", [(DXGI_FORMAT, "Format"), (UINT, "SampleCount"), Out(Pointer(UINT), "pNumQualityLevels")]), StdMethod(Void, "CheckCounterInfo", [Out(Pointer(D3D11_COUNTER_INFO), "pCounterInfo")]), StdMethod(HRESULT, "CheckCounter", [(Pointer(Const(D3D11_COUNTER_DESC)), "pDesc"), Out(Pointer(D3D11_COUNTER_TYPE), "pType"), Out(Pointer(UINT), "pActiveCounters"), Out(LPSTR, "szName"), Out(Pointer(UINT), "pNameLength"), Out(LPSTR, "szUnits"), Out(Pointer(UINT), "pUnitsLength"), Out(LPSTR, "szDescription"), Out(Pointer(UINT), "pDescriptionLength")]), StdMethod(HRESULT, "CheckFeatureSupport", [(D3D11_FEATURE, "Feature"), Out(D3D11_FEATURE_DATA, "pFeatureSupportData"), (UINT, "FeatureSupportDataSize")]), StdMethod(HRESULT, "GetPrivateData", [(REFGUID, "guid"), Out(Pointer(UINT), "pDataSize"), Out(OpaquePointer(Void), "pData")]), StdMethod(HRESULT, "SetPrivateData", [(REFGUID, "guid"), (UINT, "DataSize"), (OpaqueBlob(Const(Void), "DataSize"), "pData")]), StdMethod(HRESULT, "SetPrivateDataInterface", [(REFGUID, "guid"), (OpaquePointer(Const(IUnknown)), "pData")]), StdMethod(D3D_FEATURE_LEVEL, "GetFeatureLevel", []), StdMethod(D3D11_CREATE_DEVICE_FLAG, "GetCreationFlags", []), StdMethod(HRESULT, "GetDeviceRemovedReason", []), StdMethod(Void, "GetImmediateContext", [Out(Pointer(ObjPointer(ID3D11DeviceContext)), "ppImmediateContext")]), StdMethod(HRESULT, "SetExceptionMode", [(D3D11_RAISE_FLAG, "RaiseFlags")]), StdMethod(UINT, "GetExceptionMode", []), ] d3d11 = API("d3d11") d3d11.addFunctions([ StdFunction(HRESULT, "D3D11CreateDevice", [(ObjPointer(IDXGIAdapter), "pAdapter"), (D3D_DRIVER_TYPE, "DriverType"), (HMODULE, "Software"), (D3D11_CREATE_DEVICE_FLAG, "Flags"), (Array(Const(D3D_FEATURE_LEVEL), "FeatureLevels"), "pFeatureLevels"), (UINT, "FeatureLevels"), (UINT, "SDKVersion"), Out(Pointer(ObjPointer(ID3D11Device)), "ppDevice"), Out(Pointer(D3D_FEATURE_LEVEL), "pFeatureLevel"), Out(Pointer(ObjPointer(ID3D11DeviceContext)), "ppImmediateContext")]), StdFunction(HRESULT, "D3D11CreateDeviceAndSwapChain", [(ObjPointer(IDXGIAdapter), "pAdapter"), (D3D_DRIVER_TYPE, "DriverType"), (HMODULE, "Software"), (D3D11_CREATE_DEVICE_FLAG, "Flags"), (Array(Const(D3D_FEATURE_LEVEL), "FeatureLevels"), "pFeatureLevels"), (UINT, "FeatureLevels"), (UINT, "SDKVersion"), (Pointer(Const(DXGI_SWAP_CHAIN_DESC)), "pSwapChainDesc"), Out(Pointer(ObjPointer(IDXGISwapChain)), "ppSwapChain"), Out(Pointer(ObjPointer(ID3D11Device)), "ppDevice"), Out(Pointer(D3D_FEATURE_LEVEL), "pFeatureLevel"), Out(Pointer(ObjPointer(ID3D11DeviceContext)), "ppImmediateContext")]), # XXX: Undocumented functions, called by d3d11sdklayers.dll when D3D11_CREATE_DEVICE_DEBUG is set StdFunction(HRESULT, "D3D11CoreRegisterLayers", [LPCVOID, DWORD], internal=True), StdFunction(SIZE_T, "D3D11CoreGetLayeredDeviceSize", [LPCVOID, DWORD], internal=True), StdFunction(HRESULT, "D3D11CoreCreateLayeredDevice", [LPCVOID, DWORD, LPCVOID, (REFIID, "riid"), Out(Pointer(ObjPointer(Void)), "ppvObj")], internal=True), StdFunction(HRESULT, "D3D11CoreCreateDevice", [DWORD, DWORD, DWORD, DWORD, DWORD, DWORD, DWORD, DWORD, DWORD], internal=True), ]) d3d11.addInterfaces([ IDXGIAdapter1, IDXGIDevice1, IDXGIResource, ID3D11Debug, ID3D11InfoQueue, ID3D11SwitchToRef, ])

8a69c6a560d7f1d6a12a9bb69281971b56733693

py

Python

setup.py

xbabka01/filetype.py

faba42b86988bd21a50d5b20919ecff0c6a84957

setup.py

xbabka01/filetype.py

faba42b86988bd21a50d5b20919ecff0c6a84957

setup.py

xbabka01/filetype.py

faba42b86988bd21a50d5b20919ecff0c6a84957

#!/usr/bin/env python # -*- coding: utf-8 -*- import codecs from setuptools import find_packages, setup setup( name='filetype', version='1.0.7', description='Infer file type and MIME type of any file/buffer. ' 'No external dependencies.', long_description=codecs.open('README.rst', 'r', encoding='utf-8', errors='ignore').read(), keywords='file libmagic magic infer numbers magicnumbers discovery mime ' 'type kind', url='https://github.com/h2non/filetype.py', download_url='https://github.com/h2non/filetype.py/tarball/master', author='Tomas Aparicio', author_email='tomas@aparicio.me', license='MIT', license_files=['LICENSE'], classifiers=[ 'Development Status :: 5 - Production/Stable', 'Environment :: Console', 'Environment :: Web Environment', 'Intended Audience :: Developers', 'Intended Audience :: System Administrators', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: System', 'Topic :: System :: Filesystems', 'Topic :: Utilities'], platforms=['any'], packages=find_packages(exclude=['dist', 'build', 'docs', 'tests', 'examples']), package_data={'filetype': ['LICENSE', '*.md']}, zip_safe=True)

