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

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from five import grok from OFS.interfaces import IObjectClonedEvent from opengever.base import _ from plone.dexterity.interfaces import IDexterityContent from zope.component.hooks import getSite @grok.subscribe(IDexterityContent, IObjectClonedEvent) def create_initial_version(obj, event): """When a object was copied, create an initial version. """ portal = getSite() pr = portal.portal_repository history = pr.getHistory(obj) if history is not None and not len(history) > 0: comment = _(u'label_initial_version_copied', default="Initial version (document copied)") # Create an initial version pr._recursiveSave(obj, {}, pr._prepareSysMetadata(comment), autoapply=pr.autoapply)

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/epidemics/utils/nested.py

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cselab/covid19

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import numpy as np from multiprocessing import Pool import pickle import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt class WorkerPool(object): def __init__(self, cores): self.pool = Pool(processes=cores) self.size = cores def map(self, function, tasks): return self.pool.map(function, tasks) def priorTransformFromJs(p,js): pt = np.zeros(len(p)) for idx in range(len(p)): lb = js['Distributions'][idx]['Minimum'] ub = js['Distributions'][idx]['Maximum'] pt[idx] = lb+p[idx]*(ub-lb) return pt def resample_equal_with_idx(samples, weights, rstate=None): if rstate is None: rstate = np.random if abs(np.sum(weights) - 1.) > 1e-9: # same tol as in np.random.choice. # Guarantee that the weights will sum to 1. warnings.warn("Weights do not sum to 1 and have been renormalized.") weights = np.array(weights) / np.sum(weights) # Make N subdivisions and choose positions with a consistent random offset. nsamples = len(weights) positions = (rstate.random() + np.arange(nsamples)) / nsamples # Resample the data. idx = np.zeros(nsamples, dtype=np.int) cumulative_sum = np.cumsum(weights) i, j = 0, 0 while i < nsamples: if positions[i] < cumulative_sum[j]: idx[i] = j i += 1 else: j += 1 return samples[idx], idx def getPosteriorFromResult(result): weights = np.exp(result.logwt - result.logz[-1]) # normalized weights samples, idx = resample_equal_with_idx(result.samples, weights) return samples, idx # Plot histogram of sampes in diagonal def plot_histogram(ax, theta): dim = theta.shape[1] num_bins = 30 for i in range(dim): if (dim == 1): ax_loc = ax else: ax_loc = ax[i, i] hist, bins, _ = ax_loc.hist( theta[:, i], num_bins, density=True, color='lightgreen', ec='black') if i == 0: # Rescale hist to scale of theta -> get correct axis titles widths = np.diff(bins) if (dim > 1): hist = hist / np.max(hist) * ( ax_loc.get_xlim()[1] - ax_loc.get_xlim()[0]) bottom = ax_loc.get_xlim()[0] ax_loc.cla() ax_loc.bar( bins[:-1], hist, widths, color='lightgreen', ec='black', bottom=bottom) ax_loc.set_ylim(ax_loc.get_xlim()) ax_loc.set_xticklabels([]) else: ax_loc.cla() ax_loc.bar(bins[:-1], hist, widths, color='lightgreen', ec='black') elif i == theta.shape[1] - 1: ax_loc.set_yticklabels([]) else: ax_loc.set_xticklabels([]) ax_loc.set_yticklabels([]) ax_loc.tick_params(axis='both', which='both', length=0) #Plot scatter plot in upper triangle of figure def plot_upper_triangle(ax, theta, lik): dim = theta.shape[1] if (dim == 1): return for i in range(dim): for j in range(i + 1, dim): if lik: ax[i, j].scatter( theta[:, j], theta[:, i], marker='o', s=3, alpha=0.5, c=lik) else: ax[i, j].plot(theta[:, j], theta[:, i], marker='.', s=1, alpha=0.5) ax[i, j].set_xticklabels([]) ax[i, j].set_yticklabels([]) ax[i, j].grid(b=True, which='both') #Plot 2d histogram in lower triangle of figure def plot_lower_triangle(ax, theta): dim = theta.shape[1] if (dim == 1): return for i in range(dim): for j in range(i): # returns bin values, bin edges and bin edges H, xe, ye = np.histogram2d(theta[:, j], theta[:, i], 10, density=True) # plot and interpolate data ax[i, j].imshow( H.T, aspect="auto", interpolation='spline16', origin='lower', extent=np.hstack((ax[j, j].get_xlim(), ax[i, i].get_xlim())), cmap=plt.get_cmap('jet')) if i < theta.shape[1] - 1: ax[i, j].set_xticklabels([]) if j > 0: ax[i, j].set_yticklabels([]) def plotNetsedResult(result, savepath=None): samples, idx = getPosteriorFromResult(result) numdim = len(samples[0]) numentries = len(samples) llk = (result.logl[idx]).tolist() fig, ax = plt.subplots(numdim, numdim, figsize=(8, 8)) samplesTmp = np.reshape(samples, (numentries, numdim)) plt.suptitle( '{0} Plotter - \nNumber of Samples {1}'.format( str("Nested Sampling"), str(numentries)).strip(), fontweight='bold', fontsize=12) plot_histogram(ax, samplesTmp) plot_upper_triangle(ax, samplesTmp, llk) plot_lower_triangle(ax, samplesTmp) # for i in range(numdim): # ax[i, 0].set_ylabel(genList[idx]['Variables'][i]['Name']) # ax[-1, i].set_xlabel(genList[idx]['Variables'][i]['Name']) if (savepath==None): print("TEST") plt.show() else: plt.savefig(savepath)

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import math def calcula_euler(x,n): contador=2 inicio=1+x while(soma<n): inicio+=x**contador/math.factorial(contador) contador+=1 return soma

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you@example.com

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/Data Structures/1-D/Help Jarvis!.py

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anshumanairy/Hacker-Earth

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def func(): T=int(input()) for i in range(T): list1=list(input()) list1.sort() check=1 for j in range(len(list1)-1): if(int(list1[j+1])!=int(list1[j])+1): check=0 print('NO') break if(check==1): print('YES') func()

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anshuman.airy04@gmail.com

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/app/travel/models/city_information.py

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kahee/MySmallTrip

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import magic from .product_base import ProductBase from django.core.files import File from django.db import models from io import BytesIO from PIL import Image class CityInformation(ProductBase): name = models.CharField('도시명', max_length=20) continent = models.CharField('대륙', max_length=20) nationality = models.CharField('나라', max_length=20) city_image = models.ImageField('도시이미지', upload_to='city') city_image_thumbnail = models.ImageField(upload_to='city-thumbnail') def save(self, *args, **kwargs): self._save_thumbnail_process() super().save(*args, **kwargs) def _save_thumbnail_process(self): """ save() 메서드 실행 도중 img_profile필드의 썸네일 생성에 관한 로직 :return: """ if self.city_image: # 이미지파일의 이름과 확장자를 가져옴 full_name = self.city_image.name.rsplit('/')[-1] full_name_split = full_name.rsplit('.', maxsplit=1) temp_file = BytesIO() temp_file.write(self.city_image.read()) temp_file.seek(0) mime_info = magic.from_buffer(temp_file.read(), mime=True) temp_file.seek(0) name = full_name_split[0] ext = mime_info.split('/')[-1] # Pillow를 사용해 이미지 파일 로드 im = Image.open(self.city_image) # 썸네일 형태로 데이터 변경 im.thumbnail((375, 199)) # 썸네일 이미지 데이터를 가지고 있을 임시 메모리 파일 생성 temp_file = BytesIO() # 임시 메모리 파일에 Pillow인스턴스의 내용을 기록 im.save(temp_file, ext) # 임시 메모리파일을 Django의 File로 한번 감싸 썸네일 필드에 저장 self.city_image_thumbnail.save(f'{name}_thumbnail.{ext}', File(temp_file), save=False) else: self.city_image_thumbnail.delete(save=False)

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hsj2334@gmail.com

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/devel/.private/hector_mapping/lib/python2.7/dist-packages/hector_mapping/msg/_HectorDebugInfo.py

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# This Python file uses the following encoding: utf-8 """autogenerated by genpy from hector_mapping/HectorDebugInfo.msg. Do not edit.""" import codecs import sys python3 = True if sys.hexversion > 0x03000000 else False import genpy import struct import hector_mapping.msg class HectorDebugInfo(genpy.Message): _md5sum = "4d33c0696c0c536f5c1447c260756674" _type = "hector_mapping/HectorDebugInfo" _has_header = False # flag to mark the presence of a Header object _full_text = """HectorIterData[] iterData ================================================================================ MSG: hector_mapping/HectorIterData float64[9] hessian float64 conditionNum float64 determinant float64 conditionNum2d float64 determinant2d """ __slots__ = ['iterData'] _slot_types = ['hector_mapping/HectorIterData[]'] def __init__(self, *args, **kwds): """ Constructor. Any message fields that are implicitly/explicitly set to None will be assigned a default value. The recommend use is keyword arguments as this is more robust to future message changes. You cannot mix in-order arguments and keyword arguments. The available fields are: iterData :param args: complete set of field values, in .msg order :param kwds: use keyword arguments corresponding to message field names to set specific fields. """ if args or kwds: super(HectorDebugInfo, self).__init__(*args, **kwds) # message fields cannot be None, assign default values for those that are if self.iterData is None: self.iterData = [] else: self.iterData = [] def _get_types(self): """ internal API method """ return self._slot_types def serialize(self, buff): """ serialize message into buffer :param buff: buffer, ``StringIO`` """ try: length = len(self.iterData) buff.write(_struct_I.pack(length)) for val1 in self.iterData: buff.write(_get_struct_9d().pack(*val1.hessian)) _x = val1 buff.write(_get_struct_4d().pack(_x.conditionNum, _x.determinant, _x.conditionNum2d, _x.determinant2d)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize(self, str): """ unpack serialized message in str into this message instance :param str: byte array of serialized message, ``str`` """ codecs.lookup_error("rosmsg").msg_type = self._type try: if self.iterData is None: self.iterData = None end = 0 start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) self.iterData = [] for i in range(0, length): val1 = hector_mapping.msg.HectorIterData() start = end end += 72 val1.hessian = _get_struct_9d().unpack(str[start:end]) _x = val1 start = end end += 32 (_x.conditionNum, _x.determinant, _x.conditionNum2d, _x.determinant2d,) = _get_struct_4d().unpack(str[start:end]) self.iterData.append(val1) return self except struct.error as e: raise genpy.DeserializationError(e) # most likely buffer underfill def serialize_numpy(self, buff, numpy): """ serialize message with numpy array types into buffer :param buff: buffer, ``StringIO`` :param numpy: numpy python module """ try: length = len(self.iterData) buff.write(_struct_I.pack(length)) for val1 in self.iterData: buff.write(val1.hessian.tostring()) _x = val1 buff.write(_get_struct_4d().pack(_x.conditionNum, _x.determinant, _x.conditionNum2d, _x.determinant2d)) except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self))))) except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self))))) def deserialize_numpy(self, str, numpy): """ unpack serialized message in str into this message instance using numpy for array types :param str: byte array of serialized message, ``str`` :param numpy: numpy python module """ codecs.lookup_error("rosmsg").msg_type = self._type try: if self.iterData is None: self.iterData = None end = 0 start = end end += 4 (length,) = _struct_I.unpack(str[start:end]) self.iterData = [] for i in range(0, length): val1 = hector_mapping.msg.HectorIterData() start = end end += 72 val1.hessian = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=9) _x = val1 start = end end += 32 (_x.conditionNum, _x.determinant, _x.conditionNum2d, _x.determinant2d,) = _get_struct_4d().unpack(str[start:end]) self.iterData.append(val1) return self except struct.error as e: raise genpy.DeserializationError(e) # most likely buffer underfill _struct_I = genpy.struct_I def _get_struct_I(): global _struct_I return _struct_I _struct_4d = None def _get_struct_4d(): global _struct_4d if _struct_4d is None: _struct_4d = struct.Struct("<4d") return _struct_4d _struct_9d = None def _get_struct_9d(): global _struct_9d if _struct_9d is None: _struct_9d = struct.Struct("<9d") return _struct_9d

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arijit.dg@hotmail.com

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from queue import PriorityQueue def prim_list(G, s): V = len(G) Q = PriorityQueue() visited = [False] * V parent = [None] * V d = [float('inf')] * V d[s] = 0 Q.put((d[s], s)) def relax(u, v, w): if d[v] > w: d[v] = w parent[v] = u Q.put((d[v], v)) while not Q.empty(): (_, u) = Q.get() visited[u] = True for v in G[u]: if not visited[v[0]]: relax(u, v[0], v[1]) result = ['MST:'] sum = 0 for i in range(V): if parent[i] is not None: for j in range(len(G[i])): if G[i][j][0] == parent[i]: sum += G[i][j][1] result.append('edge: ({}, {}) weight: {}'.format(parent[i], i, G[i][j][1])) result.append('sum of weights: {}'.format(sum)) return result G = [[(1, 4), (7, 8)], [(0, 4), (2, 8), (7, 11)], [(1, 8), (5, 4), (8, 2), (3, 7)], [(2, 7), (4, 9), (5, 14)], [(3, 9), (5, 10)], [(3, 14), (2, 4), (6, 2), (4, 10)], [(5, 2), (7, 1), (8, 6)], [(6, 1), (0, 8), (1, 11), (8, 7)], [(6, 6), (7, 7), (2, 2)]] arr = prim_list(G, 1) for i in range(len(arr)): print(arr[i]) H = [[(1, 28), (5, 10)], [(0, 28), (2, 16), (6, 14)], [(1, 16), (3, 12)], [(2, 12), (6, 18), (4, 22)], [(3, 22), (6, 24), (5, 25)], [(0, 10), (4, 25)], [(1, 14), (3, 18), (4, 24)]] print('\n') arr = prim_list(H, 5) for i in range(len(arr)): print(arr[i]) I = [[(1, 3), (2, 1)], [(0, 3), (2, 1)], [(0, 1), (1, 1)]] print('\n') arr = prim_list(I, 0) for i in range(len(arr)): print(arr[i])

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zakuro9715/atcoder

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import urllib.request import settings endpoint = 'https://game.coderunner.jp' token = settings.token def submit(text): return _request('{0}/q?str={1}&token={2}'.format(endpoint, text, token)) def update_profile(text): return _request('{0}/profile?text={1}&token={2}'.format(endpoint, text, token)) def _request(url): try: res = url, urllib.request.urlopen(url).read(), None except urllib.error.HTTPError as e: res = url, None, e finally: f = open(settings.logs, 'a') f.writelines(map(lambda x: '{0}\n'.format(x), res)) f.write('\n') f.close() return res

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# 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 # https://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 pdb import tensorflow as tf from basenji import ops def stochastic_rc(seq_1hot): """Stochastically reverse complement a one hot encoded DNA sequence.""" rcseq_1hot = tf.gather(seq_1hot, [3, 2, 1, 0], axis=-1) rcseq_1hot = tf.reverse(rcseq_1hot, axis=[1]) reverse_bool = tf.random_uniform(shape=[]) > 0.5 seq_1hot_aug = tf.cond(reverse_bool, lambda: rcseq_1hot, lambda: seq_1hot) return seq_1hot_aug, reverse_bool def reverse_preds(rp_tuple): (preds, reverse_bool) = rp_tuple preds_rev = tf.reverse(preds, axis=[1]) preds_match = tf.cond(reverse_bool, lambda: preds, lambda: preds_rev) return preds_match ################################################################################ def shift_sequence(seq, shift_amount, pad_value=0.25): """Shift a sequence left or right by shift_amount. Args: seq: a [batch_size, sequence_length, sequence_depth] sequence to shift shift_amount: the signed amount to shift (tf.int32 or int) pad_value: value to fill the padding (primitive or scalar tf.Tensor) """ if seq.shape.ndims != 3: raise ValueError('input sequence should be rank 3') input_shape = seq.shape pad = pad_value * tf.ones_like(seq[:, 0:tf.abs(shift_amount), :]) def _shift_right(_seq): sliced_seq = _seq[:, :-shift_amount:, :] return tf.concat([pad, sliced_seq], axis=1) def _shift_left(_seq): sliced_seq = _seq[:, -shift_amount:, :] return tf.concat([sliced_seq, pad], axis=1) output = tf.cond( tf.greater(shift_amount, 0), lambda: _shift_right(seq), lambda: _shift_left(seq)) output.set_shape(input_shape) return output def augment_deterministic_set(data_ops, augment_rc=False, augment_shifts=[0]): """ Args: data_ops: dict with keys 'sequence,' 'label,' and 'na.' augment_rc: Boolean augment_shifts: List of ints. Returns data_ops_list: """ augment_pairs = [] for ashift in augment_shifts: augment_pairs.append((False, ashift)) if augment_rc: augment_pairs.append((True, ashift)) data_ops_list = [] for arc, ashift in augment_pairs: data_ops_aug = augment_deterministic(data_ops, arc, ashift) data_ops_list.append(data_ops_aug) return data_ops_list def augment_deterministic(data_ops, augment_rc=False, augment_shift=0): """Apply a deterministic augmentation, specified by the parameters. Args: data_ops: dict with keys 'sequence,' 'label,' and 'na.' augment_rc: Boolean augment_shift: Int Returns data_ops: augmented data, with all existing keys transformed and 'reverse_preds' bool added. """ data_ops_aug = {} for key in data_ops: if key not in ['sequence']: data_ops_aug[key] = data_ops[key] if augment_shift == 0: data_ops_aug['sequence'] = data_ops['sequence'] else: shift_amount = tf.constant(augment_shift, shape=(), dtype=tf.int64) data_ops_aug['sequence'] = shift_sequence(data_ops['sequence'], shift_amount) if augment_rc: data_ops_aug = augment_deterministic_rc(data_ops_aug) else: data_ops_aug['reverse_preds'] = tf.zeros((), dtype=tf.bool) return data_ops_aug def augment_deterministic_rc(data_ops): """Apply a deterministic reverse complement augmentation. Args: data_ops: dict with keys 'sequence,' 'label,' and 'na.' Returns data_ops_aug: augmented data ops """ data_ops_aug = ops.reverse_complement_transform(data_ops) data_ops_aug['reverse_preds'] = tf.ones((), dtype=tf.bool) return data_ops_aug def augment_stochastic_rc(data_ops): """Apply a stochastic reverse complement augmentation. Args: data_ops: dict with keys 'sequence,' 'label,' and 'na.' Returns data_ops_aug: augmented data """ reverse_preds = tf.random_uniform(shape=[]) > 0.5 data_ops_aug = tf.cond(reverse_preds, lambda: ops.reverse_complement_transform(data_ops), lambda: data_ops.copy()) data_ops_aug['reverse_preds'] = reverse_preds return data_ops_aug def augment_stochastic_shifts(seq, augment_shifts): """Apply a stochastic shift augmentation. Args: seq: input sequence of size [batch_size, length, depth] augment_shifts: list of int offsets to sample from Returns: shifted and padded sequence of size [batch_size, length, depth] """ shift_index = tf.random_uniform(shape=[], minval=0, maxval=len(augment_shifts), dtype=tf.int64) shift_value = tf.gather(tf.constant(augment_shifts), shift_index) seq = tf.cond(tf.not_equal(shift_value, 0), lambda: shift_sequence(seq, shift_value), lambda: seq) return seq def augment_stochastic(data_ops, augment_rc=False, augment_shifts=[]): """Apply stochastic augmentations, Args: data_ops: dict with keys 'sequence,' 'label,' and 'na.' augment_rc: Boolean for whether to apply reverse complement augmentation. augment_shifts: list of int offsets to sample shift augmentations. Returns: data_ops_aug: augmented data """ if augment_shifts: data_ops['sequence'] = augment_stochastic_shifts(data_ops['sequence'], augment_shifts) if augment_rc: data_ops = augment_stochastic_rc(data_ops) else: data_ops['reverse_preds'] = tf.zeros((), dtype=tf.bool) return data_ops

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""" Physics simulation with PyODE followed by a (basic) rendering with Vapory See the result here: http://i.imgur.com/TdhxwGz.gifv Zulko 2014 This script is placed in the Public Domain (Licence Creative Commons 0) """ # =============== FIRST PART : SIMULATION WITH PyODE import ode lx, ly, lz, density = 1.0, 1.0, 1.0, 1.0 world = ode.World() world.setGravity( (0,-9.81,0) ) world.setCFM(1E-6) space = ode.Space() contactgroup = ode.JointGroup() geoms = [] def near_callback(args, geom1, geom2): """Callback function for the collide() method below. This function checks if the given geoms do collide and creates contact joints if they do. """ contacts = ode.collide(geom1, geom2) world,contactgroup = args for c in contacts: c.setBounce(0.01) c.setMu(60000) j = ode.ContactJoint(world, contactgroup, c) j.attach(geom1.getBody(), geom2.getBody()) def new_cube(xyz): """ Creates a new PyODE cude at position (x,y,z) """ body = ode.Body(world) M = ode.Mass() M.setBox(density, lx, ly, lz) body.setMass(M) body.shape = "box" body.boxsize = (lx, ly, lz) body.setPosition(xyz) geom = ode.GeomBox(space, lengths=body.boxsize) geom.setBody(body) geoms.append(geom) # avoids that geom gets trashed return body # The objects of the scene: floor = ode.GeomPlane(space, (0,1,0), 0) cubes = [new_cube(xyz) for xyz in [(0.5,3,0.5),(0.5,4,0),(0,5,0),(-0.5,6,0), (-0.5,7,-0.5),(0,8,0.5)]] # Start the simulation ! t = 0.0 dt = 0.005 duration = 4.0 trajectories = [] while t<duration: trajectories.append([(c.getPosition(), c.getRotation()) for c in cubes]) space.collide((world,contactgroup), near_callback) world.step(dt) contactgroup.empty() t+=dt # =============== SECOND PART : RENDERING WITH VAPORY from moviepy.editor import VideoClip, ipython_display from vapory import * light = LightSource( [10,10,10], 'color', [3,3,3], 'parallel', 'point_at', [0, 0, 0]) ground = Plane([0,1,0],0, Texture('Rosewood')) def vapory_box(xyz, R): """ Draws a box with at the given position and rotation""" return Box([-lx/2, -ly/2, -lz/2], [lx/2, ly/2, lz/2], Texture('T_Ruby_Glass'), Interior('ior',4), 'matrix', R+xyz) def make_frame(t): """ Returns the image of the scene rendered at time t """ boxes = [vapory_box(position, rotation) for (position, rotation) in trajectories[int(t/dt)]] scene = Scene( Camera("location", [0,3,-4], "look_at", [0,0,0]), [light, ground, Background("White")] + boxes, included=["colors.inc", "textures.inc", "glass.inc"]) return scene.render(height=300, width=400, antialiasing=0.0001) clip = VideoClip(make_frame, duration=duration) clip.write_videofile("pyODE.avi", codec='png', fps=20) # lossless format

