0 else (train_val, [])\\n new_splits.append([train, val, test])\\n return new_splits\",\n \"def stratified_kfold_cross_validation(X, y, n_splits=5):\\n\\n #Define variables\\n X_train_folds = []\\n X_test_folds = []\\n\\n #Create dictionary \\n y_dict = myutils.group_by(y)\\n\\n #Split data\\n folds = [[] for _ in range(n_splits)]\\n for category in y_dict.keys():\\n index = y_dict[category]\\n for i in range(len(index)):\\n folds[i % n_splits].append(index[i])\\n\\n #Add data to train and testing sets\\n for i in range(n_splits):\\n train = []\\n for j in range(n_splits):\\n if i != j:\\n for item in folds[j]:\\n train.append(item)\\n test = folds[i]\\n X_train_folds.append(train)\\n X_test_folds.append(test)\\n \\n return X_train_folds, X_test_folds\",\n \"def train_test_split3(*arrays, **options):\\n\\n n_arrays = len(arrays)\\n if n_arrays == 0:\\n raise ValueError(\\\"At least one array required as input\\\")\\n\\n random_state = options.pop('random_state', None)\\n shuffleresults = options.pop('shuffle', True) \\n test_fold = options.pop('test_fold', None)\\n if test_fold is None:\\n raise TypeError(\\\"Parameter test_fold is required.\\\")\\n\\n test_fold = np.array(test_fold, dtype=np.int)\\n test_fold = column_or_1d(test_fold)\\n\\n if options:\\n raise TypeError(\\\"Invalid parameters passed: %s\\\" % str(options))\\n\\n evalu=np.where(test_fold==2)[0]\\n if shuffleresults:\\n rng = check_random_state(random_state)\\n rng.shuffle(evalu)\\n cv = PredefinedThreeSplit(test_fold=test_fold, shuffle=shuffleresults, random_state=random_state)\\n train, test = next(cv.split())\\n\\n #print evalu\\n if len(evalu)==0:\\n return list(chain.from_iterable((safe_indexing(a, train),\\n safe_indexing(a, test), np.array(0)) for a in arrays))\\n return list(chain.from_iterable((safe_indexing(a, train),\\n safe_indexing(a, test),\\n safe_indexing(a, evalu)) for a in arrays))\",\n \"def fold(self, fold, shuffle = True):\\n testData = self.mergefolds[fold]\\n trainData = sum(self.mergefolds[:fold] +\\n self.mergefolds[fold + 1:], [])\\n if shuffle:\\n random.shuffle(testData)\\n random.shuffle(trainData)\\n return trainData, testData\",\n \"def cv_folds(bco_splits, resampling_ratio):\\n folds = resample_bco_splits(bco_splits, resampling_ratio)\\n # for each step, choose (n-1) train folds and 1 test fold\\n n_folds = len(folds)\\n cv_folds = []\\n for i in range(n_folds):\\n if n_folds > 1:\\n fold_indices = np.delete(np.arange(n_folds), i)\\n # select & concatenate folds[indices]\\n indices_train = combine_folds(folds, fold_indices)\\n indices_test = folds[i]\\n else:\\n indices_train = folds[i]\\n indices_test = []\\n cv_folds.append((indices_train, indices_test))\\n\\n return cv_folds\",\n \"def divide_train_test(self, sentences, tags):\\n logging.info('Dividindo dataset em 10 folds')\\n kf = KFold(n_splits=10)\\n train, test = [], []\\n for train_index, test_index in kf.split(sentences):\\n train.append(train_index)\\n test.append(test_index)\\n return train, test\",\n \"def split(self, X, y, groups):\\n for train_index, test_index in super(FilteredLeaveOneGroupOut,\\n self).split(X, y, groups):\\n train_len = len(train_index)\\n test_len = len(test_index)\\n train_index = [i for i in train_index if self.example_ids[i] in\\n self.keep]\\n test_index = [i for i in test_index if self.example_ids[i] in\\n self.keep]\\n if not self._warned and (train_len != len(train_index) or\\n test_len != len(test_index)):\\n self.logger.warning('Feature set contains IDs that are not ' +\\n 'in folds dictionary. Skipping those IDs.')\\n self._warned = True\\n\\n yield train_index, test_index\",\n \"def generate_folds(n_samples, n_folds=10, train_fraction=.8, len_blocks=5,\\n overlapping=True):\\n if overlapping:\\n splits = np.array_split(np.arange(n_samples), n_folds)\\n\\n folds = []\\n for s in range(n_folds):\\n if s == 0:\\n training = np.concatenate(splits[(s + 1):], axis=0)\\n elif s == n_folds - 1:\\n training = np.concatenate(splits[:s], axis=0)\\n else:\\n fold_splits = splits[:s] + splits[(s + 1):]\\n training = np.concatenate(fold_splits, axis=0)\\n validation = splits[s]\\n folds.append((training, validation))\\n else:\\n folds = aone.utils.generate_trnval_folds(\\n N=n_samples,\\n sampler='cv',\\n nfolds=n_folds,\\n testpct=(1 - train_fraction),\\n nchunks=len_blocks,\\n )\\n folds = [(training, validation) for training, validation in folds]\\n return folds\",\n \"def train_validation_split(self, threshold=None):\\n for train, validation in self._get_k_folds(5, threshold):\\n train_provider = train\\n validation_provider = validation\\n break\\n return train_provider, validation_provider\",\n \"def cross_validation(self, k_folds, **inputs):\\n self.cv_stats, self.cv_traces = [], []\\n self.k_folds = k_folds\\n inputs = self._clean_inputs(inputs)\\n for i, fold in enumerate(k_folds):\\n train, test = fold\\n input_train, input_test = {}, {}\\n for name, data in inputs.items():\\n if name in self.cv_vars:\\n input_train[name] = data[train]\\n input_test[name] = data[test]\\n else:\\n input_train[name] = data\\n input_test[name] = data\\n trace = self.run(**input_train)\\n stats = self.calculate_statistics(trace, **input_test)\\n self.cv_traces.append(trace)\\n self.cv_stats.append(stats)\\n return self.cv_traces, self.cv_stats\",\n \"def data_split(X, y):\\n folds = KFold(n_splits=SPLITS, shuffle=True, random_state=RANDOM_STATE)\\n train_indices, validation_indices = list(folds.split(X))[-1][0], list(folds.split(X))[-1][1]\\n\\n X_train = X.iloc[train_indices]\\n X_validation = X.iloc[validation_indices]\\n\\n y_train = y.iloc[train_indices]\\n y_validation = y.iloc[validation_indices]\\n\\n return X_train, X_validation, y_train, y_validation\",\n \"def create_cross_val_splits(\\n self,\\n cross_val_type: CrossValTypes,\\n num_splits: int\\n ) -> List[Tuple[Union[List[int], np.ndarray], Union[List[int], np.ndarray]]]:\\n # Create just the split once\\n # This is gonna be called multiple times, because the current dataset\\n # is being used for multiple pipelines. That is, to be efficient with memory\\n # we dump the dataset to memory and read it on a need basis. So this function\\n # should be robust against multiple calls, and it does so by remembering the splits\\n if not isinstance(cross_val_type, CrossValTypes):\\n raise NotImplementedError(f'The selected `cross_val_type` \\\"{cross_val_type}\\\" is not implemented.')\\n kwargs = {}\\n if cross_val_type.is_stratified():\\n # we need additional information about the data for stratification\\n kwargs[\\\"stratify\\\"] = self.train_tensors[-1]\\n splits = self.cross_validators[cross_val_type.name](\\n self.random_state, num_splits, self._get_indices(), **kwargs)\\n return splits\",\n \"def train_test_split(data, validate_size=0.3):\\r\\n\\r\\n split = len(data) * (1 - validate_size)\\r\\n split = int(split)\\r\\n train = data[:split]\\r\\n validate = data[split:]\\r\\n\\r\\n return train, validate\",\n \"def split_dataset(df_playlists, df_interactions):\\n df_train_pl, cat_pids = generate_train(df_playlists)\\n df_test_pl, df_test_itr, df_eval_itr, df_train_itr = generate_test(cat_pids, df_playlists, df_interactions)\\n\\n return df_train_pl, df_train_itr, df_test_pl, df_test_itr, df_eval_itr\",\n \"def separate_train_valid(positives, validation_split):\\n val_set = []\\n shuffled_positives = shuffle_annotations(positives)\\n upper = int(round(len(shuffled_positives)*validation_split))\\n subset = shuffled_positives[0:upper]\\n for each in subset:\\n val_set.append(each)\\n shuffled_positives.remove(each)\\n return val_set, shuffled_positives\",\n \"def get_n_folds(self, X, Y, n=10):\\n seed(2)\\n X_copy = list(X)\\n Y_copy = list(Y)\\n fold_size = int(len(X) / n)\\n # Tweets + Labels in Tupel zusammenführen\\n # Datenset in n folds splitten\\n d_split = []\\n for i in range(n):\\n foldX = []\\n foldY = []\\n while(len(foldX) < fold_size):\\n index = randrange(len(X_copy))\\n foldX.append(X_copy.pop(index))\\n foldY.append(Y_copy.pop(index))\\n d_split.append([foldX, foldY])\\n assert(len(d_split) == n)\\n return d_split\",\n \"def partition(self, data, labels):\\n\\t\\treturn self.kfold.split(labels)\",\n \"def cross_validation_score(X_data, y_data, model, folds=5):\\n\\n # Shuffle index\\n index = np.random.permutation(len(X_data))\\n\\n fold_size = int(len(X_data) / folds)\\n scores = []\\n for i in range(folds):\\n \\n # Partition Data\\n X_train, X_val = partition_data(X_data[index], folds, i + 1, fold_size)\\n y_train, y_val = partition_data(y_data[index], folds, i + 1, fold_size)\\n\\n # Train Model\\n print(f\\\"Training on Fold: {i + 1}\\\")\\n model.fit(X_train, y_train)\\n\\n # Predict Values on Validation Set\\n val_pred = model.predict(X_val)\\n\\n # Get Accuracy\\n score = accuracy_score(y_val, val_pred)\\n scores.append(score)\\n \\n return sum(scores) / len(scores)\",\n \"def train_val_test_split(data):\\n raise NotImplementedError\",\n \"def train_test_split(x, y, test_pct):\\n data = zip(x, y)\\n train, test = split_data(data, 1 - test_pct)\\n x_train, y_train = zip(*train)\\n x_test, y_test = zip(*test)\\n return x_train, y_train, x_test, y_test\",\n \"def _split_per_class(x, y, validation_split=0.):\\n shuffled_indexes = np.random.permutation(x.shape[0])\\n x = x[shuffled_indexes]\\n y = y[shuffled_indexes]\\n\\n x_val, y_val = [], []\\n x_train, y_train = [], []\\n\\n for class_id in np.unique(y):\\n class_indexes = np.where(y == class_id)[0]\\n nb_val_elts = int(class_indexes.shape[0] * validation_split)\\n\\n val_indexes = class_indexes[:nb_val_elts]\\n train_indexes = class_indexes[nb_val_elts:]\\n\\n x_val.append(x[val_indexes])\\n y_val.append(y[val_indexes])\\n x_train.append(x[train_indexes])\\n y_train.append(y[train_indexes])\\n\\n x_val, y_val = np.concatenate(x_val), np.concatenate(y_val)\\n x_train, y_train = np.concatenate(x_train), np.concatenate(y_train)\\n\\n return x_val, y_val, x_train, y_train\",\n \"def splitfeatdata(rawdata, fold=10):\\n\\n labeldata = []\\n for row in rawdata:\\n\\n # if row[2] > 0:\\n # label = 'pos'\\n # elif row[2] == 0:\\n # label = 'neutral'\\n # else:\\n # label = 'neg'\\n\\n\\n label = row[2]\\n labeldata.append((row[4], label))\\n\\n\\n random.shuffle(labeldata)\\n\\n size = int(math.floor(len(labeldata) / 10.0))\\n # train = labeldata[:split]\\n # test = labeldata[split:]\\n\\n # code for k-fold validation referred from:\\n # http://stackoverflow.com/questions/16379313/how-to-use-the-a-10-fold-cross-validation-with-naive-bayes-classifier-and-nltk\\n claccuracy = []\\n for i in range(fold):\\n test_this_round = labeldata[i*size:][:size]\\n train_this_round = labeldata[:i*size] + labeldata[(i+1)*size:]\\n\\n acc = myclassifier(train_this_round, test_this_round)\\n\\n claccuracy.append(acc)\\n\\n\\n\\n print os.getcwd()\\n\\n\\n mySentClassifier = nltk.NaiveBayesClassifier.train(labeldata)\\n f = open('../../../mySentClassifier2.pickle', 'wb')\\n dump(mySentClassifier, f)\\n f.close()\\n\\n\\n return claccuracy\",\n \"def cross_validation_split(frames, frames_json, patch_dir, n=10):\\n if os.path.isfile(frames_json):\\n print(\\\"Reading n-splits from file\\\")\\n with open(frames_json, 'r') as file:\\n fjson = json.load(file)\\n splits = fjson['splits']\\n else:\\n print(\\\"Creating and writing n-splits to file\\\")\\n frames_list = list(range(len(frames)))\\n # Divide into n-split, each containing training and test set\\n kf = KFold(n_splits=n, shuffle=True, random_state=1117)\\n print(\\\"Number of spliting iterations:\\\", kf.get_n_splits(frames_list))\\n splits = []\\n for train_index, test_index in kf.split(frames_list):\\n splits.append([train_index.tolist(), test_index.tolist()])\\n frame_split = {\\n 'splits': splits\\n }\\n if not os.path.exists(patch_dir):\\n os.makedirs(patch_dir)\\n with open(frames_json, 'w') as f:\\n json.dump(frame_split, f)\\n\\n return splits\",\n \"def splitData(groupList, trainSize):\\r\\n from sklearn.model_selection import StratifiedShuffleSplit\\r\\n\\r\\n groupList[0]['text'] = cleanRealTexts(list(groupList[0]['text']))\\r\\n\\r\\n classLabels = np.array([])\\r\\n for i, group in enumerate(groupList):\\r\\n classLabels = np.append(classLabels, np.repeat(i, len(group)))\\r\\n\\r\\n classData = pd.concat(groupList).reset_index(drop=True)\\r\\n\\r\\n splits = list(StratifiedShuffleSplit(n_splits=i,\\r\\n test_size=1-trainSize,\\r\\n train_size=trainSize,\\r\\n random_state=0).split(X=classData, y=classLabels))[0]\\r\\n trainIdx, testIdx = splits\\r\\n\\r\\n trainData = classData.iloc[trainIdx]\\r\\n testData = classData.iloc[testIdx]\\r\\n trainLabels = classLabels[trainIdx]\\r\\n testLabels = classLabels[testIdx]\\r\\n\\r\\n return [[trainData, trainLabels], [testData, testLabels]]\",\n \"def train_test_split_data(aligned_image_lists, raw_image_lists, test_ratio ,validation = True ):\\n\\n assert len(aligned_image_lists) == len(raw_image_lists), \\\"images have different size\\\"\\n mask = list(range(len(aligned_image_lists)))\\n mask_train, mask_test = train_test_split(mask, test_size= test_ratio, shuffle=True)\\n\\n aligned_lists_train = [aligned_image_lists[i] for i in mask_train]\\n aligned_lists_test = [aligned_image_lists[i] for i in mask_test]\\n\\n raw_lists_train = [raw_image_lists[i] for i in mask_train] \\n raw_lists_test = [raw_image_lists[i] for i in mask_test] \\n\\n return [aligned_lists_train, aligned_lists_test, raw_lists_train, raw_lists_test]\",\n \"def load_data(n_folds=10):\\n\\n sss = sssplit(n_splits=n_folds, test_size=.1, random_state=np.random.RandomState(830452))\\n X, y = get_transformed_data()\\n (X_train, y_train), (X_test, y_test) = ([],[]),([],[])\\n\\n for train_idx, test_idx in sss.split(X, y):\\n X_train.append(X[train_idx])\\n y_train.append(y[train_idx])\\n X_test.append(X[test_idx])\\n y_test.append(y[test_idx])\\n\\n return (X_train, y_train), (X_test, y_test)\",\n \"def train_valid_index_split_two_stage(all_index, train_size_1 = None, train_size_2 = None, valid_split = 0.3):\\n\\tall_index = np.arange(all_index) if isinstance(all_index, int) else np.array(all_index)\\n\\n\\ttrain_size_2 = len(all_index) if train_size_2 is None else train_size_2\\n\\ttrain_index_2_ = np.random.choice(all_index, train_size_2, replace = False)\\n\\ttrain_index_2, valid_index_2 = np.split(train_index_2_, [int(train_size_2*(1-valid_split))])\\n\\n\\tall_index = np.setdiff1d(all_index, train_index_2)\\n\\ttrain_index_1_ = np.random.choice(all_index, train_size_1-train_size_2, replace = False)\\n\\ttrain_index_1, valid_index_1 = np.split(train_index_1_, [int((train_size_1-train_size_2)*(1-valid_split))])\\n\\ttrain_index_1 = np.hstack([train_index_1, train_index_2])\\n\\tvalid_index_1 = np.hstack([valid_index_1, valid_index_2])\\n\\treturn train_index_1, valid_index_1, train_index_2, valid_index_2\",\n \"def split_data(train_percentage, *data):\\n train = [entry[0:int(train_percentage * len(entry))] for entry in data]\\n val = [entry[int(train_percentage * len(entry)):] for entry in data]\\n return train, val\",\n \"def get_folds(X, y, k):\\n # temporarily change the 1/-1 nature of y to 1/0\\n _y = (y + 1) / 2\\n # partition the examples into postive and negative sets\\n positive_indices = np.where(_y)[0]\\n negative_indices = np.where(_y - 1)[0]\\n assert len(positive_indices) + len(negative_indices) == len(y)\\n\\n # shuffle both lists\\n np.random.shuffle(positive_indices)\\n np.random.shuffle(negative_indices)\\n\\n # create k buckets of indices of (approximately) equal size\\n positive_folds_indices = \\\\\\n np.array(np.array_split(positive_indices, k))\\n negative_folds_indices = \\\\\\n np.array(np.array_split(negative_indices, k))\\n\\n train_X, train_y, test_X, test_y = [], [], [], []\\n for i in range(k):\\n train_folds = np.concatenate((np.arange(0, i), np.arange(i+1, k)))\\n pos_train_indices = np.concatenate(positive_folds_indices[train_folds])\\n neg_train_indices = np.concatenate(negative_folds_indices[train_folds])\\n pos_test_indices = positive_folds_indices[i]\\n neg_test_indices = negative_folds_indices[i]\\n\\n train_X.append(\\n np.concatenate((X[pos_train_indices], X[neg_train_indices]))\\n )\\n train_y.append(\\n np.concatenate((y[pos_train_indices], y[neg_train_indices]))\\n )\\n test_X.append(\\n np.concatenate((X[pos_test_indices], X[neg_test_indices]))\\n )\\n test_y.append(\\n np.concatenate((y[pos_test_indices], y[neg_test_indices]))\\n )\\n\\n return zip(train_X, train_y, test_X, test_y)\",\n \"def __init__(self, splits):\\n\\t\\tself.kfold = KFold(splits)\",\n \"def split_list_equally(list_to_split: list, num_inter_lists: int):\\n if num_inter_lists < 1:\\n raise Exception(\\\"max_items_per_line needs to be greater than 0\\\")\\n\\n max_list_items = math.ceil(len(list_to_split) / num_inter_lists)\\n return [\\n list_to_split[i : i + max_list_items]\\n for i in range(0, len(list_to_split), max_list_items)\\n ]\",\n \"def make_splits(input_pkl, test_split=0.1, val_split=0.1):\\n if (test_split > 1) or (val_split > 1) or (test_split + val_split > 1) or (test_split <= 0) or (val_split <= 0):\\n logging.warning('Check the input for make splits, quitting')\\n exit()\\n\\n main_dict = load_pickle(input_pkl)\\n data, labels = main_dict['data'], main_dict['labels']\\n idx_arr = np.random.choice(len(data), len(data))\\n data, labels = data[idx_arr], labels[idx_arr]\\n print(len(data[0][-1]))\\n # Find the split sizes\\n val_split = int(len(data) * val_split)\\n test_split = val_split + int(len(data) * test_split)\\n\\n # Make and save the splits\\n save_pickle({'data': data[:val_split], 'labels': labels[:val_split]}, 'data/val.pkl')\\n save_pickle({'data': data[val_split:test_split], 'labels': labels[val_split:test_split]}, 'data/test.pkl')\\n save_pickle({'data': data[test_split:], 'labels': labels[test_split:]}, 'data/train.pkl')\",\n \"def split(self,\\n dataset,\\n frac_train=.8,\\n frac_valid=.1,\\n frac_test=.1,\\n log_every_n=1000):\\n np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.)\\n scaffold_sets = self.generate_scaffolds(dataset)\\n\\n train_cutoff = frac_train * len(dataset)\\n valid_cutoff = (frac_train + frac_valid) * len(dataset)\\n train_inds, valid_inds, test_inds = [], [], []\\n\\n log(\\\"About to sort in scaffold sets\\\", self.verbose)\\n for scaffold_set in scaffold_sets:\\n if len(train_inds) + len(scaffold_set) > train_cutoff:\\n if len(train_inds) + len(valid_inds) + len(scaffold_set) > valid_cutoff:\\n test_inds += scaffold_set\\n else:\\n valid_inds += scaffold_set\\n else:\\n train_inds += scaffold_set\\n return train_inds, valid_inds, test_inds\",\n \"def kfold_split(subj_ids, n_splits, groups=None, **kwargs):\\n kfoldClass = KFold if groups is None else ShuffleGroupKFold\\n kfold = kfoldClass(n_splits, shuffle=True, **kwargs)\\n return [split for split in kfold.split(X=subj_ids, groups=groups)]\",\n \"def train_test_split(x, y, test_pct):\\n\\tdata = zip(x,y)\\n\\ttrain, test = split_data(data, 1 - test_pct)\\n\\tx_train, y_train = zip(*train)\\n\\tx_test, y_test = zip(*test)\\n\\treturn x_train, y_train, x_test, y_test\",\n \"def k_fold_cross_validation(X, K):\\n\\tfor k in xrange(K):\\n\\t\\ttraining = [i for i, x in enumerate(X) if i % K != k]\\n\\t\\tvalidation = [i for i, x in enumerate(X) if i % K == k]\\n\\t\\tyield training, validation\",\n \"def train_validation_test_split(\\n data: pd.DataFrame,\\n target: str,\\n val_partition: float = 0.2,\\n test_partition: float = 0.15\\n) -> list:\\n\\n assert val_partition + test_partition < 1.0\\n\\n val_samples = val_partition * data.shape[0]\\n test_samples = test_partition * data.shape[0]\\n\\n train_validation, test = train_test_split(\\n data, test_size=int(test_samples), stratify=data[target]\\n )\\n\\n train, validation = train_test_split(\\n train_validation, test_size=int(val_samples), stratify=train_validation[target]\\n )\\n\\n return [train, validation, test]\",\n \"def splits(cls, *args, **kwargs):\\n train = cls('train', *args, **kwargs)\\n val = cls('val', *args, **kwargs)\\n test = cls('test', *args, **kwargs)\\n return train, val, test\",\n \"def cross_validate(self, X, y):\\n roc_ap = []\\n kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)\\n for train_ix, test_ix in kfold.split(X, y):\\n train_X, test_X = X.iloc[train_ix, :], X.iloc[test_ix, :]\\n train_y, test_y = y[train_ix], y[test_ix]\\n self.train(train_X, train_y)\\n roc_ap.append(self.test(test_X, test_y))\\n return roc_ap\",\n \"def __implement_cross_validation(self, X, y, model):\\n\\n skfolds = StratifiedKFold(n_splits=3, random_state=42)\\n\\n for train_index, test_index in skfolds.split(X, y):\\n clone_clf = clone(model)\\n X_train_folds = X[train_index]\\n y_train_folds = y[train_index]\\n X_test_fold = X[test_index]\\n y_test_fold = y[test_index]\\n\\n clone_clf.fit(X_train_folds, y_train_folds)\\n y_pred = clone_clf.predict(X_test_fold)\\n n_correct = sum(y_pred == y_test_fold)\\n message = \\\"ratio of correct predictions: \\\", n_correct / len(y_pred)\\n self.__logger.info(message)\",\n \"def crossTrain(data, labels, outDir, modelsPer, validator, params):\\n\\tcompleted = 0\\n\\tresults = []\\n\\n\\tout = open(join(outDir, \\\"log.txt\\\"), \\\"a\\\")\\n\\n\\t#for each fold split the data and train models keeping the best\\n\\tfor i, partition in enumerate(validator.partition(data,params.eventMap.matrixToIndex(labels))):\\n\\n\\t\\tif params.limit and completed == params.limit:\\n\\t\\t\\treturn results\\n\\n\\t\\tprint(\\\"Fold {}\\\".format(i))\\n\\n\\t\\t#create the output directory\\n\\t\\tfoldDir = join(outDir, \\\"fold{}\\\".format(i))\\n\\n\\t\\tif not os.access(foldDir, os.F_OK):\\n\\t\\t\\tmkdir(foldDir)\\n\\n\\t\\t\\tprint(\\\"Training Size {}, Dev Size {}\\\".format(len(partition[0]), len(partition[1])))\\n\\n\\t\\t\\t#train models\\n\\t\\t\\tbestScore, rnd, epoch, best = trainOnFold(data, labels, foldDir, modelsPer, partition, params)\\n\\n\\t\\t\\tprint(\\\"Best Score {}\\\".format(bestScore))\\n\\n\\t\\t\\tout.write(\\\"Round {}, Epoch {}, Score {}\\\\n\\\".format(rnd, epoch, bestScore))\\n\\t\\t\\tout.flush()\\n\\t\\t\\tos.fsync(out)\\n\\n\\t\\t\\tresults.append(best)\\n\\t\\t\\tcompleted += 1\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"Fold {} already exists\\\".format(i))\\n\\n\\tout.close()\\n\\n\\treturn results\",\n \"def validate_kfold(\\n x: ndarray,\\n y: ndarray,\\n x_sample_dim: int,\\n y_sample_dim: int,\\n n_splits: int,\\n stratify: bool,\\n models: List[Model],\\n generator: RandomGenerator,\\n save_fold_accs: bool,\\n) -> List[KFoldResults]:\\n # don't work with original arrays which may have sample index in strange location\\n length = int(x.shape[x_sample_dim])\\n idx = np.arange(0, length, dtype=int)\\n # convert to labels if we have a one-hot (will fail for dummy coding)\\n # y_split = np.argmax(y, axis=1 - int(np.abs(y_sample_dim))) if y.ndim == 2 else y\\n y_split = np.argmax(y, axis=1 - np.abs(y_sample_dim).astype(int)) if y.ndim == 2 else y\\n\\n # can't trust kfold shuffling in multiprocessing, shuffle ourselves\\n generator.shuffle(idx)\\n y_split = y_split[idx]\\n\\n kfolder = StratifiedKFold if stratify else KFold\\n kfold = kfolder(n_splits=n_splits, shuffle=False)\\n\\n results = []\\n for model, (train_idx, val_idx) in zip(models, kfold.split(idx, y_split)):\\n x_train = array_indexer(x, x_sample_dim, train_idx)\\n y_train = array_indexer(y, y_sample_dim, train_idx)\\n model.fit(x_train, y_train)\\n\\n acc = None\\n y_pred = None\\n if save_fold_accs:\\n x_val = array_indexer(x, x_sample_dim, val_idx)\\n y_val = array_indexer(y, y_sample_dim, val_idx)\\n y_pred = model.predict(x_val)\\n acc = 1 - np.mean(get_y_error(y_pred, y_val, y_sample_dim))\\n results.append(KFoldResults(model, acc, y_pred, val_idx))\\n\\n return results\",\n \"def build_splits(dataset, train_size, valid_size, by=['context_id'], seed=17):\\n if isinstance(seed, RandomState):\\n rng = seed\\n else:\\n rng = RandomState(seed)\\n\\n groups = dataset.groupby(by).groups\\n context_ids = groups.keys()\\n\\n train_ids, other_ids = sklearn.cross_validation.train_test_split(\\n context_ids, train_size=train_size, random_state=rng)\\n valid_ids, test_ids = sklearn.cross_validation.train_test_split(\\n other_ids, train_size=valid_size, random_state=rng)\\n\\n train_idx = context_id_to_idx(train_ids, groups)\\n valid_idx = context_id_to_idx(valid_ids, groups)\\n test_idx = context_id_to_idx(test_ids, groups)\\n\\n return dataset.ix[train_idx, :], dataset.ix[valid_idx, :], dataset.ix[test_idx, :]\",\n \"def train_test_split(X: list, y: list, test_frac: float = 0.2, seed: int = 42) -> list:\\n return train_test_split(X, y, test_size=test_frac, random_state = seed)\",\n \"def split(self,X,y=None):\\n all_idx = pd.Series(np.arange(X.shape[0])) \\n mbrg = int(X.shape[0]*self.embargo_pct)\\n test_starts=[(i[0],i[-1]+1) for i in np.array_split(all_idx.values,self.n_splits)]\\n for i, j in test_starts:\\n t0 = all_idx.index[i] # start of test set\\n test_indices = all_idx.values[i:j]\\n maxT1Idx = all_idx.index.searchsorted(all_idx[test_indices].max())\\n train_indices = all_idx.index.searchsorted(all_idx[all_idx<=t0].index)\\n if maxT1Idx < X.shape[0]: \\n train_indices=np.concatenate((train_indices,all_idx[maxT1Idx+mbrg:]))\\n yield train_indices,test_indices\",\n \"def train_valid_split(X, y):\\n random_indexes = np.random.permutation(len(y))\\n train_inds = random_indexes[:(0.75*len(y))]\\n valid_inds = random_indexes[(0.75*len(y)):]\\n return X[train_inds], y[train_inds], X[valid_inds], y[valid_inds]\",\n \"def cv_multiclass_fold(Y,num_fold=10):\\n\\t\\n (K,N) = Y.shape\\n indices = dict()\\n Nk = dict()\\n for k in range(K):\\n # select indices belonging to class k\\n indices[k] = list((Y[k,:]==1).nonzero()[0])\\n rand.shuffle(indices[k])\\n Nk[k] = len(indices[k])/num_fold\\n\\t\\n index_list = []\\n\\n for k in range(K):\\n for i in range(num_fold-1):\\n # split class-k indices into num_fold random sets\\n try:\\n index_list[i].extend(indices[k][Nk[k]*i:Nk[k]*(i+1)])\\n except IndexError:\\n index_list.append([])\\n index_list[i].extend(indices[k][Nk[k]*i:Nk[k]*(i+1)])\\n try:\\n index_list[num_fold-1].extend(indices[k][Nk[k]*(num_fold-1):])\\n except IndexError:\\n index_list.append([])\\n index_list[num_fold-1].extend(indices[k][Nk[k]*(num_fold-1):])\\n\\n return index_list\",\n \"def __init__(self, splits):\\n\\t\\tself.kfold = StratifiedKFold(splits)\",\n \"def fold(self, k):\\n if not (0 < k < 5):\\n raise ValueError('almazan cross-evaluation proposes a k=4 k-cross evaluation, {} out of range'.format(k))\\n test_idcs = [idx for idx, fold in enumerate(self.fold_idcs) if fold == k]\\n train_idcs = list(set(range(len(self))).difference(set(test_idcs)))\\n return self.sub_set(train_idcs), self.sub_set(test_idcs)\",\n \"def _score_slices(y_true, list_y_pred, scorer, score_mode, cv):\\n scores_list = list()\\n for y_pred in list_y_pred:\\n scores = list()\\n for t, this_y_pred in enumerate(y_pred):\\n if score_mode in ['mean-fold-wise', 'fold-wise']:\\n # Estimate score within each fold\\n scores_ = list()\\n for train, test in cv:\\n scores_.append(scorer(y_true[test], this_y_pred[test]))\\n scores_ = np.array(scores_)\\n # Summarize score as average across folds\\n if score_mode == 'mean-fold-wise':\\n scores_ = np.mean(scores_, axis=0)\\n elif score_mode == 'mean-sample-wise':\\n # Estimate score across all y_pred without cross-validation.\\n scores_ = scorer(y_true, this_y_pred)\\n scores.append(scores_)\\n scores_list.append(scores)\\n return scores_list\",\n \"def split_validation_training_index(allind, splitsize, do_offset, offset_steps):\\n i = offset_steps\\n lval = splitsize\\n if not do_offset:\\n i_val = allind[:lval]\\n i_train = allind[lval:]\\n else:\\n i_val = allind[i * lval:(i + 1) * lval]\\n i_train = np.concatenate([allind[0:i * lval], allind[(i + 1) * lval:]], axis=0)\\n if len(i_val) <= 0:\\n print(\\\"Warning: #Validation data is 0, take 1 training sample instead\\\")\\n i_val = i_train[:1]\\n\\n return i_train, i_val\",\n \"def fold_data(fold, datas):\\n training, validation = fold\\n folded = {}\\n\\n # fold Xtrain\\n if datas.get('Xtrain') is not None:\\n folded['Xtrain'] = datas['Xtrain'][training, :]\\n folded['Xtest'] = datas['Xtrain'][validation, :]\\n\\n # fold Ktrain\\n if datas.get('Ktrain') is not None:\\n folded['Ktrain'] = datas['Ktrain'][training, :][:, training]\\n folded['Ktest'] = datas['Ktrain'][validation, ][:, training]\\n # if datas.get('Ktest') is not None:\\n # folded['Ktest'] = datas['Ktest'][:, validation]\\n # else:\\n # folded['Ktest'] = folded['Xtest'].dot(folded['Xtrain'].T)\\n\\n # fold Ytrain\\n if datas.get('Ytrain') is not None:\\n folded['Ytrain'] = datas['Ytrain'][training, :]\\n folded['Ytest'] = datas['Ytrain'][validation, :]\\n\\n return folded\",\n \"def _split_indices(X, y, n_folds=5):\\n # TODO: check if indices are permuted\\n n = X.shape[0]\\n print('n:', n)\\n #original_indices = np.arange(n)\\n shuffle = np.random.permutation(n)\\n subset_proportion = 1./float(n_folds)\\n fold_size = int(subset_proportion*n)\\n folds = [shuffle[i*fold_size:(i+1)*fold_size] for i in range(n_folds)]\\n return folds\",\n \"def get_train_validation_test_split(dframe):\\n skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=2)\\n result = next(skf.split(dframe, get_label(dframe)), None)\\n\\n train_validation = dframe.iloc[result[0]]\\n test = dframe.iloc[result[1]]\\n\\n result = next(skf.split(train_validation, get_label(train_validation)), None)\\n\\n train = train_validation.iloc[result[0]]\\n validation = train_validation.iloc[result[1]]\\n return train, validation, test\",\n \"def train_test_split(features, outcomes, percent_train, limited_size=-1):\\n if len(features) != len(outcomes):\\n raise IndexError('the number of feautre instances and outcome instances do not match')\\n if percent_train >= 100 or percent_train <= 0:\\n raise ValueError('percent must be between 0 and 100')\\n if limited_size > len(features):\\n raise ValueError('limited size is larger than the number of instances provided')\\n if limited_size <0:\\n limited_size = len(features)\\n\\n features_set = features[:limited_size]\\n outcomes_set = outcomes[:limited_size]\\n train_size = math.ceil(len(features_set)*percent_train/100)\\n features_train = features_set[:train_size - 1]\\n outcomes_train = outcomes_set[:train_size - 1]\\n features_test = features_set[train_size-1:]\\n outcomes_test = outcomes_set[train_size-1:]\\n return features_train, outcomes_train, features_test, outcomes_test\",\n \"def train_dev_test_split(object_list, split_ratio=(0.8, 0.1, 0.1), seed=2018):\\n train_size, dev_size, test_size = split_ratio\\n # TODO: add stratify option for split on pair level, investigate same option for document/sentence level?\\n train, tmp = train_test_split(object_list, random_state=seed, train_size=train_size)\\n dev, test = train_test_split(tmp, random_state=seed, test_size=test_size / (test_size + dev_size))\\n\\n if isinstance(train, pd.DataFrame) and isinstance(dev, pd.DataFrame) and isinstance(test, pd.DataFrame):\\n return train, dev, test\\n else:\\n # TODO: add checks that train, dev, test are lists of documents or sentences\\n train_examples = pd.concat([elem.get_examples() for elem in train]).reset_index(drop=True)\\n dev_examples = pd.concat([elem.get_examples() for elem in dev]).reset_index(drop=True)\\n test_examples = pd.concat([elem.get_examples() for elem in test]).reset_index(drop=True)\\n\\n return train_examples, dev_examples, test_examples\",\n \"def get_xy_kfolds(split_index=[0.5, 0.6, 0.7, 0.8, 0.9], timesteps=1000):\\n df = np.loadtxt('exchange_rate.txt', delimiter=',')\\n n = len(df)\\n folds = []\\n enc = MinMaxScaler()\\n df = enc.fit_transform(df)\\n for split_point in split_index:\\n train_end = int(split_point * n)\\n train_x, train_y = [], []\\n for i in range(train_end - timesteps):\\n train_x.append(df[i:i + timesteps])\\n train_y.append(df[i + timesteps])\\n train_x = np.array(train_x)\\n train_y = np.array(train_y)\\n test_x = df[train_end - timesteps + 1:train_end + 1]\\n test_y = df[train_end + 1]\\n folds.append((train_x, train_y, test_x, test_y))\\n return folds, enc\",\n \"def cross_validation(whole_train_data, whole_train_labels, k, k_fold):\\n accuracies = []\\n for i in range(k_fold):\\n train_data, train_labels, validation_data, validation_labels = split_train_and_validation(whole_train_data, whole_train_labels, i, k_fold)\\n accuracy = knn(train_data, train_labels, validation_data, validation_labels, k)\\n accuracies.append(accuracy)\\n avg_accuracy = np.mean(accuracies)\\n return avg_accuracy\",\n \"def _train_validation_test_split(self, images, centroids):\\n \\n # Get test images from images list. Test images are images for which a centroid mask does NOT exist\\n test = []\\n filtered_images = []\\n \\n # split images in test and train/val set\\n for image in images:\\n if image not in centroids:\\n test.append(image)\\n else:\\n filtered_images.append(image)\\n \\n # Generate train/validation split based on remaining images and centroids\\n train, validation = train_test_split(filtered_images, train_size=self.split, random_state=self.seed)\\n \\n return train, validation, test\",\n \"def crossValidate(dataset, folds):\\n\\tshuffle(dataset)\\n\\tcv_results = []\\n\\tprecision_recall_acc = []\\n\\tfoldSize = int(len(dataset)/folds)\\n\\tfor i in range(0,len(dataset),foldSize):\\n\\t\\t# preparing data\\n\\t\\tvalD = dataset[i:i+foldSize]\\n\\t\\ttestD = dataset[:i]+dataset[i+foldSize:] #list(set(dataset)-set(dataset[i:i+foldSize]))\\n\\t\\t# Training\\n\\t\\tprint(\\\"*\\\"*60)\\n\\t\\tprint(\\\"Training on data-set size \\\"+str(len(testD))+\\\" of batch \\\"+str(i/(foldSize)))\\n\\t\\tclassi = trainClassifier(testD)\\n\\t\\t# Prediction on validation data \\n\\t\\tprint(\\\"Predicting on heldout data-set size...\\\"+str(len(valD))+\\\" of batch \\\"+str(i/(foldSize)))\\n\\t\\ty_true = list(map(lambda t: t[1], valD))\\n\\t\\ty_pred = predictLabels(valD,classi)\\t\\t\\n\\t\\t# Performance Metrics\\t\\t\\n\\t\\t# average based on macro as it calculate metrics for each label, and find their unweighted mean.\\n\\t\\tprecision_recall = list(precision_recall_fscore_support(y_true, y_pred, average='macro'))\\n\\t\\tacc = accuracy_score(y_true,y_pred)\\n\\t\\tprecision_recall[-1] = acc\\n\\t\\tprint(precision_recall)\\n\\t\\tprecision_recall_acc.append(precision_recall)\\n\\tdf = pd.DataFrame(precision_recall_acc,columns = [\\\"Precision\\\",\\\"Recall\\\",\\\"F1 score\\\",\\\"Accuracy Score\\\"])\\n\\tprint(df)\\n\\tcv_results = df.mean().tolist()\\n\\treturn cv_results\",\n \"def _kfold_cross_val(self, training_elms: np.ndarray) -> None:\\n kf = model_selection.KFold(\\n n_splits=config.folds, shuffle=True, random_state=config.seed\\n )\\n self.df[\\\"elm_events\\\"] = training_elms\\n self.df[\\\"fold\\\"] = -1\\n for f_, (_, valid_idx) in enumerate(kf.split(X=training_elms)):\\n self.df.loc[valid_idx, \\\"fold\\\"] = f_\",\n \"def get_k_fold(examples, labels, k=10):\\n example_fold = []\\n label_fold = []\\n interval = int(len(examples)/k)\\n for i in range(k):\\n \\t#f_examples = [examples[j] for j in range(len(examples)) if j%k == i]\\n #f_labels = [labels[j] for j in range(len(labels)) if j%k == i]\\n f_examples = [examples[j] for j in range(interval*i,interval*(i+1))]\\n f_labels = [labels[j] for j in range(interval*i,interval*(i+1))]\\n example_fold.append(f_examples)\\n label_fold.append(f_labels)\\n return example_fold, label_fold\",\n \"def train_test_split(dataset, split):\\r\\n train = list()\\r\\n train_size = split * len(dataset)\\r\\n dataset_copy = list(dataset) \\r\\n while len(train) < train_size:\\r\\n index = randrange(len(dataset_copy))\\r\\n train.append(dataset_copy.pop(index))\\r\\n return train, dataset_copy\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72968256","0.719841","0.69764197","0.6945098","0.6917422","0.6894226","0.687894","0.6873099","0.68583196","0.68282145","0.6812268","0.6787838","0.6777427","0.6756678","0.67225647","0.6717021","0.6692794","0.6687437","0.6682434","0.6654291","0.66536283","0.66208285","0.6602911","0.65664124","0.654339","0.6537929","0.6528892","0.64985627","0.6482556","0.6477113","0.646001","0.6438524","0.64322287","0.6389781","0.6372824","0.6372489","0.6369183","0.63627493","0.63355213","0.6299107","0.62853694","0.6232104","0.62292033","0.62247324","0.6213816","0.6206777","0.62061566","0.6179986","0.6171327","0.6166212","0.6152663","0.6126487","0.61104065","0.61071396","0.61059237","0.6087301","0.6072244","0.60719234","0.60476357","0.6046549","0.6027668","0.6027411","0.6006618","0.60046023","0.59944665","0.5988678","0.5988209","0.5986786","0.5965873","0.59624225","0.59315157","0.5927471","0.59209085","0.5909748","0.59014225","0.5897769","0.5893273","0.5884654","0.5881579","0.58814764","0.587391","0.5850217","0.5846673","0.5831435","0.5830303","0.5807056","0.57900447","0.5782702","0.578012","0.57780534","0.577701","0.5776472","0.5769913","0.5769128","0.57631594","0.5759124","0.5750719","0.5745853","0.57452554","0.57450396"],"string":"[\n \"0.72968256\",\n \"0.719841\",\n \"0.69764197\",\n \"0.6945098\",\n \"0.6917422\",\n \"0.6894226\",\n \"0.687894\",\n \"0.6873099\",\n \"0.68583196\",\n \"0.68282145\",\n \"0.6812268\",\n \"0.6787838\",\n \"0.6777427\",\n \"0.6756678\",\n \"0.67225647\",\n \"0.6717021\",\n \"0.6692794\",\n \"0.6687437\",\n \"0.6682434\",\n \"0.6654291\",\n \"0.66536283\",\n \"0.66208285\",\n \"0.6602911\",\n \"0.65664124\",\n \"0.654339\",\n \"0.6537929\",\n \"0.6528892\",\n \"0.64985627\",\n \"0.6482556\",\n \"0.6477113\",\n \"0.646001\",\n \"0.6438524\",\n \"0.64322287\",\n \"0.6389781\",\n \"0.6372824\",\n \"0.6372489\",\n \"0.6369183\",\n \"0.63627493\",\n \"0.63355213\",\n \"0.6299107\",\n \"0.62853694\",\n \"0.6232104\",\n \"0.62292033\",\n \"0.62247324\",\n \"0.6213816\",\n \"0.6206777\",\n \"0.62061566\",\n \"0.6179986\",\n \"0.6171327\",\n \"0.6166212\",\n \"0.6152663\",\n \"0.6126487\",\n \"0.61104065\",\n \"0.61071396\",\n \"0.61059237\",\n \"0.6087301\",\n \"0.6072244\",\n \"0.60719234\",\n \"0.60476357\",\n \"0.6046549\",\n \"0.6027668\",\n \"0.6027411\",\n \"0.6006618\",\n \"0.60046023\",\n \"0.59944665\",\n \"0.5988678\",\n \"0.5988209\",\n \"0.5986786\",\n \"0.5965873\",\n \"0.59624225\",\n \"0.59315157\",\n \"0.5927471\",\n \"0.59209085\",\n \"0.5909748\",\n \"0.59014225\",\n \"0.5897769\",\n \"0.5893273\",\n \"0.5884654\",\n \"0.5881579\",\n \"0.58814764\",\n \"0.587391\",\n \"0.5850217\",\n \"0.5846673\",\n \"0.5831435\",\n \"0.5830303\",\n \"0.5807056\",\n \"0.57900447\",\n \"0.5782702\",\n \"0.578012\",\n \"0.57780534\",\n \"0.577701\",\n \"0.5776472\",\n \"0.5769913\",\n \"0.5769128\",\n \"0.57631594\",\n \"0.5759124\",\n \"0.5750719\",\n \"0.5745853\",\n \"0.57452554\",\n \"0.57450396\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58894753"},"document_rank":{"kind":"string","value":"77"}}},{"rowIdx":78894,"cells":{"query":{"kind":"string","value":"Return items to be featured on homepage"},"document":{"kind":"string","value":"def on_homepage(self):\n # While it's easy enough to just query the default manager\n # to do this, providing this convenience method abstracts\n # away the way homepage items are designated in case\n # we change the way that designation is done.\n return self.filter(on_homepage=True)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def featured(self):\n \n featured_content = []\n \n def rnd_content_items(return_items=1, **kwargs):\n if 'limit' not in kwargs:\n kwargs['limit'] = 3\n # searching due_date limits results to only assignments; if we want\n # all articles, then we don't want to limit to assignments\n #if 'due_date' not in kwargs and kwargs.get('type') == \"assignment\":\n # kwargs['due_date'] = \">now\"\n \n # AllanC - Because we were not getting new content on the site - if one of these returns gives back nothing, redo the query with a longer timeframe\n # This shouldnt be to much of a problem as this is cached server side every 10 mins .,.. once we get popular it will always break on the first try\n for days_jump in [i*7 for i in range(1,5)]:\n kwargs['update_date'] = \">\"+str(now() - datetime.timedelta(days=days_jump)) \n kwargs['exclude_content'] = \",\".join([str(content['id']) for content in featured_content])\n content_list = content_search(**kwargs)['data']['list']\n if len(content_list['items']) >= return_items:\n break\n log.info('Too few content retured for %s - attempting query again with day range of %d' % (kwargs, days_jump))\n \n random.shuffle( content_list['items'] )\n content_list['items'] = content_list['items'][:return_items]\n #content_list['count'] = len(content_list['items'])\n for content_item in content_list['items']:\n featured_content.append(content_item)\n \n return content_list\n \n \n #return to_apilist(featured_content, obj_type='contents') # AllanC - a liniear list of featured contebt\n \n # Sponsored content dictionary\n #s = {}\n #s['sponsored_responded' ] = rnd_content_items(return_items=1, sort='-num_responses', limit=3 )\n #s['sponsored_assignment' ] = rnd_content_items(return_items=1, sort='-views', type='assignment', limit=3 )\n \n # Featured content dictionary\n f = {}\n ##f['top_viewed_assignments' ] = rnd_content_items(return_items=2, sort='-views' , type='assignment', limit=5)\n f['recent' ] = rnd_content_items(return_items=3, sort='-update_date' , limit=5)\n f['most_responses' ] = rnd_content_items(return_items=3, sort='-num_responses', limit=5)\n if request.GET.get(\"location\"):\n f['near_me' ] = rnd_content_items(return_items=3, sort='distance' , limit=5, location=request.GET.get(\"location\"))\n f['recent_assignments' ] = rnd_content_items(return_items=3, sort='-update_date' , limit=5, list='assignments_active')\n \n # New members\n m ={}\n m['new_members'] = member_search(sort='-join_date', type='user' , limit=3)['data']['list']\n m['new_groups' ] = member_search(sort='-join_date', default_content_visibility='public', limit=3)['data']['list']\n \n # AllanC - HACK HACK!!!\n # The count from the query using the default_content_visibility='public' is wrong .. the content is correct .. the count is broken\n # Set the count FOR THIS LIST ONLY to match the items returned\n m['new_groups' ]['count'] = len(m['new_groups' ]['items'])\n \n return action_ok(\n data={\n #'sponsored' : s,\n 'featured' : f,\n 'members' : m,\n }\n )","def homepage(request):\n limit = 7\n featured_marketer_list = (\n PimpUser.objects\n .filter(featured=True)\n .filter(usertype=PimpUser.MARKETER)\n .order_by('-date_joined')[:limit]\n )\n featured_cause_list = (\n PimpUser.objects\n .filter(featured=True)\n .filter(usertype=PimpUser.CAUSE)\n .order_by('-date_joined')[:limit]\n )\n news_post_list = (\n NewsPost.objects\n .order_by('-created_at')[:3]\n )\n\n context = {\n 'featured_marketer_list': featured_marketer_list,\n 'featured_cause_list': featured_cause_list,\n 'news_post_list': news_post_list,\n }\n\n return render(\n request,\n 'index.html',\n context\n )","def featured(request):\n\n artist = Artist.objects.all()\n\n \n\n context = {\n 'artist': artist,\n }\n\n return render(request, 'artist/featured.html', context)","def get_queryset(self, *args, **kwargs):\n return Product.objects.featured()","def get_featured_content():\n\n return FeatureHistory.objects.filter(featured=True).order_by('updated_at')[:3]","def test_get_featured_front_page(self):\r\n\r\n app = self.create_app(None)\r\n app.owner = self.user\r\n db.session.add(app)\r\n featured = Featured(app=app)\r\n db.session.add(featured)\r\n db.session.commit()\r\n\r\n featured = cached_apps.get_featured_front_page()\r\n\r\n assert len(featured) is 1, featured","def test_get_featured_front_page_only_returns_featured(self):\r\n\r\n featured_app = self.create_app(None)\r\n non_featured_app = self.create_app(None)\r\n non_featured_app.name = 'other_app'\r\n non_featured_app.short_name = 'other_app'\r\n featured_app.owner = self.user\r\n non_featured_app.owner = self.user\r\n db.session.add(featured_app)\r\n db.session.add(non_featured_app)\r\n featured = Featured(app=featured_app)\r\n db.session.add(featured)\r\n db.session.commit()\r\n\r\n featured = cached_apps.get_featured_front_page()\r\n\r\n assert len(featured) is 1, featured","def get_feature(request):\n feature = Feature.objects.filter(published_date__lte=timezone.now()\n ).order_by('published_date')\n return render(request, \"feature.html\", {'feature': feature})","def test_featured_pages(self, client, site, homepage):\n # create pages with test content\n about = ContentPage(title=\"about\", slug=\"about\", description=\"lorem ipsum\")\n homepage.add_child(instance=about)\n homepage.save()\n consult = ContentPage(title=\"consult\", slug=\"consult\")\n homepage.add_child(instance=consult)\n homepage.save()\n response = client.get(homepage.relative_url(site))\n # should render featured pages section\n assertTemplateUsed(response, \"cdhpages/snippets/featured_pages.html\")\n # should include description from about page\n assertContains(response, \"lorem ipsum\")\n # should include link to about page\n assertContains(response, about.get_url())\n # should include link to consult page\n assertContains(response, consult.get_url())","def getItems(self): \n \n kwargs = {}\n kwargs[\"max\"] = {\"deals.mocality.co.ke\" : 1, \"www.zetu.co.ke\":1, \"manual\":2}\n from frontpage import get_deals\n items = get_deals(self.context, self.request, **kwargs)\n return items","def add_frontpage_contents(sender, **kwds):\n\n context = kwds['context']\n if 'newsitems' not in context:\n context['newsitems'] = News.query.latest().limit(5).all()\n return context","def featured_news(self):\n return self.split(self.settings.featured_news)","def home():\n\n lst = item_list()\n return render_template('index.html', sell_flag=0, items=lst)","def get_top_featured_entries(number=5):\n return list(Entry.published.filter(featured=True)[:number])","def getItems(self): \n if self.itemCount > 0:\n \n site = getSite()\n \n \n # Make string path relative to the site root\n # E.g. string path \"news\" becomes \"/yoursiteid/news\"\n site_path = site.getPhysicalPath();\n \n path = \"/\".join(site_path) + \"/\" + self.path \n \n types = [self.itemPortalType]\n \n items = []\n \n #if self.itemPortalType2 != None:\n # types.append(self.itemPortalType2) \n \n #print \"Querying by:\" + type + \" \" + path\n content_by_type = self.context.portal_catalog(path={ \"query\": path, \"depth\" :9 }, \n portal_type=self.itemPortalType, \n sort_on=\"created\", \n sort_order=\"reverse\")[0:self.itemCount]\n\n content_by_type = list(content_by_type)\n \n if self.itemPortalType2 != None:\n content_by_type2 = self.context.portal_catalog(path={ \"query\": path, \"depth\" :9 }, \n portal_type=self.itemPortalType2, \n sort_on=\"created\", \n sort_order=\"reverse\")[0:self.itemCount]\n\n content_by_type += list(content_by_type2)\n\n \n items += [ brain.getObject() for brain in content_by_type ]\n else:\n items = []\n \n #if self.title == \"Daily deals\":\n # import pdb ; pdb.set_trace()\n \n # XXX: custom hack for deals\n def is_expired_deal(i):\n \"\"\"\n \"\"\"\n if hasattr(i, \"validUntil\"):\n now = datetime.datetime.utcnow()\n if now > i.validUntil:\n return True\n \n return False\n \n items = [ i for i in items if not is_expired_deal(i) ]\n \n return items","def myfood(request):\n my_fav = Favorite.objects.filter(user=request.user)\n my_fav_products = [fav.product_favorite for fav in my_fav]\n paginator = Paginator(my_fav_products, 6)\n page_number = request.GET.get(\"page\")\n page_obj = paginator.get_page(page_number)\n context = {\n \"favorites\": page_obj\n }\n return render(request, \"myfood.html\", context)","def test_empty_featured_pages(self, client, site, homepage):\n # no about or consult page: nothing rendered\n response = client.get(homepage.relative_url(site))\n assertTemplateNotUsed(response, \"cdhpages/snippets/featured_pages.html\")\n # only about page: nothing rendered\n about = ContentPage(title=\"about\", slug=\"about\", body=\"\")\n homepage.add_child(instance=about)\n homepage.save()\n response = client.get(homepage.relative_url(site))\n assertTemplateNotUsed(response, \"cdhpages/snippets/featured_pages.html\")\n # only consults page is live: nothing rendered\n about.unpublish()\n consult = ContentPage(title=\"consult\", slug=\"consult\")\n homepage.add_child(instance=consult)\n homepage.save()\n response = client.get(homepage.relative_url(site))\n assertTemplateNotUsed(response, \"cdhpages/snippets/featured_pages.html\")\n # both pages live; should render\n about.save_revision().publish()\n response = client.get(homepage.relative_url(site))\n assertTemplateUsed(response, \"cdhpages/snippets/featured_pages.html\")","def index(request):\n featured_collections = Collection.objects.order_by('creation_date')\n template = loader.get_template('movies/index.html')\n context = {\n 'featured_collections': featured_collections,\n }\n return HttpResponse(template.render(context, request))","def homepage():\n prods = db(db.product.prod_starred == True).select()\n return dict(\n prods = prods\n )","def home(request):\n featured_image = ProductFeatured.objects.first()\n products = Product.objects.all().order_by('?')\n const_products = Product.objects.filter(section='m')\n home_products = Product.objects.filter(section='h')\n elect_products = Product.objects.filter(section='e')\n art_products = Product.objects.filter(section='a')\n furn_products = Product.objects.filter(section='f')\n bed_products = Product.objects.filter(section='s')\n kitch_products = Product.objects.filter(section='k')\n book_products = Product.objects.filter(section='b')\n drawer_products = Product.objects.filter(section='d')\n cabin_products = Product.objects.filter(section='c')\n dress_products = Product.objects.filter(section='Dressers')\n Oven_products = Product.objects.filter(section='o')\n ref_products = Product.objects.filter(section='r')\n vac_products = Product.objects.filter(section='v')\n gas_products = Product.objects.filter(section='g')\n smart_products = Product.objects.filter(section='p')\n trend_products = ProductTrend.objects.all()\n\n context = {\n \"featured_image\": featured_image,\n \"products\": products,\n \"const_products\": const_products,\n \"home_products\": home_products,\n \"elect_products\": elect_products,\n \"art_products\": art_products,\n \"furn_products\": furn_products,\n \"bed_products\": bed_products,\n \"book_products\": book_products,\n \"kitch_products\": kitch_products,\n \"drawer_products\": drawer_products,\n \"cabin_products\": cabin_products,\n \"dress_products\": dress_products,\n \"Oven_products\": Oven_products,\n \"ref_products\": ref_products,\n \"vac_products\": vac_products,\n \"gas_products\": gas_products,\n \"smart_products\":smart_products,\n \"trend_products\":trend_products\n }\n\n if request.GET:\n print request.GET\n\n return render(request, \"home.html\", context)","def home():\r\n\r\n page = 1\r\n per_page = current_app.config.get('APPS_PER_PAGE')\r\n if per_page is None: # pragma: no cover\r\n per_page = 5\r\n d = {'featured': cached_apps.get_featured_front_page(),\r\n 'top_apps': cached_apps.get_top(),\r\n 'top_users': None}\r\n\r\n # Get all the categories with apps\r\n categories = cached_cat.get_used()\r\n d['categories'] = categories\r\n d['categories_apps'] = {}\r\n for c in categories:\r\n tmp_apps, count = cached_apps.get(c['short_name'], page, per_page)\r\n d['categories_apps'][str(c['short_name'])] = tmp_apps\r\n\r\n # Add featured\r\n tmp_apps, count = cached_apps.get_featured('featured', page, per_page)\r\n if count > 0:\r\n featured = model.category.Category(name='Featured', short_name='featured')\r\n d['categories'].insert(0,featured)\r\n d['categories_apps']['featured'] = tmp_apps\r\n\r\n if current_app.config['ENFORCE_PRIVACY'] and current_user.is_authenticated():\r\n if current_user.admin:\r\n d['top_users'] = cached_users.get_top()\r\n if not current_app.config['ENFORCE_PRIVACY']:\r\n d['top_users'] = cached_users.get_top()\r\n return render_template('/home/index.html', **d)","def show_news_list():\r\n\tnews_list = Page.objects.filter(tags='news').order_by('-created')\r\n\treturn {'news_list': news_list}","def get_local_news_items(self):\n catalog = api.portal.get_tool(name='portal_catalog')\n default_lang = api.portal.get_tool(\n \"portal_languages\").getDefaultLanguage()\n results = catalog.searchResults(\n portal_type=\"News Item\",\n sort_on=\"effective\",\n sort_order=\"descending\",\n review_state='published',\n Language=[default_lang, ''],\n )\n return results","def explore_view(request):\r\n # explore items\r\n user = request.user.userprofile\r\n items = Item.objects.explore(user)\r\n context = {'items':items}\r\n return render(request, 'explore/explore.html', context)","def getContentTypeFeatured(ct):","def test_get_featured_front_page_not_returns_hidden_apps(self):\r\n\r\n featured_app = self.create_app(None)\r\n featured_app.owner = self.user\r\n featured_app.hidden = 1\r\n db.session.add(featured_app)\r\n featured = Featured(app=featured_app)\r\n db.session.add(featured)\r\n db.session.commit()\r\n\r\n featured = cached_apps.get_featured_front_page()\r\n\r\n assert len(featured) is 0, featured","def show_homepage():\n max_items = 5\n all_categories = session.execute(\n 'SELECT category.name, category.id, count(item.id) AS item_count '\n 'FROM category '\n 'LEFT JOIN item ON category.id = item.category_id '\n 'GROUP BY category.name, category.id')\n items = session.query(Item, Category).join(Category).order_by(Item.time_updated.desc(), Item.time_created.desc(),\n Item.name).limit(5).all()\n return render_template('categories_latest.html',\n all_categories=all_categories,\n items=items,\n max_items=max_items,\n login_session=login_session)","def index(request, archive=False):\n context = {'archive':archive}\n posts = Post.objects.all()\n if not archive:\n posts = posts[:10]\n context['posts'] = posts\n if request.user.is_authenticated():\n #These are the new news items the logged in user has\n context['new_posts'] = NewBlog.objects.filter(user=request.user)\n return render(request, 'blog/index.html', context)","def index(page):\r\n if cached_apps.n_featured() > 0:\r\n return app_index(page, cached_apps.get_featured, 'featured',\r\n True, False)\r\n else:\r\n categories = cached_cat.get_all()\r\n cat_short_name = categories[0].short_name\r\n return redirect(url_for('.app_cat_index', category=cat_short_name))","def get(self, request, *args, **kwargs):\n my_normal_post_lists = NormalPosts.objects.filter(uploded_by=request.user.normalprofile).order_by(\"-id\")\n return render(request, self.template_name, {\n 'my_normal_post_lists': my_normal_post_lists,\n })","def top_menu_items(request):\n return {\n 'top_menu_items': settings.TOP_MENU_ITEMS\n }","def get_items(id_name, request, client):\n result = client.quick_search(request)\n \n items_pages = []\n limit_to_x_pages = None\n for page in result.iter(limit_to_x_pages):\n items_pages.append(page.get())\n\n items = [item for page in items_pages for item in page['features']]\n \n \n return (id_name, items)","def full_listing(request, urlname):\n\tif request.user.is_authenticated():\n\t\tblog = Blog.qa_objects.get(urlname=urlname)\n\t\tposts = BlogEntry.qa_objects.filter(blog=blog, posting_time__lte=datetime.now()).order_by('-posting_time')\n\telse:\n\t\tblog = Blog.objects.get(urlname=urlname)\n\t\tposts = BlogEntry.objects.filter(blog=blog).order_by('-posting_time')\n\treturn render_to_response('blogs/full.html', {'blog': blog, 'posts': posts}, context_instance=RequestContext(request))","def get_featured_articles(request):\n try:\n count = 1\n if 'count' in request.POST and int(request.POST['count']):\n count = int(request.POST['count'])\n\n newest_list = []\n for article in Article.objects.order_by('-modified')[:count]:\n newest_list.append(article.dump_to_dict())\n\n popular_list = []\n for article in Article.objects.order_by('-views')[:count]:\n popular_list.append(article.dump_to_dict())\n\n return format_ajax_response(True, \"Featured articles retrieved successfully.\", {'newest': newest_list,'popular': popular_list})\n except Exception as ex:\n logger.error(\"Failed to get_featured_articles: %s\" % ex)\n return format_ajax_response(False, \"There was an error retrieving the featured articles.