8a69d4b012c5607f3bca22996d7b21d1f2aed261

py

Python

demos/netmiko_textfsm.py

ryanaa08/NPA

45173efa60713858bb8b1d884fe12c50fe69920c

2019-01-15T16:15:26.000Z

2021-12-05T16:03:15.000Z

demos/netmiko_textfsm.py

krishnakadiyala/NPAcourse

74f097107839d990b44adcee69d4f949696a332c

demos/netmiko_textfsm.py

krishnakadiyala/NPAcourse

74f097107839d990b44adcee69d4f949696a332c

2019-07-04T16:38:19.000Z

2020-01-31T15:38:27.000Z

# make sure templates are present and netmiko knows about them # git clone https://github.com/networktocode/ntc-templates # export NET_TEXTFSM=/home/ntc/ntc-templates/templates/ # see https://github.com/networktocode/ntc-templates/tree/master/templates # for list of templates from netmiko import ConnectHandler import json user = 'ntc' pwd = 'ntc123' d_type = 'cisco_ios' csr1 = ConnectHandler(ip='csr1', username=user, password=pwd, device_type=d_type) sh_ip_int_br = csr1.send_command("show ip int brief", use_textfsm=True) # [{'status': 'up', 'intf': 'GigabitEthernet1', 'ipaddr': '10.0.0.51', 'proto': 'up'}, {'status': 'up', 'intf': 'GigabitEthernet2', 'ipaddr': 'unassigned', 'proto': 'up'}, {'status': 'up', 'intf': 'GigabitEthernet3', 'ipaddr': 'unassigned', 'proto': 'up'}, {'status': 'up', 'intf': 'GigabitEthernet4', 'ipaddr': '5.12.1.1', 'proto': 'up'}, {'status': 'up', 'intf': 'Loopback100', 'ipaddr': '10.200.1.20', 'proto': 'up'}] # is type list print (type(sh_ip_int_br)) # list of dicts print (type(sh_ip_int_br[0])) for each_dict in sh_ip_int_br: print "\n" for key in each_dict.keys(): print key for each_dict in sh_ip_int_br: print "\n" for key, value in each_dict.items(): print key + " is " + value sh_ver_ios = csr1.send_command("show version", use_textfsm=True) # [{'running_image': 'packages.conf', 'hostname': 'csr1', 'uptime': '6 hours, 59 minutes', 'config_register': '0x2102', 'hardware': ['CSR1000V'], 'version': '16.6.2', 'serial': ['9KIBQAQ3OPE'], 'rommon': 'IOS-XE'}] # print the json nicely print (json.dumps(sh_ver_ios, indent=4)) print sh_ver_ios # list print type(sh_ver_ios) # each item is a dict print type(sh_ver_ios[0]) # list of dicts with some nested lists with the dicts for each_dict in sh_ver_ios: print "\n" for key, value in each_dict.items(): if type(value) is list: print key + " is " for list_entry in value: print list_entry if type(value) is str: print key + " is " + value

8a6c4e202130d51c730ab01bd3f2f21e5ec32862

py

Python

tools/data.py

seanys/2D-Irregular-Packing-Algorithm

cc10edff2bc2631fcbcb47acf7bb3215e5c5023c

2020-02-07T06:41:25.000Z

2022-03-16T18:04:07.000Z

tools/data.py

seanys/2D-Irregular-Packing-Algorithm

cc10edff2bc2631fcbcb47acf7bb3215e5c5023c

2020-04-27T01:36:27.000Z

2022-01-31T11:59:05.000Z

tools/data.py

seanys/2D-Irregular-Packing-Algorithm

cc10edff2bc2631fcbcb47acf7bb3215e5c5023c

2020-05-05T05:34:06.000Z

2022-03-26T07:32:46.000Z

from tools.geofunc import GeoFunc import pandas as pd import json def getData(index): '''报错数据集有（空心）：han,jakobs1,jakobs2 ''' '''形状过多暂时未处理：shapes、shirt、swim、trousers''' name=["ga","albano","blaz1","blaz2","dighe1","dighe2","fu","han","jakobs1","jakobs2","mao","marques","shapes","shirts","swim","trousers"] print("开始处理",name[index],"数据集") '''暂时没有考虑宽度，全部缩放来表示''' scale=[100,0.5,100,100,20,20,20,10,20,20,0.5,20,50] print("缩放",scale[index],"倍") df = pd.read_csv("data/"+name[index]+".csv") polygons=[] for i in range(0,df.shape[0]): for j in range(0,df['num'][i]): poly=json.loads(df['polygon'][i]) GeoFunc.normData(poly,scale[index]) polygons.append(poly) return polygons

8a6e9d6c995b4c34ef5a6722c4973c2c7fb333f1

py

Python

projects/eyetracking/gen_adhd_sin.py

nirdslab/streaminghub

a0d9f5f8be0ee6f090bd2b48b9f596695497c2bf

projects/eyetracking/gen_adhd_sin.py

nirdslab/streaminghub

a0d9f5f8be0ee6f090bd2b48b9f596695497c2bf

projects/eyetracking/gen_adhd_sin.py

nirdslab/streaminghub

a0d9f5f8be0ee6f090bd2b48b9f596695497c2bf

2020-01-22T15:35:29.000Z

2020-01-22T15:35:29.000Z

#!/usr/bin/env python3 import glob import os import pandas as pd import dfs SRC_DIR = f"{dfs.get_data_dir()}/adhd_sin_orig" OUT_DIR = f"{dfs.get_data_dir()}/adhd_sin" if __name__ == '__main__': files = glob.glob(f"{SRC_DIR}/*.csv") file_names = list(map(os.path.basename, files)) for file_name in file_names: df: pd.DataFrame = pd.read_csv(f'{SRC_DIR}/{file_name}').set_index('EyeTrackerTimestamp').sort_index()[ ['GazePointX (ADCSpx)', 'GazePointY (ADCSpx)', 'PupilLeft', 'PupilRight']].reset_index() df.columns = ['t', 'x', 'y', 'dl', 'dr'] # fill blanks (order=interpolate(inter)->bfill+ffill(edges))->zerofill df = df.apply(lambda x: x.interpolate().fillna(method="bfill").fillna(method="ffill")).fillna(0) df['x'] = df['x'] / 1920 df['y'] = df['y'] / 1080 df['d'] = (df['dl'] + df['dr']) / 2 # start with t=0, and set unit to ms df['t'] = (df['t'] - df['t'].min()) / 1000 df = df[['t', 'x', 'y', 'd']].round(6).set_index('t') df.to_csv(f'{OUT_DIR}/{file_name}') print(f'Processed: {file_name}')

8a73f2115b3d49a7048eebbbf6a7d009bf2bcb02

py

Python

TopQuarkAnalysis/TopJetCombination/python/TtSemiLepJetCombMaxSumPtWMass_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

TopQuarkAnalysis/TopJetCombination/python/TtSemiLepJetCombMaxSumPtWMass_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