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# -*- coding: utf-8 -*- #!/usr/bin/env python # # Copyright 2015-2016 BigML # # 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. """ Testing prediction creation in DEV mode """ from world import world, setup_module, teardown_module import create_source_steps as source_create import read_source_steps as source_read import create_dataset_steps as dataset_create import create_model_steps as model_create import create_prediction_steps as prediction_create class TestDevPrediction(object): def setup(self): """ Switches to DEV mode for this class methods only """ print "\n-------------------\nTests in: %s\n" % __name__ world.api.delete_project(world.project_id) world.api = world.api_dev_mode world.project_id = world.api.create_project( \ {"name": world.test_project_name})['resource'] def teardown(self): """ Debug information """ print "\nEnd of tests in: %s\n-------------------\n" % __name__ def test_scenario1(self): """ Scenario: Successfully creating a prediction in DEV mode: Given I want to use api in DEV mode When I create a data source uploading a "<data>" file And I wait until the source is ready less than <time_1> secs And the source has DEV True And I create a dataset And I wait until the dataset is ready less than <time_2> secs And I create a model And I wait until the model is ready less than <time_3> secs When I create a prediction for "<data_input>" Then the prediction for "<objective>" is "<prediction>" Examples: | data | time_1 | time_2 | time_3 | data_input | objective | prediction | | ../data/iris.csv | 10 | 10 | 10 | {"petal width": 0.5} | 000004 | Iris-setosa | """ print self.test_scenario1.__doc__ examples = [ ['data/iris.csv', '10', '10', '10', '{"petal width": 0.5}', '000004', 'Iris-setosa']] for example in examples: print "\nTesting with:\n", example source_create.i_upload_a_file(self, example[0]) source_create.the_source_is_finished(self, example[1]) source_read.source_has_dev(self, True) dataset_create.i_create_a_dataset(self) dataset_create.the_dataset_is_finished_in_less_than(self, example[2]) model_create.i_create_a_model(self) model_create.the_model_is_finished_in_less_than(self, example[3]) prediction_create.i_create_a_prediction(self, example[4]) prediction_create.the_prediction_is(self, example[5], example[6])

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# Copyright 2015 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. """Command to set IAM policy for a resource.""" from __future__ import absolute_import from __future__ import unicode_literals from googlecloudsdk.calliope import base from googlecloudsdk.command_lib.iam import iam_util from googlecloudsdk.command_lib.organizations import flags from googlecloudsdk.command_lib.organizations import orgs_base @base.ReleaseTracks( base.ReleaseTrack.GA, base.ReleaseTrack.BETA, base.ReleaseTrack.ALPHA) class SetIamPolicy(orgs_base.OrganizationCommand): """Set IAM policy for an organization. Given an organization ID and a file encoded in JSON or YAML that contains the IAM policy, this command will set the IAM policy for that organization. """ detailed_help = { 'EXAMPLES': ( '\n'.join([ 'The following command reads an IAM policy defined in a JSON', 'file `policy.json` and sets it for an organization with the ID', '`123456789`:', '', ' $ {command} 123456789 policy.json', ])) } @staticmethod def Args(parser): flags.IdArg('whose IAM policy you want to set.').AddToParser(parser) parser.add_argument( 'policy_file', help='JSON or YAML file containing the IAM policy.') def Run(self, args): messages = self.OrganizationsMessages() policy = iam_util.ParsePolicyFile(args.policy_file, messages.Policy) update_mask = iam_util.ConstructUpdateMaskFromPolicy(args.policy_file) # To preserve the existing set-iam-policy behavior of always overwriting # bindings and etag, add bindings and etag to update_mask. if 'bindings' not in update_mask: update_mask += ',bindings' if 'etag' not in update_mask: update_mask += ',etag' set_iam_policy_request = messages.SetIamPolicyRequest( policy=policy, updateMask=update_mask) policy_request = ( messages.CloudresourcemanagerOrganizationsSetIamPolicyRequest( organizationsId=args.id, setIamPolicyRequest=set_iam_policy_request)) result = self.OrganizationsClient().SetIamPolicy(policy_request) iam_util.LogSetIamPolicy(args.id, 'organization') return result

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# !/usr/bin/env python # -*-coding:utf-8 -*- # PROJECT : Python-Exercise # Time ：2020/12/8 23:02 # Warning ：The Hard Way Is Easier from apscheduler.jobstores.redis import RedisJobStore # from apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore from apscheduler.executors.pool import ThreadPoolExecutor, ProcessPoolExecutor job_stores = { "redis": RedisJobStore(), # 设置一个名为redis的job存储，后端使用 redis # 一个名为 default 的 job 存储，后端使用数据库（使用 Sqlite） # "default": SQLAlchemyJobStore(url="sqlite:///jobs.sqlite") } executors = { "default": ThreadPoolExecutor(20), # 设置一个名为 default的线程池执行器，最大线程设置为20个 "processpool": ProcessPoolExecutor(5), # 设置一个名为 processpool的进程池执行器，最大进程数设为5个 } # 开启job合并，设置job最大实例上限为3 job_default = { 'coalesce': False, 'max_instances': 3 }

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# sync_lock.py #coding=utf-8 import threading import time ''' charactors: 1. only 1 global 2. only 1 lock 3. before add, acquire lock,after add, release lock ''' g_num = 0 # 创建一个互斥锁 # 默认是未上锁的状态 mutex = threading.Lock() def work1(num): global g_num for i in range(num): mutex.acquire() # 上锁 g_num += 1 mutex.release() # 解锁 print("----in work1, g_num is %d---"%g_num) def work2(num): global g_num for i in range(num): mutex.acquire() # 上锁 g_num += 1 mutex.release() # 解锁 print("----in work2, g_num is %d---"%g_num) def main(): print("---线程创建之前g_num is %d---"%g_num) # 创建两个线程，各自对g_num进行相加 t1 = threading.Thread(target=work1, args=(10000000,)) t1.start() t2 = threading.Thread(target=work2, args=(10000000,)) t2.start() while len(threading.enumerate()) != 1: time.sleep(1) print("2个线程对同一个全局变量操作之后的最终结果是:%s" % g_num) if __name__ == "__main__": main() ''' first time: ---线程创建之前g_num is 0--- ----in work2, g_num is 19820884--- ----in work1, g_num is 20000000--- 2个线程对同一个全局变量操作之后的最终结果是:20000000 second time: ---线程创建之前g_num is 0--- ----in work2, g_num is 19739028--- ----in work1, g_num is 20000000--- 2个线程对同一个全局变量操作之后的最终结果是:20000000 锁的好处：确保了某段关键代码只能由一个线程从头到尾完整地执行锁的坏处：阻止了多线程并发执行，包含锁的某段代码实际上只能以单线程模式执行，效率就大大地下降了由于可以存在多个锁，不同的线程持有不同的锁，并试图获取对方持有的锁时，可能会造成死锁 Avoid dead lock 综上所述，银行家算法是从当前状态出发，逐个按安全序列检查各客户谁能完成其工作，然后假定其完成工作且归还全部贷款，再进而检查下一个能完成工作的客户，......。如果所有客户都能完成工作，则找到一个安全序列，银行家才是安全的 '''

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''' A host gets a set of back to back reservation requests (inquiries) for stays of various lengths. The information about these requests is stored in an array, where each value is an integer representing the length of a stay in nights. All of these inquiries are back to back, which means that stay number i+1 starts right after stay number i ends. The host wants to have time to prepare the listing for the next guest, so they don't want to accept inquiries that are adjacent to each other. In other words, if they accept inquiry i, they can't also accept either i-1 or i+1. Under these conditions, what is the maximum number of nights the host can accept? Some examples: [5, 1, 2, 6] = 11 [5,2] [5,6] [1,6] [4, 9, 6] = 10 [4, 6] [9] [4, 11, 6] = 11 [4, 10, 3, 1, 5] = 15 [1] -> [1] [1,2] -> [1] [2] [1,2,3] -> [1,3], [2], [3] [1,2,3,4] -> [1,3] [1,4] [2,4] every index will generate n-2 ''' import copy def max_nights(array): """ Dynamic programming solution. O(n) """ if len(array) < 1: return 0 elif len(array) <= 2: return max(array) else: compare = [[array[0]], array[0]] if array[1] > array[0]: gap = [[array[1]], array[1]] else: gap = copy.deepcopy(compare) for i in xrange(2, len(array)): if compare[1] + array[i] > gap[1]: compare[0].append(array[i]) compare[1] = compare[1] + array[i] compare, gap = gap, compare else: compare = copy.deepcopy(gap) return gap # assumptions # min 0 size # max infinite # all integeres in array are positive and no 0s # get subsets of from the original array based on rules to omit immediate next # request # compute the sum of subsets and return the largest value # O(n * n-2) -> O(n^2) def maximum_nights(array): if len(array) < 1: return 0 largest = 0 for i in xrange(len(array)): # O(n) temp = 0 if i == len(array)-2: if array[i] > largest: largest = array[i] break for item in array[i+2:]: # O(n-2) temp = array[i] + item if temp > largest: largest = temp return largest # O(n) # possible to update the largest with only 1 run def maximum_nights3(array): if len(array) < 1: return 0 largest = 0 for i in xrange(len(array)): if i == len(array)-2: if array[i] > largest: largest = array[i] break sliced = array[i+2:] if len(sliced) < 1: continue temp = array[i] + max(sliced) if temp > largest: largest = temp return largest if __name__ == "__main__": test1 = [5, 1, 2, 6, 20, 2] test2 = [4, 9, 6] test3 = [4, 11, 6] test4 = [4, 10, 3, 1, 5] print maximum_nights3(test1), max_nights(test1) # 11 print maximum_nights3(test2), max_nights(test2) # 10 print maximum_nights3(test3), max_nights(test3) # 11 print maximum_nights3(test4), max_nights(test4) # 15

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import ghidra.graph.job import ghidra.util.task import java.awt.geom import java.lang class MoveVertexToCenterAnimatorFunctionGraphJob(ghidra.graph.job.MoveViewAnimatorFunctionGraphJob): def __init__(self, __a0: edu.uci.ics.jung.visualization.VisualizationServer, __a1: object, __a2: bool): ... def canShortcut(self) -> bool: ... def dispose(self) -> None: ... def equals(self, __a0: object) -> bool: ... def execute(self, listener: ghidra.graph.job.GraphJobListener) -> None: ... def getClass(self) -> java.lang.Class: ... def hashCode(self) -> int: ... def isFinished(self) -> bool: ... def notify(self) -> None: ... def notifyAll(self) -> None: ... def setBusyListener(self, listener: ghidra.util.task.BusyListener) -> None: ... def setOffset(self, offsetFromOriginalPoint: java.awt.geom.Point2D) -> None: ... def shortcut(self) -> None: ... def toString(self) -> unicode: ... @overload def wait(self) -> None: ... @overload def wait(self, __a0: long) -> None: ... @overload def wait(self, __a0: long, __a1: int) -> None: ...

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# -*- coding: utf-8 -*- # BSD 3-Clause License # # Copyright (c) 2017 # All rights reserved. # Copyright 2022 Huawei Technologies Co., Ltd # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ========================================================================== # -*- coding: utf-8 -*- # BSD 3-Clause License # # Copyright (c) 2017 # All rights reserved. # Copyright 2022 Huawei Technologies Co., Ltd # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ========================================================================== _base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py'] checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b3_20220624-13b1141c.pth' # noqa model = dict( backbone=dict( init_cfg=dict(type='Pretrained', checkpoint=checkpoint), embed_dims=64, num_layers=[3, 4, 18, 3]), decode_head=dict(in_channels=[64, 128, 320, 512]))

[ "wangjiangben@huawei.com" ]

wangjiangben@huawei.com

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/dayu_widgets/examples/MMessageTest.py

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DangoWang/dayu_widgets

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refs/heads/master

2020-06-06T05:37:24.986564

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#!/usr/bin/env python # -*- coding: utf-8 -*- ################################################################### # Author: Mu yanru # Date : 2019.2 # Email : muyanru345@163.com ################################################################### import functools from dayu_widgets.MButtonGroup import MPushButtonGroup from dayu_widgets.MDivider import MDivider from dayu_widgets.MFieldMixin import MFieldMixin from dayu_widgets.MLabel import MLabel from dayu_widgets.MMessage import MMessage from dayu_widgets.MPushButton import MPushButton from dayu_widgets.qt import * class MMessageTest(QWidget, MFieldMixin): def __init__(self, parent=None): super(MMessageTest, self).__init__(parent) self._init_ui() def _init_ui(self): button3 = MPushButton(text='Normal Message', type=MPushButton.PrimaryType) button4 = MPushButton(text='Success Message', type=MPushButton.SuccessType) button5 = MPushButton(text='Warning Message', type=MPushButton.WarningType) button6 = MPushButton(text='Error Message', type=MPushButton.ErrorType) button3.clicked.connect(functools.partial(self.slot_show_message, MMessage.info, {'text': u'这是一条普通提示'})) button4.clicked.connect(functools.partial(self.slot_show_message, MMessage.success, {'text': u'恭喜你，成功啦！'})) button5.clicked.connect(functools.partial(self.slot_show_message, MMessage.warning, {'text': u'我警告你哦！'})) button6.clicked.connect(functools.partial(self.slot_show_message, MMessage.error, {'text': u'失败了！'})) sub_lay1 = QHBoxLayout() sub_lay1.addWidget(button3) sub_lay1.addWidget(button4) sub_lay1.addWidget(button5) sub_lay1.addWidget(button6) button_duration = MPushButton(text='show 5s Message') button_duration.clicked.connect(functools.partial(self.slot_show_message, MMessage.info, {'text': u'该条消息将显示5秒后关闭', 'duration': 5 })) button_closable = MPushButton(text='closable Message') button_closable.clicked.connect(functools.partial(self.slot_show_message, MMessage.info, {'text': u'可手动关闭提示', 'closable': True })) main_lay = QVBoxLayout() main_lay.addWidget(MDivider('different type')) main_lay.addLayout(sub_lay1) main_lay.addWidget(MLabel(u'不同的提示状态：普通、成功、警告、错误。默认2秒后消失')) main_lay.addWidget(MDivider('set duration')) main_lay.addWidget(button_duration) main_lay.addWidget(MLabel(u'自定义时长，config中设置duration值，单位为秒')) main_lay.addWidget(MDivider('set closable')) main_lay.addWidget(button_closable) main_lay.addWidget(MLabel(u'设置是否可关闭，config中设置closable 为 True')) button_grp = MPushButtonGroup() button_grp.set_button_list([ {'text': 'set duration to 1s', 'clicked': functools.partial(self.slot_set_config, MMessage.config, {'duration': 1})}, {'text': 'set duration to 10s', 'clicked': functools.partial(self.slot_set_config, MMessage.config, {'duration': 10})}, {'text': 'set top to 5', 'clicked': functools.partial(self.slot_set_config, MMessage.config, {'top': 5})}, {'text': 'set top to 50', 'clicked': functools.partial(self.slot_set_config, MMessage.config, {'top': 50})}, ]) loading_button = MPushButton.primary('Display a loading indicator') loading_button.clicked.connect(self.slot_show_loading) main_lay.addWidget(MDivider('set global setting')) main_lay.addWidget(button_grp) main_lay.addWidget(MLabel(u'全局设置默认duration（默认2秒）；top（离parent顶端的距离，默认24px）')) main_lay.addWidget(loading_button) main_lay.addStretch() self.setLayout(main_lay) def slot_show_message(self, func, config): func(parent=self, **config) def slot_set_config(self, func, config): func(**config) def slot_show_loading(self): msg = MMessage.loading(u'正在加载中', parent=self) msg.sig_closed.connect(functools.partial(MMessage.success, u'加载成功啦！！哈哈哈哈', self)) if __name__ == '__main__': import sys app = QApplication(sys.argv) test = MMessageTest() from dayu_widgets import dayu_theme dayu_theme.apply(test) test.show() sys.exit(app.exec_())

[ "muyanru@phenom-films.com" ]

muyanru@phenom-films.com

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/pong-client/pong.py

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[]

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elgrandt/Pong-Network

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import pygame import network from gui import server_list,events,server_connect_menu,status_cur,loading import gui import global_knowns from twisted.internet import reactor,threads import sys import thread import global_knowns as gk import elements class game: def __init__(self,IOSYS): self.IOSYS = IOSYS self.elements = [] menu_connect = server_connect_menu.server_connect() menu_connect.set_position((300-menu_connect.W/2,200-menu_connect.H/2)) self.mc = menu_connect self.elements.append(menu_connect) self.STATUS = "TOCONNECT" def handleData(self,data): if (data["packet"] == global_knowns.welcome): self.handleStart() elif (data["packet"] == gk.rooms_information): self.update_rooms(data["rooms"]) elif (data["packet"] == gk.extra_rooms_info): self.update_room_extra(data["data"]) elif (data["packet"] == gk.element_list): self.updateElements(data["data"]) elif (data["packet"] == gk.start_game): self.startSendMove() elif (data["packet"] == gk.stop_game): self.endSendMove() def logic_update(self,EVENTS): for x in range(len(self.elements)): self.elements[x].logic_update(EVENTS) if (self.STATUS == "TOCONNECT"): self.update_menu_connect() elif (self.STATUS == "CONNECTING"): self.update_connecting() elif (self.STATUS == "ROOMLIST"): self.update_room_list() elif (self.STATUS == "ERROR"): self.update_error_connect() elif (self.STATUS == "GAME"): self.updateGame() def graphic_update(self,SCREEN): for x in range(len(self.elements)): self.elements[x].graphic_update(SCREEN) def update_menu_connect(self): if (self.mc.button_connect.button.pressed): self.start_connect(self.mc.get_host(),self.mc.get_port()) def start_connect(self,host,port): self.mc.set_loading() self.STATUS = "CONNECTING" self.reactorStart( host,port,self.IOSYS.NETWORK) def update_connecting(self): pass def reactorStart(self,GAME_IP,GAME_PORT,connection): if (GAME_IP == "Host"): GAME_IP = "localhost" GAME_PORT = "9999" reactor.connectTCP(GAME_IP, int(GAME_PORT), connection) # @UndefinedVariable def handleStart(self): self.elements = [] sl = server_list(self.IOSYS.NETWORK) sl.set_position((0,0)) self.elements.append(sl) self.sl = sl if (self.STATUS == "CONNECTING"): self.STATUS = "ROOMLIST" self.get_info() def handleFail(self): if (self.STATUS == "CONNECTING"): self.STATUS = "ERROR" self.mc.set_error() def update_room_list(self): if (self.sl.end == True): self.STATUS = "GAME" self.elements = [] self.elementManager = elements.manager(self.IOSYS.NETWORK) self.elements.append(self.elementManager) def update_error_connect(self): if (self.mc.ta.button.pressed): x,y = self.mc.X,self.mc.Y self.mc.__init__() self.mc.set_position((x,y)) self.STATUS = "TOCONNECT" def get_info(self): self.IOSYS.NETWORK.protocol.sendData({"packet":gk.get_rooms_information}) def update_rooms(self,data): self.sl.update(data) def update_room_extra(self,data): if (self.STATUS == "ROOMLIST"): self.sl.update_extra(data) def updateGame(self): pass def updateElements(self,data): if (self.STATUS == "GAME"): self.elementManager.updateElements(data) def startSendMove(self): self.elementManager.startSend() def stopSendMove(self): self.elementManager.stopSend() class iosys: def __init__(self): #OUTPUT self.SCREEN = pygame.display.set_mode((600,400)) #INPUT self.EVENTS = events.events() #LOGIC self.GAME = game(self) #NETWORK self.NETWORK = network.gameClientFactory(self.GAME) #CLOCK self.CLOCK = pygame.time.Clock() #ON self.ON = True def updating(self): if (True): self.SCREEN.fill((255,255,255)) for event in pygame.event.get(): if event.type == pygame.QUIT: self.quitGame() self.EVENTS.update_keyboard(pygame.key.get_pressed()) self.EVENTS.update_mouse(pygame.mouse.get_pos(),pygame.mouse.get_pressed()) self.GAME.logic_update(self.EVENTS) self.GAME.graphic_update(self.SCREEN) status_cur.update() pygame.display.update() reactor.callLater(1./40,self.updating)# @UndefinedVariable def quitGame(self): reactor.stop()# @UndefinedVariable pygame.quit() def main(): pygame.init() io = iosys() io.updating() reactor.run()# @UndefinedVariable main()

[ "dylantasat11@gmail.com" ]

dylantasat11@gmail.com

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/case/world/different_point/work_or_day.py

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[]

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JingkaiTang/github-play

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#! /usr/bin/env python def little_time(str_arg): thing(str_arg) print('be_woman_with_new_company') def thing(str_arg): print(str_arg) if __name__ == '__main__': little_time('leave_different_person')

[ "jingkaitang@gmail.com" ]

jingkaitang@gmail.com

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/orders/migrations/0003_auto_20200509_1214.py

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[]

no_license

anthonylauly/My-Shop---Django-2-By-Example

a4a597a6f1d243aebc74e30005034da4b3a0cf4a

2e47abe19e1fc1f35ad73049f1f077ee805ad945

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2023-01-30T00:20:44.343128

2020-12-08T06:05:43

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# Generated by Django 2.2.12 on 2020-05-09 12:14 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('orders', '0002_order_braintree_id'), ] operations = [ migrations.AlterModelOptions( name='order', options={'ordering': ('-created',), 'verbose_name': 'order'}, ), ]

[ "anthony.lauly1833@gmail.com" ]

anthony.lauly1833@gmail.com

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/greedy-algorithms/assignment_2/tests/test_clustering.py