\")","def is_featured(self):\n return self._is_featured","def get_all_posts(request, show_only=None):\n if(show_only == None):\n posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('-is_important', '-published_date')\n title = \"All Posts\"\n else:\n posts = Post.objects.filter(post_type__exact=show_only.name).filter(published_date__lte=timezone.now()).order_by('-is_important', '-published_date')\n title = show_only.name\n return render(request, \"posts.html\", {\"posts\": posts, \"title\": title})","def new_in(request, search = None, page = 1):\n\tcontext_dict = {}\n\titems = []\t\n\tsearch = search\n\n\tif search != None:\n\t\tsearch = search.split(\" \")\n\t\ttoAdd = []\n\t\t\n\t\tfor word in search:\n\t\t\ttoAdd += Item.objects.filter(Q(description__contains = word ) | Q(title__contains = word)\n\t\t\t\t| Q(category = word) | Q(section = word))\n\t\t\t\n\t\t\tfor item in toAdd:\n\t\t\t\tif (date.today()- item.datePosted).days <= 7:\n\t\t\t\t\titems += [item]\n\t\t\n\t\tsearchS = \"_\".join(search)\n\t\tmaxi = 0\n\t\t\n\t\tfor item in items:\n\t\t\tif item.price > maxi:\n\t\t\t\tmaxi = item.price\n\t\t\n\t\tserch_form = Search_bar()\n\t\tcontext_dict['search_bar'] = search_form\n\t\tcontext_dict['maxi'] = maxi + 5\n\t\tcontext_dict['search'] = searchS\n\t\tcontext_dict['page'] = page\n\t\tcontext_dict['items'] = items\n\t\tcontext_dict['pages'] = int(len(items)/6)\n\t\tcontext_dict['min'] = 6 * (int(page) - 1)\n\t\tcontext_dict['max'] = 6 * (int(page))\n\t\t\n\t\treturn render(request, 'tailored/shop_bootstrap.html',context_dict)\n\telse:\n\t\treturn home_page(request)","def get_news(request):\n return get_all_posts(request, PostType.NEWS)","def test_empty_posts(self, client, site, homepage):\n response = client.get(homepage.relative_url(site))\n assertTemplateNotUsed(response, \"snippets/carousel.html\")","def index(request):\n\n # Fetches the latest 3 listings published\n listings = Listing.objects.order_by('-list_date').filter(is_published=True)[:3]\n context = {\n 'listings': listings,\n 'state_choices': state_choices,\n 'bedroom_choices': bedroom_choices,\n 'price_choices': price_choices\n }\n return render(request, \"pages/index.html\", context)","def show_homepage():\n\n posts = Post.query.order_by(Post.created_at.desc()).limit(5).all()\n\n return render_template('index.html', posts=posts)","def favorites(request):\n cur_user = request.user # Gets the current logged-in user\n fav_products = Favorite.objects.all() # Gets all \"Favorite\" model objects\n\n # Gets the favorites of the current user\n fav_prod_filtered = fav_products.filter(users_id=cur_user).order_by('-id')\n\n # Adds pagination for up to 6 products per page\n paginator = Paginator(fav_prod_filtered, 6)\n page = request.GET.get('page')\n\n try:\n products = paginator.page(page)\n except PageNotAnInteger:\n products = paginator.page(1)\n except EmptyPage:\n products = paginator.page(paginator.num_pages)\n\n context = {\n 'favorites': products,\n 'paginate': True,\n }\n return render(request, 'favorites/favorites.html', context)","def get_featured_photos(self, count = 30, page = 1):\n uri = 'photos/featured'\n options = { 'per_page': count, 'page': page }\n return self.make_request(uri, options)","def _get_catalog_results(self, featured=False, **kw):\n if 'context' in kw.keys():\n kw['path'] = {'query': '/'.join(kw['context'].getPhysicalPath())}\n\n types = ('Article', 'Blog Entry', )\n states = ('published', )\n sort = 'Date'\n \n results = self.qrymethod(portal_type=types,\n review_state=states,\n is_featured=featured,\n sort_on=sort, \n sort_order='descending',\n **kw)\n\n return results","def get_featured_websites(self):\n REGION_TAG = 'featured-website-%s' % self.request.REGION.slug\n region_filter = es_filter.Term(tags=REGION_TAG)\n GLOBAL_TAG = 'featured-website'\n global_filter = es_filter.Term(tags=GLOBAL_TAG)\n mow_query = query.Q(\n 'function_score',\n filter=es_filter.Bool(should=[region_filter, global_filter]),\n functions=[\n SF('random_score', seed=self._get_daily_seed()),\n es_function.BoostFactor(value=100.0, filter=region_filter)\n ],\n )\n es = Search(using=WebsiteIndexer.get_es())[:11]\n results = es.query(mow_query).execute().hits\n return ESWebsiteSerializer(results, many=True).data","def get_context(self, request, *args, **kwargs):\n context = super().get_context(request, *args, **kwargs)\n context[\"page_title\"] = self.title\n\n categories = NewsCategory.objects.all().order_by(\"category\")\n context[\"categories\"] = categories\n\n # Check for category\n if \"category\" in kwargs:\n category = NewsCategory.objects.filter(\n slug=kwargs[\"category\"],\n ).first()\n news_items = (\n NewsPage.objects.filter(\n news_categories__news_category_id__in=[\n category.pk,\n ],\n )\n .live()\n .public()\n .order_by(\n \"-pinned_on_home\",\n \"home_news_order_pages__order\",\n \"-first_published_at\",\n )\n )\n\n if category.lead_story:\n news_items = news_items.exclude(\n pk=category.lead_story.pk,\n )\n\n context[\"category\"] = category\n else:\n # Get all posts\n news_items = (\n NewsPage.objects.live()\n .public()\n .order_by(\n \"-pinned_on_home\",\n \"home_news_order_pages__order\",\n \"-first_published_at\",\n )\n )\n\n featured_page = NewsPage.objects.filter(\n featured_on_news_home=True,\n ).first()\n\n if featured_page:\n news_items = news_items.exclude(\n pk=featured_page.pk,\n )\n\n # Paginate all posts by 2 per page\n paginator = Paginator(news_items, 9)\n # Try to get the ?page=x value\n page = int(request.GET.get(\"page\", 1))\n\n try:\n # If the page exists and the ?page=x is an int\n posts = paginator.page(page)\n except EmptyPage:\n # If the ?page=x is out of range (too high most likely)\n # Then return the last page\n posts = paginator.page(paginator.num_pages)\n\n start = 1\n total_shown = 10\n\n if paginator.num_pages < total_shown:\n total_shown = paginator.num_pages\n\n if page > 9:\n start = page - 7\n total_shown = 10\n\n if (page + 2) > paginator.num_pages:\n start = paginator.num_pages - 9\n\n context[\"pagination_range\"] = range(start, (start + total_shown))\n\n # \"posts\" will have child pages; you'll need to use .specific in the templates\n # in order to access child properties, such as youtube_video_id and subtitle\n context[\"posts\"] = posts\n\n return context","def featured():\n try:\n where = []\n for badval in [\"Unlisted\", \"Archived\", \"Deleted\"]:\n where.append(\"featured != \\\"\" + badval + \"\\\"\")\n where = \"WHERE \" + \" AND \".join(where)\n tls = dbacc.query_entity(\"Timeline\", where)\n except ValueError as e:\n return util.serve_value_error(e)\n return util.respJSON(tls)","def get_custom_feeds(request):\n start = int(request.paginate_number) * 10\n end = start + 10\n feeds = Feed.objects.all().order_by('-id')[start: end]\n return get_feed_list(feeds)","def get_featured(category, page=1, per_page=5):\r\n\r\n count = n_featured()\r\n\r\n sql = text('''SELECT app.id, app.name, app.short_name, app.info, app.created,\r\n app.description,\r\n \"user\".fullname AS owner FROM app, featured, \"user\"\r\n WHERE app.id=featured.app_id AND app.hidden=0\r\n AND \"user\".id=app.owner_id GROUP BY app.id, \"user\".id\r\n OFFSET(:offset) LIMIT(:limit);\r\n ''')\r\n offset = (page - 1) * per_page\r\n results = db.engine.execute(sql, limit=per_page, offset=offset)\r\n apps = []\r\n for row in results:\r\n app = dict(id=row.id, name=row.name, short_name=row.short_name,\r\n created=row.created, description=row.description,\r\n overall_progress=overall_progress(row.id),\r\n last_activity=pretty_date(last_activity(row.id)),\r\n last_activity_raw=last_activity(row.id),\r\n owner=row.owner,\r\n featured=row.id,\r\n info=dict(json.loads(row.info)))\r\n apps.append(app)\r\n return apps, count","def recipes_list(request):\n recipes = Recipe.objects.all().order_by('published_date')\n\n # prawidlowy sposob zbierania URLa - object.image.url\n # recipe = recipes[0]\n # print(\"path: \" + recipe.image.url)\n\n paginator = Paginator(recipes, INITIAL_PAGE_SIZE)\n page = paginator.page(1)\n\n context = {\n 'page': page,\n 'display_likes': True,\n }\n\n return render(request, 'recipes_index.html', context)","def get(self):\n return get_all_posts()","def __folha(soup):\n news = []\n\n items = soup.find('li', class_='c-most-popular__item')\n\n for item in items:\n div = item.find('div', class_='c-most-popular__content')\n link = div.a['href']\n title = div.a.string\n news.append(\n dict(title=title, link=replace_original_link_with_outline_call(link)))\n\n return news","def education_post_list(request):\n posts = EducationBlogPost.objects.filter(published_date__lte=timezone.now()\n ).order_by('-published_date')\n return render(request, \"education_center/education_blogposts.html\", {'posts': posts})","def item_extra_kwargs(self, obj):\n result = {'tags':obj.get_meta_keywords(),\n 'short_description':obj.get_meta_description()[:90] if obj.get_meta_description() else '',\n 'image_url':''}\n try:\n page_rss_feed = PageRSSFeed.objects.get(page=obj)\n result['short_description']= page_rss_feed.short_description[:90]\n result['image_url']= page_rss_feed.image_url\n except PageRSSFeed.DoesNotExist:\n pass\n return result","def newsfeed(request):\n article_list = Article.objects.order_by('published_date')\n context = {'article_list': article_list}\n return render(request, 'sacms/newsfeed.html', context)","def show_home():\r\n posts = Post.query.order_by(Post.created_at.desc()).limit(5).all()\r\n\r\n return render_template('home.html', posts=posts)","def articles(self):\n return self.get_queryset().filter(content_type__model='article').order_by('-articles__published_at')","def item_detail(request, item_id):\n # Select product based on URL param\n item = SELECT('item', where=f'id = {item_id}', _print=False)\n\n context = {\n 'item': item,\n 'photos': [item['photo_primary']] + item['photos']\n }\n return render(request, 'item_detail.html', context)","def GET_front_recent_posts(self, *a, **kw):\r\n # Server side cache is also invalidated when new article is posted\r\n return self.render_cached('recent-promoted', RecentPromotedArticles, g.side_posts_max_age)","def all_items(request):\n\n items = Product.objects.all()\n query = None\n category = None\n sort = None\n direction = None\n\n if request.GET:\n if 'sort' in request.GET:\n sortkey = request.GET['sort']\n sort = sortkey\n if sortkey == 'name':\n sortkey = 'lower_name'\n items = items.annotate(lower_name=Lower('name'))\n if sortkey == 'category':\n sortkey = 'category__name'\n if 'direction' in request.GET:\n direction = request.GET['direction']\n if direction == 'desc':\n sortkey = f'-{sortkey}'\n items = items.order_by(sortkey)\n\n if 'category' in request.GET:\n categories = request.GET['category'].split(',')\n items = items.filter(category__name__in=categories)\n categories = Category.objects.filter(name__in=categories)\n\n\n if 'q' in request.GET:\n query = request.GET['q']\n if not query:\n messages.error(request, \"Please input the item you are looking for\")\n return redirect(reverse('items'))\n \n queries = Q(name__icontains=query) | Q(description__icontains=query)\n items = items.filter(queries)\n \n current_sorting = f'{sort}_{direction}'\n\n context = {\n 'products': items,\n 'search_term': query,\n 'current_categories': category,\n 'current_sorting': current_sorting,\n }\n\n return render(request, 'products/items.html', context)","def featured(app_id=None):\r\n try:\r\n categories = cached_cat.get_all()\r\n\r\n if request.method == 'GET':\r\n apps = {}\r\n for c in categories:\r\n n_apps = cached_apps.n_count(category=c.short_name)\r\n apps[c.short_name], n_apps = cached_apps.get(category=c.short_name,\r\n page=1,\r\n per_page=n_apps)\r\n return render_template('/admin/applications.html', apps=apps,\r\n categories=categories)\r\n else:\r\n app = db.session.query(model.app.App).get(app_id)\r\n if app:\r\n if request.method == 'POST':\r\n cached_apps.reset()\r\n f = model.featured.Featured()\r\n f.app_id = app_id\r\n require.app.update(app)\r\n # Check if the app is already in this table\r\n tmp = db.session.query(model.featured.Featured)\\\r\n .filter(model.featured.Featured.app_id == app_id)\\\r\n .first()\r\n if (tmp is None):\r\n db.session.add(f)\r\n db.session.commit()\r\n return json.dumps(f.dictize())\r\n else:\r\n msg = \"App.id %s alreay in Featured table\" % app_id\r\n return format_error(msg, 415)\r\n if request.method == 'DELETE':\r\n cached_apps.reset()\r\n f = db.session.query(model.featured.Featured)\\\r\n .filter(model.featured.Featured.app_id == app_id)\\\r\n .first()\r\n if (f):\r\n db.session.delete(f)\r\n db.session.commit()\r\n return \"\", 204\r\n else:\r\n msg = 'App.id %s is not in Featured table' % app_id\r\n return format_error(msg, 404)\r\n else:\r\n msg = 'App.id %s not found' % app_id\r\n return format_error(msg, 404)\r\n except Exception as e: # pragma: no cover\r\n current_app.logger.error(e)\r\n return abort(500)","def index(request):\n\n last_six = Property.objects.order_by('created')[:6]\n last_six_in_ascending_order = reversed(last_six)\n\n carousel_items = Property.objects.order_by('rating')[:5]\n\n context = {\n 'properties': last_six_in_ascending_order,\n 'carousel_items': carousel_items,\n }\n\n return render(request, 'home/index.html', context)","def homepage():\n\n general_news=get_sources('general')\n business_news=get_sources('business')\n sports_news=get_sources('sports')\n return render_template('sources.html',general=general_news, business=business_news, sports=sports_news)","def thumbnails_urls(request):\n return {'thumbnails_urls': ThumbnailsUrls()}","def all_news(request):\n\n all_news = News.objects.all().order_by(\"-date_added\")\n context = {\n 'news': all_news,\n 'show_without_bag': True\n }\n return render(request, 'news/news.html', context)","def featured_services(self):\n return self.split(self.settings.featured_services)","def home():\n categories = session.query(Category).all()\n latest_items = session.query(Item).order_by(\n Item.created_date.desc()).limit(ITEM_COUNT).all()\n return render_template(\n 'views/catalog.html',\n categories=categories,\n latest_items=latest_items,\n user_in_session=user_in_session())","def test_get_featured_front_page_returns_required_fields(self):\r\n\r\n app = self.create_app(None)\r\n app.owner = self.user\r\n db.session.add(app)\r\n featured = Featured(app=app)\r\n db.session.add(featured)\r\n db.session.commit()\r\n fields = ('id', 'name', 'short_name', 'info', 'n_volunteers', 'n_completed_tasks')\r\n\r\n featured = cached_apps.get_featured_front_page()[0]\r\n\r\n for field in fields:\r\n assert featured.has_key(field), \"%s not in app info\" % field","def favorites_substitutes(request):\n favorites = User_Favorites_Substitutes.objects.filter(user=request.user)\n paginator = Paginator(favorites, 1)\n page = request.GET.get('page')\n try:\n products_list_p = paginator.page(page)\n except PageNotAnInteger:\n # If page not an Integer then deliver first page.\n products_list_p = paginator.page(1)\n except EmptyPage:\n # If page over the last result page, then deliver last result page.\n products_list_p = paginator.page(paginator.num_pages)\n\n context = {\n \"products_list\": products_list_p,\n 'paginate': True\n }\n return render(request, 'store/favorites.html', context)","def select_news(self):\n data = self.soup.findAll('item')\n for item in data:\n news_data = dict()\n for tag in ['title', 'link']:\n news_data[tag] = item.find(tag).get_text()\n\n news_data['pubDate'] = parse(item.find('pubDate').get_text())\n media = item.find('media:content')\n\n if media:\n news_data['media'] = media.get('url')\n else:\n news_data['media'] = None\n\n yield news_data","def home():\n stocks = preprocess()\n\n return render_template(\"main.html\",stocks=stocks)","def all_products_view(request):\n products = Product.objects.all()\n paginator = Paginator(products, 6)\n page_number = request.GET.get('page', 1)\n page = paginator.page(page_number)\n\n \"\"\"render a products html page and within that page we will have access to products, so all_products\"\"\"\n\n if page.has_next():\n\n next_url = f'?page={page.next_page_number()}'\n\n else:\n\n next_url = ''\n\n if page.has_previous():\n\n prev_url = f'?page={page.previous_page_number()}'\n\n else:\n\n prev_url = ''\n\n \n return render(request, 'products.html', {'page': page, 'next_page_url': next_url, 'prev_page_url': prev_url})","def get_queryset(self):\n return Article.objects.filter(\n pub_date__lte=timezone.now()\n ).order_by('-pub_date')[:5]","def test_toggle_featured(self):\n story = create_story(title=\"Test Story\", summary=\"Test Summary\",\n byline=\"Test Byline\", status='published',\n on_homepage=True)\n story2 = create_story(title=\"Test Related Story\", \n summary=\"Test Related Story Summary\",\n byline=\"Test Related Story Byline\",\n status='published',\n on_homepage=False)\n self.assertEqual(story.on_homepage, True)\n self.assertEqual(story2.on_homepage, False)\n toggle_featured(None, None,\n Story.objects.filter(pk__in=[story.pk, story2.pk]))\n story = Story.objects.get(pk=story.pk)\n story2 = Story.objects.get(pk=story2.pk)\n self.assertEqual(story.on_homepage, False)\n self.assertEqual(story2.on_homepage, True)","def test_blog_posts(self, client, site, homepage, blog_posts):\n response = client.get(homepage.relative_url(site))\n assertTemplateUsed(response, \"snippets/carousel.html\")\n\n # no posts featured, so most recent ones should be in context\n assert len(response.context[\"updates\"]) == 3\n\n # feature some posts; only those should be displayed\n announcement = blog_posts[\"announcement\"]\n project_feature = blog_posts[\"project_feature\"]\n announcement.featured = True\n project_feature.featured = True\n announcement.save()\n project_feature.save()\n response = client.get(homepage.relative_url(site))\n assert len(response.context[\"updates\"]) == 2\n assert announcement in response.context[\"updates\"]\n assert project_feature in response.context[\"updates\"]\n\n # should display short title, short description, and link\n assertContains(response, \"A Big Announcement!\")\n assertContains(response, \"We're making a big digital humanities announcement.\")\n assertContains(response, announcement.get_url())\n\n # unpublished posts shouldn't be displayed\n announcement.unpublish()\n response = client.get(homepage.relative_url(site))\n assert announcement not in response.context[\"updates\"]","def get_context_data(self, **kwargs):\n context = super(AudioListView, self).get_context_data(**kwargs)\n if AudioHome.objects:\n context['audio'] = AudioHome.objects.latest('pub_date')\n return context\n else:\n return []","def get_featured_projects(self):\n featured_projs = FeaturedProject.objects.order_by('id')[0:3]\n highlighted = []\n activities = personalize_activities_dict(self.request.user)\n try:\n for featured in featured_projs:\n try:\n activity = activities[featured.project.id_label]\n if featured.description:\n activity['commentary'] = featured.description\n highlighted.append(activity)\n except KeyError:\n pass\n return highlighted\n except (ValueError, TypeError):\n return []","def getItems(self): \n items = []\n if self.itemCount > 0:\n \n site = getSite()\n \n \n # Make string path relative to the site root\n # E.g. string path \"news\" becomes \"/yoursiteid/news\"\n site_path = site.getPhysicalPath();\n \n path = \"/\".join(site_path) + \"/\" + self.path \n \n #if self.itemPortalType2 != None:\n # types.append(self.itemPortalType2) \n \n #print \"Querying by:\" + type + \" \" + path\n content_by_type = self.context.portal_catalog(path={ \"query\": path, \"depth\" :9 }, \n sort_on=\"created\", \n sort_order=\"reverse\")[0:self.itemCount]\n\n \n items += [ brain.getObject() for brain in content_by_type ]\n\n return items","def on_fetched(self, doc):\n\n docs = doc[app.config[\"ITEMS\"]]\n for item in docs:\n build_custom_hateoas(\n {\n \"self\": {\n \"title\": item[\"_type\"],\n \"href\": \"/{}/{{_id}}\".format(item[\"_type\"]),\n }\n },\n item,\n )","def render_home_page(request, **kwargs):\n category = kwargs.get(\"category\", default=\"home\")\n context = {}\n template_name = \"resources/home_page.html\"\n all_technology = Technology.objects.filter(category=category)\n context[\"tech_cards\"] = all_technology.for_home_page()[\n : settings.DISPLAY_CARD_VALUE\n ]\n context[\"show_view_all_for_tech\"] = (\n True if all_technology.count() > settings.DISPLAY_CARD_VALUE else False\n )\n return render(request, template_name, context)","def general_news():\n\n return general_scraper(['http://mesva.univaq.it/'])","def news():\n\n # ensure parameters are present\n # geo = request.args.get(\"geo\")\n geo = '95060'\n if not geo:\n raise RuntimeError(\"missing geo\")\n\n # lookup articles and store them as JSON array\n article_list = lookup(geo)\n\n # TODO\n print(article_list)\n news = jsonify(article_list) \n print(news)\n # return render_template(\"index.html\")\n return article_list","def latest_blog_posts(self, request, *args, **kwargs):\n context = self.get_context(request, *args, **kwargs)\n context[\"latest_posts\"] = MyblogDetailPage.objects.live().public()[:1] \n return render(request, \"myblog/latest_posts.html\", context)","def show_all_posts():\n post = Post.query.all()\n\n return render_template('all-posts.html', post=post)","def get(self):\n\n self.render_posts()","def _fetch_items(self):\n url = self._api.router.publication['search'].format(\n project_id=self.project_id\n )\n res_data = self._api.post(url, data=self.search_param)\n self.total = res_data['total']\n self._items = (\n Publication(item, self.project_id)\n for item in res_data['hits']\n )\n div = self.total // self.search_param['limit']\n reste = self.total % self.search_param['limit']\n self.total_page = div\n if reste != 0: self.total_page += 1\n self.search_param = self.search_param.next_page()","def home_page_view(request):\n\n\t# Set the variable jse_articles to the output of the function get_stories().\n\tjse_articles = get_stories()\n\t# Commenting the below out due to 404 error on site\n\t#coindesk_articles = crypto_news()\n\n\t# Add the jse_articles variable to the views context dictionary for use in the template.\n\tcontext = {\n\t\t'jse_articles' : jse_articles,\t\n\t\t#'coindesk_articles': coindesk_articles,\n\t}\n\treturn render(request, 'home.html', context)","def newsList(request):\n\n news_count = New.objects.count() # Pocet vsech zaznamu novinek\n news_list = New.objects.all().order_by(\"date\") # Sort by date ... and only part of list\n # misto vsech zaznamu ziskat jen ty v intervalu start - stop -> API\n\n pictureOfWeek = PhotoOfWeek.objects.last()\n context = {'news_list': news_list, 'news_count': news_count, 'pictureOfWeek': pictureOfWeek}\n return render(request, 'news/newsList.html', context)","def index(request):\n form = SearchForm()\n \n form2 = SearchFormMini(request=request)\n objects = Apartament.objects.filter(show_on_start_page=True, is_published=True).all()\n\n return direct_to_template(request, 'start_page.html', {\n 'form': form,\n 'form2': form2,\n 'objects':objects,\n 'data': {'main_page': True},\n\t\t'main_text': get_object_or_404(StaticPage, pk=10)\n })","def is_featured(self, is_featured):\n self._is_featured = is_featured","def index():\n db = get_db()\n items = db.execute(\"select item_id, location from items where location != ''\").fetchall()\n return render_template(\"tracker/index.html\", items=items)","def features(request):\n # Order features by amount of upvotes\n features_list = Feature.objects.all().order_by('-upvotes')\n \n # Pagination for features\n page = request.GET.get('page', 1)\n paginator = Paginator(features_list, 10)\n try:\n features = paginator.page(page)\n except PageNotAnInteger:\n features = paginator.page(1)\n except EmptyPage:\n features = paginator.page(paginator.num_pages)\n \n # Display graphs\n chart_total_feature = FeaturesTotalChart() \n chart_feature_daily = FeaturesDailyStatus()\n chart_feature_weekly = FeaturesWeeklyStatus()\n chart_feature_monthly = FeaturesMonthlyStatus()\n \n return render(request, \"features.html\", {\n \"features\": features,\n 'chart_total_feature': chart_total_feature,\n 'chart_feature_daily': chart_feature_daily,\n 'chart_feature_weekly': chart_feature_weekly,\n 'chart_feature_monthly': chart_feature_monthly\n })","def show_blog_list():\r\n\tblog_list = Page.objects.filter(page_type=3).order_by('-created')[:4]\r\n\treturn {'blog_list': blog_list}","def home(request):\n cart = Cart(request)\n products = Product.objects.order_by('-created')[:3]\n last = Blog.objects.order_by('-posted')[:3]\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/index.html',\n {\n 'title':'СТО',\n 'cart': cart,\n 'last': last,\n 'products': products,\n 'year':datetime.now().year,\n }\n )","def index(request):\n return render(request, 'items/index.html', {\n 'globalvars': globalvars,\n 'fart': 'fart'\n })","def test_if_featured_apps_are_displayed_correctly(self):\n # She also see a section which seems to contain some packages\n # listed as 'Featured apps'.\n featured_header_text = self.browser.find_element_by_tag_name('h1').text\n self.assertEqual(\"Featured apps\", featured_header_text)\n\n # She notices that some apps appears there randomly\n app_link = self.browser.find_element_by_id(\"featured_app_href\")\n app_link = app_link.get_attribute(\"href\")\n app_pkgname = \"None\"\n\n i = 0\n for app in FeaturedApp.objects.all():\n app_name_from_link = app_link.split(\"/apps/\")[1]\n if app_name_from_link in app.component.type_id:\n app_type_id = app.component.type_id\n break\n\n self.assertIn(app_name_from_link, app_type_id)","def index(request):\n products = Product.objects.all()\n highlights = Original.objects.filter(status='h')\n context = {\n \"index_page\": \"active\",\n \"products\": products,\n \"highlights\": highlights,\n \"title\": \"Home\"\n }\n return render(request, \"index.html\", context)","def get(self, request, pk, *args, **kwargs):\n self.restaurant = get_object_or_404(Restaurant, id=pk)\n self.page_title = \"{} Information\".format(self.restaurant.name)\n\n def get_list_or_none(klass, *args, **kwargs):\n queryset = _get_queryset(klass)\n obj_list = list(queryset.filter(*args, **kwargs))\n if obj_list:\n return obj_list\n return []\n\n self.dishes = get_list_or_none(Dish, restaurant=self.restaurant)\n self.orders = get_list_or_none(Order, restaurant=self.restaurant)\n return super().get(request, *args, **kwargs)","def get_queryset(self):\n return self.queryset.filter(theme__contest__publish_date__lte=timezone.now())","def get_context(self, request, *args, **kwargs):\n\n context = super().get_context(request, *args, **kwargs)\n context['posts'] = BlogDetailPage.objects.live().public()\n return context"],"string":"[\n \"def featured(self):\\n \\n featured_content = []\\n \\n def rnd_content_items(return_items=1, **kwargs):\\n if 'limit' not in kwargs:\\n kwargs['limit'] = 3\\n # searching due_date limits results to only assignments; if we want\\n # all articles, then we don't want to limit to assignments\\n #if 'due_date' not in kwargs and kwargs.get('type') == \\\"assignment\\\":\\n # kwargs['due_date'] = \\\">now\\\"\\n \\n # AllanC - Because we were not getting new content on the site - if one of these returns gives back nothing, redo the query with a longer timeframe\\n # This shouldnt be to much of a problem as this is cached server side every 10 mins .,.. once we get popular it will always break on the first try\\n for days_jump in [i*7 for i in range(1,5)]:\\n kwargs['update_date'] = \\\">\\\"+str(now() - datetime.timedelta(days=days_jump)) \\n kwargs['exclude_content'] = \\\",\\\".join([str(content['id']) for content in featured_content])\\n content_list = content_search(**kwargs)['data']['list']\\n if len(content_list['items']) >= return_items:\\n break\\n log.info('Too few content retured for %s - attempting query again with day range of %d' % (kwargs, days_jump))\\n \\n random.shuffle( content_list['items'] )\\n content_list['items'] = content_list['items'][:return_items]\\n #content_list['count'] = len(content_list['items'])\\n for content_item in content_list['items']:\\n featured_content.append(content_item)\\n \\n return content_list\\n \\n \\n #return to_apilist(featured_content, obj_type='contents') # AllanC - a liniear list of featured contebt\\n \\n # Sponsored content dictionary\\n #s = {}\\n #s['sponsored_responded' ] = rnd_content_items(return_items=1, sort='-num_responses', limit=3 )\\n #s['sponsored_assignment' ] = rnd_content_items(return_items=1, sort='-views', type='assignment', limit=3 )\\n \\n # Featured content dictionary\\n f = {}\\n ##f['top_viewed_assignments' ] = rnd_content_items(return_items=2, sort='-views' , type='assignment', limit=5)\\n f['recent' ] = rnd_content_items(return_items=3, sort='-update_date' , limit=5)\\n f['most_responses' ] = rnd_content_items(return_items=3, sort='-num_responses', limit=5)\\n if request.GET.get(\\\"location\\\"):\\n f['near_me' ] = rnd_content_items(return_items=3, sort='distance' , limit=5, location=request.GET.get(\\\"location\\\"))\\n f['recent_assignments' ] = rnd_content_items(return_items=3, sort='-update_date' , limit=5, list='assignments_active')\\n \\n # New members\\n m ={}\\n m['new_members'] = member_search(sort='-join_date', type='user' , limit=3)['data']['list']\\n m['new_groups' ] = member_search(sort='-join_date', default_content_visibility='public', limit=3)['data']['list']\\n \\n # AllanC - HACK HACK!!!\\n # The count from the query using the default_content_visibility='public' is wrong .. the content is correct .. the count is broken\\n # Set the count FOR THIS LIST ONLY to match the items returned\\n m['new_groups' ]['count'] = len(m['new_groups' ]['items'])\\n \\n return action_ok(\\n data={\\n #'sponsored' : s,\\n 'featured' : f,\\n 'members' : m,\\n }\\n )\",\n \"def homepage(request):\\n limit = 7\\n featured_marketer_list = (\\n PimpUser.objects\\n .filter(featured=True)\\n .filter(usertype=PimpUser.MARKETER)\\n .order_by('-date_joined')[:limit]\\n )\\n featured_cause_list = (\\n PimpUser.objects\\n .filter(featured=True)\\n .filter(usertype=PimpUser.CAUSE)\\n .order_by('-date_joined')[:limit]\\n )\\n news_post_list = (\\n NewsPost.objects\\n .order_by('-created_at')[:3]\\n )\\n\\n context = {\\n 'featured_marketer_list': featured_marketer_list,\\n 'featured_cause_list': featured_cause_list,\\n 'news_post_list': news_post_list,\\n }\\n\\n return render(\\n request,\\n 'index.html',\\n context\\n )\",\n \"def featured(request):\\n\\n artist = Artist.objects.all()\\n\\n \\n\\n context = {\\n 'artist': artist,\\n }\\n\\n return render(request, 'artist/featured.html', context)\",\n \"def get_queryset(self, *args, **kwargs):\\n return Product.objects.featured()\",\n \"def get_featured_content():\\n\\n return FeatureHistory.objects.filter(featured=True).order_by('updated_at')[:3]\",\n \"def test_get_featured_front_page(self):\\r\\n\\r\\n app = self.create_app(None)\\r\\n app.owner = self.user\\r\\n db.session.add(app)\\r\\n featured = Featured(app=app)\\r\\n db.session.add(featured)\\r\\n db.session.commit()\\r\\n\\r\\n featured = cached_apps.get_featured_front_page()\\r\\n\\r\\n assert len(featured) is 1, featured\",\n \"def test_get_featured_front_page_only_returns_featured(self):\\r\\n\\r\\n featured_app = self.create_app(None)\\r\\n non_featured_app = self.create_app(None)\\r\\n non_featured_app.name = 'other_app'\\r\\n non_featured_app.short_name = 'other_app'\\r\\n featured_app.owner = self.user\\r\\n non_featured_app.owner = self.user\\r\\n db.session.add(featured_app)\\r\\n db.session.add(non_featured_app)\\r\\n featured = Featured(app=featured_app)\\r\\n db.session.add(featured)\\r\\n db.session.commit()\\r\\n\\r\\n featured = cached_apps.get_featured_front_page()\\r\\n\\r\\n assert len(featured) is 1, featured\",\n \"def get_feature(request):\\n feature = Feature.objects.filter(published_date__lte=timezone.now()\\n ).order_by('published_date')\\n return render(request, \\\"feature.html\\\", {'feature': feature})\",\n \"def test_featured_pages(self, client, site, homepage):\\n # create pages with test content\\n about = ContentPage(title=\\\"about\\\", slug=\\\"about\\\", description=\\\"lorem ipsum\\\")\\n homepage.add_child(instance=about)\\n homepage.save()\\n consult = ContentPage(title=\\\"consult\\\", slug=\\\"consult\\\")\\n homepage.add_child(instance=consult)\\n homepage.save()\\n response = client.get(homepage.relative_url(site))\\n # should render featured pages section\\n assertTemplateUsed(response, \\\"cdhpages/snippets/featured_pages.html\\\")\\n # should include description from about page\\n assertContains(response, \\\"lorem ipsum\\\")\\n # should include link to about page\\n assertContains(response, about.get_url())\\n # should include link to consult page\\n assertContains(response, consult.get_url())\",\n \"def getItems(self): \\n \\n kwargs = {}\\n kwargs[\\\"max\\\"] = {\\\"deals.mocality.co.ke\\\" : 1, \\\"www.zetu.co.ke\\\":1, \\\"manual\\\":2}\\n from frontpage import get_deals\\n items = get_deals(self.context, self.request, **kwargs)\\n return items\",\n \"def add_frontpage_contents(sender, **kwds):\\n\\n context = kwds['context']\\n if 'newsitems' not in context:\\n context['newsitems'] = News.query.latest().limit(5).all()\\n return context\",\n \"def featured_news(self):\\n return self.split(self.settings.featured_news)\",\n \"def home():\\n\\n lst = item_list()\\n return render_template('index.html', sell_flag=0, items=lst)\",\n \"def get_top_featured_entries(number=5):\\n return list(Entry.published.filter(featured=True)[:number])\",\n \"def getItems(self): \\n if self.itemCount > 0:\\n \\n site = getSite()\\n \\n \\n # Make string path relative to the site root\\n # E.g. string path \\\"news\\\" becomes \\\"/yoursiteid/news\\\"\\n site_path = site.getPhysicalPath();\\n \\n path = \\\"/\\\".join(site_path) + \\\"/\\\" + self.path \\n \\n types = [self.itemPortalType]\\n \\n items = []\\n \\n #if self.itemPortalType2 != None:\\n # types.append(self.itemPortalType2) \\n \\n #print \\\"Querying by:\\\" + type + \\\" \\\" + path\\n content_by_type = self.context.portal_catalog(path={ \\\"query\\\": path, \\\"depth\\\" :9 }, \\n portal_type=self.itemPortalType, \\n sort_on=\\\"created\\\", \\n sort_order=\\\"reverse\\\")[0:self.itemCount]\\n\\n content_by_type = list(content_by_type)\\n \\n if self.itemPortalType2 != None:\\n content_by_type2 = self.context.portal_catalog(path={ \\\"query\\\": path, \\\"depth\\\" :9 }, \\n portal_type=self.itemPortalType2, \\n sort_on=\\\"created\\\", \\n sort_order=\\\"reverse\\\")[0:self.itemCount]\\n\\n content_by_type += list(content_by_type2)\\n\\n \\n items += [ brain.getObject() for brain in content_by_type ]\\n else:\\n items = []\\n \\n #if self.title == \\\"Daily deals\\\":\\n # import pdb ; pdb.set_trace()\\n \\n # XXX: custom hack for deals\\n def is_expired_deal(i):\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n if hasattr(i, \\\"validUntil\\\"):\\n now = datetime.datetime.utcnow()\\n if now > i.validUntil:\\n return True\\n \\n return False\\n \\n items = [ i for i in items if not is_expired_deal(i) ]\\n \\n return items\",\n \"def myfood(request):\\n my_fav = Favorite.objects.filter(user=request.user)\\n my_fav_products = [fav.product_favorite for fav in my_fav]\\n paginator = Paginator(my_fav_products, 6)\\n page_number = request.GET.get(\\\"page\\\")\\n page_obj = paginator.get_page(page_number)\\n context = {\\n \\\"favorites\\\": page_obj\\n }\\n return render(request, \\\"myfood.html\\\", context)\",\n \"def test_empty_featured_pages(self, client, site, homepage):\\n # no about or consult page: nothing rendered\\n response = client.get(homepage.relative_url(site))\\n assertTemplateNotUsed(response, \\\"cdhpages/snippets/featured_pages.html\\\")\\n # only about page: nothing rendered\\n about = ContentPage(title=\\\"about\\\", slug=\\\"about\\\", body=\\\"\\\")\\n homepage.add_child(instance=about)\\n homepage.save()\\n response = client.get(homepage.relative_url(site))\\n assertTemplateNotUsed(response, \\\"cdhpages/snippets/featured_pages.html\\\")\\n # only consults page is live: nothing rendered\\n about.unpublish()\\n consult = ContentPage(title=\\\"consult\\\", slug=\\\"consult\\\")\\n homepage.add_child(instance=consult)\\n homepage.save()\\n response = client.get(homepage.relative_url(site))\\n assertTemplateNotUsed(response, \\\"cdhpages/snippets/featured_pages.html\\\")\\n # both pages live; should render\\n about.save_revision().publish()\\n response = client.get(homepage.relative_url(site))\\n assertTemplateUsed(response, \\\"cdhpages/snippets/featured_pages.html\\\")\",\n \"def index(request):\\n featured_collections = Collection.objects.order_by('creation_date')\\n template = loader.get_template('movies/index.html')\\n context = {\\n 'featured_collections': featured_collections,\\n }\\n return HttpResponse(template.render(context, request))\",\n \"def homepage():\\n prods = db(db.product.prod_starred == True).select()\\n return dict(\\n prods = prods\\n )\",\n \"def home(request):\\n featured_image = ProductFeatured.objects.first()\\n products = Product.objects.all().order_by('?')\\n const_products = Product.objects.filter(section='m')\\n home_products = Product.objects.filter(section='h')\\n elect_products = Product.objects.filter(section='e')\\n art_products = Product.objects.filter(section='a')\\n furn_products = Product.objects.filter(section='f')\\n bed_products = Product.objects.filter(section='s')\\n kitch_products = Product.objects.filter(section='k')\\n book_products = Product.objects.filter(section='b')\\n drawer_products = Product.objects.filter(section='d')\\n cabin_products = Product.objects.filter(section='c')\\n dress_products = Product.objects.filter(section='Dressers')\\n Oven_products = Product.objects.filter(section='o')\\n ref_products = Product.objects.filter(section='r')\\n vac_products = Product.objects.filter(section='v')\\n gas_products = Product.objects.filter(section='g')\\n smart_products = Product.objects.filter(section='p')\\n trend_products = ProductTrend.objects.all()\\n\\n context = {\\n \\\"featured_image\\\": featured_image,\\n \\\"products\\\": products,\\n \\\"const_products\\\": const_products,\\n \\\"home_products\\\": home_products,\\n \\\"elect_products\\\": elect_products,\\n \\\"art_products\\\": art_products,\\n \\\"furn_products\\\": furn_products,\\n \\\"bed_products\\\": bed_products,\\n \\\"book_products\\\": book_products,\\n \\\"kitch_products\\\": kitch_products,\\n \\\"drawer_products\\\": drawer_products,\\n \\\"cabin_products\\\": cabin_products,\\n \\\"dress_products\\\": dress_products,\\n \\\"Oven_products\\\": Oven_products,\\n \\\"ref_products\\\": ref_products,\\n \\\"vac_products\\\": vac_products,\\n \\\"gas_products\\\": gas_products,\\n \\\"smart_products\\\":smart_products,\\n \\\"trend_products\\\":trend_products\\n }\\n\\n if request.GET:\\n print request.GET\\n\\n return render(request, \\\"home.html\\\", context)\",\n \"def home():\\r\\n\\r\\n page = 1\\r\\n per_page = current_app.config.get('APPS_PER_PAGE')\\r\\n if per_page is None: # pragma: no cover\\r\\n per_page = 5\\r\\n d = {'featured': cached_apps.get_featured_front_page(),\\r\\n 'top_apps': cached_apps.get_top(),\\r\\n 'top_users': None}\\r\\n\\r\\n # Get all the categories with apps\\r\\n categories = cached_cat.get_used()\\r\\n d['categories'] = categories\\r\\n d['categories_apps'] = {}\\r\\n for c in categories:\\r\\n tmp_apps, count = cached_apps.get(c['short_name'], page, per_page)\\r\\n d['categories_apps'][str(c['short_name'])] = tmp_apps\\r\\n\\r\\n # Add featured\\r\\n tmp_apps, count = cached_apps.get_featured('featured', page, per_page)\\r\\n if count > 0:\\r\\n featured = model.category.Category(name='Featured', short_name='featured')\\r\\n d['categories'].insert(0,featured)\\r\\n d['categories_apps']['featured'] = tmp_apps\\r\\n\\r\\n if current_app.config['ENFORCE_PRIVACY'] and current_user.is_authenticated():\\r\\n if current_user.admin:\\r\\n d['top_users'] = cached_users.get_top()\\r\\n if not current_app.config['ENFORCE_PRIVACY']:\\r\\n d['top_users'] = cached_users.get_top()\\r\\n return render_template('/home/index.html', **d)\",\n \"def show_news_list():\\r\\n\\tnews_list = Page.objects.filter(tags='news').order_by('-created')\\r\\n\\treturn {'news_list': news_list}\",\n \"def get_local_news_items(self):\\n catalog = api.portal.get_tool(name='portal_catalog')\\n default_lang = api.portal.get_tool(\\n \\\"portal_languages\\\").getDefaultLanguage()\\n results = catalog.searchResults(\\n portal_type=\\\"News Item\\\",\\n sort_on=\\\"effective\\\",\\n sort_order=\\\"descending\\\",\\n review_state='published',\\n Language=[default_lang, ''],\\n )\\n return results\",\n \"def explore_view(request):\\r\\n # explore items\\r\\n user = request.user.userprofile\\r\\n items = Item.objects.explore(user)\\r\\n context = {'items':items}\\r\\n return render(request, 'explore/explore.html', context)\",\n \"def getContentTypeFeatured(ct):\",\n \"def test_get_featured_front_page_not_returns_hidden_apps(self):\\r\\n\\r\\n featured_app = self.create_app(None)\\r\\n featured_app.owner = self.user\\r\\n featured_app.hidden = 1\\r\\n db.session.add(featured_app)\\r\\n featured = Featured(app=featured_app)\\r\\n db.session.add(featured)\\r\\n db.session.commit()\\r\\n\\r\\n featured = cached_apps.get_featured_front_page()\\r\\n\\r\\n assert len(featured) is 0, featured\",\n \"def show_homepage():\\n max_items = 5\\n all_categories = session.execute(\\n 'SELECT category.name, category.id, count(item.id) AS item_count '\\n 'FROM category '\\n 'LEFT JOIN item ON category.id = item.category_id '\\n 'GROUP BY category.name, category.id')\\n items = session.query(Item, Category).join(Category).order_by(Item.time_updated.desc(), Item.time_created.desc(),\\n Item.name).limit(5).all()\\n return render_template('categories_latest.html',\\n all_categories=all_categories,\\n items=items,\\n max_items=max_items,\\n login_session=login_session)\",\n \"def index(request, archive=False):\\n context = {'archive':archive}\\n posts = Post.objects.all()\\n if not archive:\\n posts = posts[:10]\\n context['posts'] = posts\\n if request.user.is_authenticated():\\n #These are the new news items the logged in user has\\n context['new_posts'] = NewBlog.objects.filter(user=request.user)\\n return render(request, 'blog/index.html', context)\",\n \"def index(page):\\r\\n if cached_apps.n_featured() > 0:\\r\\n return app_index(page, cached_apps.get_featured, 'featured',\\r\\n True, False)\\r\\n else:\\r\\n categories = cached_cat.get_all()\\r\\n cat_short_name = categories[0].short_name\\r\\n return redirect(url_for('.app_cat_index', category=cat_short_name))\",\n \"def get(self, request, *args, **kwargs):\\n my_normal_post_lists = NormalPosts.objects.filter(uploded_by=request.user.normalprofile).order_by(\\\"-id\\\")\\n return render(request, self.template_name, {\\n 'my_normal_post_lists': my_normal_post_lists,\\n })\",\n \"def top_menu_items(request):\\n return {\\n 'top_menu_items': settings.TOP_MENU_ITEMS\\n }\",\n \"def get_items(id_name, request, client):\\n result = client.quick_search(request)\\n \\n items_pages = []\\n limit_to_x_pages = None\\n for page in result.iter(limit_to_x_pages):\\n items_pages.append(page.get())\\n\\n items = [item for page in items_pages for item in page['features']]\\n \\n \\n return (id_name, items)\",\n \"def full_listing(request, urlname):\\n\\tif request.user.is_authenticated():\\n\\t\\tblog = Blog.qa_objects.get(urlname=urlname)\\n\\t\\tposts = BlogEntry.qa_objects.filter(blog=blog, posting_time__lte=datetime.now()).order_by('-posting_time')\\n\\telse:\\n\\t\\tblog = Blog.objects.get(urlname=urlname)\\n\\t\\tposts = BlogEntry.objects.filter(blog=blog).order_by('-posting_time')\\n\\treturn render_to_response('blogs/full.html', {'blog': blog, 'posts': posts}, context_instance=RequestContext(request))\",\n \"def get_featured_articles(request):\\n try:\\n count = 1\\n if 'count' in request.POST and int(request.POST['count']):\\n count = int(request.POST['count'])\\n\\n newest_list = []\\n for article in Article.objects.order_by('-modified')[:count]:\\n newest_list.append(article.dump_to_dict())\\n\\n popular_list = []\\n for article in Article.objects.order_by('-views')[:count]:\\n popular_list.append(article.dump_to_dict())\\n\\n return format_ajax_response(True, \\\"Featured articles retrieved successfully.\\\", {'newest': newest_list,'popular': popular_list})\\n except Exception as ex:\\n logger.error(\\\"Failed to get_featured_articles: %s\\\" % ex)\\n return format_ajax_response(False, \\\"There was an error retrieving the featured articles.\\\")\",\n \"def is_featured(self):\\n return self._is_featured\",\n \"def get_all_posts(request, show_only=None):\\n if(show_only == None):\\n posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('-is_important', '-published_date')\\n title = \\\"All Posts\\\"\\n else:\\n posts = Post.objects.filter(post_type__exact=show_only.name).filter(published_date__lte=timezone.now()).order_by('-is_important', '-published_date')\\n title = show_only.name\\n return render(request, \\\"posts.html\\\", {\\\"posts\\\": posts, \\\"title\\\": title})\",\n \"def new_in(request, search = None, page = 1):\\n\\tcontext_dict = {}\\n\\titems = []\\t\\n\\tsearch = search\\n\\n\\tif search != None:\\n\\t\\tsearch = search.split(\\\" \\\")\\n\\t\\ttoAdd = []\\n\\t\\t\\n\\t\\tfor word in search:\\n\\t\\t\\ttoAdd += Item.objects.filter(Q(description__contains = word ) | Q(title__contains = word)\\n\\t\\t\\t\\t| Q(category = word) | Q(section = word))\\n\\t\\t\\t\\n\\t\\t\\tfor item in toAdd:\\n\\t\\t\\t\\tif (date.today()- item.datePosted).days <= 7:\\n\\t\\t\\t\\t\\titems += [item]\\n\\t\\t\\n\\t\\tsearchS = \\\"_\\\".join(search)\\n\\t\\tmaxi = 0\\n\\t\\t\\n\\t\\tfor item in items:\\n\\t\\t\\tif item.price > maxi:\\n\\t\\t\\t\\tmaxi = item.price\\n\\t\\t\\n\\t\\tserch_form = Search_bar()\\n\\t\\tcontext_dict['search_bar'] = search_form\\n\\t\\tcontext_dict['maxi'] = maxi + 5\\n\\t\\tcontext_dict['search'] = searchS\\n\\t\\tcontext_dict['page'] = page\\n\\t\\tcontext_dict['items'] = items\\n\\t\\tcontext_dict['pages'] = int(len(items)/6)\\n\\t\\tcontext_dict['min'] = 6 * (int(page) - 1)\\n\\t\\tcontext_dict['max'] = 6 * (int(page))\\n\\t\\t\\n\\t\\treturn render(request, 'tailored/shop_bootstrap.html',context_dict)\\n\\telse:\\n\\t\\treturn home_page(request)\",\n \"def get_news(request):\\n return get_all_posts(request, PostType.NEWS)\",\n \"def test_empty_posts(self, client, site, homepage):\\n response = client.get(homepage.relative_url(site))\\n assertTemplateNotUsed(response, \\\"snippets/carousel.html\\\")\",\n \"def index(request):\\n\\n # Fetches the latest 3 listings published\\n listings = Listing.objects.order_by('-list_date').filter(is_published=True)[:3]\\n context = {\\n 'listings': listings,\\n 'state_choices': state_choices,\\n 'bedroom_choices': bedroom_choices,\\n 'price_choices': price_choices\\n }\\n return render(request, \\\"pages/index.html\\\", context)\",\n \"def show_homepage():\\n\\n posts = Post.query.order_by(Post.created_at.desc()).limit(5).all()\\n\\n return render_template('index.html', posts=posts)\",\n \"def favorites(request):\\n cur_user = request.user # Gets the current logged-in user\\n fav_products = Favorite.objects.all() # Gets all \\\"Favorite\\\" model objects\\n\\n # Gets the favorites of the current user\\n fav_prod_filtered = fav_products.filter(users_id=cur_user).order_by('-id')\\n\\n # Adds pagination for up to 6 products per page\\n paginator = Paginator(fav_prod_filtered, 6)\\n page = request.GET.get('page')\\n\\n try:\\n products = paginator.page(page)\\n except PageNotAnInteger:\\n products = paginator.page(1)\\n except EmptyPage:\\n products = paginator.page(paginator.num_pages)\\n\\n context = {\\n 'favorites': products,\\n 'paginate': True,\\n }\\n return render(request, 'favorites/favorites.html', context)\",\n \"def get_featured_photos(self, count = 30, page = 1):\\n uri = 'photos/featured'\\n options = { 'per_page': count, 'page': page }\\n return self.make_request(uri, options)\",\n \"def _get_catalog_results(self, featured=False, **kw):\\n if 'context' in kw.keys():\\n kw['path'] = {'query': '/'.join(kw['context'].getPhysicalPath())}\\n\\n types = ('Article', 'Blog Entry', )\\n states = ('published', )\\n sort = 'Date'\\n \\n results = self.qrymethod(portal_type=types,\\n review_state=states,\\n is_featured=featured,\\n sort_on=sort, \\n sort_order='descending',\\n **kw)\\n\\n return results\",\n \"def get_featured_websites(self):\\n REGION_TAG = 'featured-website-%s' % self.request.REGION.slug\\n region_filter = es_filter.Term(tags=REGION_TAG)\\n GLOBAL_TAG = 'featured-website'\\n global_filter = es_filter.Term(tags=GLOBAL_TAG)\\n mow_query = query.Q(\\n 'function_score',\\n filter=es_filter.Bool(should=[region_filter, global_filter]),\\n functions=[\\n SF('random_score', seed=self._get_daily_seed()),\\n es_function.BoostFactor(value=100.0, filter=region_filter)\\n ],\\n )\\n es = Search(using=WebsiteIndexer.get_es())[:11]\\n results = es.query(mow_query).execute().hits\\n return ESWebsiteSerializer(results, many=True).data\",\n \"def get_context(self, request, *args, **kwargs):\\n context = super().get_context(request, *args, **kwargs)\\n context[\\\"page_title\\\"] = self.title\\n\\n categories = NewsCategory.objects.all().order_by(\\\"category\\\")\\n context[\\\"categories\\\"] = categories\\n\\n # Check for category\\n if \\\"category\\\" in kwargs:\\n category = NewsCategory.objects.filter(\\n slug=kwargs[\\\"category\\\"],\\n ).first()\\n news_items = (\\n NewsPage.objects.filter(\\n news_categories__news_category_id__in=[\\n category.pk,\\n ],\\n )\\n .live()\\n .public()\\n .order_by(\\n \\\"-pinned_on_home\\\",\\n \\\"home_news_order_pages__order\\\",\\n \\\"-first_published_at\\\",\\n )\\n )\\n\\n if category.lead_story:\\n news_items = news_items.exclude(\\n pk=category.lead_story.pk,\\n )\\n\\n context[\\\"category\\\"] = category\\n else:\\n # Get all posts\\n news_items = (\\n NewsPage.objects.live()\\n .public()\\n .order_by(\\n \\\"-pinned_on_home\\\",\\n \\\"home_news_order_pages__order\\\",\\n \\\"-first_published_at\\\",\\n )\\n )\\n\\n featured_page = NewsPage.objects.filter(\\n featured_on_news_home=True,\\n ).first()\\n\\n if featured_page:\\n news_items = news_items.exclude(\\n pk=featured_page.pk,\\n )\\n\\n # Paginate all posts by 2 per page\\n paginator = Paginator(news_items, 9)\\n # Try to get the ?page=x value\\n page = int(request.GET.get(\\\"page\\\", 1))\\n\\n try:\\n # If the page exists and the ?page=x is an int\\n posts = paginator.page(page)\\n except EmptyPage:\\n # If the ?page=x is out of range (too high most likely)\\n # Then return the last page\\n posts = paginator.page(paginator.num_pages)\\n\\n start = 1\\n total_shown = 10\\n\\n if paginator.num_pages < total_shown:\\n total_shown = paginator.num_pages\\n\\n if page > 9:\\n start = page - 7\\n total_shown = 10\\n\\n if (page + 2) > paginator.num_pages:\\n start = paginator.num_pages - 9\\n\\n context[\\\"pagination_range\\\"] = range(start, (start + total_shown))\\n\\n # \\\"posts\\\" will have child pages; you'll need to use .specific in the templates\\n # in order to access child properties, such as youtube_video_id and subtitle\\n context[\\\"posts\\\"] = posts\\n\\n return context\",\n \"def featured():\\n try:\\n where = []\\n for badval in [\\\"Unlisted\\\", \\\"Archived\\\", \\\"Deleted\\\"]:\\n where.append(\\\"featured != \\\\\\\"\\\" + badval + \\\"\\\\\\\"\\\")\\n where = \\\"WHERE \\\" + \\\" AND \\\".join(where)\\n tls = dbacc.query_entity(\\\"Timeline\\\", where)\\n except ValueError as e:\\n return util.serve_value_error(e)\\n return util.respJSON(tls)\",\n \"def get_custom_feeds(request):\\n start = int(request.paginate_number) * 10\\n end = start + 10\\n feeds = Feed.objects.all().order_by('-id')[start: end]\\n return get_feed_list(feeds)\",\n \"def get_featured(category, page=1, per_page=5):\\r\\n\\r\\n count = n_featured()\\r\\n\\r\\n sql = text('''SELECT app.id, app.name, app.short_name, app.info, app.created,\\r\\n app.description,\\r\\n \\\"user\\\".fullname AS owner FROM app, featured, \\\"user\\\"\\r\\n WHERE app.id=featured.app_id AND app.hidden=0\\r\\n AND \\\"user\\\".id=app.owner_id GROUP BY app.id, \\\"user\\\".id\\r\\n OFFSET(:offset) LIMIT(:limit);\\r\\n ''')\\r\\n offset = (page - 1) * per_page\\r\\n results = db.engine.execute(sql, limit=per_page, offset=offset)\\r\\n apps = []\\r\\n for row in results:\\r\\n app = dict(id=row.id, name=row.name, short_name=row.short_name,\\r\\n created=row.created, description=row.description,\\r\\n overall_progress=overall_progress(row.id),\\r\\n last_activity=pretty_date(last_activity(row.id)),\\r\\n last_activity_raw=last_activity(row.id),\\r\\n owner=row.owner,\\r\\n featured=row.id,\\r\\n info=dict(json.loads(row.info)))\\r\\n apps.append(app)\\r\\n return apps, count\",\n \"def recipes_list(request):\\n recipes = Recipe.objects.all().order_by('published_date')\\n\\n # prawidlowy sposob zbierania URLa - object.image.url\\n # recipe = recipes[0]\\n # print(\\\"path: \\\" + recipe.image.url)\\n\\n paginator = Paginator(recipes, INITIAL_PAGE_SIZE)\\n page = paginator.page(1)\\n\\n context = {\\n 'page': page,\\n 'display_likes': True,\\n }\\n\\n return render(request, 'recipes_index.html', context)\",\n \"def get(self):\\n return get_all_posts()\",\n \"def __folha(soup):\\n news = []\\n\\n items = soup.find('li', class_='c-most-popular__item')\\n\\n for item in items:\\n div = item.find('div', class_='c-most-popular__content')\\n link = div.a['href']\\n title = div.a.string\\n news.append(\\n dict(title=title, link=replace_original_link_with_outline_call(link)))\\n\\n return news\",\n \"def education_post_list(request):\\n posts = EducationBlogPost.objects.filter(published_date__lte=timezone.now()\\n ).order_by('-published_date')\\n return render(request, \\\"education_center/education_blogposts.html\\\", {'posts': posts})\",\n \"def item_extra_kwargs(self, obj):\\n result = {'tags':obj.get_meta_keywords(),\\n 'short_description':obj.get_meta_description()[:90] if obj.get_meta_description() else '',\\n 'image_url':''}\\n try:\\n page_rss_feed = PageRSSFeed.objects.get(page=obj)\\n result['short_description']= page_rss_feed.short_description[:90]\\n result['image_url']= page_rss_feed.image_url\\n except PageRSSFeed.DoesNotExist:\\n pass\\n return result\",\n \"def newsfeed(request):\\n article_list = Article.objects.order_by('published_date')\\n context = {'article_list': article_list}\\n return render(request, 'sacms/newsfeed.html', context)\",\n \"def show_home():\\r\\n posts = Post.query.order_by(Post.created_at.desc()).limit(5).all()\\r\\n\\r\\n return render_template('home.html', posts=posts)\",\n \"def articles(self):\\n return self.get_queryset().filter(content_type__model='article').order_by('-articles__published_at')\",\n \"def item_detail(request, item_id):\\n # Select product based on URL param\\n item = SELECT('item', where=f'id = {item_id}', _print=False)\\n\\n context = {\\n 'item': item,\\n 'photos': [item['photo_primary']] + item['photos']\\n }\\n return render(request, 'item_detail.html', context)\",\n \"def GET_front_recent_posts(self, *a, **kw):\\r\\n # Server side cache is also invalidated when new article is posted\\r\\n return self.render_cached('recent-promoted', RecentPromotedArticles, g.side_posts_max_age)\",\n \"def all_items(request):\\n\\n items = Product.objects.all()\\n query = None\\n category = None\\n sort = None\\n direction = None\\n\\n if request.GET:\\n if 'sort' in request.GET:\\n sortkey = request.GET['sort']\\n sort = sortkey\\n if sortkey == 'name':\\n sortkey = 'lower_name'\\n items = items.annotate(lower_name=Lower('name'))\\n if sortkey == 'category':\\n sortkey = 'category__name'\\n if 'direction' in request.GET:\\n direction = request.GET['direction']\\n if direction == 'desc':\\n sortkey = f'-{sortkey}'\\n items = items.order_by(sortkey)\\n\\n if 'category' in request.GET:\\n categories = request.GET['category'].split(',')\\n items = items.filter(category__name__in=categories)\\n categories = Category.objects.filter(name__in=categories)\\n\\n\\n if 'q' in request.GET:\\n query = request.GET['q']\\n if not query:\\n messages.error(request, \\\"Please input the item you are looking for\\\")\\n return redirect(reverse('items'))\\n \\n queries = Q(name__icontains=query) | Q(description__icontains=query)\\n items = items.filter(queries)\\n \\n current_sorting = f'{sort}_{direction}'\\n\\n context = {\\n 'products': items,\\n 'search_term': query,\\n 'current_categories': category,\\n 'current_sorting': current_sorting,\\n }\\n\\n return render(request, 'products/items.html', context)\",\n \"def featured(app_id=None):\\r\\n try:\\r\\n categories = cached_cat.get_all()\\r\\n\\r\\n if request.method == 'GET':\\r\\n apps = {}\\r\\n for c in categories:\\r\\n n_apps = cached_apps.n_count(category=c.short_name)\\r\\n apps[c.short_name], n_apps = cached_apps.get(category=c.short_name,\\r\\n page=1,\\r\\n per_page=n_apps)\\r\\n return render_template('/admin/applications.html', apps=apps,\\r\\n categories=categories)\\r\\n else:\\r\\n app = db.session.query(model.app.App).get(app_id)\\r\\n if app:\\r\\n if request.method == 'POST':\\r\\n cached_apps.reset()\\r\\n f = model.featured.Featured()\\r\\n f.app_id = app_id\\r\\n require.app.update(app)\\r\\n # Check if the app is already in this table\\r\\n tmp = db.session.query(model.featured.Featured)\\\\\\r\\n .filter(model.featured.Featured.app_id == app_id)\\\\\\r\\n .first()\\r\\n if (tmp is None):\\r\\n db.session.add(f)\\r\\n db.session.commit()\\r\\n return json.dumps(f.dictize())\\r\\n else:\\r\\n msg = \\\"App.id %s alreay in Featured table\\\" % app_id\\r\\n return format_error(msg, 415)\\r\\n if request.method == 'DELETE':\\r\\n cached_apps.reset()\\r\\n f = db.session.query(model.featured.Featured)\\\\\\r\\n .filter(model.featured.Featured.app_id == app_id)\\\\\\r\\n .first()\\r\\n if (f):\\r\\n db.session.delete(f)\\r\\n db.session.commit()\\r\\n return \\\"\\\", 204\\r\\n else:\\r\\n msg = 'App.id %s is not in Featured table' % app_id\\r\\n return format_error(msg, 404)\\r\\n else:\\r\\n msg = 'App.id %s not found' % app_id\\r\\n return format_error(msg, 404)\\r\\n except Exception as e: # pragma: no cover\\r\\n current_app.logger.error(e)\\r\\n return abort(500)\",\n \"def index(request):\\n\\n last_six = Property.objects.order_by('created')[:6]\\n last_six_in_ascending_order = reversed(last_six)\\n\\n carousel_items = Property.objects.order_by('rating')[:5]\\n\\n context = {\\n 'properties': last_six_in_ascending_order,\\n 'carousel_items': carousel_items,\\n }\\n\\n return render(request, 'home/index.html', context)\",\n \"def homepage():\\n\\n general_news=get_sources('general')\\n business_news=get_sources('business')\\n sports_news=get_sources('sports')\\n return render_template('sources.html',general=general_news, business=business_news, sports=sports_news)\",\n \"def thumbnails_urls(request):\\n return {'thumbnails_urls': ThumbnailsUrls()}\",\n \"def all_news(request):\\n\\n all_news = News.objects.all().order_by(\\\"-date_added\\\")\\n context = {\\n 'news': all_news,\\n 'show_without_bag': True\\n }\\n return render(request, 'news/news.html', context)\",\n \"def featured_services(self):\\n return self.split(self.settings.featured_services)\",\n \"def home():\\n categories = session.query(Category).all()\\n latest_items = session.query(Item).order_by(\\n Item.created_date.desc()).limit(ITEM_COUNT).all()\\n return render_template(\\n 'views/catalog.html',\\n categories=categories,\\n latest_items=latest_items,\\n user_in_session=user_in_session())\",\n \"def test_get_featured_front_page_returns_required_fields(self):\\r\\n\\r\\n app = self.create_app(None)\\r\\n app.owner = self.user\\r\\n db.session.add(app)\\r\\n featured = Featured(app=app)\\r\\n db.session.add(featured)\\r\\n db.session.commit()\\r\\n fields = ('id', 'name', 'short_name', 'info', 'n_volunteers', 'n_completed_tasks')\\r\\n\\r\\n featured = cached_apps.get_featured_front_page()[0]\\r\\n\\r\\n for field in fields:\\r\\n assert featured.has_key(field), \\\"%s not in app info\\\" % field\",\n \"def favorites_substitutes(request):\\n favorites = User_Favorites_Substitutes.objects.filter(user=request.user)\\n paginator = Paginator(favorites, 1)\\n page = request.GET.get('page')\\n try:\\n products_list_p = paginator.page(page)\\n except PageNotAnInteger:\\n # If page not an Integer then deliver first page.\\n products_list_p = paginator.page(1)\\n except EmptyPage:\\n # If page over the last result page, then deliver last result page.\\n products_list_p = paginator.page(paginator.num_pages)\\n\\n context = {\\n \\\"products_list\\\": products_list_p,\\n 'paginate': True\\n }\\n return render(request, 'store/favorites.html', context)\",\n \"def select_news(self):\\n data = self.soup.findAll('item')\\n for item in data:\\n news_data = dict()\\n for tag in ['title', 'link']:\\n news_data[tag] = item.find(tag).get_text()\\n\\n news_data['pubDate'] = parse(item.find('pubDate').get_text())\\n media = item.find('media:content')\\n\\n if media:\\n news_data['media'] = media.get('url')\\n else:\\n news_data['media'] = None\\n\\n yield news_data\",\n \"def home():\\n stocks = preprocess()\\n\\n return render_template(\\\"main.html\\\",stocks=stocks)\",\n \"def all_products_view(request):\\n products = Product.objects.all()\\n paginator = Paginator(products, 6)\\n page_number = request.GET.get('page', 1)\\n page = paginator.page(page_number)\\n\\n \\\"\\\"\\\"render a products html page and within that page we will have access to products, so all_products\\\"\\\"\\\"\\n\\n if page.has_next():\\n\\n next_url = f'?page={page.next_page_number()}'\\n\\n else:\\n\\n next_url = ''\\n\\n if page.has_previous():\\n\\n prev_url = f'?page={page.previous_page_number()}'\\n\\n else:\\n\\n prev_url = ''\\n\\n \\n return render(request, 'products.html', {'page': page, 'next_page_url': next_url, 'prev_page_url': prev_url})\",\n \"def get_queryset(self):\\n return Article.objects.filter(\\n pub_date__lte=timezone.now()\\n ).order_by('-pub_date')[:5]\",\n \"def test_toggle_featured(self):\\n story = create_story(title=\\\"Test Story\\\", summary=\\\"Test Summary\\\",\\n byline=\\\"Test Byline\\\", status='published',\\n on_homepage=True)\\n story2 = create_story(title=\\\"Test Related Story\\\", \\n summary=\\\"Test Related Story Summary\\\",\\n byline=\\\"Test Related Story Byline\\\",\\n status='published',\\n on_homepage=False)\\n self.assertEqual(story.on_homepage, True)\\n self.assertEqual(story2.on_homepage, False)\\n toggle_featured(None, None,\\n Story.objects.filter(pk__in=[story.pk, story2.pk]))\\n story = Story.objects.get(pk=story.pk)\\n story2 = Story.objects.get(pk=story2.pk)\\n self.assertEqual(story.on_homepage, False)\\n self.assertEqual(story2.on_homepage, True)\",\n \"def test_blog_posts(self, client, site, homepage, blog_posts):\\n response = client.get(homepage.relative_url(site))\\n assertTemplateUsed(response, \\\"snippets/carousel.html\\\")\\n\\n # no posts featured, so most recent ones should be in context\\n assert len(response.context[\\\"updates\\\"]) == 3\\n\\n # feature some posts; only those should be displayed\\n announcement = blog_posts[\\\"announcement\\\"]\\n project_feature = blog_posts[\\\"project_feature\\\"]\\n announcement.featured = True\\n project_feature.featured = True\\n announcement.save()\\n project_feature.save()\\n response = client.get(homepage.relative_url(site))\\n assert len(response.context[\\\"updates\\\"]) == 2\\n assert announcement in response.context[\\\"updates\\\"]\\n assert project_feature in response.context[\\\"updates\\\"]\\n\\n # should display short title, short description, and link\\n assertContains(response, \\\"A Big Announcement!\\\")\\n assertContains(response, \\\"We're making a big digital humanities announcement.\\\")\\n assertContains(response, announcement.get_url())\\n\\n # unpublished posts shouldn't be displayed\\n announcement.unpublish()\\n response = client.get(homepage.relative_url(site))\\n assert announcement not in response.context[\\\"updates\\\"]\",\n \"def get_context_data(self, **kwargs):\\n context = super(AudioListView, self).get_context_data(**kwargs)\\n if AudioHome.objects:\\n context['audio'] = AudioHome.objects.latest('pub_date')\\n return context\\n else:\\n return []\",\n \"def get_featured_projects(self):\\n featured_projs = FeaturedProject.objects.order_by('id')[0:3]\\n highlighted = []\\n activities = personalize_activities_dict(self.request.user)\\n try:\\n for featured in featured_projs:\\n try:\\n activity = activities[featured.project.id_label]\\n if featured.description:\\n activity['commentary'] = featured.description\\n highlighted.append(activity)\\n except KeyError:\\n pass\\n return highlighted\\n except (ValueError, TypeError):\\n return []\",\n \"def getItems(self): \\n items = []\\n if self.itemCount > 0:\\n \\n site = getSite()\\n \\n \\n # Make string path relative to the site root\\n # E.g. string path \\\"news\\\" becomes \\\"/yoursiteid/news\\\"\\n site_path = site.getPhysicalPath();\\n \\n path = \\\"/\\\".join(site_path) + \\\"/\\\" + self.path \\n \\n #if self.itemPortalType2 != None:\\n # types.append(self.itemPortalType2) \\n \\n #print \\\"Querying by:\\\" + type + \\\" \\\" + path\\n content_by_type = self.context.portal_catalog(path={ \\\"query\\\": path, \\\"depth\\\" :9 }, \\n sort_on=\\\"created\\\", \\n sort_order=\\\"reverse\\\")[0:self.itemCount]\\n\\n \\n items += [ brain.getObject() for brain in content_by_type ]\\n\\n return items\",\n \"def on_fetched(self, doc):\\n\\n docs = doc[app.config[\\\"ITEMS\\\"]]\\n for item in docs:\\n build_custom_hateoas(\\n {\\n \\\"self\\\": {\\n \\\"title\\\": item[\\\"_type\\\"],\\n \\\"href\\\": \\\"/{}/{{_id}}\\\".format(item[\\\"_type\\\"]),\\n }\\n },\\n item,\\n )\",\n \"def render_home_page(request, **kwargs):\\n category = kwargs.get(\\\"category\\\", default=\\\"home\\\")\\n context = {}\\n template_name = \\\"resources/home_page.html\\\"\\n all_technology = Technology.objects.filter(category=category)\\n context[\\\"tech_cards\\\"] = all_technology.for_home_page()[\\n : settings.DISPLAY_CARD_VALUE\\n ]\\n context[\\\"show_view_all_for_tech\\\"] = (\\n True if all_technology.count() > settings.DISPLAY_CARD_VALUE else False\\n )\\n return render(request, template_name, context)\",\n \"def general_news():\\n\\n return general_scraper(['http://mesva.univaq.it/'])\",\n \"def news():\\n\\n # ensure parameters are present\\n # geo = request.args.get(\\\"geo\\\")\\n geo = '95060'\\n if not geo:\\n raise RuntimeError(\\\"missing geo\\\")\\n\\n # lookup articles and store them as JSON array\\n article_list = lookup(geo)\\n\\n # TODO\\n print(article_list)\\n news = jsonify(article_list) \\n print(news)\\n # return render_template(\\\"index.html\\\")\\n return article_list\",\n \"def latest_blog_posts(self, request, *args, **kwargs):\\n context = self.get_context(request, *args, **kwargs)\\n context[\\\"latest_posts\\\"] = MyblogDetailPage.objects.live().public()[:1] \\n return render(request, \\\"myblog/latest_posts.html\\\", context)\",\n \"def show_all_posts():\\n post = Post.query.all()\\n\\n return render_template('all-posts.html', post=post)\",\n \"def get(self):\\n\\n self.render_posts()\",\n \"def _fetch_items(self):\\n url = self._api.router.publication['search'].format(\\n project_id=self.project_id\\n )\\n res_data = self._api.post(url, data=self.search_param)\\n self.total = res_data['total']\\n self._items = (\\n Publication(item, self.project_id)\\n for item in res_data['hits']\\n )\\n div = self.total // self.search_param['limit']\\n reste = self.total % self.search_param['limit']\\n self.total_page = div\\n if reste != 0: self.total_page += 1\\n self.search_param = self.search_param.next_page()\",\n \"def home_page_view(request):\\n\\n\\t# Set the variable jse_articles to the output of the function get_stories().