How to use generated_content method in tox

Best Python code snippet using tox_python

protocol_analysis.py

Source:protocol_analysis.py Github

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1class Protocol:2 """3 一种通用的网络协议解析方法,比如tls,一般格式为（内容长度(固定字节数)+内容）4 修改日期 202202115 """6 def __init__(self, struct: dict, content: bytes = None, strict=False):7 if content: # 输入内容，对内容进行解析8 # 分别返回解析的内容以及内容对应的属性9 self.parsed_content, self._parsed_content = self._parse(struct, content, strict)10 else: # 没有内容，根据struct生成字节串11 self.generated_content = self.generate(struct)12 self.parsed_content = None # 不能少，用于判断是解析还是生成字节串13 self.__prefix = "" # 用于repr格式化显示14 def __bool__(self):15 return bool(self.parsed_content)16 @classmethod17 def generate(cls, struct):18 """19 根据struct,生成字节串20 :param struct: 输入的字典21 :return: 输出字节串22 """23 generated_content = b"" # 最后生成的字节串24 length_num = None # 根据内容的大小，生成内容的长度，用lengthNum个字节表示。None表示不需要计算长度25 for k, v in struct.items():26 if isinstance(v, tuple):27 num, content = v # 对应的字节数和实际内容28 if content is None:29 if not isinstance(num, int):30 raise TypeError(f"类型错误，{k}对应的元组的第一位，必须是一个整数值，来限定字节数")31 length_num = num # 下面内容的长度未知，之后再添加,先记下位置和字节数32 elif isinstance(content, bytes): # content是bytes类型，没有问题，对num进行验证33 if num is not None:34 if isinstance(num, int):35 if len(content) != num:36 # num不是None,证明内容具有固定长度,因此对内容长度进行检验37 raise ValueError(38 f"值错误，{k} 对应的值(元组第二位)的字节数应该是{num},实际字节数是{len(content)}")39 else: # num不是None,也不是int类型，有问题40 raise TypeError(f"类型错误，{k} 需要限定字节数,必须是一个整数值")41 if length_num: # 需要补上本内容的长度，用lengthNum个字节数表示42 if len(content) > 256 ** length_num - 1: # 内容长度超出lengthNum个字节数表示范围43 raise ValueError(44 f"内容超出限制，{k} 内容(元组第二位)最大允许长度是{256 ** length_num - 1},实际内容长度是{len(content)}")45 else:46 generated_content += bytes.fromhex(47 hex(len(content))[2:].zfill(length_num * 2)) # 先加上本次内容的长度48 length_num = None # 这个字节的内容已经确定了，lengthNum肯定是None49 generated_content += content # 加上本次内容50 else: # 必须是字节类型或者None51 raise TypeError(f"类型错误，{k} 对应的值(元组第二位)应该是内容应该是bytes类型或者是None,实际类型是{type(content)}")52 elif isinstance(v, dict): # 对应的值是字典53 content = cls.generate(v)54 if length_num: # 需要填充之前的长度段55 if len(content) > 256 ** length_num - 1: # 内容长度超出lengthNum个字节数表示范围56 raise ValueError(f"内容超出限制，{k} 内容最大长度是{256 ** length_num - 1},实际内容长度是{len(content)}")57 else:58 generated_content += bytes.fromhex(hex(len(content))[2:].zfill(num * 2)) # 先加上本次内容的长度59 length_num = None # 这个字节的内容已经确定了，lengthNum肯定是None60 generated_content += content # 加上本次内容61 else:62 raise TypeError(f"类型错误，{k} 对应的应该是数组或者字典,输入的类型是{type(v)}")63 return generated_content64 @classmethod65 def _parse(cls, struct, content, strict=False, lastone=True):66 """67 将字节串(content)解析为具有一定结构(struct)的字典,建议使用match68 :param struct:字典，表示协议的结构69 :param content: 字节串内容70 :param strict: 严格模式下，需要根据参考值对内容的值进行校验71 :param lastone: 最上层，只返回需要的内容,不是最上层，要返回更多参数供上层使用72 :return: 返回content解析后的字典结构,以及带有详细内容的字典结构73 """74 try:75 if isinstance(content, bytes):76 parsed_content = {} # 最后返回的content解析后的字典结构77 _parsed_content = {} # 具有详细的属性，但使用不方便，主要用于显示78 _length = hex(len(content))[2:] # 用十六进制表示内容的长度79 if len(_length) % 2 != 0: # 字节串长度一定是8位的整除，一定是有偶数个十六进制值80 _length = "0" + _length # 补0，形成正确的16进制81 _length = bytes.fromhex(_length) # 16进制字节串82 for k, v in struct.items():83 