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[]

no_license

ybim/stanford-algs

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refs/heads/master

2021-03-04T03:45:13.444826

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import os import os.path import logging from ..clustering import read_file, maximum_spacing import pytest test_dir = os.path.join(os.path.dirname(__file__), 'cluster_cases') def lookup_spacing(f): f = f.replace('input', 'output') with open(f, 'r') as handle: return int(handle.readlines()[0]) def pytest_generate_tests(metafunc): idlist = [] argvalues = [] for case in metafunc.cls.cases: idlist.append(case) argvalues.append(os.path.join(test_dir, case)) metafunc.parametrize('_in', argvalues, ids=idlist, scope='class') class TestCluster: cases = [f for f in os.listdir(test_dir) if f.startswith('input') and f.endswith('.txt')] def test_clustering(self, _in): nodes, edges = read_file(_in) assert maximum_spacing(nodes, edges) == lookup_spacing(_in)

[ "trendahl@slac.stanford.edu" ]

trendahl@slac.stanford.edu

c87160c690087c80faa23886e3d2eb5ad52a67c4

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/solutions_python/Problem_53/143.py

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[]

no_license

dr-dos-ok/Code_Jam_Webscraper

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26a35bf114a3aa30fc4c677ef069d95f41665cc0

refs/heads/master

2020-04-06T08:17:40.938460

2018-10-14T10:12:47

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py

#!/usr/bin/env python f = open('A-large.in', 'r') n = int(f.readline()) count = 0 for line in f: res = 'OFF' count+=1 (n,k) = line.split() n = int(n) k = int(k) if k>0: if k%(2**n) == (2**n-1): res = 'ON' print 'Case #' + str(count) + ': '+ res

[ "miliar1732@gmail.com" ]

miliar1732@gmail.com

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/src/ch4/predictData_2015_0.25.py

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[]

no_license

Relph1119/machine-learning-blueprints

283cc1a6ee843ae4c806466af8f9a0df2325ecb6

c46c5bd93bd781f96d41a80a03a797ca3e673627

refs/heads/master

2022-12-09T11:34:34.883337

2020-08-26T08:48:24

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import getFeature as gft from sklearn.ensemble import RandomForestClassifier from sklearn import linear_model import pandas as pd import matplotlib.pyplot as plt X = gft.X ipos = gft.ipos X_train, X_test = X[173:], X[:173] y_train = ipos['$ Chg Open to Close'][173:].map(lambda x: 1 if x>=.25 else 0) y_test = ipos['$ Chg Open to Close'][:173].map(lambda x: 1 if x>=.25 else 0) clf = linear_model.LogisticRegression() clf.fit(X_train, y_train) clf.score(X_test, y_test) #print(ipos[(ipos['Date']>='2015-01-01')]['$ Chg Open to Close'].describe()) pred_label=clf.predict(X_test) results=[] for pl, tl, idx, chg in zip(pred_label, y_test, y_test.index, ipos.iloc[y_test.index]['$ Chg Open to Close']): if pl == tl: results.append([idx, chg, pl, tl, 1]) else: results.append([idx, chg, pl, tl, 0]) rf = pd.DataFrame(results, columns=['index', '$ chg', 'predicted', 'actual', 'correct']) print(rf[rf['predicted']==1]['$ chg'].describe()) fig, ax = plt.subplots(figsize=(15, 10)) rf[rf['predicted']==1]['$ chg'].plot(kind='bar') ax.set_title('Model Predicted Buys', y=1.01) ax.set_ylabel('$ Change Open to Close') ax.set_xlabel('Index') plt.show()

[ "huruifeng1202@163.com" ]

huruifeng1202@163.com

0e9b6c56ee63c623900c65faf8d6ad30c2b7eb88

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/nvis/base.py

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[]

no_license

sinhrks/nvis

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refs/heads/master

2021-01-12T13:26:41.363157

2016-09-25T22:14:19

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#!/usr/bin/env python # coding: utf-8 from __future__ import unicode_literals import collections import six import traitlets from enum import Enum import nvis.common as com class _JSObject(traitlets.HasTraits): """ Base class for JS instances, which can be converted to JavaScript instance """ def __eq__(self, other): # conmpare with script if isinstance(other, _JSObject): return self.script == other.script return False @property def _klass(self): return "Cesium.{0}".format(self.__class__.__name__) @property def _props(self): raise NotImplementedError('must be overriden in child classes') @property def _property_dict(self): props = collections.OrderedDict() for p in self._props: props[p] = getattr(self, p) return props @property def script(self): props = self._property_dict results = com.to_jsobject(props) return ''.join(results) class _Enum(Enum): @property def script(self): return self.value class RistrictedList(_JSObject): widget = traitlets.Instance(klass=_JSObject) def __init__(self, widget, allowed, propertyname): self.widget = widget self._items = [] self._allowed = allowed self._propertyname = propertyname def add(self, item, **kwargs): if com.is_listlike(item): for i in item: self.add(i, **kwargs) elif isinstance(item, self._allowed): for key, value in six.iteritems(kwargs): setattr(item, key, value) self._items.append(item) else: msg = 'item must be {allowed} instance: {item}' if isinstance(self._allowed, tuple): allowed = ', '.join([a.__name__ for a in self._allowed]) else: allowed = self._allowed raise ValueError(msg.format(allowed=allowed, item=item)) def clear(self): self._items = [] def __len__(self): return len(self._items) def __getitem__(self, item): return self._items[item] @property def script(self): """ return list of scripts built from entities each script may be a list of comamnds also """ results = [] for item in self._items: script = """{varname}.{propertyname}.add({item});""" script = script.format(varname=self.widget._varname, propertyname=self._propertyname, item=item.script) results.append(script) return results

[ "sinhrks@gmail.com" ]

sinhrks@gmail.com

90f1ed30332f1619a1573ce61dab44dbb952e827

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/ray/do.py

a0649d1bcc56f44ebc03bdcbe163b03e6ef80cbd

[]

no_license

cottrell/notebooks

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9eaf3d0500067fccb294d064ab78d7aaa03e8b4d

refs/heads/master

2023-08-09T22:41:01.996938

2023-08-04T22:41:51

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2023-03-04T03:58:03

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import numpy as np import ray def init(): return ray.init() def bounce(): ray.disconnect() # might be incorrect return ray.init() def f(x): return x ** 2 f_remote = ray.remote(f) def g(x, seed=1): np.random.seed(seed) x = np.random.randn(10, 5) + x return x g_remote = ray.remote(g)

[ "cottrell@users.noreply.github.com" ]

cottrell@users.noreply.github.com

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/tools/coverage/gcda_clean.py

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[ "Apache-2.0" ]

permissive

Thunderbrook/Paddle

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refs/heads/develop

2022-11-05T09:53:16.633465

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Apache-2.0

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py

#!/usr/bin/env python # -*- coding: utf-8 -*- """ usage: gcda_clean.py pull_id. """ import os import sys from github import Github def get_pull(pull_id): """Get pull. Args: pull_id (int): Pull id. Returns: github.PullRequest.PullRequest """ token = os.getenv('GITHUB_API_TOKEN', 'e1f9c3cf211d5c20e65bd9ab7ec07983da284bca') github = Github(token, timeout=60) repo = github.get_repo('PaddlePaddle/Paddle') pull = repo.get_pull(pull_id) return pull def get_files(pull_id): """Get files. Args: pull_id (int): Pull id. Returns: iterable: The generator will yield every filename. """ pull = get_pull(pull_id) for file in pull.get_files(): yield file.filename def clean(pull_id): """Clean. Args: pull_id (int): Pull id. Returns: None. """ changed = [] for file in get_files(pull_id): changed.append('/paddle/build/{}.gcda'.format(file)) for parent, dirs, files in os.walk('/paddle/build/'): for gcda in files: if gcda.endswith('.gcda'): trimmed = parent # convert paddle/fluid/imperative/CMakeFiles/layer.dir/layer.cc.gcda # to paddle/fluid/imperative/layer.cc.gcda if trimmed.endswith('.dir'): trimmed = os.path.dirname(trimmed) if trimmed.endswith('CMakeFiles'): trimmed = os.path.dirname(trimmed) # remove no changed gcda if os.path.join(trimmed, gcda) not in changed: gcda = os.path.join(parent, gcda) os.remove(gcda) if __name__ == '__main__': pull_id = sys.argv[1] pull_id = int(pull_id) clean(pull_id)

[ "luotao02@baidu.com" ]

luotao02@baidu.com

c0a77d989d2367e946c726db66d379941cb95e58

191a7f83d964f74a2b3c7faeb4fc47d9c63d521f

/.history/main_20210529115051.py

1db23d5522089aab30d37049645fc3bb97d85fce

[]

no_license

AndreLiu1225/Kinder-Values-Survey

2a317feee8d5b17c27da2b2116742656e35d8ab9

090c27da0c822abb7dfc0ec6e13ae1b3dcb7bbf3

refs/heads/master

2023-05-03T00:26:00.481423

2021-06-04T03:24:19

371,989,154

0

null

UTF-8

Python

false

6,895

py

from flask import Flask, render_template, redirect, url_for, flash, request from flask_sqlalchemy import SQLAlchemy from flask_wtf import FlaskForm from wtforms import StringField, TextField, SubmitField, IntegerField, SelectField, RadioField from wtforms.validators import DataRequired, Email, EqualTo, Length, ValidationError import datetime import matplotlib.pyplot as plt app = Flask(__name__) app.config['SECRET_KEY'] = "0c8973c8a5e001bb0c816a7b56c84f3a" app.config['SQLALCHEMY_DATABASE_URI'] = "sqlite:///site.db" db = SQLAlchemy(app) class Survey(db.Model): age = db.Column(db.Integer, nullable=False, primary_key=True) email = db.Column(db.String(50), unique=False, nullable=False) profession = db.Column(db.String(50), nullable=False) power = db.Column(db.Integer, nullable=False) tradition = db.Column(db.Integer, nullable=False) achievement = db.Column(db.Integer, nullable=False) stimulation = db.Column(db.Integer, nullable=False) hedonism = db.Column(db.Integer, nullable=False) conformity = db.Column(db.Integer, nullable=False) security = db.Column(db.Integer, nullable=False) self_direction = db.Column(db.Integer, nullable=False) benevolence = db.Column(db.Integer, nullable=False) universalism = db.Column(db.Integer, nullable=False) date_posted = db.Column(db.DateTime, nullable=False, default=datetime.datetime.utcnow) def __repr__(self): return f"Survey('{self.age}', '{self.name}', '{self.date_posted}')" class MCQ(FlaskForm): email = StringField("What is your email?", validators=[DataRequired(), Email(message=('Not a valid email address')), Length(max=50)]) age = IntegerField("Please enter your age", validators=[DataRequired()]) profession = StringField("What is your profession?", validators=[DataRequired(), Length(max=30)]) # Self-Enhancement power = IntegerField("Do you desire a higher social status and dominance over others? (4- It is my utmost priority, 3-It is important, 2-Doesn't bother me, 1-Not even a thought)", validators=[DataRequired()]) hedonism = IntegerField("Is personal gratification the most important? (4- It is my utmost priority, 3-It is important, 2-Doesn't bother me, 1-Not even a thought)", validators=[DataRequired()]) achievement = IntegerField("Is achievement according to social standards important? (4- It is my utmost priority, 3-It is important, 2-Doesn't bother me, 1-Not even a thought)", validators=[DataRequired()]) # Conservation tradition = IntegerField("Do you care about preserving traditions? (4- It is my utmost priority, 3-It is important, 2-Doesn't bother me, 1-Not even a thought)", validators=[DataRequired()]) conformity = IntegerField("Do you think restraint of actions against social norms is important? (4- It is my utmost priority, 3-It is important, 2-Doesn't bother me, 1-Not even a thought)", validators=[DataRequired()]) security = IntegerField("Do you value safety, harmony and stability of society, of relationships, and of self? (4- It is my utmost priority, 3-It is important, 2-Doesn't bother me, 1-Not even a thought)", validators=[DataRequired()]) # Openness to change stimulation = IntegerField("Do you prefer novel and exciting challenges in life? (4- It is my utmost priority, 3-It is important, 2-Doesn't bother me, 1-Not even a thought)", validators=[DataRequired()]) self_direction = IntegerField("Do you think independent thought and action are important (4- It is my utmost priority, 3-It is important, 2-Doesn't bother me, 1-Not even a thought)", validators=[DataRequired()]) # Self-transcendence benevolence = IntegerField("Are preserving and enhancing the welfare of your friends and family the most important? (4- It is my utmost priority, 3-It is important, 2-Doesn't bother me, 1-Not even a thought)", validators=[DataRequired()]) universalism = IntegerField("I find it important to understand, tolerate, appreciate and protect all ethnicities and people. (4- It is my utmost priority, 3-It is important, 2-Doesn't bother me, 1-Not even a thought)", validators=[DataRequired()]) submit = SubmitField("Submit") @app.route('/', methods=['POST','GET']) def values_quiz(): form = MCQ() if form.validate_on_submit(): post = Survey(age=form.age.data, email=form.email.data, profession=form.profession.data, power=form.power.data, tradition=form.tradition.data, achievement=form.achievement.data, stimulation=form.stimulation.data, hedonism=form.hedonism.data, conformity=form.conformity.data, self_direction=form.self_direction.data, benevolence=form.benevolence.data, universalism=form.universalism.data, security=form.security.data) # if Survey.is_email_in_database(form.email.data): # flash(f"The user with {form.email.data} has already filled the survey", "danger") db.session.add(post) db.session.commit() flash(f'Survey is completed by {form.email.data}', 'success') return redirect(url_for('data_dashboard')) else: flash('Ensure all questions are answered correctly', 'warning') return render_template('MCQ.html', form=form) @app.route('/results', methods=['GET']) def data_dashboard(): power = request.form['power'] tradition = request.form['tradition'] achievement = request.form['achievement'] stimulation = request.form['stimulation'] hedonism = request.form['hedonism'] conformity = request.form['conformity'] security = request.form['security'] self_direction = request['self_direction'] benevolence = request['benevolence'] universalism = request['universalism'] values = [power, tradition, achievement, stimulation, hedonism, conformity, security, self_direction, benevolence, universalism] values_labels = ['Openness to Change', 'Self-Transcendence', 'Conservation', 'Self-Enchancement'] openness = [hedonism, stimulation, self_direction] self_enhancement = [hedonism, achievement, power] conservation = [tradition, conformity, security] self_trans = [universalism, benevolence] total_sum = sum(values) open_sum = round(sum(openness)/total_sum*100) enhance_sum = round(sum(self_enhancement)/total_sum*100) trans_sum = round(sum(self_trans)/total_sum*100) cons_sum = round(sum(conservation)/total_sum*100) sum_v = [open_sum, enhance_sum, trans_sum, cons_sum] # initiating the range of y ticks ran = [20,40,60,80,100] plt.xticks(ran, values_labels) # Calling bar plot function plt.bar(ran, sum_v) plt.title('Percentage obtained on each dynamic values') plt.ylabel('Percentage') plt.xlabel('Dynamic value types') return render_template('data_dashboard.html', image=plt.show()) if __name__ == "__main__": app.run(debug=True)

[ "andreliu2004@gmail.com" ]

andreliu2004@gmail.com

fb7165c445a5220131bc572f6c89d995c80cc8bb

e27eebd9cacba56eb0e161cf9584b856db4543b5

/code/version-demo/venv/bin/django-admin

f3b2a60f6246b15c8ef89d632a053b19ff026f34

[]

no_license

Dosimz/django-rest-framework-notes

fd374f9793057f9fbfe85c072912a019ef26b73a

1d0cf0e9cd9042e432f883cd9c3fa2504e5b9a22

refs/heads/master

2020-06-25T15:36:33.700321

2019-08-03T04:26:40

199,354,041

0

null

UTF-8

Python

false

345

#!/run/media/yuyi/068AE93F8AE92BBD/python/django-rest-framework/code/version-demo/venv/bin/python # -*- coding: utf-8 -*- import re import sys from django.core.management import execute_from_command_line if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(execute_from_command_line())

[ "Vhzix24@gmail.com" ]

Vhzix24@gmail.com

2678e12e0ef37185b07dd5045587321dab24ceab

15f321878face2af9317363c5f6de1e5ddd9b749

/solutions_python/Problem_116/1538.py

f953c8478ab10037f18a92cc014c6473535f8868

[]

no_license

dr-dos-ok/Code_Jam_Webscraper

c06fd59870842664cd79c41eb460a09553e1c80a

26a35bf114a3aa30fc4c677ef069d95f41665cc0

refs/heads/master

2020-04-06T08:17:40.938460

2018-10-14T10:12:47

null

0

null

UTF-8

Python

false

2,103

py

#!/usr/bin/env python row = 4 col = 4 def getState(matrix): hasEmpty = False # Check row for i in range(4): flag = matrix[i][0] if (matrix[i][1] == flag or matrix[i][1] == 'T') \ and (matrix[i][2] == flag or matrix[i][2] == 'T') \ and (matrix[i][3] == flag or matrix[i][3] == 'T'): if flag == "X": return "X won" if flag == "O": return "O won" for j in range(1,4): if matrix[i][j] == '.': hasEmpty = True # Check col for i in range(4): flag = matrix[0][i] if (matrix[1][i] == flag or matrix[1][i] == 'T') \ and (matrix[2][i] == flag or matrix[2][i] == 'T') \ and (matrix[3][i] == flag or matrix[3][i] == 'T'): if flag == "X": return "X won" if flag == "O": return "O won" for j in range(1,4): if matrix[j][i] == '.': hasEmpty = True # Check diagonal flag = matrix[0][0] if (matrix[1][1] == flag or matrix[1][1] == 'T') \ and (matrix[2][2] == flag or matrix[2][2] == 'T') \ and (matrix[3][3] == flag or matrix[3][3] == 'T'): if flag == "X": return "X won" if flag == "O": return "O won" flag = matrix[0][3] if (matrix[1][2] == flag or matrix[1][2] == 'T') \ and (matrix[2][1] == flag or matrix[2][1] == 'T') \ and (matrix[3][0] == flag or matrix[3][0] == 'T'): if flag == "X": return "X won" if flag == "O": return "O won" if hasEmpty: return "Game has not completed" else: return "Draw" #f = open("small") f = open("A-small-attempt0.in") lines = f.readlines() num = int(lines[0]) wfile = open("lk", "w") index = 1 for i in range(num): matrix = [] for j in range(index, index+4): ns = list(lines[j].strip()) matrix.append(ns) wfile.write("Case #%d: %s\n" % (i+1, getState(matrix))) index += 5

[ "miliar1732@gmail.com" ]

miliar1732@gmail.com

41187fee24fba11e59e9c8b13bd1af25a462c4bd

3854700ad938a19efbb934fb0706793d70848ff1

/docs/python/algorytmy/heron.py

378735c8ff661796f1d4195070da01ca9670741d

[]

no_license

xinulsw/linetc

0e0c94f3bd814a9ef8da9f2613785f605c04ed6e

7311063d640ca1889dca1cd9d6f8b40541c8809a

refs/heads/master

2022-06-21T05:40:32.139937

2022-05-27T15:33:05

28,744,486

1

5

null

2016-12-30T14:13:33

2015-01-03T13:30:50

Python

UTF-8

Python

false

724

py

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Obliczanie wartości pierwiastka kwadratowego z podanej liczby # przy użyciu metody Herona: a(n) = (a(n-1) + x / a(n-1)) / 2 # <eCG> def pierwiastek(x, d): a = x # inicjalizacja szkuanej wartości while abs(a - (x / a)) > d: a = (a + (x / a)) / 2 return a def main(args): # liczba dla której szukamy pierwiastka x = float(raw_input("Podaj liczbę: ")) # dokładność obliczeń d = float(raw_input("Podaj dokładność: ")) # drukujemy wynik z dokładnością do 6 miejsc print "Pierwiastek kwadratowy: {:.6f}".format(pierwiastek(x, d)) return 0 if __name__ == '__main__': import sys sys.exit(main(sys.argv))

[ "xinulsw@gmail.com" ]

xinulsw@gmail.com

671a4259b297caaf1b440af131b996017d579c02

4c3467d15408362150528c98022016e3af48a481

/day05/day05.py

97d0ddbbe0a70fad197e58908870483548377405

[]

no_license

Ha-Young/nomad_python_challenge

7659fc1f5d08b9925156f48e5150bd6981593452

d4b476ad27fcc039851f7c7acbc4d925d678d6b2

refs/heads/master

2022-12-12T10:59:38.154297

2020-09-06T14:16:52

257,329,572

0

null

UTF-8

Python

false

1,100

py

import os import requests from bs4 import BeautifulSoup os.system("clear") url = "https://www.iban.com/currency-codes" req = requests.get(url) soup = BeautifulSoup(req.text, "html.parser") table = soup.find('table', {'class': 'table'}) tbody = table.find('tbody') trs = tbody.find_all('tr') dict_country_code = {} for i, tr in enumerate(trs): tds = tr.find_all('td') country = tds[0].text.strip() currency = tds[1].text.strip() code = tds[2].text.strip() number = tds[3].text.strip() dict_country_code[i] = { 'country':country, 'currency': currency, 'code': code, 'number':number } print("Hello Please Choose select a country") for key, value in dict_country_code.items(): print(key,value['country']) while(True): try: inputNum = int(input('#: ')) if inputNum not in dict_country_code.keys(): print("Choose a number from the list.") else : print('You Chose',dict_country_code[inputNum]['country']) print('The currency code is',dict_country_code[inputNum]['code']) break except: print("That wasn't a number")

[ "hayeong28@naver.com" ]

hayeong28@naver.com

fb05f1a8484b67ea8c719f094c1624f8289d9513

d7db8cd9cf18d21a57c6bdb1a3ead018d81801a9

/字节跳动/数组与排序/数组中第k个最大元素.py

d961475a12f37bea9f668fbae3335b8f3f7edfb3

[]

no_license

Eleanoryuyuyu/LeetCode

a1e8c230162a3a850e6714ed7c2d4d946bc5f33b

44b65f3ab6cc6ca3ef2a2fd7ef39d4df0cd7f7eb

refs/heads/master

2021-06-01T15:25:44.812448

2020-08-06T07:32:44

144,653,156

3

2

null

UTF-8

Python

false

1,027

py

from typing import List class Solution: def findKthLargest(self, nums: List[int], k: int) -> int: def partition(left, right): paviot = nums[left] while left < right: while left < right and nums[right] >= paviot: right -= 1 nums[left] = nums[right] while left < right and nums[left] <= paviot: left += 1 nums[right] = nums[left] nums[left] = paviot return left n = len(nums) p = n - k left, right = 0, n - 1 index = partition(left, right) while index != p: if index < p: left = index + 1 index = partition(left, right) elif index > p: right = index - 1 index = partition(left, right) else: return nums[index] return nums[index] nums = [3,2,3,1,2,4,5,5,6] print(Solution().findKthLargest(nums, 4))

