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How to use is_variable method in Robotframework
Best Python code snippet using robotframework
embtopktable.py
Source:embtopktable.py 
...89 return False90 s = t[0]91 o = t[1]92 score = t[2]93 if not s.is_variable() and not o.is_variable() and score.is_variable():94 return True95 if s.is_variable() and o.is_variable():96 return False97 if not score.is_variable():98 return False99 n_vars = 0100 unique_vars = set()101 if s.is_variable():102 unique_vars.add(s.get_value())103 n_vars += 1104 if o.is_variable():105 unique_vars.add(o.get_value())106 n_vars += 1107 if score.is_variable():108 unique_vars.add(score.get_value())109 n_vars += 1110 if len(unique_vars) != n_vars:111 return False112 return True113 def is_query_allowed(self, t: tuple) -> bool:114 return self._is_query_allowed(t)115 def get_cardinality(self, t: tuple) -> int:116 if not self._is_query_allowed(t):117 if t[0].is_variable() and t[1].is_variable() and t[2].is_variable() and t[0].get_value() != t[1].get_value() and t[1].get_value() != t[2].get_value():118 return self.n_terms * self.top_k119 else:120 print("EMBTopkTable: Tuple is not allowed!")121 raise Exception("Tuple is not allowed")122 elif not t[0].is_variable() and not t[1].is_variable():123 return 1124 return self.top_k125 def get_unique_values_in_column(self, t: tuple, column_nr) -> int:126 raise Exception("Not implemented!")127 def get_n_terms(self) -> int:128 self.n_terms + 1000 # I assume I return scores with three decimal digits of precision129 def _normalize_scores_simple(self, t : tuple, scores):130 sum = 0.0131 for s in scores:132 sum += 1 / s133 for idx, s in enumerate(scores):134 scores[idx] = 1 / (s.item()) / sum.item()135 return scores136 def _normalize_scores(self, t : tuple, scores):137 # Collect up to k max/min scores138 true_answers = self._get_true_answers(t)139 if len(true_answers) > 0:140 true_scores = self._get_answer_scores(t, true_answers)141 else:142 if t[0].is_variable():143 true_scores = self.known_min_score_s144 else:145 true_scores = self.known_min_score_o146 false_answers = self._get_false_answers(t)147 false_scores = self._get_answer_scores(t, false_answers)148 # Normalise "scores" depending on true_scores and false_scores149 best_score = torch.max(true_scores).item()150 assert(best_score <= 0)151 worst_score = torch.mean(false_scores).item()152 assert (worst_score <= 0)153 assert(best_score >= worst_score)154 clamped_scores = torch.clamp(scores, min=worst_score, max=best_score)155 scores = clamped_scores - worst_score156 range = best_score - worst_score157 prob = scores / range158 return prob159 def _get_answer_scores(self, t : tuple, a : torch.Tensor):160 if t[0].is_variable():161 # Retrieve ID of the object162 value = t[1].get_value()163 value_id = self.entities_dict[value]164 o = torch.Tensor([value_id, ]).long()165 p = torch.Tensor([self.rel_id, ]).long()166 scores = self.model.score_po(p, o, a)[0]167 #elif t[1].is_variable():168 else:169 # Retrieve ID of the subject170 value = t[0].get_value()171 value_id = self.entities_dict[value]172 s = torch.Tensor([value_id, ]).long()173 p = torch.Tensor([self.rel_id, ]).long()174 scores = self.model.score_sp(s, p, a)[0]175 #else:176 # raise Exception("Not implemented")177 return scores178 def _get_true_answers(self, t : tuple):179 if t[0].is_variable():180 # Retrieve ID of the object181 value = t[1].get_value()182 value_id = self.entities_dict[value]183 o = torch.Tensor([value_id, ]).long().item()184 p = torch.Tensor([self.rel_id, ]).long().item()185 answers = self.known_answers_po.get([p, o])186 #elif t[1].is_variable():187 else:188 # Retrieve ID of the subject189 value = t[0].get_value()190 value_id = self.entities_dict[value]191 s = torch.Tensor([value_id, ]).long().item()192 p = torch.Tensor([self.rel_id, ]).long().item()193 