if isinstance(v, tuple):84 _num, _content_reference = v # 所占的位数和参考值，如果strict为True,会根据参考值对内容进行判断85 if _num is None: # 证明这个长度不是固定的，而是由之前的值指定的86 _length = int.from_bytes(_length, "big") # length是上个字节串的内容，表示这次的内容长度87 parsed_content[k] = content[:_length]88 _parsed_content[k] = (content[:_length], _length)89 content = content[_length:]90 elif isinstance(_num, int):91 _length = _temp_content = content[:_num] # content可能是下一个片段的长度,保存到_length92 if strict and _content_reference is not None: # 严格模式下且有参考值的时候，需要判断内容是否正确93 if isinstance(_content_reference, list): # 参考内容是列表，对应的值应该是其中一个94 if _temp_content not in _content_reference:95 raise ValueError(96 f"内容错误,{k} 内容应该是 {_content_reference} 中的一个,实际内容是 {_temp_content}")97 elif isinstance(_content_reference, bytes): # 参考内容不是列表而是单一的字节串98 if _temp_content != _content_reference:99 raise ValueError(f"内容错误,{k} 内容应该是 {_content_reference},实际内容是 {_temp_content}")100 else:101 raise TypeError(f"结构错误,{k} 的参考值类型应该是列表、字节串，实际类型是 {type(_content_reference)}")102 parsed_content[k] = _temp_content # 记录内容长度和内容103 _parsed_content[k] = (_temp_content, _num) # 记录内容长度和内容104 content = content[_num:]105 else:106 raise TypeError(f"结构错误,{k} 的长度限定值,必须是一个整数值或者是None,实际是{type(_num)}")107 elif isinstance(v, dict): # 下一级协议或者之前长度值所包含的内容108 _length = int.from_bytes(_length, "big") # length是之前保存的本片段长度，转化成整数109 _pc, __pc, content = cls._parse(v, content[:_length], lastone=False)110 parsed_content[k] = _pc111 _parsed_content[k] = (__pc, _length)112 elif isinstance(v, list): # 不定长元素，一定在最末尾113 list_parsed_content = []114 _list_parsed_content = {}115 _ele_num = 0 # 表示解析出的各个元素，从0开始计数116 _clength = _length = len(content)117 if len(v) != 1 or not isinstance(v[0], dict):118 raise TypeError(f"结构错误,{k} 参考值list应该只有一个元素，且元素类型是字典")119 while True:120 _pc, __pc, content = cls._parse(v[0], content, lastone=False)121 ll = len(content)122 list_parsed_content.append((_pc))123 _list_parsed_content[str(_ele_num)] = (__pc, _length - ll)124 _length = ll125 _ele_num += 1126 if not content:127 break128 parsed_content[k] = list_parsed_content129 _parsed_content[k] = (_list_parsed_content, _clength)130 else:131 raise TypeError(f"结构错误,{k} 参考值的类型应该是dict、tuple、list中的一个,实际内容是 {type(v)}")132 if lastone:133 return (parsed_content, _parsed_content)134 else:135 return (parsed_content, _parsed_content, content)136 else:137 raise TypeError(f"content 对应的值应该是bytes类型,输入的类型是{type(content)}")138 except Exception:139 return (None, None)140 @classmethod141 def match(cls, struct, content, strict=False):142 """143 查看字节串(content)是否符合struct的结构144 :param struct: 字典，表示协议的结构145 :param content: 字节串内容146 :param strict: 严格模式下，需要根据参考值对内容的值进行校验147 :return: 如果匹配返回字典，如果匹配失败，返回False148 """149 try:150 parsed_content = cls._parse(struct, content, strict)[0]151 return parsed_content152 except Exception:153 return False154 def _to_string(self, _parsed_content):155 repr_str = ""156 self.__prefix += " "157 for k, v in _parsed_content.items():158 repr_str += f"\n{self.__prefix}{k}:"159 if isinstance(v[0], dict):160 repr_str += f"({v[1]}) {self._to_string(v[0])}"161 else:162 repr_str += f"({v[1]}) {v[0]}"163 self.__prefix = self.__prefix[2:]164 return repr_str165 def __repr__(self):166 return self._to_string(self._parsed_content)167 def __bytes__(self):...