[ "yangjieyu@zju.edu.cn" ]

yangjieyu@zju.edu.cn

bad84b93239fac0a1e4e0d53a6650b8a3c99b62a

bdec175f02173938f99e546e772ce8b3730a3f48

/lists/ex48.py

83c5d0115be5792c374e8c614d5037b552d518fe

[]

no_license

hmunduri/MyPython

99f637f6665a733903968047aa46b763d9557858

af26f3a4ffb9b786d682114635b432480010ffc8

refs/heads/master

2020-03-09T13:13:59.873228

2018-04-20T22:33:30

128,805,444

0

null

UTF-8

Python

false

164

py

#Python program print a nested lists using the print() function. import sys colors = [['Red'], ['Green'], ['Black']] print(' '.join([str(lst) for lst in colors]))

[ "root@himagiri0275.mylabserver.com" ]

root@himagiri0275.mylabserver.com

f2c38ad89b093cb62129bdcd1e825b1f8d27054b

fc0a6e0f9ffa90a2473fec77bc52ea02e9b21f55

/venv/lib/python3.7/site-packages/akshare/news/news_east_money.py

b585d6d945b5f41bbe2d071cbf638da2a4c7259b

[]

no_license

YixuanSeanZhou/COVID19_Scraping

3903e697caf406c7d357afd8cc43811d62896244

b84890c4a5ddef589cd76d1ed8fd4a1976f4e3c4

refs/heads/master

2022-09-08T16:14:33.292096

2020-05-23T04:26:02

266,261,823

0

null

UTF-8

Python

false

464

py

# -*- coding:utf-8 -*- # /usr/bin/env python """ Author: Albert King date: 2019/12/12 15:08 contact: jindaxiang@163.com desc: """ import newspaper sina_paper = newspaper.build('http://futures.hexun.com/domestic/', language='zh') for article in sina_paper.articles: print(article) sina_paper.size() first_article = sina_paper.articles[5] first_article.download() first_article.parse() print(first_article.title) print(first_article.text)

[ "thomaszhou2333@gmail.com" ]

thomaszhou2333@gmail.com

d75c687734fdd3778c4f88537fea21cdca140a57

1ee910d6602123eb1328f56419b04e31b3761b6b

/lib/python3.5/site-packages/twilio/rest/sync/v1/__init__.py

c1aab31243a7200a57ff60b634bef12ef3b66f88

[ "MIT" ]

permissive

mraza007/Pizza-or-Not-a-Pizza

7fc89e0905c86fbd3c77a9cc834a4b6098912aeb

6ad59d046adbd6be812c7403d9cb8ffbdbd6b0b8

refs/heads/master

2022-12-15T15:47:34.779838

2018-07-04T02:28:56

127,992,302

30

4

MIT

2022-11-22T00:43:51

2018-04-04T01:56:26

Python

UTF-8

Python

false

938

py

# coding=utf-8 """ This code was generated by \ / _ _ _| _ _ | (_)\/(_)(_|\/| |(/_ v1.0.0 / / """ from twilio.base.version import Version from twilio.rest.sync.v1.service import ServiceList class V1(Version): def __init__(self, domain): """ Initialize the V1 version of Sync :returns: V1 version of Sync :rtype: twilio.rest.sync.v1.V1.V1 """ super(V1, self).__init__(domain) self.version = 'v1' self._services = None @property def services(self): """ :rtype: twilio.rest.sync.v1.service.ServiceList """ if self._services is None: self._services = ServiceList(self) return self._services def __repr__(self): """ Provide a friendly representation :returns: Machine friendly representation :rtype: str """ return '<Twilio.Sync.V1>'

[ "muhammadraza0047@gmail.com" ]

muhammadraza0047@gmail.com

b02dbf261aa073b6ee44836616403995bc7d2945

590a3dab75527bb8742165e84d7108d056826351

/py_tutorials/py_tutorials/1_supervised/train_svm.py

0d87c8c10e65447b4213b9ff822672393d37ac32

[]

no_license

onkarganjewar/ipam-tutorials

2cfa5055484531f2cc2fdd051ddf0ea771a08a37

a8451dd121d0a4029bc26c27ee28b45dfc6e51c0

refs/heads/master

2021-01-13T12:48:50.944781

2016-10-30T02:57:09

72,319,739

0

null

2016-10-30T02:05:48

2016-10-30T02:05:47

null

UTF-8

Python

false

1,996

py

import logging import sys import time import numpy as np from numpy import arange, dot, maximum, ones, tanh, zeros from numpy.random import randn from skdata import mnist from autodiff import fmin_sgd, fmin_l_bfgs_b from utils import show_filters def main(): # -- top-level parameters of this script dtype = 'float32' # XXX n_examples = 50000 online_batch_size = 1 online_epochs = 2 batch_epochs = 30 lbfgs_m = 20 # -- load and prepare the data set data_view = mnist.views.OfficialVectorClassification(x_dtype=dtype) n_classes = 10 x = data_view.train.x[:n_examples] y = data_view.train.y[:n_examples] y1 = -1 * ones((len(y), n_classes)).astype(dtype) y1[arange(len(y)), y] = 1 # --initialize the SVM model w = zeros((x.shape[1], n_classes), dtype=dtype) b = zeros(n_classes, dtype=dtype) def svm(ww, bb, xx=x, yy=y1): # -- one vs. all linear SVM loss margin = yy * (dot(xx, ww) + bb) hinge = maximum(0, 1 - margin) cost = hinge.mean(axis=0).sum() return cost # -- stage-1 optimization by stochastic gradient descent print 'Starting SGD' n_batches = n_examples / online_batch_size w, b = fmin_sgd(svm, (w, b), streams={ 'xx': x.reshape((n_batches, online_batch_size, x.shape[1])), 'yy': y1.reshape((n_batches, online_batch_size, y1.shape[1]))}, loops=online_epochs, stepsize=0.001, print_interval=10000, ) print 'SGD complete, about to start L-BFGS' show_filters(w.T, (28, 28), (2, 5,)) # -- stage-2 optimization by L-BFGS print 'Starting L-BFGS' w, b = fmin_l_bfgs_b(svm, (w, b), maxfun=batch_epochs, iprint=1, m=lbfgs_m) print 'L-BFGS complete' show_filters(w.T, (28, 28), (2, 5,)) if __name__ == '__main__': logging.basicConfig(stream=sys.stdout, level=logging.INFO) sys.exit(main())

[ "james.bergstra@gmail.com" ]

james.bergstra@gmail.com

9039e65c812006506a37289b4b5417f47fc945cf

ecf62aae48e02420cd99008f58c4725c6da56d22

/models/review.py

094c86885b823cd171680a2c1fc29a9485940188

[]

no_license

ThibautBernard/AirBnB_clone

e3110415acd98b56134928eee0d2befb6bd68a25

d495dd85add4332880eacf00b338704c2799d3e5

refs/heads/main

2023-03-08T15:51:46.968249

2021-03-03T15:58:29

337,568,140

0

null

UTF-8

Python

false

261

py

#!/usr/bin/python3 from models.base_model import BaseModel """ Class that represent a Review """ class Review(BaseModel): place_id = "" user_id = "" text = "" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)

[ "thibautbernard@sfr.fr" ]

thibautbernard@sfr.fr

db81a9b45a6e7f46cba1807fbdb51d5e43c86df0

65b4522c04c2be071c2d42095956fe950fe1cebe

/lib/viscojapan/plots/plot_L_curve.py

b89df84bae00664d50f081bdee6e2e51759c6520

[]

no_license

geodesy/viscojapan

ac0cd93f7a2134cd2651623b94879dcc21c0c46a

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import glob import h5py from pylab import loglog, xlabel, ylabel, grid, text __all__=['plot_L_from_res_h5','plot_L'] def plot_L_from_res_h5(pathname, x_key, y_key, **kwargs): flist = glob.glob(pathname) flist = sorted(flist) xs = [] ys = [] for f in flist: with h5py.File(f,'r') as fid: x = fid[x_key][...] y = fid[y_key][...] xs.append(x) ys.append(y) handle = loglog(xs,ys, **kwargs) xlabel(x_key) ylabel(y_key) grid('on') return handle def plot_L(nres,nsol,alphas=None,lanos=None, label=None,color='blue'): ''' alphas - regularization parameters array lanos - array that indicates which alphas pairs are labeled. None means label every two. label - L-curve label ''' assert len(nres)==len(nsol) if alphas is not None: assert len(nres)==len(alphas) # plot L-curve loglog(nres,nsol,'o',label=label,color=color) if alphas is not None: if lanos is None: lanos = range(0,len(alphas),2) for ano in lanos: text(nres[ano],nsol[ano],'%d/%.2G'%(ano,alphas[ano]),color='red') xlabel('Residual Norm ($\log_{10}{||Gm-d||_2}$)') ylabel('Solution Roughness ($\log_{10}{||Lm||_2}$)') grid('on')

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zy31415@gmail.com

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/xai/brain/wordbase/nouns/_frolicking.py

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from xai.brain.wordbase.nouns._frolic import _FROLIC #calss header class _FROLICKING(_FROLIC, ): def __init__(self,): _FROLIC.__init__(self) self.name = "FROLICKING" self.specie = 'nouns' self.basic = "frolic" self.jsondata = {}

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xingwang1991@gmail.com

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[]

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zhanghe06/migration_project

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#!/usr/bin/env python # encoding: utf-8 """ @author: zhanghe @software: PyCharm @file: enum.py @time: 2019-04-26 15:55 """ from __future__ import unicode_literals structure_key_item = 'enum' structure_key_items = 'enums' structure_key_item_cn = '枚举类型' structure_key_items_cn = '枚举类型'

[ "zhang_he06@163.com" ]

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LiuFang816/SALSTM_py_data

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import tensorflow as tf import tensorflow.contrib.layers as layers from datetime import datetime # MNIST input data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) def multilayer_perceptron(x): W_fc1 = tf.Variable(tf.random_normal([784, 256], mean=0, stddev=1)) b_fc1 = tf.Variable([0] * 256) # ??????? fc1 = tf.nn.xw_plus_b(x, W_fc1, b_fc1) fc2 = layers.fully_connected(fc1, 256, activation_fn=tf.nn.relu, scope='fc2') out = layers.fully_connected(fc2, 10, activation_fn=None, scope='out') return out # build model, loss, and train op x = tf.placeholder(tf.float32, [None, 784], name='placeholder_x') y = tf.placeholder(tf.float32, [None, 10], name='placeholder_y') pred = multilayer_perceptron(x) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) def train(session): batch_size = 200 session.run(tf.global_variables_initializer()) # Training cycle for epoch in range(10): epoch_loss = 0.0 batch_steps = mnist.train.num_examples / batch_size for i in range(batch_steps): batch_x, batch_y = mnist.train.next_batch(batch_size) _, c = session.run([train_op, loss], feed_dict={x: batch_x, y: batch_y}) epoch_loss += c / batch_steps print "[%s] Epoch %02d, Loss = %.6f" % (datetime.now(), epoch, epoch_loss) # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}) def main(): with tf.Session(config=tf.ConfigProto( gpu_options=tf.GPUOptions(allow_growth=True), device_count={'GPU': 1})) as session: train(session) if __name__ == '__main__': main()

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/Tree/[508]Most Frequent Subtree Sum.py

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dstch/my_leetcode

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# # Given the root of a tree, you are asked to find the most frequent subtree sum. # The subtree sum of a node is defined as the sum of all the node values formed b # y the subtree rooted at that node (including the node itself). So what is the mo # st frequent subtree sum value? If there is a tie, return all the values with the # highest frequency in any order. # # # Examples 1 # Input: # # 5 # / \ # 2 -3 # # return [2, -3, 4], since all the values happen only once, return all of them i # n any order. # # # Examples 2 # Input: # # 5 # / \ # 2 -5 # # return [2], since 2 happens twice, however -5 only occur once. # # # Note: # You may assume the sum of values in any subtree is in the range of 32-bit sign # ed integer. # Related Topics Hash Table Tree # 👍 840 👎 143 # leetcode submit region begin(Prohibit modification and deletion) # Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right class Solution: def findFrequentTreeSum(self, root: TreeNode) -> List[int]: dic = {} m = 0 def func(node, m): if node is not None: m = func(node.left, m) m = func(node.right, m) s = node.val if node.left: s += node.left.val if node.right: s += node.right.val node.val = s if node.val in dic: dic[node.val] += 1 else: dic[node.val] = 1 if dic[node.val] > m: m = dic[node.val] return m m = func(root, m) print(dic) return [x for x in dic if dic[x] == m] # leetcode submit region end(Prohibit modification and deletion)

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ndjman7/Algorithm

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class NumArray(object): def __init__(self, nums): """ :type nums: List[int] """ self.cache = [0 for i in range(len(nums)+1)] for i in range(len(nums)): self.cache[i + 1] = self.cache[i] + nums[i] def sumRange(self, i, j): """ :type i: int :type j: int :rtype: int """ return self.cache[j + 1] - self.cache[i] # Your NumArray object will be instantiated and called as such: # obj = NumArray(nums) # param_1 = obj.sumRange(i,j)

[ "ndjman7@gmail.com" ]

ndjman7@gmail.com

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/test7/myapp/models.py

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853695319/learningdjango

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from django.db import models from django.contrib.auth.models import User class Note(models.Model): user = models.ForeignKey(User) pub_date = models.DateTimeField() title = models.CharField(max_length=200) body = models.TextField() def __unicode__(self): return self.title

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/blog/models.py

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Ankrate/venezoproject

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from django.db import models import os, random from django.db.models.signals import pre_save from .utils import unique_slug_generator def get_filename_ext(filepath): base_name = os.path.basename(filepath) name, ext = os.path.splitext(base_name) return name, ext def upload_image_path(instance, filename): print (instance) print (filename) name, ext = get_filename_ext(filename) new_filename = random.randint(1, 234982304) final_filename = '{}{}'.format(new_filename, ext) return f'blog/{new_filename}/{final_filename}' class BlogCategories(models.Model): title = models.CharField(max_length=2555) slug = models.SlugField(blank=True, unique=True, max_length=255) class Meta: verbose_name_plural = 'Категории блога' def __str__(self): return self.title class BlogModel(models.Model): title = models.CharField(max_length=120) slug = models.SlugField(blank=True, unique=True, max_length=255) description = models.TextField() short_desc = models.TextField(blank=True) image = models.ImageField(upload_to=upload_image_path, null=True, blank=True) category = models.ForeignKey(BlogCategories,related_name='category', on_delete=models.CASCADE, default=1) publish = models.DateField(auto_now=True) class Meta: verbose_name_plural = 'Статьи' def __str__(self): return self.title def blog_pre_save_receiver(sender, instance, *args, **kwargs): if not instance.slug: instance.slug = unique_slug_generator(instance) pre_save.connect(blog_pre_save_receiver, sender=BlogModel) pre_save.connect(blog_pre_save_receiver, sender=BlogCategories)

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angara99@gmail.com

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/geeksforgeeks/python/python_all/124_1.py

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qmnguyenw/python_py4e

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2023-06-01T07:58:13.996965

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Python | Intersection in Tuple Records Data Sometimes, while working with data, we may have a problem in which we require to find the matching records between two lists that we receive. This is a very common problem and records usually occur as a tuple. Let’s discuss certain ways in which this problem can be solved. **Method #1 : Using list comprehension** List comprehension can opt as method to perform this task in one line rather than running a loop to find the common element. In this, we just iterate for single list and check if any element occurs in other one. __ __ __ __ __ __ __ # Python3 code to demonstrate working of # Intersection in Tuple Records Data # Using list comprehension # Initializing lists test_list1 = [('gfg', 1), ('is', 2), ('best', 3)] test_list2 = [('i', 3), ('love', 4), ('gfg', 1)] # printing original lists print("The original list 1 is : " + str(test_list1)) print("The original list 2 is : " + str(test_list2)) # Intersection in Tuple Records Data # Using list comprehension res = [ele1 for ele1 in test_list1 for ele2 in test_list2 if ele1 == ele2] # printing result print("The Intersection of data records is : " + str(res)) --- __ __ **Output :** The original list 1 is : [('gfg', 1), ('is', 2), ('best', 3)] The original list 2 is : [('i', 3), ('love', 4), ('gfg', 1)] The Intersection of data records is : [('gfg', 1)] **Method #2 : Usingset.intersection()** This task can also be performed in smaller way using the generic set intersection. In this, we first convert the list of records to a set and then perform its intersection using intersection(). __ __ __ __ __ __ __ # Python3 code to demonstrate working of # Intersection in Tuple Records Data # Using set.intersection() # Initializing lists test_list1 = [('gfg', 1), ('is', 2), ('best', 3)] test_list2 = [('i', 3), ('love', 4), ('gfg', 1)] # printing original lists print("The original list 1 is : " + str(test_list1)) print("The original list 2 is : " + str(test_list2)) # Intersection in Tuple Records Data # set.intersection() res = list(set(test_list1).intersection(set(test_list2))) # printing result print("The Intersection of data records is : " + str(res)) --- __ __ **Output :** The original list 1 is : [('gfg', 1), ('is', 2), ('best', 3)] The original list 2 is : [('i', 3), ('love', 4), ('gfg', 1)] The Intersection of data records is : [('gfg', 1)] Attention geek! Strengthen your foundations with the **Python Programming Foundation** Course and learn the basics. To begin with, your interview preparations Enhance your Data Structures concepts with the **Python DS** Course. My Personal Notes _arrow_drop_up_ Save

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qmnguyenw@gmail.com

a767ea85226a3a33df945842b2bedd5921f415aa

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"""Models for the ``test_app`` app.""" from django.db import models from django.utils.translation import ugettext_lazy as _ from simple_translation.translation_pool import translation_pool from ...models_mixins import ( SimpleTranslationMixin, SimpleTranslationPublishedManager, ) class DummyProfile(SimpleTranslationMixin, models.Model): """Just a dummy profile model for testing purposes.""" user = models.ForeignKey('auth.User') dummy_field = models.CharField( verbose_name=_('Dummy Field'), max_length=128, ) objects = SimpleTranslationPublishedManager() class DummyProfileTranslation(models.Model): """Just a translation of the dummy profile.""" dummy_translation = models.CharField(max_length=128) is_published = models.BooleanField(default=True) language = models.CharField(max_length=8, default='en') dummyprofile = models.ForeignKey(DummyProfile) translation_pool.register_translation(DummyProfile, DummyProfileTranslation)

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# Generated by Django 2.1.11 on 2019-10-02 17:17 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ("contenttypes", "0002_remove_content_type_name"), ("course_catalog", "0040_add_program_published"), ] operations = [ migrations.RenameModel(old_name="CourseRun", new_name="LearningResourceRun") ]

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paulhendricks/inference-server

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# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! import grpc import inference_server_pb2 as inference__server__pb2 class InferenceStub(object): # missing associated documentation comment in .proto file pass def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.Compute = channel.unary_unary( '/inference_server.Inference/Compute', request_serializer=inference__server__pb2.Input.SerializeToString, response_deserializer=inference__server__pb2.Output.FromString, ) class InferenceServicer(object): # missing associated documentation comment in .proto file pass def Compute(self, request, context): """Sends a greeting """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_InferenceServicer_to_server(servicer, server): rpc_method_handlers = { 'Compute': grpc.unary_unary_rpc_method_handler( servicer.Compute, request_deserializer=inference__server__pb2.Input.FromString, response_serializer=inference__server__pb2.Output.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'inference_server.Inference', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,))

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Aasthaengg/IBMdataset

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s=input() n=len(s) res=0 for i in range(n): if s[i]=='A' or s[i]=='C' or s[i]=='G' or s[i]=='T': ans=1 j=i+1 while j<n: if s[j]=='A' or s[j]=='C' or s[j]=='G' or s[j]=='T': ans+=1 j+=1 else: break else: continue res=max(res,ans) print(res)

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# Copyright 2018 Open Source Robotics Foundation, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ros2cli.command import add_subparsers_on_demand from ros2cli.command import CommandExtension class ParamCommand(CommandExtension): """Various param related sub-commands.""" def add_arguments(self, parser, cli_name, *, argv=None): self._subparser = parser # add arguments and sub-commands of verbs add_subparsers_on_demand( parser, cli_name, '_verb', 'ros2param.verb', required=False, argv=argv) def main(self, *, parser, args): if not hasattr(args, '_verb'): # in case no verb was passed self._subparser.print_help() return 0 extension = getattr(args, '_verb') # call the verb's main method return extension.main(args=args)