TopQuarkAnalysis/TopJetCombination/python/TtSemiLepJetCombMaxSumPtWMass_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms # # module to make the MaxSumPtWMass jet combination # findTtSemiLepJetCombMaxSumPtWMass = cms.EDProducer("TtSemiLepJetCombMaxSumPtWMass", ## jet input jets = cms.InputTag("selectedPatJets"), ## lepton input leps = cms.InputTag("selectedPatMuons"), ## maximum number of jets to be considered maxNJets = cms.int32(4), ## nominal WMass parameter (in GeV) wMass = cms.double(80.4), ## use b-tagging two distinguish between light and b jets useBTagging = cms.bool(False), ## choose algorithm for b-tagging bTagAlgorithm = cms.string("trackCountingHighEffBJetTags"), ## minimum b discriminator value required for b jets and ## maximum b discriminator value allowed for non-b jets minBDiscBJets = cms.double(1.0), maxBDiscLightJets = cms.double(3.0) )

8a78745915eb3a4aaf90865a024b4d8bafd46ca7

py

Python

research/gnn/sgcn/postprocess.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

2021-11-18T08:17:44.000Z

2021-11-18T08:17:44.000Z

research/gnn/sgcn/postprocess.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

research/gnn/sgcn/postprocess.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

2019-09-01T06:17:04.000Z

2019-10-04T08:39:45.000Z

# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ postprocess. """ import os import argparse import numpy as np from src.ms_utils import calculate_auc from mindspore import context, load_checkpoint def softmax(x): t_max = np.max(x, axis=1, keepdims=True) # returns max of each row and keeps same dims e_x = np.exp(x - t_max) # subtracts each row with its max value t_sum = np.sum(e_x, axis=1, keepdims=True) # returns sum of each row and keeps same dims f_x = e_x / t_sum return f_x def score_model(preds, test_pos, test_neg, weight, bias): """ Score the model on the test set edges in each epoch. Args: epoch (LongTensor): Training epochs. Returns: auc(Float32): AUC result. f1(Float32): F1-Score result. """ score_positive_edges = np.array(test_pos, dtype=np.int32).T score_negative_edges = np.array(test_neg, dtype=np.int32).T test_positive_z = np.concatenate((preds[score_positive_edges[0, :], :], preds[score_positive_edges[1, :], :]), axis=1) test_negative_z = np.concatenate((preds[score_negative_edges[0, :], :], preds[score_negative_edges[1, :], :]), axis=1) # operands could not be broadcast together with shapes (4288,128) (128,3) scores = np.dot(np.concatenate((test_positive_z, test_negative_z), axis=0), weight) + bias probability_scores = np.exp(softmax(scores)) predictions = probability_scores[:, 0]/probability_scores[:, 0:2].sum(1) # predictions = predictions.asnumpy() targets = [0]*len(test_pos) + [1]*len(test_neg) auc, f1 = calculate_auc(targets, predictions) return auc, f1 def get_acc(): """get infer Accuracy.""" parser = argparse.ArgumentParser(description='postprocess') parser.add_argument('--dataset_name', type=str, default='bitcoin-otc', choices=['bitcoin-otc', 'bitcoin-alpha'], help='dataset name') parser.add_argument('--result_path', type=str, default='./ascend310_infer/input/', help='result Files') parser.add_argument('--label_path', type=str, default='', help='y_test npy Files') parser.add_argument('--mask_path', type=str, default='', help='test_mask npy Files') parser.add_argument("--checkpoint_file", type=str, default='sgcn_alpha_f1.ckpt', help="Checkpoint file path.") parser.add_argument("--edge_path", nargs="?", default="./input/bitcoin_alpha.csv", help="Edge list csv.") parser.add_argument("--features-path", nargs="?", default="./input/bitcoin_alpha.csv", help="Edge list csv.") parser.add_argument("--test-size", type=float, default=0.2, help="Test dataset size. Default is 0.2.") parser.add_argument("--seed", type=int, default=42, help="Random seed for sklearn pre-training. Default is 42.") parser.add_argument("--spectral-features", default=True, dest="spectral_features", action="store_true") parser.add_argument("--reduction-iterations", type=int, default=30, help="Number of SVD iterations. Default is 30.") parser.add_argument("--reduction-dimensions", type=int, default=64, help="Number of SVD feature extraction dimensions. Default is 64.") args_opt = parser.parse_args() # Runtime context.set_context(mode=context.GRAPH_MODE, device_target='Ascend', device_id=0) # Create network test_pos = np.load(os.path.join(args_opt.result_path, 'pos_test.npy')) test_neg = np.load(os.path.join(args_opt.result_path, 'neg_test.npy')) # Load parameters from checkpoint into network param_dict = load_checkpoint(args_opt.checkpoint_file) print(type(param_dict)) print(param_dict) print(type(param_dict['regression_weights'])) print(param_dict['regression_weights']) # load_param_into_net(net, param_dict) pred = np.fromfile('./result_Files/repos_0.bin', np.float32) if args_opt.dataset_name == 'bitcoin-otc': pred = pred.reshape(5881, 64) else: pred = pred.reshape(3783, 64) auc, f1 = score_model(pred, test_pos, test_neg, param_dict['regression_weights'].asnumpy(), param_dict['regression_bias'].asnumpy()) print("Test set results:", "auc=", "{:.5f}".format(auc), "f1=", "{:.5f}".format(f1)) if __name__ == '__main__': get_acc()