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metagenerate.py

Source:metagenerate.py Github

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1from presenter import *2import wiki3import sys4from outbuffer import *5from visitor import *6from entities import SpecificEnabler, DeprecatedSpecificEnabler, Application, PrettyPrinter7import logging8from fidoc import FIdoc9def generate_page(dw, outpage, meta): 10 # out = FileBuffer(outfile)11 out = PageBuffer(dw, outpage)12 out << dw.heading(1, "Generated output from FIcontent's Meta-Structure")13 14 generated_content = []15 16 pp = PrettyPrinter()17 18 # Overall timeline of experiments19 #######################################20 21 generated_content += [22 ("Timeline of Experiments", ExperimentTimelinePresenter()),23 ]24 25 26 # Experiments per site27 #######################################28 29 sites = ["Zurich", "Brittany", "Lancaster", "Cologne", "Berlin", "Barcelona"]30 generated_content += [31 ("Experiments in %s" % s, ExperimentTimelinePresenter(s)) for s in sites32 ]33 34 # All tested scenarios35 #######################################36 37 generated_content += [38 ("All Tested Scenarios", ListPresenter(TestedScenariosVisitor(), pp.print_Scenario)),39 ]40 41 # All SEs and their relations42 #######################################43 44 generated_content += [(45 "Relations of %s SE" % se.get_name(),46 SEGraphPresenter(se, pp.dispatch)47 ) for se in meta.get_specific_enablers()48 ]49 50 # All SEs and their descriptions51 #######################################52 53 generated_content += [(54 "Description of %s SE" % se.get_name(),55 PropertyPresenter(se, '/spec/documentation/what-it-does')56 ) for se in meta.get_specific_enablers()57 ]58 # All SEs and their resources59 #######################################60 61 generated_content += [(62 "Resources of %s SE" % se.get_name(),63 ResourcesPresenter(dw, se, pp.dispatch)64 ) for se in meta.get_specific_enablers()65 ]66 # All SEs and their release cycle67 #######################################68 69 generated_content += [(70 "Release cycle of %s SE" % se.get_name(),71 ReleaseCyclePresenter(dw, se, pp.dispatch)72 ) for se in meta.get_specific_enablers()73 ]74 # Dependencies per scenario75 #######################################76 77 # v = ExperimentsVisitor()78 # v.visit(meta_structure)79 80 # experiments = list(set([(e.scenario, e.site) for e in v.result]))81 82 # Dependencies per scenario (only actual usage)83 # generated_content += [84 # ('Scenario "%s" on Site %s - USES' % e, DependencyPresenter(e[0], e[1], ['USES'])) for e in experiments85 # ]86 # Dependencies per scenario (actual and planned usage)87 # relations = ['USES', 'WILL USE', 'MAY USE']88 # generated_content += [89 # ('Scenario "%s" on Site %s - ALL' % e, DependencyPresenter(e[0], e[1], relations)) for e in experiments90 # ]91 92 # Enablers used in experiments93 # niceenabler = lambda e: e.identifier + ' ' + e.entity94 95 # experiments = v.result # [e for e in v.result if (e.site == "Barcelona") and (e.application.identifier == "Smart City Guide (Android App)")]96 # generated_content += [(97 # 'Enablers tested in Scenario "%s" on Site %s at %s' % (e.scenario, e.site, e.date),98 # ListPresenter(99 # EnablersTestedVisitor(e.application, ts = e.date),100 # niceenabler101 # )102 # ) for e in experiments103 # ]104 105 # GE Utilization106 #######################################107 108 generated_content += [(109 "Utilization of %s GE" % ge.get_name(),110 ListPresenter(UsedByVisitor(111 ge,112 follow_relations = ['USES'],113 collect_entities = [SpecificEnabler, DeprecatedSpecificEnabler, Application]114 ), pp.dispatch)115 ) for ge in meta.get_generic_enablers()116 ]117 118 119 # Overall Uptake of Generic Enablers120 #######################################121 122 generated_content += [123 ("Overall Uptake of Generic Enablers", UptakePresenter(pp.dispatch, hideunused=True))124 ]125 126 127 # FI-PPP SEis Usage and General Information128 #######################################129 col_fippp = ['name', 'owner', 'product', 'open-source', 'mode', 'last-update', 'next-update', 'assets', 'catalog']130 col_overview = ['name', 'owner', 'final-release']131 generated_content += [132 ("FI-PPP SEis Usage and General Information", CockpitPresenter(col_fippp, pp.dispatch)),133 ("Overview of FIcontent SEs", CockpitPresenter(col_overview, pp.dispatch, sort = ['name']))134 ]135 136 # SE Discovery Summary137 #######################################138 139 generated_content += [140 ("SE Discovery Summary", SummaryPresenter())141 ]142 143 # Incomplete/invalid SEis144 #######################################145 generated_content += [146 ("Incomplete and/or invalid SEs", ListPresenter(InvalidEntitiesVisitor('SE'), pp.dispatch))147 ]148 149 # GE Validation Survey150 #######################################151 # generated_content += [152 # ("GE Validation Survey", GESurveyPresenter())153 # ]154 155 # Roadmap Releases156 #######################################157 # releases = set([rel.get_name() for rel in meta.get_releases()])158 roadmaps = ['socialtv', 'smartcity', 'gaming', 'common']159 160 for rel in meta.get_releases():161 generated_content += [(162 "Roadmap %s - %s" % (road, rel.get_name()),163 RoadmapPresenter(dw, road, rel)164 ) for road in roadmaps165 ]166 167 #######################################168 # main generation loop169 #######################################170 171 for h, p in generated_content:172 logging.info('Generating -> %s ...' % h)173 p.present(meta)174 out << dw.heading(2, h)175 p.dump(out)176 out << ''177 178 logging.info("Flushing generated content ...")179 out.flush()180def generate_meta_information(fidoc, generatedpage):181 dw = fidoc.get_wiki()182 meta = fidoc.get_meta_structure()183 # pub = fidoc.get_publisher()184 185 if meta is None:186 logging.fatal("Invalid meta structure.")187 188 generate_page(dw, generatedpage, meta)189 190 191 192 193if __name__ == "__main__":194 195 import wikiconfig196 metapage = ":FIcontent:private:meta:"197 if len(sys.argv) > 1:198 metapage = sys.argv[1]199 generatedpage = ":FIcontent:private:meta:generated"200 if len(sys.argv) > 2:201 generatedpage = sys.argv[2]202 try:203 logging.info("Connecting to remote DokuWiki at %s" % wikiconfig.url)204 # dw = wiki.DokuWikiLocal(url, 'pages', 'media')205 dw = wiki.DokuWikiRemote(wikiconfig.url, wikiconfig.user, wikiconfig.passwd)206 skipchecks = [207 # tv208 # 'Content Similarity', 'Audio Fingerprinting',209 # city210 # 'Local Information', 'Recommendation Services',211 # gaming212 # 'Visual Agent Design', 'Augmented Reality - Marker Tracking', 'Networked Virtual Character',213 # common214 # 'POI Storage', 'Content Sharing'215 ]216 217 logging.info("Loading FIdoc object ...")218 fidoc = FIdoc(dw, skipchecks)219 220 generate_meta_information(fidoc, generatedpage)221 222 logging.info("Finished")223 224 except logging.FatalError:225 pass...