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sidbmw/PyCharm-Settings

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5,547

py

# encoding: utf-8 # module _signal # from (built-in) # by generator 1.146 """ This module provides mechanisms to use signal handlers in Python. Functions: alarm() -- cause SIGALRM after a specified time [Unix only] setitimer() -- cause a signal (described below) after a specified float time and the timer may restart then [Unix only] getitimer() -- get current value of timer [Unix only] signal() -- set the action for a given signal getsignal() -- get the signal action for a given signal pause() -- wait until a signal arrives [Unix only] default_int_handler() -- default SIGINT handler signal constants: SIG_DFL -- used to refer to the system default handler SIG_IGN -- used to ignore the signal NSIG -- number of defined signals SIGINT, SIGTERM, etc. -- signal numbers itimer constants: ITIMER_REAL -- decrements in real time, and delivers SIGALRM upon expiration ITIMER_VIRTUAL -- decrements only when the process is executing, and delivers SIGVTALRM upon expiration ITIMER_PROF -- decrements both when the process is executing and when the system is executing on behalf of the process. Coupled with ITIMER_VIRTUAL, this timer is usually used to profile the time spent by the application in user and kernel space. SIGPROF is delivered upon expiration. *** IMPORTANT NOTICE *** A signal handler function is called with two arguments: the first is the signal number, the second is the interrupted stack frame. """ # no imports # Variables with simple values CTRL_BREAK_EVENT = 1 CTRL_C_EVENT = 0 NSIG = 23 SIGABRT = 22 SIGBREAK = 21 SIGFPE = 8 SIGILL = 4 SIGINT = 2 SIGSEGV = 11 SIGTERM = 15 SIG_DFL = 0 SIG_IGN = 1 # functions def default_int_handler(*more): # real signature unknown; restored from __doc__ """ default_int_handler(...) The default handler for SIGINT installed by Python. It raises KeyboardInterrupt. """ pass def getsignal(*args, **kwargs): # real signature unknown """ Return the current action for the given signal. The return value can be: SIG_IGN -- if the signal is being ignored SIG_DFL -- if the default action for the signal is in effect None -- if an unknown handler is in effect anything else -- the callable Python object used as a handler """ pass def set_wakeup_fd(fd, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__ """ set_wakeup_fd(fd, *, warn_on_full_buffer=True) -> fd Sets the fd to be written to (with the signal number) when a signal comes in. A library can use this to wakeup select or poll. The previous fd or -1 is returned. The fd must be non-blocking. """ pass def signal(): # real signature unknown; restored from __doc__ """ Set the action for the given signal. The action can be SIG_DFL, SIG_IGN, or a callable Python object. The previous action is returned. See getsignal() for possible return values. *** IMPORTANT NOTICE *** A signal handler function is called with two arguments: the first is the signal number, the second is the interrupted stack frame. """ pass # classes class __loader__(object): """ Meta path import for built-in modules. All methods are either class or static methods to avoid the need to instantiate the class. """ @classmethod def create_module(cls, *args, **kwargs): # real signature unknown """ Create a built-in module """ pass @classmethod def exec_module(cls, *args, **kwargs): # real signature unknown """ Exec a built-in module """ pass @classmethod def find_module(cls, *args, **kwargs): # real signature unknown """ Find the built-in module. If 'path' is ever specified then the search is considered a failure. This method is deprecated. Use find_spec() instead. """ pass @classmethod def find_spec(cls, *args, **kwargs): # real signature unknown pass @classmethod def get_code(cls, *args, **kwargs): # real signature unknown """ Return None as built-in modules do not have code objects. """ pass @classmethod def get_source(cls, *args, **kwargs): # real signature unknown """ Return None as built-in modules do not have source code. """ pass @classmethod def is_package(cls, *args, **kwargs): # real signature unknown """ Return False as built-in modules are never packages. """ pass @classmethod def load_module(cls, *args, **kwargs): # real signature unknown """ Load the specified module into sys.modules and return it. This method is deprecated. Use loader.exec_module instead. """ pass def module_repr(module): # reliably restored by inspect """ Return repr for the module. The method is deprecated. The import machinery does the job itself. """ pass def __init__(self, *args, **kwargs): # real signature unknown pass __weakref__ = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """list of weak references to the object (if defined)""" __dict__ = None # (!) real value is '' # variables with complex values __spec__ = None # (!) real value is ''

[ "siddharthnatamai@gmail.com" ]

siddharthnatamai@gmail.com

37a1db073986ca0971ba4944632aafaec7a0d5ff

f57529f95a0fd10676f46063fdcd273fb5a81427

/boj/05001-06000/5063.py

6a2433d4269d1553b50fb5ef00f2d4236113033c

[]

no_license

hoyasmh/PS

a9b83b0044e483586590c9b7c6bf8a77236b67e7

6bbaa0ce77b2726f6af782af049d73720820f761

refs/heads/master

2023-04-23T10:43:27.349785

2021-05-17T13:43:53

311,239,034

0

null

UTF-8

Python

false

211

py

n=int(input()) for i in range(n): r,e,c=map(int, input().split()) a=e-c if a>r: print('advertise') elif a==r: print('does not matter') else: print('do not advertise')

[ "hoyasmh@gmail.com" ]

hoyasmh@gmail.com

600a02249547f25a443efccb2f227a4daf743e72

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p03626/s691421903.py

12b73d2e8e8b33454998cc794473feec516fdf85

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

933

py

# https://atcoder.jp/contests/abc071/tasks/arc081_b # 横ドミノと縦ドミノの場合わけを考える # 横ドミノ→横ドミノの場合 : いままでの通り*3通り # 横ドミノ→縦ドミノ : いままで*1通り ok # 縦ドミノ→横ドミノ : いままで*2通り ok # 縦ドミノ→縦ドミノ : いままで*2通り ok MOD = 10**9+7 N = int(input()) S1 = input() S2 = input() pre1 = S1[0] pre2 = S2[0] if pre1 != pre2: ans = 6 else: ans = 3 for s1, s2 in zip(S1[1:], S2[1:]): if pre1 == s1 and pre2 == s2: pass elif pre1 != pre2 and s1 != s2: # 横→横 ans *= 3 elif pre1 != pre2 and s1 == s2: # 横→縦 pass elif pre1 == pre2 and s1 != s2: # 縦→横 ans *= 2 elif pre1 == pre2 and s1 == s2: # 縦→縦 ans *= 2 if ans >= MOD: ans %= MOD pre1, pre2 = s1, s2 print(ans % MOD)

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

511cd08228a358eb20555f4016aa0f102b17db57

487ce91881032c1de16e35ed8bc187d6034205f7

/codes/CodeJamCrawler/16_0_2/HazelParr/pancake_revenge.py

0d9c90b6f19e36f7eff6120670502de3018d5d1b

[]

no_license

DaHuO/Supergraph

9cd26d8c5a081803015d93cf5f2674009e92ef7e

c88059dc66297af577ad2b8afa4e0ac0ad622915

refs/heads/master

2021-06-14T16:07:52.405091

2016-08-21T13:39:13

49,829,508

2

0

null

2021-03-19T21:55:46

2016-01-17T18:23:00

Python

UTF-8

Python

false

381

py

T = int(input()) for case in range(T): x = input() count = 0 while "-" in x: new_string = "" unchanged = "" y = x.rfind("-") fragment = x[:y+1] unchanged = x[y+1:] for i in fragment: if i == "-": new_string += "+" else: new_string += "-" count += 1 x = new_string + unchanged print("Case #{}: {}".format(case+1, count))

[ "[dhuo@tcd.ie]" ]

[dhuo@tcd.ie]

8cbdf7ab2ec66f8f1e82cb4824f60c32e935e855

e23a4f57ce5474d468258e5e63b9e23fb6011188

/120_design_patterns/001_SOLID_design_principles/_exercises/_exercises/isp/ISP/Book/IBook.py

01dad5411393ea75f180ee3e25c50e409867ecae

[]

no_license

syurskyi/Python_Topics

52851ecce000cb751a3b986408efe32f0b4c0835

be331826b490b73f0a176e6abed86ef68ff2dd2b

refs/heads/master

2023-06-08T19:29:16.214395

2023-05-29T17:09:11

220,583,118

3

2

null

2023-02-16T03:08:10

2019-11-09T02:58:47

Python

UTF-8

Python

false

220

py

# ___ ISP.IL.. ______ IL.. # # # c_ IBookIL.. # # ?p.. # ___ pages -> ? # r_ N... # # ??.? # ___ pages num_pages ? # r_ N... # # ?p.. # ___ title __ ? # r_ N... # # ??.? # ___ title titleName ? # r_ N...

[ "sergejyurskyj@yahoo.com" ]

sergejyurskyj@yahoo.com

f6b1a79848c4fc34a82d807b82b44e98bececaf5

163bbb4e0920dedd5941e3edfb2d8706ba75627d

/Code/CodeRecords/2320/60716/267286.py

b7837b56224bdc4c298a9aada04e063f0c2c204d

[]

no_license

AdamZhouSE/pythonHomework

a25c120b03a158d60aaa9fdc5fb203b1bb377a19

ffc5606817a666aa6241cfab27364326f5c066ff

refs/heads/master

2022-11-24T08:05:22.122011

2020-07-28T16:21:24

259,576,640

2

1

null

UTF-8

Python

false

385

py

strs = input() k = int(input()) if k==1: lists = list() lists.append(strs) for i in range(len(strs)): strlist = list(strs) k = strlist.pop(0) strlist.append(k) strs = ''.join(strlist) lists.append(strs) lists.sort() print(lists[0]) else: strlist = list(strs) strlist.sort() strs = ''.join(strlist) print(strs)

[ "1069583789@qq.com" ]

1069583789@qq.com

391f37b3390c8466064f8f93b5a3f7eba2852de0

cdd79cef15bdf6a0b9098e27028bbe38607bc288

/AOJ/Vol02/0241_Quaternion Multiplication.py

a94a35629d177665c61f8dcc76a162305205fbfc

[]

no_license

nord2sudjp/atcoder

ee35a3eb35717485dc62627172de24c9dac102fb

6b1cc5102a615492cc7ff8a33813bbb954641782

refs/heads/master

2023-08-25T11:27:14.205593

2021-09-27T05:43:04

302,855,505

0

null

UTF-8

Python

false

562

py

# Volume2-0241 Quaternion Multiplication #l1=[1,2,3,4] #l2=[7,6,7,8] while True: N=int(input()) if N==0:break for _ in range(N): *t,=list(map(int,input().split())) l1=t[0:4] l2=t[4:] s=[x*y for x in l1 for y in l2] ans=[0]*4 #1,i,j,k ans[0]+=s[0] ans[1]+=s[1]+s[4] ans[2]+=s[2]+s[8] ans[3]+=s[3]+s[12] ans[0]+=-1*s[5]-1*s[10]-1*s[15] ans[1]+=s[11]-1*s[14] ans[2]+=-1*s[7]+s[13] ans[3]+=s[6]-1*s[9] print(' '.join(map(str,ans)))

[ "nord2sudjp@gmail.com" ]

nord2sudjp@gmail.com

e6c627876e58af236e4193f1e7495a258b610ed7

2f38331b8a0bc8867859d853d352f9f5cc9cd1b5

/day07/code/value.py

ae8c35d75226626552aef586887bad597c2a649a

[]

no_license

Chenhuaqi6/python_net

aaf66a15a2f438e1f3fc67f338abd15e2bbfd6a3

56efd53bbaa1212a86c65e9cd3b29d2f5f30b752

refs/heads/master

2020-04-09T08:30:20.817513

2019-01-19T06:51:41

160,196,516

0

null

UTF-8

Python

false

523

py

from multiprocessing import Process,Value import time import random #创建共享内存 money = Value("i",5000) #挣钱 def boy(): for i in range(30): time.sleep(0.2) money.value += random.randint(1,1500) def girl(): for i in range(30): time.sleep(0.15) money.value -= random.randint(100,1200) b = Process(target = boy) g = Process(target = girl) b.start() g.start() b.join() g.join() print("一个月余额:",money.value)

[ "466940759@qq.com" ]

466940759@qq.com

4621879f40c0fc877a782f9a6e4748cf4c1db4cc

077a17b286bdd6c427c325f196eb6e16b30c257e

/00_BofVar-unit-tests/05_32/remenissions-work/exploit-BofFunc-28.py

a140ada92f16b731fc6c68505ca91b55c8fc744a

[]

no_license

KurSh/remenissions_test

626daf6e923459b44b82521aa4cb944aad0dbced

9dec8085b62a446f7562adfeccf70f8bfcdbb738

refs/heads/master

2023-07-08T20:25:04.823318

2020-10-05T06:45:16

null

0

null

UTF-8

Python

false

920

py

from pwn import * import time import sys import signal import sf target = process("./chall-test_BofVar-05-x86") gdb.attach(target, execute="verify_exploit") bof_payload = sf.BufferOverflow(arch=32) bof_payload.set_input_start(0x66) bof_payload.add_int32(0x34, 0xdeab) bof_payload.add_int32(0x30, 0xbeef) bof_payload.add_int32(0x2c, 0xfacadf) bof_payload.add_int32(0x28, 0xbef0) bof_payload.add_int32(0x24, 0xfacade) bof_payload.add_int32(0x20, 0xdeac) bof_payload.set_ret(0x8048456) payload = bof_payload.generate_payload() target.sendline(payload) # Exploit Verification starts here 15935728 def handler(signum, frame): raise Exception("Timed out") def check_verification_done(): while True: if os.path.exists("pwned") or os.path.exists("rip"): sys.exit(0) signal.signal(signal.SIGALRM, handler) signal.alarm(2) try: while True: check_verification_done() except Exception: print("Exploit timed out")

[ "ryancmeinke@gmail.com" ]

ryancmeinke@gmail.com

4ce830497d5d62e49c120012bdfb1463222c1714

0b406d2c041c76d9ef8789539e0e3af9a50e3613

/Extract_refactor/Ocr/lib/BusinessImagen.py

1efc3e6dc179167f58ae293994b0d864ef57c812

[]

no_license

aise17/ExtractPdf

221b47c5f0e75a823284b4f52981917962042592

7e1bfbc759cb7473d727574e5df78eaaac9fa8a4

refs/heads/master

2022-02-26T06:39:14.265795

2019-06-04T15:01:39

184,154,301

1

0

null

UTF-8

Python

false

3,186

py

import glob # de aki cojeremos la calse Image de PIL que es necesaria para pasarsela a tresseract from PIL import Image # con treseract extaeremos eltexto de la imagen png pasada por escala de grises import pytesseract # se usea OpenCv para aplicar escala de grises sobre imagen import cv2 # usamos sistema para crear una imagen/archivo temporal y eliminarla por su PID import os from Ocr.Conf.Config import configuracion from Extract_refactor.settings import MEDIA_URL , IMAGENES_PATH, JPG_PATH class BusinessImagen(): def configurarEscalaDeGrisesDefecto(self,image): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) return gray def configurarEscalaDeGrisesBlur(self, image): gray = cv2.medianBlur(self.configurarEscalaDeGrisesDefecto(image), 3) return gray def configurarEscalaDeGrisesThresh(self, image): gray = cv2.threshold(self.configurarEscalaDeGrisesDefecto(image), 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] return gray def configuracionEscalaDeColoresThresBinary(self, image): gray = cv2.adaptiveThreshold(self.configurarEscalaDeGrisesDefecto(image), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \ cv2.THRESH_BINARY, 11, 2) return gray def contarImagenesGeneradas(self): """ cuenta el numero de archivos acabados en jpg que existen en la carpeta output :return: numero de imagenes """ number_of_images = len(glob.glob( JPG_PATH + configuracion['rutas_de_todas_imagenes'])) print(" [+] NUMERO DE PAGINAS -> " + number_of_images.__str__()) return number_of_images def cargaImagen (self, count): """ carga las imagenes generadas aprtir del pdf :param count: para iterar entreimagenessi son mas de una :return: debulve una imagen tipo OpenCV """ if os.path.exists(JPG_PATH + 'image_name.jpg'): image = cv2.imread(JPG_PATH + 'image_name.jpg') else: image = cv2.imread(JPG_PATH + 'image_name-' + count.__str__() + '.jpg') return image def aplicarEcalaDeGrises(self, gray): """ escribimos la imagen procesada(en escala de grises) en el disco como una imagen/fichero temporal, y sobre este aplicamos openCV :param gray: :return: ruta filename temporal creado """ filename = "{}.jpg".format(os.getpid()) cv2.imwrite(filename, gray) #cv2.imshow('output', gray) #cv2.waitKey(0) return filename def aplicarORC(self, filename): """ cargamos la imagen con la variable tipo Image de PIL/Pillow, y se aplica el ORC :param filename: ruta de imagen temporal :return: str texto extraido de imagen con tresseract-orc """ text = pytesseract.image_to_string(Image.open(filename)) # y eliminamos la imagen temporal os.remove(filename) return text def borrarImagenesCreadas(self): for imagen in glob.glob(JPG_PATH +configuracion['rutas_de_todas_imagenes']): os.remove(imagen)

[ "sergio.martinez-g@hotmail.com" ]

sergio.martinez-g@hotmail.com

bbefdb7f7b0c7af6bc87b4dd1079b37651970d18

8fc653ed827dc185cc92196826c94e01c0a532c4

/setup.py

90e957663dd75dd44cbb3931a9637eb2385ae74f

[ "MIT" ]

permissive

Deepakdubey90/prometheus_flask_exporter

825dd1d1b8f4d81c7c78d9b9d2107b17f673748d

ae613e94573a28c72ef19dcd1372cc52fa07b5a7

refs/heads/master

2020-04-08T19:36:43.364482

2018-11-13T11:21:35

null

0

null

UTF-8

Python

false

1,361

py

from setuptools import setup with open('README.md') as readme: long_description = readme.read() setup( name='prometheus_flask_exporter', packages=['prometheus_flask_exporter'], version='0.4.0', description='Prometheus metrics exporter for Flask', long_description=long_description, long_description_content_type='text/markdown', license='MIT', author='Viktor Adam', author_email='rycus86@gmail.com', url='https://github.com/rycus86/prometheus_flask_exporter', download_url='https://github.com/rycus86/prometheus_flask_exporter/archive/0.4.0.tar.gz', keywords=['prometheus', 'flask', 'monitoring', 'exporter'], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: System Administrators', 'Topic :: System :: Monitoring', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7' ], install_requires=['prometheus_client', 'flask'], )

[ "rycus86@gmail.com" ]

rycus86@gmail.com

e26e6b3fb181566bc0a2c8b7ecbf18f53dc42fef

e27333261b8e579564016c71d2061cc33972a8b8

/development_codes/Backend/.history/BERTimplementation_20210810215300.py

e2cbc16e68d4af0fc027546c697ad4f250821dcd

[]

no_license

Dustyik/NewsTweet_InformationRetrieval

882e63dd20bc9101cbf48afa6c3302febf1989b1

d9a6d92b51c288f5bcd21ea1cc54772910fa58f7

refs/heads/master

2023-07-01T09:12:53.215563

2021-08-12T08:28:33

382,780,359

0

null

UTF-8

Python

false

1,561

py

from sentence_transformers import SentenceTransformer from sklearn.metrics.pairwise import cosine_similarity import numpy as np from tfidfImplementation import * from utils import QueryParsers #First segment involves retrieving K documents with tf-idf #Second segment involves reranking them with a BERT encoder K = 100 #BERT_MODEL = 'bert-base-nli-mean-tokens' BERT_MODEL = "paraphrase-multilingual-mpnet-base-v2" class BERTmodel: def __init__(self, tweets_data): self.tweets_data = tweets_data self.cosineSimilarity = CosineSimilarity(tweets_data, return_size=K) self.BERT_model = SentenceTransformer(BERT_MODEL) def tfidf_retrieve_K_tweets(self, article_id, article_title): topKResults = self.cosineSimilarity.query(query_id=article_id, query_text=article_title) return topKResults def return_BERT_query(self, article_id, article_title): topKResults = self.tfidf_retrieve_K_tweets(article_id, article_title) queryStemmed = " ".join(QueryParsers(article_title).query) query_vector_embedding = self.BERT_model.encode(queryStemmed) topKResults['vector_embedding'] = topKResults.apply(lambda row: self.BERT_model.encode(row.clean_text), axis = 1) topKResults["BERT_similarity"] = topKResults.apply(lambda row: cosine_similarity(np.array(query_vector_embedding).reshape(1, -1), np.array(row.vector_embedding).reshape(1, -1)).item(), axis = 1) topKResults.sort_values(by='BERT_similarity',ascending=False,inplace=True) return topKResults

[ "chiayik_tan@mymail.sutd.edu.sg" ]

chiayik_tan@mymail.sutd.edu.sg

fc2adf8051fdd9f97147f4dc02075974b54ab7ac

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p03265/s414771268.py

b56306c65e375123788a4b9e6db496b3f1d196d4

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

147

py

x1, y1, x2, y2 = map(int, input().split()) a = x2 - x1 b = y2 - y1 x3 = x2 - b y3 = y2 + a x4 = x3 - a y4 = y3 - b print(f'{x3} {y3} {x4} {y4}')

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

a22223b26b8737255728236d842d299cecf4eb12

d91a0186cec0452a8eb54fd6fabe0ef9e75cd738

/chapter_8/exercise_8.16/build_prof_module.py

301a4e69f18d34e27417fae0a4179c61e1060dee

[]

no_license

MaximZolotukhin/erik_metiz

31a6f5146b8bb58b8f04a6b9635b36a67830e52a

8afde60aa2bddd6858a5f7a7189169a82bde4322

refs/heads/main

2023-05-03T07:39:06.731413

2021-05-30T19:04:31

361,544,267

0

null

UTF-8

Python

false

350

py

# Передача неограниченного числа именованных аргументов def build_profile(firs_name, last_name, **user_info): """Строит словарь с информацией о пользователе""" user_info['firs_name'] = firs_name user_info['last_name'] = last_name return user_info

[ "ScorpionEvil777@gmail.com" ]

ScorpionEvil777@gmail.com

407d88f8bd05212b374fa5dd67bf6d0bcceb9d30

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/thor/constants.py

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import numpy as np class Constants: # km to au KM_P_AU = 149597870.700 # seconds to days S_P_DAY = 86400.0 # Speed of light: AU per day (173.14463267424034) (299792.458 km/s -- DE431/DE430) C = 299792.458 / KM_P_AU * S_P_DAY # Gravitational constant: AU**3 / M_sun / d**2 (0.295912208285591100E-3 -- DE431/DE430) G = 0.295912208285591100E-3 # Solar Mass: M_sun (1.0) M_SUN = 1.0 # Earth Equatorial Radius: km (6378.1363 km -- DE431/DE430) R_EARTH = 6378.1363 / KM_P_AU # Mean Obliquity at J2000: radians (84381.448 arcseconds -- DE431/DE430) OBLIQUITY = 84381.448 * np.pi / (180.0 * 3600.0) # Transformation matrix from Equatorial J2000 to Ecliptic J2000 TRANSFORM_EQ2EC = np.array([ [1, 0, 0], [0, np.cos(OBLIQUITY), np.sin(OBLIQUITY)], [0, -np.sin(OBLIQUITY), np.cos(OBLIQUITY) ]]) # Transformation matrix from Ecliptic J2000 to Equatorial J2000 TRANSFORM_EC2EQ = TRANSFORM_EQ2EC.T

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# Copyright 2020 kubeflow.org. # # 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. # coding: utf-8 """ KServe Python SDK for KServe # noqa: E501 The version of the OpenAPI document: v0.1 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import unittest import datetime import kserve from kserve.models.v1beta1_explainer_config import V1beta1ExplainerConfig # noqa: E501 from kserve.rest import ApiException class TestV1beta1ExplainerConfig(unittest.TestCase): """V1beta1ExplainerConfig unit test stubs""" def setUp(self): pass def tearDown(self): pass def make_instance(self, include_optional): """Test V1beta1ExplainerConfig include_option is a boolean, when False only required params are included, when True both required and optional params are included """ # model = kserve.models.v1beta1_explainer_config.V1beta1ExplainerConfig() # noqa: E501 if include_optional : return V1beta1ExplainerConfig( default_image_version = '0', image = '0' ) else : return V1beta1ExplainerConfig( default_image_version = '0', image = '0', ) def testV1beta1ExplainerConfig(self): """Test V1beta1ExplainerConfig""" inst_req_only = self.make_instance(include_optional=False) inst_req_and_optional = self.make_instance(include_optional=True) if __name__ == '__main__': unittest.main()