\\n\\tjse_articles = get_stories()\\n\\t# Commenting the below out due to 404 error on site\\n\\t#coindesk_articles = crypto_news()\\n\\n\\t# Add the jse_articles variable to the views context dictionary for use in the template.\\n\\tcontext = {\\n\\t\\t'jse_articles' : jse_articles,\\t\\n\\t\\t#'coindesk_articles': coindesk_articles,\\n\\t}\\n\\treturn render(request, 'home.html', context)\",\n \"def newsList(request):\\n\\n news_count = New.objects.count() # Pocet vsech zaznamu novinek\\n news_list = New.objects.all().order_by(\\\"date\\\") # Sort by date ... and only part of list\\n # misto vsech zaznamu ziskat jen ty v intervalu start - stop -> API\\n\\n pictureOfWeek = PhotoOfWeek.objects.last()\\n context = {'news_list': news_list, 'news_count': news_count, 'pictureOfWeek': pictureOfWeek}\\n return render(request, 'news/newsList.html', context)\",\n \"def index(request):\\n form = SearchForm()\\n \\n form2 = SearchFormMini(request=request)\\n objects = Apartament.objects.filter(show_on_start_page=True, is_published=True).all()\\n\\n return direct_to_template(request, 'start_page.html', {\\n 'form': form,\\n 'form2': form2,\\n 'objects':objects,\\n 'data': {'main_page': True},\\n\\t\\t'main_text': get_object_or_404(StaticPage, pk=10)\\n })\",\n \"def is_featured(self, is_featured):\\n self._is_featured = is_featured\",\n \"def index():\\n db = get_db()\\n items = db.execute(\\\"select item_id, location from items where location != ''\\\").fetchall()\\n return render_template(\\\"tracker/index.html\\\", items=items)\",\n \"def features(request):\\n # Order features by amount of upvotes\\n features_list = Feature.objects.all().order_by('-upvotes')\\n \\n # Pagination for features\\n page = request.GET.get('page', 1)\\n paginator = Paginator(features_list, 10)\\n try:\\n features = paginator.page(page)\\n except PageNotAnInteger:\\n features = paginator.page(1)\\n except EmptyPage:\\n features = paginator.page(paginator.num_pages)\\n \\n # Display graphs\\n chart_total_feature = FeaturesTotalChart() \\n chart_feature_daily = FeaturesDailyStatus()\\n chart_feature_weekly = FeaturesWeeklyStatus()\\n chart_feature_monthly = FeaturesMonthlyStatus()\\n \\n return render(request, \\\"features.html\\\", {\\n \\\"features\\\": features,\\n 'chart_total_feature': chart_total_feature,\\n 'chart_feature_daily': chart_feature_daily,\\n 'chart_feature_weekly': chart_feature_weekly,\\n 'chart_feature_monthly': chart_feature_monthly\\n })\",\n \"def show_blog_list():\\r\\n\\tblog_list = Page.objects.filter(page_type=3).order_by('-created')[:4]\\r\\n\\treturn {'blog_list': blog_list}\",\n \"def home(request):\\n cart = Cart(request)\\n products = Product.objects.order_by('-created')[:3]\\n last = Blog.objects.order_by('-posted')[:3]\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'app/index.html',\\n {\\n 'title':'СТО',\\n 'cart': cart,\\n 'last': last,\\n 'products': products,\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def index(request):\\n return render(request, 'items/index.html', {\\n 'globalvars': globalvars,\\n 'fart': 'fart'\\n })\",\n \"def test_if_featured_apps_are_displayed_correctly(self):\\n # She also see a section which seems to contain some packages\\n # listed as 'Featured apps'.\\n featured_header_text = self.browser.find_element_by_tag_name('h1').text\\n self.assertEqual(\\\"Featured apps\\\", featured_header_text)\\n\\n # She notices that some apps appears there randomly\\n app_link = self.browser.find_element_by_id(\\\"featured_app_href\\\")\\n app_link = app_link.get_attribute(\\\"href\\\")\\n app_pkgname = \\\"None\\\"\\n\\n i = 0\\n for app in FeaturedApp.objects.all():\\n app_name_from_link = app_link.split(\\\"/apps/\\\")[1]\\n if app_name_from_link in app.component.type_id:\\n app_type_id = app.component.type_id\\n break\\n\\n self.assertIn(app_name_from_link, app_type_id)\",\n \"def index(request):\\n products = Product.objects.all()\\n highlights = Original.objects.filter(status='h')\\n context = {\\n \\\"index_page\\\": \\\"active\\\",\\n \\\"products\\\": products,\\n \\\"highlights\\\": highlights,\\n \\\"title\\\": \\\"Home\\\"\\n }\\n return render(request, \\\"index.html\\\", context)\",\n \"def get(self, request, pk, *args, **kwargs):\\n self.restaurant = get_object_or_404(Restaurant, id=pk)\\n self.page_title = \\\"{} Information\\\".format(self.restaurant.name)\\n\\n def get_list_or_none(klass, *args, **kwargs):\\n queryset = _get_queryset(klass)\\n obj_list = list(queryset.filter(*args, **kwargs))\\n if obj_list:\\n return obj_list\\n return []\\n\\n self.dishes = get_list_or_none(Dish, restaurant=self.restaurant)\\n self.orders = get_list_or_none(Order, restaurant=self.restaurant)\\n return super().get(request, *args, **kwargs)\",\n \"def get_queryset(self):\\n return self.queryset.filter(theme__contest__publish_date__lte=timezone.now())\",\n \"def get_context(self, request, *args, **kwargs):\\n\\n context = super().get_context(request, *args, **kwargs)\\n context['posts'] = BlogDetailPage.objects.live().public()\\n return context\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7098284","0.6752768","0.65510976","0.6305554","0.6284082","0.6271135","0.62244487","0.6103396","0.6074179","0.60473037","0.5998711","0.58087254","0.5795663","0.57800406","0.57797366","0.5759702","0.57143676","0.57087076","0.57039094","0.5650906","0.5648793","0.5642826","0.564081","0.56323904","0.5629458","0.562608","0.55548203","0.5547333","0.55402553","0.5535326","0.5524673","0.5520509","0.5497882","0.54897815","0.5486327","0.5481437","0.5451287","0.5450938","0.54374737","0.5424787","0.53894603","0.5377259","0.5376787","0.536759","0.5364478","0.5362268","0.5357259","0.5354596","0.5351506","0.534616","0.53148085","0.53060263","0.5304882","0.52981","0.5293733","0.52935094","0.5269827","0.5260872","0.5251578","0.5243297","0.5227034","0.5221464","0.5214752","0.52108836","0.52016145","0.51954186","0.5192848","0.519054","0.51806176","0.51677054","0.516501","0.51607627","0.51542103","0.51511246","0.51437443","0.51391095","0.51351136","0.51344156","0.5129474","0.510363","0.5103463","0.5102166","0.5095136","0.50872576","0.5082519","0.50824064","0.50817287","0.50798875","0.5078223","0.5077717","0.50750566","0.5073194","0.5072361","0.5071968","0.50709766","0.5068338","0.50578237","0.50550574","0.50484616","0.504729"],"string":"[\n \"0.7098284\",\n \"0.6752768\",\n \"0.65510976\",\n \"0.6305554\",\n \"0.6284082\",\n \"0.6271135\",\n \"0.62244487\",\n \"0.6103396\",\n \"0.6074179\",\n \"0.60473037\",\n \"0.5998711\",\n \"0.58087254\",\n \"0.5795663\",\n \"0.57800406\",\n \"0.57797366\",\n \"0.5759702\",\n \"0.57143676\",\n \"0.57087076\",\n \"0.57039094\",\n \"0.5650906\",\n \"0.5648793\",\n \"0.5642826\",\n \"0.564081\",\n \"0.56323904\",\n \"0.5629458\",\n \"0.562608\",\n \"0.55548203\",\n \"0.5547333\",\n \"0.55402553\",\n \"0.5535326\",\n \"0.5524673\",\n \"0.5520509\",\n \"0.5497882\",\n \"0.54897815\",\n \"0.5486327\",\n \"0.5481437\",\n \"0.5451287\",\n \"0.5450938\",\n \"0.54374737\",\n \"0.5424787\",\n \"0.53894603\",\n \"0.5377259\",\n \"0.5376787\",\n \"0.536759\",\n \"0.5364478\",\n \"0.5362268\",\n \"0.5357259\",\n \"0.5354596\",\n \"0.5351506\",\n \"0.534616\",\n \"0.53148085\",\n \"0.53060263\",\n \"0.5304882\",\n \"0.52981\",\n \"0.5293733\",\n \"0.52935094\",\n \"0.5269827\",\n \"0.5260872\",\n \"0.5251578\",\n \"0.5243297\",\n \"0.5227034\",\n \"0.5221464\",\n \"0.5214752\",\n \"0.52108836\",\n \"0.52016145\",\n \"0.51954186\",\n \"0.5192848\",\n \"0.519054\",\n \"0.51806176\",\n \"0.51677054\",\n \"0.516501\",\n \"0.51607627\",\n \"0.51542103\",\n \"0.51511246\",\n \"0.51437443\",\n \"0.51391095\",\n \"0.51351136\",\n \"0.51344156\",\n \"0.5129474\",\n \"0.510363\",\n \"0.5103463\",\n \"0.5102166\",\n \"0.5095136\",\n \"0.50872576\",\n \"0.5082519\",\n \"0.50824064\",\n \"0.50817287\",\n \"0.50798875\",\n \"0.5078223\",\n \"0.5077717\",\n \"0.50750566\",\n \"0.5073194\",\n \"0.5072361\",\n \"0.5071968\",\n \"0.50709766\",\n \"0.5068338\",\n \"0.50578237\",\n \"0.50550574\",\n \"0.50484616\",\n \"0.504729\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63614005"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":78895,"cells":{"query":{"kind":"string","value":"Says when a member joined."},"document":{"kind":"string","value":"async def joined(self, ctx, member: discord.Member):\n await ctx.send('{0.name} joined in {0.joined_at}'.format(member))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["async def on_member_join(self, member: Member):\n\n if not self._is_tracked(member.guild, EventPriority.join):\n return\n\n em = self.em_base(\n member,\n f\"User {member.mention} ({member.name}) joined\",\n EventColors.join.value\n )\n\n em.add_field(\n name=\"Account Creation Timestamp\",\n value=self._get_timestamp()\n )\n\n await self.log_event(em, member.guild, priority=EventPriority.join)","async def joined(ctx, member: discord.Member):\n await bot.say('{0.name} joined in {0.joined_at}'.format(member))","async def joined(member : discord.Member):\n await bot.say('{0.name} joined in {0.joined_at}'.format(member))","async def joined(member : discord.Member):\n await bot.say('{0.name} joined in {0.joined_at}'.format(member))","async def joined(member : discord.Member):\n await bot.say('{0.name} joined in {0.joined_at}'.format(member))","async def joined(ctx, member: discord.Member):\n await bot.send('{0.name} joined in {0.joined_at}'.format(member))","async def joined(ctx, member: discord.Member):\n await ctx.send(\"{0.name} joined in {0.joined_at}\".format(member))","async def joined(ctx, member: discord.Member):\n await ctx.send('{0.name} joined in {0.joined_at}'.format(member))","async def on_member_join(member: Member):\n await member_handler.member_joined(member)","async def on_member_join(member):\r\n pass","async def on_member_join(member):\n if boterate.has_member(member):\n boterate.update_member(member)\n else:\n boterate.insert_user(member)","async def on_member_join(self, member: discord.Member) -> None:\n\n await add_user_in_db(member, member.guild)\n\n guild_from_db = await Guilds.get(guild_id=member.guild.id)\n role_saver = guild_from_db.role_saver\n if role_saver:\n user_roles = await UserRoles.get_many(guild_id=member.guild.id, user_id=member.id)\n if user_roles:\n for rol in user_roles:\n role = discord.utils.get(member.guild.roles, id=rol.role_id)\n if role.name == '@everyone':\n continue\n else:\n await member.add_roles(role)\n\n await Profiles.update(user_id=member.id,\n guild_id=member.guild.id,\n set=[\"joins = joins + 1\"])\n await Guilds.update(guild_id=member.guild.id,\n set=[\"day_joins = day_joins + 1\"])\n\n await self.refresh_user_count_channel(member.guild)","async def on_member_join(self, member: Member) -> None:\n if member.guild.id != Guild.id:\n return\n\n self.bot.stats.gauge(\"guild.total_members\", len(member.guild.members))","async def on_member_join(self, member: discord.Member) -> None:\n\n # retrieve logging information\n if logging_info := (await typed_retrieve_query(\n self.bot.database,\n PartialLoggingAction,\n 'SELECT CHANNEL_ID, BITS FROM LOGGING WHERE GUILD_ID=?',\n (member.guild.id,))\n ):\n await log_to_channel(\n self.bot,\n LoggingActions.USER_JOINED,\n logging_info[0].bits,\n logging_info[0].channel_id,\n f'**{str(member)}** joined the guild.'\n )","async def on_member_join(self, member):\n if channel is not None:\n await channel.send(\n 'I, too, wonder why I am here, {0.mention}?'.format(member)\n )","async def on_guild_join(self, guild):\n l.info(f\"Joined {guild.name} with {guild.member_count} users!\")","async def on_member_join(self, member):\n verified = get(member.guild.roles, name='verified')\n verify_channel = get(member.guild.channels, name='verify')\n db_discord_user = PostgreSQL.get_discord_user(member.id)\n # Checks if the verified role exists, if it doesn't a DM is sent to the server owner to configure it\n if verified is None:\n await verify_channel.send(f'{member.guild.owner.mention} The verified role doesn\\'t exist in the server `{member.guild.name}`. Please type `!build` in one of the text channels in that server')\n return\n\n # Checks if the user exists in the database, if it doesn't a DM is sent to the user to tell them to get verified\n if db_discord_user is None:\n await verify_channel.send(f'{member.mention} You have not been verified yet. Please visit {WEBSITE} to get verified (VPN is required)')\n return\n \n db_openid_user = PostgreSQL.get_openid_user(db_discord_user[\"openidc_id\"])\n email = db_openid_user[\"username\"]\n await member.add_roles(verified, reason='Assigning user the verified role')\n\n if check_shelve_file(member.guild.id):\n await member.edit(nick=f'{member.name} [{email}]', reason=\"Changing users\\'s nickname\")","def on_me_joined(self, raw_msg, **kwargs):","def joined(message):\n room = session.get('room')\n join_room(room)\n emit('status', {'msg': session.get('name') + ' joined'}, room=room)","def on_join(data):\r\n\r\n username = data[\"username\"]\r\n room = data[\"room\"]\r\n join_room(room)\r\n\r\n # Broadcast that new user has joined\r\n send({\"msg\": username + \" has joined the \" + room + \" room.\"}, room=room)","def joined(message):\n\tglobal GLOBAL_NUM_USERS\n\tGLOBAL_NUM_USERS = GLOBAL_NUM_USERS + 1\n\tprint(message)\n\tsession['name'] = message['name']\n\tsession['room'] = message['room']\n\troom = session.get('room')\n\tjoin_room(room)\n\tprint('%s : joined' % session)\n\temit('_joined', {'user_name': session.get('name'), 'num_users' : GLOBAL_NUM_USERS}, room=room)","async def on_member_join(self, member):\n sid = member.server.id\n role = await self.get_role(member.server)\n try:\n muterole = self.riceCog2[server.id][\"muterole\"]\n except:\n muterole = default_muterole \n\n if 'poop' in self.riceCog2[sid]:\n if self.riceCog2[sid]['poop'] == True:\n if member.id in self.riceCog[sid]:\n if count >= 1:\n count = self.riceCog[sid][member.id][\"Count\"]\n poops = \"\\U0001f528\" * count\n role_name = \"Warning {}\".format(poops)\n is_there = False\n colour = 0xbc7642\n for role in member.server.roles:\n if role.name == role_name:\n poop_role = role\n is_there = True\n if not is_there:\n server = member.server\n poop_role = await self.bot.create_role(server)\n await self.bot.edit_role(role=poop_role,\n name=role_name,\n server=server)\n try:\n await self.bot.add_roles(member,\n poop_role)\n except discord.errors.Forbidden:\n await self.bot.say(\"No permission to add roles\")\n else:\n pass\n if member.id in self.norole[sid]:\n if self.norole[sid]['role'] == True:\n role = discord.utils.get(member.server.roles, name=\"NoBNL\")\n await self.bot.add_roles(member, role)\n \n if not role or not (sid in self.json and member.id in self.json[sid]):\n return\n\n duration = self.json[sid][member.id]['until'] - time.time()\n if duration > 0:\n role = discord.utils.get(member.server.roles, name=muterole)\n await self.bot.add_roles(member, role)\n\n reason = 'Punishment re-added on rejoin. '\n if self.json[sid][member.id]['reason']:\n reason += self.json[sid][member.id]['reason']\n\n if member.id not in self.handles[sid]:\n self.schedule_unpunish(duration, member, reason)","def joined(message):\n room = session.get('room')\n join_room(room)\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)","def joined(message):\n room = session.get('room')\n join_room(room)\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)","async def on_member_leave(self, member: Member) -> None:\n if member.guild.id != Guild.id:\n return\n\n self.bot.stats.gauge(\"guild.total_members\", len(member.guild.members))","def joined(self, channel):\n log.msg(\"[I have joined %s]\" % channel)\n self.msg(channel, \"user1: bonbon\")","async def user_joined_button(self, payload: discord.RawReactionActionEvent) -> None:\n\n self.bits = flip_action_bits(LoggingActions.USER_JOINED, self.bits)\n await self.update_embed()","async def react_join(a: Message):\n if a.action.member_id == club_id:\n await a.answer(r_register_help)\n stats.jincr()","def userJoined(self, user, channel):\n self.dispatch('population', 'userJoined', user, channel)","def userJoined(self, user, channel):\n # Send messasge to Server bot.\n self.data_in(text=\"\", type=\"joined\", user=\"server\", channel=channel,\n nicklist=[user])","def on_join(self, raw_msg, source, **kwargs):","def on_join(data):\n username = request.sid\n room = data\n join_room(room)\n logging.info(username + ' has entered the room.')\n send(username + ' has entered the room.', room=room)","def on_joinuser(self, data):\n user_data = {\n 'un': data[3], # nick\n 'ml': data[4], # mod level\n 'st': data[5], # status related\n 'id': data[6], # ezcapechat user id\n 'su': data[7] # ?\n }\n if data[3] == self.users.client.nick:\n self.users.add_client_data(user_data)\n else:\n _user = self.users.add(data[3], user_data)\n print ('%s Joined the room.' % _user.nick)\n\n #BOT\n if (_user.nick.lower() in self.autogreet):\n self.send_public(\"%s, %s\" % (_user.nick, self.autogreet[_user.nick.lower()]))","def joined(message):\n #room = session.get('room')\n room='abc'\n join_room(room)\n #emit('status', {'msg': session.get('name') + ' has entered the room.' + message['msg']}, room=room)\n emit('status', {'msg': 'Yao has entered the room.'}, room=room)\n #emit('status', {'msg': 'Yao has entered the room.'}, room='room1')","def join_room(self, client, room):\n if room.verify_if_is_invited(client):\n room.add_member(client)\n self.send_message('Te has unido a la sala {}'.format(room.get_name()), client)\n else:\n self.send_message('No estas invitado a la sala.', client)","def join(var, wrapper, message):\n # keep this and the event in fjoin() in sync\n evt = Event(\"join\", {\n \"join_player\": join_player,\n \"join_deadchat\": join_deadchat,\n \"vote_gamemode\": vote_gamemode\n })\n if not evt.dispatch(var, wrapper, message, forced=False):\n return\n if var.PHASE in (\"none\", \"join\"):\n if wrapper.private:\n return\n if var.ACCOUNTS_ONLY:\n if wrapper.source.account is None:\n wrapper.pm(messages[\"not_logged_in\"])\n return\n if evt.data[\"join_player\"](var, wrapper) and message:\n evt.data[\"vote_gamemode\"](var, wrapper, message.lower().split()[0], doreply=False)\n\n else: # join deadchat\n if wrapper.private and wrapper.source is not wrapper.target:\n evt.data[\"join_deadchat\"](var, wrapper.source)","def on_player_join(self, func):\n self.loop.create_task(self._rpc.register_event('ACTIVITY_JOIN', func))\n return func","async def on_members_added_activity(self, members_added: [ChannelAccount], turn_context: TurnContext):\n for member in members_added:\n if member.id != turn_context.activity.recipient.id:\n await turn_context.send_activity(\n f\"Hi there. \" + self.WELCOME_MESSAGE\n )","def joined(self, channel):\n log.info(\"Joined %s.\", channel)\n # ask for the current list of users in the channel\n self.dispatch('presence', 'joined', channel)","def test_double_join(self):\n now = datetime.datetime.now()\n\n channel = ChannelStatus.get_channel(channel_spec=self.channel)\n greet = channel.update_user_join_status(self.user, self.greeting, now=now)\n now += datetime.timedelta(seconds=0.1)\n\n greet = channel.update_user_join_status(self.user, self.greeting, now=now)\n self.assertEqual(False, greet)","def joined(self, channel):\n self.logger.log(\"[I have joined %s]\" % channel)","def test_join_after_invite(self) -> None:\n\n self._perform_background_initial_update()\n\n u1 = self.register_user(\"u1\", \"pass\")\n u1token = self.login(\"u1\", \"pass\")\n r1 = self.helper.create_room_as(u1, tok=u1token)\n\n u2 = self.register_user(\"u2\", \"pass\")\n u2token = self.login(\"u2\", \"pass\")\n\n self.helper.invite(r1, u1, u2, tok=u1token)\n\n r1stats_ante = self._get_current_stats(\"room\", r1)\n assert r1stats_ante is not None\n\n self.helper.join(r1, u2, tok=u2token)\n\n r1stats_post = self._get_current_stats(\"room\", r1)\n assert r1stats_post is not None\n\n self.assertEqual(\n r1stats_post[\"current_state_events\"] - r1stats_ante[\"current_state_events\"],\n 0,\n )\n self.assertEqual(\n r1stats_post[\"joined_members\"] - r1stats_ante[\"joined_members\"], +1\n )\n self.assertEqual(\n r1stats_post[\"invited_members\"] - r1stats_ante[\"invited_members\"], -1\n )","def test_join(self):\n self.node.fake_message(Join(), sender='F999')\n self.assertMessage(['F999'], Welcome(state='state', slot=2,\n decisions={1: PROPOSAL1}))","def on_join(data):\n print(str(data))\n if models.Leaderboard.query.filter_by(\n username=data['user']).first() is None:\n add_user(data['user'])\n users, scores = calculate_scores()\n socketio.emit('leaderboard_info', {'users': users, 'scores': scores})","async def join(self, ctx, invite : discord.Invite):\r\n if ctx.message.author.id == \"481270883701358602\":\r\n await self.client.accept_invite(invite)\r\n await self.client.say(\"Joined the server.\")\r\n else:\r\n await self.client.say(\"**Owner only command.**\")","def on_join(bot, trigger):\n\tfor channel in trigger.args[0].split(','):\n\t\tlog(bot, channel, '*** {} has joined {}', trigger.nick, channel);","def joined_days(self):\n return (timezone.now() - self.user.date_joined).days","async def on_member_update(self, before, after):\n if after and after.activity is not None:\n if after.activity.name == \"Guild Wars 2\":\n print(after.id)\n await self.startGw2Session(after.id)\n print(\"Started GW2 Session {} {}\".format(after, dt.utcnow()))\n\n elif before and before.activity is not None:\n if before.activity.name == \"Guild Wars 2\":\n await self.endGw2Session(before.id)\n print(\"Ending GW2 Session {} {}\".format(before, dt.utcnow()))","async def join(ctx):\n if ctx.message.channel.name.lower() not in tod_channels:\n return\n\n room = ctx.message.channel.name.lower()\n if room not in tod_games:\n await amor_manager.say(\"Truth Or Dare not in progress in {}\".format(room))\n return\n player = ctx.message.author.name\n if player.lower() in list(tod_games[room]['participants'].keys()):\n await amor_manager.say(\"{}... you're already playing Truth or Dare here!\".format(room))\n else:\n tod_games[room]['participants'][player.lower()] = {'spins': 0}\n await amor_manager.say(\"{} has joined Truth or Dare!\".format(player))","async def chat_join(self, event):\n await self.send_json(\n return_value(\n ACTION_JOIN,\n event['label'],\n event['username'],\n MSG_JOIN,\n NO_MESSAGE\n )\n )","async def on_member_update(self, before: Member, after: Member):\n\n if not self._is_tracked(before.guild, EventPriority.update):\n return\n\n if before.name != after.name or before.discriminator != after.discriminator:\n em = self.em_base(\n after,\n f\"Member {before.mention} ({before.name}#{before.discriminator}) \"\n f\"changed their name to {after.name}#{after.discriminator}\",\n EventColors.name_change.value\n )\n\n await self.log_event(em, before.guild, priority=EventPriority.update)\n\n if before.roles != after.roles:\n added, removed = None, None\n\n for role in before.roles:\n if role not in after.roles:\n removed = f\"{role.mention}\\n({role.name})\"\n\n for role in after.roles:\n if role not in before.roles:\n added = f\"{role.mention}\\n({role.name})\"\n\n found, errored, mod, _ = await self._get_last_audit_action(\n after.guild,\n AuditLogAction.member_role_update,\n after\n )\n\n if added:\n\n em = self.em_base(\n after,\n f\"Member {after.mention} ({after.name}) roles changed\",\n EventColors.role_added.value\n )\n\n em.add_field(\n name=\"Role Added\",\n value=added\n )\n\n if found:\n em.add_field(\n name=\"Mod Responsible\",\n value=f\"{mod.mention}\\n({mod.name})\"\n )\n\n await self.log_event(em, before.guild, priority=EventPriority.update)\n\n if removed:\n\n em = self.em_base(\n after,\n f\"Member {after.mention} ({after.name}) roles changed\",\n EventColors.role_removed.value\n )\n\n em.add_field(\n name=\"Role Removed\",\n value=removed\n )\n\n if found:\n em.add_field(\n name=\"Mod Responsible\",\n value=f\"{mod.mention}\\n({mod.name})\"\n )\n\n await self.log_event(em, before.guild, priority=EventPriority.update)\n\n if before.nick != after.nick:\n\n if after.nick is None:\n em = self.em_base(\n after,\n f\"Member {before.mention} ({before.name}) reset their nickname\",\n EventColors.nickname_change.value\n )\n\n else:\n em = self.em_base(\n after,\n f\"Member {before.mention} ({before.name}) changed their nickname \"\n f\"from {before.nick} to {after.nick}\",\n EventColors.nickname_change.value\n )\n\n await self.log_event(em, after.guild, priority=EventPriority.update)","def joined(message):\n global list_messages\n room = session.get('room')\n join_room(room)\n print ('joined session list_messages ' + str(len(list_messages)) + ' , session ' + str(session) +'\\n')\n emit('status', {'msg': str(clients)})\n for x in list_messages:\n emit('status', {'msg': x})\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)","async def on_ready():\n print(\"{} has joined the server\".format(client.user.name))","def joined(self, channel):\n self.logger.log(\"[I have joined %s]\" % channel)\n #IRC Keepalive\n self.startHeartbeat()\n #Custom heartbeat\n self.loopcall.start(5.0)","def on_join_data(self, data):\n self.users.client.key = data[7] # unique user identifier ?\n self.users.client.join_time = data[11] # join time as unix including milliseconds ?\n self._room_id = data[13] # room id\n\n self.send_connection_ok()\n\n if config.DEBUG_TO_CONSOLE:\n print ('Join Data:')\n for i, v in enumerate(data):\n print ('\\t[%s] - %s' % (i, v))","def join(bot, event, *args):\n yield from _joinEvent(bot, event, list(args))","def on_join(self, event):\n self.pre_check(event)\n state = event.guild.get_member(event.author).get_voice_state()\n if not state:\n return api_loop(\n event.channel.send_message,\n \"You must be in a voice channel to use that command.\",\n )\n if event.guild.id not in self.guilds:\n try:\n client = state.channel.connect(mute=False)\n except VoiceException as e:\n return api_loop(\n event.channel.send_message,\n \"Failed to connect to voice: `{}`\".format(e),\n )\n else:\n self.guilds[event.guild.id] = psuedo_queue(\n self,\n player=Player(client),\n guild_id=event.guild.id,\n )\n return","def query_member_status():\n notify_member_status()\n logger.info('signal sent for status report')","def getJoiningTime(self):\n return self.__joinTime","def signedOn(self):\n log.msg(\"Signed on\")\n if self.nickname != self.factory.nickname:\n log.msg('Name taken, new is ''\"{}\".'.format(self.nickname))\n self.join(self.factory.channel)","async def update_member_count():\n guild = bot.get_guild(SERVER_ID)\n channel_prefix = \"Members\"\n vc = discord.utils.find(lambda c: channel_prefix in c.name, guild.voice_channels)\n mem_count = guild.member_count\n joined_today = len([m for m in guild.members if m.joined_at.date() == datetime.datetime.today().date()])\n left_channel = discord.utils.get(guild.text_channels, name=CHANNEL_LEAVE)\n left_messages = await left_channel.history(limit=200).flatten()\n left_today = len([m for m in left_messages if m.created_at.date() == datetime.datetime.today().date()])\n await vc.edit(name=f\"{mem_count} Members (+{joined_today}/-{left_today})\")\n print(\"Refreshed member count.\")","async def newusers(self, ctx, *, count=5):\n\n count = min(max(count, 5), 25)\n\n now = datetime.now(timezone.utc)\n e = disnake.Embed()\n\n for idx, member in enumerate(\n sorted(ctx.guild.members, key=lambda m: m.joined_at, reverse=True)\n ):\n if idx >= count:\n break\n\n value = \"Joined {0} ago\\nCreated {1} ago\".format(\n pretty_timedelta(now - member.joined_at),\n pretty_timedelta(now - member.created_at),\n )\n e.add_field(name=po(member), value=value, inline=False)\n\n await ctx.send(embed=e)","def fjoin(var, wrapper, message):\n # keep this and the event in def join() in sync\n evt = Event(\"join\", {\n \"join_player\": join_player,\n \"join_deadchat\": join_deadchat,\n \"vote_gamemode\": vote_gamemode\n })\n\n if not evt.dispatch(var, wrapper, message, forced=True):\n return\n noticed = False\n fake = False\n if not message.strip():\n evt.data[\"join_player\"](var, wrapper, forced=True)\n\n parts = re.split(\" +\", message)\n possible_users = {u.lower().nick for u in wrapper.target.users}\n to_join = []\n if not botconfig.DEBUG_MODE:\n match = complete_one_match(users.lower(parts[0]), possible_users)\n if match:\n to_join.append(match)\n else:\n for i, s in enumerate(parts):\n match = complete_one_match(users.lower(s), possible_users)\n if match:\n to_join.append(match)\n else:\n to_join.append(s)\n for tojoin in to_join:\n tojoin = tojoin.strip()\n # Allow joining single number fake users in debug mode\n if users.predicate(tojoin) and botconfig.DEBUG_MODE:\n user = users._add(wrapper.client, nick=tojoin) # FIXME\n evt.data[\"join_player\"](var, type(wrapper)(user, wrapper.target), forced=True, who=wrapper.source)\n continue\n # Allow joining ranges of numbers as fake users in debug mode\n if \"-\" in tojoin and botconfig.DEBUG_MODE:\n first, hyphen, last = tojoin.partition(\"-\")\n if first.isdigit() and last.isdigit():\n if int(last)+1 - int(first) > var.MAX_PLAYERS - len(list_players()):\n wrapper.send(messages[\"too_many_players_to_join\"].format(wrapper.source.nick))\n break\n fake = True\n for i in range(int(first), int(last)+1):\n user = users._add(wrapper.client, nick=str(i)) # FIXME\n evt.data[\"join_player\"](var, type(wrapper)(user, wrapper.target), forced=True, who=wrapper.source)\n continue\n if not tojoin:\n continue\n\n maybe_user = None\n\n for user in wrapper.target.users:\n if users.equals(user.nick, tojoin):\n maybe_user = user\n break\n else:\n if not users.predicate(tojoin) or botconfig.DEBUG_MODE:\n if not noticed: # important\n wrapper.send(\"{0}{1}\".format(wrapper.source, messages[\"fjoin_in_chan\"]))\n noticed = True\n continue\n\n if maybe_user is not None:\n if not botconfig.DEBUG_MODE and var.ACCOUNTS_ONLY:\n if maybe_user.account is None:\n wrapper.pm(messages[\"account_not_logged_in\"].format(maybe_user))\n return\n elif botconfig.DEBUG_MODE:\n fake = True\n\n if maybe_user is not users.Bot:\n if maybe_user is None and users.predicate(tojoin) and botconfig.DEBUG_MODE:\n maybe_user = users._add(wrapper.client, nick=tojoin) # FIXME\n evt.data[\"join_player\"](var, type(wrapper)(maybe_user, wrapper.target), forced=True, who=wrapper.source)\n else:\n wrapper.pm(messages[\"not_allowed\"])\n if fake:\n wrapper.send(messages[\"fjoin_success\"].format(wrapper.source, len(list_players())))","def joined(self, channel):\n # Return user list to Server bot.\n self.get_nicklist()","async def on_member_update(old, updated):\n if old.nick != updated.nick:\n boterate.update_member(updated)","async def monitor(self, ctx, member: Member, channel: TextChannel):\n guild_timezone = await self.get_tz(ctx.guild.id)\n if not guild_timezone:\n return await ctx.send(\n f\"Please set timezone using `{ctx.prefix}settz` command.\"\n )\n\n user = User(self.bot.db)\n log = Log(self.bot.db)\n\n monitor = await user.get(multi=False, guild_id=ctx.guild.id, user_id=member.id)\n if monitor:\n return await ctx.send(f\"{member} has already been monitored.\")\n\n await user.insert(\n mentor_id=ctx.author.id,\n guild_id=ctx.guild.id,\n user_id=member.id,\n channel_id=channel.id,\n )\n now = datetime.now(timezone(guild_timezone))\n\n await log.insert(\n status=member.status.name,\n user_id=member.id,\n guild_id=ctx.guild.id,\n started=now,\n )\n\n self.bot.monitored_users.add(member.id)\n\n await ctx.send(f\"{member} will get monitored from now.\")","async def join(self, ctx):\n voice = ctx.author.voice\n\n if voice and voice.channel:\n channel = voice.channel\n \n try:\n await channel.connect()\n except discord.errors.ClientException:\n await ctx.guild.get_member(self.bot.user.id).move_to(channel) \n \n await ctx.send(f\"Joined `{channel.name}`.\")\n else:\n await ctx.message.add_reaction('\\U0001F615');\n await ctx.send(\"You're not in a voice channel.\")","async def on_guild_join(self, guild: discord.Guild) -> None:\n logger.info(f'Added to new guild: {guild.name} ({guild.id})')\n\n with self.get_session() as session:\n _guild: Guild = session.query(Guild).get(guild.id)\n if _guild is None:\n session.add(Guild(id=guild.id))\n else:\n # Guild has been seen before. Update last_joined and set as active again.\n _guild.active = True\n _guild.last_joined = datetime.utcnow()","def on_join(data):\n quiz = store.get_quiz_by_user_id(data['user_id'])\n room = quiz.quiz_id\n\n # get and clean the users (no score)\n users = store.get_users_by_id(store.get_quiz_by_id(room).users)\n users_cleaned = [user.name for user in users]\n\n # emit the new users the to the room\n if room is not None:\n join_room(room)\n emit(\"current_players\", {\"users\": users_cleaned}, room=room)","def joinedChannel(self, channel, users):\n pass","def test_join_first_time(self) -> None:\n\n self._perform_background_initial_update()\n\n u1 = self.register_user(\"u1\", \"pass\")\n u1token = self.login(\"u1\", \"pass\")\n r1 = self.helper.create_room_as(u1, tok=u1token)\n\n u2 = self.register_user(\"u2\", \"pass\")\n u2token = self.login(\"u2\", \"pass\")\n\n r1stats_ante = self._get_current_stats(\"room\", r1)\n assert r1stats_ante is not None\n\n self.helper.join(r1, u2, tok=u2token)\n\n r1stats_post = self._get_current_stats(\"room\", r1)\n assert r1stats_post is not None\n\n self.assertEqual(\n r1stats_post[\"current_state_events\"] - r1stats_ante[\"current_state_events\"],\n 1,\n )\n self.assertEqual(\n r1stats_post[\"joined_members\"] - r1stats_ante[\"joined_members\"], 1\n )","async def on_member_join(member: discord.Member):\n for channel in member.server.channels:\n print(channel)\n if channel == \"general\":\n await member.send(f\"\"\"Welcome to the server {member.mention}!\"\"\")","def date_joined(self):\n return self._date_joined","def PartyCheckIn(sc, event):\n now = datetime.datetime.now()\n vice = event['text'][:5].lower()\n user = sc.api_call('users.info', user=event['user'])\n user = user['user']['name']\n db = pymysql.connect(host='localhost', user='pizzabot', db='pizzachat')\n cursor = db.cursor()\n cursor.execute('DELETE FROM party WHERE date < '\n '(NOW() - INTERVAL 120 MINUTE) AND who=\"%s\"' % user)\n cursor.execute('INSERT INTO party (who, vice) VALUES (\"%s\", \"%s\")' % (user, vice))\n db.commit()\n cursor.execute('SELECT who FROM party WHERE who=\"%s\" AND vice=\"%s\"' % (user, vice))\n status = cursor.fetchall()\n party_message = PartyMessage(user, vice, len(status))\n sc.api_call('chat.postMessage', as_user='true', channel=event['channel'],\n text=party_message)\n db.close()","def test_join_after_leave(self) -> None:\n\n self._perform_background_initial_update()\n\n u1 = self.register_user(\"u1\", \"pass\")\n u1token = self.login(\"u1\", \"pass\")\n r1 = self.helper.create_room_as(u1, tok=u1token)\n\n u2 = self.register_user(\"u2\", \"pass\")\n u2token = self.login(\"u2\", \"pass\")\n\n self.helper.join(r1, u2, tok=u2token)\n self.helper.leave(r1, u2, tok=u2token)\n\n r1stats_ante = self._get_current_stats(\"room\", r1)\n assert r1stats_ante is not None\n\n self.helper.join(r1, u2, tok=u2token)\n\n r1stats_post = self._get_current_stats(\"room\", r1)\n assert r1stats_post is not None\n\n self.assertEqual(\n r1stats_post[\"current_state_events\"] - r1stats_ante[\"current_state_events\"],\n 0,\n )\n self.assertEqual(\n r1stats_post[\"joined_members\"] - r1stats_ante[\"joined_members\"], +1\n )\n self.assertEqual(\n r1stats_post[\"left_members\"] - r1stats_ante[\"left_members\"], -1\n )","def join(coro):\n return JoinEvent(coro)","def signedOn(self):\n self.join(self.factory.channel)","async def on_start(self):\n m = \"**{}** has started a game of {}! To participate, say `I`! **{} players needed.**\".format(\n self.message.author.display_name, self.name, self.num)\n await client.say(self.message, m)","def join(self) -> \"cocotb.triggers.Join\":\n return cocotb.triggers.Join(self)","async def on_guild_join(self, guild):\n embed = discord.Embed(title=\"New server!\", description=guild.name, colour=GREEN)\n embed.add_field(name=\"Owner\", value=guild.owner.name)\n embed.add_field(name=\"Members\", value=str(len(guild.members)))\n embed.set_author(name=guild.owner.nick if guild.owner.nick else guild.owner.name, icon_url=guild.owner.avatar_url)\n embed.set_footer(text=f\"ID: {guild.id}\")\n await self.bot.get_channel(753495117767377016).send(embed=embed)","async def tod_join(self, ctx, *args):\n if ctx.author not in self.players:\n self.players.append(ctx.author)\n message = f\"{ctx.author.mention} has been added to the game!\"\n await ctx.send(message)\n else:\n message = f\"{ctx.author.mention} has already joined!\"\n await ctx.send(message)\n\n # Updates the role if channel exists\n for channel in ctx.guild.channels:\n if channel.name.startswith(\"truth-or-dare\"):\n role = discord.utils.get(ctx.guild.roles, name=\"Player\")\n await ctx.author.add_roles(role)\n return\n\n # Creates the channel if it doesn't exist\n role = discord.utils.get(ctx.guild.roles, name=\"Player\")\n bots = discord.utils.get(ctx.guild.roles, name=\"Bots\")\n overwrites = {\n ctx.guild.default_role: discord.PermissionOverwrite(read_messages=False, send_messages=False),\n bots: discord.PermissionOverwrite(read_messages=True, send_messages=True),\n role: discord.PermissionOverwrite(read_messages=True, send_messages=True, connect=True, speak=True)\n }\n await ctx.guild.create_text_channel('truth-or-dare', overwrites=overwrites)\n await ctx.guild.create_voice_channel('secret-voice', overwrites=overwrites)\n\n # Adds the role\n role = discord.utils.get(ctx.guild.roles, name=\"Player\")\n await ctx.author.add_roles(role)","async def info(self, ctx, *, member: disnake.Member = None):\n\n member = member or ctx.author\n\n e = disnake.Embed(description=\"\")\n\n if member.bot:\n e.description = \"This account is a bot.\\n\\n\"\n\n e.description += member.mention\n\n e.add_field(name=\"Status\", value=member.status)\n\n if member.activity:\n e.add_field(name=\"Activity\", value=member.activity.name)\n\n e.set_author(name=str(member), icon_url=member.display_avatar.url)\n\n now = datetime.now(timezone.utc)\n created = member.created_at\n joined = member.joined_at\n\n e.add_field(\n name=\"Account age\",\n value=\"{0} • Created <t:{1}:F>\".format(\n pretty_timedelta(now - created), round(created.timestamp())\n ),\n inline=False,\n )\n\n e.add_field(\n name=\"Member for\",\n value=\"{0} • Joined <t:{1}:F>\".format(\n pretty_timedelta(now - joined), round(joined.timestamp())\n ),\n )\n\n if len(member.roles) > 1:\n e.add_field(\n name=\"Roles\",\n value=\" \".join(role.mention for role in reversed(member.roles[1:])),\n inline=False,\n )\n\n e.set_footer(text=\"ID: \" + str(member.id))\n\n await ctx.send(embed=e)","def joined(self, channel):\n self.logger.info(\"Joined %s\" % channel)","async def join(message):\n voice_client = await get_voice_client(message.author, move_to=True, any_channel=True)\n return \"I'm in {}\".format(voice_client.channel.mention)","async def level(self, ctx, member: discord.Member = None):\n member = member or ctx.author\n if member.bot:\n return\n await get_user_level(ctx, member)","async def on_member_update(before: discord.Member, after: discord.Member):\n # Return if the server doesn't have any notify channels setup\n if not twitch_config.data[\"servers\"].get(after.server.id, {}).get(\"notify_channels\", False):\n return\n\n # Make sure the member just started streaming\n if not started_streaming(before, after):\n return\n\n # Tru getting the id and also log some possibly useful info during exceptions\n try:\n twitch_id = await twitch.get_id(after)\n except twitch.RequestFailed as e: # Could not find the streamer due to a request error\n logging.info(\"Could not get twitch id of {}: {}\".format(after, e))\n return\n except twitch.UserNotResolved as e: # Ignore them if the id was not found.\n logging.debug(e)\n return\n\n # Return the stream info of the specified user\n try:\n stream_response = await twitch.request(\"streams/\" + twitch_id)\n except twitch.RequestFailed as e:\n logging.info(\"Could not get twitch stream of {} (id: {}): {}\".format(after, twitch_id, e))\n return\n\n # If the member isn't actually streaming, return (should not be the case as discord uses the twitch api too)\n if stream_response[\"stream\"] is None:\n return\n\n # Create the embedded message and send it to every stream channel\n embed = make_twitch_embed(after, stream_response)\n for channel_id in twitch_config.data[\"servers\"][after.server.id][\"notify_channels\"]:\n await client.send_message(after.server.get_channel(channel_id), embed=embed)","def you_won(self):\n self.end_of_level()\n self.message_holder.add_widget(self.you_win_label)\n Clock.schedule_once(self.goto_next_level, 5)","def on_join(session):\n print('session connected: {}'.format(session))\n\n def on_leave(details):\n print(\"on_leave\", details)\n\n session.on_leave(on_leave)\n\n # explicitly leaving a realm will disconnect the connection\n # cleanly and not try to reconnect, but exit cleanly.\n session.leave()","def is_joined_days_passed(self, days):\n return timezone.now() >= self.user.date_joined + timedelta(days=days)","def join(self):\n if self._join_command is not None:\n return\n\n if self._joined:\n self.emit('joined', True, None)\n return\n\n print('%r: joining', self)\n\n self._join_command = _JoinCommand(self.telepathy_conn,\n self.room_handle)\n self._join_command.connect('finished', self.__joined_cb)\n self._join_command.run()","def signedOn(self):\n self.join(self.channel)","def irc_JOIN(self, prefix, params):\n user = re.match(self.user_regex, prefix)\n channel = params[0]\n\n self.logger.debug(\n \"%s!%s@%s joined %s\" %\n (user.group(1), user.group(2), user.group(3), channel)\n )\n\n self.event_manager.fire(\"irc.join\", user, channel)","def join(self, message, db_session):\n username = self.ts.get_user(message)\n user = db_session.query(db.User).filter(db.User.name == username).one_or_none()\n if not user:\n user = db.User(name=username)\n db_session.add(user)\n try:\n self.player_queue.push(username, user.times_played)\n self._add_to_whisper_queue(username, \"You've joined the queue.\")\n user.times_played += 1\n except RuntimeError:\n self._add_to_whisper_queue(username, \"You're already in the queue and can't join again.\")\n\n # queue_snapshot = copy.deepcopy(self.player_queue.queue)\n # self.command_queue.appendleft(('_insert_into_player_queue_spreadsheet',\n # {'username': username, 'times_played':user.times_played, 'player_queue': queue_snapshot}))","async def join(self, ctx):\n player = self.bot.lavalink.players.get(ctx.guild.id)\n if player.is_connected:\n return await ctx.send(\"I'm already in a voice channel :no_entry:\")\n else:\n if not ctx.author.voice or not ctx.author.voice.channel:\n return await ctx.send(\"You are not in a voice channel :no_entry:\")\n player.store('sessionowner', ctx.author.id)\n player.store('channel', ctx.channel.id)\n await player.connect(ctx.author.voice.channel.id)\n await ctx.send(\"Summoned to `{}` <:done:403285928233402378>\".format(ctx.author.voice.channel.name))","async def tick(self):\n room = self.bot.Room.load('19961884194@chatroom')\n await room.ready()\n await room.say(f'i love you -> {datetime.now()}')","def member_action(self):\n # trigger scene signal\n self.scene().node_member.emit(self.metadata)","async def join(self, interaction: discord.Interaction, button: discord.ui.Button):\n\t\tif interaction.user.id == self.ctx.author.id:\n\t\t\tawait interaction.response.send_message(\n\t\t\t\tcontent='You have already joined the game. You can add AI players or start the game early with the other two buttons.',\n\t\t\t\tephemeral=True,\n\t\t\t)\n\t\t\treturn\n\t\tself.players.append(interaction.user)\n\t\tself.start.disabled = False\n\t\tif len(self.players) >= self.max_players:\n\t\t\tview = None\n\t\t\tself.stop()\n\t\telse:\n\t\t\tview = self\n\t\tawait interaction.response.edit_message(content=self.generate_message(), view=view)","def count_changed_handler(count: int):\n # Chat should have at least two active members\n global can_speak\n can_speak = count >= 2\n if not can_speak:\n safe_print(print_mutex, \"Too few members. Waiting another member...\")","async def on_members_added_activity(\n self, members_added: [ChannelAccount], turn_context: TurnContext\n ):\n for member in members_added:\n if member.id != turn_context.activity.recipient.id:\n reply = MessageFactory.list([])\n member_name = member.name\n message = Activity(\n type=ActivityTypes.message,\n attachments=[self.__send_welcome_card(member_name)],\n )\n\n await turn_context.send_activity(message)\n\n #await self.__send_welcome_card(turn_context, member_name)","def start(self, update, context):\n # add or update to the sqlite table.\n chat = update.message.chat\n user_tuple = self.db_manager.create_user_tuple(chat.id)\n self.db_manager.add_new_user(user_tuple)\n self.logger.info(\n 'A new user with username: %s and chat_id: %s subscribed to the list.' % (chat.username, chat.id)\n )\n update.message.reply_text('Welcome! You have successfully subscribed to real time notification for a six '\n 'scored in IPL 2019 cricket match.')"],"string":"[\n \"async def on_member_join(self, member: Member):\\n\\n if not self._is_tracked(member.guild, EventPriority.join):\\n return\\n\\n em = self.em_base(\\n member,\\n f\\\"User {member.mention} ({member.name}) joined\\\",\\n EventColors.join.value\\n )\\n\\n em.add_field(\\n name=\\\"Account Creation Timestamp\\\",\\n value=self._get_timestamp()\\n )\\n\\n await self.log_event(em, member.guild, priority=EventPriority.join)\",\n \"async def joined(ctx, member: discord.Member):\\n await bot.say('{0.name} joined in {0.joined_at}'.format(member))\",\n \"async def joined(member : discord.Member):\\n await bot.say('{0.name} joined in {0.joined_at}'.format(member))\",\n \"async def joined(member : discord.Member):\\n await bot.say('{0.name} joined in {0.joined_at}'.format(member))\",\n \"async def joined(member : discord.Member):\\n await bot.say('{0.name} joined in {0.joined_at}'.format(member))\",\n \"async def joined(ctx, member: discord.Member):\\n await bot.send('{0.name} joined in {0.joined_at}'.format(member))\",\n \"async def joined(ctx, member: discord.Member):\\n await ctx.send(\\\"{0.name} joined in {0.joined_at}\\\".format(member))\",\n \"async def joined(ctx, member: discord.Member):\\n await ctx.send('{0.name} joined in {0.joined_at}'.format(member))\",\n \"async def on_member_join(member: Member):\\n await member_handler.member_joined(member)\",\n \"async def on_member_join(member):\\r\\n pass\",\n \"async def on_member_join(member):\\n if boterate.has_member(member):\\n boterate.update_member(member)\\n else:\\n boterate.insert_user(member)\",\n \"async def on_member_join(self, member: discord.Member) -> None:\\n\\n await add_user_in_db(member, member.guild)\\n\\n guild_from_db = await Guilds.get(guild_id=member.guild.id)\\n role_saver = guild_from_db.role_saver\\n if role_saver:\\n user_roles = await UserRoles.get_many(guild_id=member.guild.id, user_id=member.id)\\n if user_roles:\\n for rol in user_roles:\\n role = discord.utils.get(member.guild.roles, id=rol.role_id)\\n if role.name == '@everyone':\\n continue\\n else:\\n await member.add_roles(role)\\n\\n await Profiles.update(user_id=member.id,\\n guild_id=member.guild.id,\\n set=[\\\"joins = joins + 1\\\"])\\n await Guilds.update(guild_id=member.guild.id,\\n set=[\\\"day_joins = day_joins + 1\\\"])\\n\\n await self.refresh_user_count_channel(member.guild)\",\n \"async def on_member_join(self, member: Member) -> None:\\n if member.guild.id != Guild.id:\\n return\\n\\n self.bot.stats.gauge(\\\"guild.total_members\\\", len(member.guild.members))\",\n \"async def on_member_join(self, member: discord.Member) -> None:\\n\\n # retrieve logging information\\n if logging_info := (await typed_retrieve_query(\\n self.bot.database,\\n PartialLoggingAction,\\n 'SELECT CHANNEL_ID, BITS FROM LOGGING WHERE GUILD_ID=?',\\n (member.guild.id,))\\n ):\\n await log_to_channel(\\n self.bot,\\n LoggingActions.USER_JOINED,\\n logging_info[0].bits,\\n logging_info[0].channel_id,\\n f'**{str(member)}** joined the guild.'\\n )\",\n \"async def on_member_join(self, member):\\n if channel is not None:\\n await channel.send(\\n 'I, too, wonder why I am here, {0.mention}?'.format(member)\\n )\",\n \"async def on_guild_join(self, guild):\\n l.info(f\\\"Joined {guild.name} with {guild.member_count} users!\\\")\",\n \"async def on_member_join(self, member):\\n verified = get(member.guild.roles, name='verified')\\n verify_channel = get(member.guild.channels, name='verify')\\n db_discord_user = PostgreSQL.get_discord_user(member.id)\\n # Checks if the verified role exists, if it doesn't a DM is sent to the server owner to configure it\\n if verified is None:\\n await verify_channel.send(f'{member.guild.owner.mention} The verified role doesn\\\\'t exist in the server `{member.guild.name}`. Please type `!build` in one of the text channels in that server')\\n return\\n\\n # Checks if the user exists in the database, if it doesn't a DM is sent to the user to tell them to get verified\\n if db_discord_user is None:\\n await verify_channel.send(f'{member.mention} You have not been verified yet. Please visit {WEBSITE} to get verified (VPN is required)')\\n return\\n \\n db_openid_user = PostgreSQL.get_openid_user(db_discord_user[\\\"openidc_id\\\"])\\n email = db_openid_user[\\\"username\\\"]\\n await member.add_roles(verified, reason='Assigning user the verified role')\\n\\n if check_shelve_file(member.guild.id):\\n await member.edit(nick=f'{member.name} [{email}]', reason=\\\"Changing users\\\\'s nickname\\\")\",\n \"def on_me_joined(self, raw_msg, **kwargs):\",\n \"def joined(message):\\n room = session.get('room')\\n join_room(room)\\n emit('status', {'msg': session.get('name') + ' joined'}, room=room)\",\n \"def on_join(data):\\r\\n\\r\\n username = data[\\\"username\\\"]\\r\\n room = data[\\\"room\\\"]\\r\\n join_room(room)\\r\\n\\r\\n # Broadcast that new user has joined\\r\\n send({\\\"msg\\\": username + \\\" has joined the \\\" + room + \\\" room.\\\"}, room=room)\",\n \"def joined(message):\\n\\tglobal GLOBAL_NUM_USERS\\n\\tGLOBAL_NUM_USERS = GLOBAL_NUM_USERS + 1\\n\\tprint(message)\\n\\tsession['name'] = message['name']\\n\\tsession['room'] = message['room']\\n\\troom = session.get('room')\\n\\tjoin_room(room)\\n\\tprint('%s : joined' % session)\\n\\temit('_joined', {'user_name': session.get('name'), 'num_users' : GLOBAL_NUM_USERS}, room=room)\",\n \"async def on_member_join(self, member):\\n sid = member.server.id\\n role = await self.get_role(member.server)\\n try:\\n muterole = self.riceCog2[server.id][\\\"muterole\\\"]\\n except:\\n muterole = default_muterole \\n\\n if 'poop' in self.riceCog2[sid]:\\n if self.riceCog2[sid]['poop'] == True:\\n if member.id in self.riceCog[sid]:\\n if count >= 1:\\n count = self.riceCog[sid][member.id][\\\"Count\\\"]\\n poops = \\\"\\\\U0001f528\\\" * count\\n role_name = \\\"Warning {}\\\".format(poops)\\n is_there = False\\n colour = 0xbc7642\\n for role in member.server.roles:\\n if role.name == role_name:\\n poop_role = role\\n is_there = True\\n if not is_there:\\n server = member.server\\n poop_role = await self.bot.create_role(server)\\n await self.bot.edit_role(role=poop_role,\\n name=role_name,\\n server=server)\\n try:\\n await self.bot.add_roles(member,\\n poop_role)\\n except discord.errors.Forbidden:\\n await self.bot.say(\\\"No permission to add roles\\\")\\n else:\\n pass\\n if member.id in self.norole[sid]:\\n if self.norole[sid]['role'] == True:\\n role = discord.utils.get(member.server.roles, name=\\\"NoBNL\\\")\\n await self.bot.add_roles(member, role)\\n \\n if not role or not (sid in self.json and member.id in self.json[sid]):\\n return\\n\\n duration = self.json[sid][member.id]['until'] - time.time()\\n if duration > 0:\\n role = discord.utils.get(member.server.roles, name=muterole)\\n await self.bot.add_roles(member, role)\\n\\n reason = 'Punishment re-added on rejoin. '\\n if self.json[sid][member.id]['reason']:\\n reason += self.json[sid][member.id]['reason']\\n\\n if member.id not in self.handles[sid]:\\n self.schedule_unpunish(duration, member, reason)\",\n \"def joined(message):\\n room = session.get('room')\\n join_room(room)\\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)\",\n \"def joined(message):\\n room = session.get('room')\\n join_room(room)\\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)\",\n \"async def on_member_leave(self, member: Member) -> None:\\n if member.guild.id != Guild.id:\\n return\\n\\n self.bot.stats.gauge(\\\"guild.total_members\\\", len(member.guild.members))\",\n \"def joined(self, channel):\\n log.msg(\\\"[I have joined %s]\\\" % channel)\\n self.msg(channel, \\\"user1: bonbon\\\")\",\n \"async def user_joined_button(self, payload: discord.RawReactionActionEvent) -> None:\\n\\n self.bits = flip_action_bits(LoggingActions.USER_JOINED, self.bits)\\n await self.update_embed()\",\n \"async def react_join(a: Message):\\n if a.action.member_id == club_id:\\n await a.answer(r_register_help)\\n stats.jincr()\",\n \"def userJoined(self, user, channel):\\n self.dispatch('population', 'userJoined', user, channel)\",\n \"def userJoined(self, user, channel):\\n # Send messasge to Server bot.\\n self.data_in(text=\\\"\\\", type=\\\"joined\\\", user=\\\"server\\\", channel=channel,\\n nicklist=[user])\",\n \"def on_join(self, raw_msg, source, **kwargs):\",\n \"def on_join(data):\\n username = request.sid\\n room = data\\n join_room(room)\\n logging.info(username + ' has entered the room.')\\n send(username + ' has entered the room.', room=room)\",\n \"def on_joinuser(self, data):\\n user_data = {\\n 'un': data[3], # nick\\n 'ml': data[4], # mod level\\n 'st': data[5], # status related\\n 'id': data[6], # ezcapechat user id\\n 'su': data[7] # ?\\n }\\n if data[3] == self.users.client.nick:\\n self.users.add_client_data(user_data)\\n else:\\n _user = self.users.add(data[3], user_data)\\n print ('%s Joined the room.' % _user.nick)\\n\\n #BOT\\n if (_user.nick.lower() in self.autogreet):\\n self.send_public(\\\"%s, %s\\\" % (_user.nick, self.autogreet[_user.nick.lower()]))\",\n \"def joined(message):\\n #room = session.get('room')\\n room='abc'\\n join_room(room)\\n #emit('status', {'msg': session.get('name') + ' has entered the room.' + message['msg']}, room=room)\\n emit('status', {'msg': 'Yao has entered the room.'}, room=room)\\n #emit('status', {'msg': 'Yao has entered the room.'}, room='room1')\",\n \"def join_room(self, client, room):\\n if room.verify_if_is_invited(client):\\n room.add_member(client)\\n self.send_message('Te has unido a la sala {}'.format(room.get_name()), client)\\n else:\\n self.send_message('No estas invitado a la sala.', client)\",\n \"def join(var, wrapper, message):\\n # keep this and the event in fjoin() in sync\\n evt = Event(\\\"join\\\", {\\n \\\"join_player\\\": join_player,\\n \\\"join_deadchat\\\": join_deadchat,\\n \\\"vote_gamemode\\\": vote_gamemode\\n })\\n if not evt.dispatch(var, wrapper, message, forced=False):\\n return\\n if var.PHASE in (\\\"none\\\", \\\"join\\\"):\\n if wrapper.private:\\n return\\n if var.ACCOUNTS_ONLY:\\n if wrapper.source.account is None:\\n wrapper.pm(messages[\\\"not_logged_in\\\"])\\n return\\n if evt.data[\\\"join_player\\\"](var, wrapper) and message:\\n evt.data[\\\"vote_gamemode\\\"](var, wrapper, message.lower().split()[0], doreply=False)\\n\\n else: # join deadchat\\n if wrapper.private and wrapper.source is not wrapper.target:\\n evt.data[\\\"join_deadchat\\\"](var, wrapper.source)\",\n \"def on_player_join(self, func):\\n self.loop.create_task(self._rpc.register_event('ACTIVITY_JOIN', func))\\n return func\",\n \"async def on_members_added_activity(self, members_added: [ChannelAccount], turn_context: TurnContext):\\n for member in members_added:\\n if member.id != turn_context.activity.recipient.id:\\n await turn_context.send_activity(\\n f\\\"Hi there. \\\" + self.WELCOME_MESSAGE\\n )\",\n \"def joined(self, channel):\\n log.info(\\\"Joined %s.\\\", channel)\\n # ask for the current list of users in the channel\\n self.dispatch('presence', 'joined', channel)\",\n \"def test_double_join(self):\\n now = datetime.datetime.now()\\n\\n channel = ChannelStatus.get_channel(channel_spec=self.channel)\\n greet = channel.update_user_join_status(self.user, self.greeting, now=now)\\n now += datetime.timedelta(seconds=0.1)\\n\\n greet = channel.update_user_join_status(self.user, self.greeting, now=now)\\n self.assertEqual(False, greet)\",\n \"def joined(self, channel):\\n self.logger.log(\\\"[I have joined %s]\\\" % channel)\",\n \"def test_join_after_invite(self) -> None:\\n\\n self._perform_background_initial_update()\\n\\n u1 = self.register_user(\\\"u1\\\", \\\"pass\\\")\\n u1token = self.login(\\\"u1\\\", \\\"pass\\\")\\n r1 = self.helper.create_room_as(u1, tok=u1token)\\n\\n u2 = self.register_user(\\\"u2\\\", \\\"pass\\\")\\n u2token = self.login(\\\"u2\\\", \\\"pass\\\")\\n\\n self.helper.invite(r1, u1, u2, tok=u1token)\\n\\n r1stats_ante = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_ante is not None\\n\\n self.helper.join(r1, u2, tok=u2token)\\n\\n r1stats_post = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_post is not None\\n\\n self.assertEqual(\\n r1stats_post[\\\"current_state_events\\\"] - r1stats_ante[\\\"current_state_events\\\"],\\n 0,\\n )\\n self.assertEqual(\\n r1stats_post[\\\"joined_members\\\"] - r1stats_ante[\\\"joined_members\\\"], +1\\n )\\n self.assertEqual(\\n r1stats_post[\\\"invited_members\\\"] - r1stats_ante[\\\"invited_members\\\"], -1\\n )\",\n \"def test_join(self):\\n self.node.fake_message(Join(), sender='F999')\\n self.assertMessage(['F999'], Welcome(state='state', slot=2,\\n decisions={1: PROPOSAL1}))\",\n \"def on_join(data):\\n print(str(data))\\n if models.Leaderboard.query.filter_by(\\n username=data['user']).first() is None:\\n add_user(data['user'])\\n users, scores = calculate_scores()\\n socketio.emit('leaderboard_info', {'users': users, 'scores': scores})\",\n \"async def join(self, ctx, invite : discord.Invite):\\r\\n if ctx.message.author.id == \\\"481270883701358602\\\":\\r\\n await self.client.accept_invite(invite)\\r\\n await self.client.say(\\\"Joined the server.\\\")\\r\\n else:\\r\\n await self.client.say(\\\"**Owner only command.**\\\")\",\n \"def on_join(bot, trigger):\\n\\tfor channel in trigger.args[0].split(','):\\n\\t\\tlog(bot, channel, '*** {} has joined {}', trigger.nick, channel);\",\n \"def joined_days(self):\\n return (timezone.now() - self.user.date_joined).days\",\n \"async def on_member_update(self, before, after):\\n if after and after.activity is not None:\\n if after.activity.name == \\\"Guild Wars 2\\\":\\n print(after.id)\\n await self.startGw2Session(after.id)\\n print(\\\"Started GW2 Session {} {}\\\".format(after, dt.utcnow()))\\n\\n elif before and before.activity is not None:\\n if before.activity.name == \\\"Guild Wars 2\\\":\\n await self.endGw2Session(before.id)\\n print(\\\"Ending GW2 Session {} {}\\\".format(before, dt.utcnow()))\",\n \"async def join(ctx):\\n if ctx.message.channel.name.lower() not in tod_channels:\\n return\\n\\n room = ctx.message.channel.name.lower()\\n if room not in tod_games:\\n await amor_manager.say(\\\"Truth Or Dare not in progress in {}\\\".format(room))\\n return\\n player = ctx.message.author.name\\n if player.lower() in list(tod_games[room]['participants'].keys()):\\n await amor_manager.say(\\\"{}... you're already playing Truth or Dare here!