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batch_functions.py

Source:batch_functions.py Github

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1import numpy as np2from PIL import Image3import itertools4from tensorflow.keras.utils import Sequence5import numpy as np6class OCR_generator(Sequence):7 """Generator for the input data to the OCR model. We're also preparing 8 arrays for the CTC loss which are related to the output dimensions"""9 def __init__(self, base_generator, batch_size, char_to_lbl_dict,10 img_h , keras_augmentor, epoch_size=500, validation=False):11 """Inputs12 base_generator: the base trdg generator13 batch_size: number of examples fed to the NN simultaneously14 char_to_lbl_dict: mapping from character to its label (int number)15 img_h: we assume that the input here is already scaled to the correct height16 keras_augmentor: Keras augmentor to add more augmenting, the current base generator doesn'17 for example zoom, translate etc"""18 self.base_generator = base_generator19 self.batch_size = batch_size20 self.char_to_lbl_dict = char_to_lbl_dict21 self.img_h = img_h22 self.epoch_size = epoch_size23 self.validation = validation24 self.keras_augmentor = keras_augmentor25 # total number of unique characters26 self.num_chars = len(char_to_lbl_dict)27 def __len__(self):28 """Denotes the number of batches per epoch29 :return: number of batches per epoch """30 return self.epoch_size31 def __getitem__(self, index):32 """Generate one batch of data"""33 # stores the length (number of characters) of each word in a batch34 label_lens = np.zeros((self.batch_size),dtype=np.float32)35 # generate content for the batch as a list of lists36 generated_content = list(list(tup) for tup in itertools.islice(self.base_generator,self.batch_size))37 # preprocess the batch content38 generated_content, img_w, max_word_len_batch = \39 self.preprocess_batch_imgs(generated_content)40 # allocate the vectors for batch labels (integers for each character in a word)41 # and the padded + preprocessed images42 batch_labels = np.zeros((self.batch_size, max_word_len_batch),dtype=np.float32)43 batch_imgs = np.zeros((self.batch_size, img_w, self.img_h, 3),dtype=np.float32)44 # the number of time distributed values, or another words the length of the time axis in the output,45 # or equivalently the width of the image after convolutions. Needed to input in the CTC loss46 # each maxpooling halves the width dimension so in our model scaling is 1/4 with 2 maxpoolings47 t_dist_dim = int(img_w / 4)48 # we need to give it for every entry49 input_length = np.full((self.batch_size),t_dist_dim,dtype=np.float32)50 # fill the batch51 for batch_ind in range(self.batch_size):52 # get a new image and a the content word for it53 img_arr, word = generated_content[batch_ind]54 batch_imgs[batch_ind,:,:] = img_arr55 # the labels for each word, even if the max number of characters is say for example 1056 # and the word is just 5 characters, the first 5 positions are filled by the character labels57 # and the rest are whatever (zeros in our implementation), however in the real loss theyre ignored58 # because of the label_length input59 labels_arr = np.array([self.char_to_lbl_dict[char] for char in word])60 batch_labels[batch_ind,0:len(labels_arr)] = labels_arr61 label_lens[batch_ind] = len(word)62 # now the hacky part63 # keras requires in the loss function to y_pred and y_true to be the same shape64 # but the ctc losses use y_pred of shape (batchsize, tdistdim, num_chars) from NN65 # and batch_labels, input_length, label_lens which are the "y_true" but these are66 # different dimension so pack them to (batchsize, tdistdim, num_chars) and later67 # unpack in the loss to stop the whining.68 y_true = np.zeros((self.batch_size, t_dist_dim, self.num_chars),dtype=np.float32)69 y_true[:, 0:max_word_len_batch, 0] = batch_labels70 y_true[:, 0, 1] = label_lens71 y_true[:, 0, 2] = input_length72 if self.validation:73 # for validation we return slightly different things so we can do fancy74 # stuff at callback75 return batch_imgs, batch_labels, input_length, label_lens76 else: #return x, y for the model77 return batch_imgs, y_true78 def preprocess_batch_imgs(self,generated_content):79 """Function to do augmentations, padd images, return longest word len etc"""80 # check the largest image width and word len in the batch81 pil_images = [img for img, word in generated_content]82 max_width = max([img.size[0] for img in pil_images])83 max_word_len_batch = max([len(word) for img, word in generated_content])84 # expand img with to mod 4_ds so that the maxpoolings wil result into85 # well defined integer length for the mapped tdist dimension ("new width")86 if max_width % 4 == 0:87 img_w = max_width88 else:89 img_w = max_width + 4 - (max_width % 4)90 #augment batch images91 for batch_ind in range(self.batch_size):92 # pad the image width with to the largest (fixed) image width93 pil_img = pil_images[batch_ind]94 width, height = pil_img.size95 new_img = Image.new(pil_img.mode, (img_w, self.img_h), (255,255,255))96 new_img.paste(pil_img, ((img_w - width) // 2, 0))97 # convert to numpy array98 img_arr = np.array(new_img)99 100 #some additional augmentation101 img_arr = self.keras_augmentor.random_transform(img_arr)102 # scale with 255 so that the values are between 0 and 1103 # and save to batch, also transpose because the "time axis" is width104 generated_content[batch_ind][0] = img_arr.transpose((1,0,2)) / 255...

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