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from output.models.nist_data.atomic.unsigned_byte.schema_instance.nistschema_sv_iv_atomic_unsigned_byte_min_exclusive_4_xsd.nistschema_sv_iv_atomic_unsigned_byte_min_exclusive_4 import NistschemaSvIvAtomicUnsignedByteMinExclusive4 obj = NistschemaSvIvAtomicUnsignedByteMinExclusive4( value=218 )

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def conta_bigramas(string): dic = dict() for i in string and x in string: if i+x in string: if i+x in dic: dic[i+x] += 1 else: dic[i+x] = 1 return dic print(conta_bigramas("banana nanica"))

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JacobLutin/Python_bmstu

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def rotate(Tb,N): for i in range(0,N//2): for j in range(i,N-1-i): tmp = Tb[i][j] Tb[i][j] = Tb[n-j-1][i] Tb[n-j-1][i] = Tb[n-i-1][n-j-1] Tb[n-i-1][n-j-1] = Tb[j][n-i-1] Tb[j][n-i-1] = tmp return Tb def PrintTable(Tb,N): for i in range(N): for j in range(N): print(Tb[i][j], end="\t") print() print() def shifravanie(mask,out,sent,k): for i in range(n): for j in range(n): if (mask[i][j] == 1): out[i][j] = sent[k] k += 1 return out,k """ def decimalToBinary(num): while num > 1: num = num // 2 def decode(arr, n): matrix = [] for i in arr: """ import random n = int(input("Введите размер шифровальной таблицы: ")) Table = [] for i in range(n): Table.append([]) for j in range(n): Table[i].append(0) c = 1 for i in range(n // 2): for j in range(n // 2): Table[i][j] = c Table[j][n - i - 1] = c Table[n - i - 1][n - j - 1] = c Table[n - j - 1][i] = c c = c + 1 c = c - 1 block = [] while (len(block) != (n // 2) ** 2): i = random.randint(0, n - 1) j = random.randint(0, n - 1) flag = True for k in range(len(block)): if (Table[i][j] == -1) or (Table[i][j] == block[k]): flag = False break if (flag == True): block.append(Table[i][j]) Table[i][j] = -1 for i in range(n): for j in range(n): if (Table[i][j] != -1): Table[i][j] = 0 else: Table[i][j] = 1 PrintTable(Table,n) key = [] for i in range(n): m = 0 for j in range(n): m += Table[i][j] * 2 ** (n - j - 1) key.append(m) print() sentense = "серегаяобьяснютебекакэтоработает" print(len(sentense)) k = 0 Shifr = [] for i in range(n): Shifr.append([]) for j in range(n): Shifr[i].append(0) Shifr,k=shifravanie(Table,Shifr,sentense,k) Table=rotate(Table,n) Shifr,k=shifravanie(Table,Shifr,sentense,k) Table=rotate(Table,n) Shifr,k=shifravanie(Table,Shifr,sentense,k) Table=rotate(Table,n) Shifr,k=shifravanie(Table,Shifr,sentense,k) Table=rotate(Table,n) PrintTable(Table,n) PrintTable(Shifr,n) print() print(key)

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cache = {} def egcd(a, b): """ greatest common denominator :param a: :param b: :return: """ if a == 0: return (b, 0, 1) else: g, y, x = egcd(b % a, a) return (g, x - (b // a) * y, y) def modinv(a, m): """ calculate the multiplicative inverse of a modulus m such that (x * result) % m == (x / a) for any integer between 0 and m :param a: the number we wish to divide by :param m: the size of the modular field :return: the number we can multiply by to actually divide by a """ if(a in cache): sub_cache = cache[a] if m in sub_cache: return sub_cache[m] else: cache[a] = {} g, x, y = egcd(a, m) if g != 1: raise Exception('modular inverse does not exist') else: result = x % m cache[a][m] = result return result

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import dmc2gym from tf2rl.algos.curl_sac import CURLSAC from tf2rl.envs.dmc_wrapper import DMCWrapper from tf2rl.experiments.trainer import Trainer def main(): dm_envs = { 'finger': ['finger', 'spin', 2], 'cartpole': ['cartpole', 'balance', 4], 'reacher': ['reacher', 'easy', 4], 'cheetah': ['cheetah', 'run', 4], 'walker': ['walker', 'walk', 2], 'ball': ['ball_in_cup', 'catch', 4], 'humanoid': ['humanoid', 'stand', 4], 'bring_ball': ['manipulator', 'bring_ball', 4], 'bring_peg': ['manipulator', 'bring_peg', 4], 'insert_ball': ['manipulator', 'insert_ball', 4], 'insert_peg': ['manipulator', 'insert_peg', 4]} parser = Trainer.get_argument() parser = CURLSAC.get_argument(parser) parser.add_argument('--env-name', type=str, default="cartpole", choices=dm_envs.keys()) parser.add_argument('--seed', type=int, default=1) parser.set_defaults(batch_size=256) parser.set_defaults(n_warmup=10000) parser.set_defaults(max_steps=3e6) parser.set_defaults(save_summary_interval=100) args = parser.parse_args() domain_name, task_name, action_repeat = dm_envs[args.env_name] original_obs_shape = (100, 100, 9) input_obs_shape = (84, 84, 9) def make_env(): return DMCWrapper( dmc2gym.make( domain_name=domain_name, task_name=task_name, seed=args.seed, visualize_reward=False, from_pixels=True, height=100, width=100, frame_skip=action_repeat, channels_first=False), obs_shape=original_obs_shape, k=3, channel_first=False) env = make_env() test_env = make_env() # see Table 3 of CURL paper lr_sac = lr_curl = 2e-4 if args.env_name == "cheetah" else 1e-3 policy = CURLSAC( obs_shape=input_obs_shape, action_dim=env.action_space.high.size, gpu=args.gpu, memory_capacity=int(1e5), n_warmup=int(1e3), max_action=env.action_space.high[0], batch_size=512, actor_units=(1024, 1024), critic_units=(1024, 1024), lr_sac=lr_sac, lr_curl=lr_curl, lr_alpha=1e-4, tau=0.01, init_temperature=0.1, auto_alpha=True, stop_q_grad=args.stop_q_grad) trainer = Trainer(policy, env, args, test_env=test_env) if args.evaluate: trainer.evaluate_policy_continuously() else: trainer() if __name__ == "__main__": main()

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from functools import lru_cache class Solution: """ @param n: an integer @return: an integer f(n) """ @lru_cache(maxsize=10000) def fibonacci(self, n): if n == 1: return 0 elif n in [2, 3]: return 1 return self.fibonacci(n - 1) + self.fibonacci(n - 2)

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#!/home/afroz/PycharmProjects/to_do_list/venv/bin/python # EASY-INSTALL-ENTRY-SCRIPT: 'pip==10.0.1','console_scripts','pip3.6' __requires__ = 'pip==10.0.1' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('pip==10.0.1', 'console_scripts', 'pip3.6')() )

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config = { "arch": "multi-head", "backbone": "se_resnext50_32x4d", "pretrained": True, "in-bn": True, 'opt': 'sgd', 'loss_aggregate_fn': 'mean', 'schedule': 'cosine_annealing_warm_restarts', 'T_0': 6, 'lr': 0.1, 'train_aug': 'augmentations.geom.v1', 'valid_aug': 'augmentations.geom.v0', }

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# -*- coding: utf-8 -*- from odoo import models, fields, api, _ from odoo.exceptions import UserError, ValidationError class ExpenseRequest(models.Model): _name = 'expense.request' _description = 'Custom expense request' _inherit = ['mail.thread', 'mail.activity.mixin'] _order = 'date desc' @api.model def _default_employee_id(self): return self.env.user.employee_id @api.model def _get_default_requested_by(self): return self.env['res.users'].browse(self.env.uid) def _get_default_name(self): return self.env["ir.sequence"].next_by_code("expense.request.code") name = fields.Char(default=_get_default_name, readonly=True) description = fields.Char('Description', required=True) state = fields.Selection(selection=[ ('draft', 'Draft'), ('submit', 'Submitted'), ('validate', 'Validate'), ('to_approve', 'To Approve'), ('approve', 'Approved'), ('post', 'Paid'), ('refuse', 'Refused'), ('cancel', 'Cancelled') ], string='Status', index=True, readonly=True, tracking=True, copy=False, default='draft', required=True, help='Expense Report State')

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n,m = map(int,input().split()) # 1ケースでかかる時間を求める total = m*1900 + (n-m)*100 # 全てのケースで正解する確率の分母 prob_all = 2**m print(total * prob_all)

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#!/usr/bin/env python3 # Copyright 2022 The Chromium Authors # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Prints android-binary-size result for a given commit or commit range.""" import argparse import collections import concurrent.futures import csv import json import os import posixpath import re import subprocess import sys # Max number of commits to show when given a range and no -n parameter. _COMMIT_LIMIT = 200 # Commit ranges where size bot was giving invalid results. _BAD_COMMIT_RANGES = [ range(1045024, 1045552), # https://crbug.com/1361952 ] _COMMIT_RE = re.compile(r'^commit (?:(?!^commit).)*', re.DOTALL | re.MULTILINE) _MAIN_FIELDS_RE = re.compile( r'^commit (\S+).*?' r'^Date:\s+(.*?)$.*?' r'^ (\S.*?)$', re.DOTALL | re.MULTILINE) _REVIEW_RE = re.compile(r'^ Reviewed-on: (\S+)', re.MULTILINE) _CRREV_RE = re.compile(r'^ Cr-Commit-Position:.*?(\d+)', re.MULTILINE) _GERRIT_RE = re.compile(r'https://([^/]+)/c/(.*?)/\+/(\d+)') _CommitInfo = collections.namedtuple( '_CommitInfo', 'git_hash date subject review_url cr_position') def _parse_commit(text): git_hash, date, subject = _MAIN_FIELDS_RE.match(text).groups() review_url = ([''] + _REVIEW_RE.findall(text))[-1] cr_position = int((['0'] + _CRREV_RE.findall(text))[-1]) return _CommitInfo(git_hash, date, subject, review_url, cr_position) def _git_log(git_log_args): cmd = ['git', 'log'] if len(git_log_args) == 1 and '..' not in git_log_args[0]: # Single commit rather than commit range. cmd += ['-n1'] elif not any(x.startswith('-n') for x in git_log_args): # Ensure there's a limit on number of commits. cmd += [f'-n{_COMMIT_LIMIT}'] cmd += git_log_args log_output = subprocess.check_output(cmd, encoding='utf8') ret = [_parse_commit(x) for x in _COMMIT_RE.findall(log_output)] if len(ret) == _COMMIT_LIMIT: sys.stderr.write( f'Limiting to {_COMMIT_LIMIT} commits. Use -n## to override\n') return ret def _query_size(review_url, internal): if not review_url: return '<missing>' m = _GERRIT_RE.match(review_url) if not m: return '<bad URL>' host, project, change_num = m.groups() if internal: project = 'chrome' builder = 'android-internal-binary-size' else: project = 'chromium' builder = 'android-binary-size' cmd = ['bb', 'ls', '-json', '-p'] # Request results for all patchsets, assuming fewer than 30. for patchset in range(1, 30): cmd += [ '-predicate', """{ "builder":{"project":"%s","bucket":"try","builder":"%s"}, "gerrit_changes":[{ "host":"%s","project":"%s", "change":"%s","patchset":"%d"} ]}""" % (project, builder, host, project, change_num, patchset) ] result = subprocess.run(cmd, check=False, stdout=subprocess.PIPE, encoding='utf8') if result.returncode: return '<missing>' # Take the last one that has a size set (output is in reverse order already). for json_str in result.stdout.splitlines(): try: obj = json.loads(json_str) except json.JSONDecodeError: sys.stderr.write(f'Problem JSON:\n{json_str}\n') sys.exit(1) properties = obj.get('output', {}).get('properties', {}) listings = properties.get('binary_size_plugin', {}).get('listings', []) for listing in listings: if listing['name'] == 'Android Binary Size': return listing['delta'] return '<unknown>' def _maybe_rewrite_crrev(git_log_args): if len(git_log_args) != 1: return values = git_log_args[0].split('..') if len(values) != 2 or not values[0].isdigit() or not values[1].isdigit(): return values = [ subprocess.check_output(['git-crrev-parse', v], text=True).rstrip() for v in values ] git_log_args[0] = '..'.join(values) print(f'Converted crrev to commits: {git_log_args[0]}') def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('--csv', action='store_true', help='Print as CSV') parser.add_argument('--internal', action='store_true', help='Query android-internal-binary-size (Googlers only)') args, git_log_args = parser.parse_known_args() # Ensure user has authenticated. result = subprocess.run(['bb', 'auth-info'], check=False, stdout=subprocess.DEVNULL) if result.returncode: sys.stderr.write('First run: bb auth-login\n') sys.exit(1) _maybe_rewrite_crrev(git_log_args) commit_infos = _git_log(git_log_args) if not commit_infos: sys.stderr.write('Did not find any commits.\n') sys.exit(1) print(f'Fetching bot results for {len(commit_infos)} commits...') if args.csv: print_func = csv.writer(sys.stdout).writerow else: print_func = lambda v: print('{:<12}{:14}{:12}{:32}{}'.format(*v)) print_func(('Commit #', 'Git Hash', 'Size', 'Date', 'Subject')) num_bad_commits = 0 with concurrent.futures.ThreadPoolExecutor(max_workers=20) as pool: sizes = [ pool.submit(_query_size, info.review_url, args.internal) for info in commit_infos ] for info, size in zip(commit_infos, sizes): if any(info.cr_position in r for r in _BAD_COMMIT_RANGES): num_bad_commits += 1 size_str = size.result().replace(' bytes', '').lstrip('+') crrev_str = info.cr_position or '' print_func( (crrev_str, info.git_hash[:12], size_str, info.date, info.subject)) if num_bad_commits: print(f'Includes {num_bad_commits} commits from known bad revision range.') if __name__ == '__main__': main()

[ "chromium-scoped@luci-project-accounts.iam.gserviceaccount.com" ]

chromium-scoped@luci-project-accounts.iam.gserviceaccount.com

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tatubola/xpto

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# Generated by Django 2.0.8 on 2018-08-31 17:28 import django.core.validators from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('core', '0004_auto_20180827_1752'), ] operations = [ migrations.AlterField( model_name='fatura', name='boleto_url', field=models.CharField(blank=True, error_messages={'invalid': 'Insira uma url válida.'}, max_length=255, null=True, validators=[django.core.validators.URLValidator()]), ), migrations.AlterField( model_name='servico', name='data_expiracao', field=models.DateField(), ), ]

[ "dmoniz@nic.br" ]

dmoniz@nic.br

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# Do not edit. File was generated by node-gyp's "configure" step { "target_defaults": { "cflags": [], "default_configuration": "Release", "defines": [], "include_dirs": [], "libraries": [] }, "variables": { "asan": 0, "build_v8_with_gn": "false", "coverage": "false", "dcheck_always_on": 0, "debug_nghttp2": "false", "debug_node": "false", "enable_lto": "false", "enable_pgo_generate": "false", "enable_pgo_use": "false", "error_on_warn": "false", "force_dynamic_crt": 0, "host_arch": "x64", "icu_data_in": "../../deps/icu-tmp/icudt68l.dat", "icu_endianness": "l", "icu_gyp_path": "tools/icu/icu-generic.gyp", "icu_path": "deps/icu-small", "icu_small": "false", "icu_ver_major": "68", "is_debug": 0, "llvm_version": "11.0", "napi_build_version": "8", "node_byteorder": "little", "node_debug_lib": "false", "node_enable_d8": "false", "node_install_npm": "true", "node_module_version": 83, "node_no_browser_globals": "false", "node_prefix": "/usr/local", "node_release_urlbase": "https://nodejs.org/download/release/", "node_shared": "false", "node_shared_brotli": "false", "node_shared_cares": "false", "node_shared_http_parser": "false", "node_shared_libuv": "false", "node_shared_nghttp2": "false", "node_shared_openssl": "false", "node_shared_zlib": "false", "node_tag": "", "node_target_type": "executable", "node_use_bundled_v8": "true", "node_use_dtrace": "true", "node_use_etw": "false", "node_use_node_code_cache": "true", "node_use_node_snapshot": "true", "node_use_openssl": "true", "node_use_v8_platform": "true", "node_with_ltcg": "false", "node_without_node_options": "false", "openssl_fips": "", "openssl_is_fips": "false", "ossfuzz": "false", "shlib_suffix": "83.dylib", "target_arch": "x64", "v8_enable_31bit_smis_on_64bit_arch": 0, "v8_enable_gdbjit": 0, "v8_enable_i18n_support": 1, "v8_enable_inspector": 1, "v8_enable_lite_mode": 0, "v8_enable_object_print": 1, "v8_enable_pointer_compression": 0, "v8_no_strict_aliasing": 1, "v8_optimized_debug": 1, "v8_promise_internal_field_count": 1, "v8_random_seed": 0, "v8_trace_maps": 0, "v8_use_siphash": 1, "want_separate_host_toolset": 0, "xcode_version": "11.0", "nodedir": "/Users/mac/Library/Caches/node-gyp/14.17.0", "standalone_static_library": 1, "dry_run": "", "legacy_bundling": "", "save_dev": "", "browser": "", "commit_hooks": "true", "only": "", "viewer": "man", "also": "", "rollback": "true", "sign_git_commit": "", "audit": "true", "usage": "", "globalignorefile": "/usr/local/etc/npmignore", "init_author_url": "", "maxsockets": "50", "shell": "/bin/zsh", "metrics_registry": "https://registry.npmjs.org/", "parseable": "", "shrinkwrap": "true", "init_license": "ISC", "timing": "", "if_present": "", "cache_max": "Infinity", "init_author_email": "", "sign_git_tag": "", "cert": "", "git_tag_version": "true", "local_address": "", "long": "", "preid": "", "fetch_retries": "2", "registry": "https://registry.npmjs.org/", "key": "", "message": "%s", "versions": "", "globalconfig": "/usr/local/etc/npmrc", "always_auth": "", "logs_max": "10", "prefer_online": "", "cache_lock_retries": "10", "global_style": "", "update_notifier": "true", "audit_level": "low", "heading": "npm", "fetch_retry_mintimeout": "10000", "offline": "", "read_only": "", "searchlimit": "20", "access": "", "json": "", "allow_same_version": "", "description": "true", "engine_strict": "", "https_proxy": "", "init_module": "/Users/mac/.npm-init.js", "userconfig": "/Users/mac/.npmrc", "cidr": "", "node_version": "14.17.0", "user": "", "auth_type": "legacy", "editor": "vi", "ignore_prepublish": "", "save": "true", "script_shell": "", "tag": "latest", "before": "", "global": "", "progress": "true", "ham_it_up": "", "optional": "true", "searchstaleness": "900", "bin_links": "true", "force": "", "save_prod": "", "searchopts": "", "depth": "Infinity", "node_gyp": "/usr/local/lib/node_modules/npm/node_modules/node-gyp/bin/node-gyp.js", "rebuild_bundle": "true", "sso_poll_frequency": "500", "unicode": "true", "fetch_retry_maxtimeout": "60000", "ca": "", "save_prefix": "^", "scripts_prepend_node_path": "warn-only", "sso_type": "oauth", "strict_ssl": "true", "tag_version_prefix": "v", "dev": "", "fetch_retry_factor": "10", "group": "20", "save_exact": "", "cache_lock_stale": "60000", "prefer_offline": "", "version": "", "cache_min": "10", "otp": "", "cache": "/Users/mac/.npm", "searchexclude": "", "color": "true", "package_lock": "true", "fund": "true", "package_lock_only": "", "save_optional": "", "user_agent": "npm/6.14.13 node/v14.17.0 darwin x64", "ignore_scripts": "", "cache_lock_wait": "10000", "production": "", "save_bundle": "", "send_metrics": "", "init_version": "1.0.0", "node_options": "", "umask": "0022", "scope": "", "git": "git", "init_author_name": "", "onload_script": "", "tmp": "/var/folders/cn/tv09tq6n7r10g948dc0phgyh0000gn/T", "unsafe_perm": "true", "format_package_lock": "true", "link": "", "prefix": "/usr/local" } }

[ "mac@Macs-MacBook-Pro.local" ]

mac@Macs-MacBook-Pro.local

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/WebMirror/management/rss_parser_funcs/feed_parse_extractSadhoovysinhumantranslationsWordpressCom.py

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def extractSadhoovysinhumantranslationsWordpressCom(item): ''' Parser for 'sadhoovysinhumantranslations.wordpress.com' ''' vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title']) if not (chp or vol) or "preview" in item['title'].lower(): return None tagmap = [ ('PRC', 'PRC', 'translated'), ('Loiterous', 'Loiterous', 'oel'), ] for tagname, name, tl_type in tagmap: if tagname in item['tags']: return buildReleaseMessageWithType(item, name, vol, chp, frag=frag, postfix=postfix, tl_type=tl_type) return False

[ "something@fake-url.com" ]

something@fake-url.com

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TsiyonW/pracdjango

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"""ethiostockdemo URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path from django.views.decorators.csrf import csrf_exempt from graphene_django.views import GraphQLView urlpatterns = [ path('admin/', admin.site.urls), path('graphql/', csrf_exempt(GraphQLView.as_view(graphiql=True))), ]

[ "=" ]