\\\".format(room))\\n else:\\n tod_games[room]['participants'][player.lower()] = {'spins': 0}\\n await amor_manager.say(\\\"{} has joined Truth or Dare!\\\".format(player))\",\n \"async def chat_join(self, event):\\n await self.send_json(\\n return_value(\\n ACTION_JOIN,\\n event['label'],\\n event['username'],\\n MSG_JOIN,\\n NO_MESSAGE\\n )\\n )\",\n \"async def on_member_update(self, before: Member, after: Member):\\n\\n if not self._is_tracked(before.guild, EventPriority.update):\\n return\\n\\n if before.name != after.name or before.discriminator != after.discriminator:\\n em = self.em_base(\\n after,\\n f\\\"Member {before.mention} ({before.name}#{before.discriminator}) \\\"\\n f\\\"changed their name to {after.name}#{after.discriminator}\\\",\\n EventColors.name_change.value\\n )\\n\\n await self.log_event(em, before.guild, priority=EventPriority.update)\\n\\n if before.roles != after.roles:\\n added, removed = None, None\\n\\n for role in before.roles:\\n if role not in after.roles:\\n removed = f\\\"{role.mention}\\\\n({role.name})\\\"\\n\\n for role in after.roles:\\n if role not in before.roles:\\n added = f\\\"{role.mention}\\\\n({role.name})\\\"\\n\\n found, errored, mod, _ = await self._get_last_audit_action(\\n after.guild,\\n AuditLogAction.member_role_update,\\n after\\n )\\n\\n if added:\\n\\n em = self.em_base(\\n after,\\n f\\\"Member {after.mention} ({after.name}) roles changed\\\",\\n EventColors.role_added.value\\n )\\n\\n em.add_field(\\n name=\\\"Role Added\\\",\\n value=added\\n )\\n\\n if found:\\n em.add_field(\\n name=\\\"Mod Responsible\\\",\\n value=f\\\"{mod.mention}\\\\n({mod.name})\\\"\\n )\\n\\n await self.log_event(em, before.guild, priority=EventPriority.update)\\n\\n if removed:\\n\\n em = self.em_base(\\n after,\\n f\\\"Member {after.mention} ({after.name}) roles changed\\\",\\n EventColors.role_removed.value\\n )\\n\\n em.add_field(\\n name=\\\"Role Removed\\\",\\n value=removed\\n )\\n\\n if found:\\n em.add_field(\\n name=\\\"Mod Responsible\\\",\\n value=f\\\"{mod.mention}\\\\n({mod.name})\\\"\\n )\\n\\n await self.log_event(em, before.guild, priority=EventPriority.update)\\n\\n if before.nick != after.nick:\\n\\n if after.nick is None:\\n em = self.em_base(\\n after,\\n f\\\"Member {before.mention} ({before.name}) reset their nickname\\\",\\n EventColors.nickname_change.value\\n )\\n\\n else:\\n em = self.em_base(\\n after,\\n f\\\"Member {before.mention} ({before.name}) changed their nickname \\\"\\n f\\\"from {before.nick} to {after.nick}\\\",\\n EventColors.nickname_change.value\\n )\\n\\n await self.log_event(em, after.guild, priority=EventPriority.update)\",\n \"def joined(message):\\n global list_messages\\n room = session.get('room')\\n join_room(room)\\n print ('joined session list_messages ' + str(len(list_messages)) + ' , session ' + str(session) +'\\\\n')\\n emit('status', {'msg': str(clients)})\\n for x in list_messages:\\n emit('status', {'msg': x})\\n emit('status', {'msg': session.get('name') + ' has entered the room.'}, room=room)\",\n \"async def on_ready():\\n print(\\\"{} has joined the server\\\".format(client.user.name))\",\n \"def joined(self, channel):\\n self.logger.log(\\\"[I have joined %s]\\\" % channel)\\n #IRC Keepalive\\n self.startHeartbeat()\\n #Custom heartbeat\\n self.loopcall.start(5.0)\",\n \"def on_join_data(self, data):\\n self.users.client.key = data[7] # unique user identifier ?\\n self.users.client.join_time = data[11] # join time as unix including milliseconds ?\\n self._room_id = data[13] # room id\\n\\n self.send_connection_ok()\\n\\n if config.DEBUG_TO_CONSOLE:\\n print ('Join Data:')\\n for i, v in enumerate(data):\\n print ('\\\\t[%s] - %s' % (i, v))\",\n \"def join(bot, event, *args):\\n yield from _joinEvent(bot, event, list(args))\",\n \"def on_join(self, event):\\n self.pre_check(event)\\n state = event.guild.get_member(event.author).get_voice_state()\\n if not state:\\n return api_loop(\\n event.channel.send_message,\\n \\\"You must be in a voice channel to use that command.\\\",\\n )\\n if event.guild.id not in self.guilds:\\n try:\\n client = state.channel.connect(mute=False)\\n except VoiceException as e:\\n return api_loop(\\n event.channel.send_message,\\n \\\"Failed to connect to voice: `{}`\\\".format(e),\\n )\\n else:\\n self.guilds[event.guild.id] = psuedo_queue(\\n self,\\n player=Player(client),\\n guild_id=event.guild.id,\\n )\\n return\",\n \"def query_member_status():\\n notify_member_status()\\n logger.info('signal sent for status report')\",\n \"def getJoiningTime(self):\\n return self.__joinTime\",\n \"def signedOn(self):\\n log.msg(\\\"Signed on\\\")\\n if self.nickname != self.factory.nickname:\\n log.msg('Name taken, new is ''\\\"{}\\\".'.format(self.nickname))\\n self.join(self.factory.channel)\",\n \"async def update_member_count():\\n guild = bot.get_guild(SERVER_ID)\\n channel_prefix = \\\"Members\\\"\\n vc = discord.utils.find(lambda c: channel_prefix in c.name, guild.voice_channels)\\n mem_count = guild.member_count\\n joined_today = len([m for m in guild.members if m.joined_at.date() == datetime.datetime.today().date()])\\n left_channel = discord.utils.get(guild.text_channels, name=CHANNEL_LEAVE)\\n left_messages = await left_channel.history(limit=200).flatten()\\n left_today = len([m for m in left_messages if m.created_at.date() == datetime.datetime.today().date()])\\n await vc.edit(name=f\\\"{mem_count} Members (+{joined_today}/-{left_today})\\\")\\n print(\\\"Refreshed member count.\\\")\",\n \"async def newusers(self, ctx, *, count=5):\\n\\n count = min(max(count, 5), 25)\\n\\n now = datetime.now(timezone.utc)\\n e = disnake.Embed()\\n\\n for idx, member in enumerate(\\n sorted(ctx.guild.members, key=lambda m: m.joined_at, reverse=True)\\n ):\\n if idx >= count:\\n break\\n\\n value = \\\"Joined {0} ago\\\\nCreated {1} ago\\\".format(\\n pretty_timedelta(now - member.joined_at),\\n pretty_timedelta(now - member.created_at),\\n )\\n e.add_field(name=po(member), value=value, inline=False)\\n\\n await ctx.send(embed=e)\",\n \"def fjoin(var, wrapper, message):\\n # keep this and the event in def join() in sync\\n evt = Event(\\\"join\\\", {\\n \\\"join_player\\\": join_player,\\n \\\"join_deadchat\\\": join_deadchat,\\n \\\"vote_gamemode\\\": vote_gamemode\\n })\\n\\n if not evt.dispatch(var, wrapper, message, forced=True):\\n return\\n noticed = False\\n fake = False\\n if not message.strip():\\n evt.data[\\\"join_player\\\"](var, wrapper, forced=True)\\n\\n parts = re.split(\\\" +\\\", message)\\n possible_users = {u.lower().nick for u in wrapper.target.users}\\n to_join = []\\n if not botconfig.DEBUG_MODE:\\n match = complete_one_match(users.lower(parts[0]), possible_users)\\n if match:\\n to_join.append(match)\\n else:\\n for i, s in enumerate(parts):\\n match = complete_one_match(users.lower(s), possible_users)\\n if match:\\n to_join.append(match)\\n else:\\n to_join.append(s)\\n for tojoin in to_join:\\n tojoin = tojoin.strip()\\n # Allow joining single number fake users in debug mode\\n if users.predicate(tojoin) and botconfig.DEBUG_MODE:\\n user = users._add(wrapper.client, nick=tojoin) # FIXME\\n evt.data[\\\"join_player\\\"](var, type(wrapper)(user, wrapper.target), forced=True, who=wrapper.source)\\n continue\\n # Allow joining ranges of numbers as fake users in debug mode\\n if \\\"-\\\" in tojoin and botconfig.DEBUG_MODE:\\n first, hyphen, last = tojoin.partition(\\\"-\\\")\\n if first.isdigit() and last.isdigit():\\n if int(last)+1 - int(first) > var.MAX_PLAYERS - len(list_players()):\\n wrapper.send(messages[\\\"too_many_players_to_join\\\"].format(wrapper.source.nick))\\n break\\n fake = True\\n for i in range(int(first), int(last)+1):\\n user = users._add(wrapper.client, nick=str(i)) # FIXME\\n evt.data[\\\"join_player\\\"](var, type(wrapper)(user, wrapper.target), forced=True, who=wrapper.source)\\n continue\\n if not tojoin:\\n continue\\n\\n maybe_user = None\\n\\n for user in wrapper.target.users:\\n if users.equals(user.nick, tojoin):\\n maybe_user = user\\n break\\n else:\\n if not users.predicate(tojoin) or botconfig.DEBUG_MODE:\\n if not noticed: # important\\n wrapper.send(\\\"{0}{1}\\\".format(wrapper.source, messages[\\\"fjoin_in_chan\\\"]))\\n noticed = True\\n continue\\n\\n if maybe_user is not None:\\n if not botconfig.DEBUG_MODE and var.ACCOUNTS_ONLY:\\n if maybe_user.account is None:\\n wrapper.pm(messages[\\\"account_not_logged_in\\\"].format(maybe_user))\\n return\\n elif botconfig.DEBUG_MODE:\\n fake = True\\n\\n if maybe_user is not users.Bot:\\n if maybe_user is None and users.predicate(tojoin) and botconfig.DEBUG_MODE:\\n maybe_user = users._add(wrapper.client, nick=tojoin) # FIXME\\n evt.data[\\\"join_player\\\"](var, type(wrapper)(maybe_user, wrapper.target), forced=True, who=wrapper.source)\\n else:\\n wrapper.pm(messages[\\\"not_allowed\\\"])\\n if fake:\\n wrapper.send(messages[\\\"fjoin_success\\\"].format(wrapper.source, len(list_players())))\",\n \"def joined(self, channel):\\n # Return user list to Server bot.\\n self.get_nicklist()\",\n \"async def on_member_update(old, updated):\\n if old.nick != updated.nick:\\n boterate.update_member(updated)\",\n \"async def monitor(self, ctx, member: Member, channel: TextChannel):\\n guild_timezone = await self.get_tz(ctx.guild.id)\\n if not guild_timezone:\\n return await ctx.send(\\n f\\\"Please set timezone using `{ctx.prefix}settz` command.\\\"\\n )\\n\\n user = User(self.bot.db)\\n log = Log(self.bot.db)\\n\\n monitor = await user.get(multi=False, guild_id=ctx.guild.id, user_id=member.id)\\n if monitor:\\n return await ctx.send(f\\\"{member} has already been monitored.\\\")\\n\\n await user.insert(\\n mentor_id=ctx.author.id,\\n guild_id=ctx.guild.id,\\n user_id=member.id,\\n channel_id=channel.id,\\n )\\n now = datetime.now(timezone(guild_timezone))\\n\\n await log.insert(\\n status=member.status.name,\\n user_id=member.id,\\n guild_id=ctx.guild.id,\\n started=now,\\n )\\n\\n self.bot.monitored_users.add(member.id)\\n\\n await ctx.send(f\\\"{member} will get monitored from now.\\\")\",\n \"async def join(self, ctx):\\n voice = ctx.author.voice\\n\\n if voice and voice.channel:\\n channel = voice.channel\\n \\n try:\\n await channel.connect()\\n except discord.errors.ClientException:\\n await ctx.guild.get_member(self.bot.user.id).move_to(channel) \\n \\n await ctx.send(f\\\"Joined `{channel.name}`.\\\")\\n else:\\n await ctx.message.add_reaction('\\\\U0001F615');\\n await ctx.send(\\\"You're not in a voice channel.\\\")\",\n \"async def on_guild_join(self, guild: discord.Guild) -> None:\\n logger.info(f'Added to new guild: {guild.name} ({guild.id})')\\n\\n with self.get_session() as session:\\n _guild: Guild = session.query(Guild).get(guild.id)\\n if _guild is None:\\n session.add(Guild(id=guild.id))\\n else:\\n # Guild has been seen before. Update last_joined and set as active again.\\n _guild.active = True\\n _guild.last_joined = datetime.utcnow()\",\n \"def on_join(data):\\n quiz = store.get_quiz_by_user_id(data['user_id'])\\n room = quiz.quiz_id\\n\\n # get and clean the users (no score)\\n users = store.get_users_by_id(store.get_quiz_by_id(room).users)\\n users_cleaned = [user.name for user in users]\\n\\n # emit the new users the to the room\\n if room is not None:\\n join_room(room)\\n emit(\\\"current_players\\\", {\\\"users\\\": users_cleaned}, room=room)\",\n \"def joinedChannel(self, channel, users):\\n pass\",\n \"def test_join_first_time(self) -> None:\\n\\n self._perform_background_initial_update()\\n\\n u1 = self.register_user(\\\"u1\\\", \\\"pass\\\")\\n u1token = self.login(\\\"u1\\\", \\\"pass\\\")\\n r1 = self.helper.create_room_as(u1, tok=u1token)\\n\\n u2 = self.register_user(\\\"u2\\\", \\\"pass\\\")\\n u2token = self.login(\\\"u2\\\", \\\"pass\\\")\\n\\n r1stats_ante = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_ante is not None\\n\\n self.helper.join(r1, u2, tok=u2token)\\n\\n r1stats_post = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_post is not None\\n\\n self.assertEqual(\\n r1stats_post[\\\"current_state_events\\\"] - r1stats_ante[\\\"current_state_events\\\"],\\n 1,\\n )\\n self.assertEqual(\\n r1stats_post[\\\"joined_members\\\"] - r1stats_ante[\\\"joined_members\\\"], 1\\n )\",\n \"async def on_member_join(member: discord.Member):\\n for channel in member.server.channels:\\n print(channel)\\n if channel == \\\"general\\\":\\n await member.send(f\\\"\\\"\\\"Welcome to the server {member.mention}!\\\"\\\"\\\")\",\n \"def date_joined(self):\\n return self._date_joined\",\n \"def PartyCheckIn(sc, event):\\n now = datetime.datetime.now()\\n vice = event['text'][:5].lower()\\n user = sc.api_call('users.info', user=event['user'])\\n user = user['user']['name']\\n db = pymysql.connect(host='localhost', user='pizzabot', db='pizzachat')\\n cursor = db.cursor()\\n cursor.execute('DELETE FROM party WHERE date < '\\n '(NOW() - INTERVAL 120 MINUTE) AND who=\\\"%s\\\"' % user)\\n cursor.execute('INSERT INTO party (who, vice) VALUES (\\\"%s\\\", \\\"%s\\\")' % (user, vice))\\n db.commit()\\n cursor.execute('SELECT who FROM party WHERE who=\\\"%s\\\" AND vice=\\\"%s\\\"' % (user, vice))\\n status = cursor.fetchall()\\n party_message = PartyMessage(user, vice, len(status))\\n sc.api_call('chat.postMessage', as_user='true', channel=event['channel'],\\n text=party_message)\\n db.close()\",\n \"def test_join_after_leave(self) -> None:\\n\\n self._perform_background_initial_update()\\n\\n u1 = self.register_user(\\\"u1\\\", \\\"pass\\\")\\n u1token = self.login(\\\"u1\\\", \\\"pass\\\")\\n r1 = self.helper.create_room_as(u1, tok=u1token)\\n\\n u2 = self.register_user(\\\"u2\\\", \\\"pass\\\")\\n u2token = self.login(\\\"u2\\\", \\\"pass\\\")\\n\\n self.helper.join(r1, u2, tok=u2token)\\n self.helper.leave(r1, u2, tok=u2token)\\n\\n r1stats_ante = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_ante is not None\\n\\n self.helper.join(r1, u2, tok=u2token)\\n\\n r1stats_post = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_post is not None\\n\\n self.assertEqual(\\n r1stats_post[\\\"current_state_events\\\"] - r1stats_ante[\\\"current_state_events\\\"],\\n 0,\\n )\\n self.assertEqual(\\n r1stats_post[\\\"joined_members\\\"] - r1stats_ante[\\\"joined_members\\\"], +1\\n )\\n self.assertEqual(\\n r1stats_post[\\\"left_members\\\"] - r1stats_ante[\\\"left_members\\\"], -1\\n )\",\n \"def join(coro):\\n return JoinEvent(coro)\",\n \"def signedOn(self):\\n self.join(self.factory.channel)\",\n \"async def on_start(self):\\n m = \\\"**{}** has started a game of {}! To participate, say `I`! **{} players needed.**\\\".format(\\n self.message.author.display_name, self.name, self.num)\\n await client.say(self.message, m)\",\n \"def join(self) -> \\\"cocotb.triggers.Join\\\":\\n return cocotb.triggers.Join(self)\",\n \"async def on_guild_join(self, guild):\\n embed = discord.Embed(title=\\\"New server!\\\", description=guild.name, colour=GREEN)\\n embed.add_field(name=\\\"Owner\\\", value=guild.owner.name)\\n embed.add_field(name=\\\"Members\\\", value=str(len(guild.members)))\\n embed.set_author(name=guild.owner.nick if guild.owner.nick else guild.owner.name, icon_url=guild.owner.avatar_url)\\n embed.set_footer(text=f\\\"ID: {guild.id}\\\")\\n await self.bot.get_channel(753495117767377016).send(embed=embed)\",\n \"async def tod_join(self, ctx, *args):\\n if ctx.author not in self.players:\\n self.players.append(ctx.author)\\n message = f\\\"{ctx.author.mention} has been added to the game!\\\"\\n await ctx.send(message)\\n else:\\n message = f\\\"{ctx.author.mention} has already joined!\\\"\\n await ctx.send(message)\\n\\n # Updates the role if channel exists\\n for channel in ctx.guild.channels:\\n if channel.name.startswith(\\\"truth-or-dare\\\"):\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Player\\\")\\n await ctx.author.add_roles(role)\\n return\\n\\n # Creates the channel if it doesn't exist\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Player\\\")\\n bots = discord.utils.get(ctx.guild.roles, name=\\\"Bots\\\")\\n overwrites = {\\n ctx.guild.default_role: discord.PermissionOverwrite(read_messages=False, send_messages=False),\\n bots: discord.PermissionOverwrite(read_messages=True, send_messages=True),\\n role: discord.PermissionOverwrite(read_messages=True, send_messages=True, connect=True, speak=True)\\n }\\n await ctx.guild.create_text_channel('truth-or-dare', overwrites=overwrites)\\n await ctx.guild.create_voice_channel('secret-voice', overwrites=overwrites)\\n\\n # Adds the role\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Player\\\")\\n await ctx.author.add_roles(role)\",\n \"async def info(self, ctx, *, member: disnake.Member = None):\\n\\n member = member or ctx.author\\n\\n e = disnake.Embed(description=\\\"\\\")\\n\\n if member.bot:\\n e.description = \\\"This account is a bot.\\\\n\\\\n\\\"\\n\\n e.description += member.mention\\n\\n e.add_field(name=\\\"Status\\\", value=member.status)\\n\\n if member.activity:\\n e.add_field(name=\\\"Activity\\\", value=member.activity.name)\\n\\n e.set_author(name=str(member), icon_url=member.display_avatar.url)\\n\\n now = datetime.now(timezone.utc)\\n created = member.created_at\\n joined = member.joined_at\\n\\n e.add_field(\\n name=\\\"Account age\\\",\\n value=\\\"{0} • Created <t:{1}:F>\\\".format(\\n pretty_timedelta(now - created), round(created.timestamp())\\n ),\\n inline=False,\\n )\\n\\n e.add_field(\\n name=\\\"Member for\\\",\\n value=\\\"{0} • Joined <t:{1}:F>\\\".format(\\n pretty_timedelta(now - joined), round(joined.timestamp())\\n ),\\n )\\n\\n if len(member.roles) > 1:\\n e.add_field(\\n name=\\\"Roles\\\",\\n value=\\\" \\\".join(role.mention for role in reversed(member.roles[1:])),\\n inline=False,\\n )\\n\\n e.set_footer(text=\\\"ID: \\\" + str(member.id))\\n\\n await ctx.send(embed=e)\",\n \"def joined(self, channel):\\n self.logger.info(\\\"Joined %s\\\" % channel)\",\n \"async def join(message):\\n voice_client = await get_voice_client(message.author, move_to=True, any_channel=True)\\n return \\\"I'm in {}\\\".format(voice_client.channel.mention)\",\n \"async def level(self, ctx, member: discord.Member = None):\\n member = member or ctx.author\\n if member.bot:\\n return\\n await get_user_level(ctx, member)\",\n \"async def on_member_update(before: discord.Member, after: discord.Member):\\n # Return if the server doesn't have any notify channels setup\\n if not twitch_config.data[\\\"servers\\\"].get(after.server.id, {}).get(\\\"notify_channels\\\", False):\\n return\\n\\n # Make sure the member just started streaming\\n if not started_streaming(before, after):\\n return\\n\\n # Tru getting the id and also log some possibly useful info during exceptions\\n try:\\n twitch_id = await twitch.get_id(after)\\n except twitch.RequestFailed as e: # Could not find the streamer due to a request error\\n logging.info(\\\"Could not get twitch id of {}: {}\\\".format(after, e))\\n return\\n except twitch.UserNotResolved as e: # Ignore them if the id was not found.\\n logging.debug(e)\\n return\\n\\n # Return the stream info of the specified user\\n try:\\n stream_response = await twitch.request(\\\"streams/\\\" + twitch_id)\\n except twitch.RequestFailed as e:\\n logging.info(\\\"Could not get twitch stream of {} (id: {}): {}\\\".format(after, twitch_id, e))\\n return\\n\\n # If the member isn't actually streaming, return (should not be the case as discord uses the twitch api too)\\n if stream_response[\\\"stream\\\"] is None:\\n return\\n\\n # Create the embedded message and send it to every stream channel\\n embed = make_twitch_embed(after, stream_response)\\n for channel_id in twitch_config.data[\\\"servers\\\"][after.server.id][\\\"notify_channels\\\"]:\\n await client.send_message(after.server.get_channel(channel_id), embed=embed)\",\n \"def you_won(self):\\n self.end_of_level()\\n self.message_holder.add_widget(self.you_win_label)\\n Clock.schedule_once(self.goto_next_level, 5)\",\n \"def on_join(session):\\n print('session connected: {}'.format(session))\\n\\n def on_leave(details):\\n print(\\\"on_leave\\\", details)\\n\\n session.on_leave(on_leave)\\n\\n # explicitly leaving a realm will disconnect the connection\\n # cleanly and not try to reconnect, but exit cleanly.\\n session.leave()\",\n \"def is_joined_days_passed(self, days):\\n return timezone.now() >= self.user.date_joined + timedelta(days=days)\",\n \"def join(self):\\n if self._join_command is not None:\\n return\\n\\n if self._joined:\\n self.emit('joined', True, None)\\n return\\n\\n print('%r: joining', self)\\n\\n self._join_command = _JoinCommand(self.telepathy_conn,\\n self.room_handle)\\n self._join_command.connect('finished', self.__joined_cb)\\n self._join_command.run()\",\n \"def signedOn(self):\\n self.join(self.channel)\",\n \"def irc_JOIN(self, prefix, params):\\n user = re.match(self.user_regex, prefix)\\n channel = params[0]\\n\\n self.logger.debug(\\n \\\"%s!%s@%s joined %s\\\" %\\n (user.group(1), user.group(2), user.group(3), channel)\\n )\\n\\n self.event_manager.fire(\\\"irc.join\\\", user, channel)\",\n \"def join(self, message, db_session):\\n username = self.ts.get_user(message)\\n user = db_session.query(db.User).filter(db.User.name == username).one_or_none()\\n if not user:\\n user = db.User(name=username)\\n db_session.add(user)\\n try:\\n self.player_queue.push(username, user.times_played)\\n self._add_to_whisper_queue(username, \\\"You've joined the queue.\\\")\\n user.times_played += 1\\n except RuntimeError:\\n self._add_to_whisper_queue(username, \\\"You're already in the queue and can't join again.\\\")\\n\\n # queue_snapshot = copy.deepcopy(self.player_queue.queue)\\n # self.command_queue.appendleft(('_insert_into_player_queue_spreadsheet',\\n # {'username': username, 'times_played':user.times_played, 'player_queue': queue_snapshot}))\",\n \"async def join(self, ctx):\\n player = self.bot.lavalink.players.get(ctx.guild.id)\\n if player.is_connected:\\n return await ctx.send(\\\"I'm already in a voice channel :no_entry:\\\")\\n else:\\n if not ctx.author.voice or not ctx.author.voice.channel:\\n return await ctx.send(\\\"You are not in a voice channel :no_entry:\\\")\\n player.store('sessionowner', ctx.author.id)\\n player.store('channel', ctx.channel.id)\\n await player.connect(ctx.author.voice.channel.id)\\n await ctx.send(\\\"Summoned to `{}` <:done:403285928233402378>\\\".format(ctx.author.voice.channel.name))\",\n \"async def tick(self):\\n room = self.bot.Room.load('19961884194@chatroom')\\n await room.ready()\\n await room.say(f'i love you -> {datetime.now()}')\",\n \"def member_action(self):\\n # trigger scene signal\\n self.scene().node_member.emit(self.metadata)\",\n \"async def join(self, interaction: discord.Interaction, button: discord.ui.Button):\\n\\t\\tif interaction.user.id == self.ctx.author.id:\\n\\t\\t\\tawait interaction.response.send_message(\\n\\t\\t\\t\\tcontent='You have already joined the game. You can add AI players or start the game early with the other two buttons.',\\n\\t\\t\\t\\tephemeral=True,\\n\\t\\t\\t)\\n\\t\\t\\treturn\\n\\t\\tself.players.append(interaction.user)\\n\\t\\tself.start.disabled = False\\n\\t\\tif len(self.players) >= self.max_players:\\n\\t\\t\\tview = None\\n\\t\\t\\tself.stop()\\n\\t\\telse:\\n\\t\\t\\tview = self\\n\\t\\tawait interaction.response.edit_message(content=self.generate_message(), view=view)\",\n \"def count_changed_handler(count: int):\\n # Chat should have at least two active members\\n global can_speak\\n can_speak = count >= 2\\n if not can_speak:\\n safe_print(print_mutex, \\\"Too few members. Waiting another member...\\\")\",\n \"async def on_members_added_activity(\\n self, members_added: [ChannelAccount], turn_context: TurnContext\\n ):\\n for member in members_added:\\n if member.id != turn_context.activity.recipient.id:\\n reply = MessageFactory.list([])\\n member_name = member.name\\n message = Activity(\\n type=ActivityTypes.message,\\n attachments=[self.__send_welcome_card(member_name)],\\n )\\n\\n await turn_context.send_activity(message)\\n\\n #await self.__send_welcome_card(turn_context, member_name)\",\n \"def start(self, update, context):\\n # add or update to the sqlite table.\\n chat = update.message.chat\\n user_tuple = self.db_manager.create_user_tuple(chat.id)\\n self.db_manager.add_new_user(user_tuple)\\n self.logger.info(\\n 'A new user with username: %s and chat_id: %s subscribed to the list.' % (chat.username, chat.id)\\n )\\n update.message.reply_text('Welcome! You have successfully subscribed to real time notification for a six '\\n 'scored in IPL 2019 cricket match.')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78859025","0.7684899","0.76168865","0.76168865","0.76168865","0.76117444","0.7605289","0.7578849","0.7570187","0.7469505","0.7328319","0.72358775","0.7097512","0.6860103","0.6832718","0.66840136","0.664821","0.65940976","0.65013736","0.64450365","0.6405919","0.63756734","0.6334497","0.6334497","0.6278375","0.62650555","0.62643343","0.6246457","0.6193901","0.60370153","0.59691834","0.59461606","0.59383017","0.59205216","0.58919305","0.5865398","0.58455086","0.58400255","0.583536","0.58142895","0.58101004","0.57955766","0.57813567","0.57805014","0.5770844","0.575654","0.57506776","0.57422435","0.5716284","0.57131004","0.57073843","0.5698458","0.5697734","0.5692713","0.56865317","0.56193656","0.5618889","0.5597236","0.55821085","0.55706835","0.55645216","0.5558013","0.554588","0.5542725","0.5507813","0.5491523","0.5488251","0.54816633","0.54630053","0.54500407","0.5447463","0.5443998","0.54293865","0.5423332","0.54191434","0.5413729","0.54129046","0.5406512","0.540125","0.5376322","0.53674656","0.5363155","0.53399885","0.53304785","0.5324466","0.53225505","0.5318149","0.53006864","0.52777755","0.5264826","0.5264339","0.526225","0.5247702","0.5234483","0.52260196","0.52238715","0.52184266","0.5213315","0.52068204","0.51826566"],"string":"[\n \"0.78859025\",\n \"0.7684899\",\n \"0.76168865\",\n \"0.76168865\",\n \"0.76168865\",\n \"0.76117444\",\n \"0.7605289\",\n \"0.7578849\",\n \"0.7570187\",\n \"0.7469505\",\n \"0.7328319\",\n \"0.72358775\",\n \"0.7097512\",\n \"0.6860103\",\n \"0.6832718\",\n \"0.66840136\",\n \"0.664821\",\n \"0.65940976\",\n \"0.65013736\",\n \"0.64450365\",\n \"0.6405919\",\n \"0.63756734\",\n \"0.6334497\",\n \"0.6334497\",\n \"0.6278375\",\n \"0.62650555\",\n \"0.62643343\",\n \"0.6246457\",\n \"0.6193901\",\n \"0.60370153\",\n \"0.59691834\",\n \"0.59461606\",\n \"0.59383017\",\n \"0.59205216\",\n \"0.58919305\",\n \"0.5865398\",\n \"0.58455086\",\n \"0.58400255\",\n \"0.583536\",\n \"0.58142895\",\n \"0.58101004\",\n \"0.57955766\",\n \"0.57813567\",\n \"0.57805014\",\n \"0.5770844\",\n \"0.575654\",\n \"0.57506776\",\n \"0.57422435\",\n \"0.5716284\",\n \"0.57131004\",\n \"0.57073843\",\n \"0.5698458\",\n \"0.5697734\",\n \"0.5692713\",\n \"0.56865317\",\n \"0.56193656\",\n \"0.5618889\",\n \"0.5597236\",\n \"0.55821085\",\n \"0.55706835\",\n \"0.55645216\",\n \"0.5558013\",\n \"0.554588\",\n \"0.5542725\",\n \"0.5507813\",\n \"0.5491523\",\n \"0.5488251\",\n \"0.54816633\",\n \"0.54630053\",\n \"0.54500407\",\n \"0.5447463\",\n \"0.5443998\",\n \"0.54293865\",\n \"0.5423332\",\n \"0.54191434\",\n \"0.5413729\",\n \"0.54129046\",\n \"0.5406512\",\n \"0.540125\",\n \"0.5376322\",\n \"0.53674656\",\n \"0.5363155\",\n \"0.53399885\",\n \"0.53304785\",\n \"0.5324466\",\n \"0.53225505\",\n \"0.5318149\",\n \"0.53006864\",\n \"0.52777755\",\n \"0.5264826\",\n \"0.5264339\",\n \"0.526225\",\n \"0.5247702\",\n \"0.5234483\",\n \"0.52260196\",\n \"0.52238715\",\n \"0.52184266\",\n \"0.5213315\",\n \"0.52068204\",\n \"0.51826566\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7771115"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":78896,"cells":{"query":{"kind":"string","value":"Given a triangulation object and a field `q` defined at the vertices of the triangulation, return integral |grad q|^2 dx"},"document":{"kind":"string","value":"def square_gradient(tri, q):\n\n finder = tri.get_trifinder()\n interp = LinearTriInterpolator(tri, q, trifinder = finder)\n\n integral = 0.0\n num_triangles, _ = np.shape(tri.triangles)\n\n for n in range(num_triangles):\n ele = tri.triangles[n, :]\n x = tri.x[ele]\n y = tri.y[ele]\n\n a = area(x, y)\n q_x, q_y = interp.gradient(sum(x) / 3, sum(y) / 3)\n\n integral += a * (q_x.data**2 + q_y.data**2)\n\n return integral"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def gradh(self,q):\n gx = self.d_i(q) / self.e1u\n gy = self.d_j(q) / self.e2v\n #\n return gx,gy","def integrate_dphi_phi(self, i, j):\n def func(x):\n return self.shape_function_deriv(i, x) * self.shape_function(j, x)\n i, err = quadrature(func, -1, 1)\n return i","def gradFun(self, x):\n tmp = x.reshape(self.inp_shape)\n g = np.ravel(np.asarray(self.calcGrad(np.asarray(tmp,dtype=np.float32)),dtype=np.float64)) + 2*self.alpha*x\n return g","def eval_numerical_gradient(f,x):\n\n\tgrad = np.zeros(x.shape)\n\th = 0.0001\n\n\t# iterate over all indexes in x\n\tit = np.nditer(x, flag = ['multi_index'], op_flags = ['readwrite'])\n\n\twhile not it.finished:\n\t\tix = it.multi_index\n\t\told_value = x[ix]\n\n\t\tx[ix] = old_value + h\n\t\tfxh_left = f(x)\n\n\t\tx[ix] = old_value - h\n\t\tfxh_right = f(x)\n\n\t\tx[ix] = old_value\n\n\t\t# compute the partial derivative\n\t\tgrad[ix] = (fxh_left - fxh_right) / (2 * h)\n\t\tit.iterate()\n\n\treturn grad","def gradient(scalar_field, coord_sys=None, doit=True):\n coord_sys = _get_coord_sys_from_expr(scalar_field, coord_sys)\n\n if coord_sys is None:\n return Vector.zero\n else:\n from sympy.vector.functions import express\n scalar_field = express(scalar_field, coord_sys,\n variables=True)\n vx = Derivative(scalar_field, coord_sys._x) / coord_sys._h1\n vy = Derivative(scalar_field, coord_sys._y) / coord_sys._h2\n vz = Derivative(scalar_field, coord_sys._z) / coord_sys._h3\n\n if doit:\n return (vx * coord_sys._i + vy * coord_sys._j + vz * coord_sys._k).doit()\n return vx * coord_sys._i + vy * coord_sys._j + vz * coord_sys._k","def integral( self ):\n integral = 0.0\n for i in range( 1, self.GetN() ):\n previousPoint = ( ROOT.Double(), ROOT.Double() )\n thisPoint = ( ROOT.Double(), ROOT.Double() )\n self.GetPoint( i-1, previousPoint[0], previousPoint[1] )\n self.GetPoint( i, thisPoint[0], thisPoint[1] )\n \n integral += (thisPoint[0]-previousPoint[0]) * (thisPoint[1]+previousPoint[1])/2.0\n return integral","def get_grad(self, X):\n raise NotImplementedError","def gradient(self):\n result = np.zeros(len(self.variables))\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\n return result","def _grad_sub_obj(self, X, q, coeffs_ext):\n n_features = self.n_features\n n_samples = self.n_samples\n if self.fit_intercept:\n coeffs = coeffs_ext[:n_features + 1] - coeffs_ext[n_features + 1:]\n coeffs_0 = coeffs[0]\n coeffs = coeffs[1:]\n else:\n coeffs_0 = 0\n coeffs = coeffs_ext[:n_features] - coeffs_ext[n_features:]\n grad_pen = self._grad_pen(coeffs)\n u = coeffs_0 + X.dot(coeffs)\n if self.fit_intercept:\n X = np.concatenate((np.ones(n_samples).reshape(1, n_samples).T, X),\n axis=1)\n grad_pen = np.concatenate([[0], grad_pen[:n_features], [0],\n grad_pen[n_features:]])\n grad = (X * (q - self.logistic_grad(-u)).reshape(n_samples, 1)).mean(\n axis=0)\n grad_sub_obj = np.concatenate([grad, -grad])\n return grad_sub_obj + grad_pen","def wrap_fq_grad(atoms, qbin=.1, sum_type='fq'):\n q = atoms.get_positions().astype(np.float32)\n qbin = np.float32(qbin)\n # get scatter array\n if sum_type == 'fq':\n scatter_array = atoms.get_array('F(Q) scatter')\n else:\n scatter_array = atoms.get_array('PDF scatter')\n\n # define scatter_q information and initialize constants\n qmax_bin = scatter_array.shape[1]\n n = len(q)\n\n # Get pair coordinate distance array\n d = np.zeros((n, n, 3), np.float32)\n get_d_array(d, q)\n\n # Get pair distance array\n r = np.zeros((n, n), np.float32)\n get_r_array(r, d)\n\n # Get normalization array\n norm = np.zeros((n, n, qmax_bin), np.float32)\n get_normalization_array(norm, scatter_array)\n\n # Get omega\n omega = np.zeros((n, n, qmax_bin), np.float32)\n get_omega(omega, r, qbin)\n\n # Get grad omega\n grad_omega = np.zeros((n, n, 3, qmax_bin), np.float32)\n get_grad_omega(grad_omega, omega, r, d, qbin)\n\n # Get grad FQ\n get_grad_fq_inplace(grad_omega, norm)\n grad_fq = grad_omega\n\n # Normalize FQ\n grad_fq = grad_fq.sum(1)\n # '''\n norm = np.zeros((int(n * (n - 1) / 2.), qmax_bin), np.float32)\n flat_norm(norm, scatter_array, 0)\n na = np.mean(norm, axis=0) * np.float32(n)\n old_settings = np.seterr(all='ignore')\n grad_fq = np.nan_to_num(grad_fq / na)\n np.seterr(**old_settings)\n del d, r, scatter_array, norm, omega, grad_omega\n return grad_fq","def grad_potential(self, q: Variable) -> Tensor:\n with tf.GradientTape() as t:\n # Watch is necessary in case q is a Tensor (and not a Variable). Passing a Tensor without watch result in\n # None result.\n t.watch(q)\n potential = self.potential(q)\n return t.gradient(potential, q)","def integral(ambient_dim, dim, operand, where=None):\n\n return NodeSum(\n area_element(ambient_dim, dim, where)\n * QWeight(where)\n * operand)","def grad_log_q(self,z): \n param_count = 0\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\n for core_param in range(len(self.q)):\n for approx_param in range(self.q[core_param].param_no):\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \n param_count += 1\n return grad","def numerical_gradient(f, x: np.ndarray):\n h = 1e-4\n grad = np.zeros_like(x)\n for i in range(x.size):\n tmp_val = x.flat[i]\n x.flat[i] = tmp_val + h\n fxh1 = f(x)\n\n x.flat[i] = tmp_val - h\n fxh2 = f(x)\n grad.flat[i] = (fxh1 - fxh2) / (2 * h)\n x.flat[i] = tmp_val\n return grad","def func_grad(self, X, Y):\n Q = 0\n gradd = np.zeros((N, 2*n+1))\n for x, y in zip(X, Y):\n #pairs of required arr coordinates\n iterat = [(int(x[i] // (self.max / N)), i) for i in range(2*n+1)]\n prediction = 0\n for j, i in iterat:\n prediction += self.arr[j, i]\n delta = prediction - y\n #in a meantime I precalculate new_step and, if all right, Ill use it to make new step\n for j, i in iterat:\n gradd[j, i] += delta \n Q += delta * delta\n return Q / len(X), gradd / len(X)","def grad_f(f: FlowFieldVal, dim: int, h: float) -> FlowFieldVal:\n if dim == 0:\n df = kernel_op.apply_kernel_op_x(f, 'kdx')\n elif dim == 1:\n df = kernel_op.apply_kernel_op_y(f, 'kdy')\n elif dim == 2:\n df = kernel_op.apply_kernel_op_z(f, 'kdz', 'kdzsh')\n else:\n raise ValueError('Unsupport dimension: {}'.format(dim))\n\n return [df_i / h for df_i in df]","def grad_interp(f: FlowFieldVal, grad_dim: int, interp_dim: int,\n h: float) -> FlowFieldVal:\n return interp(grad_n(f, grad_dim, h), interp_dim)","def check_gradient(f, x, delta=1e-5, tol=1e-4):\n\n assert isinstance(x, np.ndarray)\n assert x.dtype == np.float\n \n orig_x = x.copy()\n #print('check_g, orig_x befor',orig_x)\n #print('check_g, x befor',x)\n #print('befor first pass in grad check')\n fx, analytic_grad = f(x)\n #print('after first pass in grad check')\n #print('check_g, orig_x after',orig_x)\n #print('check_g, x.shape',x.shape)\n #print('func',f(x)[0])\n #print('fx=',fx,'analityc_grad=',analytic_grad)\n \n assert np.all(np.isclose(orig_x, x, tol)), \"Functions shouldn't modify input variables\"\n\n assert analytic_grad.shape == x.shape\n #print('analitical grad.shape',analytic_grad.shape)\n analytic_grad = analytic_grad.copy()\n\n # We will go through every dimension of x and compute numeric\n # derivative for it\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\n #print('it.shape=',it.shape)\n while not it.finished:\n ix = it.multi_index\n #print('ix',ix)\n #print('x[ix]',x[ix])\n analytic_grad_at_ix = analytic_grad[ix]\n #print('analitical_grad-at_ix',analytic_grad_at_ix)\n orig_x = x.copy()\n #print('orig_x',orig_x)\n #print('x.shape befor delta',x.shape)\n orig_x[ix]+=delta\n #print('x.shape after delta',x.shape)\n #print('orig_x[ix] delta +',orig_x[ix])\n fx_plus=f(orig_x)[0]\n #fx_plus=fx_plus_full[ix[0]]\n #print('fx__plus',fx_plus)\n orig_x = x.copy()\n orig_x[ix]-=delta\n #print('orig_x[ix] delta -',orig_x[ix])\n fx_minus=f(orig_x)[0]\n #print('fx_minus',fx_minus)\n \n divider=2*delta\n #print('divider',divider)\n #numeric_grad_at_ix = np.divide((fx_plus-fx_minus),divider)\n numeric_grad_at_ix = (fx_plus-fx_minus)/divider\n #print('numeric_grad_at_ix',numeric_grad_at_ix)\n #print('fx(ix)', fx[ix])\n\n # TODO compute value of numeric gradient of f to idx\n \n if not np.isclose(numeric_grad_at_ix, analytic_grad_at_ix, tol):\n print(\"Gradients are different at %s. Analytic: %2.5f, Numeric: %2.5f\" % (ix, analytic_grad_at_ix, numeric_grad_at_ix))\n return False\n\n it.iternext()\n\n print(\"Gradient check passed!\")\n return True","def grad(ambient_dim, operand):\n return grad_mv(ambient_dim, operand).as_vector()","def fieldGradient(self):\n return self.params['fieldGradient']","def construct_gradient(f, mesh=None, op=DefaultOptions()):\n if mesh is None:\n mesh = f.function_space().mesh()\n P1_vec = VectorFunctionSpace(mesh, \"CG\", 1)\n g = TrialFunction(P1_vec)\n φ = TestFunction(P1_vec)\n # TODO: include an option to swap between these two: 'parts' vs 'L2'\n a = inner(g, φ)*dx\n # L = inner(grad(f), φ)*dx\n L = f*dot(φ, FacetNormal(mesh))*ds - f*div(φ)*dx # enables f to be P0\n g = Function(P1_vec)\n solve(a == L, g, solver_parameters=op.hessian_solver_parameters)\n return g","def g(self, q):\n # check for function in dictionary\n if self._g is None:\n self._g = self._calc_g()\n parameters = tuple(q)\n return np.array(self._g(*parameters), dtype='float32').flatten()","def approx_grad(theta, X, y):\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\n for e in np.identity(len(theta)) * 1e-5])\n return grad_a","def field_line_integrate(\n r0, z0, phis, field, params={}, rtol=1e-8, atol=1e-8, maxstep=1000\n):\n r0, z0, phis = map(jnp.asarray, (r0, z0, phis))\n assert r0.shape == z0.shape, \"r0 and z0 must have the same shape\"\n rshape = r0.shape\n r0 = r0.flatten()\n z0 = z0.flatten()\n x0 = jnp.array([r0, phis[0] * jnp.ones_like(r0), z0]).T\n\n @jit\n def odefun(rpz, s):\n rpz = rpz.reshape((3, -1)).T\n r = rpz[:, 0]\n br, bp, bz = field.compute_magnetic_field(rpz, params, basis=\"rpz\").T\n return jnp.array(\n [r * br / bp * jnp.sign(bp), jnp.sign(bp), r * bz / bp * jnp.sign(bp)]\n ).squeeze()\n\n intfun = lambda x: odeint(odefun, x, phis, rtol=rtol, atol=atol, mxstep=maxstep)\n x = jnp.vectorize(intfun, signature=\"(k)->(n,k)\")(x0)\n r = x[:, :, 0].T.reshape((len(phis), *rshape))\n z = x[:, :, 2].T.reshape((len(phis), *rshape))\n return r, z","def _grad_j(q_j, A_j, b_j, b_j_norm, a_1_j, a_2_j, m):\n return (A_j.t() @ q_j / (-m)) + (b_j * (a_1_j / b_j_norm + a_2_j))","def _compute_func_grad(self, w):\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)","def compute_gradient(self, grad=None):\n if grad is None:\n grad = backend.ones_like(self.output_value)\n dx = -grad\n return dx","def eval_numerical_gradient_array(f, x, df, h=1e-5):\n grad = np.zeros_like(x)\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\n while not it.finished:\n ix = it.multi_index\n\n oldval = x[ix]\n x[ix] = oldval + h\n pos = f(x).copy()\n x[ix] = oldval - h\n neg = f(x).copy()\n x[ix] = oldval\n\n grad[ix] = np.sum((pos - neg) * df) / (2 * h)\n it.iternext()\n return grad","def calculate_diff(grad_theta, grad_approx):\n # np.linalg.norm apply for matric equal to Frobenius norm\n numerator = np.linalg.norm(grad_theta - grad_approx)\n denominator = np.linalg.norm(grad_theta) + np.linalg.norm(grad_approx)\n diff = numerator / denominator\n return diff","def grad_f3(x):\n pass","def eval_numerical_gradient_array(f, x, df, h = 1e-5):\n grad = np.zeros_like(x)\n it = np.nditer(x, flags = ['multi_index'], op_flags =['readwrite'])\n while not it.finished:\n ix = it.multi_index\n \n oldval = x[ix]\n x[ix] = oldval + h\n pos = f(x).copy()\n x[ix] = oldval - h\n neg = f(x).copy()\n x[ix] = oldval\n \n grad[ix] = np.sum((pos - neg) * df) / (2 * h)\n it.iternext()\n return grad","def grad(self,w):\n # Calculate the vector -sigma(-y_i * x_i.w)\n s = -np.array([sigmoid(-yi * np.dot(xi,w)) for xi,yi in zip(self.x,self.y)])\n # Multiply it by xy\n g = np.array([np.dot(xyj,s) for xyj in self.xy.transpose()])\n # Add regularisation\n g += self.alpha*w\n return g\n #g = np.array([self.grad_j(w,j) for j in xrange(len(w))])","def eval_numerical_gradient(f, x, verbose = True, h = 0.00001):\n fx = f(x) # evaluate function value at original point\n grad = np.zeros_like(x) # iterate over all indexese in x\n it = np.nditer(x, flags = ['multi_index'], op_flags = ['readwrite'])\n while not it.finished:\n # evaluate function at x+h\n ix = it.multi_index\n oldval = x[ix]\n x[ix] = oldval + h # increment by h\n fxph = f(x) # evaluate f(x+h)\n x[ix] = oldval - h\n fxmh = f(x) # evaluate f(x-h)\n x[ix] = oldval # restore\n \n #compute the partial derivative with centered fromula.\n grad[ix] = (fxph - fxmh) / (2 * h)\n if verbose:\n print(ix, grad[ix])\n it.iternext()\n return grad","def vert_integrate(self, u, d='up', Q='self'):\n s = \"::: vertically integrating function :::\"\n print_text(s, cls=self)\n\n if type(Q) != FunctionSpace:\n Q = self.Q\n ff = self.ff\n phi = TestFunction(Q)\n v = TrialFunction(Q)\n bcs = []\n # integral is zero on bed (ff = 3,5) \n if d == 'up':\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_B_GND)) # grounded\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_B_FLT)) # shelves\n a = v.dx(2) * phi * dx\n # integral is zero on surface (ff = 2,6) \n elif d == 'down':\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_S_GND)) # grounded\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_S_FLT)) # shelves\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_U_GND)) # grounded\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_U_FLT)) # shelves\n a = -v.dx(2) * phi * dx\n L = u * phi * dx\n name = 'value integrated %s' % d \n v = Function(Q, name=name)\n solve(a == L, v, bcs, annotate=False)\n print_min_max(u, 'vertically integrated function', cls=self)\n return v","def dphi(x):\n return grad(phi)(x)","def __call__(self, x):\n\n self.dbeads.q = x\n e = self.dforces.pot # Energy\n g = -self.dforces.f # Gradient\n\n return e, g","def gradient(poly):\n return differential(\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\"GR\"))","def gradient_check(op, *args, **kwargs):\n\n if( not 'id_list' in kwargs.keys() ):\n kwargs.update({\"id_list\":[0]})\n\n id_list = kwargs.get(\"id_list\", [0])\n\n for i in id_list:\n\n if(not isinstance(args[i], Variable)):\n raise Exception(\"input {:g} is not a variable\".format(i))\n\n if(isinstance(args[i], Variable) and not args[i].requires_grad):\n raise Exception(\"input {:g} doesn't require gradient\".format(i))\n\n nelems = args[i].numel()\n\n \"\"\" numerical gradient \"\"\"\n\n wrapper, p = numdiff_wrapper(op, args, kwargs, i)\n jacobian_numerical = numdiff_unified(wrapper, p)\n\n \"\"\" analytic gradient \"\"\"\n\n jacobian_analytic = []\n\n if(len(kwargs.keys()) > 1):\n \"\"\"function has dictionary inputs\"\"\"\n f = op(*args, **kwargs)\n else:\n f = op(*args)\n\n output_nelems = f.data.numel()\n\n for k in range(output_nelems):\n\n output_grad = torch.zeros(f.data.size())\n output_grad.view(output_nelems, 1)[k] = 1\n\n f.backward(output_grad, retain_variables=True)\n\n jacobian_analytic.append( np.copy( args[i].grad.data.view( nelems ).numpy() ) )\n\n for params_i in args:\n if(isinstance(params_i, torch.autograd.Variable) and params_i.requires_grad):\n params_i.grad.data.zero_()\n\n jacobian_analytic = np.asarray(jacobian_analytic)\n\n \"\"\"\n compare jacobian_analytic with jacobian_numerical\n \"\"\"\n\n if( np.allclose(jacobian_analytic, jacobian_numerical) ):\n\n print \"gradient is correct\"\n\n else:\n\n rel_error = np.linalg.norm( jacobian_analytic - jacobian_numerical ) / \\\n np.maximum( np.linalg.norm( jacobian_analytic ), np.linalg.norm( jacobian_numerical) )\n\n print 'analytic jacobian :'\n print jacobian_analytic\n\n print 'numerical jacobian :'\n print jacobian_numerical\n\n print 'jacobian difference :'\n print jacobian_analytic - jacobian_numerical\n\n print 'relative error:'\n print rel_error","def interp_1d_grad_coef(ix, x, gamma):\n # verifying arguments\n gamma = float(gamma)\n assert x.ndim == 1\n n = x.size; N = min(n, 50)\n # construct X\n X = zeros([N, n], dtype=float)\n for i in range(N):\n X[i,:] = gamma**(i+1) / factorial(i+1) * (x - x[ix]) ** (i+1)\n # construct diagonal G matrix for the Lagrange residual\n G = gamma**(N+1) / factorial(N+1) * (x - x[ix])**(N+1)\n # construct c\n c = ones(n)\n # first try to assemble the diagonal of matrix A, and sort by its diagonal\n diagA = (X**2).sum(0) + G**2\n finite = numpy.isfinite(diagA)\n isort = sorted(range(n), key=diagA.__getitem__)\n irevt = sorted(range(n), key=isort.__getitem__)\n # drop first one -- all 0\n assert isort[0] == ix\n isort = isort[1:]\n # permute columns of X and diagonal of G\n X = X[:,isort]\n G = G[isort]\n c = c[isort]\n finite = finite[isort]\n # solve linear system\n X = X[:,finite]\n G = G[finite]\n # QR decomposition of X, R is now cholesky factor of A\n n1, n2 = X.shape\n A = zeros([n1+n2, n2])\n A[:n1,:] = X\n A[range(n1,n1+n2), range(n2)] = G\n R = numpy.linalg.qr(A, mode='r')\n # ------------------- gradient part -----------------\n # calculate rhs = dA * a\n rhs = - gamma * X[0,:];\n # solve for da\n da = zeros(c.size + 1)\n tmp = solve_L(R.transpose(), rhs)\n da[1:][finite] = solve_R(R, tmp)\n da[0] = -dot(da[1:], c)\n # reverse sorting permutation to get a and b and normalize\n da = da[irevt]\n return -da","def gradient(self, x):\n u = np.asarray([x[0]])\n C = self.C_func(u)\n dC = self.dC_func(u, order=1)\n P = self.P\n numerator = np.sum((C - P) * dC, axis=0)\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\n if np.abs(denominator) > 0:\n gradient = numerator/denominator\n else:\n gradient = np.asarray(0)[np.newaxis]\n return gradient","def magnitude_of_gradient(grad_f):\n return np.sqrt(np.ufunc.reduce(np.add, [x**2 for x in grad_f]))","def integral(self, dmax, dmin=0):\r\n # more expressive\r\n #return integrate.quad(lambda r: 2*np.pi*r*self(r), dmin, dmax)[0]\r\n return self.bpsf.integral(dmax, dmin)","def elementwise_grad(func):\n return vmap(grad(func))","def gradient(self,i,f):\n\n diff = self.points[f, :] - self.points[i, :]\n gradient = diff[1]/diff[0]\n\n return gradient","def compute_gradient(self, function, arguments):","def test_arg_grad(self):\r\n\r\n x = matrix()\r\n cost = argmax(x, axis=0).sum()\r\n gx = grad(cost, x)\r\n val = tensor.get_scalar_constant_value(gx)\r\n assert val == 0.0","def grad_fn(grad: np.ndarray) -> np.ndarray:\n return grad * np.ones_like(t.data)","def gradient(self, x):\n g = self._grad(\n time_series=self.observed_data,\n a=x[0],\n b=x[1],\n c=x[2],\n sigma=self.sigma\n )\n return g","def scl_grad(X, phi):\r\n shape = list(phi.shape)\r\n shape.insert(0,3)\r\n grad = _np.zeros(shape,dtype=phi.dtype)\r\n\r\n polynomial = _interp(X[0], phi, axis=0)\r\n grad[0,...] = polynomial(X[0], nu=1)\r\n\r\n polynomial = _interp(X[1], phi, axis=1)\r\n grad[1,...] = polynomial(X[1], nu=1)\r\n\r\n polynomial = _interp(X[2], phi, axis=2)\r\n grad[2,...] = polynomial(X[2], nu=1)\r\n\r\n return grad","def integral_function(w,i,dprime):\n factors = 1\n for j in xrange(Number_of_locs): #MOD Number_of_locs deleted\n factors *= stats.norm.cdf(\n (\n -2.0*np.log(Post_probs[j]/Post_probs[i]) +\n dprime[j]**2 +\n 2.0*dprime[i]*w +\n dprime[i]**2\n ) / (\n 2.0*dprime[j]\n )\n ) #MOD Post_probs deleted\n return stats.norm.pdf(w) * factors","def GradDescent_Armijo(q, gradq, x0, tol, kmax, a_low=1e-9, a_high=0.9,\r\n N=20, c_low=0.1, c_high=0.9, CD_tao=1e-5):\r\n\r\n # Use CentralDifferences to approximate the gradient\r\n if isinstance(gradq, str) and gradq == \"CD\":\r\n # Lambda function so that we can pass function and perturbation through\r\n gradq = (lambda x: _CentralDifferencesGradient(q, x, CD_tao))\r\n # If not a function and not \"CD\" then error\r\n elif not callable(gradq):\r\n raise InvalidArgumentError(\"Undefined gradq - should be a callable or CD\")\r\n\r\n # Iteration Counter\r\n k = 1\r\n\r\n # Make a copy in case we try to change it\r\n xk = x0.copy()\r\n\r\n # Cache gradq(xk), q(xk), and norm(gradq(xk)) to speed up computation\r\n gq_cache = gradq(xk)\r\n q_cache = q(xk)\r\n n_gq_cache = LA.norm(gq_cache)\r\n\r\n # Alpha grid for step size search\r\n alpha = np.logspace(np.log10(a_low), np.log10(a_high), N, endpoint=True)\r\n\r\n while n_gq_cache > tol*(1 + np.abs(q_cache)) and k < kmax:\r\n # Track minimum phi value in the grid\r\n phimin = None\r\n imin = None\r\n\r\n # Move along the direction of the gradient, testing q at each grid point\r\n for i in range(N):\r\n phi = q(xk - alpha[i]*gq_cache) - q_cache\r\n h = -c_low*alpha[i]*n_gq_cache*n_gq_cache\r\n l = -c_high*alpha[i]*n_gq_cache*n_gq_cache\r\n\r\n # Want the minimum phi value s.t. phi <= h and phi >= l\r\n if (l <= phi <= h) and (phimin is None or phi < phimin):\r\n phimin = phi\r\n imin = i\r\n\r\n # Not good, probably a step size issue\r\n if phimin is None or phimin > 0:\r\n # return xk, k\r\n warnings.warn(\"No more steps to take downward, don't trust answer. \\\r\n Probably a step size issue. Consider increasing N\",\r\n NonConvergenceWarning)\r\n\r\n # Update xk\r\n xk -= alpha[imin]*gq_cache\r\n\r\n # Cache new values\r\n gq_cache = gradq(xk)\r\n n_gq_cache = LA.norm(gq_cache)\r\n q_cache = q(xk)\r\n\r\n # Increase iteration count\r\n k += 1\r\n\r\n # If kmax gets exceeded, we can't trust the answer so issue warning\r\n if k >= kmax:\r\n warnings.warn(\"kmax exceeded, consider raising it\", NonConvergenceWarning)\r\n\r\n # Otherwise, we stopped the above loop because we're within tolerance so the answer is good\r\n return xk, k","def grad(self, x):\n return self.smoothed_grad(x)","def grad(self, x):\n return self.smoothed_grad(x)","def grad(self, x):\n return self.smoothed_grad(x)","def eval_numerical_gradient(f, x, verbose=True, h=0.00001):\n\n fx = f(x) # evaluate function value at original point\n grad = np.zeros_like(x)\n # iterate over all indexes in x\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\n while not it.finished:\n\n # evaluate function at x+h\n ix = it.multi_index\n oldval = x[ix]\n x[ix] = oldval + h # increment by h\n fxph = f(x) # evalute f(x + h)\n x[ix] = oldval - h\n fxmh = f(x) # evaluate f(x - h)\n x[ix] = oldval # restore\n\n # compute the partial derivative with centered formula\n grad[ix] = (fxph - fxmh) / (2 * h) # the slope\n if verbose:\n print(ix, grad[ix])\n it.iternext() # step to next dimension\n\n return grad","def IntegratePolynomialPartLogExt(poly, fieldTower):\n #print(poly)\n #print(poly.degree)\n #print(poly.fieldTower)\n if poly == 0 or poly.isZero():\n return Int.Integral()\n red = poly.reduceToLowestPossibleFieldTower()\n if red.fieldTower.towerHeight<fieldTower.towerHeight:\n return Integrate(red)\n p = poly.getCoefficients()\n l = len(p)-1\n q = [ZERO]*(len(p)+1)\n b = [ZERO]*(len(p)+1)\n d = [ZERO]*(len(p)+1)\n \n \n u = fieldTower.getLastExtension().argFunction # last fieldExtension with T=log(u)\n log_diff_u = u.logDifferentiate()#u.differentiate()/u # u'/u\n \n # p_l *T^l + p_(l-1)*T^(l-1) + ... + p_0 = (q_(l+1)*T^(l+1) + q_l*T^l + ... + q_0)' + (c_1 *v_1'/v_i + ... + c_m *v_m'/v_m)\n # (q_i*T^i)' = q_i'*T^i + q_i*i*T'*T^(i-1)\n # -> 0 = q_(l+1)'\n # p_l = (l+1)*q_(l+1)*T' + q_l'\n # ...\n # p_0 = q_1*T' + (qe_0)', qe_0 = q_0+(c_1 *log(v_1) + ... + c_m *log(v_m))\n for i in range(l,-1,-1):\n # q_(i+1) = d_(i+1) + b_(i+1)\n # integral(p_i-(i+1)*d_(i+1)*T') = l*b_(i+1)*T' + q_i\n #i = Rational.fromFloat(i)\n integrand = p[i]+(-ONE)*(i+1)*d[i+1]*log_diff_u\n if type(integrand)==float or type(integrand)==int:\n integrand = Rational.fromFloat(integrand)\n int_reduced = integrand.reduceToLowestPossibleFieldTower()\n if int_reduced!=None:\n integrand = int_reduced\n P_i = Integrate(integrand)\n if i>0:\n IntegratePolynomialPartLogExtCheckIntegralConditions(P_i, fieldTower)\n else:\n if P_i==None:\n raise Int.IntegralNotElementaryError()\n \n prev = fieldTower.prevTower()\n #logs = P_i.getNewLogExpressionsInFieldTower(prev,fieldTower)\n logTerm = P_i.getLogExpression(u)\n otherLogs = [log for log in P_i.logExpressions if not log==logTerm]#in logs]\n ci = ZERO if logTerm==None else logTerm.factor # integral = P_i = c_i*T+d_i, d_i = P_i\\logs\n d[i] = Int.Integral(poly_rationals=P_i.poly_rational_partExpressions,logs=otherLogs,rootSums=P_i.rootSums).asFunction()\n b[i+1] = ci/(i+1)\n q[i+1] = d[i+1]+b[i+1]\n \n #integrand = p[0]+(-1)*d[1]*log_diff_u\n #P0 = Integrate(integrand)\n #if P0==None:\n # raise Int.IntegralNotElementaryError()\n #b1 = 0\n #for log in P0.logExpressions:\n # if log.argFunction==u:\n # b1 = log.factor\n \n if b[1]==0:\n q_0 = P_i.asFunction()\n else:\n q_0 = (P_i+Int.Integral(logs=[Int.LogFunction(u,(-1)*b[1])])).asFunction()\n q[0] = q_0\n \n integralPoly = Pol.Polynomial(variable=fieldTower.getLastVariable())\n for i in range(l+1,-1,-1):\n integralPoly.setCoefficient(i,q[i], callUpdates=False)\n integralPoly.updateCoefficientsAll()\n \n return Int.Integral(poly_rationals=[integralPoly])","def get_quad_length(q):\n P0, P1, P2, P3 = q\n p0 = Vector.fromPoint(P0) # fromPoint converts to ECEF\n p1 = Vector.fromPoint(P1)\n qlength = (p1 - p0).mag() / 1000\n return qlength","def internal2external_grad(xi,bounds):\n \n ge = np.empty_like(xi)\n\n for i,(v,bound) in enumerate(zip(xi,bounds)):\n \n a = bound[0] # minimum\n b = bound[1] # maximum\n\n if a == None and b == None: # No constraints\n ge[i] = 1.0\n\n elif b == None: # only min\n ge[i] = v/np.sqrt(v**2+1)\n\n elif a == None: # only max\n ge[i] = -v/np.sqrt(v**2+1)\n\n else: # both min and max\n ge[i] = (b-a)*np.cos(v)/2.\n\n return ge","def grad(self, var):\n return Circuit.grad(self, var)","def grad(self, A, y, x):\n z = y - A.dot(x) # Error for each observation\n grad_x = -1 * A.T.dot(np.sign(z))\n # Gradient normalized by the num obs\n return grad_x / y.size","def gradient(self, x_in):\r\n\r\n return 1 - torch.pow(self.forward(x_in), 2)","def integral (self):\n dx = self.xbins[1] - self.xbins[0]\n dy = self.ybins[1] - self.ybins[0]\n return self.sum * (dx * dy)","def grad(x):\n\n return x - np.arange(1, cfg.n + 1, dtype=np.float_)","def cv_gradient(self,z):\n gradient = np.zeros(np.sum(self.approx_param_no))\n z_t = z.T \n log_q = self.normal_log_q(z.T)\n log_p = self.log_p(z.T)\n grad_log_q = self.grad_log_q(z)\n gradient = grad_log_q*(log_p-log_q)\n\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \n\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\n\n return np.mean(vectorized,axis=1)","def tot_grad_obj(self,x0,gradient):\n tmp = self.tot_objective(self.rebuild_param(x0,**self.fixed))\n obj = tmp[0]\n if gradient:\n grad =self.fix_par(tmp[1], **self.fixed)[0]\n return obj,grad\n else:\n return obj","def FermiDirac_integral(self, y):\n return sint.quad_vec(lambda x: x**2.*np.sqrt(x**2.+y**2.)/(np.exp(x)+1.), 0., 500.)[0]","def objective_function_gradients(self, x):\r\n try:\r\n self._set_params_transformed(x)\r\n obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())\r\n self._fail_count = 0\r\n except (LinAlgError, ZeroDivisionError, ValueError) as e:\r\n if self._fail_count >= self._allowed_failures:\r\n raise e\r\n self._fail_count += 1\r\n obj_grads = np.clip(-self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients()), -1e100, 1e100)\r\n return obj_grads","def log_q(xy, x, y, h):\n return -linalg.norm(y - x - h*xy.grad_log_density(x))/(4*h)","def integrate_f_from0(b):\n integral, err = scipy.integrate.quad(f, 0, b)\n return integral","def gradient(self, x):\n pass","def compute_gradient(self, grad=None):\n x = self.input_nodes[0].output_value\n if grad is None:\n grad = backend.ones_like(self.output_value)\n if x == float('inf') or x == float('-inf'):\n return grad * float('inf')\n else:\n return grad * 1 / x","def sp_integrate_1D ( func , xmin , xmax , *args , **kwargs ) : \n from scipy import integrate\n ##\n result = integrate.quad ( func , xmin , xmax , *args , **kwargs )\n return result[0]","def integrate_quantity(self, q, mask=None, layer=None):\n if type(q) == str:\n qarr = self.get_scalar_quantity(q)\n else:\n qarr = q\n if layer is not None:\n l = self.layers[layer]\n z_vals = self.Z[l.get_slice(self.Z)]\n qarr = qarr[l.get_slice(self.Z)]\n if mask is not None and len(mask.shape) == 3:\n mask = mask[l.get_slice(self.Z)]\n else:\n z_vals = self.Z\n if mask is not None:\n qarr = qarr*mask\n z_integral = intg.trapz(qarr, x=z_vals, axis=0)\n x_integral = intg.trapz(z_integral, x=self.X, axis=0)\n y_integral = intg.trapz(x_integral, x=self.Y, axis=0)\n return y_integral","def objective_function_gradients(self, x):\n self._set_params_transformed(x)\n LL_gradients = self._transform_gradients(self._log_likelihood_gradients())\n prior_gradients = self._transform_gradients(self._log_prior_gradients())\n return - LL_gradients - prior_gradients","def get_numerical_derived_accelerations(self):\n # calculate numerical 2° order derivative and return it\n return np.gradient(np.gradient(self.trajectory,axis=1),axis=1)","def grad_n(f: FlowFieldVal, dim: int, h: float) -> FlowFieldVal:\n if dim == 0:\n df = kernel_op.apply_kernel_op_x(f, 'kDx')\n elif dim == 1:\n df = kernel_op.apply_kernel_op_y(f, 'kDy')\n elif dim == 2:\n df = kernel_op.apply_kernel_op_z(f, 'kDz', 'kDzsh')\n else:\n raise ValueError('Unsupport dimension: {}'.format(dim))\n\n return [df_i / (2.0 * h) for df_i in df]","def dalf(x):\n return grad(alf)(x)","def grad(self, x):\n grad = np.zeros((x.shape[0], 1))\n grad[x >= utils.TOLERANCE] = 1.0\n grad[x <= -utils.TOLERANCE] = -1.0\n between = (x > -utils.TOLERANCE) & (x < utils.TOLERANCE)\n grad[between] = self.rng(between.sum())\n\n return self.l * grad","def integrate_fun(fun: Callable, low_b: float, upp_b: float) -> float:\n return integrate.quad(fun, low_b, upp_b)[0]","def gauss_quad_1d(q, n=150):\n z, w = leggauss(n)\n theta = (THETA_HIGH-THETA_LOW)*(z + 1)/2 + THETA_LOW\n sin_theta = abs(sin(theta))\n Zq = kernel_1d(q=q, theta=theta)\n return np.sum(Zq*w*sin_theta)*(THETA_HIGH-THETA_LOW)/2","def _grad(ys, xs, v=None):\n if framework.in_dygraph_mode():\n # paddle.grad returns a list though the inputs is a signle Tensor. The\n # follow code snippet fixes the problem by return the first element of\n # xs_grad when the xs is a signle Tensor.\n xs_grad = paddle.grad(ys, xs, v, create_graph=True, allow_unused=True)\n if (\n isinstance(xs, paddle.fluid.framework.Variable)\n and isinstance(xs_grad, typing.Sequence)\n and len(xs_grad) > 0\n ):\n xs_grad = xs_grad[0]\n else:\n xs_grad = paddle.incubate.autograd.grad(ys, xs, v)\n return _replace_none_with_zero_tensor(xs_grad, xs)","def integrate(self, t):","def grad(self, x):\n norm_beta = np.linalg.norm(x)\n if norm_beta > utils.TOLERANCE:\n return (self.l / norm_beta) * x\n else:\n D = x.shape[0]\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\n norm_u = np.linalg.norm(u)\n a = self.rng() # [0, 1]\n\n return (self.l * (a / norm_u)) * u","def grad_phi_mesh(self):\n fy = -0.5 * (np.roll(self.phi, 1, axis = 1) - np.roll(self.phi, -1, axis=1)) \n fx = -0.5 * (np.roll(self.phi, 1, axis = 0) - np.roll(self.phi, -1, axis=0))\n return fx,fy","def grad(self):\n\n return self._grad","def cv_gradient(self, z): \n z_t = np.transpose(z)\n log_q = self.normal_log_q(z_t)\n log_p = self.log_p(z_t)\n grad_log_q = self.grad_log_q(z)\n gradient = grad_log_q*np.repeat((log_p - log_q).T,2,axis=0)\n\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \n \n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\n\n return np.mean(vectorized,axis=1)","def gradient_func(self, grad_f, topology, tmax, eta, step_size):\n x_func = partial(self._optimize_form, topology=topology, tmax=tmax, eta=eta)\n return partial(grad_f, x_func=x_func, step_size=step_size)","def gass_hermite_quad(f, degree, m, c):\n\n points, weights = np.polynomial.hermite.hermgauss( degree)\n\n #function values at given points\n f_x = f(points, m= m, c= c)\n\n #weighted sum of function values\n F = np.sum( f_x * weights)\n\n return F","def gravity_gradients(self, p):\n Le = self._Le(p)\n wf = self._wf(p)\n EeLe = np.einsum(\"i...,i\", self.Ee, Le)\n FfWf = np.einsum(\"i...,i\", self.Ff, wf)\n return G * self.d * (EeLe - FfWf)","def _test_gradgrad_array(func, optimized, *args):\n\n def tangent_func():\n func.__globals__['np'] = np\n df = tangent.grad(func, motion='joint', optimized=optimized, verbose=True)\n ddf = tangent.grad(df, motion='joint', optimized=optimized, verbose=True)\n return ddf(*args)\n\n def reference_func():\n func.