answers = self.known_answers_sp.get([s, p])194 #else:195 # raise Exception("Not implemented")196 return answers197 def _get_false_answers(self, t : tuple):198 random_entities = np.random.randint(0, self.n_terms, 10)199 return torch.from_numpy(random_entities)200 def get_iterator(self, t: tuple) -> PyIterator:201 if not self._is_query_allowed(t):202 print("EMBTopkTable: Tuple is not allowed!")203 raise Exception("Tuple is not allowed")204 answers = []205 if t[0].is_variable():206 # Retrieve ID of the object207 value = t[1].get_value()208 value_id = self.entities_dict[value]209 o = torch.Tensor([value_id, ]).long()210 p = torch.Tensor([self.rel_id, ]).long()211 scores = self.model.score_po(p, o)[0]212 s = torch.argsort(scores, descending=True)213 top_k_s = s[:self.top_k]214 top_k_scores = scores[top_k_s]215 top_k_scores = self._normalize_scores(t, top_k_scores)216 if self.score_threshold is not None:217 # Filter values with low scores218 top_k_scores = top_k_scores.double()219 top_k_scores = torch.where(top_k_scores < self.score_threshold, 0., top_k_scores)220 retained_entities = torch.nonzero(top_k_scores, as_tuple=True)221 top_k_s = top_k_s[retained_entities]222 top_k_scores = top_k_scores[retained_entities]223 txt_top_k_s = self.model.dataset.entity_strings(top_k_s)224 for idx, e in enumerate(txt_top_k_s):225 answers.append((e, value, top_k_scores[idx].item()))226 elif t[1].is_variable():227 # Retrieve ID of the subject228 value = t[0].get_value()229 value_id = self.entities_dict[value]230 s = torch.Tensor([value_id,]).long()231 p = torch.Tensor([self.rel_id, ]).long()232 scores = self.model.score_sp(s, p)[0]233 o = torch.argsort(scores, descending=True)234 top_k_o = o[:self.top_k]235 top_k_scores = scores[top_k_o]236 top_k_scores = self._normalize_scores(t, top_k_scores)237 if self.score_threshold is not None:238 # Filter values with low scores239 top_k_scores = top_k_scores.double()240 top_k_scores = torch.where(top_k_scores < self.score_threshold, 0., top_k_scores)...
get_instances.py
Source:get_instances.py
1import itertools2from geosolver.diagram.computational_geometry import polygon_is_convex, angle_in_radian3from geosolver.diagram.states import GraphParse4from geosolver.ontology.instantiator_definitions import instantiators5from geosolver.ontology.ontology_definitions import FormulaNode, signatures6import numpy as np7from geosolver.ontology.ontology_semantics import MeasureOf8__author__ = 'minjoon'9def get_instances(graph_parse, instance_type_name, is_variable, *args):10 assert instance_type_name in instantiators11 if instance_type_name in ["triangle", "quad", 'hexagon', 'polygon']:12 return _get_polygons(graph_parse, instance_type_name, is_variable, *args)13 else:14 return eval("_get_%ss(graph_parse, is_variable, *args)" % instance_type_name)15def get_all_instances(graph_parse, instance_type_name, is_variable=False):16 if instance_type_name == 'polygon':17 triangles = _get_all_polygons(graph_parse, 'triangle', 3, is_variable)18 quads = _get_all_polygons(graph_parse, 'quad', 4, is_variable)19 hexagons = _get_all_polygons(graph_parse, 'hexagon', 6, is_variable)20 polygons = dict(triangles.items() + quads.items() + hexagons.items())21 return polygons22 elif instance_type_name in ["triangle", "quad", "hexagon"]:23 if instance_type_name == 'triangle': n = 324 elif instance_type_name == 'quad': n = 425 elif instance_type_name == 'hexagon': n = 626 return _get_all_polygons(graph_parse, instance_type_name, n, is_variable)27 else:28 return eval("_get_all_%ss(graph_parse, is_variable)" % instance_type_name)29def _get_points(graph_parse, is_variable, key):30 if key in graph_parse.intersection_points:31 if is_variable:32 return {key: graph_parse.core_parse.point_variables[key]}33 else:34 return {key: graph_parse.intersection_points[key]}35 else:36 return {}37def _get_all_points(graph_parse, is_variable):38 items = []39 for key in graph_parse.intersection_points.keys():40 items.extend(_get_points(graph_parse, is_variable, key).iteritems())41 return dict(items)42def _get_lines(graph_parse, is_variable, a_key, b_key):43 assert isinstance(graph_parse, GraphParse)44 if graph_parse.line_graph.has_edge(a_key, b_key):45 if is_variable:46 line = graph_parse.line_graph[a_key][b_key]['variable']47 else:48 line = graph_parse.line_graph[a_key][b_key]['instance']49 return {(a_key, b_key): line}50 else:51 return {}52def _get_all_lines(graph_parse, is_variable):53 assert isinstance(graph_parse, GraphParse)54 items = []55 for a_key, b_key in graph_parse.line_graph.edges():56 items.extend(_get_lines(graph_parse, is_variable, a_key, b_key).iteritems())57 return dict(items)58def _get_circles(graph_parse, is_variable, center_key):59 assert isinstance(graph_parse, GraphParse)60 if center_key in graph_parse.circle_dict:61 circles = {}62 for radius_key, d in graph_parse.circle_dict[center_key].iteritems():63 if is_variable:64 circle = d['variable']65 else:66 circle = d['instance']67 circles[(center_key, radius_key)] = circle68 return circles69 else:70 return {}71def _get_all_circles(graph_parse, is_variable):72 assert isinstance(graph_parse, GraphParse)73 items = []74 for center_key in graph_parse.circle_dict:75 items.extend(_get_circles(graph_parse, is_variable, center_key).iteritems())76 return dict(items)77def _get_arcs(graph_parse, is_variable, a_key, b_key):78 assert isinstance(graph_parse, GraphParse)79 arcs = {}80 for circle_key in _get_all_circles(graph_parse, is_variable):81 if graph_parse.arc_graphs[circle_key].has_edge(a_key, b_key):82 test_arc = graph_parse.arc_graphs[circle_key][a_key][b_key]['instance']83 circle, a, b = test_arc84 test_angle = instantiators['angle'](a, circle.center, b)85 if angle_in_radian(test_angle) > np.pi:86 a_key, b_key = b_key, a_key87 if is_variable:88 arc = graph_parse.arc_graphs[circle_key][a_key][b_key]['variable']89 else:90 arc = graph_parse.arc_graphs[circle_key][a_key][b_key]['instance']91 arc_key = (circle_key, a_key, b_key)92 arcs[arc_key] = arc93 return arcs94def _get_all_arcs(graph_parse, is_variable):95 assert isinstance(graph_parse, GraphParse)96 items = []97 for a_key, b_key in itertools.combinations(graph_parse.intersection_points, 2):98 items.extend(_get_arcs(graph_parse, is_variable, a_key, b_key).iteritems())99 return dict(items)100def _get_polygons(graph_parse, name, is_variable, *args):101 # TODO : include ignore_trivial parameter. This ignores area-0 polygons.102 assert isinstance(graph_parse, GraphParse)103 line_graph = graph_parse.line_graph104 if all(line_graph.has_edge(args[idx-1], arg) for idx, arg in enumerate(args)):105 convex = polygon_is_convex(tuple(graph_parse.intersection_points[key] for key in args))106 if is_variable:107 points = list(graph_parse.core_parse.point_variables[key] for key in args)108 else:109 points = list(graph_parse.intersection_points[key] for key in args)110 if not convex:111 points.reverse()112 polygon = FormulaNode(signatures[name.capitalize()], points)113 polygon_key = tuple(args)114 return {polygon_key: polygon}115 else:116 return {}117def _get_all_polygons(graph_parse, name, n, is_variable):118 polygons = {}119 frozensets = set()120 line_graph = graph_parse.line_graph121 for keys in itertools.permutations(graph_parse.intersection_points, n):122 if frozenset(keys) in frozensets:123 continue124 if not all(line_graph.has_edge(keys[idx-1], key) for idx, key in enumerate(keys)):125 continue126 angles = []127 for idx, key in enumerate(keys):128 angles.extend(_get_angles(graph_parse, False, keys[idx-2], keys[idx-1], key).values())129 if len(angles) < n:130 continue131 convex = polygon_is_convex(tuple(graph_parse.intersection_points[key] for key in keys))132 if