=

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permissive

HOLL95/Cytochrome_SV

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refs/heads/master

2022-08-01T05:58:16.161510

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#!/usr/bin/env python3 # # Tests the Rosenbrock toy problems. # # This file is part of PINTS. # Copyright (c) 2017-2018, University of Oxford. # For licensing information, see the LICENSE file distributed with the PINTS # software package. # import pints import pints.toy import unittest import numpy as np class TestRosenbrock(unittest.TestCase): """ Tests the Rosenbrock toy problems. """ def test_error(self): f = pints.toy.RosenbrockError() self.assertEqual(f.n_parameters(), 2) fx = f([10, 10]) self.assertTrue(np.isscalar(fx)) self.assertEqual(fx, 810081) xopt = f.optimum() fopt = f(xopt) self.assertEqual(fopt, 0) np.random.seed(1) for x in np.random.uniform(-5, 5, size=(10, 2)): self.assertTrue(f(x) > fopt) def test_log_pdf(self): f = pints.toy.RosenbrockLogPDF() self.assertEqual(f.n_parameters(), 2) fx = f([0.5, 6.0]) self.assertTrue(np.isscalar(fx)) self.assertAlmostEqual(fx, np.log(1.0 / 3307.5)) xopt = f.optimum() fopt = f(xopt) self.assertEqual(fopt, 0) # sensitivity test l, dl = f.evaluateS1([3, 4]) self.assertEqual(l, -np.log(2505)) self.assertEqual(len(dl), 2) self.assertEqual(dl[0], float(-6004.0 / 2505.0)) self.assertEqual(dl[1], float(200.0 / 501.0)) # suggested bounds and distance measure bounds = f.suggested_bounds() bounds = [[-2, 4], [-1, 12]] bounds = np.transpose(bounds).tolist() self.assertTrue(np.array_equal(bounds, f.suggested_bounds())) x = np.ones((100, 3)) self.assertRaises(ValueError, f.distance, x) x = np.ones((100, 3, 2)) self.assertRaises(ValueError, f.distance, x) # there is no simple way to generate samples from Rosenbrock nsamples = 10000 g = pints.toy.GaussianLogPDF([1, 1], [1, 1]) samples = g.sample(nsamples) self.assertTrue(f.distance(samples) > 0) x = np.ones((100, 3)) self.assertRaises(ValueError, f.distance, x) x = np.ones((100, 2, 2)) self.assertRaises(ValueError, f.distance, x) # generate samples with mean and variance closer to true values g1 = pints.toy.GaussianLogPDF([0.86935785, 2.59978086], [[1.80537968, 2.70257559], [2.70257559, 8.52658308]]) samples1 = g1.sample(nsamples) self.assertTrue(f.distance(samples1) > 0) self.assertTrue(f.distance(samples) > f.distance(samples1)) if __name__ == '__main__': print('Add -v for more debug output') import sys if '-v' in sys.argv: debug = True unittest.main()

[ "henney@localhost.localdomain" ]

henney@localhost.localdomain

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/src/leetcode/P968.py

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[]

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stupidchen/leetcode

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refs/heads/master

2022-03-14T21:15:47.263954

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py

# Definition for a binary tree node. from functools import lru_cache INF = 0xffffffff class TreeNode: def __init__(self, x): self.val = x self.left = None self.right = None class Solution: def minCameraCover(self, root): """ :type root: TreeNode :rtype: int """ @lru_cache(maxsize=None) def solve(node, t): if node is None: return 0 if node.left is None and node.right is None: if t == 1: return INF if t == 0: return 1 if t == -1: return 0 l = [] for i in range(3): l.append(solve(node.left, i - 1)) r = [] for i in range(3): r.append(solve(node.right, i - 1)) if t == -1: if node.left is not None and node.right is not None: return min(min(l[2], l[1]) + min(r[2], r[1]), min(r[2], r[1]) + min(l[2], l[1])) if node.left is not None: return min(l[2], l[1]) else: return min(r[2], r[1]) if t == 0: return 1 + min(l) + min(r) if t == 1: if node.left is not None and node.right is not None: return min(l[1] + min(r[2], r[1]), r[1] + min(l[2], l[1])) if node.left is not None: return l[1] else: return r[1] return min(solve(root, 0), solve(root, 1)) if __name__ == '__main__': node = TreeNode(1) node.left = TreeNode(2) node.left.left = TreeNode(3) node.left.left.left = TreeNode(4) node.left.left.left.left = TreeNode(5) node.left.left.left.left.left = TreeNode(6) print(Solution().minCameraCover(node))

[ "stupidchen@foxmail.com" ]

stupidchen@foxmail.com

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/dag1/live_koding/gjettelek.py

b593a9791de1a0e0394b71192b55604fdfbfa7a4

[]

no_license

kodeskolen/tekna_agder_h20_2

6eb52a21fa2425b82cb88108686cce0079ac71ab

16e869ad48b2411b3f2b133f3adbb382863a744d

refs/heads/main

2023-01-13T16:35:06.870744

2020-11-20T13:24:49

310,009,393

0

null

UTF-8

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false

838

py

# -*- coding: utf-8 -*- """ Created on Thu Nov 12 19:28:47 2020 @author: Marie """ # Vi "tenker" på et tall mellom 0 og 100 # Spilleren gjetter et tall # Dersom gjettet er rett så er spillet over # Dersom gjettet er for lavt eller høyt, får spilleren beskjed # Spilleren får gjette på nytt # Spilleren har begrenset med gjett from random import randint riktig_tall = randint(0, 100) maks_forsøk = 3 gjett = int(input("Gjett et tall mellom 0 og 100: ")) forsøk = 1 while gjett != riktig_tall and forsøk < maks_forsøk: if gjett < riktig_tall: print("For lavt!") else: print("For høyt!") gjett = int(input("Gjett igjen: ")) forsøk += 1 if riktig_tall == gjett: print("Det var riktig! :D") else: print(f"Du har brukt opp dine {maks_forsøk} forsøk!") print("GAME OVER!!")

[ "roald.marie@gmail.com" ]

roald.marie@gmail.com

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/py3/torch_motion_retarget_autoencoder/main_train.py

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[]

no_license

Daiver/jff

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33d6a781af8d7f6ae60c25e10051977af2fef1b9

refs/heads/master

2023-04-07T06:33:41.487938

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2023-04-03T19:25:00

2013-08-17T15:03:14

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import numpy as np import cv2 import torch import torch.optim as optim import torch.nn as nn from torch.utils.data import DataLoader import np_draw_tools from data_generation import make_rectangle_dataset, make_circle_dataset from models import Encoder, Decoder from utils import numpy_images_to_torch def main(): n_samples_to_generate = 1500 epochs = 50 device = "cuda" batch_size = 8 circle_set = make_circle_dataset(n_samples_to_generate) rect_set = make_rectangle_dataset(n_samples_to_generate) train_rect_set = numpy_images_to_torch(rect_set) train_circle_set = numpy_images_to_torch(circle_set) print(f"N rect samples {len(train_rect_set)} N circle samples {len(train_circle_set)}") train_rect_loader = DataLoader(train_rect_set, batch_size=batch_size, shuffle=True, drop_last=True) val_rect_loader = DataLoader(train_rect_set, batch_size=batch_size * 16, shuffle=False) train_circle_loader = DataLoader(train_circle_set, batch_size=batch_size, shuffle=True, drop_last=True) encoder = Encoder().to(device) decoder_rect = Decoder().to(device) decoder_circle = Decoder().to(device) optimizer = optim.Adam(list(encoder.parameters()) + list(decoder_rect.parameters()) + list(decoder_circle.parameters()), lr=1e-4) # criterion = nn.MSELoss() # criterion = nn.L1Loss() criterion = nn.BCELoss() for epoch_ind in range(epochs): losses = [] losses_rect = [] losses_circle = [] encoder.train() decoder_rect.train() decoder_circle.train() for sample_rect, sample_circle in zip(train_rect_loader, train_circle_loader): sample_rect = sample_rect.to(device) pred_rect = encoder(sample_rect) pred_rect = decoder_rect(pred_rect) loss_rect = criterion(pred_rect, sample_rect) sample_circle = sample_circle.to(device) pred_circle = encoder(sample_circle) pred_circle = decoder_circle(pred_circle) loss_circle = criterion(pred_circle, sample_circle) loss = loss_rect + loss_circle losses.append(loss.item()) losses_rect.append(loss_rect.item()) losses_circle.append(loss_circle.item()) optimizer.zero_grad() loss.backward() optimizer.step() encoder.eval() decoder_rect.eval() decoder_circle.eval() print(f"{epoch_ind + 1} / {epochs} loss {np.mean(losses)} loss rect {np.mean(losses_rect)} loss circle {np.mean(losses_circle)}") for sample_rect in val_rect_loader: sample_rect = sample_rect.to(device) pred_rect = encoder(sample_rect) # pred_rect = decoder_rect(pred_rect) pred_rect = decoder_circle(pred_rect) pred_rect = (pred_rect.detach().permute(0, 2, 3, 1).cpu().numpy() * 255).astype(np.uint8) sample_rect = (sample_rect.detach().permute(0, 2, 3, 1).cpu().numpy() * 255).astype(np.uint8) to_show = [] for p, s in zip(pred_rect, sample_rect): to_show.append(p) to_show.append(s) to_show = np_draw_tools.make_grid(to_show[:64], 8) cv2.imshow("", to_show) cv2.waitKey(100) break torch.save(encoder.state_dict(), "encoder.pt") torch.save(decoder_circle.state_dict(), "decoder_circle.pt") torch.save(decoder_rect.state_dict(), "decoder_rect.pt") cv2.waitKey() if __name__ == '__main__': main()

[ "ra22341@ya.ru" ]

ra22341@ya.ru

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/wagtail_shell/test/urls.py

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kaedroho/wagtail-shell

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from django.conf.urls import include, url from django.contrib import admin from wagtail.admin import urls as wagtailadmin_urls from wagtail.documents import urls as wagtaildocs_urls from wagtail.core import urls as wagtail_urls urlpatterns = [ url(r"^django-admin/", admin.site.urls), url(r"^admin/", include(wagtailadmin_urls)), url(r"^documents/", include(wagtaildocs_urls)), url(r"", include(wagtail_urls)), ]

[ "karl@kaed.uk" ]

karl@kaed.uk

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/18_mmaction/lib/mmcv/mmcv/ops/__init__.py

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karndeepsingh/Monk_Object_Detection

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2022-12-22T18:26:53.933397

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from .bbox import bbox_overlaps from .carafe import CARAFE, CARAFENaive, CARAFEPack, carafe, carafe_naive from .cc_attention import CrissCrossAttention from .corner_pool import CornerPool from .deform_conv import DeformConv2d, DeformConv2dPack, deform_conv2d from .deform_roi_pool import (DeformRoIPool, DeformRoIPoolPack, ModulatedDeformRoIPoolPack, deform_roi_pool) from .focal_loss import (SigmoidFocalLoss, SoftmaxFocalLoss, sigmoid_focal_loss, softmax_focal_loss) from .info import get_compiler_version, get_compiling_cuda_version from .masked_conv import MaskedConv2d, masked_conv2d from .modulated_deform_conv import (ModulatedDeformConv2d, ModulatedDeformConv2dPack, modulated_deform_conv2d) from .nms import batched_nms, nms, nms_match, soft_nms from .point_sample import (SimpleRoIAlign, point_sample, rel_roi_point_to_rel_img_point) from .psa_mask import PSAMask from .roi_align import RoIAlign, roi_align from .roi_pool import RoIPool, roi_pool from .saconv import SAConv2d from .sync_bn import SyncBatchNorm from .tin_shift import TINShift, tin_shift from .wrappers import Conv2d, ConvTranspose2d, Linear, MaxPool2d __all__ = [ 'bbox_overlaps', 'CARAFE', 'CARAFENaive', 'CARAFEPack', 'carafe', 'carafe_naive', 'CornerPool', 'DeformConv2d', 'DeformConv2dPack', 'deform_conv2d', 'DeformRoIPool', 'DeformRoIPoolPack', 'ModulatedDeformRoIPoolPack', 'deform_roi_pool', 'SigmoidFocalLoss', 'SoftmaxFocalLoss', 'sigmoid_focal_loss', 'softmax_focal_loss', 'get_compiler_version', 'get_compiling_cuda_version', 'MaskedConv2d', 'masked_conv2d', 'ModulatedDeformConv2d', 'ModulatedDeformConv2dPack', 'modulated_deform_conv2d', 'batched_nms', 'nms', 'soft_nms', 'nms_match', 'RoIAlign', 'roi_align', 'RoIPool', 'roi_pool', 'SyncBatchNorm', 'Conv2d', 'ConvTranspose2d', 'Linear', 'MaxPool2d', 'CrissCrossAttention', 'PSAMask', 'point_sample', 'rel_roi_point_to_rel_img_point', 'SimpleRoIAlign', 'SAConv2d', 'TINShift', 'tin_shift' ]

[ "abhishek4273@gmail.com" ]

abhishek4273@gmail.com

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/setup.py

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winkidney/PyMonitor

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import os from setuptools import setup, find_packages here = os.path.abspath(os.path.dirname(__file__)) requires = [ 'psutil', ] setup(name='PyMonitor', version='0.1', description='Monitor system status', classifiers=[ "Programming Language :: Python", ], author='', author_email='', url='', keywords='system tools', packages=find_packages(), include_package_data=True, zip_safe=False, install_requires=requires, tests_require=requires, test_suite="none", )

[ "winkidney@gmail.com" ]

winkidney@gmail.com

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/Part_1/Алгоритмы и структуры данных/Homeworks/Sort1/quick_sort_new.py

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no_license

akoshel/MADE

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refs/heads/main

2023-04-28T14:25:53.210880

2021-05-19T16:23:52

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from random import randint list_lenght = int(input()) raw_list = list(map(int, input().split(' '))) assert(len(raw_list) == list_lenght) def quick_sort_step(input_list, l, r): ll = l i = l hh = r x = input_list[l] while i <= hh: if input_list[i] < x: input_list[ll], input_list[i] = input_list[i], input_list[ll] ll += 1 i += 1 elif input_list[i] > x: input_list[i], input_list[hh] = input_list[hh], input_list[i] hh -= 1 else: i += 1 return ll, hh def quick_sort(init_list, l, r): if l < r: m = randint(l, r) init_list[m], init_list[l] = init_list[l], init_list[m] ll, hh = quick_sort_step(init_list, l, r) quick_sort(init_list, l, ll - 1) quick_sort(init_list, hh + 1, r) quick_sort(raw_list, 0, len(raw_list)-1) print(' '.join(list(map(str, raw_list))))

[ "johndoe@example.com" ]

johndoe@example.com

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/nn_ns/data_structure/TreeNodeOps/UnbalancedMultiWayTreeNodeOps/IUnbalancedMultiWayTreeNodeOps.py

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[]

no_license

edt-yxz-zzd/python3_src

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refs/heads/master

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__all__ = ''' IUnbalancedMultiWayTreeNodeOps '''.split() from .abc import not_implemented, override from ..TreeNodeOps.IOrientedTreeNodeOps__inorder import \ IOrientedTreeNodeOps__inorder class IUnbalancedMultiWayTreeNodeOps(IOrientedTreeNodeOps__inorder): ''' assume nonleaf.num_children > 0 since num_entities = num_children-1 assume nonleaf.num_children >= 2 since inorder entity_position == innode_position except: entity_position != innode_last == entity_end new methods: `get_entity_at `_iter_entities_of_nonleaf_ `_iter_reversed_entities_of_nonleaf_ iter_entities_of_nonleaf iter_reversed_entities_of_nonleaf get_num_entities_of_nonleaf calc_num_entities_of_subtree iter_innode_position_entity_pairs_of_nonleaf iter_reversed_innode_position_entity_pairs_of_nonleaf get_innode_entity_begin get_innode_entity_end get_innode_entity_begin_or_end iter_entities_of_subtree iter_reversed_entities_of_subtree leaf_to_iter_entities_of_subtree leaf_to_iter_reversed_entities_of_subtree ''' __slots__ = () @override def why_not_subtree_ok(ops, self, **kwargs): # kwargs readonly, should not remove key from it # i.e. donot override: def is_subtree_ok(ops, self, *, as_root=..., **kwargs) return (ops.why_not_num_entities_of_nonleaf_ok(self) + super().why_not_subtree_ok(self, **kwargs) ) def why_not_num_entities_of_nonleaf_ok(ops, self): # num_entities_of_nonleaf = num_children-1 # is_leaf = ops.is_leaf unstable_iter_nodes_of_subtree = ops.unstable_iter_nodes_of_subtree get_num_children = ops.get_num_children get_num_entities_of_nonleaf = ops.get_num_entities_of_nonleaf #if ops.is_leaf(self): return () for node in unstable_iter_nodes_of_subtree(self): if is_leaf(node): continue nonleaf = node; del node num_children = get_num_children(nonleaf) num_entities_of_nonleaf = get_num_entities_of_nonleaf(nonleaf) if num_children != 1+num_entities_of_nonleaf: return ('num_children != 1+num_entities_of_nonleaf',) return () def get_num_entities_of_nonleaf(ops, self): assert not ops.is_leaf(self) return ops.get_num_children(self) - 1 ########## require num_children > 0 ################ #def get_num_entities_of_subtree(ops, self): def calc_num_entities_of_subtree(ops, self): on_leaf = lambda _: 0 on_nonleaf = ops.get_num_entities_of_nonleaf combine = lambda a, bs: sum(bs, a) return ops.bottomup_eval_unoriented_subtree( self, on_leaf, on_nonleaf, combine) if ops.is_leaf(self): return 0 return sum(map(ops.calc_num_entities_of_subtree , ops.unstable_iter_children(self)) , ops.get_num_entities_of_nonleaf(self)) # nonleaf @not_implemented def _iter_entities_of_nonleaf_(ops, self): # self as node, not as subtree assert not ops.is_leaf(self) ... @not_implemented def _iter_reversed_entities_of_nonleaf_(ops, self): # self as node, not as subtree assert not ops.is_leaf(self) ... def iter_entities_of_nonleaf(ops, self, *, reverse=False): if not reverse: f = ops._iter_entities_of_nonleaf_ else: f = ops._iter_reversed_entities_of_nonleaf_ return f(self) def iter_reversed_entities_of_nonleaf(ops, self, *, reverse=False): return ops.iter_entities_of_nonleaf(self, reverse=not reverse) def iter_innode_position_entity_pairs_of_nonleaf(ops, self, *, reverse=False): # NOTE: not output innode_last (== child_last == entity_end) return zip(ops.iter_innode_positions(self, reverse=reverse) , ops.iter_entities_of_nonleaf(self, reverse=reverse)) def iter_reversed_innode_position_entity_pairs_of_nonleaf(ops, self, *, reverse=False): # NOTE: not output innode_last (== child_last == entity_end) return ops.iter_innode_position_entity_pairs_of_nonleaf(self, reverse=not reverse) # entity_begin == child_begin # entity_end == child_last def get_innode_entity_begin_or_end(ops, self, end:bool): return ops.get_innode_first_or_last_position(self) def get_innode_entity_begin(ops, self): return ops.get_innode_first_position(self) def get_innode_entity_end(ops, self): return ops.get_innode_last_position(self) @not_implemented def get_entity_at(ops, self, entity_position): # like get_child_at assert entity_position != ops.get_innode_entity_end(self) ... def iter_reversed_entities_of_subtree(ops, self, *, reverse=False): return ops.iter_entities_of_subtree(self, reverse=not reverse) def iter_entities_of_subtree(ops, self, *, reverse=False): # reverse ==>> last leaf last = reverse = bool(reverse) leaf, depth = ops.get_first_or_last_leaf_ex(self, 0, last) return ops.leaf_to_iter_entities_of_subtree(leaf, depth, reverse=reverse) @staticmethod def __nonleaf_triples2entities(get_entity_at, triples): # triple = (nonleaf, entity_position, depth) # get_entity_at = ops.get_entity_at for nonleaf, entity_position, depth in triples: yield get_entity_at(nonleaf, entity_position) def leaf_to_iter_reversed_entities_of_subtree(ops, self, depth, *, reverse=False): f = ops.leaf_to_iter_entities_of_subtree return f(self, depth, reverse=not reverse) def leaf_to_iter_entities_of_subtree(ops, self, depth, *, reverse=False): assert ops.is_leaf(self) f = ops.leaf_to_inorder_iter_nonleaf_entity_position_triples it = f(self, depth, reverse=reverse) return __class__.__nonleaf_triples2entities(ops.get_entity_at, it) if __name__ == '__main__': XXX = IUnbalancedMultiWayTreeNodeOps from seed.helper.print_methods import print_methods print_methods(XXX)

[ "wuming_zher@zoho.com.cn" ]

wuming_zher@zoho.com.cn

f2c89a5b91a1fc71833013689a89d7bf15352771

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/supervised_learning/0x12-transformer_apps/1-dataset.py

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[]

no_license

95ktsmith/holbertonschool-machine_learning

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refs/heads/master

2023-07-26T16:02:26.399758

2021-09-09T15:57:57

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#!/usr/bin/env python3 """ Dataset """ import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds class Dataset: """ Class to load an prepare a dataset for machine translation """ def __init__(self): """ Init """ data = tfds.load( "ted_hrlr_translate/pt_to_en", as_supervised=True ) self.data_train = data['train'] self.data_valid = data['validation'] tokenizer_pt, tokenizer_en = self.tokenize_dataset(self.data_train) self.tokenizer_pt = tokenizer_pt self.tokenizer_en = tokenizer_en def tokenize_dataset(self, data): """ Creates sub-word tokenizers for a dataset data: tf.data.Dataset whose examples are formatted as a table (pt, en) pt: tf.Tensor containing the Portuguese sentence en: tf.Tensor containing the English sentence Returns: tokenizer_pt, tokenizer_en tokenizer_pt: Portuguese tokenizer tokenizer_en: English tokenizer """ encoder = tfds.deprecated.text.SubwordTextEncoder tokenizer_pt = encoder.build_from_corpus( (pt.numpy() for pt, en in data), target_vocab_size=2 ** 15 ) tokenizer_en = encoder.build_from_corpus( (en.numpy() for pt, en in data), target_vocab_size=2 ** 15 ) return tokenizer_pt, tokenizer_en def encode(self, pt, en): """ Encodes a translation into tokens pt: tf.Tensor containing the Portuguese sentence en: tf.Tensor containing the corresponding English sentence Returns: pt_tokens, en_tokens pt_tokens: np.ndarray containing Portuguese tokens en_tokens: np.ndarray containing the English tokens """ pt_tokens = [self.tokenizer_pt.vocab_size] pt_tokens += self.tokenizer_pt.encode(pt.numpy()) pt_tokens += [pt_tokens[0] + 1] en_tokens = [self.tokenizer_en.vocab_size] en_tokens += self.tokenizer_en.encode(en.numpy()) en_tokens += [en_tokens[0] + 1] return pt_tokens, en_tokens

[ "95ktsmith@gmail.com" ]

95ktsmith@gmail.com

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/LSTM/SegWords/BI-LSTM/Demo4/seqlib.py