8a790aaa3beecccbae1e5fe2d0bb1478dbadd597

py

Python

VENV/lib/python3.6/site-packages/PyInstaller/hooks/hook-PyQt5.py

workingyifei/display-pattern-generator

b27be84c6221fa93833f283109870737b05bfbf6

2018-11-27T06:30:23.000Z

2021-05-30T15:56:32.000Z

VENV/lib/python3.6/site-packages/PyInstaller/hooks/hook-PyQt5.py

workingyifei/display-pattern-generator

b27be84c6221fa93833f283109870737b05bfbf6

2018-11-15T02:00:31.000Z

2021-12-06T02:20:32.000Z

VENV/lib/python3.6/site-packages/PyInstaller/hooks/hook-PyQt5.py

workingyifei/display-pattern-generator

b27be84c6221fa93833f283109870737b05bfbf6

2020-11-06T18:46:35.000Z

2020-11-06T18:46:35.000Z

#----------------------------------------------------------------------------- # Copyright (c) 2005-2017, PyInstaller Development Team. # # Distributed under the terms of the GNU General Public License with exception # for distributing bootloader. # # The full license is in the file COPYING.txt, distributed with this software. #----------------------------------------------------------------------------- import os from PyInstaller.utils.hooks import ( get_module_attribute, is_module_satisfies, qt_menu_nib_dir, get_module_file_attribute, collect_data_files) from PyInstaller.compat import getsitepackages, is_darwin, is_win # On Windows system PATH has to be extended to point to the PyQt5 directory. # The PySide directory contains Qt dlls. We need to avoid including different # version of Qt libraries when there is installed another application (e.g. QtCreator) if is_win: from PyInstaller.utils.win32.winutils import extend_system_path extend_system_path([os.path.join(x, 'PyQt5') for x in getsitepackages()]) extend_system_path([os.path.join(os.path.dirname(get_module_file_attribute('PyQt5')), 'Qt', 'bin')]) # In the new consolidated mode any PyQt depends on _qt hiddenimports = ['sip', 'PyQt5.Qt'] # Collect just the qt.conf file. datas = [x for x in collect_data_files('PyQt5', False, os.path.join('Qt', 'bin')) if x[0].endswith('qt.conf')] # For Qt<5.4 to work on Mac OS X it is necessary to include `qt_menu.nib`. # This directory contains some resource files necessary to run PyQt or PySide # app. if is_darwin: # Version of the currently installed Qt 5.x shared library. qt_version = get_module_attribute('PyQt5.QtCore', 'QT_VERSION_STR') if is_module_satisfies('Qt < 5.4', qt_version): datas = [(qt_menu_nib_dir('PyQt5'), '')]

8a7ecd71a92cf19cd5b6422ac30a671d4195653c

py

Python

experiments/bst/setup.py

bigchaindb/privacy-protocols

d220f642c7c056e5ec179b47a8d0863dbc373d9d

2017-08-02T14:22:59.000Z

2022-02-19T05:27:42.000Z

experiments/bst/setup.py

bigchaindb/privacy-protocols

d220f642c7c056e5ec179b47a8d0863dbc373d9d

2017-08-05T18:30:14.000Z

2017-08-22T19:54:53.000Z

experiments/bst/setup.py

bigchaindb/privacy-protocols

d220f642c7c056e5ec179b47a8d0863dbc373d9d

2017-08-22T16:04:26.000Z

2022-03-13T10:36:02.000Z

"""bst: BigchainDB Sharing Tools""" from setuptools import setup, find_packages install_requires = [ 'base58~=0.2.2', 'PyNaCl~=1.1.0', 'bigchaindb-driver', 'click==6.7', 'colorama', ] setup( name='bst', version='0.1.0', description='bst: BigchainDB Sharing Tools', long_description=( 'A collection of scripts with different patterns to share' 'private data on BigchainDB.'), url='https://github.com/vrde/bst/', author='Alberto Granzotto', author_email='alberto@bigchaindb.com', license='AGPLv3', zip_safe=False, classifiers=[ 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'Topic :: Database', 'Topic :: Database :: Database Engines/Servers', 'Topic :: Software Development', 'Natural Language :: English', 'License :: OSI Approved :: GNU Affero General Public License v3', 'Programming Language :: Python :: 3 :: Only', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Operating System :: MacOS :: MacOS X', 'Operating System :: POSIX :: Linux', ], packages=find_packages(), entry_points={ 'console_scripts': [ 'bst=bst.cli:main' ], }, install_requires=install_requires )

8a8aa73cf4c767bf7b906925d1382b404b94f301

py

Python

Google/google_books/scrape_google_books.py

dimitryzub/blog-posts-archive

0978aaa0c9f0142d6f996b81ce391930c5e3be35

Google/google_books/scrape_google_books.py

dimitryzub/blog-posts-archive

0978aaa0c9f0142d6f996b81ce391930c5e3be35

Google/google_books/scrape_google_books.py

dimitryzub/blog-posts-archive

0978aaa0c9f0142d6f996b81ce391930c5e3be35

from parsel import Selector import requests, json, re params = { "q": "richard branson", "tbm": "bks", "gl": "us", "hl": "en" } headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.87 Safari/537.36", } html = requests.get("https://www.google.com/search", params=params, headers=headers, timeout=30) selector = Selector(text=html.text) books_results = [] # https://regex101.com/r/mapBs4/1 book_thumbnails = re.findall(r"s=\\'data:image/jpg;base64,(.*?)\\'", str(selector.css("script").getall()), re.DOTALL) for book_thumbnail, book_result in zip(book_thumbnails, selector.css(".Yr5TG")): title = book_result.css(".DKV0Md::text").get() link = book_result.css(".bHexk a::attr(href)").get() displayed_link = book_result.css(".tjvcx::text").get() snippet = book_result.css(".cmlJmd span::text").get() author = book_result.css(".fl span::text").get() author_link = f'https://www.google.com/search{book_result.css(".N96wpd .fl::attr(href)").get()}' date_published = book_result.css(".fl+ span::text").get() preview_link = book_result.css(".R1n8Q a.yKioRe:nth-child(1)::attr(href)").get() more_editions_link = book_result.css(".R1n8Q a.yKioRe:nth-child(2)::attr(href)").get() books_results.append({ "title": title, "link": link, "displayed_link": displayed_link, "snippet": snippet, "author": author, "author_link": author_link, "date_published": date_published, "preview_link": preview_link, "more_editions_link": f"https://www.google.com{more_editions_link}" if more_editions_link is not None else None, "thumbnail": bytes(bytes(book_thumbnail, "ascii").decode("unicode-escape"), "ascii").decode("unicode-escape") })

8a8bbdd35a1d135f6e6a32befca7b762678940d4

py

Python

Python/Higher-Or-Lower/hol/__init__.py

AustinTSchaffer/DailyProgrammer

b16d9babb298ac5e879c514f9c4646b99c6860a8

2020-07-28T17:07:35.000Z

2020-07-28T17:07:35.000Z

Python/Higher-Or-Lower/hol/__init__.py

AustinTSchaffer/DailyProgrammer

b16d9babb298ac5e879c514f9c4646b99c6860a8

2021-04-06T18:25:29.000Z

2021-04-10T15:13:28.000Z

Python/Higher-Or-Lower/hol/__init__.py

AustinTSchaffer/DailyProgrammer

b16d9babb298ac5e879c514f9c4646b99c6860a8

r""" Contains classes and methods that can be used when simulating the game Higher-or-Lower and performing statistical analysis on different games. """ from hol import ( cards, constants, ) from hol._hol import ( generate_all_games, should_pick_higher, is_a_winning_game, generate_win_statistics, )

8a995f399ed25fbe111acb3f8ad5749b538eef0a

py

Python

python/re_user.py

seckcoder/lang-learn

1e0d6f412bbd7f89b1af00293fd907ddb3c1b571

2017-10-14T04:23:45.000Z

2017-10-14T04:23:45.000Z

python/re_user.py

seckcoder/lang-learn

1e0d6f412bbd7f89b1af00293fd907ddb3c1b571

python/re_user.py

seckcoder/lang-learn

1e0d6f412bbd7f89b1af00293fd907ddb3c1b571

#!/usr/bin/env python #-*- coding=utf-8 -*- # # Copyright 2012 Jike Inc. All Rights Reserved. # Author: liwei@jike.com import re from urlparse import urlparse def parse1(): p = re.compile(r"/(?P<uid>\d+)/(?P<mid>\w+)") o = urlparse("http://weibo.com/2827699110/yz62AlEjF") m = p.search(o.path) print m.group('uid') print m.group('mid') def parse2(): exc_type_str = "<type 'exceptions.IndexError'>" parse1()