__globals__['np'] = ag_np\n return ag_grad(ag_grad(func))(*args)\n\n def backup_reference_func():\n return utils.numeric_grad(utils.numeric_grad(func))(*args)\n\n utils.assert_result_matches_reference(\n tangent_func, reference_func, backup_reference_func,\n tolerance=1e-2) # extra loose bounds for 2nd order grad","def _transform_gradients(self, g):\r\n x = self._get_params()\r\n [np.put(x, i, x * t.gradfactor(x[i])) for i, t in zip(self.constrained_indices, self.constraints)]\r\n [np.put(g, i, v) for i, v in [(t[0], np.sum(g[t])) for t in self.tied_indices]]\r\n if len(self.tied_indices) or len(self.fixed_indices):\r\n to_remove = np.hstack((self.fixed_indices + [t[1:] for t in self.tied_indices]))\r\n return np.delete(g, to_remove)\r\n else:\r\n return g","def quad_interp(x,y,xi) :\n f = interp1d(x,y,kind='quadratic')\n yi = f(xi)\n \n return yi","def integrate(self, x, dx):\n nq, nv, nx, ndx = self.model.nq, self.model.nv, self.nx, self.ndx\n assert (x.shape == (nx, ) and dx.shape == (ndx, ))\n q = x[:nq]\n v = x[-nv:]\n dq = dx[:nv]\n dv = dx[-nv:]\n qn = pinocchio.integrate(self.model, a2m(q), a2m(dq))\n return np.concatenate([qn.flat, v + dv])","def get_grad_vector(args, pp, grad_dims):\n grads = torch.Tensor(sum(grad_dims))\n if args.cuda: grads = grads.cuda()\n\n grads.fill_(0.0)\n cnt = 0\n for param in pp():\n if param.grad is not None:\n beg = 0 if cnt == 0 else sum(grad_dims[:cnt])\n en = sum(grad_dims[:cnt + 1])\n grads[beg: en].copy_(param.grad.data.view(-1))\n cnt += 1\n return grads","def _UpdateGradient(self):\n self.mol.GetGradient('analytic')","def get_quad_down_dip_vector(q):\n P0, P1, P2, P3 = q\n p0 = Vector.fromPoint(P0) # fromPoint converts to ECEF\n p1 = Vector.fromPoint(P1)\n p0p1 = p1 - p0\n qnv = get_quad_normal(q)\n ddv = Vector.cross(p0p1, qnv).norm()\n return ddv","def clgrad(obj, exe, arg, delta=DELTA):\n f, x = get_method_and_copy_of_attribute(obj, exe, arg)\n def grad_f(*args, **kwargs):\n grad_val = numpy.zeros(x.shape)\n it = numpy.nditer(x, op_flags=['readwrite'], flags=['multi_index'])\n for xi in it:\n i = it.multi_index\n xi += delta/2\n fp = f(*args, **kwargs)\n xi -= delta\n fm = f(*args, **kwargs)\n xi += delta/2\n grad_val[i] = (fp - fm)/delta\n return grad_val\n return grad_f","def get_input_grad(self, Y, output_grad):\n return np.multiply(logistic_deriv(Y), output_grad)","def _get_grad_direction_cost(self, p, q):\n dp = (self.grad_y[p[0]][p[1]], -self.grad_x[p[0]][p[1]])\n dq = (self.grad_y[q[0]][q[1]], -self.grad_x[q[0]][q[1]])\n \n l = np.array([q[0]-p[0], q[1]-p[1]], np.float)\n if 0 not in l:\n l *= SQRT_0_5\n \n dp_l = np.dot(dp, l)\n l_dq = np.dot(l, dq)\n if dp_l < 0:\n dp_l = -dp_l\n l_dq = -l_dq\n \n # 2/3pi * ...\n return 0.212206590789 * (np.arccos(dp_l)+np.arccos(l_dq))","def GradDescent_ILS(q, gradq, x0, tol, kmax, a_low=1e-9, a_high=0.9, N=20, CD_tao=1e-5):\r\n\r\n # Use CentralDifferences to approximate the gradient\r\n if isinstance(gradq, str) and gradq == \"CD\":\r\n # Lambda function so that we can pass function and perturbation through\r\n gradq = (lambda x: _CentralDifferencesGradient(q, x, CD_tao))\r\n # If not a function and not \"CD\" then error\r\n elif not callable(gradq):\r\n raise InvalidArgumentError(\"Undefined gradq - should be a callable or CD\")\r\n\r\n\r\n # Iteration Counter\r\n k = 1\r\n\r\n # Make a copy in case we try to change it\r\n xk = x0.copy()\r\n\r\n # Cache gradq(xk) and q(xk) to speed up computation\r\n gq_cache = gradq(xk)\r\n q_cache = q(xk)\r\n n_gq_cache = LA.norm(gq_cache)\r\n normalized_gq_cache = gq_cache/n_gq_cache\r\n\r\n # Alpha grid for step size search\r\n alpha = np.logspace(np.log10(a_low), np.log10(a_high), N, endpoint=True)\r\n\r\n while n_gq_cache > tol*(1 + np.abs(q_cache)) and k < kmax:\r\n # Track minimum phi value in the grid\r\n phimin = None\r\n imin = None\r\n\r\n # Move along the direction of the gradient, testing q at each grid point\r\n for i in range(N):\r\n phi = q(xk - alpha[i]*normalized_gq_cache) - q_cache\r\n\r\n # Want the minimum phi value\r\n if phimin is None or phi < phimin:\r\n phimin = phi\r\n imin = i\r\n\r\n # Not good, probably a step size issue\r\n if phimin > 0:\r\n warnings.warn(\"No more steps to take downward, don't trust answer.\\\r\n Probably a step size issue. Consider increasing N\",\r\n NonConvergenceWarning)\r\n\r\n # Update xk\r\n xk -= alpha[imin]*(gq_cache/n_gq_cache)\r\n\r\n # Cache new values\r\n gq_cache = gradq(xk)\r\n q_cache = q(xk)\r\n n_gq_cache = LA.norm(gq_cache)\r\n normalized_gq_cache = gq_cache/n_gq_cache\r\n\r\n # Increase iteration count\r\n k += 1\r\n\r\n # If kmax gets exceeded, we can't trust the answer so issue a warning\r\n if k >= kmax:\r\n warnings.warn(\"kmax exceeded, consider raising it\", NonConvergenceWarning)\r\n\r\n # Otherwise, we stopped the above loop because we're within tolerance so the answer is good\r\n return xk, k"],"string":"[\n \"def gradh(self,q):\\n gx = self.d_i(q) / self.e1u\\n gy = self.d_j(q) / self.e2v\\n #\\n return gx,gy\",\n \"def integrate_dphi_phi(self, i, j):\\n def func(x):\\n return self.shape_function_deriv(i, x) * self.shape_function(j, x)\\n i, err = quadrature(func, -1, 1)\\n return i\",\n \"def gradFun(self, x):\\n tmp = x.reshape(self.inp_shape)\\n g = np.ravel(np.asarray(self.calcGrad(np.asarray(tmp,dtype=np.float32)),dtype=np.float64)) + 2*self.alpha*x\\n return g\",\n \"def eval_numerical_gradient(f,x):\\n\\n\\tgrad = np.zeros(x.shape)\\n\\th = 0.0001\\n\\n\\t# iterate over all indexes in x\\n\\tit = np.nditer(x, flag = ['multi_index'], op_flags = ['readwrite'])\\n\\n\\twhile not it.finished:\\n\\t\\tix = it.multi_index\\n\\t\\told_value = x[ix]\\n\\n\\t\\tx[ix] = old_value + h\\n\\t\\tfxh_left = f(x)\\n\\n\\t\\tx[ix] = old_value - h\\n\\t\\tfxh_right = f(x)\\n\\n\\t\\tx[ix] = old_value\\n\\n\\t\\t# compute the partial derivative\\n\\t\\tgrad[ix] = (fxh_left - fxh_right) / (2 * h)\\n\\t\\tit.iterate()\\n\\n\\treturn grad\",\n \"def gradient(scalar_field, coord_sys=None, doit=True):\\n coord_sys = _get_coord_sys_from_expr(scalar_field, coord_sys)\\n\\n if coord_sys is None:\\n return Vector.zero\\n else:\\n from sympy.vector.functions import express\\n scalar_field = express(scalar_field, coord_sys,\\n variables=True)\\n vx = Derivative(scalar_field, coord_sys._x) / coord_sys._h1\\n vy = Derivative(scalar_field, coord_sys._y) / coord_sys._h2\\n vz = Derivative(scalar_field, coord_sys._z) / coord_sys._h3\\n\\n if doit:\\n return (vx * coord_sys._i + vy * coord_sys._j + vz * coord_sys._k).doit()\\n return vx * coord_sys._i + vy * coord_sys._j + vz * coord_sys._k\",\n \"def integral( self ):\\n integral = 0.0\\n for i in range( 1, self.GetN() ):\\n previousPoint = ( ROOT.Double(), ROOT.Double() )\\n thisPoint = ( ROOT.Double(), ROOT.Double() )\\n self.GetPoint( i-1, previousPoint[0], previousPoint[1] )\\n self.GetPoint( i, thisPoint[0], thisPoint[1] )\\n \\n integral += (thisPoint[0]-previousPoint[0]) * (thisPoint[1]+previousPoint[1])/2.0\\n return integral\",\n \"def get_grad(self, X):\\n raise NotImplementedError\",\n \"def gradient(self):\\n result = np.zeros(len(self.variables))\\n result[self.bivariateGradInd] = (self.shape-1)/self.variable - self.rate\\n return result\",\n \"def _grad_sub_obj(self, X, q, coeffs_ext):\\n n_features = self.n_features\\n n_samples = self.n_samples\\n if self.fit_intercept:\\n coeffs = coeffs_ext[:n_features + 1] - coeffs_ext[n_features + 1:]\\n coeffs_0 = coeffs[0]\\n coeffs = coeffs[1:]\\n else:\\n coeffs_0 = 0\\n coeffs = coeffs_ext[:n_features] - coeffs_ext[n_features:]\\n grad_pen = self._grad_pen(coeffs)\\n u = coeffs_0 + X.dot(coeffs)\\n if self.fit_intercept:\\n X = np.concatenate((np.ones(n_samples).reshape(1, n_samples).T, X),\\n axis=1)\\n grad_pen = np.concatenate([[0], grad_pen[:n_features], [0],\\n grad_pen[n_features:]])\\n grad = (X * (q - self.logistic_grad(-u)).reshape(n_samples, 1)).mean(\\n axis=0)\\n grad_sub_obj = np.concatenate([grad, -grad])\\n return grad_sub_obj + grad_pen\",\n \"def wrap_fq_grad(atoms, qbin=.1, sum_type='fq'):\\n q = atoms.get_positions().astype(np.float32)\\n qbin = np.float32(qbin)\\n # get scatter array\\n if sum_type == 'fq':\\n scatter_array = atoms.get_array('F(Q) scatter')\\n else:\\n scatter_array = atoms.get_array('PDF scatter')\\n\\n # define scatter_q information and initialize constants\\n qmax_bin = scatter_array.shape[1]\\n n = len(q)\\n\\n # Get pair coordinate distance array\\n d = np.zeros((n, n, 3), np.float32)\\n get_d_array(d, q)\\n\\n # Get pair distance array\\n r = np.zeros((n, n), np.float32)\\n get_r_array(r, d)\\n\\n # Get normalization array\\n norm = np.zeros((n, n, qmax_bin), np.float32)\\n get_normalization_array(norm, scatter_array)\\n\\n # Get omega\\n omega = np.zeros((n, n, qmax_bin), np.float32)\\n get_omega(omega, r, qbin)\\n\\n # Get grad omega\\n grad_omega = np.zeros((n, n, 3, qmax_bin), np.float32)\\n get_grad_omega(grad_omega, omega, r, d, qbin)\\n\\n # Get grad FQ\\n get_grad_fq_inplace(grad_omega, norm)\\n grad_fq = grad_omega\\n\\n # Normalize FQ\\n grad_fq = grad_fq.sum(1)\\n # '''\\n norm = np.zeros((int(n * (n - 1) / 2.), qmax_bin), np.float32)\\n flat_norm(norm, scatter_array, 0)\\n na = np.mean(norm, axis=0) * np.float32(n)\\n old_settings = np.seterr(all='ignore')\\n grad_fq = np.nan_to_num(grad_fq / na)\\n np.seterr(**old_settings)\\n del d, r, scatter_array, norm, omega, grad_omega\\n return grad_fq\",\n \"def grad_potential(self, q: Variable) -> Tensor:\\n with tf.GradientTape() as t:\\n # Watch is necessary in case q is a Tensor (and not a Variable). Passing a Tensor without watch result in\\n # None result.\\n t.watch(q)\\n potential = self.potential(q)\\n return t.gradient(potential, q)\",\n \"def integral(ambient_dim, dim, operand, where=None):\\n\\n return NodeSum(\\n area_element(ambient_dim, dim, where)\\n * QWeight(where)\\n * operand)\",\n \"def grad_log_q(self,z): \\n param_count = 0\\n grad = np.zeros((np.sum(self.approx_param_no),self.sims))\\n for core_param in range(len(self.q)):\\n for approx_param in range(self.q[core_param].param_no):\\n grad[param_count] = self.q[core_param].vi_score(z[core_param],approx_param) \\n param_count += 1\\n return grad\",\n \"def numerical_gradient(f, x: np.ndarray):\\n h = 1e-4\\n grad = np.zeros_like(x)\\n for i in range(x.size):\\n tmp_val = x.flat[i]\\n x.flat[i] = tmp_val + h\\n fxh1 = f(x)\\n\\n x.flat[i] = tmp_val - h\\n fxh2 = f(x)\\n grad.flat[i] = (fxh1 - fxh2) / (2 * h)\\n x.flat[i] = tmp_val\\n return grad\",\n \"def func_grad(self, X, Y):\\n Q = 0\\n gradd = np.zeros((N, 2*n+1))\\n for x, y in zip(X, Y):\\n #pairs of required arr coordinates\\n iterat = [(int(x[i] // (self.max / N)), i) for i in range(2*n+1)]\\n prediction = 0\\n for j, i in iterat:\\n prediction += self.arr[j, i]\\n delta = prediction - y\\n #in a meantime I precalculate new_step and, if all right, Ill use it to make new step\\n for j, i in iterat:\\n gradd[j, i] += delta \\n Q += delta * delta\\n return Q / len(X), gradd / len(X)\",\n \"def grad_f(f: FlowFieldVal, dim: int, h: float) -> FlowFieldVal:\\n if dim == 0:\\n df = kernel_op.apply_kernel_op_x(f, 'kdx')\\n elif dim == 1:\\n df = kernel_op.apply_kernel_op_y(f, 'kdy')\\n elif dim == 2:\\n df = kernel_op.apply_kernel_op_z(f, 'kdz', 'kdzsh')\\n else:\\n raise ValueError('Unsupport dimension: {}'.format(dim))\\n\\n return [df_i / h for df_i in df]\",\n \"def grad_interp(f: FlowFieldVal, grad_dim: int, interp_dim: int,\\n h: float) -> FlowFieldVal:\\n return interp(grad_n(f, grad_dim, h), interp_dim)\",\n \"def check_gradient(f, x, delta=1e-5, tol=1e-4):\\n\\n assert isinstance(x, np.ndarray)\\n assert x.dtype == np.float\\n \\n orig_x = x.copy()\\n #print('check_g, orig_x befor',orig_x)\\n #print('check_g, x befor',x)\\n #print('befor first pass in grad check')\\n fx, analytic_grad = f(x)\\n #print('after first pass in grad check')\\n #print('check_g, orig_x after',orig_x)\\n #print('check_g, x.shape',x.shape)\\n #print('func',f(x)[0])\\n #print('fx=',fx,'analityc_grad=',analytic_grad)\\n \\n assert np.all(np.isclose(orig_x, x, tol)), \\\"Functions shouldn't modify input variables\\\"\\n\\n assert analytic_grad.shape == x.shape\\n #print('analitical grad.shape',analytic_grad.shape)\\n analytic_grad = analytic_grad.copy()\\n\\n # We will go through every dimension of x and compute numeric\\n # derivative for it\\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\\n #print('it.shape=',it.shape)\\n while not it.finished:\\n ix = it.multi_index\\n #print('ix',ix)\\n #print('x[ix]',x[ix])\\n analytic_grad_at_ix = analytic_grad[ix]\\n #print('analitical_grad-at_ix',analytic_grad_at_ix)\\n orig_x = x.copy()\\n #print('orig_x',orig_x)\\n #print('x.shape befor delta',x.shape)\\n orig_x[ix]+=delta\\n #print('x.shape after delta',x.shape)\\n #print('orig_x[ix] delta +',orig_x[ix])\\n fx_plus=f(orig_x)[0]\\n #fx_plus=fx_plus_full[ix[0]]\\n #print('fx__plus',fx_plus)\\n orig_x = x.copy()\\n orig_x[ix]-=delta\\n #print('orig_x[ix] delta -',orig_x[ix])\\n fx_minus=f(orig_x)[0]\\n #print('fx_minus',fx_minus)\\n \\n divider=2*delta\\n #print('divider',divider)\\n #numeric_grad_at_ix = np.divide((fx_plus-fx_minus),divider)\\n numeric_grad_at_ix = (fx_plus-fx_minus)/divider\\n #print('numeric_grad_at_ix',numeric_grad_at_ix)\\n #print('fx(ix)', fx[ix])\\n\\n # TODO compute value of numeric gradient of f to idx\\n \\n if not np.isclose(numeric_grad_at_ix, analytic_grad_at_ix, tol):\\n print(\\\"Gradients are different at %s. Analytic: %2.5f, Numeric: %2.5f\\\" % (ix, analytic_grad_at_ix, numeric_grad_at_ix))\\n return False\\n\\n it.iternext()\\n\\n print(\\\"Gradient check passed!\\\")\\n return True\",\n \"def grad(ambient_dim, operand):\\n return grad_mv(ambient_dim, operand).as_vector()\",\n \"def fieldGradient(self):\\n return self.params['fieldGradient']\",\n \"def construct_gradient(f, mesh=None, op=DefaultOptions()):\\n if mesh is None:\\n mesh = f.function_space().mesh()\\n P1_vec = VectorFunctionSpace(mesh, \\\"CG\\\", 1)\\n g = TrialFunction(P1_vec)\\n φ = TestFunction(P1_vec)\\n # TODO: include an option to swap between these two: 'parts' vs 'L2'\\n a = inner(g, φ)*dx\\n # L = inner(grad(f), φ)*dx\\n L = f*dot(φ, FacetNormal(mesh))*ds - f*div(φ)*dx # enables f to be P0\\n g = Function(P1_vec)\\n solve(a == L, g, solver_parameters=op.hessian_solver_parameters)\\n return g\",\n \"def g(self, q):\\n # check for function in dictionary\\n if self._g is None:\\n self._g = self._calc_g()\\n parameters = tuple(q)\\n return np.array(self._g(*parameters), dtype='float32').flatten()\",\n \"def approx_grad(theta, X, y):\\n grad_a = np.array([(cost(theta + e, X, y) - cost(theta - e, X, y)) / (2 * 1e-5)\\n for e in np.identity(len(theta)) * 1e-5])\\n return grad_a\",\n \"def field_line_integrate(\\n r0, z0, phis, field, params={}, rtol=1e-8, atol=1e-8, maxstep=1000\\n):\\n r0, z0, phis = map(jnp.asarray, (r0, z0, phis))\\n assert r0.shape == z0.shape, \\\"r0 and z0 must have the same shape\\\"\\n rshape = r0.shape\\n r0 = r0.flatten()\\n z0 = z0.flatten()\\n x0 = jnp.array([r0, phis[0] * jnp.ones_like(r0), z0]).T\\n\\n @jit\\n def odefun(rpz, s):\\n rpz = rpz.reshape((3, -1)).T\\n r = rpz[:, 0]\\n br, bp, bz = field.compute_magnetic_field(rpz, params, basis=\\\"rpz\\\").T\\n return jnp.array(\\n [r * br / bp * jnp.sign(bp), jnp.sign(bp), r * bz / bp * jnp.sign(bp)]\\n ).squeeze()\\n\\n intfun = lambda x: odeint(odefun, x, phis, rtol=rtol, atol=atol, mxstep=maxstep)\\n x = jnp.vectorize(intfun, signature=\\\"(k)->(n,k)\\\")(x0)\\n r = x[:, :, 0].T.reshape((len(phis), *rshape))\\n z = x[:, :, 2].T.reshape((len(phis), *rshape))\\n return r, z\",\n \"def _grad_j(q_j, A_j, b_j, b_j_norm, a_1_j, a_2_j, m):\\n return (A_j.t() @ q_j / (-m)) + (b_j * (a_1_j / b_j_norm + a_2_j))\",\n \"def _compute_func_grad(self, w):\\n W = w.reshape((self.X.shape[1], self.Y.shape[1]))\\n self.nll_, self.grad_ = calculate_gradient(self.X, self.Y, W, self.prior, self.weighted,0)\",\n \"def compute_gradient(self, grad=None):\\n if grad is None:\\n grad = backend.ones_like(self.output_value)\\n dx = -grad\\n return dx\",\n \"def eval_numerical_gradient_array(f, x, df, h=1e-5):\\n grad = np.zeros_like(x)\\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\\n while not it.finished:\\n ix = it.multi_index\\n\\n oldval = x[ix]\\n x[ix] = oldval + h\\n pos = f(x).copy()\\n x[ix] = oldval - h\\n neg = f(x).copy()\\n x[ix] = oldval\\n\\n grad[ix] = np.sum((pos - neg) * df) / (2 * h)\\n it.iternext()\\n return grad\",\n \"def calculate_diff(grad_theta, grad_approx):\\n # np.linalg.norm apply for matric equal to Frobenius norm\\n numerator = np.linalg.norm(grad_theta - grad_approx)\\n denominator = np.linalg.norm(grad_theta) + np.linalg.norm(grad_approx)\\n diff = numerator / denominator\\n return diff\",\n \"def grad_f3(x):\\n pass\",\n \"def eval_numerical_gradient_array(f, x, df, h = 1e-5):\\n grad = np.zeros_like(x)\\n it = np.nditer(x, flags = ['multi_index'], op_flags =['readwrite'])\\n while not it.finished:\\n ix = it.multi_index\\n \\n oldval = x[ix]\\n x[ix] = oldval + h\\n pos = f(x).copy()\\n x[ix] = oldval - h\\n neg = f(x).copy()\\n x[ix] = oldval\\n \\n grad[ix] = np.sum((pos - neg) * df) / (2 * h)\\n it.iternext()\\n return grad\",\n \"def grad(self,w):\\n # Calculate the vector -sigma(-y_i * x_i.w)\\n s = -np.array([sigmoid(-yi * np.dot(xi,w)) for xi,yi in zip(self.x,self.y)])\\n # Multiply it by xy\\n g = np.array([np.dot(xyj,s) for xyj in self.xy.transpose()])\\n # Add regularisation\\n g += self.alpha*w\\n return g\\n #g = np.array([self.grad_j(w,j) for j in xrange(len(w))])\",\n \"def eval_numerical_gradient(f, x, verbose = True, h = 0.00001):\\n fx = f(x) # evaluate function value at original point\\n grad = np.zeros_like(x) # iterate over all indexese in x\\n it = np.nditer(x, flags = ['multi_index'], op_flags = ['readwrite'])\\n while not it.finished:\\n # evaluate function at x+h\\n ix = it.multi_index\\n oldval = x[ix]\\n x[ix] = oldval + h # increment by h\\n fxph = f(x) # evaluate f(x+h)\\n x[ix] = oldval - h\\n fxmh = f(x) # evaluate f(x-h)\\n x[ix] = oldval # restore\\n \\n #compute the partial derivative with centered fromula.\\n grad[ix] = (fxph - fxmh) / (2 * h)\\n if verbose:\\n print(ix, grad[ix])\\n it.iternext()\\n return grad\",\n \"def vert_integrate(self, u, d='up', Q='self'):\\n s = \\\"::: vertically integrating function :::\\\"\\n print_text(s, cls=self)\\n\\n if type(Q) != FunctionSpace:\\n Q = self.Q\\n ff = self.ff\\n phi = TestFunction(Q)\\n v = TrialFunction(Q)\\n bcs = []\\n # integral is zero on bed (ff = 3,5) \\n if d == 'up':\\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_B_GND)) # grounded\\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_B_FLT)) # shelves\\n a = v.dx(2) * phi * dx\\n # integral is zero on surface (ff = 2,6) \\n elif d == 'down':\\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_S_GND)) # grounded\\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_S_FLT)) # shelves\\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_U_GND)) # grounded\\n bcs.append(DirichletBC(Q, 0.0, ff, self.GAMMA_U_FLT)) # shelves\\n a = -v.dx(2) * phi * dx\\n L = u * phi * dx\\n name = 'value integrated %s' % d \\n v = Function(Q, name=name)\\n solve(a == L, v, bcs, annotate=False)\\n print_min_max(u, 'vertically integrated function', cls=self)\\n return v\",\n \"def dphi(x):\\n return grad(phi)(x)\",\n \"def __call__(self, x):\\n\\n self.dbeads.q = x\\n e = self.dforces.pot # Energy\\n g = -self.dforces.f # Gradient\\n\\n return e, g\",\n \"def gradient(poly):\\n return differential(\\n poly, chaospy.poly.collection.basis(1, 1, poly.dim, sort=\\\"GR\\\"))\",\n \"def gradient_check(op, *args, **kwargs):\\n\\n if( not 'id_list' in kwargs.keys() ):\\n kwargs.update({\\\"id_list\\\":[0]})\\n\\n id_list = kwargs.get(\\\"id_list\\\", [0])\\n\\n for i in id_list:\\n\\n if(not isinstance(args[i], Variable)):\\n raise Exception(\\\"input {:g} is not a variable\\\".format(i))\\n\\n if(isinstance(args[i], Variable) and not args[i].requires_grad):\\n raise Exception(\\\"input {:g} doesn't require gradient\\\".format(i))\\n\\n nelems = args[i].numel()\\n\\n \\\"\\\"\\\" numerical gradient \\\"\\\"\\\"\\n\\n wrapper, p = numdiff_wrapper(op, args, kwargs, i)\\n jacobian_numerical = numdiff_unified(wrapper, p)\\n\\n \\\"\\\"\\\" analytic gradient \\\"\\\"\\\"\\n\\n jacobian_analytic = []\\n\\n if(len(kwargs.keys()) > 1):\\n \\\"\\\"\\\"function has dictionary inputs\\\"\\\"\\\"\\n f = op(*args, **kwargs)\\n else:\\n f = op(*args)\\n\\n output_nelems = f.data.numel()\\n\\n for k in range(output_nelems):\\n\\n output_grad = torch.zeros(f.data.size())\\n output_grad.view(output_nelems, 1)[k] = 1\\n\\n f.backward(output_grad, retain_variables=True)\\n\\n jacobian_analytic.append( np.copy( args[i].grad.data.view( nelems ).numpy() ) )\\n\\n for params_i in args:\\n if(isinstance(params_i, torch.autograd.Variable) and params_i.requires_grad):\\n params_i.grad.data.zero_()\\n\\n jacobian_analytic = np.asarray(jacobian_analytic)\\n\\n \\\"\\\"\\\"\\n compare jacobian_analytic with jacobian_numerical\\n \\\"\\\"\\\"\\n\\n if( np.allclose(jacobian_analytic, jacobian_numerical) ):\\n\\n print \\\"gradient is correct\\\"\\n\\n else:\\n\\n rel_error = np.linalg.norm( jacobian_analytic - jacobian_numerical ) / \\\\\\n np.maximum( np.linalg.norm( jacobian_analytic ), np.linalg.norm( jacobian_numerical) )\\n\\n print 'analytic jacobian :'\\n print jacobian_analytic\\n\\n print 'numerical jacobian :'\\n print jacobian_numerical\\n\\n print 'jacobian difference :'\\n print jacobian_analytic - jacobian_numerical\\n\\n print 'relative error:'\\n print rel_error\",\n \"def interp_1d_grad_coef(ix, x, gamma):\\n # verifying arguments\\n gamma = float(gamma)\\n assert x.ndim == 1\\n n = x.size; N = min(n, 50)\\n # construct X\\n X = zeros([N, n], dtype=float)\\n for i in range(N):\\n X[i,:] = gamma**(i+1) / factorial(i+1) * (x - x[ix]) ** (i+1)\\n # construct diagonal G matrix for the Lagrange residual\\n G = gamma**(N+1) / factorial(N+1) * (x - x[ix])**(N+1)\\n # construct c\\n c = ones(n)\\n # first try to assemble the diagonal of matrix A, and sort by its diagonal\\n diagA = (X**2).sum(0) + G**2\\n finite = numpy.isfinite(diagA)\\n isort = sorted(range(n), key=diagA.__getitem__)\\n irevt = sorted(range(n), key=isort.__getitem__)\\n # drop first one -- all 0\\n assert isort[0] == ix\\n isort = isort[1:]\\n # permute columns of X and diagonal of G\\n X = X[:,isort]\\n G = G[isort]\\n c = c[isort]\\n finite = finite[isort]\\n # solve linear system\\n X = X[:,finite]\\n G = G[finite]\\n # QR decomposition of X, R is now cholesky factor of A\\n n1, n2 = X.shape\\n A = zeros([n1+n2, n2])\\n A[:n1,:] = X\\n A[range(n1,n1+n2), range(n2)] = G\\n R = numpy.linalg.qr(A, mode='r')\\n # ------------------- gradient part -----------------\\n # calculate rhs = dA * a\\n rhs = - gamma * X[0,:];\\n # solve for da\\n da = zeros(c.size + 1)\\n tmp = solve_L(R.transpose(), rhs)\\n da[1:][finite] = solve_R(R, tmp)\\n da[0] = -dot(da[1:], c)\\n # reverse sorting permutation to get a and b and normalize\\n da = da[irevt]\\n return -da\",\n \"def gradient(self, x):\\n u = np.asarray([x[0]])\\n C = self.C_func(u)\\n dC = self.dC_func(u, order=1)\\n P = self.P\\n numerator = np.sum((C - P) * dC, axis=0)\\n denominator = np.sum(np.sum((C - P) ** 2, axis=0) ** (1 / 2))\\n if np.abs(denominator) > 0:\\n gradient = numerator/denominator\\n else:\\n gradient = np.asarray(0)[np.newaxis]\\n return gradient\",\n \"def magnitude_of_gradient(grad_f):\\n return np.sqrt(np.ufunc.reduce(np.add, [x**2 for x in grad_f]))\",\n \"def integral(self, dmax, dmin=0):\\r\\n # more expressive\\r\\n #return integrate.quad(lambda r: 2*np.pi*r*self(r), dmin, dmax)[0]\\r\\n return self.bpsf.integral(dmax, dmin)\",\n \"def elementwise_grad(func):\\n return vmap(grad(func))\",\n \"def gradient(self,i,f):\\n\\n diff = self.points[f, :] - self.points[i, :]\\n gradient = diff[1]/diff[0]\\n\\n return gradient\",\n \"def compute_gradient(self, function, arguments):\",\n \"def test_arg_grad(self):\\r\\n\\r\\n x = matrix()\\r\\n cost = argmax(x, axis=0).sum()\\r\\n gx = grad(cost, x)\\r\\n val = tensor.get_scalar_constant_value(gx)\\r\\n assert val == 0.0\",\n \"def grad_fn(grad: np.ndarray) -> np.ndarray:\\n return grad * np.ones_like(t.data)\",\n \"def gradient(self, x):\\n g = self._grad(\\n time_series=self.observed_data,\\n a=x[0],\\n b=x[1],\\n c=x[2],\\n sigma=self.sigma\\n )\\n return g\",\n \"def scl_grad(X, phi):\\r\\n shape = list(phi.shape)\\r\\n shape.insert(0,3)\\r\\n grad = _np.zeros(shape,dtype=phi.dtype)\\r\\n\\r\\n polynomial = _interp(X[0], phi, axis=0)\\r\\n grad[0,...] = polynomial(X[0], nu=1)\\r\\n\\r\\n polynomial = _interp(X[1], phi, axis=1)\\r\\n grad[1,...] = polynomial(X[1], nu=1)\\r\\n\\r\\n polynomial = _interp(X[2], phi, axis=2)\\r\\n grad[2,...] = polynomial(X[2], nu=1)\\r\\n\\r\\n return grad\",\n \"def integral_function(w,i,dprime):\\n factors = 1\\n for j in xrange(Number_of_locs): #MOD Number_of_locs deleted\\n factors *= stats.norm.cdf(\\n (\\n -2.0*np.log(Post_probs[j]/Post_probs[i]) +\\n dprime[j]**2 +\\n 2.0*dprime[i]*w +\\n dprime[i]**2\\n ) / (\\n 2.0*dprime[j]\\n )\\n ) #MOD Post_probs deleted\\n return stats.norm.pdf(w) * factors\",\n \"def GradDescent_Armijo(q, gradq, x0, tol, kmax, a_low=1e-9, a_high=0.9,\\r\\n N=20, c_low=0.1, c_high=0.9, CD_tao=1e-5):\\r\\n\\r\\n # Use CentralDifferences to approximate the gradient\\r\\n if isinstance(gradq, str) and gradq == \\\"CD\\\":\\r\\n # Lambda function so that we can pass function and perturbation through\\r\\n gradq = (lambda x: _CentralDifferencesGradient(q, x, CD_tao))\\r\\n # If not a function and not \\\"CD\\\" then error\\r\\n elif not callable(gradq):\\r\\n raise InvalidArgumentError(\\\"Undefined gradq - should be a callable or CD\\\")\\r\\n\\r\\n # Iteration Counter\\r\\n k = 1\\r\\n\\r\\n # Make a copy in case we try to change it\\r\\n xk = x0.copy()\\r\\n\\r\\n # Cache gradq(xk), q(xk), and norm(gradq(xk)) to speed up computation\\r\\n gq_cache = gradq(xk)\\r\\n q_cache = q(xk)\\r\\n n_gq_cache = LA.norm(gq_cache)\\r\\n\\r\\n # Alpha grid for step size search\\r\\n alpha = np.logspace(np.log10(a_low), np.log10(a_high), N, endpoint=True)\\r\\n\\r\\n while n_gq_cache > tol*(1 + np.abs(q_cache)) and k < kmax:\\r\\n # Track minimum phi value in the grid\\r\\n phimin = None\\r\\n imin = None\\r\\n\\r\\n # Move along the direction of the gradient, testing q at each grid point\\r\\n for i in range(N):\\r\\n phi = q(xk - alpha[i]*gq_cache) - q_cache\\r\\n h = -c_low*alpha[i]*n_gq_cache*n_gq_cache\\r\\n l = -c_high*alpha[i]*n_gq_cache*n_gq_cache\\r\\n\\r\\n # Want the minimum phi value s.t. phi <= h and phi >= l\\r\\n if (l <= phi <= h) and (phimin is None or phi < phimin):\\r\\n phimin = phi\\r\\n imin = i\\r\\n\\r\\n # Not good, probably a step size issue\\r\\n if phimin is None or phimin > 0:\\r\\n # return xk, k\\r\\n warnings.warn(\\\"No more steps to take downward, don't trust answer. \\\\\\r\\n Probably a step size issue. Consider increasing N\\\",\\r\\n NonConvergenceWarning)\\r\\n\\r\\n # Update xk\\r\\n xk -= alpha[imin]*gq_cache\\r\\n\\r\\n # Cache new values\\r\\n gq_cache = gradq(xk)\\r\\n n_gq_cache = LA.norm(gq_cache)\\r\\n q_cache = q(xk)\\r\\n\\r\\n # Increase iteration count\\r\\n k += 1\\r\\n\\r\\n # If kmax gets exceeded, we can't trust the answer so issue warning\\r\\n if k >= kmax:\\r\\n warnings.warn(\\\"kmax exceeded, consider raising it\\\", NonConvergenceWarning)\\r\\n\\r\\n # Otherwise, we stopped the above loop because we're within tolerance so the answer is good\\r\\n return xk, k\",\n \"def grad(self, x):\\n return self.smoothed_grad(x)\",\n \"def grad(self, x):\\n return self.smoothed_grad(x)\",\n \"def grad(self, x):\\n return self.smoothed_grad(x)\",\n \"def eval_numerical_gradient(f, x, verbose=True, h=0.00001):\\n\\n fx = f(x) # evaluate function value at original point\\n grad = np.zeros_like(x)\\n # iterate over all indexes in x\\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\\n while not it.finished:\\n\\n # evaluate function at x+h\\n ix = it.multi_index\\n oldval = x[ix]\\n x[ix] = oldval + h # increment by h\\n fxph = f(x) # evalute f(x + h)\\n x[ix] = oldval - h\\n fxmh = f(x) # evaluate f(x - h)\\n x[ix] = oldval # restore\\n\\n # compute the partial derivative with centered formula\\n grad[ix] = (fxph - fxmh) / (2 * h) # the slope\\n if verbose:\\n print(ix, grad[ix])\\n it.iternext() # step to next dimension\\n\\n return grad\",\n \"def IntegratePolynomialPartLogExt(poly, fieldTower):\\n #print(poly)\\n #print(poly.degree)\\n #print(poly.fieldTower)\\n if poly == 0 or poly.isZero():\\n return Int.Integral()\\n red = poly.reduceToLowestPossibleFieldTower()\\n if red.fieldTower.towerHeight<fieldTower.towerHeight:\\n return Integrate(red)\\n p = poly.getCoefficients()\\n l = len(p)-1\\n q = [ZERO]*(len(p)+1)\\n b = [ZERO]*(len(p)+1)\\n d = [ZERO]*(len(p)+1)\\n \\n \\n u = fieldTower.getLastExtension().argFunction # last fieldExtension with T=log(u)\\n log_diff_u = u.logDifferentiate()#u.differentiate()/u # u'/u\\n \\n # p_l *T^l + p_(l-1)*T^(l-1) + ... + p_0 = (q_(l+1)*T^(l+1) + q_l*T^l + ... + q_0)' + (c_1 *v_1'/v_i + ... + c_m *v_m'/v_m)\\n # (q_i*T^i)' = q_i'*T^i + q_i*i*T'*T^(i-1)\\n # -> 0 = q_(l+1)'\\n # p_l = (l+1)*q_(l+1)*T' + q_l'\\n # ...\\n # p_0 = q_1*T' + (qe_0)', qe_0 = q_0+(c_1 *log(v_1) + ... + c_m *log(v_m))\\n for i in range(l,-1,-1):\\n # q_(i+1) = d_(i+1) + b_(i+1)\\n # integral(p_i-(i+1)*d_(i+1)*T') = l*b_(i+1)*T' + q_i\\n #i = Rational.fromFloat(i)\\n integrand = p[i]+(-ONE)*(i+1)*d[i+1]*log_diff_u\\n if type(integrand)==float or type(integrand)==int:\\n integrand = Rational.fromFloat(integrand)\\n int_reduced = integrand.reduceToLowestPossibleFieldTower()\\n if int_reduced!=None:\\n integrand = int_reduced\\n P_i = Integrate(integrand)\\n if i>0:\\n IntegratePolynomialPartLogExtCheckIntegralConditions(P_i, fieldTower)\\n else:\\n if P_i==None:\\n raise Int.IntegralNotElementaryError()\\n \\n prev = fieldTower.prevTower()\\n #logs = P_i.getNewLogExpressionsInFieldTower(prev,fieldTower)\\n logTerm = P_i.getLogExpression(u)\\n otherLogs = [log for log in P_i.logExpressions if not log==logTerm]#in logs]\\n ci = ZERO if logTerm==None else logTerm.factor # integral = P_i = c_i*T+d_i, d_i = P_i\\\\logs\\n d[i] = Int.Integral(poly_rationals=P_i.poly_rational_partExpressions,logs=otherLogs,rootSums=P_i.rootSums).asFunction()\\n b[i+1] = ci/(i+1)\\n q[i+1] = d[i+1]+b[i+1]\\n \\n #integrand = p[0]+(-1)*d[1]*log_diff_u\\n #P0 = Integrate(integrand)\\n #if P0==None:\\n # raise Int.IntegralNotElementaryError()\\n #b1 = 0\\n #for log in P0.logExpressions:\\n # if log.argFunction==u:\\n # b1 = log.factor\\n \\n if b[1]==0:\\n q_0 = P_i.asFunction()\\n else:\\n q_0 = (P_i+Int.Integral(logs=[Int.LogFunction(u,(-1)*b[1])])).asFunction()\\n q[0] = q_0\\n \\n integralPoly = Pol.Polynomial(variable=fieldTower.getLastVariable())\\n for i in range(l+1,-1,-1):\\n integralPoly.setCoefficient(i,q[i], callUpdates=False)\\n integralPoly.updateCoefficientsAll()\\n \\n return Int.Integral(poly_rationals=[integralPoly])\",\n \"def get_quad_length(q):\\n P0, P1, P2, P3 = q\\n p0 = Vector.fromPoint(P0) # fromPoint converts to ECEF\\n p1 = Vector.fromPoint(P1)\\n qlength = (p1 - p0).mag() / 1000\\n return qlength\",\n \"def internal2external_grad(xi,bounds):\\n \\n ge = np.empty_like(xi)\\n\\n for i,(v,bound) in enumerate(zip(xi,bounds)):\\n \\n a = bound[0] # minimum\\n b = bound[1] # maximum\\n\\n if a == None and b == None: # No constraints\\n ge[i] = 1.0\\n\\n elif b == None: # only min\\n ge[i] = v/np.sqrt(v**2+1)\\n\\n elif a == None: # only max\\n ge[i] = -v/np.sqrt(v**2+1)\\n\\n else: # both min and max\\n ge[i] = (b-a)*np.cos(v)/2.\\n\\n return ge\",\n \"def grad(self, var):\\n return Circuit.grad(self, var)\",\n \"def grad(self, A, y, x):\\n z = y - A.dot(x) # Error for each observation\\n grad_x = -1 * A.T.dot(np.sign(z))\\n # Gradient normalized by the num obs\\n return grad_x / y.size\",\n \"def gradient(self, x_in):\\r\\n\\r\\n return 1 - torch.pow(self.forward(x_in), 2)\",\n \"def integral (self):\\n dx = self.xbins[1] - self.xbins[0]\\n dy = self.ybins[1] - self.ybins[0]\\n return self.sum * (dx * dy)\",\n \"def grad(x):\\n\\n return x - np.arange(1, cfg.n + 1, dtype=np.float_)\",\n \"def cv_gradient(self,z):\\n gradient = np.zeros(np.sum(self.approx_param_no))\\n z_t = z.T \\n log_q = self.normal_log_q(z.T)\\n log_p = self.log_p(z.T)\\n grad_log_q = self.grad_log_q(z)\\n gradient = grad_log_q*(log_p-log_q)\\n\\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \\n\\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\\n\\n return np.mean(vectorized,axis=1)\",\n \"def tot_grad_obj(self,x0,gradient):\\n tmp = self.tot_objective(self.rebuild_param(x0,**self.fixed))\\n obj = tmp[0]\\n if gradient:\\n grad =self.fix_par(tmp[1], **self.fixed)[0]\\n return obj,grad\\n else:\\n return obj\",\n \"def FermiDirac_integral(self, y):\\n return sint.quad_vec(lambda x: x**2.*np.sqrt(x**2.+y**2.)/(np.exp(x)+1.), 0., 500.)[0]\",\n \"def objective_function_gradients(self, x):\\r\\n try:\\r\\n self._set_params_transformed(x)\\r\\n obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())\\r\\n self._fail_count = 0\\r\\n except (LinAlgError, ZeroDivisionError, ValueError) as e:\\r\\n if self._fail_count >= self._allowed_failures:\\r\\n raise e\\r\\n self._fail_count += 1\\r\\n obj_grads = np.clip(-self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients()), -1e100, 1e100)\\r\\n return obj_grads\",\n \"def log_q(xy, x, y, h):\\n return -linalg.norm(y - x - h*xy.grad_log_density(x))/(4*h)\",\n \"def integrate_f_from0(b):\\n integral, err = scipy.integrate.quad(f, 0, b)\\n return integral\",\n \"def gradient(self, x):\\n pass\",\n \"def compute_gradient(self, grad=None):\\n x = self.input_nodes[0].output_value\\n if grad is None:\\n grad = backend.ones_like(self.output_value)\\n if x == float('inf') or x == float('-inf'):\\n return grad * float('inf')\\n else:\\n return grad * 1 / x\",\n \"def sp_integrate_1D ( func , xmin , xmax , *args , **kwargs ) : \\n from scipy import integrate\\n ##\\n result = integrate.quad ( func , xmin , xmax , *args , **kwargs )\\n return result[0]\",\n \"def integrate_quantity(self, q, mask=None, layer=None):\\n if type(q) == str:\\n qarr = self.get_scalar_quantity(q)\\n else:\\n qarr = q\\n if layer is not None:\\n l = self.layers[layer]\\n z_vals = self.Z[l.get_slice(self.Z)]\\n qarr = qarr[l.get_slice(self.Z)]\\n if mask is not None and len(mask.shape) == 3:\\n mask = mask[l.get_slice(self.Z)]\\n else:\\n z_vals = self.Z\\n if mask is not None:\\n qarr = qarr*mask\\n z_integral = intg.trapz(qarr, x=z_vals, axis=0)\\n x_integral = intg.trapz(z_integral, x=self.X, axis=0)\\n y_integral = intg.trapz(x_integral, x=self.Y, axis=0)\\n return y_integral\",\n \"def objective_function_gradients(self, x):\\n self._set_params_transformed(x)\\n LL_gradients = self._transform_gradients(self._log_likelihood_gradients())\\n prior_gradients = self._transform_gradients(self._log_prior_gradients())\\n return - LL_gradients - prior_gradients\",\n \"def get_numerical_derived_accelerations(self):\\n # calculate numerical 2° order derivative and return it\\n return np.gradient(np.gradient(self.trajectory,axis=1),axis=1)\",\n \"def grad_n(f: FlowFieldVal, dim: int, h: float) -> FlowFieldVal:\\n if dim == 0:\\n df = kernel_op.apply_kernel_op_x(f, 'kDx')\\n elif dim == 1:\\n df = kernel_op.apply_kernel_op_y(f, 'kDy')\\n elif dim == 2:\\n df = kernel_op.apply_kernel_op_z(f, 'kDz', 'kDzsh')\\n else:\\n raise ValueError('Unsupport dimension: {}'.format(dim))\\n\\n return [df_i / (2.0 * h) for df_i in df]\",\n \"def dalf(x):\\n return grad(alf)(x)\",\n \"def grad(self, x):\\n grad = np.zeros((x.shape[0], 1))\\n grad[x >= utils.TOLERANCE] = 1.0\\n grad[x <= -utils.TOLERANCE] = -1.0\\n between = (x > -utils.TOLERANCE) & (x < utils.TOLERANCE)\\n grad[between] = self.rng(between.sum())\\n\\n return self.l * grad\",\n \"def integrate_fun(fun: Callable, low_b: float, upp_b: float) -> float:\\n return integrate.quad(fun, low_b, upp_b)[0]\",\n \"def gauss_quad_1d(q, n=150):\\n z, w = leggauss(n)\\n theta = (THETA_HIGH-THETA_LOW)*(z + 1)/2 + THETA_LOW\\n sin_theta = abs(sin(theta))\\n Zq = kernel_1d(q=q, theta=theta)\\n return np.sum(Zq*w*sin_theta)*(THETA_HIGH-THETA_LOW)/2\",\n \"def _grad(ys, xs, v=None):\\n if framework.in_dygraph_mode():\\n # paddle.grad returns a list though the inputs is a signle Tensor. The\\n # follow code snippet fixes the problem by return the first element of\\n # xs_grad when the xs is a signle Tensor.\\n xs_grad = paddle.grad(ys, xs, v, create_graph=True, allow_unused=True)\\n if (\\n isinstance(xs, paddle.fluid.framework.Variable)\\n and isinstance(xs_grad, typing.Sequence)\\n and len(xs_grad) > 0\\n ):\\n xs_grad = xs_grad[0]\\n else:\\n xs_grad = paddle.incubate.autograd.grad(ys, xs, v)\\n return _replace_none_with_zero_tensor(xs_grad, xs)\",\n \"def integrate(self, t):\",\n \"def grad(self, x):\\n norm_beta = np.linalg.norm(x)\\n if norm_beta > utils.TOLERANCE:\\n return (self.l / norm_beta) * x\\n else:\\n D = x.shape[0]\\n u = (self.rng(D, 1) * 2.0) - 1.0 # [-1, 1]^D\\n norm_u = np.linalg.norm(u)\\n a = self.rng() # [0, 1]\\n\\n return (self.l * (a / norm_u)) * u\",\n \"def grad_phi_mesh(self):\\n fy = -0.5 * (np.roll(self.phi, 1, axis = 1) - np.roll(self.phi, -1, axis=1)) \\n fx = -0.5 * (np.roll(self.phi, 1, axis = 0) - np.roll(self.phi, -1, axis=0))\\n return fx,fy\",\n \"def grad(self):\\n\\n return self._grad\",\n \"def cv_gradient(self, z): \\n z_t = np.transpose(z)\\n log_q = self.normal_log_q(z_t)\\n log_p = self.log_p(z_t)\\n grad_log_q = self.grad_log_q(z)\\n gradient = grad_log_q*np.repeat((log_p - log_q).T,2,axis=0)\\n\\n alpha0 = alpha_recursion(np.zeros(np.sum(self.approx_param_no)), grad_log_q, gradient, np.sum(self.approx_param_no)) \\n \\n vectorized = gradient - ((alpha0/np.var(grad_log_q,axis=1))*grad_log_q.T).T\\n\\n return np.mean(vectorized,axis=1)\",\n \"def gradient_func(self, grad_f, topology, tmax, eta, step_size):\\n x_func = partial(self._optimize_form, topology=topology, tmax=tmax, eta=eta)\\n return partial(grad_f, x_func=x_func, step_size=step_size)\",\n \"def gass_hermite_quad(f, degree, m, c):\\n\\n points, weights = np.polynomial.hermite.hermgauss( degree)\\n\\n #function values at given points\\n f_x = f(points, m= m, c= c)\\n\\n #weighted sum of function values\\n F = np.sum( f_x * weights)\\n\\n return F\",\n \"def gravity_gradients(self, p):\\n Le = self._Le(p)\\n wf = self._wf(p)\\n EeLe = np.einsum(\\\"i...,i\\\", self.Ee, Le)\\n FfWf = np.einsum(\\\"i...,i\\\", self.Ff, wf)\\n return G * self.d * (EeLe - FfWf)\",\n \"def _test_gradgrad_array(func, optimized, *args):\\n\\n def tangent_func():\\n func.__globals__['np'] = np\\n df = tangent.grad(func, motion='joint', optimized=optimized, verbose=True)\\n ddf = tangent.grad(df, motion='joint', optimized=optimized, verbose=True)\\n return ddf(*args)\\n\\n def reference_func():\\n func.__globals__['np'] = ag_np\\n return ag_grad(ag_grad(func))(*args)\\n\\n def backup_reference_func():\\n return utils.numeric_grad(utils.numeric_grad(func))(*args)\\n\\n utils.assert_result_matches_reference(\\n tangent_func, reference_func, backup_reference_func,\\n tolerance=1e-2) # extra loose bounds for 2nd order grad\",\n \"def _transform_gradients(self, g):\\r\\n x = self._get_params()\\r\\n [np.put(x, i, x * t.gradfactor(x[i])) for i, t in zip(self.constrained_indices, self.constraints)]\\r\\n [np.put(g, i, v) for i, v in [(t[0], np.sum(g[t])) for t in self.tied_indices]]\\r\\n if len(self.tied_indices) or len(self.fixed_indices):\\r\\n to_remove = np.hstack((self.fixed_indices + [t[1:] for t in self.tied_indices]))\\r\\n return np.delete(g, to_remove)\\r\\n else:\\r\\n return g\",\n \"def quad_interp(x,y,xi) :\\n f = interp1d(x,y,kind='quadratic')\\n yi = f(xi)\\n \\n return yi\",\n \"def integrate(self, x, dx):\\n nq, nv, nx, ndx = self.model.nq, self.model.nv, self.nx, self.ndx\\n assert (x.shape == (nx, ) and dx.shape == (ndx, ))\\n q = x[:nq]\\n v = x[-nv:]\\n dq = dx[:nv]\\n dv = dx[-nv:]\\n qn = pinocchio.integrate(self.model, a2m(q), a2m(dq))\\n return np.concatenate([qn.flat, v + dv])\",\n \"def get_grad_vector(args, pp, grad_dims):\\n grads = torch.Tensor(sum(grad_dims))\\n if args.cuda: grads = grads.cuda()\\n\\n grads.fill_(0.0)\\n cnt = 0\\n for param in pp():\\n if param.grad is not None:\\n beg = 0 if cnt == 0 else sum(grad_dims[:cnt])\\n en = sum(grad_dims[:cnt + 1])\\n grads[beg: en].copy_(param.grad.data.view(-1))\\n cnt += 1\\n return grads\",\n \"def _UpdateGradient(self):\\n self.mol.GetGradient('analytic')\",\n \"def get_quad_down_dip_vector(q):\\n P0, P1, P2, P3 = q\\n p0 = Vector.fromPoint(P0) # fromPoint converts to ECEF\\n p1 = Vector.fromPoint(P1)\\n p0p1 = p1 - p0\\n qnv = get_quad_normal(q)\\n ddv = Vector.cross(p0p1, qnv).norm()\\n return ddv\",\n \"def clgrad(obj, exe, arg, delta=DELTA):\\n f, x = get_method_and_copy_of_attribute(obj, exe, arg)\\n def grad_f(*args, **kwargs):\\n grad_val = numpy.zeros(x.shape)\\n it = numpy.nditer(x, op_flags=['readwrite'], flags=['multi_index'])\\n for xi in it:\\n i = it.multi_index\\n xi += delta/2\\n fp = f(*args, **kwargs)\\n xi -= delta\\n fm = f(*args, **kwargs)\\n xi += delta/2\\n grad_val[i] = (fp - fm)/delta\\n return grad_val\\n return grad_f\",\n \"def get_input_grad(self, Y, output_grad):\\n return np.multiply(logistic_deriv(Y), output_grad)\",\n \"def _get_grad_direction_cost(self, p, q):\\n dp = (self.grad_y[p[0]][p[1]], -self.grad_x[p[0]][p[1]])\\n dq = (self.grad_y[q[0]][q[1]], -self.grad_x[q[0]][q[1]])\\n \\n l = np.array([q[0]-p[0], q[1]-p[1]], np.float)\\n if 0 not in l:\\n l *= SQRT_0_5\\n \\n dp_l = np.dot(dp, l)\\n l_dq = np.dot(l, dq)\\n if dp_l < 0:\\n dp_l = -dp_l\\n l_dq = -l_dq\\n \\n # 2/3pi * ...\\n return 0.212206590789 * (np.arccos(dp_l)+np.arccos(l_dq))\",\n \"def GradDescent_ILS(q, gradq, x0, tol, kmax, a_low=1e-9, a_high=0.9, N=20, CD_tao=1e-5):\\r\\n\\r\\n # Use CentralDifferences to approximate the gradient\\r\\n if isinstance(gradq, str) and gradq == \\\"CD\\\":\\r\\n # Lambda function so that we can pass function and perturbation through\\r\\n gradq = (lambda x: _CentralDifferencesGradient(q, x, CD_tao))\\r\\n # If not a function and not \\\"CD\\\" then error\\r\\n elif not callable(gradq):\\r\\n raise InvalidArgumentError(\\\"Undefined gradq - should be a callable or CD\\\")\\r\\n\\r\\n\\r\\n # Iteration Counter\\r\\n k = 1\\r\\n\\r\\n # Make a copy in case we try to change it\\r\\n xk = x0.copy()\\r\\n\\r\\n # Cache gradq(xk) and q(xk) to speed up computation\\r\\n gq_cache = gradq(xk)\\r\\n q_cache = q(xk)\\r\\n n_gq_cache = LA.norm(gq_cache)\\r\\n normalized_gq_cache = gq_cache/n_gq_cache\\r\\n\\r\\n # Alpha grid for step size search\\r\\n alpha = np.logspace(np.log10(a_low), np.log10(a_high), N, endpoint=True)\\r\\n\\r\\n while n_gq_cache > tol*(1 + np.abs(q_cache)) and k < kmax:\\r\\n # Track minimum phi value in the grid\\r\\n phimin = None\\r\\n imin = None\\r\\n\\r\\n # Move along the direction of the gradient, testing q at each grid point\\r\\n for i in range(N):\\r\\n phi = q(xk - alpha[i]*normalized_gq_cache) - q_cache\\r\\n\\r\\n # Want the minimum phi value\\r\\n if phimin is None or phi < phimin:\\r\\n phimin = phi\\r\\n imin = i\\r\\n\\r\\n # Not good, probably a step size issue\\r\\n if phimin > 0:\\r\\n warnings.warn(\\\"No more steps to take downward, don't trust answer.\\\\\\r\\n Probably a step size issue. Consider increasing N\\\",\\r\\n NonConvergenceWarning)\\r\\n\\r\\n # Update xk\\r\\n xk -= alpha[imin]*(gq_cache/n_gq_cache)\\r\\n\\r\\n # Cache new values\\r\\n gq_cache = gradq(xk)\\r\\n q_cache = q(xk)\\r\\n n_gq_cache = LA.norm(gq_cache)\\r\\n normalized_gq_cache = gq_cache/n_gq_cache\\r\\n\\r\\n # Increase iteration count\\r\\n k += 1\\r\\n\\r\\n # If kmax gets exceeded, we can't trust the answer so issue a warning\\r\\n if k >= kmax:\\r\\n warnings.warn(\\\"kmax exceeded, consider raising it\\\", NonConvergenceWarning)\\r\\n\\r\\n # Otherwise, we stopped the above loop because we're within tolerance so the answer is good\\r\\n return xk, k\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61055577","0.588669","0.5814507","0.57111114","0.5700496","0.5698189","0.56779283","0.5656769","0.5652398","0.5644207","0.5642131","0.56034553","0.5586597","0.55844885","0.5546834","0.5504967","0.5503718","0.54716414","0.54594284","0.5456545","0.5454004","0.5397677","0.53925854","0.5392429","0.5391397","0.5374551","0.5373389","0.5365532","0.5364785","0.5364355","0.5363418","0.535607","0.53549826","0.5350678","0.53400964","0.5338982","0.5334382","0.5332968","0.53243184","0.5320884","0.5318457","0.53021646","0.52920794","0.5272897","0.5262743","0.5262245","0.52593285","0.5254311","0.5245872","0.5245404","0.52346003","0.5225009","0.5225009","0.5225009","0.5212662","0.5201214","0.5190735","0.5190666","0.5187259","0.5184554","0.5181967","0.51805335","0.5178434","0.5174263","0.5170897","0.5151678","0.5147992","0.51463234","0.51429915","0.51416874","0.5140422","0.51378155","0.51372737","0.5135152","0.51304954","0.51221865","0.5120238","0.5119033","0.5107886","0.51072496","0.51044995","0.5100112","0.5097341","0.50971305","0.5093812","0.5090585","0.5087037","0.507436","0.50684875","0.50654095","0.50644547","0.50599873","0.5052827","0.50496745","0.5048356","0.50460273","0.50450075","0.5044155","0.5040536","0.5039984"],"string":"[\n \"0.61055577\",\n \"0.588669\",\n \"0.5814507\",\n \"0.57111114\",\n \"0.5700496\",\n \"0.5698189\",\n \"0.56779283\",\n \"0.5656769\",\n \"0.5652398\",\n \"0.5644207\",\n \"0.5642131\",\n \"0.56034553\",\n \"0.5586597\",\n \"0.55844885\",\n \"0.5546834\",\n \"0.5504967\",\n \"0.5503718\",\n \"0.54716414\",\n \"0.54594284\",\n \"0.5456545\",\n \"0.5454004\",\n \"0.5397677\",\n \"0.53925854\",\n \"0.5392429\",\n \"0.5391397\",\n \"0.5374551\",\n \"0.5373389\",\n \"0.5365532\",\n \"0.5364785\",\n \"0.5364355\",\n \"0.5363418\",\n \"0.535607\",\n \"0.53549826\",\n \"0.5350678\",\n \"0.53400964\",\n \"0.5338982\",\n \"0.5334382\",\n \"0.5332968\",\n \"0.53243184\",\n \"0.5320884\",\n \"0.5318457\",\n \"0.53021646\",\n \"0.52920794\",\n \"0.5272897\",\n \"0.5262743\",\n \"0.5262245\",\n \"0.52593285\",\n \"0.5254311\",\n \"0.5245872\",\n \"0.5245404\",\n \"0.52346003\",\n \"0.5225009\",\n \"0.5225009\",\n \"0.5225009\",\n \"0.5212662\",\n \"0.5201214\",\n \"0.5190735\",\n \"0.5190666\",\n \"0.5187259\",\n \"0.5184554\",\n \"0.5181967\",\n \"0.51805335\",\n \"0.5178434\",\n \"0.5174263\",\n \"0.5170897\",\n \"0.5151678\",\n \"0.5147992\",\n \"0.51463234\",\n \"0.51429915\",\n \"0.51416874\",\n \"0.5140422\",\n \"0.51378155\",\n \"0.51372737\",\n \"0.5135152\",\n \"0.51304954\",\n \"0.51221865\",\n \"0.5120238\",\n \"0.5119033\",\n \"0.5107886\",\n \"0.51072496\",\n \"0.51044995\",\n \"0.5100112\",\n \"0.5097341\",\n \"0.50971305\",\n \"0.5093812\",\n \"0.5090585\",\n \"0.5087037\",\n \"0.507436\",\n \"0.50684875\",\n \"0.50654095\",\n \"0.50644547\",\n \"0.50599873\",\n \"0.5052827\",\n \"0.50496745\",\n \"0.5048356\",\n \"0.50460273\",\n \"0.50450075\",\n \"0.5044155\",\n \"0.5040536\",\n \"0.5039984\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6430995"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":78897,"cells":{"query":{"kind":"string","value":"Get the errors from the Lcurve runs"},"document":{"kind":"string","value":"def get_l_curve_results(directory, overwrite = False):\n costs = []\n tikhs = []\n regs = []\n\n glacier = \"helheim\"\n extension = \".tar.gz\"\n\n # If the output hasn't been cached, retrieve it from the giant archives\n cached_output = os.path.join(directory, \"helheim_l_curve.txt\")\n if not os.path.exists(cached_output) or overwrite:\n for archive_name in [f for f in os.listdir(directory)\n if (os.path.isfile(os.path.join(directory, f))\n and\n f[-len(extension):] == extension)]:\n start_index = len(glacier) + len(\"_lambda-\")\n regularization = float(archive_name[start_index: -len(extension)])\n cost, tikh = l_curve_point(os.path.join(directory, archive_name),\n glacier, regularization, 4)\n costs.append(cost)\n tikhs.append(tikh)\n regs.append(regularization)\n\n # Save these results to a text file\n with open(cached_output, 'w') as fid:\n fid.write(\"cost,model_norm,lambda\\n\")\n for k in range(len(costs)):\n fid.write(\"{0},{1},{2}\\n\".format(costs[k], tikhs[k], regs[k]))\n\n # Open the cached output\n with open(cached_output, 'r') as fid:\n line = fid.readline()\n line = fid.readline().split('\\n')[0]\n while(line):\n c, t, r = line.split(',')\n costs.append(float(c))\n tikhs.append(float(t))\n regs.append(float(r))\n line = fid.readline().split('\\n')[0]\n\n return costs, tikhs, regs"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __call__(self, errors: List[float]) -> List[float]:","def error(self) -> Sequence[float]:\n errors = []\n for line, sign in zip(self.marker_lines, (-1, 1)):\n if self._orientation == Orientation.UP_DOWN:\n picket_pos = self._fit(line.center.y)\n mlc_pos = line.center.x\n else:\n picket_pos = self._fit(line.center.x)\n mlc_pos = line.center.y\n if (\n self._separate_leaves\n ): # offset the picket position by the DLG and nominal gap\n mag_factor = self._image.sid / 1000\n picket_pos += (\n sign * self._nominal_gap_mm * mag_factor / 2 * self._image.dpmm\n )\n errors.append((mlc_pos - picket_pos) / self._image.dpmm)\n return errors","def linpol_error(self):\n return self._linpol_error","def yerr(self, i):\n return self.errors[1][i]","def calc_errors(problem, points):\n original = problem.getp()\n try:\n ret = plugin.calc_errors(problem, points)\n except:\n import traceback\n print(\"error calculating distribution on model\")\n traceback.print_exc()\n ret = None\n finally:\n problem.setp(original)\n return ret","def calcErr(dicth,dictl,cdli): \n \n \n errh = np.square((cdli.datain_h - np.dot(dicth, np.transpose(cdli.wh))))\n errl = np.square((cdli.datain_l - np.dot(dictl, np.transpose(cdli.wl))))\n \n \n return [errh, errl]","def error_compute(self):\n self.tt_error = np.linalg.norm(self.rel_error)\n if self.global_rank==0:print('Overall error is::',self.tt_error)\n return {'NMF': self.rel_error, 'tt': self.tt_error}","def get_error_rates():\n return [ERR_RT * i for i in range(int((1 / ERR_RT) / 4))] # error up to 25%","def _step_errors(sequence: Sequence) -> List[TransformationMetrics]:\n errors = [TransformationMetrics.calc(t_pred, t_gt, time)\n for t_pred, t_gt, time in zip(sequence.prediction.transforms, sequence.ground_truth.transforms,\n sequence.times)]\n return errors","def get_linear_track_error(self):\r\n return self._arm.get_linear_track_error()","def L_curveTV(f,lam_init = 2.0, q = 0.9):\n lam = lam_init\n max_iter = 50\n residual_list = np.zeros(max_iter)\n size_list = np.zeros(max_iter)\n error = np.zeros(max_iter)\n alt_error = np.zeros(max_iter)\n \n for i in range(max_iter): #range(max_iter):\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc=True, tol = 1.0e-5)\n #u, _, j = projected_gradient_alt(f,lam, tau = 0.2, tol = 1.0e-4)\n lam = lam_init * (q ** i)\n residual_list[i] = np.linalg.norm(u - f)\n size_list[i] = np.linalg.norm(u)\n error[i] = np.linalg.norm(u - f) * np.linalg.norm(u)\n #plt.loglog(residual_list,size_list)\n #plt.show()\n opt_idx = np.argmin(error)\n t = 1.0 / (1.0 + lam_init * (q ** opt_idx))\n lam = lam_init * (q ** opt_idx)\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc = True, tol = 1.0e-5)\n return u, t","def lCurve(self): \n\n # --------------------------------------------------------------------------------------------- #\n # Read data\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\n lcTab = Table.read(fitsNnam)\n if (self.tstart is not None) and (self.tstop is not None):\n lcTab = lcTab[ (self.tstart <= lcTab['mjd']) & (lcTab['mjd'] <= self.tstop)]\n lcTab = lcTab[lcTab['flux'] != -1.] # avoid undone analyses\n\n timeMJD = lcTab['mjd']\n tref = int(np.floor( timeMJD[0] / 100.0)) * 100 # round to lowest hundred\n timeMJD -= tref\n ts = lcTab['ts']\n detect = lcTab['ts'] >= self.tsmin\n undet = lcTab['ts'] < self.tsmin\n flux = lcTab['flux'][detect]\n fluxerr = lcTab['fluxerr'][detect]\n upperl = lcTab['upperlim'][undet]\n upperl[upperl == -1.] = 0. # for when it failed\n scale = 10**int(np.floor(np.log10( np.mean( np.concatenate( (flux, upperl), axis=0) ) ))) \n\n # --------------------------------------------------------------------------------------------- #\n # Plot\n lcplt = FermiPlot(savepath='', xsize=8.5, ysize=6)\n lcplt.figname = os.path.join(self.workpath, 'LightCurve.pdf')\n lcplt.xlabel = r'Time (MJD $-$ {})'.format(tref)\n lcplt.ylabel = [r'Flux ($10^{%d}$ ph\\,cm$^{-2}$\\,s$^{-1}$)'%(int(np.log10(scale))), r'TS']\n lcplt.hline = [None, self.tsmin]\n deltaY = max(np.concatenate((flux+fluxerr, upperl), axis=0)) - min(np.concatenate((flux-fluxerr, upperl), axis=0))\n lcplt.ymin = [(min(np.concatenate((flux-fluxerr, upperl-upperl*0.1), axis=0)) - 0.05*deltaY) / scale, min(ts) - 0.05*(max(ts)-min(ts))]\n lcplt.ymax = [(max(np.concatenate((flux+fluxerr, upperl), axis=0)) + 0.05*deltaY) / scale, max(ts) + 0.05*(max(ts)-min(ts))]\n deltaX = (timeMJD[-1] + lcTab['mjderr'][-1]) - (timeMJD[0] - lcTab['mjderr'][0]) \n lcplt.xmin = timeMJD[0] - lcTab['mjderr'][0] - 0.05*deltaX\n lcplt.xmax = timeMJD[-1] + lcTab['mjderr'][-1] + 0.05*deltaX\n lcplt.fill = [item for sublist in zip( timeMJD[detect]-lcTab['mjderr'][detect], timeMJD[detect]+lcTab['mjderr'][detect] ) for item in sublist]\n lcplt.shadecol= self.loran \n if len(flux) == 0:\n lcplt.mksize = [2, 2]\n lcplt.ymode = ['linear', 'linear']\n lcplt.color = ['gray', 'black']\n lcplt.prop = [3, 1]\n lcplt.limit = [True, False]\n lcplt.multiplot(x = [ timeMJD[undet], timeMJD ],\n y = [ upperl/scale, ts ],\n xerr = [ lcTab['mjderr'][undet], lcTab['mjderr']],\n yerr = [ upperl/scale*0.1, None])\n else:\n lcplt.mksize = [2, 2, 2]\n lcplt.ymode = ['linear', 'linear', 'linear']\n lcplt.color = ['gray', 'black', 'black']\n lcplt.prop = [3, 1]\n lcplt.limit = [[True, False], False]\n lcplt.multiplot(x = [ [timeMJD[undet], timeMJD[detect]], timeMJD ],\n y = [ [upperl/scale, flux/scale], ts ],\n xerr = [ [lcTab['mjderr'][undet], lcTab['mjderr'][detect]], lcTab['mjderr']],\n yerr = [ [upperl/scale*0.1, fluxerr/scale], None])\n lcplt.save()\n\n print(\"\\t=== Figure '{}' created ===\".format(lcplt.figname)) \n return","def _getErrorFunction(self):\n\n\t\treturn (self._setpoint - self._current)","def get_errors(self):\n return {'loss': self.loss.data[0]}","def get_error_estimates(self, Y, M1, M2=None):\n # First K0 and K1\n Mminus = M1\n if M2 is None:\n Mplus = M1\n else:\n Mplus = M2\n if self.Cp0 != 0 and self.Cp1 != 0 and self.Cm != 0:\n Cp0 = self.Cp0\n Cp1 = self.Cp1\n Cm = self.Cm\n else:\n PP = self.principal_part()\n Cmax = max(PP.values())\n Kmax = 0\n for t in PP.keys():\n if isinstance(t, tuple):\n (c, l) = t\n elif isinstance(t, (int, Integer)):\n (c, l) = rn_from_D(self._space.multiplier(), t)\n else:\n raise ValueError(\"Incorrect principal part: t={0}\".format(t))\n if c in self._space.multiplier().D():\n tmp = l + self._space.multiplier().Qv[self._space.index_set().index(c)]\n elif c in range(len(self._space.multiplier().Qv)):\n tmp = l + self._space.multiplier().Qv[c]\n else:\n raise ValueError(\"Incorrect principal part: c,l={0},{1}\".format(c, l))\n if(abs(tmp) > Kmax):\n Kmax = abs(tmp)\n [Cp0, Cp1] = self._space.get_Cp(Cmax)\n Cm = self._space.get_Cm(Kmax, Cmax)\n self.Cp0 = Cp0\n self.Cp1 = Cp1\n self.Cm = Cm\n\n fak = len(self._space.index_set())\n # print \"Cp0,Cp1,Cm=\",Cp0,Cp1,Cm\n # print \"fak=\",fak\n\n er1 = fak * self._space.err_est_vv_hwmf_neg(Y, Mminus, Cm)\n er2 = fak * self._space.err_est_vv_hwmf_pos(Y, Mplus, Cp0, Cp1)\n return [er1, er2]","def compute_log_curve(log_name, num_runs=4):\n log_collector = []\n for run_idx in range(1, num_runs+1):\n # prepare log path\n log_file = join(spiker.SPIKER_EXPS+\"-run-%d\" % (run_idx),\n log_name, \"csv_history.log\")\n log_dict = utils.parse_csv_log(log_file)\n log_collector.append(log_dict)\n\n # compute for train loss\n train_loss = np.vstack(\n (log_collector[0][\"loss\"][np.newaxis, ...],\n log_collector[1][\"loss\"][np.newaxis, ...],\n log_collector[2][\"loss\"][np.newaxis, ...],\n log_collector[3][\"loss\"][np.newaxis, ...]))\n train_loss = train_loss.astype(\"float64\")\n train_loss_mean = np.mean(train_loss, axis=0)\n train_loss_std = np.std(train_loss, axis=0)\n\n # compute for test loss\n test_loss = np.vstack(\n (log_collector[0][\"val_loss\"][np.newaxis, ...],\n log_collector[1][\"val_loss\"][np.newaxis, ...],\n log_collector[2][\"val_loss\"][np.newaxis, ...],\n log_collector[3][\"val_loss\"][np.newaxis, ...]))\n test_loss = test_loss.astype(\"float64\")\n test_loss_mean = np.mean(test_loss, axis=0)\n test_loss_std = np.std(test_loss, axis=0)\n\n # compute for train mse\n train_mse = np.vstack(\n (log_collector[0][\"mean_squared_error\"][np.newaxis, ...],\n log_collector[1][\"mean_squared_error\"][np.newaxis, ...],\n log_collector[2][\"mean_squared_error\"][np.newaxis, ...],\n log_collector[3][\"mean_squared_error\"][np.newaxis, ...]))\n train_mse = train_mse.astype(\"float64\")\n train_mse_mean = np.mean(train_mse, axis=0)\n train_mse_std = np.std(train_mse, axis=0)\n\n # compute for test mse\n test_mse = np.vstack(\n (log_collector[0][\"val_mean_squared_error\"][np.newaxis, ...],\n log_collector[1][\"val_mean_squared_error\"][np.newaxis, ...],\n log_collector[2][\"val_mean_squared_error\"][np.newaxis, ...],\n log_collector[3][\"val_mean_squared_error\"][np.newaxis, ...]))\n test_mse = test_mse.astype(\"float64\")\n test_mse_mean = np.mean(test_mse, axis=0)\n test_mse_std = np.std(test_mse, axis=0)\n\n trloss = (train_loss_mean, train_loss_std)\n teloss = (test_loss_mean, test_loss_std)\n trmse = (train_mse_mean, train_mse_std)\n temse = (test_mse_mean, test_mse_std)\n\n return trloss, teloss, trmse, temse","def error():\n\n # Make data set using errors\n dataset_a = DataSet(oscillating,error_y=oscillating_error,plot='error_bar',label='Data and error')\n dataset_a.set_error(interval=5,width=1,cap=2)\n dataset_b = DataSet(oscillating,plot='error_shade',error_y=oscillating_error,order=0,colour='lightgrey',label='Error')\n dataset_c = DataSet(oscillating,plot='line',order=1,colour='firebrick',label='Data')\n\n # Make line graph with error bars\n plot_bar = Plot()\n plot_bar.set_legend(legend=True)\n plot_bar.add_dataset(dataset_a)\n plot_bar.plot()\n plot_bar.save(name='./figures/2d_error_bar',fmt='png')\n plot_bar.display()\n\n # Make line graph with shaded errors\n plot_shade = Plot()\n plot_shade.set_legend(legend=True,location='upper left')\n plot_shade.add_dataset(dataset_b)\n plot_shade.add_dataset(dataset_c)\n plot_shade.plot()\n plot_shade.save(name='./figures/2d_error_shade',fmt='png')\n plot_shade.display()","def error(self, trainset: ([], [])):\n # MSE = Σ | d – y |^2 / n\n error_sum = 0.0\n for index, example in enumerate(trainset):\n # | d – y |^2\n output = self.activate(example[0])\n\n target = example[1][0]\n\n error = target - output\n error_sum += error ** 2\n\n # Σ |error_sum| / n\n error_sum = error_sum / len(trainset)\n return error_sum","def error(self,pt,eta):\n return self._data[self.__ptBin(pt)][self.__etaBin(eta)][1]","def runErrorTests(c, startNx, endNx, stepNx=1, display=False):\n errorsArray = []\n dxs = np.empty(shape=[0])\n iteration = 0\n\n for currNx in range(startNx, endNx, stepNx):\n nx = currNx\n nt = nx\n # initialize the vector of space points, our domain is [0,1]\n x = np.linspace(0, 1, nx)\n dxs = np.append(dxs, x[1] - x[0])\n #to check convergence use smooth function\n phi_ic = ic.cosineBasedFctn(x, 0.5)\n errline, _ = runAllSchemes(x, phi_ic, nx, nt, c)\n errorsArray = np.append(errorsArray, errline)\n iteration = iteration+1\n \n # to check order of convergence we see the behaviour of log-log plots\n # just for extra safety we check >0 for log\n dxLog = np.where(dxs>0, np.log10(dxs), 0)\n ErrorsLog = np.where(errorsArray>0, np.log10(errorsArray), 0)\n ErrorsLog = ErrorsLog.reshape(iteration, len(errline)) \n ErrorsLog = np.matrix.transpose(ErrorsLog)\n methods = [\"FTBS\", \"CTCS\", \"CNCS\", \"LaxWendroff\"]\n if(display):\n for i in range (0, 4):\n plt.plot(dxLog, ErrorsLog[i], label=methods[i])\n coeff = np.polyfit(dxLog,ErrorsLog[i],1)\n print(\"Estimated order of convergence for \"+methods[i]+\\\n \": \"+str(coeff[0]))\n plt.title(\"Log-log plot of L2 errors vs dx\\nc=\"+str(c))\n plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n plt.show()","def plotErr(self):\n if self.xp and self.wp:\n # plot the spectra\n w=self.ws.value(np.array(self.xp))\n self.errcurve,=self.erraxes.plot(self.xp,self.wp-w,linewidth=0.5,linestyle='',marker='o',color='b')\n if self.dxp and self.dwp:\n # plot the spectra\n dw=self.ws.value(np.array(self.dxp))\n self.delerrcurve,=self.erraxes.plot(self.dxp,self.dwp-dw,linewidth=0.5,linestyle='',marker='x',color='b')","def error(line, data): # error function\n # Metric: Sum of squared Y-axis differences\n err = np.sum((data[:, 1] - (line[0] * data[:, 0] + line[1])) ** 2)\n return err","def getErrors(self):\n errorList = []\n\n # E0\n try:\n if not self.e0.isValid():\n errorList.append(\"Invalid first error axis in ErrorEllipse Class\")\n except (NameError, AttributeError):\n errorList.append(\"No first error axis in ErrorEllipse Class.