is_variable:133 points = list(graph_parse.core_parse.point_variables[key] for key in keys)134 else:135 points = list(graph_parse.intersection_points[key] for key in keys)136 if not convex:137 points.reverse()138 # polygon = instantiators[name](*points)139 polygon = FormulaNode(signatures[name.capitalize()], points)140 polygon_key = tuple(keys)141 polygons[polygon_key] = polygon142 frozensets.add(frozenset(keys))143 return polygons144def _get_angles(graph_parse, is_variable, a_key, b_key, c_key, ignore_trivial=True):145 assert isinstance(graph_parse, GraphParse)146 line_graph = graph_parse.line_graph147 if line_graph.has_edge(a_key, b_key) and line_graph.has_edge(b_key, c_key):148 if ignore_trivial:149 # line_a = line_graph[b_key][a_key]['instance']150 a_points = line_graph[b_key][a_key]['points']151 # line_c = line_graph[b_key][c_key]['instance']152 c_points = line_graph[b_key][c_key]['points']153 if c_key in a_points or a_key in c_points:154 return {}155 if line_graph.has_edge(a_key, c_key):156 """157 Removes flat angle (180 degrees)158 """159 b_points = line_graph[a_key][c_key]['points']160 if b_key in b_points:161 return {}162 if is_variable:163 points = graph_parse.core_parse.point_variables164 a, b, c = points[a_key], points[b_key], points[c_key]165 angle = FormulaNode(signatures['Angle'], [a, b, c])166 else:167 points = graph_parse.intersection_points168 a, b, c = points[a_key], points[b_key], points[c_key]169 angle = instantiators['angle'](a, b, c)170 angle_key = (a_key, b_key, c_key)171 return {angle_key: angle}172 else:173 return {}174def _get_all_angles(graph_parse, is_variable, ignore_trivial=True):175 """176 If ignore_trival is set to true, then 0 degree / 360 degree angles are not considered.177 :param graph_parse:178 :param ignore_trival:179 :return:180 """181 assert isinstance(graph_parse, GraphParse)182 items = []183 for a_key, b_key, c_key in itertools.permutations(graph_parse.intersection_points, 3):184 items.extend(_get_angles(graph_parse, is_variable, a_key, b_key, c_key, ignore_trivial=ignore_trivial).iteritems())...
Мелентьев Петр Алексеевич KNN.py
Source:Мелентьев Петр Алексеевич KNN.py
1#!/usr/bin/env python2# coding: utf-83# In[1]:4import pandas as pd5import numpy as np6import math7from matplotlib import pyplot as plt8from scipy.spatial import distance9filename = "datasets/heart.csv"10CLASSES = 511# In[2]:12def minmax(dataset):13 minmax = list()14 for i in range(len(dataset[0])):15 if i == len(dataset[0]) - 1:16 continue17 value_min = dataset[:, i].min()18 value_max = dataset[:, i].max()19 minmax.append([value_min, value_max])20 return minmax21# In[3]:22def normalize(_dataset, min_max):23 for row in _dataset:24 for i in range(len(row)):25 if i == len(row) - 1: # exclude labels26 continue27 dif = min_max[i][1] - min_max[i][0]28 row[i] = ((row[i] - min_max[i][0]) / dif) if dif != 0 else 029 return _dataset30# In[4]:31def draw_plot(dataset, feature1N, feature2N, target):32 iris_type_to_color = {33 1.0 : "g",34 2.0 : "b",35 3.0 : "y",36 4.0 : "m",37 5.0 : "c"38 }39 colored_irises = list(map(lambda x: iris_type_to_color[x], dataset[:, -1]))40 colored_irises.append("r")41 x = list(dataset[:, feature1N])42 y = list(dataset[:, feature2N])43 x.append(target[feature1N])44 y.append(target[feature2N])45 plt.scatter(x, y, c=colored_irises)46 plt.show()47# In[5]:48dataset = pd.read_csv(filename, dtype=np.float64)49# In[6]:50pd.DataFrame(dataset.values)51# In[7]:52min_max = minmax(dataset.values)53normalized_dataset_values = normalize(dataset.values.copy(), min_max)54pd.DataFrame(normalized_dataset_values)55# In[8]:56def kNN(dataset, distf, target, k):57 distances = list()58 for row in dataset:59 dist = distf(target, row[:-1])60 distances.append((row, dist))61 distances.sort(key=lambda tup: tup[1])62 63# neighbors = list()64# for i in