8a9d019bec9e50c7c8d759ea60e658149d43ef2a

py

Python

audiomentations/core/utils.py

jeongyoonlee/audiomentations

7f0112ae310989430e0ef7eb32c4116114810966

2021-02-03T19:12:04.000Z

2021-02-03T19:12:04.000Z

audiomentations/core/utils.py

jeongyoonlee/audiomentations

7f0112ae310989430e0ef7eb32c4116114810966

audiomentations/core/utils.py

jeongyoonlee/audiomentations

7f0112ae310989430e0ef7eb32c4116114810966

2021-07-08T07:33:10.000Z

2021-07-08T07:33:10.000Z

import os from pathlib import Path import numpy as np AUDIO_FILENAME_ENDINGS = (".aiff", ".flac", ".m4a", ".mp3", ".ogg", ".opus", ".wav") def get_file_paths( root_path, filename_endings=AUDIO_FILENAME_ENDINGS, traverse_subdirectories=True ): """Return a list of paths to all files with the given filename extensions in a directory. Also traverses subdirectories by default. """ file_paths = [] for root, dirs, filenames in os.walk(root_path): filenames = sorted(filenames) for filename in filenames: input_path = os.path.abspath(root) file_path = os.path.join(input_path, filename) if filename.lower().endswith(filename_endings): file_paths.append(Path(file_path)) if not traverse_subdirectories: # prevent descending into subfolders break return file_paths def calculate_rms(samples): """Given a numpy array of audio samples, return its Root Mean Square (RMS).""" return np.sqrt(np.mean(np.square(samples), axis=-1)) def calculate_desired_noise_rms(clean_rms, snr): """ Given the Root Mean Square (RMS) of a clean sound and a desired signal-to-noise ratio (SNR), calculate the desired RMS of a noise sound to be mixed in. Based on https://github.com/Sato-Kunihiko/audio-SNR/blob/8d2c933b6c0afe6f1203251f4877e7a1068a6130/create_mixed_audio_file.py#L20 :param clean_rms: Root Mean Square (RMS) - a value between 0.0 and 1.0 :param snr: Signal-to-Noise (SNR) Ratio in dB - typically somewhere between -20 and 60 :return: """ a = float(snr) / 20 noise_rms = clean_rms / (10 ** a) return noise_rms def convert_decibels_to_amplitude_ratio(decibels): return 10 ** (decibels / 20) def is_waveform_multichannel(samples): """ Return bool that answers the question: Is the given ndarray a multichannel waveform or not? :param samples: numpy ndarray :return: """ return len(samples.shape) > 1 def is_spectrogram_multichannel(spectrogram): """ Return bool that answers the question: Is the given ndarray a multichannel spectrogram? :param samples: numpy ndarray :return: """ return len(spectrogram.shape) > 2 and spectrogram.shape[-1] > 1 def convert_float_samples_to_int16(y): """Convert floating-point numpy array of audio samples to int16.""" if not issubclass(y.dtype.type, np.floating): raise ValueError("input samples not floating-point") return (y * np.iinfo(np.int16).max).astype(np.int16)

8a9ed7740bcb98fbae13ca6bc7e08c9cb1a32fd1

py

Python

semantic-segmentation/deeplabv3plus/dataset_utils.py

shikisawamura/nnabla-examples

baf4e4cc620dedbf4368683325c0fb868676850d

2020-08-03T12:49:25.000Z

2020-08-03T12:49:25.000Z

semantic-segmentation/deeplabv3plus/dataset_utils.py

takuseno/nnabla-examples

070d25078ad3d5458744dbfd390cdd926e20e573

semantic-segmentation/deeplabv3plus/dataset_utils.py

takuseno/nnabla-examples

070d25078ad3d5458744dbfd390cdd926e20e573

2020-04-25T06:11:28.000Z

2020-04-25T06:11:28.000Z

# Copyright (c) 2017 Sony 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. import numpy as np import os from scipy.misc import imread from args import get_args import matplotlib.pyplot as plt def get_color(): # RGB format return np.array([[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], [120, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128], [224, 224, 192]]) def encode_label(label): ''' Converting pixel values to corresponding class numbers. Assuming that the input label in 3-dim(h,w,c) and in BGR fromat read from cv2 ''' h, w, c = label.shape new_label = np.zeros((h, w, 1), dtype=np.int32) cls_to_clr_map = get_color() for i in range(cls_to_clr_map.shape[0]): #new_label[(label == cls_to_clr_map[i])[:,:,0]] = i #new_label[np.argwhere((label.astype(np.int32) == cls_to_clr_map[i]).all(axis=2))]=i print(np.where((label.astype(np.int32) == [120, 0, 128]).all(axis=2))) if i == 21: new_label[np.where( (label.astype(np.int32) == cls_to_clr_map[i]).all(axis=2))] = 255 else: new_label[np.where( (label.astype(np.int32) == cls_to_clr_map[i]).all(axis=2))] = i return new_label # this method should generate train-image.txt and train-label.txt def generate_path_files(data_dir, train_file, val_file): ti = open('train_image.txt', 'w') tl = open('train_label.txt', 'w') vi = open('val_image.txt', 'w') vl = open('val_label.txt', 'w') rootdir = data_dir train_text_file = open(train_file, "r") lines = [line[:-1] for line in train_text_file] for line in lines: if os.path.exists(data_dir+'JPEGImages/'+line+'.jpg'): ti.write(data_dir+'JPEGImages/'+line+'.jpg' + '\n') assert (os.path.isfile(data_dir+'SegmentationClass/encoded/'+line + '.npy')), "No matching label file for image : " + line + '.jpg' tl.write(data_dir+'SegmentationClass/encoded/'+line + '.npy' + '\n') val_text_file = open(val_file, "r") lines = [line[:-1] for line in val_text_file] for line in lines: if os.path.exists(data_dir+'JPEGImages/'+line+'.jpg'): vi.write(data_dir+'JPEGImages/'+line+'.jpg' + '\n') assert (os.path.isfile(data_dir+'SegmentationClass/encoded/'+line + '.npy')), "No matching label file for image : " + line + '.jpg' vl.write(data_dir+'SegmentationClass/encoded/'+line + '.npy' + '\n') ti.close() tl.close() vi.close() vl.close() def main(): ''' Arguments: train-file = txt file containing randomly selected image filenames to be taken as training set. val-file = txt file containing randomly selected image filenames to be taken as validation set. data-dir = dataset directory Usage: python dataset_utils.py --train-file="" --val-file="" --data_dir="" ''' args = get_args() data_dir = args.data_dir if not os.path.exists(data_dir+'SegmentationClass/' + 'encoded/'): os.makedirs(data_dir+'SegmentationClass/' + 'encoded/') for filename in os.listdir(data_dir+'SegmentationClass/'): if os.path.isdir(data_dir+'SegmentationClass/' + filename): continue label = imread(data_dir+'SegmentationClass/' + filename).astype('float32') label = encode_label(label) np.save(data_dir+'SegmentationClass/' + 'encoded/' + filename.split('.')[0] + '.npy', label) generate_path_files(args.data_dir, args.train_file, args.val_file) if __name__ == '__main__': main()