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[]

no_license

sunshinelu/NLPLearnNote

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refs/heads/master

2020-03-08T07:03:25.652478

2018-05-06T14:13:02

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#!/usr/bin/python #-*-coding:utf-8-*- ''' Created on 2018-04-25 15:41 @author:wangrs ''' #1.导入模块包和语料库文件 import codecs from gensim.models.word2vec import Word2Vec import numpy as np import nltk from nltk.probability import FreqDist import pandas as pd from pickle import dump,load from keras.preprocessing import sequence from keras.models import Sequential from keras.layers.embeddings import Embedding from keras.layers.recurrent import LSTM,GRU,SimpleRNN from keras.layers.core import Reshape,Flatten,Dropout,Dense,Activation from keras.regularizers import l1,l2 from keras.layers.convolutional import Convolution2D,MaxPooling2D,MaxPooling1D from sklearn.cross_validation import train_test_split from keras.optimizers import SGD,RMSprop,Adagrad from keras.utils import np_utils #2.使用分词语料库生成词向量（模型） def load_file(input_file): #读单个文本 input_data = codecs.open(input_file,'r',encoding='utf-8') input_text = input_data.read() return input_text #使用gensim的word2vec库 def trainW2V(corpus,epochs = 10,num_features=100,sg=1,min_word_count=1,num_works=4,context=4,sample=1e-5,negative=5): w2v = Word2Vec(workers=num_works,sample= sample,size=num_features,min_count=min_word_count,window=context) np.random.shuffle(corpus) #打乱顺序函数 w2v.build_vocab(corpus) w2v.train(corpus,total_examples=w2v.corpus_count,epochs=epochs) print("word2vec DONE.") return w2v #3.语料预处理 def freq_func(input_text): #nltk输入文本，输出词频 corpus = nltk.Text(input_text) fdist = FreqDist(corpus) w = list(fdist.keys()) v = list(fdist.values()) freqpd = pd.DataFrame({'word':w,'freq':v}) freqpd.sort_values(by='freq',ascending=False,inplace=True) freqpd['idx'] = np.arange(len(v)) return freqpd #初始化权重 def init_weightlist(w2v,idx2word,word2idx): init_weight_wv = [] for i in range(len(idx2word)): init_weight_wv.append(w2v[idx2word[i]]) #定义‘U’为未登录新字，‘P’为两头padding用途，并增加两个相应的向量表示 char_num = len(init_weight_wv) idx2word[char_num] = 'U' word2idx['U'] = char_num idx2word[char_num+1] = 'P' word2idx['P'] = char_num+1 init_weight_wv.append(np.random.randn(100)) init_weight_wv.append(np.zeros(100)) return init_weight_wv,idx2word,word2idx def character_tagging(input_file,output_file): #加入标注标签：BMES(B是词首，M是词中，E是词尾，S是单字词) #带BOM的utf-8编码的txt文件时开头会有一个多余的字符\ufeff，BOM被解码为一个字符\ufeff，如何去掉？ # 修改encoding为utf-8_sig或者utf_8_sig input_data = codecs.open(input_file,'r',encoding='utf-8_sig') output_data = codecs.open(output_file,'w',encoding='utf-8') for line in input_data.readlines(): word_list = line.strip().split() for word in word_list: if len(word) == 1: output_data.write(word+"/S ") else: output_data.write(word[0]+'/B ') for w in word[1:len(word)-1]: output_data.write(w+"/M ") output_data.write(word[len(word)-1]+'/E ') output_data.write('\n') output_data.close() input_data.close() def featContext(sentence,word2idx='',context = 7): predict_word_num = [] for w in sentence: #文本中的字如果在词典中则转为数字，如果不在则设置为U if w in word2idx: predict_word_num.append(word2idx[w]) else: predict_word_num.append(word2idx['U']) num = len(predict_word_num) #首尾padding pad = int((context-1)*0.5) for i in range(pad): predict_word_num.insert(0,word2idx['P']) predict_word_num.append(word2idx['P']) train_x = [] for i in range(num): train_x.append(predict_word_num[i:i+context]) return train_x #4.训练语料 class Lstm_Net(object): def __init__(self): self.init_weight=[] self.batch_size = 128 self.word_dim = 100 self.maxlen = 7 self.hidden_units = 100 self.nb_classes = 0 def buildnet(self): self.maxfeatures = self.init_weight[0].shape[0] #词典大小 self.model = Sequential() print('stacking LSTM .....')#使用了堆叠的LSTM架构 self.model.add(Embedding(self.maxfeatures,self.word_dim,input_length=self.maxlen)) self.model.add(LSTM(self.hidden_units,return_sequences=True)) self.model.add(LSTM(self.hidden_units,return_sequences=False)) self.model.add(Dropout(0.5)) self.model.add(Dense(self.nb_classes)) self.model.add(Activation('softmax')) self.model.compile(loss='categorical_crossentropy',optimizer='adam') def train(self,modelname): result= self.model.fit(self.train_X,self.Y_train,batch_size=self.batch_size,epochs=20,validation_data=(self.test_X,self.Y_test)) self.model.save_weights(modelname) def splitset(self,train_word_num,train_label,train_size=0.9,random_state=1): self.train_X,self.test_X,train_y,test_y = train_test_split(train_word_num,train_label,train_size=0.9,random_state=1) print(np.shape(self.train_X)) self.Y_train = np_utils.to_categorical(train_y,self.nb_classes) print(np.shape(self.Y_train)) self.Y_test = np_utils.to_categorical(test_y,self.nb_classes) def predict_num(self,input_num,input_txt,label_dict='',num_dict=''): #根据输入得到标注推断 input_num = np.array(input_num) predict_prob = self.model.predict_proba(input_num,verbose=False) predict_label = self.model.predict_classes(input_num,verbose=False) for i,label in enumerate(predict_label[:-1]): if i==0: #如果是首字，不可为E，M predict_prob[i,label_dict['E']] = 0 predict_prob[i,label_dict['M']] = 0 if label == label_dict['B']: #前字为B，后字不可为B,S predict_prob[i+1, label_dict['B']] = 0 predict_prob[i+1, label_dict['S']] = 0 if label == label_dict['E']: #前字为E，后字不可为M,E predict_prob[i+1, label_dict['M']] = 0 predict_prob[i+1, label_dict['E']] = 0 if label == label_dict['M']: #前字为M，后字不可为B,S predict_prob[i+1, label_dict['B']] = 0 predict_prob[i+1, label_dict['S']] = 0 if label == label_dict['S']: # 前字为S，后字不可为M,E predict_prob[i + 1, label_dict['M']] = 0 predict_prob[i + 1, label_dict['E']] = 0 predict_label[i+1] = predict_prob[i+1].argmax() predict_label_new = [num_dict[x] for x in predict_label] result = [w+'/'+label for w,label in zip(input_txt,predict_label_new)] return ' '.join(result)+'\n' def getweights(self,wfname): return self.model.load_weights(wfname)

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from rdflib import Namespace, Graph, Literal, RDF, URIRef from rdfalchemy.rdfSubject import rdfSubject from rdfalchemy import rdfSingle, rdfMultiple, rdfList from brick.brickschema.org.schema._1_0_2.Brick.Supply_Air_Flow import Supply_Air_Flow class Average_Supply_Air_Flow(Supply_Air_Flow): rdf_type = Namespace('https://brickschema.org/schema/1.0.2/Brick#').Average_Supply_Air_Flow

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# qubit number=5 # total number=41 import cirq import qiskit from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister from qiskit import BasicAer, execute, transpile from pprint import pprint from qiskit.test.mock import FakeVigo from math import log2,floor, sqrt, pi import numpy as np import networkx as nx def build_oracle(n: int, f) -> QuantumCircuit: # implement the oracle O_f^\pm # NOTE: use U1 gate (P gate) with \lambda = 180 ==> CZ gate # or multi_control_Z_gate (issue #127) controls = QuantumRegister(n, "ofc") oracle = QuantumCircuit(controls, name="Zf") for i in range(2 ** n): rep = np.binary_repr(i, n) if f(rep) == "1": for j in range(n): if rep[j] == "0": oracle.x(controls[j]) # oracle.h(controls[n]) if n >= 2: oracle.mcu1(pi, controls[1:], controls[0]) for j in range(n): if rep[j] == "0": oracle.x(controls[j]) # oracle.barrier() return oracle def make_circuit(n:int,f) -> QuantumCircuit: # circuit begin input_qubit = QuantumRegister(n,"qc") classical = ClassicalRegister(n, "qm") prog = QuantumCircuit(input_qubit, classical) prog.h(input_qubit[0]) # number=3 prog.h(input_qubit[1]) # number=4 prog.h(input_qubit[2]) # number=5 prog.h(input_qubit[3]) # number=6 prog.h(input_qubit[4]) # number=21 prog.x(input_qubit[2]) # number=26 Zf = build_oracle(n, f) repeat = floor(sqrt(2 ** n) * pi / 4) for i in range(1): prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)]) prog.h(input_qubit[0]) # number=1 prog.h(input_qubit[1]) # number=2 prog.h(input_qubit[2]) # number=7 prog.h(input_qubit[3]) # number=8 prog.y(input_qubit[3]) # number=25 prog.x(input_qubit[0]) # number=9 prog.h(input_qubit[1]) # number=32 prog.cz(input_qubit[0],input_qubit[1]) # number=33 prog.h(input_qubit[1]) # number=34 prog.h(input_qubit[1]) # number=38 prog.cz(input_qubit[0],input_qubit[1]) # number=39 prog.h(input_qubit[1]) # number=40 prog.x(input_qubit[1]) # number=36 prog.cx(input_qubit[0],input_qubit[1]) # number=37 prog.cx(input_qubit[0],input_qubit[1]) # number=30 prog.cx(input_qubit[0],input_qubit[2]) # number=22 prog.x(input_qubit[2]) # number=23 prog.y(input_qubit[3]) # number=27 prog.cx(input_qubit[0],input_qubit[2]) # number=24 prog.x(input_qubit[3]) # number=12 prog.cx(input_qubit[1],input_qubit[2]) # number=31 if n>=2: prog.mcu1(pi,input_qubit[1:],input_qubit[0]) prog.x(input_qubit[0]) # number=13 prog.x(input_qubit[1]) # number=14 prog.x(input_qubit[2]) # number=15 prog.x(input_qubit[3]) # number=16 prog.h(input_qubit[0]) # number=17 prog.h(input_qubit[1]) # number=18 prog.h(input_qubit[2]) # number=19 prog.h(input_qubit[3]) # number=20 prog.h(input_qubit[0]) prog.h(input_qubit[1]) prog.h(input_qubit[2]) prog.h(input_qubit[3]) # circuit end for i in range(n): prog.measure(input_qubit[i], classical[i]) return prog if __name__ == '__main__': key = "00000" f = lambda rep: str(int(rep == key)) prog = make_circuit(5,f) backend = BasicAer.get_backend('qasm_simulator') sample_shot =7924 info = execute(prog, backend=backend, shots=sample_shot).result().get_counts() backend = FakeVigo() circuit1 = transpile(prog,backend,optimization_level=2) writefile = open("../data/startQiskit959.csv","w") print(info,file=writefile) print("results end", file=writefile) print(circuit1.depth(),file=writefile) print(circuit1,file=writefile) writefile.close()

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from evalml.data_checks import ( DataCheck, DataCheckError, DataCheckMessageCode, DataCheckWarning ) from evalml.utils import _convert_woodwork_types_wrapper, infer_feature_types class ClassImbalanceDataCheck(DataCheck): """Checks if any target labels are imbalanced beyond a threshold. Use for classification problems""" def __init__(self, threshold=0.1, min_samples=100, num_cv_folds=3): """Check if any of the target labels are imbalanced, or if the number of values for each target are below 2 times the number of cv folds Arguments: threshold (float): The minimum threshold allowed for class imbalance before a warning is raised. This threshold is calculated by comparing the number of samples in each class to the sum of samples in that class and the majority class. For example, a multiclass case with [900, 900, 100] samples per classes 0, 1, and 2, respectively, would have a 0.10 threshold for class 2 (100 / (900 + 100)). Defaults to 0.10. min_samples (int): The minimum number of samples per accepted class. If the minority class is both below the threshold and min_samples, then we consider this severely imbalanced. Must be greater than 0. Defaults to 100. num_cv_folds (int): The number of cross-validation folds. Must be positive. Choose 0 to ignore this warning. """ if threshold <= 0 or threshold > 0.5: raise ValueError("Provided threshold {} is not within the range (0, 0.5]".format(threshold)) self.threshold = threshold if min_samples <= 0: raise ValueError("Provided value min_samples {} is not greater than 0".format(min_samples)) self.min_samples = min_samples if num_cv_folds < 0: raise ValueError("Provided number of CV folds {} is less than 0".format(num_cv_folds)) self.cv_folds = num_cv_folds * 2 def validate(self, X, y): """Checks if any target labels are imbalanced beyond a threshold for binary and multiclass problems Ignores NaN values in target labels if they appear. Arguments: X (ww.DataTable, pd.DataFrame, np.ndarray): Features. Ignored. y (ww.DataColumn, pd.Series, np.ndarray): Target labels to check for imbalanced data. Returns: dict: Dictionary with DataCheckWarnings if imbalance in classes is less than the threshold, and DataCheckErrors if the number of values for each target is below 2 * num_cv_folds. Example: >>> import pandas as pd >>> X = pd.DataFrame() >>> y = pd.Series([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) >>> target_check = ClassImbalanceDataCheck(threshold=0.10) >>> assert target_check.validate(X, y) == {"errors": [{"message": "The number of instances of these targets is less than 2 * the number of cross folds = 6 instances: [0]",\ "data_check_name": "ClassImbalanceDataCheck",\ "level": "error",\ "code": "CLASS_IMBALANCE_BELOW_FOLDS",\ "details": {"target_values": [0]}}],\ "warnings": [{"message": "The following labels fall below 10% of the target: [0]",\ "data_check_name": "ClassImbalanceDataCheck",\ "level": "warning",\ "code": "CLASS_IMBALANCE_BELOW_THRESHOLD",\ "details": {"target_values": [0]}},\ {"message": "The following labels in the target have severe class imbalance because they fall under 10% of the target and have less than 100 samples: [0]",\ "data_check_name": "ClassImbalanceDataCheck",\ "level": "warning",\ "code": "CLASS_IMBALANCE_SEVERE",\ "details": {"target_values": [0]}}],\ "actions": []} """ results = { "warnings": [], "errors": [], "actions": [] } y = infer_feature_types(y) y = _convert_woodwork_types_wrapper(y.to_series()) fold_counts = y.value_counts(normalize=False, sort=True) if len(fold_counts) == 0: return results # search for targets that occur less than twice the number of cv folds first below_threshold_folds = fold_counts.where(fold_counts < self.cv_folds).dropna() if len(below_threshold_folds): below_threshold_values = below_threshold_folds.index.tolist() error_msg = "The number of instances of these targets is less than 2 * the number of cross folds = {} instances: {}" DataCheck._add_message(DataCheckError(message=error_msg.format(self.cv_folds, below_threshold_values), data_check_name=self.name, message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS, details={"target_values": below_threshold_values}), results) counts = fold_counts / (fold_counts + fold_counts.values[0]) below_threshold = counts.where(counts < self.threshold).dropna() # if there are items that occur less than the threshold, add them to the list of results if len(below_threshold): below_threshold_values = below_threshold.index.tolist() warning_msg = "The following labels fall below {:.0f}% of the target: {}" DataCheck._add_message(DataCheckWarning(message=warning_msg.format(self.threshold * 100, below_threshold_values), data_check_name=self.name, message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD, details={"target_values": below_threshold_values}), results) sample_counts = fold_counts.where(fold_counts < self.min_samples).dropna() if len(below_threshold) and len(sample_counts): sample_count_values = sample_counts.index.tolist() severe_imbalance = [v for v in sample_count_values if v in below_threshold] warning_msg = "The following labels in the target have severe class imbalance because they fall under {:.0f}% of the target and have less than {} samples: {}" DataCheck._add_message(DataCheckWarning(message=warning_msg.format(self.threshold * 100, self.min_samples, severe_imbalance), data_check_name=self.name, message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE, details={"target_values": severe_imbalance}), results) return results

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import openmc mats = openmc.Materials() mat = openmc.Material(1) mat.name = "Plutonium nitrate solution" mat.set_density('sum') mat.add_nuclide('Pu238', 1.5406e-08) mat.add_nuclide('Pu239', 2.4294e-04) mat.add_nuclide('Pu240', 1.1886e-05) mat.add_nuclide('Pu241', 7.7338e-07) mat.add_nuclide('Pu242', 2.2727e-08) mat.add_nuclide('N14', 1.2577e-03) mat.add_nuclide('H1', 6.3655e-02) mat.add_nuclide('O16', 3.5483e-02) mat.add_s_alpha_beta('c_H_in_H2O') mats.append(mat) mat = openmc.Material(2) mat.name = "304L stainless steel" mat.set_density('sum') mat.add_element('Fe', 5.9355e-02) mat.add_element('Cr', 1.7428e-02) mat.add_element('Ni', 7.7203e-03) mat.add_element('Mn', 1.7363e-03) mats.append(mat) mat = openmc.Material(3) mat.name = "Water at 25 C" mat.set_density('sum') mat.add_nuclide('H1', 6.6655e-02) mat.add_nuclide('O16', 3.3327e-02) mat.add_s_alpha_beta('c_H_in_H2O') mats.append(mat) mats.export_to_xml()

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''' Function: Implementation of NonLocalNet Author: Zhenchao Jin ''' import copy import torch import torch.nn as nn import torch.nn.functional as F from ...backbones import * from ..base import BaseModel from .nonlocalblock import NonLocal2d '''NonLocalNet''' class NonLocalNet(BaseModel): def __init__(self, cfg, **kwargs): super(NonLocalNet, self).__init__(cfg, **kwargs) align_corners, norm_cfg, act_cfg = self.align_corners, self.norm_cfg, self.act_cfg # build non-local block nl_cfg = cfg['nonlocal'] self.conv_before_nl = nn.Sequential( nn.Conv2d(nl_cfg['in_channels'], nl_cfg['out_channels'], kernel_size=3, stride=1, padding=1, bias=False), BuildNormalizationLayer(norm_cfg['type'], (nl_cfg['out_channels'], norm_cfg['opts'])), BuildActivation(act_cfg['type'], **act_cfg['opts']), ) self.nl_block = NonLocal2d( in_channels=nl_cfg['out_channels'], reduction=nl_cfg['reduction'], use_scale=nl_cfg['use_scale'], mode=nl_cfg['mode'], norm_cfg=copy.deepcopy(norm_cfg), act_cfg=copy.deepcopy(act_cfg), ) self.conv_after_nl = nn.Sequential( nn.Conv2d(nl_cfg['out_channels'], nl_cfg['out_channels'], kernel_size=3, stride=1, padding=1, bias=False), BuildNormalizationLayer(norm_cfg['type'], (nl_cfg['out_channels'], norm_cfg['opts'])), BuildActivation(act_cfg['type'], **act_cfg['opts']), ) # build decoder decoder_cfg = cfg['decoder'] self.decoder = nn.Sequential( nn.Conv2d(decoder_cfg['in_channels'], decoder_cfg['out_channels'], kernel_size=3, stride=1, padding=1, bias=False), BuildNormalizationLayer(norm_cfg['type'], (decoder_cfg['out_channels'], norm_cfg['opts'])), BuildActivation(act_cfg['type'], **act_cfg['opts']), nn.Dropout2d(decoder_cfg['dropout']), nn.Conv2d(decoder_cfg['out_channels'], cfg['num_classes'], kernel_size=1, stride=1, padding=0) ) # build auxiliary decoder auxiliary_cfg = cfg['auxiliary'] self.auxiliary_decoder = nn.Sequential( nn.Conv2d(auxiliary_cfg['in_channels'], auxiliary_cfg['out_channels'], kernel_size=3, stride=1, padding=1, bias=False), BuildNormalizationLayer(norm_cfg['type'], (auxiliary_cfg['out_channels'], norm_cfg['opts'])), BuildActivation(act_cfg['type'], **act_cfg['opts']), nn.Dropout2d(auxiliary_cfg['dropout']), nn.Conv2d(auxiliary_cfg['out_channels'], cfg['num_classes'], kernel_size=1, stride=1, padding=0) ) # freeze normalization layer if necessary if cfg.get('is_freeze_normlayer', False): self.freezenormlayer() '''forward''' def forward(self, x, targets=None, losses_cfg=None): h, w = x.size(2), x.size(3) # feed to backbone network x1, x2, x3, x4 = self.backbone_net(x) # feed to non-local block feats = self.conv_before_nl(x4) feats = self.nl_block(feats) feats = self.conv_after_nl(feats) # feed to decoder feats = torch.cat([x4, feats], dim=1) preds = self.decoder(feats) # feed to auxiliary decoder and return according to the mode if self.mode == 'TRAIN': preds = F.interpolate(preds, size=(h, w), mode='bilinear', align_corners=self.align_corners) preds_aux = self.auxiliary_decoder(x3) preds_aux = F.interpolate(preds_aux, size=(h, w), mode='bilinear', align_corners=self.align_corners) return self.calculatelosses( predictions={'loss_cls': preds, 'loss_aux': preds_aux}, targets=targets, losses_cfg=losses_cfg ) return preds '''return all layers''' def alllayers(self): return { 'backbone_net': self.backbone_net, 'conv_before_nl': self.conv_before_nl, 'nl_block': self.nl_block, 'conv_after_nl': self.conv_after_nl, 'decoder': self.decoder, 'auxiliary_decoder': self.auxiliary_decoder }

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/packages/models-library/src/models_library/services_ui.py

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2023-07-12T09:47:00

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2023-04-18T08:04:27

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from enum import Enum from typing import Union from pydantic import BaseModel, Extra, Field from pydantic.types import PositiveInt class WidgetType(str, Enum): TextArea = "TextArea" SelectBox = "SelectBox" class TextArea(BaseModel): min_height: PositiveInt = Field( ..., alias="minHeight", description="minimum Height of the textarea" ) class Config: extra = Extra.forbid class Structure(BaseModel): key: Union[str, bool, float] label: str class Config: extra = Extra.forbid class SelectBox(BaseModel): structure: list[Structure] = Field(..., min_items=1) class Config: extra = Extra.forbid class Widget(BaseModel): widget_type: WidgetType = Field( ..., alias="type", description="type of the property" ) details: Union[TextArea, SelectBox] class Config: extra = Extra.forbid

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pcrespov.noreply@github.com