range(k):65# neighbors.append(distances[i][0])66 return distances[-1][1]67# In[9]:68def NWreg(dataset, x, dist, kernel, H, variable=False, onehot=False, lables=None):69 up = 070 gr = 071 if variable:72 H = kNN(dataset, dist, x, H)73 for idx, row in enumerate(dataset):74 weight = kernel(dist(row[:-1], x) / H)75 up += row[-1] * weight76 if onehot and lables is not None:77 up += lables[idx] * weight78 gr += weight79 return up / gr if gr != 0 else 080# In[10]:81def F_macro(CM):82 TP = []83 FP = []84 FN = []85 F_micro = 086 F_macro = 087 All = sum(list(sum(e) for e in zip(*CM)))88 89 def Prec(c):90 _gr = TP[c] + FP[c]91 return TP[c] / _gr if _gr != 0 else 092 def Recall(c):93 _gr = TP[c] + FN[c]94 return TP[c] / _gr if _gr != 0 else 095 def F_score(c, b=1):96 _prec = Prec(c)97 _recall = Recall(c)98 if _prec != 0 and _recall != 0:99 return (1 + pow(b, 2)) * _prec * _recall / (pow(b, 2) * _prec + _recall)100 else:101 return 0102 def PrecW():103 _sum = 0104 for i in range(CLASSES):105 P = sum(list(e[i] for e in CM))106 _sum += CM[i][i] * sum(CM[i]) / P if P != 0 else 0107 return _sum / All108 def RecallW():109 return sum(CM[i][i] for i in range(CLASSES)) / All110 for i in range(CLASSES):111 tp = CM[i][i]112 TP.append(tp)113 FN.append(sum(CM[i]) - tp)114 FP.append(sum(list(e[i] for e in CM)) - tp)115 F_micro += (sum(CM[i]) * F_score(i)) / All116 return 2 * PrecW() * RecallW() / (PrecW() + RecallW())117# In[11]:118euclidean = distance.euclidean119manhattan = distance.cityblock120chebyshev = distance.chebyshev121distances = [euclidean, manhattan, chebyshev]122# In[12]:123def epanechnikov(u):124 return 3 * (1 - pow(u, 2)) / 4 if u < 1 else 0125def uniform(u):126 return 0.5 if u < 1 else 0127def triangular(u):128 return 1 - abs(u) if u < 1 else 0129def quartic(u):130 return 15*pow(1-pow(u,2), 2) / 16 if u < 1 else 0131def triweight(u):132 return 35*pow(1-pow(u,2), 3) / 32 if u < 1 else 0133def tricube(u):134 return 70*pow(1-pow(abs(u),3), 3) / 81 if u < 1 else 0135def gaussian(u):136 return math.exp(-pow(u,2) / 2) / math.sqrt(2*math.pi)137def cosine(u):138 return (math.pi * math.cos((math.pi * u) / 2)) / 4 if u < 1 else 0139def logistic(u):140 return 1 / (math.exp(u) + 2 + math.exp(-u))141def sigmoid(u):142 return 2 / (math.pi * (math.exp(u) + math.exp(-u)))143kernels = [epanechnikov, uniform, triangular, quartic, triweight, tricube, gaussian, cosine, logistic, sigmoid]144# In[25]:145def regression(dataset):146 maxfscore = 0147 best = []148 bestCM = np.zeros((CLASSES, CLASSES))149 for kernel in kernels:150 for distance in distances:151 for is_variable in [False, True]:152 if is_variable:153 windows = [x for x in range(2, 25)]154 else:155 windows = [x * 0.1 for x in range(40, 250)]156 for window in windows:157 CM = np.zeros((CLASSES, CLASSES))158 for i in range(len(dataset.values)):159 curr_dataset = [row for idx, row in enumerate(dataset.values) if idx != i]160 res = NWreg(curr_dataset, dataset.values[i][:-1],161 distance, kernel, window, is_variable)162 predicted = round(res) if res >= 0.5 else 1163 expected = round(dataset.values[i][-1])164 CM[predicted-1][expected-1] += 1165 fscore = F_macro(CM)166 if fscore > maxfscore:167 maxfscore = fscore168 best = [kernel, distance, window, is_variable]169 bestCM = CM170 print(str(best))171 print(pd.DataFrame(bestCM))172 print("F score: " + maxfscore.__str__())173 return best, bestCM, maxfscore174# In[26]:175best, cm, fsc = regression(dataset)176# In[13]:177onehot = pd.get_dummies(normalized_dataset_values[:,-1])178onehot179# In[18]:180def regression(dataset, onehot): # for onehot181 maxfscore = 0182 best = []183 bestCM = np.zeros((CLASSES, CLASSES))184 for kernel in kernels:185 for distance in distances:186 for is_variable in [False, True]:187 if is_variable:188 windows = [x for x in range(2, 25)]189 else:190 windows = [x * 0.1 for x in range(40, 250)]191 for window in windows:192 CM = np.zeros((CLASSES, CLASSES))193 for i in range(len(dataset.values)):194 curr_dataset = [row for idx, row in enumerate(dataset.values) if idx != i]195 res = NWreg(curr_dataset, dataset.values[i][:-1],196 distance, kernel, window, variable=is_variable, onehot=True, lables=onehot)197 if not isinstance(res, int):198 predicted = np.where(res == max(res))[0][0]199 #print(kernel.