8aa2d7e8d015afdc94844a8b1cce4b350015d579

py

Python

Python/Examples/Macros/SettingsAxesOptimization.py

archformco/RoboDK-API

b3d0cad6a83f505811e2be273453ccb4579324f1

2018-03-23T01:27:08.000Z

2022-03-23T12:18:35.000Z

Python/Examples/Macros/SettingsAxesOptimization.py

OxideDevX/RoboDK-API

50357c38b2fcf58cf82d9b7bf61021cb900fd358

2018-11-19T10:18:58.000Z

2022-03-28T18:37:11.000Z

Python/Examples/Macros/SettingsAxesOptimization.py

OxideDevX/RoboDK-API

50357c38b2fcf58cf82d9b7bf61021cb900fd358

2018-03-22T19:25:35.000Z

2022-03-30T04:46:59.000Z

# This example shows how to read or modify the Axes Optimization settings using the RoboDK API and a JSON string. # You can select "Axes optimization" in a robot machining menu or the robot parameters to view the axes optimization settings. # It is possible to update the axes optimization settings attached to a robot or a robot machining project manually or using the API. # # More information about the RoboDK API here: # https://robodk.com/doc/en/RoboDK-API.html # For more information visit: # https://robodk.com/doc/en/PythonAPI/robolink.html from robolink import * # RoboDK API # JSON tools import json # Start the RoboDK API RDK = Robolink() # Ask the user to select a robot arm (6 axis robot wich can have external axes) robot = RDK.ItemUserPick("Select a robot arm",ITEM_TYPE_ROBOT_ARM) # Default optimization settings test template AxesOptimSettings = { # Optimization parameters: "Active": 1, # Use generic axes optimization: 0=Disabled or 1=Enabled "Algorithm": 2, # Optimization algorithm to use: 1=Nelder Mead, 2=Samples, 3=Samples+Nelder Mead "MaxIter": 650, # Max. number of iterations "Tol": 0.0016, # Tolerance to stop iterations # Absolute Reference joints (double): "AbsJnt_1": 104.17, "AbsJnt_2": 11.22, "AbsJnt_3": 15.97, "AbsJnt_4": -87.48, "AbsJnt_5": -75.36, "AbsJnt_6": 63.03, "AbsJnt_7": 174.13, "AbsJnt_8": 173.60, "AbsJnt_9": 0, # Using Absolute reference joints (0: No, 1: Yes): "AbsOn_1": 1, "AbsOn_2": 1, "AbsOn_3": 1, "AbsOn_4": 1, "AbsOn_5": 1, "AbsOn_6": 1, "AbsOn_7": 1, "AbsOn_8": 1, "AbsOn_9": 1, # Weight for absolute reference joints (double): "AbsW_1": 100, "AbsW_2": 100, "AbsW_3": 100, "AbsW_4": 89, "AbsW_5": 90, "AbsW_6": 92, "AbsW_7": 92, "AbsW_8": 96, "AbsW_9": 50, # Using for relative joint motion smoothing (0: No, 1: Yes): "RelOn_1": 1, "RelOn_2": 1, "RelOn_3": 1, "RelOn_4": 1, "RelOn_5": 1, "RelOn_6": 1, "RelOn_7": 1, "RelOn_8": 1, "RelOn_9": 1, # Weight for relative joint motion (double): "RelW_1": 5, "RelW_2": 47, "RelW_3": 44, "RelW_4": 43, "RelW_5": 36, "RelW_6": 47, "RelW_7": 53, "RelW_8": 59, "RelW_9": 0, } # Update one value, for example, make it active: ToUpdate = {} ToUpdate["Active"] = 1 json_str = json.dumps(json.dumps(ToUpdate)) status = robot.setParam("OptimAxes", json_str) print(status) # Example to make a partial or full update count = 1 while True: for i in range(7): # Partial update ToUpdate = {} ToUpdate["AbsJnt_" + str(i+1)] = (count+i)*4 ToUpdate["AbsOn_" + str(i+1)] = count % 2 ToUpdate["AbsW_" + str(i+1)] = (count+i) json_str = json.dumps(json.dumps(ToUpdate)) status = robot.setParam("OptimAxes", json_str) print(status) # Full update #OptimAxes_TEST["RefJoint_" + str(i+1)] = (count+i)*4 #OptimAxes_TEST["RefWeight_" + str(i+1)] = (count+i) #OptimAxes_TEST["RefOn_" + str(i+1)] = count % 2 # Full update #print(robot.setParam("OptimAxes", str(AxesOptimSettings))) count = count + 1 # Read settings json_data = robot.setParam("OptimAxes") json_object = json.loads(json_data) print(json.dumps(json_object, indent=4)) pause(0.2) # Example to read the current axes optimization settings: while True: json_data = robot.setParam("OptimAxes") json_object = json.loads(json_data) print(json.dumps(json_object, indent=4)) pause(0.2)

8aaa6ef648c6ab0a8f38e3df5ebf0a4f712b233a

py

Python

infrastructure-provisioning/src/general/api/install_libs.py

roolrd/incubator-datalab

2045207ecd1b381193f1a1ec143cc968716ad989

2020-10-03T08:36:48.000Z

2022-03-20T23:16:20.000Z

infrastructure-provisioning/src/general/api/install_libs.py

roolrd/incubator-datalab

2045207ecd1b381193f1a1ec143cc968716ad989

2019-02-28T12:11:33.000Z

2020-09-15T08:27:08.000Z

infrastructure-provisioning/src/general/api/install_libs.py

roolrd/incubator-datalab

2045207ecd1b381193f1a1ec143cc968716ad989

2019-01-14T10:31:55.000Z

2020-09-22T17:53:33.000Z

#!/usr/bin/python3 # ***************************************************************************** # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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 json import os import sys import subprocess if __name__ == "__main__": success = True try: subprocess.run('cd /root; fab install-libs', shell=True, check=True) except: success = False reply = dict() reply['request_id'] = os.environ['request_id'] if success: reply['status'] = 'ok' else: reply['status'] = 'err' reply['response'] = dict() try: with open("/root/result.json") as f: reply['response']['result'] = json.loads(f.read()) except: reply['response']['result'] = {"error": "Failed to open result.json"} reply['response']['log'] = "/var/log/datalab/{0}/{0}_{1}_{2}.log".format(os.environ['conf_resource'], os.environ['project_name'], os.environ['request_id']) with open("/response/{}_{}_{}.json".format(os.environ['conf_resource'], os.environ['project_name'], os.environ['request_id']), 'w') as response_file: response_file.write(json.dumps(reply)) try: subprocess.run('chmod 666 /response/*', shell=True, check=True) except: success = False if not success: sys.exit(1)