\")\n\n # E1\n try:\n if not self.e1.isValid():\n errorList.append(\"Invalid second error axis in ErrorEllipse Class\")\n except (NameError, AttributeError):\n errorList.append(\"No second error axis in ErrorEllipse Class.\")\n\n # E2\n try:\n if not self.e2.isValid():\n errorList.append(\"Invalid third error axis in ErrorEllipse Class\")\n except (NameError, AttributeError):\n errorList.append(\"No third error axis in ErrorEllipse Class.\")\n\n # maximumHorizontalProjection\n try:\n self.maximumHorizontalProjection\n except (NameError, AttributeError):\n errorList.append(\"No MaximumHorizontalProjection in ErrorEllipse Class.\")\n\n # maximumVerticalProjection\n try:\n self.maximumVerticalProjection\n except (NameError, AttributeError):\n errorList.append(\"No MaximumVerticalProjection in ErrorEllipse Class\")\n\n # equivalentHorizontalRadius\n try:\n self.equivalentHorizontalRadius\n except (NameError, AttributeError):\n errorList.append(\"No EquivalentHorizontalRadius in ErrorEllipse class\")\n\n return errorList","def calculate_error(self):\n \n delta = self.Y - self.T\n error = delta.dot(delta) / self.N\n error = format(error, '.5f')\n \n self.errors.append(error)","def tracking_error(port_returns, market_returns):\n\n return np.std(port_returns - market_returns)","def tracking_error(port_returns, market_returns):\n\n return np.std(port_returns - market_returns)","def get_accelerometer_error(self) -> (Iterable[float], Iterable[float]):\n acceleration_error = self.compute_accelerometer_error()\n upper_error = [None] * self.num_steps\n lower_error = [None] * self.num_steps\n\n upper_error[0] = lower_error[0] = self.initial_value\n upper_error[1] = lower_error[1] = (\n self.time_step * self.initial_velocity + self.initial_value)\n\n index = self.fill_values(upper_error)\n self.fill_values(lower_error)\n if index >= self.num_steps - 1:\n return (upper_error[index:], lower_error[index:])\n\n if index > 1:\n self.retrieve_value(\n index + 1, upper_error, acceleration_error=acceleration_error)\n self.retrieve_value(\n index + 1, lower_error, acceleration_error=-acceleration_error)\n else:\n self.retrieve_value(index + 1, upper_error)\n self.retrieve_value(index + 1, lower_error)\n self.retrieve_value(\n index + 2, upper_error, acceleration_error=acceleration_error)\n self.retrieve_value(\n index + 2, lower_error, acceleration_error=-acceleration_error)\n\n self.retrieve_value(self.num_steps - 1, upper_error)\n self.retrieve_value(self.num_steps - 1, lower_error)\n\n return (upper_error[index:], lower_error[index:])","def error(self): \n if not self.terminal:\n err = sum([v**2 for v in self.state + self.q[:-1]])\n else:\n err = sum([v**2 for v in LIMITS[:9]] + [1.0 - LIMITS[9]**2])\n err *= (self.max_steps - self.steps)\n return err","def errorList(self, ylist, predictedlist):\n\n e = [0 for i in range(self.size)]\n for i in range(self.size):\n e[i] = round(ylist[i] - predictedlist[i], 3)\n \n errors = Vector(self.size)\n errors.changeVals(e)\n return errors","def calc_error(self, data):\n errors = np.zeros(data.shape)\n\n # Values below minimum lidar distance are ascribed the minimum error\n errors[data <= self.min_error_dist] = self.min_error\n\n # Values above minimum have erros calculated by proportion of their value\n errors[data > self.min_error_dist] = data[data > self.min_error_dist] * self.error_prop\n return errors","def get_error(self, params):\n return self.endog - self.predict(params)","def ss_err(self):\n return np.squeeze(self._ss_err)","def display_convergence_error(train_losses, valid_losses):\n if len(valid_losses) > 0:\n plt.plot(len(train_losses), train_losses, color=\"red\")\n plt.plot(len(valid_losses), valid_losses, color=\"blue\")\n plt.legend([\"Train\", \"Valid\"])\n else:\n plt.plot(len(train_losses), train_losses, color=\"red\")\n plt.legend([\"Train\"])\n plt.xlabel('Epoch')\n plt.ylabel('Loss')\n plt.show()","def analyse_training_error(self, errors):\n # Write Training Error in an excel file\n with open(r\"Logistic Regression Results/Wine/logistic_regression_error\" + str(self.learning_rate) + r\"-\" + str(len(errors)) + r\"epochs.csv\", \"w\") as file:\n\n # Create dict writer\n csv_writer = csv.writer(file, delimiter = \",\", lineterminator=\"\\n\")\n\n # Write headers of the CSV file\n csv_writer.writerow([\"Training Error\", \"Learning Rate\"])\n\n # Write first training error with the current learning rate\n csv_writer.writerow([errors[0],self.learning_rate])\n\n # Write every row after the first line\n for error in errors:\n csv_writer.writerow([error])\n\n # Create a list of epochs\n epochs = list(range(1, len(errors) + 1))","def _ss_err(self):\n return np.sum(np.square(self._resids), axis=-2)","def OF1_CalcErrorEstimation(param_list, args):\n #return (sum( \\\n #( OF1_SumOfGauss(param_list, classNum, g_lvls) - histogram ) ** 2) / g_lvls.size) + \\\n #(abs(sum(param_list[:classNum]) - 1) * o)\n return (sum( \\\n ( OF1_SumOfGauss(param_list, args[0], args[1]) - args[2] ) ** 2) / args[1].size) + \\\n (abs(sum(param_list[:args[0]]) - 1) * args[3])","def eFieldErrorsAtPoints(resolution, lbcs, modeNames, pointSet, pointSetName):\n\n print \"\\n\\nCalculating errors of fields: \"\n print \" \", resolution\n print \" \", lbcs\n print \" \", modeNames\n print \" \", pointSetName\n\n dir = simDir(resolution, lbcs)\n\n nModes = len(modeNames)\n\n # get points for finding discrete field coeffs that minimize the L2 volume error\n volPtsIn = getVolumePoints(32, 64, \"in\")\n volPtsOut = getVolumePoints(32, 64, \"out\")\n print \"Total number of volume points for minimizing L2 error: \", len(volPtsIn) + len(volPtsOut)\n print \"Total number of surface points: \", len(pointSet)\n\n # get mx fields from simulation\n mxSurfFields = []\n mxVolFieldsIn = []\n mxVolFieldsOut = []\n for mode in modeNames:\n print \" Mode \" + mode + \":\"\n print \" calculating simulation surface field values...\"\n mxSurfFields.append(mxFieldValuesAtPoints(resolution, lbcs, 'E', mode, pointSet, pointSetName).ravel())\n print \" calculating simulation volume field values...\"\n mxVolFieldsIn.append(mxFieldValuesAtPoints(resolution, lbcs, 'E', mode, volPtsIn, \"volume_in\").ravel())\n mxVolFieldsOut.append(mxFieldValuesAtPoints(resolution, lbcs, 'E', mode, volPtsOut, \"volume_out\").ravel())\n mxSurfFields = numpy.asarray(mxSurfFields)\n\n # calculate analytic fields at points if data does not already exist in simulation directory\n anSurfFields = []\n anVolFieldsIn = []\n anVolFieldsOut = []\n for mode in modeNames:\n print \" Mode \" + mode + \":\"\n print \" calculating analytic surface field values...\"\n anSurfFields.append(analyticFieldValuesAtPoints(dir, mode, pointSet, pointSetName).ravel())\n print \" calculating analytic volume field values...\"\n anVolFieldsIn.append(analyticFieldValuesAtPoints(\"./analytic/\", mode, volPtsIn, \"volume_in\").ravel())\n anVolFieldsOut.append(analyticFieldValuesAtPoints(\"./analytic/\", mode, volPtsOut, \"volume_out\").ravel())\n\n # dot product matrix of degenerate mx fields\n dotProds = numpy.zeros((nModes, nModes))\n for i in range(nModes):\n for j in range(i, nModes):\n dotProds[i, j] = numpy.dot(mxVolFieldsIn[i], mxVolFieldsIn[j]) + numpy.dot(mxVolFieldsOut[i], mxVolFieldsOut[j])\n dotProds[j, i] = dotProds[i, j]\n invDotProds = numpy.linalg.inv(dotProds)\n\n #print normalizeMinRes(resolution, lbcs, modeNames)\n\n # calculate errors\n norm1s = []\n norm2s = []\n normInfs = []\n for anVolFieldIn, anVolFieldOut, anSurfField in zip(anVolFieldsIn, anVolFieldsOut, anSurfFields):\n rhs = numpy.zeros(nModes)\n for i in range(nModes):\n rhs[i] = numpy.dot(anVolFieldIn, mxVolFieldsIn[i]) + numpy.dot(anVolFieldOut, mxVolFieldsOut[i])\n coeffs = numpy.dot(invDotProds, rhs)\n mxSurfField = numpy.dot(mxSurfFields.T, coeffs)\n diff = mxSurfField - anSurfField\n norm1s.append(numpy.linalg.norm(diff, 1) / numpy.linalg.norm(anSurfField, 1))\n norm2s.append(numpy.linalg.norm(diff, 2) / numpy.linalg.norm(anSurfField, 2))\n normInfs.append(numpy.linalg.norm(diff, numpy.inf) / numpy.linalg.norm(anSurfField, numpy.inf))\n \n return norm1s, norm2s, normInfs","def error(self) -> list:\n return self.__err","def calc_error_dist(self):\n pass","def test_error_curve(self):\r\n\r\n scores = self.test_confidences\r\n sort = numpy.argsort(scores, axis=0)\r\n sorted_scores = scores[sort]\r\n\r\n test_errors = numpy.zeros((scores.shape[0]))\r\n thresholds = numpy.zeros((scores.shape[0]))\r\n\r\n for i in range(sort.shape[0]):\r\n thresholds[i] = sorted_scores[i]\r\n test_errors[i] = numpy.sum(self.test_errors[self.test_confidences >= thresholds[i]]) / float(numpy.sum(self.test_confidences >= thresholds[i]))\r\n\r\n return test_errors, thresholds","def getFitErr(self):\n return (self.fitSum2Err)","def err_func(x,rv,valore,specn,lcrop,models='da2014'):\n tmp = tmp_func(x[0], x[1], rv, specn, lcrop, models)\n if tmp != 1: return abs(tmp[3]-(valore+1.)) #this is quantity that gets minimized \n else: return 1E30","def diff_of_errors(self):\n self.e_of_e = self.azimuth_error - self.altitude_error\n return self.e_of_e","def kl_loss(self):\n return sum([p.kl_loss() for p in self.parameters])","def nzErr(xerr, yerr, vxerr, vyerr, year_x, year_y, mag, alnDir = '13_08_21/', chainsDir = 'efit/chains_S0-2_newRV2/'):\n\n #Read in values for error in position and velocity of sgr*\n origin_val = asciidata.open('/g/ghez/align/' + alnDir + chainsDir + 'efit_summary.txt')\n ori_x0e = origin_val[25][0]\n ori_y0e = origin_val[26][0]\n ori_vxe = origin_val[27][0]\n ori_vye = origin_val[28][0]\n t_0 = 2000.0 #hard coded t_0 of sgr*\n\n # magBins=np.array([9,11,12,13,14,15,16,17,18,19,20,21])\n # deltaArr=np.array([3.5,71.0,58.0,210.0,300.0,650.0,700.0,1100.0,1900.0,2200.0,3000.0])*1e-6\n\n# delta = mag*0.0\n# for i in range(len(mag)):\n# for j in range(len(deltaArr)):\n# if ((mag[i] > magBins[j]) & (mag[i] <= magBins[j+1])):\n# delta[i]=deltaArr[j]\n\n#pdb.set_trace()\n\n #Update errors\n xerr = np.sqrt(xerr**2 + ori_x0e**2 + ((year_x - t_0)*ori_vxe)**2)\n yerr = np.sqrt(yerr**2 + ori_y0e**2 + ((year_y - t_0)*ori_vye)**2)\n vxerr = np.sqrt(vxerr**2 + ori_vxe**2)\n vyerr = np.sqrt(vyerr**2 + ori_vye**2)\n\n return xerr, yerr, vxerr, vyerr","def xerr(self, i):\n return self.errors[0][i]","def errors(self) -> List[Error]:","def __error(self, R, P, Q, K, beta):\n e = 0\n for i in xrange(len(R)):\n for j in xrange(len(R[i])):\n if R[i][j] > 0:\n\n # loss function error sum( (y-y_hat)^2 )\n e = e + pow(R[i][j]-numpy.dot(P[i,:],Q[:,j]), 2)\n\n # add regularization\n for k in xrange(K):\n\n # error + ||P||^2 + ||Q||^2\n e = e + (beta/2) * ( pow(P[i][k], 2) + pow(Q[k][j], 2) )\n return e","def _update_errs(self, lr = 1e-8, epochs = 1000):\n if self.update_err_inf:\n log_f = np.log(self._fv())\n for _ in range(epochs):\n residual = np.log(self._gv()) - log_f\n grad = - np.mean(residual / (self.y - self.err_inf)) # gradient\n # now: descent + projection steps\n self.err_inf -= lr * grad\n self._project()","def error_values(X_train,X_test,Y_train,Y_test):\n #setting up parameters and variables for plotting \n n_train = X_train.shape[0]\n n_test = X_test.shape[0]\n d = X_train.shape[1]\n hdnode = 100\n w1 = np.random.normal(0,0.001,d*hdnode).reshape((d,hdnode))\n d1 = np.zeros((d,hdnode))\n w2 = np.random.normal(0,0.001,hdnode).reshape((hdnode,1))\n d2 = np.zeros(hdnode)\n h = np.zeros(hdnode)\n mb = 100 #minibatch size\n m = int(n_train/mb)\n batch = np.arange(m) \n lr = 0.00020\n EP = 20000 #needed for initializing \n ep = 0\n yh = np.zeros((n_train,1))\n yh2 = np.zeros((n_test,1))\n L_train= np.zeros(EP+1)\n L_test = np.zeros(EP+1)\n Y_train = Y_train.reshape(len(Y_train),1)\n #activation function for the hidden layer is tanh\n \n def g(A):\n return (np.tanh(A))\n\n def gd(A):\n return (1-np.square(np.tanh(A)))\n \n #setting up how long the epoch will run\n EP = 200\n ep = 0\n while ep < EP:\n ep += 1\n yh = g(X_train.dot(w1)).dot(w2)\n yh2 = g(X_test.dot(w1)).dot(w2)\n L_train[ep] = LA.norm(yh-Y_train.reshape(len(Y_train),1))/n_train\n L_test[ep] = LA.norm(yh2-Y_test.reshape(len(Y_test),1))/n_test\n \n np.random.shuffle(batch)\n for i in range(m):\n st = batch[i]*mb\n ed = (batch[i]+1)*mb\n h = g(X_train[st:ed].dot(w1))\n y = h.dot(w2)\n d2 = h.T.dot(Y_train[st:ed]-y)\n d1 = X_train[st:ed].T.dot(np.multiply((Y_train[st:ed]-y).dot(w2.T),gd(X_train[st:ed].dot(w1))))\n w2 += lr*d2\n w1 += lr*d1\n return yh, yh2","def compute_errors(gt, pred):\n thresh = np.maximum((gt / pred), (pred / gt))\n a1 = (thresh < 1.25 ).mean()\n a2 = (thresh < 1.25 ** 2).mean()\n a3 = (thresh < 1.25 ** 3).mean()\n\n rmse = (gt - pred) ** 2\n rmse = np.sqrt(rmse.mean())\n\n rmse_log = (np.log(gt) - np.log(pred)) ** 2\n rmse_log = np.sqrt(rmse_log.mean())\n\n abs_rel = np.mean(np.abs(gt - pred) / gt)\n\n sq_rel = np.mean(((gt - pred) ** 2) / gt)\n\n return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3","def _df_err(self):\n return self.n - self.k - 1","def post_iteration(self, step, level_number):\n\n super(error_output, self).post_iteration(step, level_number)\n\n # some abbreviations\n L = step.levels[level_number]\n P = L.prob\n\n L.sweep.compute_end_point()\n\n uex = P.u_exact(step.time + step.dt)\n err = abs(uex - L.uend)\n\n est = []\n\n for m in range(1, L.sweep.coll.num_nodes + 1):\n est.append(abs(L.uold[m] - L.u[m]))\n\n est_all = max(est)\n\n print(step.status.iter, err, est_all, L.status.residual)","def calculate_error(self):\n self.network.index_nodes()\n self._calculate_dist()\n _, relative_error = self._relative_error()\n _, absolute_error = self._absolute_error()\n\n return absolute_error, relative_error","def train_error(self):\n self.prediction = self.predict()\n pred = self.prediction.reshape(-1)\n self.error = np.sum(pred != self.label) / self.train_data.shape[0]\n return(self.error)","def mag_err(self):\n return self.photosamplers.get_estimate(mag=True)[1:]","def compute_errors(gt, pred, selector):\n gt = gt[selector]\n pred = pred[selector]\n\n thresh = np.maximum((gt / pred), (pred / gt))\n a1 = (thresh < 1.25 ).mean()\n a2 = (thresh < 1.25 ** 2).mean()\n a3 = (thresh < 1.25 ** 3).mean()\n\n rmse = (gt - pred) ** 2\n rmse = np.sqrt(rmse.mean())\n\n rmse_log = (np.log(gt) - np.log(pred)) ** 2\n rmse_log = np.sqrt(rmse_log.mean())\n\n abs_rel = np.mean(np.abs(gt - pred) / gt)\n\n sq_rel = np.mean(((gt - pred) ** 2) / gt)\n\n return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3","def validation_curve(x, y, x_val, y_val):\n lambda_vec = np.array([0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10])\n error_train = np.zeros(len(lambda_vec))\n error_val = np.zeros(len(lambda_vec))\n m = x.shape[0]\n m_val = x_val.shape[0]\n for i in range(len(lambda_vec)):\n l = lambda_vec[i]\n theta = train_linear_reg(x, y, l)\n error_train[i] = 1.0 / (2 * m) * np.sum(np.square(x.dot(theta) - y))\n error_val[i] = 1.0 / (2 * m_val) * np.sum(np.square(x_val.dot(theta) - y_val))\n\n return lambda_vec, error_train, error_val","def node_layer_error(self, errors, layer):\n weights = self.weights[layer]\n\n node_errors = []\n\n for i, input in enumerate(self.input_matrix[layer-1]):\n node_errors.append(float(log_deriv(input) * dot_product(weights[i], errors)))\n\n return node_errors","def leacurve(x,y,xval,yval,lamda):\n m1 = y.size\n m2= yval.size\n x = addOnes(x,m1)\n xval = addOnes(xval,m2)\n m,n = x.shape\n errtrain = np.zeros(m)\n errval = np.zeros(m)\n thetaInit = np.zeros(n)\n for i in range(1,m+1):\n status,theta = optimSolve(thetaInit,x[0:i],y[0:i],reg=True,lamda=lamda)\n errtrain[i-1] = costFunc(theta,x[0:i],y[0:i])\n errval[i-1] = costFunc(theta,xval,yval)\n return errtrain, errval","def get_error(intercept, slope, points):\n error_value = 0\n for i in range(0, len(points)):\n error_value += (points[i].y - (slope * points[i].x + intercept)) ** 2\n return error_value / float(len(points))","def _compute_errors(self):\n self.errors = np.sqrt(self.data)\n self.errors[self.errors == 0.] = 1.","def errors(self):\n return self.args[1]","def _create_diagnostic_lightcurves(self):\n if self.coefficients is None:\n raise ValueError(\"you need to call `correct()` first\")\n\n lcs = {}\n for idx, submatrix in enumerate(self.dmc.matrices):\n # What is the index of the first column for the submatrix?\n firstcol_idx = sum([m.shape[1] for m in self.dmc.matrices[:idx]])\n submatrix_coefficients = self.coefficients[\n firstcol_idx : firstcol_idx + submatrix.shape[1]\n ]\n # submatrix_coefficients_err = self.coefficients_err[firstcol_idx:firstcol_idx+submatrix.shape[1], firstcol_idx:firstcol_idx+submatrix.shape[1]]\n # samples = np.asarray([np.dot(submatrix.values, np.random.multivariate_normal(submatrix_coefficients, submatrix_coefficients_err)) for idx in range(100)]).T\n # model_err = np.abs(np.percentile(samples, [16, 84], axis=1) - np.median(samples, axis=1)[:, None].T).mean(axis=0)\n model_flux = u.Quantity(\n submatrix.X.dot(submatrix_coefficients), unit=self.lc.flux.unit\n )\n model_flux_err = u.Quantity(\n np.zeros(len(model_flux)), unit=self.lc.flux.unit\n )\n lcs[submatrix.name] = LightCurve(\n time=self.lc.time,\n flux=model_flux,\n flux_err=model_flux_err,\n label=submatrix.name,\n )\n return lcs","def _ms_err(self):\n return self._ss_err / self._df_err","def ms_err(self):\n return np.squeeze(self._ms_err)","def accr_lum(L_line, tracer, L_line_err = 0*u.W):\n \n a, a_err, b, b_err = rel['a'][tracer],rel['a_err'][tracer],rel['b'][tracer],rel['b_err'][tracer]\n \n log_L_acc = b + a * log10(L_line*u.W/L_sun)\n \n L_acc = 10**log_L_acc*L_sun/u.W\n \n #error propagation\n \n #c_err = (L_line_err)/(log(10) * L_line)\n #ac_err = a * log10(L_line/L_sun) * ((a_err/a)**2 + (c_err/log10(L_line/L_sun))**2)**0.5\n #log_L_acc_err = (b_err**2 + ac_err**2)**0.5\n #L_acc_err = L_acc * log(10) * log_L_acc_err\n\n return L_acc","def lsterr(inlist):\r\n return stdev(inlist) / float(math.sqrt(len(inlist)))","def prediction_errors(data, paths): \n \n #prettyprint stuff\n max_col = 20 \n print \"\".join(word.ljust(max_col) for word in [\"Network conf\",\"Mean error\",\"STD error\"])\n print '-'*max_col*3\n \n errors = np.zeros((len(paths), len(data)))\n \n for i, path in enumerate(paths):\n som = get_soms(path)\n errors[i, :] = som.quantization_error(data) \n outputs = [path, str(errors[i, :].mean()), str(errors[i, :].std())]\n print \"\".join(word.ljust(max_col) for word in outputs)\n \n return errors","def getAll(self):\n x,y,a = self.getxya()\t\n xerrs = [self.errors[0][i] for i in range(len(self.x)) if self.x[i]!=None and self.y[i]!=None]\n yerrs = [self.errors[1][i] for i in range(len(self.x)) if self.x[i]!=None and self.y[i]!=None]\t\n return x,y,a,xerrs,yerrs","def l2_error(self, X=None, y=None) -> np.ndarray:\n return np.square(self.residuals(X, y))","def _calculate_error(self, targets):\n\n def hist(d):\n f, v = histogram(array(d))\n i = len(f) if argmax(f) == len(f) - 1 else argmax(f)\n return v[i]\n\n devxs, devys = list(zip(*[r.dev_centroid for r in targets]))\n\n if len(targets) > 2 and self.use_histogram:\n dx = hist(devxs)\n dy = hist(devys)\n else:\n\n def avg(s):\n return sum(s) / len(s)\n\n dx = avg(devxs)\n dy = avg(devys)\n\n return -dx, dy","def get_errors(cov):\n return np.sqrt(np.diag(cov))","def compute_error_cross_dataset(AL, train_y):\n # print(train_y.shape)\n nb = train_y.shape[0]\n error=np.power(np.add(train_y,-AL),2)*1/nb\n return error\n # raise NotImplementedError","def cps_err(self):\n return np.sqrt(self.totalcounts) / self.exptime","def error(Y, X):\n return (Y - X) ** 2","def get_current_validation_losses(self):\n errors_ret = OrderedDict()\n for name in self.loss_names:\n if isinstance(name, str):\n errors_ret[name+'_val'] = float(getattr(self, 'loss_' + name + '_val')) # float(...) works for both scalar tensor and float number\n return errors_ret","def get_error(self, output,target):\n return [target[i]-output[i] for i in range(len(output))]","def _error(self, Y, T):\n err = np.mean((Y - T)**2)\n return err","def get_errors(self):\n return [result for result in self.values() if result.outcome == Result.ERROR]","def plot_cv_errors(errors, lambdas , K , path): \n colors = cycle([\"aqua\", \"black\", \"blue\", \"fuchsia\", \"gray\", \"green\", \"lime\", \"maroon\", \"navy\", \"olive\", \"purple\", \"red\", \"silver\", \"teal\", \"yellow\"])\n \n markers = cycle([ \".\", \",\", \"o\", \"v\" , \"^\" , \">\", \"1\", \"2\", \"3\", \"4\", \"8\", \"s\", \"p\", \"*\", \"h\"])\n \n \n for i, data in enumerate(errors):\n \n lambda_str = ('%f' % lambdas[i]).rstrip('0').rstrip('.')\n plt.plot(range(len(data)), data, marker=next(markers), label='$\\lambda$ = %s'%lambda_str);\n \n plt.ylim(0.975 , 0.99)\n #plt.xlim(0 , 50)\n plt.title('ALS-WR Learning Curve, K = %d'% K)\n plt.xlabel('Number of Epochs');\n plt.ylabel('RMSE');\n plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n plt.grid()\n plt.savefig(\"../results/\"+path)\n plt.show()","def lapse(self):\n pass","def error(clf, X, y, ntrials=100, test_size=0.2) :\n\n train_error = 0\n test_error = 0\n ### ========== TODO : START ========== ###\n # compute cross-validation error over ntrials\n # hint: use train_test_split (be careful of the parameters)\n for i in range(0,ntrials, 1):\n #get the value of the error for each division\n #train on the test data for the clf\n #test also on the data\n #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 42)\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= test_size, random_state=i)\n #now find the error\n #first train the model\n #then predict\n #check the accuracy\n clf.fit(X_train,y_train)\n y_pred = clf.predict(X_train)\n #now find the error for the train_error\n train_err = 1 - metrics.accuracy_score(y_train, y_pred, normalize=True)\n train_error += train_err\n\n y_pred = clf.predict(X_test)\n test_err = 1 - metrics.accuracy_score(y_test, y_pred, normalize=True)\n test_error += test_err\n\n\n #get the average\n train_error = float(train_error)/((1-test_size)*len(X))\n test_error = float(test_error)/((test_size)*len(X))\n ### ========== TODO : END ========== ###\n\n return train_error, test_error","def _lcResults(self):\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\n noCt = int( self.suffix.split('_')[-1] ) \n \n hdu = fits.open(fitsNnam)\n hdu['LIGHTCURVE'].data['flux'][noCt] = self.getResult(param='Flux')[0]\n hdu['LIGHTCURVE'].data['fluxerr'][noCt] = self.getResult(param='Flux')[1]\n hdu['LIGHTCURVE'].data['index'][noCt] = self.getResult(param='Index')[0]\n hdu['LIGHTCURVE'].data['indexerr'][noCt] = self.getResult(param='Index')[1]\n hdu['LIGHTCURVE'].data['ts'][noCt] = self.getResult(param='TS value')[0]\n hdu['LIGHTCURVE'].data['upperlim'][noCt] = self.getResult(param='upplim')[0] \n hdu['LIGHTCURVE'].data['status'][noCt] = 'done'\n hdu.writeto(fitsNnam, clobber=True)\n hdu.close()","def Error_estimation(params, qu=None, qu_err=None, p_maxL=None, par=False):\n \n if par is True:\n print('Estimate uncertainties for the parameters')\n sigma = par_err(params)\n return(sigma)\n elif qu is not None:\n N = len(qu[0,:])\n model_err = np.zeros((3, N))\n star_err = np.zeros((3,N))\n msg = 'Estimate model uncertainties'\n print(msg)\n if qu_err is None:\n print(' -> by varying the model')\n star_err[:-1,:] = np.std(QU_func(params, qu, star=True), axis=2)\n model_err[:-1,:] = np.std(QU_func(params, qu), axis=2)\n bkgr_err = np.std(background(params[N:,:]), axis=2) #?\n \n else:\n print('-> using data and parameters')\n err = par_err(params)\n s_ax = (p_maxL[:N]*qu_err)**2 + (qu*err[:N])**2\n s_b = np.array([(err[N:-1]*np.cos(2*p_maxL[-1]))**2 +\\\n (2*p_maxL[N:-1]*err[-1]*np.sin(2*p_maxL[-1]))**2,\\\n (err[N:-1]*np.sin(2*p_maxL[-1]))**2 +\\\n (2*p_maxL[N:-1]*err[-1]*np.sin(2*p_maxL[-1]))**2])\n model_err[:-1,:] = np.sqrt(s_ax + s_b)\n star_err[:-1,:] = np.sqrt(s_ax)\n bkgr_err = np.sqrt(s_b)\n star_err[-1,:] = np.sqrt(Cov(star_err[0,:], star_err[1,:]))\n model_err[-1,:] = np.sqrt(Cov(model_err[0,:], model_err[1,:]))\n return(model_err, star_err, bkgr_err)","def error_coeffs(self,p):\n import nodepy.rooted_trees as rt\n forest=rt.list_trees(p)\n err_coeffs=[]\n for tree in forest:\n err_coeffs.append(self.error_coefficient(tree))\n return snp.array(err_coeffs)","def backprop_error(self,t=0, external_error=0):\n\t\taux = external_error\n\t\t# Other layers' contributions\n\t\tif t != 0:\n\t\t\tfor layer in self.next_recurrent:\n\t\t\t\taux += layer.get_error_contribution(self, t=t+1)\n\t\tfor layer in self.next:\n\t\t\taux += layer.get_error_contribution(self, t=t)\n\n\t\tif t != 0:\n\t\t\taux += np.dot(\n\t\t\t\tself.get_o_error(t=t+1) * self.__dev_sigm__(self.get_o(t=t+1)),\n\t\t\t\tnp.transpose(self.W_o_atprev)\n\t\t\t) + \\\n\t\t\tnp.dot(\n\t\t\t\tself.get_c_error(t=t+1) * self.get_i(t=t+1) * self.__dev_tanh__(self.get_c(t=t+1)),\n\t\t\t\tnp.transpose(self.W_c_atprev)\n\t\t\t) + \\\n\t\t\tnp.dot(\n\t\t\t\tself.get_i_error(t=t+1) * self.__dev_sigm__(self.get_i(t=t+1)),\n\t\t\t\tnp.transpose(self.W_i_atprev)\n\t\t\t) + \\\n\t\t\tnp.dot(\n\t\t\t\tself.get_f_error(t=t+1) * self.__dev_sigm__(self.get_f(t=t+1)),\n\t\t\t\tnp.transpose(self.W_f_atprev)\n\t\t\t)\n\t\t\n\t\tif isinstance(aux, np.ndarray):\n\t\t\tself.error_a = [aux]+self.error_a\n\n\t\t# error in o\n\t\tself.error_o = [self.get_a_error(t=t) * self.__tanh__(self.get_c(t=t))] + self.error_o\n\n\t\t# error in c\n\t\taux = self.w_o_c * self.get_o_error(t=t) * self.__dev_sigm__(self.get_o(t=t)) \\\n\t\t+ self.get_a_error(t=t) * self.get_o(t=t) * self.__dev_tanh_z__(self.get_c(t=t))\n\t\tif t!=0:\n\t\t\taux += self.get_c_error(t=t+1) * self.get_f(t=t+1) \\\n\t\t\t+ self.w_i_ctprev * self.get_i_error(t=t+1) * self.__dev_sigm__(self.get_i(t=t+1)) \\\n\t\t\t+ self.w_f_ctprev * self.get_f_error(t=t+1) * self.__dev_sigm__(self.get_f(t=t+1))\n\t\tself.error_c = [aux] + self.error_c\n\n\t\t# error in f\n\t\tself.error_f = [self.get_c(t=t-1)*self.get_c_error(t=t)] + self.error_f\n\n\t\t# error in i\n\t\tself.error_i = [self.get_c_error(t=t)*self.get_tanh_zc(t=t)] + self.error_i","def __error(self,node_set):\n error=0\n for n in node_set:\n if(n.seq_num!=0):\n error+=LA.norm(n.node_vol-node_set[n.neighbor.parent].node_vol-n.impedance*n.branch_cur)\n #print n.node_vol, '\\n', node_set[n.neighbor.parent].node_vol\n \n return error","def get_error(self, err_type):\n self.tmp = self.location_errors[0].errors[err_type] * \\\n self.location_errors[0].errors[\"N\"]\n N = self.location_errors[0].errors[\"N\"]\n\n for lerr in self.location_errors[1:]:\n self.tmp = np.add(self.tmp, lerr.errors[err_type] *\n lerr.errors[\"N\"])\n N += lerr.errors[\"N\"]\n\n # print(self.tmp, N, self.tmp/ N)\n return self.tmp / N","def kl_loss_batch(self):\n return sum([e for m in self.modules for e in m._kl_losses])","def main():\n error_LU, h_LU = read_2_col_file(\"error_LU_data.txt\") \n error, h = read_2_col_file(\"error_data.txt\")\n \n log_h_LU = np.log10(h_LU)\n log_error_LU = np.log10(error_LU)\n log_h = np.log10(h)\n log_error = np.log10(error)\n\n plt.plot(log_h_LU, log_error_LU, label=\"LU-decomposition\")\n plt.plot(log_h, log_error, label=\"general Thomas algo.\")\n plt.legend()\n plt.title(\"Logarithmic plot of relative error \")\n plt.xlabel(\"$log_{10}(time \\, step)$\")\n plt.ylabel(\"$log_{10}(error)$\")\n plt.show()","def ee_err_1(sav, *args):\n w, r, labor_supply, b_init, s, t = args\n\n error = u_prime(w[t] * labor_supply[s] + (1 + r[t]) * b_init[s] - sav) - beta * (1 + r[t+1]) * u_prime((1 + r[t+1]) * sav + w[t+1] * labor_supply[s+1])\n\n return error","def calc_error(case, numerical, func, title='Percentage Error',\n kolom=15, prec=6):\n nodes, L = get_valdict(case, 'nodes,L')\n y_num = numerical[1]\n \n node = np.arange(1, nodes+1)\n distance = create_axis(case)[1:-1]\n sol_exact = func(distance, case)\n y_num = y_num[1:-1]\n \n diff, percenterr = [], []\n for i, val in enumerate(sol_exact):\n dval = val - y_num[i]\n diff.append(dval)\n percenterr.append(dval/val*100)\n \n lebar = kolom*6+7\n print('='*lebar)\n print('{:^{l:d}s}'.format(title, l=lebar))\n print('='*lebar)\n print('|{:^{k:d}s}|{:^{k:d}s}|{:^{k:d}s}|{:^{k:d}s}|{:^{k:d}s}|{:^{k:d}s}|'.format(\n 'node', 'distance', 'numeric', 'exact', 'diff', 'error', k=kolom))\n print('-'*lebar)\n \n for i in range(0, nodes):\n print('| {:^{k:d}d} | {:> {k:d}.{p:d}f} | {:> {k:d}.{p:d}f} | {:> {k:d}.{p:d}f} | {:> {k:d}.{p:d}f} | {:> {k:d}.{p:d}f} |'.format(\n node[i], distance[i], y_num[i], sol_exact[i], diff[i], percenterr[i], k=kolom-2, p=prec\n ))\n print('='*lebar)","def error_dropoff(data):\n # 2009-05-05 08:58 IJC: Adapted to Python from Matlab.\n # 2006/06/06 IJC: Made it work with arrays of column vectors.\n # Added the '--nomean' option.\n \n \n# PARSE INPUTS\n data = array(data).copy()\n \n#interval = max([1 round(extract_from_options('--interval=', 1, options))]);\n \n \n if len(data)==len(data.ravel()):\n data = data.ravel()\n data = data.reshape(len(data),1)\n \n nsets = data.shape[1]\n npts_vec = arange(data.shape[0]/2)+1.0\n errors = zeros((data.shape[0]/2, nsets))\n \n# LOOP AND CALCULATE STUFF\n for ii in range(len(npts_vec)):\n npts = npts_vec[ii] # number of points we average over\n nsamp = floor(data.shape[0]/npts) # number of subsamples\n dre = reshape(data[0:nsamp*npts,:], (npts, nsamp, nsets))\n error_values = std(dre.mean(1))\n errors[ii,:] = error_values\n \n return (errors, npts_vec)","def getErrorsList(self):\n return self.__errors","def logistic_derivative(errors):\n return [log_deriv(error) for error in errors]","def get_errors(id, vector):\n for i in vector: assert -10<=abs(i)<=10\n assert len(vector) == MAX_DEG\n\n return json.loads(send_request(id, vector, 'geterrors'))","def get_errors(id, vector):\n for i in vector: assert -10<=abs(i)<=10\n assert len(vector) == MAX_DEG\n\n return json.loads(send_request(id, vector, 'geterrors'))","def learning_curve():\n loss = []\n val_loss = []\n data_size = []\n\n x_slid, y_slid = sliding_window_main(x, y)\n x_train, y_train, x_val, y_val, x_test, y_test = data_splitting_main(x_slid, y_slid)\n m_tot = x_train.shape[0]\n\n batch_step = 50\n try:\n for m in range(batch_size, m_tot, batch_step*batch_size):\n print(\"Training: \", m)\n net = create_network()\n history = trainer(net, x_train[:m], y_train[:m], x_val, y_val)\n loss.append(history.history[\"loss\"][-1])\n val_loss.append(history.history[\"val_loss\"][-1])\n data_size.append(m)\n\n print(\"Loss:\", loss[-1])\n print()\n\n finally:\n plt.plot(data_size, loss, label=\"Loss\", marker=\"o\")\n plt.plot(data_size, val_loss, label=\"Val Loss\", marker=\"o\")\n plt.xlabel(\"m\")\n plt.ylabel(\"Losses\")\n plt.title(\"Model Loss\")\n plt.legend()\n plt.savefig(\"img/\" + datetime.now().strftime(\"%y%m%d_%H%M\") + \"_learning_curve.png\")\n plt.show()\n plt.close()\n\n return loss, val_loss","def get_func_errors(subset_size, args, filenames):\n pA, pA_list, pdMdh, pdMdh_list = args\n\n r, s, A, As = load_svA(filenames[0])\n\n Sigma = dict(zip(r, list(pA['Sigma'])))\n eta = dict(zip(r, list(pdMdh['eta'])))\n\n Sigma_list = [pA[0] for pA in pA_list]\n eta_list = [pdMdh[1] for pdMdh in pdMdh_list]\n\n Sigma_std = std_funcs(r, subset_size, Sigma_list)\n eta_std = std_funcs(r, subset_size, eta_list)\n\n return Sigma, Sigma_std, eta, eta_std","def compute_errors(u_exact, u):\n\n # Compute error norm (for very small errors, the value can be\n # negative so we run abs(assemble(error)) to avoid failure in sqrt\n\n V = u.function_space()\n\n # Function - Expression\n error = (u - u_exact)**2*dx\n E1 = sqrt(abs(assemble(error)))\n\n # Explicit interpolation of u_e onto the same space as u:\n u_e = interpolate(u_exact, V)\n error = (u - u_e)**2*dx\n E2 = sqrt(abs(assemble(error)))\n\n # Explicit interpolation of u_exact to higher-order elements,\n # u will also be interpolated to the space Ve before integration\n Ve = FunctionSpace(V.mesh(), 'P', 5)\n u_e = interpolate(u_exact, Ve)\n error = (u - u_e)**2*dx\n E3 = sqrt(abs(assemble(error)))\n\n # fenics.errornorm interpolates u and u_e to a space with\n # given degree, and creates the error field by subtracting\n # the degrees of freedom, then the error field is integrated\n # TEMPORARY BUG - doesn't accept Expression for u_e\n #E4 = errornorm(u_e, u, normtype='l2', degree=3)\n # Manual implementation errornorm to get around the bug:\n def errornorm(u_exact, u, Ve):\n u_Ve = interpolate(u, Ve)\n u_e_Ve = interpolate(u_exact, Ve)\n e_Ve = Function(Ve)\n # Subtract degrees of freedom for the error field\n e_Ve.vector()[:] = u_e_Ve.vector().array() - u_Ve.vector().array()\n # More efficient computation (avoids the rhs array result above)\n #e_Ve.assign(u_e_Ve) # e_Ve = u_e_Ve\n #e_Ve.vector().axpy(-1.0, u_Ve.vector()) # e_Ve += -1.0*u_Ve\n error = e_Ve**2*dx(Ve.mesh())\n return sqrt(abs(assemble(error))), e_Ve\n E4, e_Ve = errornorm(u_exact, u, Ve)\n\n # Infinity norm based on nodal values\n u_e = interpolate(u_exact, V)\n E5 = abs(u_e.vector().array() - u.vector().array()).max()\n\n # H1 seminorm\n error = dot(grad(e_Ve), grad(e_Ve))*dx\n E6 = sqrt(abs(assemble(error)))\n\n # Collect error measures in a dictionary with self-explanatory keys\n errors = {'u - u_exact': E1,\n 'u - interpolate(u_exact,V)': E2,\n 'interpolate(u,Ve) - interpolate(u_exact,Ve)': E3,\n 'errornorm': E4,\n 'infinity norm (of dofs)': E5,\n 'grad(error) H1 seminorm': E6}\n\n return errors"],"string":"[\n \"def __call__(self, errors: List[float]) -> List[float]:\",\n \"def error(self) -> Sequence[float]:\\n errors = []\\n for line, sign in zip(self.marker_lines, (-1, 1)):\\n if self._orientation == Orientation.UP_DOWN:\\n picket_pos = self._fit(line.center.y)\\n mlc_pos = line.center.x\\n else:\\n picket_pos = self._fit(line.center.x)\\n mlc_pos = line.center.y\\n if (\\n self._separate_leaves\\n ): # offset the picket position by the DLG and nominal gap\\n mag_factor = self._image.sid / 1000\\n picket_pos += (\\n sign * self._nominal_gap_mm * mag_factor / 2 * self._image.dpmm\\n )\\n errors.append((mlc_pos - picket_pos) / self._image.dpmm)\\n return errors\",\n \"def linpol_error(self):\\n return self._linpol_error\",\n \"def yerr(self, i):\\n return self.errors[1][i]\",\n \"def calc_errors(problem, points):\\n original = problem.getp()\\n try:\\n ret = plugin.calc_errors(problem, points)\\n except:\\n import traceback\\n print(\\\"error calculating distribution on model\\\")\\n traceback.print_exc()\\n ret = None\\n finally:\\n problem.setp(original)\\n return ret\",\n \"def calcErr(dicth,dictl,cdli): \\n \\n \\n errh = np.square((cdli.datain_h - np.dot(dicth, np.transpose(cdli.wh))))\\n errl = np.square((cdli.datain_l - np.dot(dictl, np.transpose(cdli.wl))))\\n \\n \\n return [errh, errl]\",\n \"def error_compute(self):\\n self.tt_error = np.linalg.norm(self.rel_error)\\n if self.global_rank==0:print('Overall error is::',self.tt_error)\\n return {'NMF': self.rel_error, 'tt': self.tt_error}\",\n \"def get_error_rates():\\n return [ERR_RT * i for i in range(int((1 / ERR_RT) / 4))] # error up to 25%\",\n \"def _step_errors(sequence: Sequence) -> List[TransformationMetrics]:\\n errors = [TransformationMetrics.calc(t_pred, t_gt, time)\\n for t_pred, t_gt, time in zip(sequence.prediction.transforms, sequence.ground_truth.transforms,\\n sequence.times)]\\n return errors\",\n \"def get_linear_track_error(self):\\r\\n return self._arm.get_linear_track_error()\",\n \"def L_curveTV(f,lam_init = 2.0, q = 0.9):\\n lam = lam_init\\n max_iter = 50\\n residual_list = np.zeros(max_iter)\\n size_list = np.zeros(max_iter)\\n error = np.zeros(max_iter)\\n alt_error = np.zeros(max_iter)\\n \\n for i in range(max_iter): #range(max_iter):\\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc=True, tol = 1.0e-5)\\n #u, _, j = projected_gradient_alt(f,lam, tau = 0.2, tol = 1.0e-4)\\n lam = lam_init * (q ** i)\\n residual_list[i] = np.linalg.norm(u - f)\\n size_list[i] = np.linalg.norm(u)\\n error[i] = np.linalg.norm(u - f) * np.linalg.norm(u)\\n #plt.loglog(residual_list,size_list)\\n #plt.show()\\n opt_idx = np.argmin(error)\\n t = 1.0 / (1.0 + lam_init * (q ** opt_idx))\\n lam = lam_init * (q ** opt_idx)\\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc = True, tol = 1.0e-5)\\n return u, t\",\n \"def lCurve(self): \\n\\n # --------------------------------------------------------------------------------------------- #\\n # Read data\\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\\n lcTab = Table.read(fitsNnam)\\n if (self.tstart is not None) and (self.tstop is not None):\\n lcTab = lcTab[ (self.tstart <= lcTab['mjd']) & (lcTab['mjd'] <= self.tstop)]\\n lcTab = lcTab[lcTab['flux'] != -1.] # avoid undone analyses\\n\\n timeMJD = lcTab['mjd']\\n tref = int(np.floor( timeMJD[0] / 100.0)) * 100 # round to lowest hundred\\n timeMJD -= tref\\n ts = lcTab['ts']\\n detect = lcTab['ts'] >= self.tsmin\\n undet = lcTab['ts'] < self.tsmin\\n flux = lcTab['flux'][detect]\\n fluxerr = lcTab['fluxerr'][detect]\\n upperl = lcTab['upperlim'][undet]\\n upperl[upperl == -1.] = 0. # for when it failed\\n scale = 10**int(np.floor(np.log10( np.mean( np.concatenate( (flux, upperl), axis=0) ) ))) \\n\\n # --------------------------------------------------------------------------------------------- #\\n # Plot\\n lcplt = FermiPlot(savepath='', xsize=8.5, ysize=6)\\n lcplt.figname = os.path.join(self.workpath, 'LightCurve.pdf')\\n lcplt.xlabel = r'Time (MJD $-$ {})'.format(tref)\\n lcplt.ylabel = [r'Flux ($10^{%d}$ ph\\\\,cm$^{-2}$\\\\,s$^{-1}$)'%(int(np.log10(scale))), r'TS']\\n lcplt.hline = [None, self.tsmin]\\n deltaY = max(np.concatenate((flux+fluxerr, upperl), axis=0)) - min(np.concatenate((flux-fluxerr, upperl), axis=0))\\n lcplt.ymin = [(min(np.concatenate((flux-fluxerr, upperl-upperl*0.1), axis=0)) - 0.05*deltaY) / scale, min(ts) - 0.05*(max(ts)-min(ts))]\\n lcplt.ymax = [(max(np.concatenate((flux+fluxerr, upperl), axis=0)) + 0.05*deltaY) / scale, max(ts) + 0.05*(max(ts)-min(ts))]\\n deltaX = (timeMJD[-1] + lcTab['mjderr'][-1]) - (timeMJD[0] - lcTab['mjderr'][0]) \\n lcplt.xmin = timeMJD[0] - lcTab['mjderr'][0] - 0.05*deltaX\\n lcplt.xmax = timeMJD[-1] + lcTab['mjderr'][-1] + 0.05*deltaX\\n lcplt.fill = [item for sublist in zip( timeMJD[detect]-lcTab['mjderr'][detect], timeMJD[detect]+lcTab['mjderr'][detect] ) for item in sublist]\\n lcplt.shadecol= self.loran \\n if len(flux) == 0:\\n lcplt.mksize = [2, 2]\\n lcplt.ymode = ['linear', 'linear']\\n lcplt.color = ['gray', 'black']\\n lcplt.prop = [3, 1]\\n lcplt.limit = [True, False]\\n lcplt.multiplot(x = [ timeMJD[undet], timeMJD ],\\n y = [ upperl/scale, ts ],\\n xerr = [ lcTab['mjderr'][undet], lcTab['mjderr']],\\n yerr = [ upperl/scale*0.1, None])\\n else:\\n lcplt.mksize = [2, 2, 2]\\n lcplt.ymode = ['linear', 'linear', 'linear']\\n lcplt.color = ['gray', 'black', 'black']\\n lcplt.prop = [3, 1]\\n lcplt.limit = [[True, False], False]\\n lcplt.multiplot(x = [ [timeMJD[undet], timeMJD[detect]], timeMJD ],\\n y = [ [upperl/scale, flux/scale], ts ],\\n xerr = [ [lcTab['mjderr'][undet], lcTab['mjderr'][detect]], lcTab['mjderr']],\\n yerr = [ [upperl/scale*0.1, fluxerr/scale], None])\\n lcplt.save()\\n\\n print(\\\"\\\\t=== Figure '{}' created ===\\\".format(lcplt.figname)) \\n return\",\n \"def _getErrorFunction(self):\\n\\n\\t\\treturn (self._setpoint - self._current)\",\n \"def get_errors(self):\\n return {'loss': self.loss.data[0]}\",\n \"def get_error_estimates(self, Y, M1, M2=None):\\n # First K0 and K1\\n Mminus = M1\\n if M2 is None:\\n Mplus = M1\\n else:\\n Mplus = M2\\n if self.Cp0 != 0 and self.Cp1 != 0 and self.Cm != 0:\\n Cp0 = self.Cp0\\n Cp1 = self.Cp1\\n Cm = self.Cm\\n else:\\n PP = self.principal_part()\\n Cmax = max(PP.values())\\n Kmax = 0\\n for t in PP.keys():\\n if isinstance(t, tuple):\\n (c, l) = t\\n elif isinstance(t, (int, Integer)):\\n (c, l) = rn_from_D(self._space.multiplier(), t)\\n else:\\n raise ValueError(\\\"Incorrect principal part: t={0}\\\".format(t))\\n if c in self._space.multiplier().D():\\n tmp = l + self._space.multiplier().Qv[self._space.index_set().index(c)]\\n elif c in range(len(self._space.multiplier().Qv)):\\n tmp = l + self._space.multiplier().Qv[c]\\n else:\\n raise ValueError(\\\"Incorrect principal part: c,l={0},{1}\\\".format(c, l))\\n if(abs(tmp) > Kmax):\\n Kmax = abs(tmp)\\n [Cp0, Cp1] = self._space.get_Cp(Cmax)\\n Cm = self._space.get_Cm(Kmax, Cmax)\\n self.Cp0 = Cp0\\n self.Cp1 = Cp1\\n self.Cm = Cm\\n\\n fak = len(self._space.index_set())\\n # print \\\"Cp0,Cp1,Cm=\\\",Cp0,Cp1,Cm\\n # print \\\"fak=\\\",fak\\n\\n er1 = fak * self._space.err_est_vv_hwmf_neg(Y, Mminus, Cm)\\n er2 = fak * self._space.err_est_vv_hwmf_pos(Y, Mplus, Cp0, Cp1)\\n return [er1, er2]\",\n \"def compute_log_curve(log_name, num_runs=4):\\n log_collector = []\\n for run_idx in range(1, num_runs+1):\\n # prepare log path\\n log_file = join(spiker.SPIKER_EXPS+\\\"-run-%d\\\" % (run_idx),\\n log_name, \\\"csv_history.log\\\")\\n log_dict = utils.parse_csv_log(log_file)\\n log_collector.append(log_dict)\\n\\n # compute for train loss\\n train_loss = np.vstack(\\n (log_collector[0][\\\"loss\\\"][np.newaxis, ...],\\n log_collector[1][\\\"loss\\\"][np.newaxis, ...],\\n log_collector[2][\\\"loss\\\"][np.newaxis, ...],\\n log_collector[3][\\\"loss\\\"][np.newaxis, ...]))\\n train_loss = train_loss.astype(\\\"float64\\\")\\n train_loss_mean = np.mean(train_loss, axis=0)\\n train_loss_std = np.std(train_loss, axis=0)\\n\\n # compute for test loss\\n test_loss = np.vstack(\\n (log_collector[0][\\\"val_loss\\\"][np.newaxis, ...],\\n log_collector[1][\\\"val_loss\\\"][np.newaxis, ...],\\n log_collector[2][\\\"val_loss\\\"][np.newaxis, ...],\\n log_collector[3][\\\"val_loss\\\"][np.newaxis, ...]))\\n test_loss = test_loss.astype(\\\"float64\\\")\\n test_loss_mean = np.mean(test_loss, axis=0)\\n test_loss_std = np.std(test_loss, axis=0)\\n\\n # compute for train mse\\n train_mse = np.vstack(\\n (log_collector[0][\\\"mean_squared_error\\\"][np.newaxis, ...],\\n log_collector[1][\\\"mean_squared_error\\\"][np.newaxis, ...],\\n log_collector[2][\\\"mean_squared_error\\\"][np.newaxis, ...],\\n log_collector[3][\\\"mean_squared_error\\\"][np.newaxis, ...]))\\n train_mse = train_mse.astype(\\\"float64\\\")\\n train_mse_mean = np.mean(train_mse, axis=0)\\n train_mse_std = np.std(train_mse, axis=0)\\n\\n # compute for test mse\\n test_mse = np.vstack(\\n (log_collector[0][\\\"val_mean_squared_error\\\"][np.newaxis, ...],\\n log_collector[1][\\\"val_mean_squared_error\\\"][np.newaxis, ...],\\n log_collector[2][\\\"val_mean_squared_error\\\"][np.newaxis, ...],\\n log_collector[3][\\\"val_mean_squared_error\\\"][np.newaxis, ...]))\\n test_mse = test_mse.astype(\\\"float64\\\")\\n test_mse_mean = np.mean(test_mse, axis=0)\\n test_mse_std = np.std(test_mse, axis=0)\\n\\n trloss = (train_loss_mean, train_loss_std)\\n teloss = (test_loss_mean, test_loss_std)\\n trmse = (train_mse_mean, train_mse_std)\\n temse = (test_mse_mean, test_mse_std)\\n\\n return trloss, teloss, trmse, temse\",\n \"def error():\\n\\n # Make data set using errors\\n dataset_a = DataSet(oscillating,error_y=oscillating_error,plot='error_bar',label='Data and error')\\n dataset_a.set_error(interval=5,width=1,cap=2)\\n dataset_b = DataSet(oscillating,plot='error_shade',error_y=oscillating_error,order=0,colour='lightgrey',label='Error')\\n dataset_c = DataSet(oscillating,plot='line',order=1,colour='firebrick',label='Data')\\n\\n # Make line graph with error bars\\n plot_bar = Plot()\\n plot_bar.set_legend(legend=True)\\n plot_bar.add_dataset(dataset_a)\\n plot_bar.plot()\\n plot_bar.save(name='./figures/2d_error_bar',fmt='png')\\n plot_bar.display()\\n\\n # Make line graph with shaded errors\\n plot_shade = Plot()\\n plot_shade.set_legend(legend=True,location='upper left')\\n plot_shade.add_dataset(dataset_b)\\n plot_shade.add_dataset(dataset_c)\\n plot_shade.plot()\\n plot_shade.save(name='./figures/2d_error_shade',fmt='png')\\n plot_shade.display()\",\n \"def error(self, trainset: ([], [])):\\n # MSE = Σ | d – y |^2 / n\\n error_sum = 0.0\\n for index, example in enumerate(trainset):\\n # | d – y |^2\\n output = self.activate(example[0])\\n\\n target = example[1][0]\\n\\n error = target - output\\n error_sum += error ** 2\\n\\n # Σ |error_sum| / n\\n error_sum = error_sum / len(trainset)\\n return error_sum\",\n \"def error(self,pt,eta):\\n return self._data[self.__ptBin(pt)][self.__etaBin(eta)][1]\",\n \"def runErrorTests(c, startNx, endNx, stepNx=1, display=False):\\n errorsArray = []\\n dxs = np.empty(shape=[0])\\n iteration = 0\\n\\n for currNx in range(startNx, endNx, stepNx):\\n nx = currNx\\n nt = nx\\n # initialize the vector of space points, our domain is [0,1]\\n x = np.linspace(0, 1, nx)\\n dxs = np.append(dxs, x[1] - x[0])\\n #to check convergence use smooth function\\n phi_ic = ic.cosineBasedFctn(x, 0.5)\\n errline, _ = runAllSchemes(x, phi_ic, nx, nt, c)\\n errorsArray = np.append(errorsArray, errline)\\n iteration = iteration+1\\n \\n # to check order of convergence we see the behaviour of log-log plots\\n # just for extra safety we check >0 for log\\n dxLog = np.where(dxs>0, np.log10(dxs), 0)\\n ErrorsLog = np.where(errorsArray>0, np.log10(errorsArray), 0)\\n ErrorsLog = ErrorsLog.reshape(iteration, len(errline)) \\n ErrorsLog = np.matrix.transpose(ErrorsLog)\\n methods = [\\\"FTBS\\\", \\\"CTCS\\\", \\\"CNCS\\\", \\\"LaxWendroff\\\"]\\n if(display):\\n for i in range (0, 4):\\n plt.plot(dxLog, ErrorsLog[i], label=methods[i])\\n coeff = np.polyfit(dxLog,ErrorsLog[i],1)\\n print(\\\"Estimated order of convergence for \\\"+methods[i]+\\\\\\n \\\": \\\"+str(coeff[0]))\\n plt.title(\\\"Log-log plot of L2 errors vs dx\\\\nc=\\\"+str(c))\\n plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\\n plt.show()\",\n \"def plotErr(self):\\n if self.xp and self.wp:\\n # plot the spectra\\n w=self.ws.value(np.array(self.xp))\\n self.errcurve,=self.erraxes.plot(self.xp,self.wp-w,linewidth=0.5,linestyle='',marker='o',color='b')\\n if self.dxp and self.dwp:\\n # plot the spectra\\n dw=self.ws.value(np.array(self.dxp))\\n self.delerrcurve,=self.erraxes.plot(self.dxp,self.dwp-dw,linewidth=0.5,linestyle='',marker='x',color='b')\",\n \"def error(line, data): # error function\\n # Metric: Sum of squared Y-axis differences\\n err = np.sum((data[:, 1] - (line[0] * data[:, 0] + line[1])) ** 2)\\n return err\",\n \"def getErrors(self):\\n errorList = []\\n\\n # E0\\n try:\\n if not self.e0.isValid():\\n errorList.append(\\\"Invalid first error axis in ErrorEllipse Class\\\")\\n except (NameError, AttributeError):\\n errorList.append(\\\"No first error axis in ErrorEllipse Class.\\\")\\n\\n # E1\\n try:\\n if not self.e1.isValid():\\n errorList.append(\\\"Invalid second error axis in ErrorEllipse Class\\\")\\n except (NameError, AttributeError):\\n errorList.append(\\\"No second error axis in ErrorEllipse Class.\\\")\\n\\n # E2\\n try:\\n if not self.e2.isValid():\\n errorList.append(\\\"Invalid third error axis in ErrorEllipse Class\\\")\\n except (NameError, AttributeError):\\n errorList.append(\\\"No third error axis in ErrorEllipse Class.\\\")\\n\\n # maximumHorizontalProjection\\n try:\\n self.maximumHorizontalProjection\\n except (NameError, AttributeError):\\n errorList.append(\\\"No MaximumHorizontalProjection in ErrorEllipse Class.\\\")\\n\\n # maximumVerticalProjection\\n try:\\n self.maximumVerticalProjection\\n except (NameError, AttributeError):\\n errorList.append(\\\"No MaximumVerticalProjection in ErrorEllipse Class\\\")\\n\\n # equivalentHorizontalRadius\\n try:\\n self.equivalentHorizontalRadius\\n except (NameError, AttributeError):\\n errorList.append(\\\"No EquivalentHorizontalRadius in ErrorEllipse class\\\")\\n\\n return errorList\",\n \"def calculate_error(self):\\n \\n delta = self.Y - self.T\\n error = delta.dot(delta) / self.N\\n error = format(error, '.5f')\\n \\n self.errors.append(error)\",\n \"def tracking_error(port_returns, market_returns):\\n\\n return np.std(port_returns - market_returns)\",\n \"def tracking_error(port_returns, market_returns):\\n\\n return np.std(port_returns - market_returns)\",\n \"def get_accelerometer_error(self) -> (Iterable[float], Iterable[float]):\\n acceleration_error = self.compute_accelerometer_error()\\n upper_error = [None] * self.num_steps\\n lower_error = [None] * self.num_steps\\n\\n upper_error[0] = lower_error[0] = self.initial_value\\n upper_error[1] = lower_error[1] = (\\n self.time_step * self.initial_velocity + self.initial_value)\\n\\n index = self.fill_values(upper_error)\\n self.fill_values(lower_error)\\n if index >= self.num_steps - 1:\\n return (upper_error[index:], lower_error[index:])\\n\\n if index > 1:\\n self.retrieve_value(\\n index + 1, upper_error, acceleration_error=acceleration_error)\\n self.retrieve_value(\\n index + 1, lower_error, acceleration_error=-acceleration_error)\\n else:\\n self.retrieve_value(index + 1, upper_error)\\n self.retrieve_value(index + 1, lower_error)\\n self.retrieve_value(\\n index + 2, upper_error, acceleration_error=acceleration_error)\\n self.retrieve_value(\\n index + 2, lower_error, acceleration_error=-acceleration_error)\\n\\n self.retrieve_value(self.num_steps - 1, upper_error)\\n self.retrieve_value(self.num_steps - 1, lower_error)\\n\\n return (upper_error[index:], lower_error[index:])\",\n \"def error(self): \\n if not self.terminal:\\n err = sum([v**2 for v in self.state + self.q[:-1]])\\n else:\\n err = sum([v**2 for v in LIMITS[:9]] + [1.0 - LIMITS[9]**2])\\n err *= (self.max_steps - self.steps)\\n return err\",\n \"def errorList(self, ylist, predictedlist):\\n\\n e = [0 for i in range(self.size)]\\n for i in range(self.size):\\n e[i] = round(ylist[i] - predictedlist[i], 3)\\n \\n errors = Vector(self.size)\\n errors.changeVals(e)\\n return errors\",\n \"def calc_error(self, data):\\n errors = np.zeros(data.shape)\\n\\n # Values below minimum lidar distance are ascribed the minimum error\\n errors[data <= self.min_error_dist] = self.min_error\\n\\n # Values above minimum have erros calculated by proportion of their value\\n errors[data > self.min_error_dist] = data[data > self.min_error_dist] * self.error_prop\\n return errors\",\n \"def get_error(self, params):\\n return self.endog - self.predict(params)\",\n \"def ss_err(self):\\n return np.squeeze(self._ss_err)\",\n \"def display_convergence_error(train_losses, valid_losses):\\n if len(valid_losses) > 0:\\n plt.plot(len(train_losses), train_losses, color=\\\"red\\\")\\n plt.plot(len(valid_losses), valid_losses, color=\\\"blue\\\")\\n plt.legend([\\\"Train\\\", \\\"Valid\\\"])\\n else:\\n plt.plot(len(train_losses), train_losses, color=\\\"red\\\")\\n plt.legend([\\\"Train\\\"])\\n plt.xlabel('Epoch')\\n plt.ylabel('Loss')\\n plt.show()\",\n \"def analyse_training_error(self, errors):\\n # Write Training Error in an excel file\\n with open(r\\\"Logistic Regression Results/Wine/logistic_regression_error\\\" + str(self.learning_rate) + r\\\"-\\\" + str(len(errors)) + r\\\"epochs.csv\\\", \\\"w\\\") as file:\\n\\n # Create dict writer\\n csv_writer = csv.writer(file, delimiter = \\\",\\\", lineterminator=\\\"\\\\n\\\")\\n\\n # Write headers of the CSV file\\n csv_writer.writerow([\\\"Training Error\\\", \\\"Learning Rate\\\"])\\n\\n # Write first training error with the current learning rate\\n csv_writer.writerow([errors[0],self.learning_rate])\\n\\n # Write every row after the first line\\n for error in errors:\\n csv_writer.writerow([error])\\n\\n # Create a list of epochs\\n epochs = list(range(1, len(errors) + 1))\",\n \"def _ss_err(self):\\n return np.sum(np.square(self._resids), axis=-2)\",\n \"def OF1_CalcErrorEstimation(param_list, args):\\n #return (sum( \\\\\\n #( OF1_SumOfGauss(param_list, classNum, g_lvls) - histogram ) ** 2) / g_lvls.size) + \\\\\\n #(abs(sum(param_list[:classNum]) - 1) * o)\\n return (sum( \\\\\\n ( OF1_SumOfGauss(param_list, args[0], args[1]) - args[2] ) ** 2) / args[1].size) + \\\\\\n (abs(sum(param_list[:args[0]]) - 1) * args[3])\",\n \"def eFieldErrorsAtPoints(resolution, lbcs, modeNames, pointSet, pointSetName):\\n\\n print \\\"\\\\n\\\\nCalculating errors of fields: \\\"\\n print \\\" \\\", resolution\\n print \\\" \\\", lbcs\\n print \\\" \\\", modeNames\\n print \\\" \\\", pointSetName\\n\\n dir = simDir(resolution, lbcs)\\n\\n nModes = len(modeNames)\\n\\n # get points for finding discrete field coeffs that minimize the L2 volume error\\n volPtsIn = getVolumePoints(32, 64, \\\"in\\\")\\n volPtsOut = getVolumePoints(32, 64, \\\"out\\\")\\n print \\\"Total number of volume points for minimizing L2 error: \\\", len(volPtsIn) + len(volPtsOut)\\n print \\\"Total number of surface points: \\\", len(pointSet)\\n\\n # get mx fields from simulation\\n mxSurfFields = []\\n mxVolFieldsIn = []\\n mxVolFieldsOut = []\\n for mode in modeNames:\\n print \\\" Mode \\\" + mode + \\\":\\\"\\n print \\\" calculating simulation surface field values...\\\"\\n mxSurfFields.append(mxFieldValuesAtPoints(resolution, lbcs, 'E', mode, pointSet, pointSetName).ravel())\\n print \\\" calculating simulation volume field values...\\\"\\n mxVolFieldsIn.append(mxFieldValuesAtPoints(resolution, lbcs, 'E', mode, volPtsIn, \\\"volume_in\\\").ravel())\\n mxVolFieldsOut.append(mxFieldValuesAtPoints(resolution, lbcs, 'E', mode, volPtsOut, \\\"volume_out\\\").ravel())\\n mxSurfFields = numpy.asarray(mxSurfFields)\\n\\n # calculate analytic fields at points if data does not already exist in simulation directory\\n anSurfFields = []\\n anVolFieldsIn = []\\n anVolFieldsOut = []\\n for mode in modeNames:\\n print \\\" Mode \\\" + mode + \\\":\\\"\\n print \\\" calculating analytic surface field values...\\\"\\n anSurfFields.append(analyticFieldValuesAtPoints(dir, mode, pointSet, pointSetName).ravel())\\n print \\\" calculating analytic volume field values...\\\"\\n anVolFieldsIn.append(analyticFieldValuesAtPoints(\\\"./analytic/\\\", mode, volPtsIn, \\\"volume_in\\\").ravel())\\n anVolFieldsOut.append(analyticFieldValuesAtPoints(\\\"./analytic/\\\", mode, volPtsOut, \\\"volume_out\\\").ravel())\\n\\n # dot product matrix of degenerate mx fields\\n dotProds = numpy.zeros((nModes, nModes))\\n for i in range(nModes):\\n for j in range(i, nModes):\\n dotProds[i, j] = numpy.dot(mxVolFieldsIn[i], mxVolFieldsIn[j]) + numpy.dot(mxVolFieldsOut[i], mxVolFieldsOut[j])\\n dotProds[j, i] = dotProds[i, j]\\n invDotProds = numpy.linalg.inv(dotProds)\\n\\n #print normalizeMinRes(resolution, lbcs, modeNames)\\n\\n # calculate errors\\n norm1s = []\\n norm2s = []\\n normInfs = []\\n for anVolFieldIn, anVolFieldOut, anSurfField in zip(anVolFieldsIn, anVolFieldsOut, anSurfFields):\\n rhs = numpy.zeros(nModes)\\n for i in range(nModes):\\n rhs[i] = numpy.dot(anVolFieldIn, mxVolFieldsIn[i]) + numpy.dot(anVolFieldOut, mxVolFieldsOut[i])\\n coeffs = numpy.dot(invDotProds, rhs)\\n mxSurfField = numpy.dot(mxSurfFields.T, coeffs)\\n diff = mxSurfField - anSurfField\\n norm1s.append(numpy.linalg.norm(diff, 1) / numpy.linalg.norm(anSurfField, 1))\\n norm2s.append(numpy.linalg.norm(diff, 2) / numpy.linalg.norm(anSurfField, 2))\\n normInfs.append(numpy.linalg.norm(diff, numpy.inf) / numpy.linalg.norm(anSurfField, numpy.inf))\\n \\n return norm1s, norm2s, normInfs\",\n \"def error(self) -> list:\\n return self.__err\",\n \"def calc_error_dist(self):\\n pass\",\n \"def test_error_curve(self):\\r\\n\\r\\n scores = self.test_confidences\\r\\n sort = numpy.argsort(scores, axis=0)\\r\\n sorted_scores = scores[sort]\\r\\n\\r\\n test_errors = numpy.zeros((scores.shape[0]))\\r\\n thresholds = numpy.zeros((scores.shape[0]))\\r\\n\\r\\n for i in range(sort.shape[0]):\\r\\n thresholds[i] = sorted_scores[i]\\r\\n test_errors[i] = numpy.sum(self.test_errors[self.test_confidences >= thresholds[i]]) / float(numpy.sum(self.test_confidences >= thresholds[i]))\\r\\n\\r\\n return test_errors, thresholds\",\n \"def getFitErr(self):\\n return (self.fitSum2Err)\",\n \"def err_func(x,rv,valore,specn,lcrop,models='da2014'):\\n tmp = tmp_func(x[0], x[1], rv, specn, lcrop, models)\\n if tmp != 1: return abs(tmp[3]-(valore+1.)) #this is quantity that gets minimized \\n else: return 1E30\",\n \"def diff_of_errors(self):\\n self.e_of_e = self.azimuth_error - self.altitude_error\\n return self.e_of_e\",\n \"def kl_loss(self):\\n return sum([p.kl_loss() for p in self.parameters])\",\n \"def nzErr(xerr, yerr, vxerr, vyerr, year_x, year_y, mag, alnDir = '13_08_21/', chainsDir = 'efit/chains_S0-2_newRV2/'):\\n\\n #Read in values for error in position and velocity of sgr*\\n origin_val = asciidata.open('/g/ghez/align/' + alnDir + chainsDir + 'efit_summary.txt')\\n ori_x0e = origin_val[25][0]\\n ori_y0e = origin_val[26][0]\\n ori_vxe = origin_val[27][0]\\n ori_vye = origin_val[28][0]\\n t_0 = 2000.0 #hard coded t_0 of sgr*\\n\\n # magBins=np.array([9,11,12,13,14,15,16,17,18,19,20,21])\\n # deltaArr=np.array([3.5,71.0,58.0,210.0,300.0,650.0,700.0,1100.0,1900.0,2200.0,3000.0])*1e-6\\n\\n# delta = mag*0.0\\n# for i in range(len(mag)):\\n# for j in range(len(deltaArr)):\\n# if ((mag[i] > magBins[j]) & (mag[i] <= magBins[j+1])):\\n# delta[i]=deltaArr[j]\\n\\n#pdb.set_trace()\\n\\n #Update errors\\n xerr = np.sqrt(xerr**2 + ori_x0e**2 + ((year_x - t_0)*ori_vxe)**2)\\n yerr = np.sqrt(yerr**2 + ori_y0e**2 + ((year_y - t_0)*ori_vye)**2)\\n vxerr = np.sqrt(vxerr**2 + ori_vxe**2)\\n vyerr = np.sqrt(vyerr**2 + ori_vye**2)\\n\\n return xerr, yerr, vxerr, vyerr\",\n \"def xerr(self, i):\\n return self.errors[0][i]\",\n \"def errors(self) -> List[Error]:\",\n \"def __error(self, R, P, Q, K, beta):\\n e = 0\\n for i in xrange(len(R)):\\n for j in xrange(len(R[i])):\\n if R[i][j] > 0:\\n\\n # loss function error sum( (y-y_hat)^2 )\\n e = e + pow(R[i][j]-numpy.dot(P[i,:],Q[:,j]), 2)\\n\\n # add regularization\\n for k in xrange(K):\\n\\n # error + ||P||^2 + ||Q||^2\\n e = e + (beta/2) * ( pow(P[i][k], 2) + pow(Q[k][j], 2) )\\n return e\",\n \"def _update_errs(self, lr = 1e-8, epochs = 1000):\\n if self.update_err_inf:\\n log_f = np.log(self._fv())\\n for _ in range(epochs):\\n residual = np.log(self._gv()) - log_f\\n grad = - np.mean(residual / (self.y - self.err_inf)) # gradient\\n # now: descent + projection steps\\n self.err_inf -= lr * grad\\n self._project()\",\n \"def error_values(X_train,X_test,Y_train,Y_test):\\n #setting up parameters and variables for plotting \\n n_train = X_train.shape[0]\\n n_test = X_test.shape[0]\\n d = X_train.shape[1]\\n hdnode = 100\\n w1 = np.random.normal(0,0.001,d*hdnode).reshape((d,hdnode))\\n d1 = np.zeros((d,hdnode))\\n w2 = np.random.normal(0,0.001,hdnode).reshape((hdnode,1))\\n d2 = np.zeros(hdnode)\\n h = np.zeros(hdnode)\\n mb = 100 #minibatch size\\n m = int(n_train/mb)\\n batch = np.arange(m) \\n lr = 0.00020\\n EP = 20000 #needed for initializing \\n ep = 0\\n yh = np.zeros((n_train,1))\\n yh2 = np.zeros((n_test,1))\\n L_train= np.zeros(EP+1)\\n L_test = np.zeros(EP+1)\\n Y_train = Y_train.reshape(len(Y_train),1)\\n #activation function for the hidden layer is tanh\\n \\n def g(A):\\n return (np.tanh(A))\\n\\n def gd(A):\\n return (1-np.square(np.tanh(A)))\\n \\n #setting up how long the epoch will run\\n EP = 200\\n ep = 0\\n while ep < EP:\\n ep += 1\\n yh = g(X_train.dot(w1)).dot(w2)\\n yh2 = g(X_test.dot(w1)).dot(w2)\\n L_train[ep] = LA.norm(yh-Y_train.reshape(len(Y_train),1))/n_train\\n L_test[ep] = LA.norm(yh2-Y_test.reshape(len(Y_test),1))/n_test\\n \\n np.random.shuffle(batch)\\n for i in range(m):\\n st = batch[i]*mb\\n ed = (batch[i]+1)*mb\\n h = g(X_train[st:ed].dot(w1))\\n y = h.dot(w2)\\n d2 = h.T.dot(Y_train[st:ed]-y)\\n d1 = X_train[st:ed].T.dot(np.multiply((Y_train[st:ed]-y).dot(w2.T),gd(X_train[st:ed].dot(w1))))\\n w2 += lr*d2\\n w1 += lr*d1\\n return yh, yh2\",\n \"def compute_errors(gt, pred):\\n thresh = np.maximum((gt / pred), (pred / gt))\\n a1 = (thresh < 1.25 ).mean()\\n a2 = (thresh < 1.25 ** 2).mean()\\n a3 = (thresh < 1.25 ** 3).mean()\\n\\n rmse = (gt - pred) ** 2\\n rmse = np.sqrt(rmse.mean())\\n\\n rmse_log = (np.log(gt) - np.log(pred)) ** 2\\n rmse_log = np.sqrt(rmse_log.mean())\\n\\n abs_rel = np.mean(np.abs(gt - pred) / gt)\\n\\n sq_rel = np.mean(((gt - pred) ** 2) / gt)\\n\\n return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3\",\n \"def _df_err(self):\\n return self.n - self.k - 1\",\n \"def post_iteration(self, step, level_number):\\n\\n super(error_output, self).post_iteration(step, level_number)\\n\\n # some abbreviations\\n L = step.levels[level_number]\\n P = L.prob\\n\\n L.sweep.compute_end_point()\\n\\n uex = P.u_exact(step.time + step.dt)\\n err = abs(uex - L.uend)\\n\\n est = []\\n\\n for m in range(1, L.sweep.coll.num_nodes + 1):\\n est.append(abs(L.uold[m] - L.u[m]))\\n\\n est_all = max(est)\\n\\n print(step.status.iter, err, est_all, L.status.residual)\",\n \"def calculate_error(self):\\n self.network.index_nodes()\\n self._calculate_dist()\\n _, relative_error = self._relative_error()\\n _, absolute_error = self._absolute_error()\\n\\n return absolute_error, relative_error\",\n \"def train_error(self):\\n self.prediction = self.predict()\\n pred = self.prediction.reshape(-1)\\n self.error = np.sum(pred != self.label) / self.train_data.shape[0]\\n return(self.error)\",\n \"def mag_err(self):\\n return self.photosamplers.get_estimate(mag=True)[1:]\",\n \"def compute_errors(gt, pred, selector):\\n gt = gt[selector]\\n pred = pred[selector]\\n\\n thresh = np.maximum((gt / pred), (pred / gt))\\n a1 = (thresh < 1.25 ).mean()\\n a2 = (thresh < 1.25 ** 2).mean()\\n a3 = (thresh < 1.25 ** 3).mean()\\n\\n rmse = (gt - pred) ** 2\\n rmse = np.sqrt(rmse.mean())\\n\\n rmse_log = (np.log(gt) - np.log(pred)) ** 2\\n rmse_log = np.sqrt(rmse_log.mean())\\n\\n abs_rel = np.mean(np.abs(gt - pred) / gt)\\n\\n sq_rel = np.mean(((gt - pred) ** 2) / gt)\\n\\n return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3\",\n \"def validation_curve(x, y, x_val, y_val):\\n lambda_vec = np.array([0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10])\\n error_train = np.zeros(len(lambda_vec))\\n error_val = np.zeros(len(lambda_vec))\\n m = x.shape[0]\\n m_val = x_val.shape[0]\\n for i in range(len(lambda_vec)):\\n l = lambda_vec[i]\\n theta = train_linear_reg(x, y, l)\\n error_train[i] = 1.0 / (2 * m) * np.sum(np.square(x.dot(theta) - y))\\n error_val[i] = 1.0 / (2 * m_val) * np.sum(np.square(x_val.dot(theta) - y_val))\\n\\n return lambda_vec, error_train, error_val\",\n \"def node_layer_error(self, errors, layer):\\n weights = self.weights[layer]\\n\\n node_errors = []\\n\\n for i, input in enumerate(self.input_matrix[layer-1]):\\n node_errors.append(float(log_deriv(input) * dot_product(weights[i], errors)))\\n\\n return node_errors\",\n \"def leacurve(x,y,xval,yval,lamda):\\n m1 = y.size\\n m2= yval.size\\n x = addOnes(x,m1)\\n xval = addOnes(xval,m2)\\n m,n = x.shape\\n errtrain = np.zeros(m)\\n errval = np.zeros(m)\\n thetaInit = np.zeros(n)\\n for i in range(1,m+1):\\n status,theta = optimSolve(thetaInit,x[0:i],y[0:i],reg=True,lamda=lamda)\\n errtrain[i-1] = costFunc(theta,x[0:i],y[0:i])\\n errval[i-1] = costFunc(theta,xval,yval)\\n return errtrain, errval\",\n \"def get_error(intercept, slope, points):\\n error_value = 0\\n for i in range(0, len(points)):\\n error_value += (points[i].y - (slope * points[i].x + intercept)) ** 2\\n return error_value / float(len(points))\",\n \"def _compute_errors(self):\\n self.errors = np.sqrt(self.data)\\n self.errors[self.errors == 0.] = 1.