__str__(), str(distance), str(window), str(is_variable), str(predicted))200 else:201 predicted = 1202 expected = round(dataset.values[i][-1])203 CM[predicted][expected-1] += 1204 fscore = F_macro(CM)205 if fscore > maxfscore:206 maxfscore = fscore207 best = [kernel, distance, window, is_variable]208 bestCM = CM209 print(str(best))210 print(pd.DataFrame(bestCM))211 print("F score: " + maxfscore.__str__())212 return best, bestCM, maxfscore213# In[19]:214best1h, cm1h, fsc1h = regression(dataset, onehot.values)215# In[21]:216def plot1(dataset):217 fscs = []218 winsize = []219 windows = [x * 0.1 for x in range(20, 250)]220 for window in windows:221 CM = np.zeros((CLASSES, CLASSES))222 for i in range(len(dataset.values)):223 curr_dataset = [row for idx, row in enumerate(dataset.values) if idx != i]224 res = NWreg(curr_dataset, dataset.values[i][:-1], manhattan, logistic, window)225 predicted = round(res) if res >= 0.5 else 1226 expected = round(dataset.values[i][-1])227 CM[predicted-1][expected-1] += 1228 fscs.append(F_macro(CM))229 winsize.append(window)230 plt.plot(winsize, fscs)231plot1(dataset)...
test_regex.py
Source:test_regex.py
1from functionrefactor.parser import Regex2from tests.common_functions import *3def test_comment_regex():4 r = Regex()5 assert r.comment_single_line.match("//comment")6 assert r.comment_single_line.match(" //comment something")7 assert r.comment_multiline.match(" /*comment something*/")8 assert r.comment_multiline.match(" /*comment \n something*/")9 assert r.comment_multiline_begin.match(10 " /*comment \n something \n \n \n \n")11 assert r.comment_multiline_begin.match(" /*c++")12 assert r.comment_multiline_end.match("comment */")13 assert r.comment_multiline_end.match("comment \n something \n */ ")14def test_preprocessor_regex():15 r = Regex()16 assert r.preprocessor_c.match("#define")17 assert r.preprocessor_c.match("#define MACRO_ SOMETHING(){} \ ")18 assert r.includes.match("#include <string>")19 assert r.includes.match("#include \"_header\" ")20 assert r.generic_preprocessor.match("#ifdef SOMETHING")21 assert r.generic_preprocessor.match("#endif //SOMETHING ELSE")22 assert r.line_continuation.match(r"#define MACRO \ ")23 assert r.line_continuation.match(r"return CAPSLOCKMANDATORY; \ ")24def test_class_namespace_regex():25 r = Regex()26 assert r.namespace.match("namespace std \n {")27 assert r.namespace.match(" namespace SuperNamespace{")28 assert r.class_.match("class Nutella{")29 assert not r.class_.match(" class;")30 assert r.class_.match("class Porridge{")31 assert r.struct_.match("struct{")32 assert r.struct_.match(" struct \n \n Cereal{")33 assert r.union_.match(' union Oats{ }')34 assert r.union_.match(' union Variant{ ')35def test_template_arg_packing():36 r = Regex()37 assert r.template.match(' template typename<>') # look for template start38 assert r.template.match(' template') # look for template start39 assert r.template_args.search("template <typename T>")40 assert r.template_args.search(41 "template <class T0, class T1, \n int X = 10 >")42 assert r.template_args.search(43 "template <class T0, class T1, \n int X = 10 > /* */")44def test_text_quotes():45 r = Regex()46 assert r.string_block.search(r" \" something ))) \" ")47 assert r.char_block.search(r" \' c \' ")48def test_variable_and_function_regex():49 r = Regex()50 assert r.is_variable.match("int var;")51 assert r.is_variable.match("auto i{0};")52 assert r.is_variable.match("int