8aab4acf40735c2dc3547887c3be02d0b2808eff

py

Python

model_zoo/official/nlp/bert_thor/src/evaluation_config.py

GuoSuiming/mindspore

48afc4cfa53d970c0b20eedfb46e039db2a133d5

2020-12-17T10:26:06.000Z

2022-03-28T07:18:26.000Z

model_zoo/official/nlp/bert_thor/src/evaluation_config.py

forwhat461/mindspore

59a277756eb4faad9ac9afcc7fd526e8277d4994

2020-12-29T06:46:38.000Z

2020-12-29T06:46:38.000Z

model_zoo/official/nlp/bert_thor/src/evaluation_config.py

forwhat461/mindspore

59a277756eb4faad9ac9afcc7fd526e8277d4994

2021-01-29T02:39:47.000Z

2022-03-23T05:00:26.000Z

# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ config settings, will be used in finetune.py """ from easydict import EasyDict as edict import mindspore.common.dtype as mstype from .bert_model import BertConfig cfg = edict({ 'task': 'NER', 'num_labels': 41, 'data_file': '', 'schema_file': None, 'finetune_ckpt': '', 'use_crf': False, 'clue_benchmark': False, }) bert_net_cfg = BertConfig( batch_size=8 if not cfg.clue_benchmark else 1, seq_length=512, vocab_size=30522, hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, intermediate_size=4096, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, use_relative_positions=False, input_mask_from_dataset=True, token_type_ids_from_dataset=True, dtype=mstype.float32, compute_type=mstype.float16, )

8aad8de20813d57dc973493fe2b63ad495089392

py

Python

setup.py

swfrench/nginx-access-tailer

5e060396ca749935c622e8e9c50b659b39e3675b

setup.py

swfrench/nginx-access-tailer

5e060396ca749935c622e8e9c50b659b39e3675b

setup.py

swfrench/nginx-access-tailer

5e060396ca749935c622e8e9c50b659b39e3675b

"""TODO.""" from setuptools import setup setup( name='nginx-access-tailer', version='0.1', author='swfrench', url='https://github.com/swfrench/nginx-tailer', packages=['nginx_access_tailer',], license='BSD three-clause license', entry_points={ 'console_scripts': ['nginx-access-tailer = nginx_access_tailer.__main__:main'], }, install_requires=[ 'python-gflags >= 3.1.1', 'google-cloud-monitoring >= 0.25.0', ], test_suite='nose.collector', tests_require=['nose', 'mock'], )

8aaee662db93c29bfc4e01c664b5f8c132a76382

py

Python

setup.py

richardARPANET/persistent-celery-beat-scheduler

d2cbdd12394eec282ccb97ac5ff894353c2e4ffd

2018-04-04T13:03:08.000Z

2018-04-16T18:50:45.000Z

setup.py

richardARPANET/persistent-celery-beat-scheduler

d2cbdd12394eec282ccb97ac5ff894353c2e4ffd

setup.py

richardARPANET/persistent-celery-beat-scheduler

d2cbdd12394eec282ccb97ac5ff894353c2e4ffd

#!/usr/bin/env python # -*- coding: utf-8 -* import os from setuptools import find_packages, setup # allow setup.py to be run from any path os.chdir(os.path.normpath(os.path.join(os.path.abspath(__file__), os.pardir))) with open('requirements.txt') as f: install_requires = f.read().splitlines() setup( name='persistent-celery-beat-scheduler', version='0.1.1.dev0', packages=find_packages('src', exclude=('tests',)), package_dir={'': 'src'}, include_package_data=True, zip_safe=False, description=( 'Celery Beat Scheduler that stores the scheduler data in Redis.' ), author='Richard O\'Dwyer', author_email='richard@richard.do', license='Apache 2', long_description='https://github.com/richardasaurus/persistent-celery-beat-scheduler', install_requires=install_requires, classifiers=[ 'Intended Audience :: Developers', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Internet :: WWW/HTTP', ], )

8ab2d6d56bce4e65f9e2921fdc0ec8fdc7ecb7fb

py

Python

venv/Lib/site-packages/patsy/test_regressions.py

EkremBayar/bayar

aad1a32044da671d0b4f11908416044753360b39

2015-01-07T20:08:59.000Z

2022-03-08T14:30:13.000Z

venv/Lib/site-packages/patsy/test_regressions.py

EkremBayar/bayar

aad1a32044da671d0b4f11908416044753360b39

2015-01-07T02:20:27.000Z

2021-11-15T04:23:02.000Z

venv/Lib/site-packages/patsy/test_regressions.py

EkremBayar/bayar

aad1a32044da671d0b4f11908416044753360b39

2015-01-15T16:35:12.000Z

2022-02-19T06:50:02.000Z

# This file is part of Patsy # Copyright (C) 2013 Nathaniel Smith <njs@pobox.com> # See file LICENSE.txt for license information. # Regression tests for fixed bugs (when not otherwise better covered somewhere # else) from patsy import (EvalEnvironment, dmatrix, build_design_matrices, PatsyError, Origin) def test_issue_11(): # Give a sensible error message for level mismatches # (At some points we've failed to put an origin= on these errors) env = EvalEnvironment.capture() data = {"X" : [0,1,2,3], "Y" : [1,2,3,4]} formula = "C(X) + Y" new_data = {"X" : [0,0,1,2,3,3,4], "Y" : [1,2,3,4,5,6,7]} info = dmatrix(formula, data) try: build_design_matrices([info.design_info], new_data) except PatsyError as e: assert e.origin == Origin(formula, 0, 4) else: assert False