\",\n \"def errors(self):\\n return self.args[1]\",\n \"def _create_diagnostic_lightcurves(self):\\n if self.coefficients is None:\\n raise ValueError(\\\"you need to call `correct()` first\\\")\\n\\n lcs = {}\\n for idx, submatrix in enumerate(self.dmc.matrices):\\n # What is the index of the first column for the submatrix?\\n firstcol_idx = sum([m.shape[1] for m in self.dmc.matrices[:idx]])\\n submatrix_coefficients = self.coefficients[\\n firstcol_idx : firstcol_idx + submatrix.shape[1]\\n ]\\n # submatrix_coefficients_err = self.coefficients_err[firstcol_idx:firstcol_idx+submatrix.shape[1], firstcol_idx:firstcol_idx+submatrix.shape[1]]\\n # samples = np.asarray([np.dot(submatrix.values, np.random.multivariate_normal(submatrix_coefficients, submatrix_coefficients_err)) for idx in range(100)]).T\\n # model_err = np.abs(np.percentile(samples, [16, 84], axis=1) - np.median(samples, axis=1)[:, None].T).mean(axis=0)\\n model_flux = u.Quantity(\\n submatrix.X.dot(submatrix_coefficients), unit=self.lc.flux.unit\\n )\\n model_flux_err = u.Quantity(\\n np.zeros(len(model_flux)), unit=self.lc.flux.unit\\n )\\n lcs[submatrix.name] = LightCurve(\\n time=self.lc.time,\\n flux=model_flux,\\n flux_err=model_flux_err,\\n label=submatrix.name,\\n )\\n return lcs\",\n \"def _ms_err(self):\\n return self._ss_err / self._df_err\",\n \"def ms_err(self):\\n return np.squeeze(self._ms_err)\",\n \"def accr_lum(L_line, tracer, L_line_err = 0*u.W):\\n \\n a, a_err, b, b_err = rel['a'][tracer],rel['a_err'][tracer],rel['b'][tracer],rel['b_err'][tracer]\\n \\n log_L_acc = b + a * log10(L_line*u.W/L_sun)\\n \\n L_acc = 10**log_L_acc*L_sun/u.W\\n \\n #error propagation\\n \\n #c_err = (L_line_err)/(log(10) * L_line)\\n #ac_err = a * log10(L_line/L_sun) * ((a_err/a)**2 + (c_err/log10(L_line/L_sun))**2)**0.5\\n #log_L_acc_err = (b_err**2 + ac_err**2)**0.5\\n #L_acc_err = L_acc * log(10) * log_L_acc_err\\n\\n return L_acc\",\n \"def lsterr(inlist):\\r\\n return stdev(inlist) / float(math.sqrt(len(inlist)))\",\n \"def prediction_errors(data, paths): \\n \\n #prettyprint stuff\\n max_col = 20 \\n print \\\"\\\".join(word.ljust(max_col) for word in [\\\"Network conf\\\",\\\"Mean error\\\",\\\"STD error\\\"])\\n print '-'*max_col*3\\n \\n errors = np.zeros((len(paths), len(data)))\\n \\n for i, path in enumerate(paths):\\n som = get_soms(path)\\n errors[i, :] = som.quantization_error(data) \\n outputs = [path, str(errors[i, :].mean()), str(errors[i, :].std())]\\n print \\\"\\\".join(word.ljust(max_col) for word in outputs)\\n \\n return errors\",\n \"def getAll(self):\\n x,y,a = self.getxya()\\t\\n xerrs = [self.errors[0][i] for i in range(len(self.x)) if self.x[i]!=None and self.y[i]!=None]\\n yerrs = [self.errors[1][i] for i in range(len(self.x)) if self.x[i]!=None and self.y[i]!=None]\\t\\n return x,y,a,xerrs,yerrs\",\n \"def l2_error(self, X=None, y=None) -> np.ndarray:\\n return np.square(self.residuals(X, y))\",\n \"def _calculate_error(self, targets):\\n\\n def hist(d):\\n f, v = histogram(array(d))\\n i = len(f) if argmax(f) == len(f) - 1 else argmax(f)\\n return v[i]\\n\\n devxs, devys = list(zip(*[r.dev_centroid for r in targets]))\\n\\n if len(targets) > 2 and self.use_histogram:\\n dx = hist(devxs)\\n dy = hist(devys)\\n else:\\n\\n def avg(s):\\n return sum(s) / len(s)\\n\\n dx = avg(devxs)\\n dy = avg(devys)\\n\\n return -dx, dy\",\n \"def get_errors(cov):\\n return np.sqrt(np.diag(cov))\",\n \"def compute_error_cross_dataset(AL, train_y):\\n # print(train_y.shape)\\n nb = train_y.shape[0]\\n error=np.power(np.add(train_y,-AL),2)*1/nb\\n return error\\n # raise NotImplementedError\",\n \"def cps_err(self):\\n return np.sqrt(self.totalcounts) / self.exptime\",\n \"def error(Y, X):\\n return (Y - X) ** 2\",\n \"def get_current_validation_losses(self):\\n errors_ret = OrderedDict()\\n for name in self.loss_names:\\n if isinstance(name, str):\\n errors_ret[name+'_val'] = float(getattr(self, 'loss_' + name + '_val')) # float(...) works for both scalar tensor and float number\\n return errors_ret\",\n \"def get_error(self, output,target):\\n return [target[i]-output[i] for i in range(len(output))]\",\n \"def _error(self, Y, T):\\n err = np.mean((Y - T)**2)\\n return err\",\n \"def get_errors(self):\\n return [result for result in self.values() if result.outcome == Result.ERROR]\",\n \"def plot_cv_errors(errors, lambdas , K , path): \\n colors = cycle([\\\"aqua\\\", \\\"black\\\", \\\"blue\\\", \\\"fuchsia\\\", \\\"gray\\\", \\\"green\\\", \\\"lime\\\", \\\"maroon\\\", \\\"navy\\\", \\\"olive\\\", \\\"purple\\\", \\\"red\\\", \\\"silver\\\", \\\"teal\\\", \\\"yellow\\\"])\\n \\n markers = cycle([ \\\".\\\", \\\",\\\", \\\"o\\\", \\\"v\\\" , \\\"^\\\" , \\\">\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\", \\\"8\\\", \\\"s\\\", \\\"p\\\", \\\"*\\\", \\\"h\\\"])\\n \\n \\n for i, data in enumerate(errors):\\n \\n lambda_str = ('%f' % lambdas[i]).rstrip('0').rstrip('.')\\n plt.plot(range(len(data)), data, marker=next(markers), label='$\\\\lambda$ = %s'%lambda_str);\\n \\n plt.ylim(0.975 , 0.99)\\n #plt.xlim(0 , 50)\\n plt.title('ALS-WR Learning Curve, K = %d'% K)\\n plt.xlabel('Number of Epochs');\\n plt.ylabel('RMSE');\\n plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\\n plt.grid()\\n plt.savefig(\\\"../results/\\\"+path)\\n plt.show()\",\n \"def lapse(self):\\n pass\",\n \"def error(clf, X, y, ntrials=100, test_size=0.2) :\\n\\n train_error = 0\\n test_error = 0\\n ### ========== TODO : START ========== ###\\n # compute cross-validation error over ntrials\\n # hint: use train_test_split (be careful of the parameters)\\n for i in range(0,ntrials, 1):\\n #get the value of the error for each division\\n #train on the test data for the clf\\n #test also on the data\\n #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 42)\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= test_size, random_state=i)\\n #now find the error\\n #first train the model\\n #then predict\\n #check the accuracy\\n clf.fit(X_train,y_train)\\n y_pred = clf.predict(X_train)\\n #now find the error for the train_error\\n train_err = 1 - metrics.accuracy_score(y_train, y_pred, normalize=True)\\n train_error += train_err\\n\\n y_pred = clf.predict(X_test)\\n test_err = 1 - metrics.accuracy_score(y_test, y_pred, normalize=True)\\n test_error += test_err\\n\\n\\n #get the average\\n train_error = float(train_error)/((1-test_size)*len(X))\\n test_error = float(test_error)/((test_size)*len(X))\\n ### ========== TODO : END ========== ###\\n\\n return train_error, test_error\",\n \"def _lcResults(self):\\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\\n noCt = int( self.suffix.split('_')[-1] ) \\n \\n hdu = fits.open(fitsNnam)\\n hdu['LIGHTCURVE'].data['flux'][noCt] = self.getResult(param='Flux')[0]\\n hdu['LIGHTCURVE'].data['fluxerr'][noCt] = self.getResult(param='Flux')[1]\\n hdu['LIGHTCURVE'].data['index'][noCt] = self.getResult(param='Index')[0]\\n hdu['LIGHTCURVE'].data['indexerr'][noCt] = self.getResult(param='Index')[1]\\n hdu['LIGHTCURVE'].data['ts'][noCt] = self.getResult(param='TS value')[0]\\n hdu['LIGHTCURVE'].data['upperlim'][noCt] = self.getResult(param='upplim')[0] \\n hdu['LIGHTCURVE'].data['status'][noCt] = 'done'\\n hdu.writeto(fitsNnam, clobber=True)\\n hdu.close()\",\n \"def Error_estimation(params, qu=None, qu_err=None, p_maxL=None, par=False):\\n \\n if par is True:\\n print('Estimate uncertainties for the parameters')\\n sigma = par_err(params)\\n return(sigma)\\n elif qu is not None:\\n N = len(qu[0,:])\\n model_err = np.zeros((3, N))\\n star_err = np.zeros((3,N))\\n msg = 'Estimate model uncertainties'\\n print(msg)\\n if qu_err is None:\\n print(' -> by varying the model')\\n star_err[:-1,:] = np.std(QU_func(params, qu, star=True), axis=2)\\n model_err[:-1,:] = np.std(QU_func(params, qu), axis=2)\\n bkgr_err = np.std(background(params[N:,:]), axis=2) #?\\n \\n else:\\n print('-> using data and parameters')\\n err = par_err(params)\\n s_ax = (p_maxL[:N]*qu_err)**2 + (qu*err[:N])**2\\n s_b = np.array([(err[N:-1]*np.cos(2*p_maxL[-1]))**2 +\\\\\\n (2*p_maxL[N:-1]*err[-1]*np.sin(2*p_maxL[-1]))**2,\\\\\\n (err[N:-1]*np.sin(2*p_maxL[-1]))**2 +\\\\\\n (2*p_maxL[N:-1]*err[-1]*np.sin(2*p_maxL[-1]))**2])\\n model_err[:-1,:] = np.sqrt(s_ax + s_b)\\n star_err[:-1,:] = np.sqrt(s_ax)\\n bkgr_err = np.sqrt(s_b)\\n star_err[-1,:] = np.sqrt(Cov(star_err[0,:], star_err[1,:]))\\n model_err[-1,:] = np.sqrt(Cov(model_err[0,:], model_err[1,:]))\\n return(model_err, star_err, bkgr_err)\",\n \"def error_coeffs(self,p):\\n import nodepy.rooted_trees as rt\\n forest=rt.list_trees(p)\\n err_coeffs=[]\\n for tree in forest:\\n err_coeffs.append(self.error_coefficient(tree))\\n return snp.array(err_coeffs)\",\n \"def backprop_error(self,t=0, external_error=0):\\n\\t\\taux = external_error\\n\\t\\t# Other layers' contributions\\n\\t\\tif t != 0:\\n\\t\\t\\tfor layer in self.next_recurrent:\\n\\t\\t\\t\\taux += layer.get_error_contribution(self, t=t+1)\\n\\t\\tfor layer in self.next:\\n\\t\\t\\taux += layer.get_error_contribution(self, t=t)\\n\\n\\t\\tif t != 0:\\n\\t\\t\\taux += np.dot(\\n\\t\\t\\t\\tself.get_o_error(t=t+1) * self.__dev_sigm__(self.get_o(t=t+1)),\\n\\t\\t\\t\\tnp.transpose(self.W_o_atprev)\\n\\t\\t\\t) + \\\\\\n\\t\\t\\tnp.dot(\\n\\t\\t\\t\\tself.get_c_error(t=t+1) * self.get_i(t=t+1) * self.__dev_tanh__(self.get_c(t=t+1)),\\n\\t\\t\\t\\tnp.transpose(self.W_c_atprev)\\n\\t\\t\\t) + \\\\\\n\\t\\t\\tnp.dot(\\n\\t\\t\\t\\tself.get_i_error(t=t+1) * self.__dev_sigm__(self.get_i(t=t+1)),\\n\\t\\t\\t\\tnp.transpose(self.W_i_atprev)\\n\\t\\t\\t) + \\\\\\n\\t\\t\\tnp.dot(\\n\\t\\t\\t\\tself.get_f_error(t=t+1) * self.__dev_sigm__(self.get_f(t=t+1)),\\n\\t\\t\\t\\tnp.transpose(self.W_f_atprev)\\n\\t\\t\\t)\\n\\t\\t\\n\\t\\tif isinstance(aux, np.ndarray):\\n\\t\\t\\tself.error_a = [aux]+self.error_a\\n\\n\\t\\t# error in o\\n\\t\\tself.error_o = [self.get_a_error(t=t) * self.__tanh__(self.get_c(t=t))] + self.error_o\\n\\n\\t\\t# error in c\\n\\t\\taux = self.w_o_c * self.get_o_error(t=t) * self.__dev_sigm__(self.get_o(t=t)) \\\\\\n\\t\\t+ self.get_a_error(t=t) * self.get_o(t=t) * self.__dev_tanh_z__(self.get_c(t=t))\\n\\t\\tif t!=0:\\n\\t\\t\\taux += self.get_c_error(t=t+1) * self.get_f(t=t+1) \\\\\\n\\t\\t\\t+ self.w_i_ctprev * self.get_i_error(t=t+1) * self.__dev_sigm__(self.get_i(t=t+1)) \\\\\\n\\t\\t\\t+ self.w_f_ctprev * self.get_f_error(t=t+1) * self.__dev_sigm__(self.get_f(t=t+1))\\n\\t\\tself.error_c = [aux] + self.error_c\\n\\n\\t\\t# error in f\\n\\t\\tself.error_f = [self.get_c(t=t-1)*self.get_c_error(t=t)] + self.error_f\\n\\n\\t\\t# error in i\\n\\t\\tself.error_i = [self.get_c_error(t=t)*self.get_tanh_zc(t=t)] + self.error_i\",\n \"def __error(self,node_set):\\n error=0\\n for n in node_set:\\n if(n.seq_num!=0):\\n error+=LA.norm(n.node_vol-node_set[n.neighbor.parent].node_vol-n.impedance*n.branch_cur)\\n #print n.node_vol, '\\\\n', node_set[n.neighbor.parent].node_vol\\n \\n return error\",\n \"def get_error(self, err_type):\\n self.tmp = self.location_errors[0].errors[err_type] * \\\\\\n self.location_errors[0].errors[\\\"N\\\"]\\n N = self.location_errors[0].errors[\\\"N\\\"]\\n\\n for lerr in self.location_errors[1:]:\\n self.tmp = np.add(self.tmp, lerr.errors[err_type] *\\n lerr.errors[\\\"N\\\"])\\n N += lerr.errors[\\\"N\\\"]\\n\\n # print(self.tmp, N, self.tmp/ N)\\n return self.tmp / N\",\n \"def kl_loss_batch(self):\\n return sum([e for m in self.modules for e in m._kl_losses])\",\n \"def main():\\n error_LU, h_LU = read_2_col_file(\\\"error_LU_data.txt\\\") \\n error, h = read_2_col_file(\\\"error_data.txt\\\")\\n \\n log_h_LU = np.log10(h_LU)\\n log_error_LU = np.log10(error_LU)\\n log_h = np.log10(h)\\n log_error = np.log10(error)\\n\\n plt.plot(log_h_LU, log_error_LU, label=\\\"LU-decomposition\\\")\\n plt.plot(log_h, log_error, label=\\\"general Thomas algo.\\\")\\n plt.legend()\\n plt.title(\\\"Logarithmic plot of relative error \\\")\\n plt.xlabel(\\\"$log_{10}(time \\\\, step)$\\\")\\n plt.ylabel(\\\"$log_{10}(error)$\\\")\\n plt.show()\",\n \"def ee_err_1(sav, *args):\\n w, r, labor_supply, b_init, s, t = args\\n\\n error = u_prime(w[t] * labor_supply[s] + (1 + r[t]) * b_init[s] - sav) - beta * (1 + r[t+1]) * u_prime((1 + r[t+1]) * sav + w[t+1] * labor_supply[s+1])\\n\\n return error\",\n \"def calc_error(case, numerical, func, title='Percentage Error',\\n kolom=15, prec=6):\\n nodes, L = get_valdict(case, 'nodes,L')\\n y_num = numerical[1]\\n \\n node = np.arange(1, nodes+1)\\n distance = create_axis(case)[1:-1]\\n sol_exact = func(distance, case)\\n y_num = y_num[1:-1]\\n \\n diff, percenterr = [], []\\n for i, val in enumerate(sol_exact):\\n dval = val - y_num[i]\\n diff.append(dval)\\n percenterr.append(dval/val*100)\\n \\n lebar = kolom*6+7\\n print('='*lebar)\\n print('{:^{l:d}s}'.format(title, l=lebar))\\n print('='*lebar)\\n print('|{:^{k:d}s}|{:^{k:d}s}|{:^{k:d}s}|{:^{k:d}s}|{:^{k:d}s}|{:^{k:d}s}|'.format(\\n 'node', 'distance', 'numeric', 'exact', 'diff', 'error', k=kolom))\\n print('-'*lebar)\\n \\n for i in range(0, nodes):\\n print('| {:^{k:d}d} | {:> {k:d}.{p:d}f} | {:> {k:d}.{p:d}f} | {:> {k:d}.{p:d}f} | {:> {k:d}.{p:d}f} | {:> {k:d}.{p:d}f} |'.format(\\n node[i], distance[i], y_num[i], sol_exact[i], diff[i], percenterr[i], k=kolom-2, p=prec\\n ))\\n print('='*lebar)\",\n \"def error_dropoff(data):\\n # 2009-05-05 08:58 IJC: Adapted to Python from Matlab.\\n # 2006/06/06 IJC: Made it work with arrays of column vectors.\\n # Added the '--nomean' option.\\n \\n \\n# PARSE INPUTS\\n data = array(data).copy()\\n \\n#interval = max([1 round(extract_from_options('--interval=', 1, options))]);\\n \\n \\n if len(data)==len(data.ravel()):\\n data = data.ravel()\\n data = data.reshape(len(data),1)\\n \\n nsets = data.shape[1]\\n npts_vec = arange(data.shape[0]/2)+1.0\\n errors = zeros((data.shape[0]/2, nsets))\\n \\n# LOOP AND CALCULATE STUFF\\n for ii in range(len(npts_vec)):\\n npts = npts_vec[ii] # number of points we average over\\n nsamp = floor(data.shape[0]/npts) # number of subsamples\\n dre = reshape(data[0:nsamp*npts,:], (npts, nsamp, nsets))\\n error_values = std(dre.mean(1))\\n errors[ii,:] = error_values\\n \\n return (errors, npts_vec)\",\n \"def getErrorsList(self):\\n return self.__errors\",\n \"def logistic_derivative(errors):\\n return [log_deriv(error) for error in errors]\",\n \"def get_errors(id, vector):\\n for i in vector: assert -10<=abs(i)<=10\\n assert len(vector) == MAX_DEG\\n\\n return json.loads(send_request(id, vector, 'geterrors'))\",\n \"def get_errors(id, vector):\\n for i in vector: assert -10<=abs(i)<=10\\n assert len(vector) == MAX_DEG\\n\\n return json.loads(send_request(id, vector, 'geterrors'))\",\n \"def learning_curve():\\n loss = []\\n val_loss = []\\n data_size = []\\n\\n x_slid, y_slid = sliding_window_main(x, y)\\n x_train, y_train, x_val, y_val, x_test, y_test = data_splitting_main(x_slid, y_slid)\\n m_tot = x_train.shape[0]\\n\\n batch_step = 50\\n try:\\n for m in range(batch_size, m_tot, batch_step*batch_size):\\n print(\\\"Training: \\\", m)\\n net = create_network()\\n history = trainer(net, x_train[:m], y_train[:m], x_val, y_val)\\n loss.append(history.history[\\\"loss\\\"][-1])\\n val_loss.append(history.history[\\\"val_loss\\\"][-1])\\n data_size.append(m)\\n\\n print(\\\"Loss:\\\", loss[-1])\\n print()\\n\\n finally:\\n plt.plot(data_size, loss, label=\\\"Loss\\\", marker=\\\"o\\\")\\n plt.plot(data_size, val_loss, label=\\\"Val Loss\\\", marker=\\\"o\\\")\\n plt.xlabel(\\\"m\\\")\\n plt.ylabel(\\\"Losses\\\")\\n plt.title(\\\"Model Loss\\\")\\n plt.legend()\\n plt.savefig(\\\"img/\\\" + datetime.now().strftime(\\\"%y%m%d_%H%M\\\") + \\\"_learning_curve.png\\\")\\n plt.show()\\n plt.close()\\n\\n return loss, val_loss\",\n \"def get_func_errors(subset_size, args, filenames):\\n pA, pA_list, pdMdh, pdMdh_list = args\\n\\n r, s, A, As = load_svA(filenames[0])\\n\\n Sigma = dict(zip(r, list(pA['Sigma'])))\\n eta = dict(zip(r, list(pdMdh['eta'])))\\n\\n Sigma_list = [pA[0] for pA in pA_list]\\n eta_list = [pdMdh[1] for pdMdh in pdMdh_list]\\n\\n Sigma_std = std_funcs(r, subset_size, Sigma_list)\\n eta_std = std_funcs(r, subset_size, eta_list)\\n\\n return Sigma, Sigma_std, eta, eta_std\",\n \"def compute_errors(u_exact, u):\\n\\n # Compute error norm (for very small errors, the value can be\\n # negative so we run abs(assemble(error)) to avoid failure in sqrt\\n\\n V = u.function_space()\\n\\n # Function - Expression\\n error = (u - u_exact)**2*dx\\n E1 = sqrt(abs(assemble(error)))\\n\\n # Explicit interpolation of u_e onto the same space as u:\\n u_e = interpolate(u_exact, V)\\n error = (u - u_e)**2*dx\\n E2 = sqrt(abs(assemble(error)))\\n\\n # Explicit interpolation of u_exact to higher-order elements,\\n # u will also be interpolated to the space Ve before integration\\n Ve = FunctionSpace(V.mesh(), 'P', 5)\\n u_e = interpolate(u_exact, Ve)\\n error = (u - u_e)**2*dx\\n E3 = sqrt(abs(assemble(error)))\\n\\n # fenics.errornorm interpolates u and u_e to a space with\\n # given degree, and creates the error field by subtracting\\n # the degrees of freedom, then the error field is integrated\\n # TEMPORARY BUG - doesn't accept Expression for u_e\\n #E4 = errornorm(u_e, u, normtype='l2', degree=3)\\n # Manual implementation errornorm to get around the bug:\\n def errornorm(u_exact, u, Ve):\\n u_Ve = interpolate(u, Ve)\\n u_e_Ve = interpolate(u_exact, Ve)\\n e_Ve = Function(Ve)\\n # Subtract degrees of freedom for the error field\\n e_Ve.vector()[:] = u_e_Ve.vector().array() - u_Ve.vector().array()\\n # More efficient computation (avoids the rhs array result above)\\n #e_Ve.assign(u_e_Ve) # e_Ve = u_e_Ve\\n #e_Ve.vector().axpy(-1.0, u_Ve.vector()) # e_Ve += -1.0*u_Ve\\n error = e_Ve**2*dx(Ve.mesh())\\n return sqrt(abs(assemble(error))), e_Ve\\n E4, e_Ve = errornorm(u_exact, u, Ve)\\n\\n # Infinity norm based on nodal values\\n u_e = interpolate(u_exact, V)\\n E5 = abs(u_e.vector().array() - u.vector().array()).max()\\n\\n # H1 seminorm\\n error = dot(grad(e_Ve), grad(e_Ve))*dx\\n E6 = sqrt(abs(assemble(error)))\\n\\n # Collect error measures in a dictionary with self-explanatory keys\\n errors = {'u - u_exact': E1,\\n 'u - interpolate(u_exact,V)': E2,\\n 'interpolate(u,Ve) - interpolate(u_exact,Ve)': E3,\\n 'errornorm': E4,\\n 'infinity norm (of dofs)': E5,\\n 'grad(error) H1 seminorm': E6}\\n\\n return errors\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6367377","0.63289833","0.63179725","0.62470627","0.6190272","0.6103401","0.6064777","0.60606426","0.6049611","0.6049317","0.60475177","0.60305744","0.5965578","0.59578276","0.5941514","0.5910615","0.5905703","0.5876154","0.5869304","0.5868508","0.58317065","0.5827314","0.5809045","0.5789792","0.5769182","0.5769182","0.57628804","0.57479566","0.574542","0.57301116","0.57242846","0.57209194","0.5715798","0.57130516","0.5707273","0.57014096","0.5700137","0.5697417","0.5694461","0.56941885","0.5690313","0.5688422","0.56703746","0.56548685","0.56530756","0.5648974","0.5640136","0.5634789","0.5625668","0.56128734","0.5598508","0.55933636","0.55932605","0.55901235","0.55893415","0.5586208","0.5570732","0.5566119","0.55547625","0.5544432","0.5541505","0.55174166","0.55108255","0.55096346","0.5508587","0.5501997","0.54931414","0.549264","0.5488799","0.5484077","0.5483239","0.5478029","0.5470837","0.54623437","0.54604286","0.54496425","0.54494345","0.5448327","0.54482836","0.54340494","0.54262733","0.54201293","0.5419839","0.54195946","0.541939","0.5412299","0.5407777","0.53979754","0.53798527","0.53782064","0.5373698","0.53689003","0.5351833","0.5349471","0.5349013","0.5347669","0.5342567","0.5342567","0.53381455","0.53329533","0.5330225"],"string":"[\n \"0.6367377\",\n \"0.63289833\",\n \"0.63179725\",\n \"0.62470627\",\n \"0.6190272\",\n \"0.6103401\",\n \"0.6064777\",\n \"0.60606426\",\n \"0.6049611\",\n \"0.6049317\",\n \"0.60475177\",\n \"0.60305744\",\n \"0.5965578\",\n \"0.59578276\",\n \"0.5941514\",\n \"0.5910615\",\n \"0.5905703\",\n \"0.5876154\",\n \"0.5869304\",\n \"0.5868508\",\n \"0.58317065\",\n \"0.5827314\",\n \"0.5809045\",\n \"0.5789792\",\n \"0.5769182\",\n \"0.5769182\",\n \"0.57628804\",\n \"0.57479566\",\n \"0.574542\",\n \"0.57301116\",\n \"0.57242846\",\n \"0.57209194\",\n \"0.5715798\",\n \"0.57130516\",\n \"0.5707273\",\n \"0.57014096\",\n \"0.5700137\",\n \"0.5697417\",\n \"0.5694461\",\n \"0.56941885\",\n \"0.5690313\",\n \"0.5688422\",\n \"0.56703746\",\n \"0.56548685\",\n \"0.56530756\",\n \"0.5648974\",\n \"0.5640136\",\n \"0.5634789\",\n \"0.5625668\",\n \"0.56128734\",\n \"0.5598508\",\n \"0.55933636\",\n \"0.55932605\",\n \"0.55901235\",\n \"0.55893415\",\n \"0.5586208\",\n \"0.5570732\",\n 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function and produces the Lcurve plot."},"document":{"kind":"string","value":"def analyze(argv):\n costs, tikhs, regs = get_l_curve_results(argv[0])\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n plt.scatter(tikhs, costs)\n for k in range(len(regs)):\n exponent = math.log(regs[k], 10)\n ax.annotate(r'$10^{{{0:.2f}}}$'.format(exponent),\n xy = (tikhs[k], costs[k]),\n xytext = (0, 30),\n textcoords = 'offset points',\n rotation = 45,\n fontsize = 16)\n plt.xlabel(r'Model norm (MPa $\\cdot$ m / a)', fontsize = 16)\n plt.ylabel(r'Cost function (MPa $\\cdot$ m${}^3$ / a)', fontsize = 16)\n plt.xlim(0, 1.15 * np.max(tikhs))\n plt.ylim(0.95 * np.min(costs), 1.05 * np.max(costs))\n plt.show()\n\n return regs, tikhs, costs"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def plot():\n xvals = np.arange(-50, 250, step=0.1)\n\n fig = plt.figure()\n plt.suptitle(\"Gaussian with smooth transition to power law\")\n\n A0vals = [10, 11]\n avals = [5*10**-3, 10**-3, 5*10**-4]\n ttvals = [10., 50., 100.]\n cvals = [-0.1, -0.9, -5./3., -4.]\n offset = [-30, 0.0, 30]\n\n paramvals = [A0vals, avals, ttvals,cvals, offset]\n titles, labels = return_parameter_names()\n\n nplots = len(paramvals)\n\n for i in range(nplots):\n plt.subplot(nplots, 1, i+1)\n vals = paramvals[i]\n for j in range(len(vals)):\n pset = list(default())\n pset[i] = vals[j]\n yvals=[]\n ypower=[]\n ypeak=[]\n for x in xvals:\n yvals.append(fitfunc(x, pset))\n ypeak.append(logpeak(x,pset))\n if x > 0:\n ypower.append(logpowerlaw(x,pset))\n label = labels[i] + \"=\"+str(vals[j])\n plt.plot(xvals, yvals, label = label)\n\n plt.title(titles[i])\n plt.legend()\n\n fig.set_size_inches(15, 30)\n plt.savefig(\"graphs/misc/lightcurve_models.pdf\")\n plt.close()","def plot_LvsL(modeldata,line1='CIV1548',line2='CIII1908',plotname='./TESTPLOT.pdf',\n Zgas=False,logU=False,xid=0.3,nh=100,COratio=0.38,Mcutoff=100,\n logx=False,logy=False,logp1=False,logp2=False,fixxrange=False,fixyrange=False,\n showobs=None,noobserr=False,verbose=True):\n NFalse = 0\n freeparam = []\n inforstr = \"\"\n # - - - - - - - - - - - - - - - - - - - - - - - -\n legenddic = {}\n legenddic['Zgas'] = r'Z$_\\textrm{gas}$'\n legenddic['logUs'] = r'log$_\\textrm{10}$(U)'\n legenddic['xid'] = r'$\\xi_\\textrm{d}$'\n legenddic['nh'] = r'n$_\\textrm{H}$ / [cm$^3$]'\n legenddic['COCOsol'] = r'C/O / [C/O]$_\\textrm{sun}$'\n legenddic['mup'] = r'M$_\\textrm{cut IMF}$ / [M$_\\textrm{sun}]$'\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not Zgas:\n Zgasrange = [0.0,1.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' Zgas:vary, '\n freeparam.append('Zgas')\n else:\n Zgasrange = [Zgas-1e-6,Zgas+1e-6]\n inforstr = inforstr+' '+legenddic['Zgas']+'='+str(Zgas)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not logU:\n logUrange = [-5.0,0.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' logU:vary, '\n freeparam.append('logUs')\n else:\n logUrange = [logU-0.1,logU+0.1]\n inforstr = inforstr+' '+legenddic['logUs']+'='+str(logU)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not xid:\n xidrange = [0.0,0.6]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' xid:vary, '\n freeparam.append('xid')\n else:\n xidrange = [xid-0.01,xid+0.01]\n inforstr = inforstr+' '+legenddic['xid']+'='+str(xid)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not nh:\n nhrange = [0.0,1.0e6]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' nH:vary, '\n freeparam.append('nh')\n else:\n nhrange = [nh-1.0,nh+1.0]\n inforstr = inforstr+' '+legenddic['nh']+'='+str(nh)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not COratio:\n COratiorange = [0.0,2.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' C/O:vary, '\n freeparam.append('COCOsol')\n else:\n COratiorange = [COratio-0.001,COratio+0.001]\n inforstr = inforstr+' '+legenddic['COCOsol']+'='+str(COratio)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not Mcutoff:\n Mcutoffrange = [0.0,400.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' Mcutoff:vary, '\n freeparam.append('mup')\n else:\n Mcutoffrange = [Mcutoff-1.0,Mcutoff+1.0]\n inforstr = inforstr+' '+legenddic['mup']+'='+str(Mcutoff)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n\n if NFalse != 2:\n sys.exit(' Two and only two of the model parameters (Zgas,logU,xid,nh,COratio,Mcutoff) '\n 'should be set to Flase to define the model grid; however it appears '+str(NFalse)+\n ' parameters where not set')\n\n goodent = np.where( (modeldata['Zgas'] > Zgasrange[0]) & (modeldata['Zgas'] < Zgasrange[1]) &\n (modeldata['logUs'] > logUrange[0]) & (modeldata['logUs'] < logUrange[1]) &\n (modeldata['xid'] > xidrange[0]) & (modeldata['xid'] < xidrange[1]) &\n (modeldata['nh'] > nhrange[0]) & (modeldata['nh'] < nhrange[1]) &\n (modeldata['COCOsol'] > COratiorange[0]) & (modeldata['COCOsol'] < COratiorange[1]) &\n (modeldata['mup'] > Mcutoffrange[0]) & (modeldata['mup'] < Mcutoffrange[1]) )\n\n Ngoodent = len(goodent[0])\n\n if Ngoodent > 1:\n if verbose: print(' - Getting data for '+str(Ngoodent)+' data points satisfying model selection ')\n param1 = modeldata[freeparam[0]][goodent]\n if logp1:\n param1 = np.log10(param1)\n\n param2 = modeldata[freeparam[1]][goodent]\n if logp2:\n param2 = np.log10(param2)\n\n lum1 = modeldata[line1][goodent]\n lum2 = modeldata[line2][goodent]\n else:\n if verbose: print(' WARNING: Less than 2 model grid points to plot; no output generated')\n return\n\n # - - - - - - - - - - - PLOTTING - - - - - - - - - - -\n if verbose: print(' - Setting up and generating plot')\n plotname = plotname\n fig = plt.figure(figsize=(9, 5))\n fig.subplots_adjust(wspace=0.1, hspace=0.1,left=0.1, right=0.99, bottom=0.10, top=0.95)\n Fsize = 10\n lthick = 1\n marksize = 3\n plt.rc('text', usetex=True)\n plt.rc('font', family='serif',size=Fsize)\n plt.rc('xtick', labelsize=Fsize)\n plt.rc('ytick', labelsize=Fsize)\n plt.clf()\n plt.ioff()\n plt.title(inforstr[:-2],fontsize=Fsize)\n\n margin = 0.1\n dx = np.abs(np.max(lum1)-np.min(lum1))\n dy = np.abs(np.max(lum2)-np.min(lum2))\n\n\n if fixxrange:\n xrange = fixxrange\n else:\n if logx:\n xrange = [np.min(lum1)-np.min(lum1)/2.,np.max(lum1)+np.max(lum1)/2.]\n else:\n xrange = [np.min(lum1)-dx*margin,np.max(lum1)+dx*margin]\n\n if fixyrange:\n yrange = fixyrange\n else:\n if logy:\n yrange = [np.min(lum2)-np.min(lum2)/2.,np.max(lum2)+np.max(lum2)/2.]\n else:\n yrange = [np.min(lum2)-dy*margin,np.max(lum2)+dy*margin]\n\n # ------------ PARAM1 ------------\n cmap = plt.cm.get_cmap('winter')\n cmin = np.min(param1)\n cmax = np.max(param1)\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\n mm = plt.cm.ScalarMappable(cmap=cmap)\n mm.set_array(cmaparr)\n cb1 = plt.colorbar(mm)#shrink=0.25\n\n pstr1 = legenddic[freeparam[0]]\n if logp1:\n pstr1 = r'log$_\\textrm{10}$('+pstr1+')'\n\n cb1.set_label(pstr1+' (outer circle) - Fixed: black line')\n\n for p1 in np.unique(param1):\n p1col = cmap(colnorm(p1))\n p1ent = np.where(param1 == p1)\n\n plt.plot(lum1[p1ent],lum2[p1ent],'-',lw=lthick, color='k',zorder=1)\n\n plt.errorbar(lum1[p1ent],lum2[p1ent],xerr=None,yerr=None,\n marker='o',lw=0, markersize=marksize*3,\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\n\n # ------------ PARAM2 ------------\n cmap = plt.cm.get_cmap('spring')\n cmin = np.min(param2)\n cmax = np.max(param2)\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\n mm = plt.cm.ScalarMappable(cmap=cmap)\n mm.set_array(cmaparr)\n cb2 = plt.colorbar(mm)#shrink=0.25\n\n pstr2 = legenddic[freeparam[1]]\n if logp2:\n pstr2 = 'log10('+pstr2+')'\n\n cb2.set_label(pstr2+' (inner circle) - Fixed: gray line')\n\n for p2 in np.unique(param2):\n p2col = cmap(colnorm(p2))\n p2ent = np.where(param2 == p2)\n\n plt.plot(lum1[p2ent],lum2[p2ent],'-',lw=lthick, color='gray',zorder=2)\n\n plt.errorbar(lum1[p2ent],lum2[p2ent],xerr=None,yerr=None,\n marker='o',lw=0, markersize=marksize*1.5,\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\n\n if showobs != None:\n for ii, objid in enumerate(showobs[:,0]):\n if (showobs[:,7][ii] > xrange[0]) & (showobs[:,7][ii] < xrange[1]) & \\\n (showobs[:,9][ii] > yrange[0]) & (showobs[:,9][ii] < yrange[1]):\n\n if noobserr:\n obsxerr = None\n obsyerr = None\n else:\n obsxerr = showobs[:,8][ii]\n obsyerr = showobs[:,10][ii]\n plt.errorbar(showobs[:,7][ii],showobs[:,9][ii],xerr=obsxerr,yerr=obsyerr,\n marker='*',lw=lthick, markersize=marksize*2,\n markerfacecolor='k',ecolor='k',markeredgecolor = 'k',zorder=30)\n\n plt.xlabel(r'L$_\\textrm{'+line1+r'}$ / [3.826$\\times$1e33 erg/s]/[Msun/yr]')\n plt.ylabel(r'L$_\\textrm{'+line2+r'}$ / [3.826$\\times$1e33 erg/s]/[Msun/yr]')\n\n plt.xlim(xrange)\n plt.ylim(yrange)\n\n if logx:\n plt.xscale('log')\n if logy:\n plt.yscale('log')\n\n #--------- LEGEND ---------\n # plt.errorbar(-1,-1,xerr=None,yerr=None,fmt='o',lw=lthick,ecolor='white', markersize=marksize*2,\n # markerfacecolor='white',markeredgecolor = 'k',label='Ground-based spec')\n #\n # leg = plt.legend(fancybox=True, loc='upper center',prop={'size':Fsize},ncol=1,numpoints=1)\n # #bbox_to_anchor=(1.25, 1.03)) # add the legend\n # leg.get_frame().set_alpha(0.7)\n #--------------------------\n\n if verbose: print(' Saving plot to'+plotname)\n plt.savefig(plotname)\n plt.clf()\n plt.close('all')","def lCurve(self): \n\n # --------------------------------------------------------------------------------------------- #\n # Read data\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\n lcTab = Table.read(fitsNnam)\n if (self.tstart is not None) and (self.tstop is not None):\n lcTab = lcTab[ (self.tstart <= lcTab['mjd']) & (lcTab['mjd'] <= self.tstop)]\n lcTab = lcTab[lcTab['flux'] != -1.] # avoid undone analyses\n\n timeMJD = lcTab['mjd']\n tref = int(np.floor( timeMJD[0] / 100.0)) * 100 # round to lowest hundred\n timeMJD -= tref\n ts = lcTab['ts']\n detect = lcTab['ts'] >= self.tsmin\n undet = lcTab['ts'] < self.tsmin\n flux = lcTab['flux'][detect]\n fluxerr = lcTab['fluxerr'][detect]\n upperl = lcTab['upperlim'][undet]\n upperl[upperl == -1.] = 0. # for when it failed\n scale = 10**int(np.floor(np.log10( np.mean( np.concatenate( (flux, upperl), axis=0) ) ))) \n\n # --------------------------------------------------------------------------------------------- #\n # Plot\n lcplt = FermiPlot(savepath='', xsize=8.5, ysize=6)\n lcplt.figname = os.path.join(self.workpath, 'LightCurve.pdf')\n lcplt.xlabel = r'Time (MJD $-$ {})'.format(tref)\n lcplt.ylabel = [r'Flux ($10^{%d}$ ph\\,cm$^{-2}$\\,s$^{-1}$)'%(int(np.log10(scale))), r'TS']\n lcplt.hline = [None, self.tsmin]\n deltaY = max(np.concatenate((flux+fluxerr, upperl), axis=0)) - min(np.concatenate((flux-fluxerr, upperl), axis=0))\n lcplt.ymin = [(min(np.concatenate((flux-fluxerr, upperl-upperl*0.1), axis=0)) - 0.05*deltaY) / scale, min(ts) - 0.05*(max(ts)-min(ts))]\n lcplt.ymax = [(max(np.concatenate((flux+fluxerr, upperl), axis=0)) + 0.05*deltaY) / scale, max(ts) + 0.05*(max(ts)-min(ts))]\n deltaX = (timeMJD[-1] + lcTab['mjderr'][-1]) - (timeMJD[0] - lcTab['mjderr'][0]) \n lcplt.xmin = timeMJD[0] - lcTab['mjderr'][0] - 0.05*deltaX\n lcplt.xmax = timeMJD[-1] + lcTab['mjderr'][-1] + 0.05*deltaX\n lcplt.fill = [item for sublist in zip( timeMJD[detect]-lcTab['mjderr'][detect], timeMJD[detect]+lcTab['mjderr'][detect] ) for item in sublist]\n lcplt.shadecol= self.loran \n if len(flux) == 0:\n lcplt.mksize = [2, 2]\n lcplt.ymode = ['linear', 'linear']\n lcplt.color = ['gray', 'black']\n lcplt.prop = [3, 1]\n lcplt.limit = [True, False]\n lcplt.multiplot(x = [ timeMJD[undet], timeMJD ],\n y = [ upperl/scale, ts ],\n xerr = [ lcTab['mjderr'][undet], lcTab['mjderr']],\n yerr = [ upperl/scale*0.1, None])\n else:\n lcplt.mksize = [2, 2, 2]\n lcplt.ymode = ['linear', 'linear', 'linear']\n lcplt.color = ['gray', 'black', 'black']\n lcplt.prop = [3, 1]\n lcplt.limit = [[True, False], False]\n lcplt.multiplot(x = [ [timeMJD[undet], timeMJD[detect]], timeMJD ],\n y = [ [upperl/scale, flux/scale], ts ],\n xerr = [ [lcTab['mjderr'][undet], lcTab['mjderr'][detect]], lcTab['mjderr']],\n yerr = [ [upperl/scale*0.1, fluxerr/scale], None])\n lcplt.save()\n\n print(\"\\t=== Figure '{}' created ===\".format(lcplt.figname)) \n return","def main():\n error_LU, h_LU = read_2_col_file(\"error_LU_data.txt\") \n error, h = read_2_col_file(\"error_data.txt\")\n \n log_h_LU = np.log10(h_LU)\n log_error_LU = np.log10(error_LU)\n log_h = np.log10(h)\n log_error = np.log10(error)\n\n plt.plot(log_h_LU, log_error_LU, label=\"LU-decomposition\")\n plt.plot(log_h, log_error, label=\"general Thomas algo.\")\n plt.legend()\n plt.title(\"Logarithmic plot of relative error \")\n plt.xlabel(\"$log_{10}(time \\, step)$\")\n plt.ylabel(\"$log_{10}(error)$\")\n plt.show()","def test_lcurve(self):\n from astropy.io.fits import Header\n command = ('{0} -e {1} {2} --safe-interval '\n '{3} {4} --nproc 2 -b 0.5 -o {5}').format(\n os.path.join(self.datadir, 'monol_testA_nustar_fpma_ev_calib' +\n HEN_FILE_EXTENSION),\n 3, 50, 100, 300,\n os.path.join(self.datadir, 'monol_testA_E3-50_lc' +\n HEN_FILE_EXTENSION)\n )\n hen.lcurve.main(command.split())\n\n new_filename = \\\n os.path.join(os.path.join(self.datadir,\n 'monol_testA_E3-50_lc' +\n HEN_FILE_EXTENSION))\n assert os.path.exists(new_filename)\n lc = hen.io.load_lcurve(new_filename)\n assert hasattr(lc, 'header')\n # Test that the header is correctly conserved\n Header.fromstring(lc.header)\n assert hasattr(lc, 'gti')\n gti_to_test = hen.io.load_events(self.first_event_file).gti\n assert np.allclose(gti_to_test, lc.gti)","def plot_lightcurve(t, lc, lcerr, popt, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params,\n x = None, y = None, Pns = None, errors = False, binsize = 50,\n name = None, channel = None, orbit = None, savefile = False, TT_hjd = None,\n method = 'PLD', color = 'r', scale = None, filext = None, foldext = '',\n showCuts = False, ncutstarts = None, cutstartTime = None,\n cutends = False, eclipse = False, extraoutputs = False):\n # dictionary to save the reduced data, the model, the corrected data, the corrected model\n plottingdict = {}\n\n binsize = int(binsize)\n\n if name == None:\n warnings.warn( \"What planetary system are we looking at!? -- setting to 'unknown'\" )\n name = 'unknown'\n if channel == None:\n warnings.warn( \"What channel are we looking at!? -- setting to 'unknown'\" )\n channel = 'unknown'\n\n if method == 'poly':\n\n transit, F, ramp = model_poly(popt, t, x, y, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, components = True, eclipse = eclipse)\n optflux = transit*F*ramp\n\n # Correct the lightcurve and bin the data and the optimum values\n corrected_data = lc / (F*ramp)\n binned_data = custom_bin(corrected_data, binsize)\n binned_opt = custom_bin(transit, binsize)\n binned_times = custom_bin(t, binsize)\n\n # Calculate the residuals\n residuals = lc - optflux\n rms = np.sqrt(np.sum(residuals**2)/len(residuals))\n chi2 = chi(popt, t, lc, lcerr, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, x=x, y=y, method = 'poly', eclipse = eclipse)/(len(lc)-len(popt))\n bic = BIC(popt, t, lc, lcerr, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, x=x, y=y, method = 'poly', eclipse = eclipse)\n\n binned_residuals = []\n for i in range(len(binned_data)):\n binned_residuals.append(binned_data[i] - binned_opt[i])\n\n # Make the plot, 3 plots in one.\n fig = plt.figure(figsize=(15, 6))\n #plt.title(\"{0}: rms={1}, red_chi2={2}, BIC={3}\".format(name, rms, chi2, bic))\n frame1=fig.add_axes((.1,.6,.8,.4))\n frame1.axes.get_xaxis().set_visible(False)\n frame2=fig.add_axes((.1,.2,.8,.4))\n frame2.axes.get_xaxis().set_visible(False)\n frame3=fig.add_axes((.1,.0,.8,.2))\n\n frame1.plot(t, lc*scale, 'ko', markersize=2, label='Raw data')\n frame1.plot(t, optflux*scale, color, label='Best fit poly model', linewidth =2)\n frame1.set_title(\"{0} - {1} - {2} lightcurve\".format(name, channel, orbit))\n frame1.set_ylabel(\"Raw [e-]\")\n frame1.legend(loc = 'best')\n\n\n frame2.plot(binned_times, binned_data, 'ko', markersize = 4, label='Binned data (x{})'.format(binsize))\n frame2.plot(binned_times, binned_opt, color = color, label='Best fit transit model')\n frame2.set_ylabel(\"Corrected & Normalised\")\n frame2.legend(loc = 'lower left')\n frame2.annotate('RMS={0:.3e}\\n'.format(rms) + r'$\\chi_{red}^2$' + '={0:.3e} \\nBIC={1:.3e}'.format(chi2, bic),\n xy=(0.85, 0.2), xycoords='axes fraction',bbox={'facecolor':color, 'alpha':0.5, 'pad':10})\n\n\n frame3.plot(binned_times,binned_residuals, 'ko', markersize = 4)\n frame3.axhline(0, color = color)\n frame3.set_ylabel(\"Residuals\")\n\n plottingdict['Times'] = t\n plottingdict['Raw Data'] = lc*scale\n plottingdict['Full Poly Model'] = optflux*scale\n\n plottingdict['Corrected Data'] = lc / (F*ramp)\n plottingdict['Transit Model'] = transit\n plottingdict['Temporal Ramp'] = ramp\n plottingdict['Poly'] = F\n\n\n if showCuts:\n for j in range(ncutstarts):\n if cutends:\n frame1.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n frame2.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n frame3.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n else:\n frame1.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n frame2.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n frame3.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n\n plt.xlabel(\"Time [bjd]\")\n\n if savefile:\n plt.savefig(\"{5}/PhD/SpitzerTransits/{0}{4}/{0}_{1}_{2}_Poly_lc_{3}.png\".format(name, orbit, channel, filext, foldext, os.getenv('HOME')),bbox_inches='tight')\n plt.close()\n else:\n plt.show()\n plt.close()\n\n elif method == 'PLD':\n\n DE, pixels, ramp = model_PLD(popt, t, Pns, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, components = True, eclipse = eclipse)\n\n optflux = DE + pixels + ramp\n\n # Correct the lightcurve and bin the data and the optimum values\n corrected_data = lc - pixels - ramp\n binned_data = custom_bin(corrected_data, binsize)\n binned_opt = custom_bin(DE, binsize)\n binned_times = custom_bin(t, binsize)\n\n # Calculate the residuals\n residuals = lc - optflux\n rms = np.sqrt(np.sum(residuals**2)/len(residuals))\n chi2 = chi(popt, t, lc, lcerr, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, Pns=Pns, method = 'PLD', eclipse = eclipse )/(len(lc)-len(popt))\n bic = BIC(popt, t, lc, lcerr, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, Pns=Pns, method = 'PLD', eclipse = eclipse)\n\n binned_residuals = []\n for i in range(len(binned_data)):\n binned_residuals.append(binned_data[i] - binned_opt[i])\n\n # Make the plot, 3 plots in one.\n fig = plt.figure(figsize=(15, 6))\n frame1=fig.add_axes((.1,.6,.8,.4))\n frame1.axes.get_xaxis().set_visible(False)\n frame2=fig.add_axes((.1,.2,.8,.4))\n frame2.axes.get_xaxis().set_visible(False)\n frame3=fig.add_axes((.1,.0,.8,.2))\n\n frame1.plot(t, lc*scale, 'ko', markersize=2, label='Raw data')\n frame1.plot(t, optflux*scale, color, label='Best fit PLD model', linewidth =2)\n frame1.set_title(\"{0} - {1} - {2} lightcurve\".format(name, channel, orbit))\n frame1.set_ylabel(\"Raw [e-]\")\n frame1.legend(loc = 'best')\n\n frame2.plot(binned_times, binned_data, 'ko', markersize = 4, label='Binned data (x{})'.format(binsize))\n frame2.plot(binned_times, binned_opt, color = color, label='Best fit transit model')\n frame2.set_ylabel(\"Corrected & Normalised\")\n frame2.legend(loc = 'lower left')\n frame2.annotate('RMS={0:.3e}\\n'.format(rms) + r'$\\chi_{red}^2$' + '={0:.3e} \\nBIC={1:.3e}'.format(chi2, bic),\n xy=(0.85, 0.2), xycoords='axes fraction',bbox={'facecolor':color, 'alpha':0.5, 'pad':10})\n\n frame3.plot(binned_times,binned_residuals, 'ko', markersize = 4)\n frame3.axhline(0, color = color)\n frame3.set_ylabel(\"Residuals\")\n\n plt.xlabel(\"Time [bjd]\")\n\n plottingdict['Times'] = t\n plottingdict['Raw Data'] = lc*scale\n plottingdict['Full PLD Model'] = optflux*scale\n\n plottingdict['Corrected Data'] = lc - pixels - ramp\n plottingdict['Transit Model'] = DE\n plottingdict['Temporal Ramp'] = ramp\n plottingdict['PLD'] = pixels\n\n if showCuts:\n for j in range(ncutstarts):\n if cutends:\n frame1.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n frame2.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n frame3.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n else:\n frame1.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n frame2.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n frame3.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\n\n if savefile:\n plt.savefig(\"{5}/PhD/SpitzerTransits/{0}{4}/{0}_{1}_{2}_PLD_lc_{3}.png\".format(name, orbit, channel,filext,foldext, os.getenv('HOME')),bbox_inches='tight')\n plt.close()\n else:\n plt.show()\n plt.close()\n\n else:\n warnings.warn( \"What model do you want to plot?!\" )\n\n if extraoutputs:\n return plottingdict\n else:\n pass","def plot_results(delaytype):\n \n if delaytype == 'Polynomial':\n # results for ind_obs=graph.indlinks.keys()\n tolls_collected1 = [[164.76921455176952, 690.919364787199, 1003.9715271443032], [164.76920846294715, 690.9196375266908, 1003.971503332828]]\n so_costs1 = [[1952.813985915695, 2991.422528084584, 4716.77478205995], [1952.813764068618, 2991.4225756924197, 4716.774942888717]]\n costs1 = [[1961.4088544768533, 3035.621746689017, 4751.282035585294], [1961.4088092322784, 3035.619283467948, 4751.282202058287]]\n ue_costs1 = [[1993.9477013089333, 3087.4591784440836, 4889.555908205202], [1993.9532544651581, 3087.4592286945117, 4889.562102247506]]\n \n # results for ind_obs=[(36,37,1), (13,14,1), (17,8,1), (24,17,1), (28,22,1), (14,13,1), (17,24,1), (24,40,1), (14,21,1), (16,23,1)]\n tolls_collected2 = [[165.05685117271955, 354.1634370314711, 1015.9141858070569], [165.0567022829941, 354.1917223859498, 1015.9119098321756]]\n so_costs2 = [[1952.4102665477528, 2990.2968764530697, 4714.092736177128], [1952.813764068618, 2991.4225756924197, 4716.774942888717]]\n costs2 = [[1961.0182151023969, 3019.353015234613, 4750.051821659878], [1961.4090940730164, 3020.4668352547183, 4752.675436210774]]\n ue_costs2 = [[1993.5324364880598, 3086.401670312004, 4886.656461616093], [1993.9532544651581, 3087.4592286945117, 4889.562102247506]]\n \n # results for ind_obs=[(17,24,1), (24,40,1), (14,21,1), (16,23,1)]\n tolls_collected3 = [[122.37203827953705, 369.5962596851687, 993.1260548913893], [124.00144138458633, 375.65796385498777, 1018.6147484597942]]\n so_costs3 = [[1819.720988976306, 2777.3939885366685, 4677.595258102356], [1952.813764068618, 2991.4225756924197, 4716.774942888717]]\n costs3 = [[1834.6351015726038, 2802.3016403566235, 4714.102145227426], [1965.8049083549738, 3020.126090534662, 4750.071391283308]]\n ue_costs3 = [[1872.3492340077084, 2897.9834368843217, 4886.769726452165], [1993.9532544651581, 3087.4592286945117, 4889.562102247506]]\n \n # results for ind_obs=[(10,9,1), (19,18,1), (4,5,1), (29,21,1)]\n tolls_collected4 = [[602.0313412901077, 596.7300601881633, 833.3678073460065], [609.094482012133, 542.8579545671112, 843.1817520139325]]\n so_costs4 = [[13252.728980891832, 20178.927282098706, 28549.614451166774], [1952.813764068618, 2991.4225756924197, 4716.774942888717]]\n costs4 = [[13254.342842900576, 20239.30287102026, 28549.614731411177], [1992.3969007442322, 3130.0522527298062, 4805.6728306302175]]\n ue_costs4 = [[13259.433117552415, 20185.90937054816, 28556.779443740656], [1993.9532544651581, 3087.4592286945117, 4889.562102247506]]\n \n \n if delaytype == 'Hyperbolic':\n # results for ind_obs=graph.indlinks.keys() \n tolls_collected1 = [[161.15742640370462, 416.80264009965265, 948.124688583883], [161.4013529462645, 417.25218843773354, 950.1674351402539]]\n so_costs1 = [[2519.5329969919685, 3679.4257321044365, 5325.940489651684], [2544.920874812245, 3718.292867749051, 5383.190501656683]]\n costs1 = [[2525.7590340322095, 3679.4261220632625, 5356.8071818038225], [2551.0113714206195, 3718.3028736461647, 5412.310857035818]]\n ue_costs1 = [[2548.8303153671195, 3736.67165067958, 5444.499035875823], [2574.155371287541, 3775.672784740404, 5503.564654849874]]\n \n # results for ind_obs=[(36,37,1), (13,14,1), (17,8,1), (24,17,1), (28,22,1), (14,13,1), (17,24,1), (24,40,1), (14,21,1), (16,23,1)]\n tolls_collected2 = [[355.56391097372205, 701.3544774585599, 806.6640717434449], [374.34816990991186, 736.3763770517197, 842.0768966826593]]\n so_costs2 = [[2174.1387842065237, 3306.974167934956, 4969.5560792612605], [2544.920874812245, 3718.292867749051, 5383.190501656683]]\n costs2 = [[2201.273656230071, 3338.7536674213156, 4988.732654672769], [2564.0153714222406, 3746.115605602468, 5398.046518880896]]\n ue_costs2 = [[2209.622435372754, 3380.7186533697286, 5104.755602389524], [2574.155371287541, 3775.672784740404, 5503.564654849874]]\n \n # results for ind_obs=[(17,24,1), (24,40,1), (14,21,1), (16,23,1)]\n tolls_collected3 = [[339.37838703976513, 737.9887571945254, 930.0531221139565], [353.28118085588176, 763.3708450755438, 955.644104623869]]\n so_costs3 = [[2520.1983680076073, 3833.234908857907, 5645.320052639795], [2544.920874812245, 3718.292867749051, 5383.190501656683]]\n costs3 = [[2536.138622203524, 3862.614483834193, 5668.430527845267], [2562.897499637454, 3750.9322754235955, 5406.402328441233]]\n ue_costs3 = [[2556.0266644367803, 3903.9758481975805, 5741.545038028959], [2574.155371287541, 3775.672784740404, 5503.564654849874]]\n \n # results for ind_obs=[(10,9,1), (19,18,1), (4,5,1), (29,21,1)]\n tolls_collected4 = [[443.85669649193784, 837.5428841560781, 1380.117295219297], [479.57423528502, 931.2309202734045, 1499.6972922042573]]\n so_costs4 = [[2158.995768390798, 3843.4202195503713, 7595.092411648515], [2544.920874812245, 3718.292867749051, 5383.190501656683]]\n costs4 = [[2192.900868595389, 3888.0172745668465, 7626.17585256234], [2575.5748442305207, 3755.844017718783, 5450.304046277645]]\n ue_costs4 = [[2231.1674179937672, 3999.772800567613, 7867.415688946321], [2574.155371287541, 3775.672784740404, 5503.564654849874]]\n \n # effects of toll pricing\n opacity = 0.4\n width = 0.2\n index = np.arange(4)\n plt.xticks(index, ('all', '10', '4 good', '4 bad') )\n colors = ['y', 'r', 'b']\n demands = ['94', '118', '142']\n for i in [0,1,2]:\n so = so_costs1[1][i]\n ue = ue_costs1[1][i]\n delta = ue-so\n plt.bar(index-width/2+(i-1)*width,\n [100.0*(costs1[1][i]-so)/delta, \n 100.0*(costs2[1][i]-so)/delta, \n 100.0*(costs3[1][i]-so)/delta, \n 100.0*(costs4[1][i]-so)/delta],\n width, alpha=opacity, color=colors[i], label='demand='+demands[i])\n plt.xlabel('Sets of observed links')\n plt.ylabel('Relative loss (%)')\n plt.title('Effects of toll pricing with '+delaytype+' delay function')\n plt.legend(loc=0)\n plt.show()\n \n # error in predicted UE\n for i in [0,1,2]:\n ue = ue_costs1[1][i]\n plt.bar(index-width/2+(i-1)*width,\n [100.0*abs(ue_costs1[0][i]-ue)/ue,\n 100.0*abs(ue_costs2[0][i]-ue)/ue,\n 100.0*abs(ue_costs3[0][i]-ue)/ue,\n 100.0*abs(ue_costs4[0][i]-ue)/ue],\n width, alpha=opacity, color=colors[i], label='demand='+demands[i])\n plt.xlabel('Sets of observed links')\n plt.ylabel('Relative error (%)')\n plt.title('Error in predicted UE with '+delaytype+' delay function')\n plt.legend(loc=0)\n plt.show()\n \n # error in predicted SO\n for i in [0,1,2]:\n so = so_costs1[1][i]\n plt.bar(index-width/2+(i-1)*width,\n [100.0*abs(so_costs1[0][i]-so)/so,\n 100.0*abs(so_costs2[0][i]-so)/so,\n 100.0*abs(so_costs3[0][i]-so)/so,\n 100.0*abs(so_costs4[0][i]-so)/so],\n width, alpha=opacity, color=colors[i], label='demand='+demands[i])\n plt.xlabel('Sets of observed links')\n plt.ylabel('Relative error (%)')\n plt.title('Error in predicted SO with '+delaytype+' delay function')\n plt.legend(loc=0)\n plt.show()\n \n # error in predicted tolled ue\n for i in [0,1,2]:\n plt.bar(index-width/2+(i-1)*width,\n [100.0*abs(costs1[0][i]-costs1[1][i])/costs1[1][i],\n 100.0*abs(costs2[0][i]-costs2[1][i])/costs2[1][i],\n 100.0*abs(costs3[0][i]-costs3[1][i])/costs3[1][i],\n 100.0*abs(costs4[0][i]-costs4[1][i])/costs4[1][i]],\n width, alpha=opacity, color=colors[i], label='demand='+demands[i])\n plt.xlabel('Sets of observed links')\n plt.ylabel('Relative error (%)')\n plt.title('Error in predicted tooled UE with '+delaytype+' delay function')\n plt.legend(loc=0)\n plt.show()","def L_curveTV(f,lam_init = 2.0, q = 0.9):\n lam = lam_init\n max_iter = 50\n residual_list = np.zeros(max_iter)\n size_list = np.zeros(max_iter)\n error = np.zeros(max_iter)\n alt_error = np.zeros(max_iter)\n \n for i in range(max_iter): #range(max_iter):\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc=True, tol = 1.0e-5)\n #u, _, j = projected_gradient_alt(f,lam, tau = 0.2, tol = 1.0e-4)\n lam = lam_init * (q ** i)\n residual_list[i] = np.linalg.norm(u - f)\n size_list[i] = np.linalg.norm(u)\n error[i] = np.linalg.norm(u - f) * np.linalg.norm(u)\n #plt.loglog(residual_list,size_list)\n #plt.show()\n opt_idx = np.argmin(error)\n t = 1.0 / (1.0 + lam_init * (q ** opt_idx))\n lam = lam_init * (q ** opt_idx)\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc = True, tol = 1.0e-5)\n return u, t","def test_lpp():\n \n tce_filename = \"tce0001234567_dvt.fits\"\n \n #Populate the tce class by reading in a file\n #At this point a phases array, \n #and (int) transit_number would get populated.\n \n tce = vet.create_tce(file = tce_filename, ext = 1)\n \n #This populates the LPP metric fields in this class\n #It also populates where the plotting data is stored\n #This function relies on inputs from a precomputed model stored\n #in something called mapInfo. In theory if someone trains up another \n #model we could input it here.\n tce.run_lpp(mapInfo = None)\n \n norm_TLpp = tce.lpp.norm\n raw_TLpp = tce.lpp.raw\n binned_lpp_transit = tce.lpp.binned_transit\n \n plot_out = tce.lpp.plot(filename=\"lpp_plot.png\")\n \n assert norm_TLpp > 0\n assert norm_TLpp < 10\n assert len(binned_lpp_transit) > 50","def get_l_curve_results(directory, overwrite = False):\n costs = []\n tikhs = []\n regs = []\n\n glacier = \"helheim\"\n extension = \".tar.gz\"\n\n # If the output hasn't been cached, retrieve it from the giant archives\n cached_output = os.path.join(directory, \"helheim_l_curve.txt\")\n if not os.path.exists(cached_output) or overwrite:\n for archive_name in [f for f in os.listdir(directory)\n if (os.path.isfile(os.path.join(directory, f))\n and\n f[-len(extension):] == extension)]:\n start_index = len(glacier) + len(\"_lambda-\")\n regularization = float(archive_name[start_index: -len(extension)])\n cost, tikh = l_curve_point(os.path.join(directory, archive_name),\n glacier, regularization, 4)\n costs.append(cost)\n tikhs.append(tikh)\n regs.append(regularization)\n\n # Save these results to a text file\n with open(cached_output, 'w') as fid:\n fid.write(\"cost,model_norm,lambda\\n\")\n for k in range(len(costs)):\n fid.write(\"{0},{1},{2}\\n\".format(costs[k], tikhs[k], regs[k]))\n\n # Open the cached output\n with open(cached_output, 'r') as fid:\n line = fid.readline()\n line = fid.readline().split('\\n')[0]\n while(line):\n c, t, r = line.split(',')\n costs.append(float(c))\n tikhs.append(float(t))\n regs.append(float(r))\n line = fid.readline().split('\\n')[0]\n\n return costs, tikhs, regs","def main():\n \n data_base = '/local/duman/SIMULATIONS/many_polymers_5/'\n save_base = '/usr/users/iff_th2/duman/RolfData/many_polymers_5'\n \n \n ## Index the data\n# density = [0.08, 0.2, 0.4]\n# xi_L = [0.05, 0.1, 0.2, 1.0, 2.5, 5.0, 8.0, 16.0]\n# Pe = [3.0, 7.0, 15.0, 50.0, 150.0, 500.0, 750.0, 1500.0, 4375.0, 8000.0, 10000.0]\n# kappa = [1.25, 2.5, 5.0, 25.0, 62.5, 125.0, 200.0, 400.0]\n# fp = [0.0, 0.0048, 0.0112, 0.024, 0.08, 0.24, 0.8, 1.2, 2.4, 7.0]\n \n density = [0.2]\n kappa = [1.25, 2.5, 5.0, 25.0, 62.5, 125.0, 200.0, 400.0]\n xi_L = [0.05, 0.1, 0.2, 1.0, 2.5, 5.0, 8.0, 16.0]\n fp = [0.0048, 0.0112, 0.024, 0.08, 0.24, 0.8, 1.2, 2.4, 7.0]\n Pe = [3.0, 7.0, 15.0, 50.0, 150.0, 500.0, 750.0, 1500.0, 4375.0]\n \n ## Create points\n points = []\n for i, x in enumerate(xi_L):\n for j, p in enumerate(Pe):\n points.append( Phase(x, p, kappa[i], fp[j], 'short') ) \n \n for point in points:\n point.analyse_type()\n point.set_plot_props()\n \n long_xil = [0.05, 0.2, 1.0, 2.5, 16.0]\n long_pe = [3.0, 150.0, 750.0, 8000.0, 10000.0]\n long_kappa = [5.0, 20.0, 100.0, 250.0, 1600.0]\n long_fp = [0.0003, 0.015, 0.075, 0.0, 0.0]\n long_points = []\n for i, x in enumerate(long_xil):\n for j, p in enumerate(long_pe):\n long_points.append( Phase(x, p, long_kappa[i], long_fp[j], 'long') ) \n \n for point in long_points:\n point.determine_type()\n point.set_plot_props()\n \n plot_data(points, long_points, save_base, xi_L, Pe)","def main():\n df = pd.read_csv('data/ch5_q8_simulation.csv')\n\n # Part b\n plt.figure()\n plt.scatter(df['x'], df['y'])\n plt.xlabel('x')\n plt.ylabel('y')\n plt.title('Scatterplot y vs. x')\n plt.savefig('plots/8a.png')\n\n # Part c\n response_var = 'y'\n pred_vars_lin = ['x']\n pred_vars_quad = ['x', 'x2']\n pred_vars_cub = ['x', 'x2', 'x3']\n pred_vars_quar = ['x', 'x2', 'x3', 'x4']\n\n poly_terms = pd.DataFrame({'x2': np.power(df['x'], 2),\n 'x3': np.power(df['x'], 3),\n 'x4': np.power(df['x'], 4)})\n df = pd.concat([df, poly_terms], axis=1)\n\n CV_error_lin = loocv(df, response_var, pred_vars_lin)\n CV_error_quad = loocv(df, response_var, pred_vars_quad)\n CV_error_cub = loocv(df, response_var, pred_vars_cub)\n CV_error_quar = loocv(df, response_var, pred_vars_quar)\n\n print('Part c')\n print('CV error (linear) = {:.3f}'.format(CV_error_lin))\n print('CV error (quadratic) = {:.3f}'.format(CV_error_quad))\n print('CV error (cubic) = {:.3f}'.format(CV_error_cub))\n print('CV error (quartic) = {:.3f}'.format(CV_error_quar))\n\n # Part d\n np.random.seed(801)\n y = np.random.randn(100)\n x = np.random.randn(100)\n y = x - 2 * np.power(x, 2) + np.random.randn(100)\n\n df = pd.DataFrame({'x': x,\n 'x2': np.power(x, 2),\n 'x3': np.power(x, 3),\n 'x4': np.power(x, 4),\n 'y': y})\n\n CV_error_lin = loocv(df, response_var, pred_vars_lin)\n CV_error_quad = loocv(df, response_var, pred_vars_quad)\n CV_error_cub = loocv(df, response_var, pred_vars_cub)\n CV_error_quar = loocv(df, response_var, pred_vars_quar)\n\n print('Part d')\n print('CV error (linear) = {:.3f}'.format(CV_error_lin))\n print('CV error (quadratic) = {:.3f}'.format(CV_error_quad))\n print('CV error (cubic) = {:.3f}'.format(CV_error_cub))\n print('CV error (quartic) = {:.3f}'.format(CV_error_quar))\n\n # Part f\n model = sm.OLS(df.loc[:, response_var], df.loc[:, pred_vars_quar]).fit()\n print(model.summary())","def llcm_plot():\n x = np.arange(0, 10, 0.5) # x in [0, 10)\n y_1 = 2**x\n y_2 = x**2\n\n plt.figure()\n # specify color, linestyle, and marker w/ keyword argument\n # plt.plot(x, y_1, label='$2^x$', color='g', linestyle='--', marker='o')\n plt.plot(x, y_1, 'g--o', label='$2^x$')\n # plt.plot(x, y_2, label='$x^2$', color='r', linestyle='-', marker='*')\n plt.plot(x, y_2, 'r-*', label='$x^2$')\n plt.xlabel('$x$')\n plt.ylabel('$y$')\n plt.legend(loc='upper left')\n plt.title('Line plots w/ Legend, linestyle, color, and marker')\n plt.show()\n\n\n return None","def main(argv):\n import scipy as sc\n import scipy.integrate as integrate\n import matplotlib.pylab as p\n\n def dCR_dt(pops, t=0):\n \"\"\"returns the growth rate of consumer and resource population at any given time step\"\"\"\n\n R = pops[0]\n C = pops[1]\n K = 10000000\n dRdt = r * R * (1-(R/K)) - a * R * C\n dCdt = -z * C + e * a * R * C\n\n return sc.array([dRdt, dCdt])\n\n #integrate from time point 0 to 15, using 1000 sub-divisions of time\n t = sc.linspace(0, 15, 1000)\n\n def plot(pops):\n f1 = p.figure()\n p.plot(t, pops[:,0], 'g-', label='Resource density') # Plot\n p.plot(t, pops[:,1] , 'b-', label='Consumer density')\n p.grid()\n p.legend(loc='best')\n p.xlabel('Time')\n p.ylabel('Population density')\n p.title('Consumer-Resource population dynamics')\n #p.show()# To display the figure\n f1.savefig('../Results/LV2_model.pdf')\n\n f2 = p.figure()\n p.plot(pops[:,0], pops[:,1], 'r-', label='Consumer density') # Plot\n p.grid()\n p.xlabel('Resource Density')\n p.ylabel('Consumer density')\n p.title('Consumer-Resource population dynamics')\n #p.show()# To display the figure\n f2.savefig('../Results/LV2_second_model.pdf')\n\n return f1,f2\n\n if len(argv) < 5:\n r = 1\n a = 0.1\n z = 1.5\n e = 0.75\n else:\n r,a,z,e = float(sys.argv[1]), float(sys.argv[2]), float(sys.argv[3]), float(sys.argv[4])\n #integrate from time point 0 to 15, using 1000 sub-divisions of time\n t = sc.linspace(0, 15, 1000)\n #Set the initial conditions for the two populations\n R0 = 10\n C0 = 5\n RC0 = sc.array([R0, C0])\n #numerically integrate this system forward from those starting conditions\n pops, infodict = integrate.odeint(dCR_dt, RC0, t, full_output=True)\n print(\"final Consumer and Resource population values are:\", pops[-1]) #prints final values\n plot(pops)\n return 0","def main():\n\tplt.clf()\n\taxes = setup_axes()\n\tplot_output(axes, \"../../simulations/default\", \"black\")\n\tplot_output(axes, \"../../simulations/yccsr_zero\", \"crimson\")\n\tplot_output(axes, \"../../simulations/yccsr_linear\", \"lime\")\n\tplot_output(axes, \"../../simulations/yccsr_1-exp\", \"deepskyblue\")\n\tvisuals.plot_track_points_intervals(axes[0],\n\t\tvice.history(\"../../simulations/default\"), element = \"Sr\",\n\t\treference = \"Fe\")\n\tplot_legend(axes[1])\n\tplt.tight_layout()\n\tvisuals.yticklabel_formatter(axes[1])\n\tplt.savefig(sys.argv[1])\n\tplt.clf()","def main():\n ### read parameters from input file\n charfile, headerfile, ofname, nfil, nskip, nbin, rmax, ntotal = read_settings()\n ### loop over the entire trajectory and compute the center of mass, correct for pbc\n r, pol_corr = compute_polarity_correlation(charfile, headerfile, nfil, nskip, nbin, rmax, ntotal)\n\n ### write results to file and generage a plot\n # generate folder structure\n os.system('mkdir ' + ofname)\n # write data of the averaged polarity correlation\n ofile = open(ofname + '/pol_correlation.data', 'w')\n ofile.write('polarity correlation function\\n\\n')\n ofile.write('r_min\\tr_max\\tpol_corrlation\\n')\n for i in range(nbin):\n ofile.write(str(r[i]) + '\\t' + str(r[i+1]) + '\\t' + str(pol_corr[i]) + '\\n')\n ofile.close()\n # gen figure of the averaged polarity correlation\n fig = plt.figure()\n ax = plt.subplot(111)\n ax.plot(0.5*(r[:-1]+r[1:]), pol_corr)\n ax.set_xlabel(r'r [$\\sigma]')\n ax.set_ylabel(r'g_p(r)')\n ax.set_title('Polarity Correlation Function')\n plt.savefig(ofname + '/pol_correlation.png')\n plt.close()\n return","def display4(*args):\n #-------------------- unpack\n twiss_func = args[0]\n cos_like = args[1]\n sin_like = args[2]\n lat_plot = args[3]\n #-------------------- beta x,y & dispersion x\n s = [twiss_func(i,'s') for i in range(twiss_func.nbpoints)] # Abszisse\n bx = [twiss_func(i,'bx') for i in range(twiss_func.nbpoints)] # beta x\n by = [twiss_func(i,'by') for i in range(twiss_func.nbpoints)] # beta y\n dx = [twiss_func(i,'dx') for i in range(twiss_func.nbpoints)] # dispersion x\n#-------------------- longitudinal trajectories\n z1= [cos_like(i,'s') for i in range(cos_like.nbpoints)]\n cz= [cos_like(i,'cz') for i in range(cos_like.nbpoints)]\n cdp= [cos_like(i,'cdp') for i in range(cos_like.nbpoints)]\n\n z2= [sin_like(i,'s') for i in range(sin_like.nbpoints)]\n sz= [sin_like(i,'sz') for i in range(sin_like.nbpoints)]\n sdp= [sin_like(i,'sdp') for i in range(sin_like.nbpoints)]\n #-------------------- lattice viseo\n vzero = [0. for i in range(lat_plot.nbpoints)] # zero line\n vis_abszisse = [lat_plot(i,'s') for i in range(lat_plot.nbpoints)]\n vis_ordinate = [lat_plot(i,'viseo') for i in range(lat_plot.nbpoints)]\n #-------------------- figure frame\n width=14; height=7.6\n # fighdr = 'lattice version = {}, input file = {}'.format(PARAMS['lattice_version'],PARAMS['input_file'])\n fig = plt.figure(num=1,figsize=(width,height),facecolor='#eaecef',tight_layout=False)\n\n #-------------------- beta functions\n splot211=plt.subplot(211)\n splot211.set_title('beta x,y')\n # mapping box\n splot211.text(0.01, 1.1, UTIL.FLAGS.get('mapping'),transform=splot211.transAxes,fontsize=8,bbox=dict(boxstyle='round',facecolor='wheat',alpha=0.5),verticalalignment='top')\n # function plots\n plt.plot(s,bx, label=r\"$\\beta$x [m]\", color='black', linestyle='-')\n plt.plot(s,by, label=r\"$\\beta$y [m]\", color='red', linestyle='-')\n plt.plot(s,dx, label=r'$\\eta_x$ [m]' , color='green', linestyle='-') # dispersion x\n vscale=splot211.axis()[3]*0.25\n viseoz = [x*vscale for x in vis_ordinate]\n plt.plot(vis_abszisse,viseoz,label='',color='black')\n plt.plot(vis_abszisse,vzero,color='green',linestyle='--')\n # zero line\n splot211.plot(vis_abszisse,vzero,color='green',linestyle='--')\n plt.legend(loc='lower right',fontsize='x-small')\n\n #-------------------- longitudinal tracks z, dP/P\n # ax_l = left abszisse\n ax_l=plt.subplot(212)\n # ax_l=plt.subplot(10,1,(7,9))\n ax_l.set_title('synchrotron oscillation')\n ax_l.set_ylabel(r\"z [mm]\")\n ax_l.tick_params(axis='y', colors='green')\n ax_l.yaxis.label.set_color('green')\n ax_l.plot(z1,cz,label='C',color='green')\n ax_l.plot(z2,sz,label='S',color='green',linestyle=':')\n plt.legend(loc='lower left',fontsize='x-small')\n # ax_r = right abszisse\n ax_r = ax_l.twinx()\n ax_r.set_ylabel(r'$\\Delta$p/p [%]')\n ax_r.tick_params(axis='y', colors='red')\n ax_r.yaxis.label.set_color('red')\n ax_r.plot(z2,cdp,label='C',color='red')\n ax_r.plot(z2,sdp,label='S',color='red',linestyle=':')\n ax_r.plot(vis_abszisse,vzero,color='red', linestyle='--')\n plt.legend(loc='lower right',fontsize='x-small')\n # lattice elements\n vscale=ax_l.axis()[3]*0.25\n viseoz = [x*vscale for x in vis_ordinate]\n ax_l.plot(vis_abszisse,viseoz,label='',color='black')\n ax_l.plot(vis_abszisse,vzero,color='green',linestyle='--')","def test_plot_lin(self):\n pname = os.path.join(self.datadir, 'monol_testA_E3-50_pds_fit') + \\\n HEN_FILE_EXTENSION\n cname = os.path.join(self.datadir, 'monol_test_E3-50_cpds_fit') + \\\n HEN_FILE_EXTENSION\n lname = os.path.join(self.datadir, 'monol_testA_E3-50_lc') + \\\n HEN_FILE_EXTENSION\n hen.plot.main([pname, cname, lname, '--noplot', '--xlin', '--ylin',\n '-o', 'dummy.qdp'])\n hen.plot.main([lname, '--noplot',\n '--axes', 'time', 'counts', '--xlin', '--ylin',\n '-o', 'dummy.qdp'])","def EPI():\n TE = np.array([4.22, 33.81, 63.39, 92.98, 122.6, 152.2, 181.7, 211.3, 240.9, 270.5])\n upper_left = np.array([697.3, 367.0, 217.5, 115.8, 51.8, 23.2, 14.8, 8.7, 6.1, 4.6])\n center = np.array([1110.2, 907.8, 813.6, 745.2, 692.8, 637.0, 564.9, 521.0, 450.2, 401.6])\n lower_right = np.array([723.0, 419.2, 224.1, 126.4, 61.8, 32.4, 15.1, 8.8, 3.9, 3.8])\n upper_center = np.array([782.2, 499.4, 279.5, 154.5, 88.6, 58.2, 43.8, 38.2, 38.2, 36.0])\n\n area = [upper_left, center, upper_center, lower_right]\n colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\"]\n name = [\"Upper left area\", \"Center area\", \"Up center area\", \"Lower right area\"]\n x_new = np.linspace(4.22, 270.5, 10000)\n for i, j, k in zip(area, colors, name):\n popt, _ = curve_fit(M_xy, TE, i, p0=np.array([200, 300]))\n M0, T2 = popt[0], popt[1]\n y_new = M_xy(x_new, M0, T2)\n plt.scatter(TE, i)\n plt.plot(x_new, y_new, \"--\", c=j, label=\"Fit: %s\" % k + f\", $T_2$={T2:.2f}\")\n plt.legend(loc=\"best\")\n plt.grid()\n plt.ylabel(\"Mean Signal Intensity\")\n plt.xlabel(\"TE [ms]\")\n plt.show()","def plot(self, noTLS, path_plots, interactive):\n fig = plt.figure(figsize=(10,12))\n ax1 = fig.add_subplot(4, 1, 1)\n ax2 = fig.add_subplot(4, 1, 2)\n ax3 = fig.add_subplot(4, 2, 5)\n ax4 = fig.add_subplot(4, 2, 6)\n ax5 = fig.add_subplot(4, 2, 7)\n ax6 = fig.add_subplot(4, 2, 8)\n\n # First panel: data from each sector\n colors = self._get_colors(self.nlc)\n for i, lci in enumerate(self.alllc):\n p = lci.normalize().remove_outliers(sigma_lower=5.0, sigma_upper=5.0)\n p.bin(5).scatter(ax=ax1, label='Sector %d' % self.sectors[i], color=colors[i])\n self.trend.plot(ax=ax1, color='orange', lw=2, label='Trend')\n ax1.legend(fontsize='small', ncol=4)\n\n # Second panel: Detrended light curve\n self.lc.remove_outliers(sigma_lower=5.0, sigma_upper=5.0).bin(5).scatter(ax=ax2,\n color='black',\n label='Detrended')\n\n # Third panel: BLS\n self.BLS.bls.plot(ax=ax3, label='_no_legend_', color='black')\n mean_SR = np.mean(self.BLS.power)\n std_SR = np.std(self.BLS.power)\n best_power = self.BLS.power[np.where(self.BLS.period.value == self.BLS.period_max)[0]]\n SDE = (best_power - mean_SR)/std_SR\n ax3.axvline(self.BLS.period_max, alpha=0.4, lw=4)\n for n in range(2, 10):\n if n*self.BLS.period_max <= max(self.BLS.period.value):\n ax3.axvline(n*self.BLS.period_max, alpha=0.4, lw=1, linestyle=\"dashed\")\n ax3.axvline(self.BLS.period_max / n, alpha=0.4, lw=1, linestyle=\"dashed\")\n sx, ex = ax3.get_xlim()\n sy, ey = ax3.get_ylim()\n ax3.text(ex-(ex-sx)/3, ey-(ey-sy)/3,\n 'P$_{MAX}$ = %.3f d\\nT0 = %.2f\\nDepth = %.4f\\nDuration = %.2f d\\nSDE = %.3f' %\n (self.BLS.period_max, self.BLS.t0_max,\n self.BLS.depth_max, self.BLS.duration_max, SDE))\n\n\n # Fourth panel: lightcurve folded to the best period from the BLS\n self.folded.bin(1*self.nlc).scatter(ax=ax4, label='_no_legend_', color='black',\n marker='.', alpha=0.5)\n l = max(min(4*self.BLS.duration_max/self.BLS.period_max, 0.5), 0.02)\n nbins = int(50*0.5/l)\n r1, dt1 = binningx0dt(self.folded.phase, self.folded.flux, x0=-0.5, nbins=nbins)\n ax4.plot(r1[::,0], r1[::,1], marker='o', ls='None',\n color='orange', markersize=5, markeredgecolor='orangered', label='_no_legend_')\n\n lc_model = self.BLS.bls.get_transit_model(period=self.BLS.period_max,\n duration=self.BLS.duration_max,\n transit_time=self.BLS.t0_max)\n lc_model_folded = lc_model.fold(self.BLS.period_max, t0=self.BLS.t0_max)\n ax4.plot(lc_model_folded.phase, lc_model_folded.flux, color='green', lw=2)\n ax4.set_xlim(-l, l)\n h = max(lc_model.flux)\n l = min(lc_model.flux)\n ax4.set_ylim(l-4.