i =0;")53 assert r.is_variable.match("auto i{0};")54 assert r.is_variable.match("std::vector<std::string> vec{};")55 assert r.is_variable.match(56 r"std::vector<std::string> vec={\"a\",\"b\",\"c\"}; //abc")57 assert r.is_variable.match(58 r"std::vector<std::string> vec=(\"a\",\"b\",\"c\"); //abc")59 assert r.is_variable.match(r"std::vector<std::string> vec=(\n); //abc")60 assert r.is_variable.match("std::vector<std::string> vec\n{\n}\n;")61 assert r.is_variable.match(62 "constexpr f = [&](auto& a, auto& b)->{return a+b;}")63 assert r.is_variable.match(64 "constexpr f = [&](auto& a, auto& b)->{\n return a+b; \n}")65 assert not r.is_variable.match("int main();")66 assert not r.is_variable.match("int main() { }")67 assert not r.is_variable.match(68 "virtual std::string get_tag(const std::string& key)=0; ")69 # assert r.is_variable.match(r"int i(0); //abc") note this is not a valid variable declaration 70 # in a C++ header file, so no test for this is needed71 full_regex_match("auto i =0;", r.variable_capture)72 full_regex_match("auto i{0};", r.variable_capture)73 full_regex_match("std::vector<std::string> vec{};", r.variable_capture)74 full_regex_match(r"std::vector<std::string> vec={\"a\",\"b\",\"c\"};",75 r.variable_capture)76 full_regex_match(r"std::vector<std::string> vec=(\"a\",\"b\",\"c\");",77 r.variable_capture)78 full_regex_match(r"std::vector<std::string> vec=(\n);", r.variable_capture)79 full_regex_match("std::vector<std::string> vec\n{\n}\n;",80 r.variable_capture)81 #lambda test82 full_regex_match("constexpr f = [&](auto& a, auto& b)->{return a+b;};",83 r.variable_capture)84 full_regex_match(85 "constexpr f = [&](auto& a, auto& b)->{\n return a+b; \n};",86 r.variable_capture)87 #function match88 assert r.is_function.match(" int main();")89 assert r.is_function.match("int main() { }")90 assert r.is_function.match("void do_things(const i* const ptr) {}")91 assert r.is_function.match(92 " virtual std::string get_tag(const std::string& key)=0; ")93 assert not r.is_function.match("auto i =0;")94 assert not r.is_function.match("auto i{0};")95 assert not r.is_function.match("std::vector<std::string> vec{};")96 assert not r.is_function.match(97 r"std::vector<std::string> vec={\"a\",\"b\",\"c\"}; //abc")98 assert not r.is_function.match(99 r"std::vector<std::string> vec=(\"a\",\"b\",\"c\"); //abc")100 assert not r.is_function.match(r"std::vector<std::string> vec=(\n); //abc")101 assert not r.is_function.match("std::vector<std::string> vec\n{\n}\n;")102 full_regex_match("int main();", r.function_capture)103 full_regex_match("int main(){}", r.function_capture)104 full_regex_match("long long get_key(){\n return 42; \n}",105 r.function_capture)106 full_regex_match("long long get_key(int k){\n return k+42; \n}",107 r.function_capture)108 full_regex_match("long long \n get_key() \n {\n return 42; \n}",109 r.function_capture)110 #function parameters extraction111 assert r.function_parameters.search(112 "int main(const std::string& a, const std::vector<int>& vec);")113 assert r.function_parameters.search("int main();")114 assert r.function_parameters.search(115 "int main(const std::string& a, const std::vector<int>& vec = { 1, 2, 3, 4} );"116 )117 #verification that the full parameter list is extracted in all cases118 full_regex_search(119 "int main(const std::string& a, const std::vector<int>& vec);",120 r.function_parameters,121 "(const std::string& a, const std::vector<int>& vec)")122 full_regex_search(123 "int main(const std::string& a, const std::vector<int>& vec = {1,2,3,4});",124 r.function_parameters,125 "(const std::string& a, const std::vector<int>& vec = {1,2,3,4})")126 full_regex_search(127 "int main(const std::string& a, \n const std::vector<int>& vec \n = {1,2,3,4});",128 r.function_parameters,...
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