8ab404c67e6f07e674ae9c5b07f6e6e0e0f914ac

py

Python

skimage/io/_plugins/pil_plugin.py

smheidrich/scikit-image

e9cf8b850c4c2800cc221be6f1dfff6a2a32a4eb

2019-02-28T16:05:36.000Z

2020-04-03T17:29:07.000Z

Lib/site-packages/skimage/io/_plugins/pil_plugin.py

caiyongji/Anaconda-py36.5-tensorflow-built-env

f4eb40b5ca3f49dfc929ff3ad2b4bb877e9663e2

2020-03-24T18:07:06.000Z

2022-03-12T00:12:27.000Z

Lib/site-packages/skimage/io/_plugins/pil_plugin.py

caiyongji/Anaconda-py36.5-tensorflow-built-env

f4eb40b5ca3f49dfc929ff3ad2b4bb877e9663e2

2019-12-31T23:21:40.000Z

2020-04-03T17:29:08.000Z

__all__ = ['imread', 'imsave'] import numpy as np from PIL import Image from ...util import img_as_ubyte, img_as_uint def imread(fname, dtype=None, img_num=None, **kwargs): """Load an image from file. Parameters ---------- fname : str or file File name or file-like-object. dtype : numpy dtype object or string specifier Specifies data type of array elements. img_num : int, optional Specifies which image to read in a file with multiple images (zero-indexed). kwargs : keyword pairs, optional Addition keyword arguments to pass through. Notes ----- Files are read using the Python Imaging Library. See PIL docs [1]_ for a list of supported formats. References ---------- .. [1] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html """ if isinstance(fname, str): with open(fname, 'rb') as f: im = Image.open(f) return pil_to_ndarray(im, dtype=dtype, img_num=img_num) else: im = Image.open(fname) return pil_to_ndarray(im, dtype=dtype, img_num=img_num) def pil_to_ndarray(image, dtype=None, img_num=None): """Import a PIL Image object to an ndarray, in memory. Parameters ---------- Refer to ``imread``. """ try: # this will raise an IOError if the file is not readable image.getdata()[0] except IOError as e: site = "http://pillow.readthedocs.org/en/latest/installation.html#external-libraries" pillow_error_message = str(e) error_message = ('Could not load "%s" \n' 'Reason: "%s"\n' 'Please see documentation at: %s' % (image.filename, pillow_error_message, site)) raise ValueError(error_message) frames = [] grayscale = None i = 0 while 1: try: image.seek(i) except EOFError: break frame = image if img_num is not None and img_num != i: image.getdata()[0] i += 1 continue if image.format == 'PNG' and image.mode == 'I' and dtype is None: dtype = 'uint16' if image.mode == 'P': if grayscale is None: grayscale = _palette_is_grayscale(image) if grayscale: frame = image.convert('L') else: if image.format == 'PNG' and 'transparency' in image.info: frame = image.convert('RGBA') else: frame = image.convert('RGB') elif image.mode == '1': frame = image.convert('L') elif 'A' in image.mode: frame = image.convert('RGBA') elif image.mode == 'CMYK': frame = image.convert('RGB') if image.mode.startswith('I;16'): shape = image.size dtype = '>u2' if image.mode.endswith('B') else '<u2' if 'S' in image.mode: dtype = dtype.replace('u', 'i') frame = np.fromstring(frame.tobytes(), dtype) frame.shape = shape[::-1] else: frame = np.array(frame, dtype=dtype) frames.append(frame) i += 1 if img_num is not None: break if hasattr(image, 'fp') and image.fp: image.fp.close() if img_num is None and len(frames) > 1: return np.array(frames) elif frames: return frames[0] elif img_num: raise IndexError('Could not find image #%s' % img_num) def _palette_is_grayscale(pil_image): """Return True if PIL image in palette mode is grayscale. Parameters ---------- pil_image : PIL image PIL Image that is in Palette mode. Returns ------- is_grayscale : bool True if all colors in image palette are gray. """ assert pil_image.mode == 'P' # get palette as an array with R, G, B columns palette = np.asarray(pil_image.getpalette()).reshape((256, 3)) # Not all palette colors are used; unused colors have junk values. start, stop = pil_image.getextrema() valid_palette = palette[start:stop + 1] # Image is grayscale if channel differences (R - G and G - B) # are all zero. return np.allclose(np.diff(valid_palette), 0) def ndarray_to_pil(arr, format_str=None): """Export an ndarray to a PIL object. Parameters ---------- Refer to ``imsave``. """ if arr.ndim == 3: arr = img_as_ubyte(arr) mode = {3: 'RGB', 4: 'RGBA'}[arr.shape[2]] elif format_str in ['png', 'PNG']: mode = 'I;16' mode_base = 'I' if arr.dtype.kind == 'f': arr = img_as_uint(arr) elif arr.max() < 256 and arr.min() >= 0: arr = arr.astype(np.uint8) mode = mode_base = 'L' else: arr = img_as_uint(arr) else: arr = img_as_ubyte(arr) mode = 'L' mode_base = 'L' try: array_buffer = arr.tobytes() except AttributeError: array_buffer = arr.tostring() # Numpy < 1.9 if arr.ndim == 2: im = Image.new(mode_base, arr.T.shape) try: im.frombytes(array_buffer, 'raw', mode) except AttributeError: im.fromstring(array_buffer, 'raw', mode) # PIL 1.1.7 else: image_shape = (arr.shape[1], arr.shape[0]) try: im = Image.frombytes(mode, image_shape, array_buffer) except AttributeError: im = Image.fromstring(mode, image_shape, array_buffer) # PIL 1.1.7 return im def imsave(fname, arr, format_str=None, **kwargs): """Save an image to disk. Parameters ---------- fname : str or file-like object Name of destination file. arr : ndarray of uint8 or float Array (image) to save. Arrays of data-type uint8 should have values in [0, 255], whereas floating-point arrays must be in [0, 1]. format_str: str Format to save as, this is defaulted to PNG if using a file-like object; this will be derived from the extension if fname is a string kwargs: dict Keyword arguments to the Pillow save function (or tifffile save function, for Tiff files). These are format dependent. For example, Pillow's JPEG save function supports an integer ``quality`` argument with values in [1, 95], while TIFFFile supports a ``compress`` integer argument with values in [0, 9]. Notes ----- Use the Python Imaging Library. See PIL docs [1]_ for a list of other supported formats. All images besides single channel PNGs are converted using `img_as_uint8`. Single Channel PNGs have the following behavior: - Integer values in [0, 255] and Boolean types -> img_as_uint8 - Floating point and other integers -> img_as_uint16 References ---------- .. [1] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html """ # default to PNG if file-like object if not isinstance(fname, str) and format_str is None: format_str = "PNG" # Check for png in filename if (isinstance(fname, str) and fname.lower().endswith(".png")): format_str = "PNG" arr = np.asanyarray(arr) if arr.dtype.kind == 'b': arr = arr.astype(np.uint8) if arr.ndim not in (2, 3): raise ValueError("Invalid shape for image array: %s" % (arr.shape, )) if arr.ndim == 3: if arr.shape[2] not in (3, 4): raise ValueError("Invalid number of channels in image array.") img = ndarray_to_pil(arr, format_str=format_str) img.save(fname, format=format_str, **kwargs)

8ac004a4f19bb41d9cfa8a39529011d30c5a08dc

py

Python

main.py

jonodrew/matchex

531e7cd1c328cb9dc34b601a06648bd2c3e709e6