*(h-l), h+5.*(h-l))\n del lc_model, lc_model_folded, r1, dt1\n\n\n if not noTLS:\n # Fifth panel: TLS periodogram\n ax5.axvline(self.tls.period, alpha=0.4, lw=3)\n ax5.set_xlim(np.min(self.tls.periods), np.max(self.tls.periods))\n for n in range(2, 10):\n ax5.axvline(n*self.tls.period, alpha=0.4, lw=1, linestyle=\"dashed\")\n ax5.axvline(self.tls.period / n, alpha=0.4, lw=1, linestyle=\"dashed\")\n ax5.set_ylabel(r'SDE')\n ax5.set_xlabel('Period (days)')\n ax5.plot(self.tls.periods, self.tls.power, color='black', lw=0.5)\n ax5.set_xlim(0, max(self.tls.periods))\n\n period_tls = self.tls.period\n T0_tls = self.tls.T0\n depth_tls = self.tls.depth\n duration_tls = self.tls.duration\n FAP_tls = self.tls.FAP\n\n sx, ex = ax5.get_xlim()\n sy, ey = ax5.get_ylim()\n ax5.text(ex-(ex-sx)/3, ey-(ey-sy)/3,\n 'P$_{MAX}$ = %.3f d\\nT0 = %.1f\\nDepth = %.4f\\nDuration = %.2f d\\nFAP = %.4f' %\n (period_tls, T0_tls, 1.-depth_tls, duration_tls, FAP_tls))\n\n # Sixth panel: folded light curve to the best period from the TLS\n ax6.plot(self.tls.folded_phase, self.tls.folded_y, color='black', marker='.',\n alpha=0.5, ls='None', markersize=0.7)\n l = max(min(4*duration_tls/period_tls, 0.5), 0.02)\n nbins = int(50*0.5/l)\n r1, dt1 = binningx0dt(self.tls.folded_phase, self.tls.folded_y,\n x0=0.0, nbins=nbins, useBinCenter=True)\n ax6.plot(r1[::,0], r1[::,1], marker='o', ls='None', color='orange',\n markersize=5, markeredgecolor='orangered', label='_no_legend_')\n ax6.plot(self.tls.model_folded_phase, self.tls.model_folded_model, color='green', lw=2)\n ax6.set_xlim(0.5-l, 0.5+l)\n h = max(self.tls.model_folded_model)\n l = min(self.tls.model_folded_model)\n ax6.set_ylim(l-4.*(h-l), h+5.*(h-l))\n ax6.set_xlabel('Phase')\n ax6.set_ylabel('Relative flux')\n del r1, dt1\n\n fig.subplots_adjust(top=0.98, bottom=0.05, wspace=0.25, left=0.1, right=0.97)\n fig.savefig(os.path.join(path_plots, 'TIC%d.pdf' % self.TIC))\n if interactive:\n plt.show()\n plt.close('all')\n del fig","def plot_learning_curve(model, X_train, X_test, y_train, y_test):\n\n m, train_scores, valid_scores = learning_curve(estimator = model, \n X = X_train, y = y_train.ravel(), train_sizes = np.linspace(0.1,1.0, 80))\n\n train_cv_err = np.mean(train_scores, axis=1)\n test_cv_err = np.mean(valid_scores, axis=1)\n tr, = plt.plot(m, train_cv_err)\n ts, = plt.plot(m, test_cv_err)\n plt.legend((tr, ts), ('training error', 'test error'), loc = 'best')\n plt.title('Learning Curve')\n plt.xlabel('Data Points')\n plt.ylabel('Accuracy')","def plot_LvsL_multiple(line1='CIV1548',line2='CIII1908',line1range=[1e3,1e8],line2range=[1e0,1e8],\n outputdir='./',verbose=True):\n modeldata = nm.load_model('combined',verbose=verbose)\n\n if verbose: print(' - Putting together permutations of chosen setups for plotting')\n infodic = {}\n infodic['Zgas'] = [False,0.0001,0.006,0.040], True\n infodic['logUs'] = [False,-1.0,-2.5,-4.0] , False\n infodic['xid'] = [False,0.1,0.3,0.5] , False\n infodic['nh'] = [False,10,100,1000,10000] , False\n infodic['CO'] = [False,0.1,0.38,1.4] , False\n infodic['Mcut'] = [False,100,300] , False\n\n variables = [infodic['Zgas'][0],infodic['logUs'][0],infodic['xid'][0],\n infodic['nh'][0],infodic['CO'][0],infodic['Mcut'][0]]\n\n permutations = list(itertools.product(*variables))\n permutations_with2false = [sublist for sublist in permutations if sublist.count(False) == 2.]\n Nplots = len(permutations_with2false)\n\n if verbose: print(' - With the restriction Nfalse=2 the setup will results in '+str(Nplots)+\\\n ' plots (if model data allows)')\n if verbose: print(' - These will be saved to the output directory: '+outputdir)\n for pp, perm in enumerate(permutations_with2false):\n Zval = perm[0]\n Uval = perm[1]\n Xival = perm[2]\n Nhval = perm[3]\n COval = perm[4]\n Mval = perm[5]\n\n plotname = outputdir+'NEOGALmodelgrid_Zgas'+str(Zval).replace('.','p')+\\\n '_logU'+str(Uval).replace('.','p')+\\\n '_xid'+str(Xival).replace('.','p')+\\\n '_nH'+str(Nhval).replace('.','p')+\\\n '_CO'+str(COval).replace('.','p')+\\\n '_Mcut'+str(Mval).replace('.','p')+'.pdf'\n\n plotname = plotname.replace('False','Free')\n\n if verbose:\n plotno = pp+1\n infostr = ' - Generating plot '+str(\"%.4d\" % plotno)+'/'+str(\"%.4d\" % Nplots)+': '+plotname.split('/')[-1]+' '\n sys.stdout.write(\"%s\\r\" % infostr)\n sys.stdout.flush()\n\n if not Zval:\n logp1 = True\n else:\n logp1 = False\n\n nm.plot_LvsL(modeldata,line1=line1,line2=line2,logx=True,logy=True,logp1=logp1,logp2=False,verbose=False,\n Zgas=Zval,logU=Uval,xid=Xival,nh=Nhval,COratio=COval,Mcutoff=Mval,\n fixxrange=line1range,fixyrange=line2range,plotname=plotname)\n\n print('\\n ... done')","def convergence():\n fig, axes = plt.subplots(nrows=2, figsize=figsize(aspect=1.2))\n\n # label names\n label1 = str(league.lambda1)\n label2_list = [str(lambda2) for lambda2 in league.lambda2_list]\n\n # point spread and point total subplots\n subplots = [\n (False, [-0.5, 0.5], league.spreads, 'probability spread > 0.5'),\n (True, [200.5], league.totals, 'probability total > 200.5'),\n ]\n\n for ax, (commutes, lines, values, ylabel) in zip(axes, subplots):\n\n # train margin-dependent Elo model\n melo = Melo(lines=lines, commutes=commutes, k=1e-4)\n melo.fit(league.times, league.labels1, league.labels2, values)\n\n line = lines[-1]\n\n for label2 in label2_list:\n\n # evaluation times and labels\n times = np.arange(league.times.size)[::1000]\n labels1 = times.size * [label1]\n labels2 = times.size * [label2]\n\n # observed win probability\n prob = melo.probability(times, labels1, labels2, lines=line)\n ax.plot(times, prob)\n\n # true (analytic) win probability\n if ax.is_first_row():\n prob = skellam.sf(line, int(label1), int(label2))\n ax.axhline(prob, color='k')\n else:\n prob = poisson.sf(line, int(label1) + int(label2))\n ax.axhline(prob, color='k')\n\n # axes labels\n if ax.is_last_row():\n ax.set_xlabel('Iterations')\n ax.set_ylabel(ylabel)\n\n set_tight(w_pad=.5)","def main():\n parser = argparse.ArgumentParser(\n description=\"Generate bar plots comparing Matlab solvers to RLSO\"\n )\n parser.add_argument(\n \"--path\", type=str, default=None, help=\"the path to saved testing data for RLSO\"\n )\n parser.add_argument(\n \"--trials\",\n type=int,\n default=1000,\n help=\"Number of test problems to use defaults to 1000\",\n )\n parser.add_argument(\n \"--savestring\",\n type=str,\n default=None,\n help=\"Name to save our plot as. Defaults to random int .png\",\n )\n parser.add_argument(\n \"--titlestring\",\n type=str,\n default=None,\n help=\"String to append to our plot title. Defaults to None\",\n )\n parser.add_argument(\n \"--excludeinter\",\n type=bool,\n default=True,\n help=\"Exclude interior point method form testing. Reasons Convex only, and cannot set starting point. Default True\",\n )\n\n args = parser.parse_args()\n\n if args.path is None:\n print(\"Path to pickle data needed!\")\n return\n\n pickle_savepath = args.path\n with open(pickle_savepath, \"rb\") as pickle_file:\n pickle_tuple = pickle.load(pickle_file)\n\n complex_dic = pickle_tuple[4]\n lastbest = unravel_c_dic_lastbest(complex_dic)\n Abx0 = generate_Abx0(complex_dic, lastbest)\n\n if args.trials is None:\n trials = 1000\n else:\n trials = args.trials\n if args.savestring is None:\n save_string = str(np.random.randint(low=1e15, high=9e15)) + \".png\"\n else:\n save_string = args.savestring\n\n start = time.time()\n print(f\"start time {start}\")\n\n solutions = []\n for tuples in Abx0[:trials]:\n solutions.append(quad_solve(tuples, printout=False))\n end = time.time()\n print(f\"Matlab run time: {end - start}\")\n\n rlso = []\n active = []\n inter = []\n trust = []\n for [x, y, z, w] in solutions:\n rlso.append(x)\n active.append(y)\n inter.append(z)\n trust.append(w)\n title_string = args.titlestring\n plot_box(\n rlso,\n active,\n inter,\n trust,\n title_string,\n save_string,\n exclude_inter=args.excludeinter,\n )\n\n return","def main() -> None:\n xlim = L\n x = np.arange(xlim)\n xi = np.arange(0, xlim - 1 + STEP, STEP)\n kappas = sleplet.wavelet_methods.create_kappas(xlim, B, J_MIN)\n yi = scipy.interpolate.pchip(x, kappas[0])\n plt.semilogx(xi, yi(xi), label=r\"$\\Phi_{\\ell0}$\")\n for j, k in enumerate(kappas[1:]):\n yi = scipy.interpolate.pchip(x, k)\n plt.semilogx(xi, yi(xi), label=rf\"$\\Psi^{{{j+J_MIN}}}_{{\\ell0}}$\")\n plt.xlim(1, xlim)\n ticks = 2 ** np.arange(np.log2(xlim) + 1, dtype=int)\n plt.xticks(ticks, ticks)\n plt.xlabel(r\"$\\ell$\")\n plt.legend(loc=6)\n print(f\"Opening: axisymmetric_tiling_L{L}\")\n plt.tight_layout()\n plt.show(block=False)\n plt.pause(10)\n plt.close()","def lcfig( z=7 ):\n\n for modelname in ['z15G','z25G','z40G'] :\n\n # initialize a supernova model :\n snmodel = sncosmo.Model(source=modelname)\n\n # Fix the redshift for this instantiation of the model\n # (NOTE: this does not apply any cosmological dimming. It only\n # shifts the wavelengths)\n snmodel.set( z = z )\n\n # generate the H band light curve\n tobs = np.arange( 0, 1000, 10 )\n M160 = snmodel.bandmag( 'f160w', 'ab', tobs ) # Absolute Magnitude\n m160 = M160 + cosmo.distmod( z ).value # apparent magnitude\n\n pl.plot( tobs, m160 )\n ax = pl.gca()\n ax.invert_yaxis()\n ax.set_xlabel('Time (observer-frame days)')\n ax.set_ylabel('Apparent Magnitude in F160W')\n ax.set_xlim( 0, 1000 )\n ax.set_ylim( 36, 28 )\n\n pl.draw()","def plot_evaluation(parameters_dict, log_df, settings, evaluation_set_kde, plotname):\n\n\n plots = []\n\n\n ### setup the colors for each component\n if int(settings['nr_components']) < 3:\n colors = ['rgb(228,26,28)', 'rgb(55,126,184)']\n elif int(settings['nr_components']) < 13:\n colors = np.array(cl.scales[str(settings['nr_components'])]['qual']['Paired'])\n else:\n colors = cl.interp(cl.scales['10']['qual']['Paired'], 20)\n\n\n ### set up ab list\n ab_list = evaluation_set_kde['contact'].keys()\n\n\n\n\n ####################### plotting of settings\n print_to_table = {}\n for key in sorted(settings.keys()):\n if key not in ['fold_id_dir','plot_name', 'fixed_parameters', 'threads_proteins', 'qijab_dir',\n 'debug_mode', 'parameter_file', 'settings_file', 'optimization_log_file', 'braw_dir', 'pdb_dir', 'paramdir',\n 'mask_sse', 'lambda_w_fix', 'lfactor', 'plotdir', 'psicov_dir', 'contact', 'hessian_pseudocount']:\n print_to_table[key] = settings[key]\n\n print(\"Generate settings table...\")\n table_settings_1 = plot_settings_table(print_to_table, 1)\n table_settings_2 = plot_settings_table(print_to_table, 2)\n table_settings_3 = plot_settings_table(print_to_table, 3)\n plots.append(table_settings_1)\n plots.append(table_settings_2)\n plots.append(table_settings_3)\n\n\n ####################### negLL and realted plots\n if 'step' in log_df.columns and 'pass' in log_df.columns:\n\n if 'negLL' in log_df.columns:\n plot_negll = plot_convergence_trace_plotly(log_df,\n name=['negLL', 'negLL_crossval'],\n plot_title='neg LL trace for training and cross-val set')\n plots.append(plot_negll)\n\n plot_expfit_negll = plot_exponentialFit_negLL(log_df, plot_title='exponential Fit neg LL')\n plots.append(plot_expfit_negll)\n\n if 'timestamp' in log_df.columns:\n plot_timestamps = plot_convergence_trace_plotly(log_df,\n name=['timestamp'],\n plot_title='time (s) per iteration')\n plots.append(plot_timestamps)\n\n\n if 'gradient_norm_weights' in log_df.columns:\n plot_grad_norm_weights = plot_convergence_trace_plotly(log_df,\n name=['gradient_norm_weights'],\n plot_title='norm of weight gradients')\n plots.append(plot_grad_norm_weights)\n\n if 'gradient_norm_means' in log_df.columns:\n plot_grad_norm_means = plot_convergence_trace_plotly(log_df,\n name=['gradient_norm_means'],\n plot_title='norm of mean gradients')\n plots.append(plot_grad_norm_means)\n\n if 'gradient_norm_prec' in log_df.columns:\n plot_grad_norm_prec = plot_convergence_trace_plotly(log_df,\n name=['gradient_norm_prec'],\n plot_title='norm of precMat gradients')\n plots.append(plot_grad_norm_prec)\n\n\n ####################### plotting of parameters\n print(\"Generate distribution of parameters...\")\n\n #weights\n weights_dict = {}\n for component in range(settings['nr_components']):\n weights_dict['component ' + str(component)] = {\n 'weights (contact)': parameters_dict[\"weight_contact_\" + str(component)][0],\n 'weights (bg)': parameters_dict[\"weight_bg_\" + str(component)][0]\n }\n plot_weights = plot_barplot(\n weights_dict,\n 'Distribution of weights',\n 'component weights',\n type='group',\n colors=colors\n #,plot_out=\"/home/vorberg/weights.html\"\n )\n\n #mu\n mu_df = pd.DataFrame.from_dict(dict((k, parameters_dict[k]) for k in sorted(parameters_dict.keys()) if 'mu' in k))\n plot_means = plot_boxplot(\n mu_df,\n 'Distribution of Means',\n \"values of mean parameters\",\n colors=colors\n #,plot_out=\"/home/vorberg/mus.html\"\n )\n\n #std deviation\n prec_df = pd.DataFrame.from_dict(dict((k, parameters_dict[k]) for k in sorted(parameters_dict.keys()) if 'prec' in k))\n try:\n std_dev = prec_df.apply(lambda p: np.sqrt(1.0/p))\n if settings['prec_wrt_L']:\n std_dev = prec_df.apply(lambda p: np.sqrt(1.0/(p*142))) #in case precision is specified depending on L=142\n except ZeroDivisionError as e:\n print(e)\n std_dev=prec_df\n\n std_dev.columns = [column_name.replace(\"prec\", \"std\") for column_name in std_dev.columns]\n plot_stddev = plot_boxplot(\n std_dev,\n 'Distribution of std deviations',\n \"values of std deviation parameters\",\n colors=colors\n #,plot_out=\"/home/vorberg/std.html\"\n )\n\n\n plots.append(plot_weights)\n plots.append(plot_means)\n plots.append(plot_stddev)\n\n ####################### Scatterplot mu vs std dev\n print(\"Generate scatter plot mu vs std...\")\n scatter_dict = {}\n for component in range(settings['nr_components']):\n scatter_dict['mu_'+str(component)] = [\n mu_df['mu_'+str(component)].tolist(),\n std_dev['std_'+str(component)].tolist(),\n AB.values()\n ]\n plot_mu_vs_stddev = plot_scatter(scatter_dict,\n 'Mean vs std deviation',\n 'mean',\n \"std deviation\",\n False,\n colors\n #,plot_out=\"/home/vorberg/mu_vs_std.html\"\n )\n\n plots.append(plot_mu_vs_stddev)\n\n\n ############################################## plotting of gradient norms\n print(\"Generate gradient norms plot...\")\n\n #gradients for mu\n mu_grad_dict = {}\n annotations_dict = {}\n for component in range(settings['nr_components']):\n key = 'mu_'+str(component)\n mu_grad_dict[key] = log_df[key].tolist()[-1]\n annotations_dict[key] = AB\n\n\n plot_gradient_mu_stats = jitter_plot(mu_grad_dict,\n 'Distribution of gradients for mean in last iteration',\n annotations_dict,\n colors,\n None)\n plots.append(plot_gradient_mu_stats)\n\n\n #gradients for precMat\n precMat_grad_dict = {}\n annotations_dict = {}\n for component in range(settings['nr_components']):\n key = 'prec_'+str(component)\n precMat_grad_dict['diagPrecMat_'+str(component)] = log_df[key].tolist()[-1]\n annotations_dict['diagPrecMat_'+str(component)] = AB\n\n\n plot_gradient_precMat_stats = jitter_plot(\n precMat_grad_dict,\n 'Distribution of gradients for precMat in last iteration',\n annotations_dict,\n colors,\n None\n )\n plots.append(plot_gradient_precMat_stats)\n\n ##################################### plotting of gradient trace of a specific ab pair for all components\n print(\"Generate gradient trace plot...\")\n\n gradient_df = log_df.filter(regex=(\"mu_[0-9]*\"))\n plot_gradient_mu_ab_trace = plot_gradient_ab_trace(gradient_df,\n ab_list,\n colors\n )\n plots.append(plot_gradient_mu_ab_trace)\n\n gradient_df = log_df.filter(regex=(\"prec_[0-9]*\"))\n plot_gradient_prec_ab_trace = plot_gradient_ab_trace(\n gradient_df,\n ab_list,\n colors\n )\n plots.append(plot_gradient_prec_ab_trace)\n\n\n ##################################### plotting of univariate mixtures\n if len(evaluation_set_kde['contact']) == 0 or len(evaluation_set_kde['bg']) == 0:\n print \"Evaluation set is empty. Cannot plot Mixture Visualization.\"\n else:\n print(\"Generate parameter visualization 1d plots...\")\n plots.append(plot_parameter_visualisation_1d(parameters_dict, evaluation_set_kde, settings, colors, settings['prec_wrt_L']))\n # plot_parameter_visualisation_1d(parameters_dict, evaluation_set_kde, settings, colors, settings['prec_wrt_L'], plot_out=\"/home/vorberg/1d_vis.html\")\n\n # ------------------------------------------------------------------------------\n ### define merged plot\n # ------------------------------------------------------------------------------\n cols = 3.0\n rows = int(np.ceil((len(plots)-1) / cols)) + 2\n subplot_titles = []\n\n # set up titles\n for plot in range(len(plots)-1):\n subplot_titles.append(plots[plot]['layout']['title'])\n if len(subplot_titles) < (cols * (rows-2)):\n for i in range(int((cols * (rows-2))) - len(subplot_titles) ):\n subplot_titles.append(\" \")\n subplot_titles.append(plots[-1]['layout']['title'])\n\n\n # plot all plots as subplots\n fig = tools.make_subplots(rows=rows,\n cols=3,\n specs = [ [{} for col in range(int(cols))] for row in range(rows-2)] + \\\n [[{'rowspan':2, 'colspan': 3}, None, None], [None, None, None]],\n subplot_titles=tuple(subplot_titles),\n print_grid=False)\n\n\n\n\n for i, plot in enumerate(plots[:-1]):\n col = i % int(cols)\n row = (i - col) / int(cols)\n\n #add traces to subplot\n for trace in plot['data']:\n trace['showlegend']=False\n fig.append_trace(trace, row + 1, col + 1)\n\n # adjust x and y axis for table plotting\n if 'annotations' in plot['layout'].keys():\n for cell in plot['layout']['annotations']:\n cell['yref'] = 'y' + str(i + 1)\n cell['xref'] = 'x' + str(i + 1)\n fig['layout']['annotations'] += plot['layout']['annotations']\n\n # adjust axis for all plots\n fig['layout']['xaxis' + str(i + 1)].update(plot['layout']['xaxis1'])\n fig['layout']['yaxis' + str(i + 1)].update(plot['layout']['yaxis1'])\n\n ## add mixture visualisation plot - spans 3 columns\n for trace in plots[-1]['data']:\n fig.append_trace(trace, int(rows)-1, 1)\n fig['layout']['xaxis' + str(int(cols * (rows-2) + 1))].update(plots[-1]['layout']['xaxis1'])\n fig['layout']['yaxis' + str(int(cols * (rows-2) + 1))].update(plots[-1]['layout']['yaxis1'])\n\n #check which plots are visible/invisible according to menu selection\n trace_visibility_ab = {}\n for ab in range(len(ab_list)):\n trace_visibility_ab[ab] = []\n for i, plot in enumerate(plots):\n if 'updatemenus' not in plot['layout'].keys():\n trace_visibility_ab[ab].extend([True] * len(plot['data']))\n else:\n trace_visibility_ab[ab].extend(plot['layout']['updatemenus'][0]['buttons'][ab]['args'][1])\n\n\n #use menu of last plot (=vis of mixture) as template for multiplot menu\n fig['layout']['updatemenus'] = plots[-1]['layout']['updatemenus']\n for ab in range(len(ab_list)):\n fig['layout']['updatemenus'][0]['buttons'][ab]['args'][1] = trace_visibility_ab[ab]\n\n\n fig['layout']['legend']['yanchor'] = 'bottom'\n fig['layout']['legend']['y'] = 0\n fig['layout']['height'] = rows * 250\n fig['layout']['font'] = {'size': 18} # set global font size\n\n plotly_plot(fig, filename=plotname, auto_open=False)","def plot_lc(self, show=False, result = None, **kwargs):\n for i in ['color', 'ylabel', 'normalize']:\n try:\n del kwargs[i]\n except KeyError:\n pass\n\n ax: Axes = self.lc.scatter()\n\n model = result if result is not None and len(result) >0 else self.result\n\n if len(model) > 0:\n params = []\n\n for i, j, k in zip(model[self._result.significant==True].amp\n , model[self._result.significant==True].frequency\n , model[self._result.significant==True].phase):\n params.append(i.nominal_value)\n params.append(j.nominal_value)\n params.append(k.nominal_value)\n\n y = sin_multiple(self.lc.time, *params)\n ax.plot(self.lc.time, y, color='red', linewidth=1)\n\n if show:\n pl.show()","def main():\n save = False\n show = True\n\n #hd_parameter_plots = HDparameterPlots(save=save)\n #hd_parameter_plots.flow_parameter_distribution_for_non_lake_cells_for_current_HD_model()\n #hd_parameter_plots.flow_parameter_distribution_current_HD_model_for_current_HD_model_reprocessed_without_lakes_and_wetlands()\n #hd_parameter_plots.flow_parameter_distribution_ten_minute_data_from_virna_0k_ALG4_sinkless_no_true_sinks_oceans_lsmask_plus_upscale_rdirs()\n #hd_parameter_plots.flow_parameter_distribution_ten_minute_data_from_virna_0k_ALG4_sinkless_no_true_sinks_oceans_lsmask_plus_upscale_rdirs_no_tuning()\n #ice5g_comparison_plots = Ice5GComparisonPlots(save=save)\n #ice5g_comparison_plots.plotLine()\n #ice5g_comparison_plots.plotFilled()\n #ice5g_comparison_plots.plotCombined()\n #ice5g_comparison_plots.plotCombinedIncludingOceanFloors()\n #flowmapplot = FlowMapPlots(save)\n #flowmapplot.FourFlowMapSectionsFromDeglaciation()\n #flowmapplot.Etopo1FlowMap()\n #flowmapplot.ICE5G_data_all_points_0k()\n #flowmapplot.ICE5G_data_all_points_0k_no_sink_filling()\n #flowmapplot.ICE5G_data_all_points_0k_alg4_two_color()\n #flowmapplot.ICE5G_data_all_points_21k_alg4_two_color()\n #flowmapplot.Etopo1FlowMap_two_color()\n #flowmapplot.Etopo1FlowMap_two_color_directly_upscaled_fields()\n #flowmapplot.Corrected_HD_Rdirs_FlowMap_two_color()\n #flowmapplot.ICE5G_data_ALG4_true_sinks_21k_And_ICE5G_data_ALG4_true_sinks_0k_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_Etopo1_ALG4_sinkless_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_Etopo1_ALG4_true_sinks_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_sinkless_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_true_sinks_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_corr_orog_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_no_true_sinks_corr_orog_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_HD_as_data_ALG4_true_sinks_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Upscaled_Rdirs_vs_Directly_Upscaled_fields_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplot.Ten_Minute_Data_from_Virna_data_ALG4_corr_orog_downscaled_lsmask_no_sinks_21k_vs_0k_FlowMap_comparison()\n #flowmapplot.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n flowmapplotwithcatchment = FlowMapPlotsWithCatchments(save)\n #flowmapplotwithcatchment.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_virna_data_plus_tarasov_style_orog_corrs_for_both()\n #flowmapplotwithcatchment.compare_present_day_river_directions_with_catchments_virna_data_with_vs_without_tarasov_style_orog_corrs()\n #flowmapplotwithcatchment.compare_lgm_river_directions_with_catchments_virna_data_with_vs_without_tarasov_style_orog_corrs()\n #flowmapplotwithcatchment.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.upscaled_rdirs_with_and_without_tarasov_upscaled_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.\\\n #upscaled_rdirs_with_and_without_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.\\\n #Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.\\\n #Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_glcc_olson_lsmask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_ICE5G_plus_tarasov_style_orog_corrs_for_both()\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_ICE6G_plus_tarasov_style_orog_corrs_for_both()\n #flowmapplotwithcatchment.compare_ICE5G_and_ICE6G_with_catchments_tarasov_style_orog_corrs_for_both()\n #flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs()\n #flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs_original_ts()\n flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs_new_ts_10min()\n outflowplots = OutflowPlots(save)\n #outflowplots.Compare_Upscaled_Rdirs_vs_Directly_Upscaled_fields_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_as_HD_data_ALG4_sinkless_all_points_0k()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_as_HD_data_ALG4_true_sinks_all_points_0k()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_sinkless_all_points_0k_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_true_sinks_all_points_0k_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_corr_orog_all_points_0k_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_corr_orog_downscaled_ls_mask_all_points_0k_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_Etopo1_ALG4_sinkless_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_Etopo1_ALG4_true_sinks_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_plus_tarasov_upscaled_srtm30_ALG4_corr_orog_0k_directly_upscaled_fields()\n #outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k()\n outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k_new_truesinks()\n #outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k_new_truesinks_individual_rivers()\n #outflowplots.Compare_ICE5G_with_and_without_tarasov_upscaled_srtm30_ALG4_corr_orog_0k_directly_upscaled_fields()\n #hd_output_plots = HDOutputPlots()\n #hd_output_plots.check_water_balance_of_1978_for_constant_forcing_of_0_01()\n #hd_output_plots.plot_comparison_using_1990_rainfall_data()\n #hd_output_plots.plot_comparison_using_1990_rainfall_data_adding_back_to_discharge()\n #coupledrunoutputplots = CoupledRunOutputPlots(save=save)\n #coupledrunoutputplots.ice6g_rdirs_lgm_run_discharge_plot()\n #coupledrunoutputplots.extended_present_day_rdirs_lgm_run_discharge_plot()\n #coupledrunoutputplots.ocean_grid_extended_present_day_rdirs_vs_ice6g_rdirs_lgm_run_discharge_plot()\n #coupledrunoutputplots.extended_present_day_rdirs_vs_ice6g_rdirs_lgm_echam()\n #coupledrunoutputplots.extended_present_day_rdirs_vs_ice6g_rdirs_lgm_mpiom_pem()\n #lake_plots = LakePlots()\n #lake_plots.plotLakeDepths()\n #lake_plots.LakeAndRiverMap()\n #lake_plots.LakeAndRiverMaps()\n if show:\n plt.show()","def plot_lineratios(modeldata,modeldata2='None',line1='CIV1551',line2='CIII1908',line3='CIV1551',line4='HeII1640',\n plotname='./TESTPLOT.pdf',Zgas=False,logU=False,xid=0.3,nh=100,COratio=0.38,Mcutoff=100,\n logx=True,logy=True,logp1=False,logp2=False,fixxrange=False,fixyrange=False,\n showobs=None,noobserr=False,verbose=True):\n NFalse = 0\n freeparam = []\n inforstr = \"\"\n # - - - - - - - - - - - - - - - - - - - - - - - -\n legenddic = {}\n legenddic['Zgas'] = r'Z$_\\textrm{gas}$'\n legenddic['logUs'] = r'log$_\\textrm{10}$(U)'\n legenddic['xid'] = r'$\\xi_\\textrm{d}$'\n legenddic['nh'] = r'n$_\\textrm{H}$ / [cm$^3$]'\n legenddic['COCOsol'] = r'C/O / [C/O]$_\\textrm{sun}$'\n legenddic['mup'] = r'M$_\\textrm{cut IMF}$ / [M$_\\textrm{sun}]$'\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not Zgas:\n Zgasrange = [0.0,1.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' Zgas:vary, '\n freeparam.append('Zgas')\n else:\n Zgasrange = [Zgas-1e-6,Zgas+1e-6]\n inforstr = inforstr+' '+legenddic['Zgas']+'='+str(Zgas)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not logU:\n logUrange = [-5.0,0.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' logU:vary, '\n freeparam.append('logUs')\n else:\n logUrange = [logU-0.1,logU+0.1]\n inforstr = inforstr+' '+legenddic['logUs']+'='+str(logU)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not xid:\n xidrange = [0.0,0.6]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' xid:vary, '\n freeparam.append('xid')\n else:\n xidrange = [xid-0.01,xid+0.01]\n inforstr = inforstr+' '+legenddic['xid']+'='+str(xid)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not nh:\n nhrange = [0.0,1.0e6]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' nH:vary, '\n freeparam.append('nh')\n else:\n nhrange = [nh-1.0,nh+1.0]\n inforstr = inforstr+' '+legenddic['nh']+'='+str(nh)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not COratio:\n COratiorange = [0.0,2.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' C/O:vary, '\n freeparam.append('COCOsol')\n else:\n COratiorange = [COratio-0.001,COratio+0.001]\n inforstr = inforstr+' '+legenddic['COCOsol']+'='+str(COratio)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not Mcutoff:\n Mcutoffrange = [0.0,400.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' Mcutoff:vary, '\n freeparam.append('mup')\n else:\n Mcutoffrange = [Mcutoff-1.0,Mcutoff+1.0]\n inforstr = inforstr+' '+legenddic['mup']+'='+str(Mcutoff)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n\n if NFalse != 2:\n sys.exit(' Two and only two of the model parameters (Zgas,logU,xid,nh,COratio,Mcutoff) '\n 'should be set to Flase to define the model grid; however it appears '+str(NFalse)+\n ' parameters where not set')\n\n # - - - - - - - - - - - - - - - - - - - - - - - -\n goodent = np.where( (modeldata['Zgas'] > Zgasrange[0]) & (modeldata['Zgas'] < Zgasrange[1]) &\n (modeldata['logUs'] > logUrange[0]) & (modeldata['logUs'] < logUrange[1]) &\n (modeldata['xid'] > xidrange[0]) & (modeldata['xid'] < xidrange[1]) &\n (modeldata['nh'] > nhrange[0]) & (modeldata['nh'] < nhrange[1]) &\n (modeldata['COCOsol'] > COratiorange[0]) & (modeldata['COCOsol'] < COratiorange[1]) &\n (modeldata['mup'] > Mcutoffrange[0]) & (modeldata['mup'] < Mcutoffrange[1]) )\n\n Ngoodent = len(goodent[0])\n\n if Ngoodent > 1:\n if verbose: print(' - Getting data for '+str(Ngoodent)+' data points satisfying (SFR)model selection ')\n param1_1 = modeldata[freeparam[0]][goodent]\n if logp1:\n param1_1 = np.log10(param1_1)\n\n param1_2 = modeldata[freeparam[1]][goodent]\n if logp2:\n param1_2 = np.log10(param1_2)\n\n ratio1_1 = modeldata[line1][goodent]/modeldata[line2][goodent]\n ratio1_2 = modeldata[line3][goodent]/modeldata[line4][goodent]\n else:\n if verbose: print(' WARNING: Less than 2 (SFR)model grid points to plot; no output generated')\n return\n\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if modeldata2 != 'None':\n goodent2 = np.where( (modeldata2['Zgas'] > Zgasrange[0]) & (modeldata2['Zgas'] < Zgasrange[1]) &\n (modeldata2['logUs'] > logUrange[0]) & (modeldata2['logUs'] < logUrange[1]) &\n (modeldata2['xid'] > xidrange[0]) & (modeldata2['xid'] < xidrange[1]) &\n (modeldata2['nh'] > nhrange[0]) & (modeldata2['nh'] < nhrange[1]) )\n\n Ngoodent2 = len(goodent2[0])\n\n if Ngoodent > 1:\n if verbose: print(' - Getting data for '+str(Ngoodent2)+' data points satisfying (AGN)model selection ')\n param2_1 = modeldata2[freeparam[0]][goodent2]\n if logp1:\n param2_1 = np.log10(param2_1)\n\n param2_2 = modeldata2[freeparam[1]][goodent2]\n if logp2:\n param2_2 = np.log10(param2_2)\n\n l2s = ['x','x','x','x'] # line names to use for Feltre+16 file\n for ll, linestr in enumerate([line1,line2,line3,line4]):\n if '1908' in linestr:\n l2 = linestr.replace('1908','1907')\n else:\n l2 = linestr\n\n l2s[ll] = l2\n\n ratio2_1 = modeldata2[l2s[0]][goodent2]/modeldata2[l2s[1]][goodent2]\n ratio2_2 = modeldata2[l2s[2]][goodent2]/modeldata2[l2s[3]][goodent2]\n else:\n if verbose: print(' WARNING: Less than 2 (AGN)model grid points to plot; no output generated')\n return\n\n # - - - - - - - - - - - PLOTTING - - - - - - - - - - -\n if verbose: print(' - Setting up and generating plot')\n plotname = plotname\n fig = plt.figure(figsize=(9, 5))\n fig.subplots_adjust(wspace=0.1, hspace=0.1,left=0.1, right=0.99, bottom=0.10, top=0.95)\n Fsize = 10\n lthick = 1\n marksize = 3\n plt.rc('text', usetex=True)\n plt.rc('font', family='serif',size=Fsize)\n plt.rc('xtick', labelsize=Fsize)\n plt.rc('ytick', labelsize=Fsize)\n plt.clf()\n plt.ioff()\n plt.title(inforstr[:-2],fontsize=Fsize)\n\n margin = 0.1\n dx = np.abs(np.max(ratio1_1)-np.min(ratio1_1))\n dy = np.abs(np.max(ratio1_2)-np.min(ratio1_2))\n\n\n if fixxrange:\n xrange = fixxrange\n else:\n if logx:\n xrange = [np.min(ratio1_1)-np.min(ratio1_1)/2.,np.max(ratio1_1)+np.max(ratio1_1)/2.]\n else:\n xrange = [np.min(ratio1_1)-dx*margin,np.max(ratio1_1)+dx*margin]\n\n if fixyrange:\n yrange = fixyrange\n else:\n if logy:\n yrange = [np.min(ratio1_2)-np.min(ratio1_2)/2.,np.max(ratio1_2)+np.max(ratio1_2)/2.]\n else:\n yrange = [np.min(ratio1_2)-dy*margin,np.max(ratio1_2)+dy*margin]\n\n # ------------ PARAM1 ------------\n cmap = plt.cm.get_cmap('winter')\n cmin = np.min(param1_1)\n cmax = np.max(param1_1)\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\n mm = plt.cm.ScalarMappable(cmap=cmap)\n mm.set_array(cmaparr)\n cb1 = plt.colorbar(mm)#shrink=0.25\n\n pstr1 = legenddic[freeparam[0]]\n if logp1:\n pstr1 = r'log$_\\textrm{10}$('+pstr1+')'\n\n cb1.set_label(pstr1+' (outer circle) - Fixed: black line')\n\n for p1 in np.unique(param1_1):\n p1col = cmap(colnorm(p1))\n p1ent = np.where(param1_1 == p1)\n\n plt.plot(ratio1_1[p1ent],ratio1_2[p1ent],'-',lw=lthick, color='k',zorder=1)\n\n plt.errorbar(ratio1_1[p1ent],ratio1_2[p1ent],xerr=None,yerr=None,\n marker='o',lw=0, markersize=marksize*3,\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\n\n if modeldata2 is not 'None':\n p1ent = np.where(param2_1 == p1)\n\n plt.plot(ratio2_1[p1ent],ratio2_2[p1ent],'-',lw=lthick, color='k',zorder=1)\n\n plt.errorbar(ratio2_1[p1ent],ratio2_2[p1ent],xerr=None,yerr=None,\n marker='D',lw=0, markersize=marksize*3,\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\n\n\n # ------------ PARAM2 ------------\n cmap = plt.cm.get_cmap('spring')\n cmin = np.min(param1_2)\n cmax = np.max(param1_2)\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\n mm = plt.cm.ScalarMappable(cmap=cmap)\n mm.set_array(cmaparr)\n cb2 = plt.colorbar(mm)#shrink=0.25\n\n pstr2 = legenddic[freeparam[1]]\n if logp2:\n pstr2 = 'log10('+pstr2+')'\n\n cb2.set_label(pstr2+' (inner circle) - Fixed: gray line')\n\n for p2 in np.unique(param1_2):\n p2col = cmap(colnorm(p2))\n p2ent = np.where(param1_2 == p2)\n\n plt.plot(ratio1_1[p2ent],ratio1_2[p2ent],'-',lw=lthick, color='gray',zorder=2)\n\n plt.errorbar(ratio1_1[p2ent],ratio1_2[p2ent],xerr=None,yerr=None,\n marker='o',lw=0, markersize=marksize*1.5,\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\n\n if modeldata2 is not 'None':\n p2ent = np.where(param2_2 == p2)\n\n plt.plot(ratio2_1[p2ent],ratio2_2[p2ent],'-',lw=lthick, color='gray',zorder=2)\n\n plt.errorbar(ratio2_1[p2ent],ratio2_2[p2ent],xerr=None,yerr=None,\n marker='D',lw=0, markersize=marksize*1.5,\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\n\n\n if showobs != None:\n for ii, objid in enumerate(showobs[:,0]):\n if (showobs[:,7][ii] > xrange[0]) & (showobs[:,7][ii] < xrange[1]) & \\\n (showobs[:,9][ii] > yrange[0]) & (showobs[:,9][ii] < yrange[1]):\n\n if noobserr:\n obsxerr = None\n obsyerr = None\n else:\n obsxerr = showobs[:,8][ii]\n obsyerr = showobs[:,10][ii]\n plt.errorbar(showobs[:,7][ii],showobs[:,9][ii],xerr=obsxerr,yerr=obsyerr,\n marker='*',lw=lthick, markersize=marksize*2,\n markerfacecolor='k',ecolor='k',markeredgecolor = 'k',zorder=30)\n\n plt.xlabel(line1+'/'+line2)\n plt.ylabel(line3+'/'+line4)\n\n plt.xlim(xrange)\n plt.ylim(yrange)\n\n if logx:\n plt.xscale('log')\n if logy:\n plt.yscale('log')\n\n #--------- LEGEND ---------\n # plt.errorbar(-1,-1,xerr=None,yerr=None,fmt='o',lw=lthick,ecolor='white', markersize=marksize*2,\n # markerfacecolor='white',markeredgecolor = 'k',label='Ground-based spec')\n #\n # leg = plt.legend(fancybox=True, loc='upper center',prop={'size':Fsize},ncol=1,numpoints=1)\n # #bbox_to_anchor=(1.25, 1.03)) # add the legend\n # leg.get_frame().set_alpha(0.7)\n #--------------------------\n\n if verbose: print(' Saving plot to'+plotname)\n plt.savefig(plotname)\n plt.clf()\n plt.close('all')","def plot_learning_curve(X_train_all, X_val_all, y_train_all, y_val_all, train_sizes, title):\n\n errors_df = pd.DataFrame(columns = ['train_size', 'train_acc', 'val_acc'])\n\n # Loop through example sizes and get the training and validation error\n for train_size in train_sizes:\n # Select Subset of Data\n X_train = X_train_all[:train_size]\n X_val = X_val_all[:train_size]\n y_train = y_train_all[:train_size]\n y_val = y_val_all[:train_size]\n\n # Initialize Model\n model = svm.SVC(kernel='linear')\n\n # Fit model\n print(f\"Training {title} using {train_size} examples\")\n model.fit(X_train, y_train)\n\n # Get Predictions \n train_pred = model.predict(X_train)\n val_pred = model.predict(X_val)\n\n # Get Accuracy Score for X_Train and X_Val\n errors = pd.DataFrame({\n 'train_size': [train_size],\n 'train_acc': [accuracy_score(y_train, train_pred)],\n 'val_acc': [accuracy_score(y_val, val_pred)]\n })\n \n # Concatenate Dataframes\n errors_df = pd.concat([errors_df, errors])\n\n # Plot Learning Curve\n fig, ax = plt.subplots()\n\n errors_df.plot(x='train_size', y='train_acc',kind='line', ax=ax)\n errors_df.plot(x='train_size', y='val_acc',kind='line', color='red', ax=ax)\n\n ax.set_xlabel(\"Training Size\")\n ax.set_ylabel(\"Accuracy\")\n ax.set_title(title)\n\n # Save Figure\n plt.savefig('figs/' + title + '_learning_curve.png')","def plot_curves_lenstool(label = ' ', courves_file = 'ce.dat', marker = 'm.', \\\n plt_show = False):\n# x_ca, y_ca = np.loadtxt( courves_file, usecols=(3, 4), unpack=True )\n# x_cc, y_cc = np.loadtxt( courves_file, usecols=(1, 2), unpack=True )\n\n x_cc, y_cc, x_ca, y_ca = \\\n np.loadtxt( courves_file, usecols=(1, 2, 3, 4), unpack=True )\n\n plt.figure(1, figsize=(16, 8))\n\n plt.subplot(1, 2, 1).set_aspect(1)\n plt.plot(x_ca, y_ca, marker, linewidth=3, label = label)\n axis_limit = max( 1.075*max(np.absolute(x_ca)), \\\n 1.075*max(np.absolute(y_ca)) )\n plt.axis([-axis_limit, axis_limit, -axis_limit, axis_limit])\n\n plt.subplot (1, 2, 2).set_aspect(1)\n plt.plot(x_cc, y_cc, marker, linewidth=3, label = label)\n axis_limit = max( 1.075*max(np.absolute(x_cc)), \\\n 1.075*max(np.absolute(y_cc)) )\n plt.axis([-axis_limit, axis_limit, -axis_limit, axis_limit])\n plt.legend()\n\n if plt_show:\n plt.show()\n plt.close()","def energies():\n # Hardcoded initial values\n numsteps = 10000\n time_max = 1\n # Running the calculation in the solver class using the velocity verlet method\n # for better accuracy.\n verlet = solver(input_matrix, 'verlet', time_max, numsteps)\n output_matrix, KE, PE, AM = verlet.main()\n # Creating a simple time axis for plotting\n x = np.linspace(0, 1, numsteps+1)\n\n # Plotting kinetic energy over time\n plt.figure(1, figsize=(10, 10))\n plt.plot(x, KE)\n plt.suptitle('Total kinetic energy in the Earth-Sun system.', fontsize=24)\n plt.xlabel('time [yr]', fontsize=16)\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\n plt.legend(['KE'])\n\n # Plotting potential energy over time\n plt.figure(2, figsize=(10, 10))\n plt.plot(x, PE)\n plt.suptitle('Total potential energy in the Earth-Sun system.', fontsize=24)\n plt.xlabel('time [yr]', fontsize=16)\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\n plt.legend(['PE'])\n\n # Plotting total energy against time\n plt.figure(3, figsize=(10, 10))\n plt.plot(x, PE+KE)\n plt.suptitle('Total energy in the Earth-Sun system.', fontsize=24)\n plt.xlabel('time [yr]', fontsize=16)\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\n plt.legend(['KE+PE'])\n\n # Plotting angular momentum against time. print the amplitude to terminal\n amplitude = max(AM)-min(AM)\n print('Amplitude of angular momentum during 1 year: %g[AU²/yr²]' %(amplitude))\n plt.figure(4, figsize=(10, 10))\n plt.plot(x, AM)\n plt.suptitle('Total angular momentum in the Earth-Sun system.', fontsize=24)\n plt.xlabel('time [yr]', fontsize=16)\n plt.ylabel('energy [AU²/yr²]', fontsize=16)\n plt.legend(['AM'])\n\n # Plotting the kinetic, potential and total energy against time to see\n # how great the variations are\n plt.figure(5, figsize=(10, 10))\n plt.plot(x, PE, x, KE, x, KE+PE)\n plt.suptitle('Total energy in the Earth-Sun system.', fontsize=24)\n plt.xlabel('time [yr]', fontsize=16)\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\n plt.legend(['PE', 'KE', 'KE+PE'])\n plt.show()","def prob4():\n domain = np.linspace(-1, 1, 400)\n n_array = [2**i for i in range(2,9)]\n f = lambda x: 1 / (1 + 25*x**2)\n plt.ion()\n poly_error = []\n cheby_error = []\n for n in n_array:\n x = np.linspace(-1, 1, n)\n poly = BarycentricInterpolator(x)\n poly.set_yi(f(x))\n poly_error.append(la.norm(f(domain) - poly(domain), ord=np.inf))\n y = np.array([(1/2)*(2*np.cos(j*np.pi/n)) for j in range(n+1)])\n cheby = BarycentricInterpolator(y)\n cheby.set_yi(f(y))\n cheby_error.append(la.norm(f(domain) - cheby(domain), ord=np.inf))\n plt.loglog(n_array, poly_error, label=\"equally spaced points\", basex=2)\n plt.loglog(n_array, cheby_error, label=\"Chebyshev extremal points\", basex=2)\n plt.legend()\n plt.show()","def main():\n data1, data2 = [], []\n\n # Compute results for sizes in the range 0...40,000\n max_size = 40000\n # Sizes used are 0**2 = 0, 20**2 = 400, 40**2 = 1600, 60**2 = 3600, \n # etc. up to 200**2 = 40,000\n for size in (x**2 for x in range(0, round(sqrt(max_size)) + 1, 20)):\n compute_time(size, data1, data2)\n\n # Create a plotter object\n plt = Plotter(600, 600, 0, max_size, 0, 120)\n\n # Plot the curves\n plt.pen.width(4)\n plt.plot_data(data1, \"blue\")\n plt.plot_data(data2, \"red\")\n\n # Wait for user interaction\n plt.interact()","def main():\n\n print(\"Finding maximum clade credibility tree...\")\n find_mcc_tree()\n print(\"Computing clade credibilities...\")\n compute_clade_probabilities()\n if _CAN_PLOT:\n print(\"Plotting maximum clade credibility tree...\")\n plot_mcc_tree()\n else:\n print(\"Skipping plotting tree due to lack of PyQt4 support. :(\")\n print(\"Computing posterior mean paramter estimates...\")\n ranked_means = utils.write_means(\"indoeuropean.log\", \"parameter_means.csv\")\n print(\"Computing ranking correlations...\")\n compute_ranking_correls(ranked_means)\n print(\"Generating LaTeX table...\")\n make_table(ranked_means)\n print(\"Generating rate variation figure...\")\n make_figure(\"category_rates.eps\")","def plot_tuning_curves(self, baseline_rate=10.):\n x = np.arange(0, 1 + 0.01, 0.01)\n l0 = self.data['L0']\n l1 = self.data['L1']\n y_on = np.exp(np.log(l0) + x * np.log(l1 / l0))\n y_off = np.exp(np.log(l0) + (1 - x) * np.log(l1 / l0))\n plt.plot(x, y_on, label='ON')\n plt.plot(x, y_off, label='OFF')\n plt.plot(x, baseline_rate + 0 * x, '--')\n # plt.xlabel('Stimulus intensity')\n # plt.ylabel('Firing Rate (Hz)')\n # plt.title('Firing rate as a function \\n of Stimulus Intensity')\n # plt.legend()","def pr_curve(model, X_train, y_train, X_test, y_test, train=True):\n from sklearn.metrics import precision_recall_curve\n if train==True:\n ypredTrain = model.predict(X_train) \n precisions, recalls, thresholds = precision_recall_curve(y_train, ypredTrain)\n plt.plot(precisions, recalls, linewidth=3, color='r', linestyle='-')\n plt.rc('xtick', labelsize=10) \n plt.rc('ytick', labelsize=10) \n plt.xlabel(\"Precision\", size=12)\n plt.ylabel(\"Recall\", size=12)\n plt.grid()\n plt.rcParams['figure.facecolor'] = '#F2F3F4' \n plt.rcParams['axes.facecolor'] = '#F2F3F4' \n plt.title(\"PR Curve: Precision/Recall Trade-off\\n\\n(Train)\\n\", size=14) \n plt.show()\n elif train==False:\n ypredTest = model.predict(X_test)\n precisions, recalls, thresholds = precision_recall_curve(y_test, ypredTest)\n plt.plot(precisions, recalls, linewidth=3, color='b', linestyle='-')\n plt.rc('xtick', labelsize=10) \n plt.rc('ytick', labelsize=10) \n plt.xlabel(\"Precision\", size=12)\n plt.ylabel(\"Recall\", size=12)\n plt.grid()\n plt.rcParams['figure.facecolor'] = '#F2F3F4' \n plt.rcParams['axes.facecolor'] = '#F2F3F4'\n plt.title(\"PR Curve: Precision/Recall Trade-off\\n\\n(Test)\\n\", size=14)\n plt.show()","def fit_lorentzian(comp_key,p0,time_step,conn,func = fitting.fun_lorentzian,fig=None,wind=3):\n\n (fin,) = conn.execute(\"select fout from comps where comp_key = ?\",(comp_key,)).fetchone()\n Fin = h5py.File(fin,'r')\n g = Fin[fd('vanHove',comp_key)]\n\n temp = g.attrs['temperature']\n dtime = g.attrs['dtime']\n j = int(time_step/dtime)\n count = g[fd('step',j)]['y']['disp_count'][:]\n count += g[fd('step',j)]['x']['disp_count'][:]\n edges = g[fd('step',j)]['y']['disp_edges'][:]\n\n \n edges = np.array([np.mean(edges[j:j+wind]) for j in range(1,len(count)-wind,wind)])\n count = np.array([np.sum(count[j:j+wind]) for j in range(1,len(count)-wind,wind)])\n \n \n edges = edges[count>30]\n count = count[count>30]\n \n\n out = fitting.fit_curve(edges,count,p0,func)\n fig = fitting.display_fit(edges,count,out.beta,func,fig)\n print out.beta\n return out,fig","def solve_rossler(alpha = 1.0, beta = 1.0, gamma = 1.0, max_time = 10, N = 5):\n resolution = 72\n \n fig1 = plt.figure(dpi = resolution)\n ax1, ax2, ax3 = fig1.subplots(3, 1)\n\n fig = plt.figure()\n ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n # ax.axis('off')\n \n def rossler_deriv(xi, t0, alpha=alpha, beta = beta, gamma = gamma):\n \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n \n xi1, xi2, xi3 = xi\n \n xi1_dot = -xi1 - exp(xi3)\n xi2_dot = xi1 + alpha * xi2\n xi3_dot = gamma * exp(-xi3) + xi1 - beta\n \n return [xi1_dot, xi2_dot, xi3_dot]\n\n # Choose random starting points, uniformly distributed from -15 to 15\n np.random.seed(1)\n xi0 = -15 + 30 * np.random.random((N, 3))\n xi0[:,2] = np.log(abs(xi0[:,2]))\n\n # Solve for the trajectories\n t = np.linspace(0, max_time, int(250*max_time))\n xi_t = np.asarray([integrate.odeint(rossler_deriv, xi0i, t)\n for xi0i in xi0])\n \n # choose a different color for each trajectory\n colors = plt.cm.viridis(np.linspace(0, 1, N))\n\n for i in range(N):\n xi1, xi2, xi3= xi_t[i,:,:].T\n \n ax1.plot(t, xi1, '-', c=colors[i])\n ax1.set_title('xi1')\n \n ax2.plot(t, xi2, '-', c=colors[i])\n ax2.set_title('xi2')\n \n ax3.plot(t, xi3, '-', c=colors[i])\n ax3.set_title('xi3')\n \n lines = ax.plot(xi1, xi2, xi3, '-', c=colors[i])\n plt.setp(lines, linewidth=2)\n angle = 104\n ax.view_init(30, angle)\n plt.show()\n\n return t, xi_t","def plot_cv_errors(errors, lambdas , K , path): \n colors = cycle([\"aqua\", \"black\", \"blue\", \"fuchsia\", \"gray\", \"green\", \"lime\", \"maroon\", \"navy\", \"olive\", \"purple\", \"red\", \"silver\", \"teal\", \"yellow\"])\n \n markers = cycle([ \".\", \",\", \"o\", \"v\" , \"^\" , \">\", \"1\", \"2\", \"3\", \"4\", \"8\", \"s\", \"p\", \"*\", \"h\"])\n \n \n for i, data in enumerate(errors):\n \n lambda_str = ('%f' % lambdas[i]).rstrip('0').rstrip('.')\n plt.plot(range(len(data)), data, marker=next(markers), label='$\\lambda$ = %s'%lambda_str);\n \n plt.ylim(0.975 , 0.99)\n #plt.xlim(0 , 50)\n plt.title('ALS-WR Learning Curve, K = %d'% K)\n plt.xlabel('Number of Epochs');\n plt.ylabel('RMSE');\n plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n plt.grid()\n plt.savefig(\"../results/\"+path)\n plt.show()","def lorenz_data_gen(init_x,init_y,init_z,init_r):\n # Set initial state x,y,z and parameters r,sigma,b\n sxin,syin,szin = init_x,init_y,init_z\n state = np.zeros(3)\n state[0] = float(sxin); state[1] = float(syin); state[2] = float(szin)\n \n \n r = init_r\n sigma = 10 # Parameter sigma\n b = 8./3. # Parameter b\n param = np.array([r, sigma, b]) # Vector of parameters passed to rka\n tau = .02 # Timestep from lorenz with n=500\n #err = 1.e-3 # Error tolerance\n \n # Loop over the desired number of steps\n time = 0\n nstep = 500\n # initialize arrays\n tplot=np.array([]); tauplot=np.array([])\n xplot=np.array([]); yplot=np.array([]); zplot=np.array([])\n \n for istep in range(0,nstep):\n # Record values for plotting\n x = state[0]\n y = state[1] \n z = state[2]\n tplot = np.append(tplot,time)\n tauplot = np.append(tauplot,tau)\n xplot = np.append(xplot,x)\n yplot = np.append(yplot,y)\n zplot = np.append(zplot,z)\n #if( istep%50 ==0 ):\n #print('Finished %d steps out of %d '%(istep,nstep))\n # Find new state using Runge-Kutta4\n state = rk4(state,time,tau,lorzrk,param)\n time += tau\n \"\"\"\n # Graph the time series x(t)\n plt.figure(1) \n plt.clf() # Clear figure 1 window and bring forward\n plt.plot(tplot,xplot,'-')\n plt.xlabel('Time'); plt.ylabel('x(t)')\n plt.title('Lorenz model time series')\n # plt.show()\n \n # Graph the x,y,z phase space trajectory\n \n fig=plt.figure(2)\n ax=p3.Axes3D(fig)\n ax.plot3D(xplot,yplot,zplot)\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('z')\n ax.grid(True)\n # title('Lorenz model phase space')\n #ax.set_aspect('equal')\n plt.show()\n \"\"\"\n return xplot,yplot,zplot,tplot","def figure2():\n # sim_data_XPP = pd.read_csv(\"XPP.dat\", delimiter=\" \", header=None) # Load the XPP simulation\n\n plot_settings = {'y_limits': [-25, 0],\n 'x_limits': None,\n 'y_ticks': [-25, -20, -15, -10, -5, 0],\n 'locator_size': 2.5,\n 'y_label': 'Current (nA)',\n 'x_ticks': [],\n 'scale_size': 0,\n 'x_label': \"\",\n 'scale_loc': 4,\n 'figure_name': 'figure_2',\n 'legend': ['I-Na', 'I-NaP'],\n 'legend_size': 8,\n 'y_on': True}\n\n t, y = solver(100) # Integrate solution\n t_short = np.where((t >= 8) & (t <= 18))[0] # shorter time bounds for plots A and C\n v, m, h, m_nap, h_na_p, n, m_t, h_t, m_p, m_n, h_n, z_sk, m_a, h_a, m_h, ca = y[:, ].T # Extract all variables\n\n \"\"\"\n Explicitly calculate all currents: Extra constants duplicated from function dydt to calculate currents\n \"\"\"\n g_na_bar = 0.7\n g_nap_bar = 0.05\n g_k_bar = 1.3\n g_p_bar = 0.05\n g_leak = 0.005\n g_a_bar = 1.0\n e_na = 60\n e_k = -80\n e_leak = -50\n e_ca = 40\n g_t_bar = 0.1\n g_n_bar = 0.05\n g_sk_bar = 0.3\n\n \"\"\"\n Calculate currents used in the plot\n \"\"\"\n i_na = g_na_bar * (m ** 3) * h * (v - e_na)\n i_na_p = g_nap_bar * m_nap * h_na_p * (v - e_na)\n i_k = g_k_bar * (n ** 4) * (v - e_k)\n i_leak = g_leak * (v - e_leak)\n i_t = g_t_bar * m_t * h_t * (v - e_ca)\n i_n = g_n_bar * m_n * h_n * (v - e_ca)\n i_p = g_p_bar * m_p * (v - e_ca)\n i_sk = g_sk_bar * (z_sk ** 2) * (v - e_k)\n i_a = g_a_bar * m_a * h_a * (v - e_k)\n\n plt.figure(figsize=(5, 3), dpi=96) # Create figure\n\n plt.subplot(2, 2, 1) # Generate subplot 1 (top left)\n plt.plot(t[t_short], i_na[t_short], 'k-')\n plt.plot(t[t_short], i_na_p[t_short], c='k', linestyle='dotted')\n alter_figure(plot_settings) # Alter figure for publication\n\n plt.subplot(2, 2, 2) # Generate subplot 2 (top right)\n plt.plot(t, i_t + i_n + i_p, 'k-')\n plt.plot(t, i_t, c='k', linestyle='dotted')\n plt.plot(t, i_p, 'k--')\n plt.plot(t, i_n, 'k-.')\n\n plot_settings['y_limits'] = [-2.5, 0]\n plot_settings['y_ticks'] = [-2.5, -2, -1.5, -1, -0.5, 0]\n plot_settings['locator_size'] = 0.25\n plot_settings['y_label'] = \"\"\n plot_settings['legend'] = ['I-Ca', 'I-T', 'I-P', 'I-N']\n alter_figure(plot_settings) # Alter figure for publication\n\n plt.subplot(2, 2, 3) # Generate subplot 3 (bottom left)\n plt.plot(t[t_short], i_k[t_short], 'k-')\n plt.plot(t[t_short], i_a[t_short], c='k', linestyle='dotted')\n plt.plot(t[t_short], i_leak[t_short], 'k-.')\n\n plot_settings['y_limits'] = [0, 25]\n plot_settings['y_ticks'] = [0, 5, 10, 15, 20, 25]\n plot_settings['locator_size'] = 2.5\n plot_settings['y_label'] = \"Current (nA)\"\n plot_settings['legend'] = ['I-K', 'I-A', 'I-leak']\n plot_settings['scale_size'] = 2\n plot_settings['scale_loc'] = 2\n alter_figure(plot_settings) # Alter figure for publication\n\n plt.subplot(2, 2, 4) # Generate subplot 4 (bottom left)\n\n plt.plot(t, i_sk, 'k-')\n # plt.plot(sim_data_XPP[0][900:]-200,sim_data_XPP[34][900:]) # Isk for XPP data\n\n plot_settings['y_limits'] = [0, 1]\n plot_settings['y_ticks'] = [0, 0.2, 0.4, 0.6, 0.8, 1]\n plot_settings['locator_size'] = 0.2\n plot_settings['y_label'] = \"\"\n plot_settings['legend'] = ['I-SK']\n plot_settings['scale_size'] = 20\n plot_settings['scale_loc'] = 2\n alter_figure(plot_settings, close=True) # Alter figure for publication","def main():\n # Load properties that will be needed\n store = [Storage.Storage(2), Storage.Storage(4)] \n pre_energy = [s.get(\"free_energy\") for s in store]\n post_energy = [s.get(\"post_energy\") for s in store]\n x_range = store[0].get(\"x_range\")\n xlocs = np.arange(x_range[0], x_range[1], x_range[2])\n y_range = store[0].get(\"y_range\")\n ylocs = np.arange(y_range[0], y_range[1], y_range[2])\n # Calculate step size\n xb2steps = stepsize(pre_energy[0], post_energy[0], xlocs) \n xb4steps = stepsize(pre_energy[1], post_energy[1], xlocs) \n # Set up the figure\n fig = plt.figure(1, figsize=(7.5,2.5)) \n axe = (fig.add_subplot(1, 2, 1), fig.add_subplot(1, 2, 2))\n # Plot the results\n axe[0].plot(ylocs, xb4steps, color='#FF466F', lw=4)\n axe[1].plot(ylocs, xb2steps, color='#76D753', lw=4)\n # Annotate the plots\n axe[0].set_title(\"4sXB step size\")\n axe[0].set_xlabel(\"Lattice spacing (nm)\") \n axe[0].set_ylabel(\"Step size (nm)\")\n axe[0].set_xlim((25.5, 39))\n axe[0].set_ylim((1, 8))\n axe[1].set_title(\"2sXB step size\")\n axe[1].set_xlabel(\"Lattice spacing (nm)\") \n axe[1].set_ylabel(\"Step size (nm)\")\n axe[1].set_xlim((25.5, 39))\n axe[1].set_ylim((1, 8))\n # Display the plots\n fig.subplots_adjust(wspace=0.25, hspace=0.48,\n left=0.08, right=0.98,\n top=0.85, bottom=0.21)\n plt.show()","def plot(self):\n \n \n x_ibs=[] \n x_gss=[]\n y_ibs=[] \n y_gss=[]\n x_pso=[]\n x_bgd=[]\n y_bgd=[]\n y_pso=[]\n x_gd=[]\n y_gd=[]\n \n i=0.0000001\n \n # for k in range(1,51):\n # i= random.uniform(0.00000001, 1)\n # t_avg_ibs=[]\n # t_avg_gss=[]\n # for j in range(1,51):\n #L=random.randint(-100, 0)\n #U=random.randint(0, 100)\n max_iter=self.Max_iter \n L=self.Lower_bound\n U=self.Upper_bound\n \n minima=self.gss(L,U,i,1000)\n #print(\"minima at X = \",minima[1])\n x_ibs.append(self.I_bisection(L,U,minima[1],max_iter)[0])\n x_gss.append(self.gss(L,U,i,max_iter)[0])\n x_pso.append(self.particle_Swarm(self.func, L, U, 2, max_iter)[0])\n x_gd.append(self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)[0])\n x_bgd.append(self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)[0])\n #print(x_pso)\n for i in x_ibs[0]:\n #print(self.Func(i)) \n y_ibs.append(self.Func(i))\n for i in x_gss[0]:\n y_gss.append(self.Func(i)) \n for i in x_pso[0]:\n y_pso.append(self.Func(i)) \n for i in x_gd[0]:\n y_gd.append(self.Func(i)) \n for i in x_bgd[0]:\n y_bgd.append(self.Func(i)) \n #print(y_gss)\n\n plt.plot(x_ibs[0], y_ibs, 'r.')\n plt.plot(x_gss[0], y_gss, '.')\n plt.plot(x_pso[0], y_pso, 'y.')\n #plt.plot(x_gd[0], y_gd, 'y.')\n #plt.plot(x_bgd[0], y_bgd, 'k.')\n plt.xlabel('x')\n plt.ylabel('y')\n \n plt.suptitle('Interval Bisection Search (Red) vs Golden Section Search (Blue) vs Particle swarm optimization (Green)')\n #plt.axis([0, 100, 0.00000001, 1]) \n plt.show()\n plt.plot(x_gd[0], y_gd, 'r.')\n plt.plot(x_bgd[0], y_bgd, 'k.')\n plt.xlabel('x')\n plt.ylabel('y') \n plt.suptitle('Gradient Descent (Red) vs Batch Gradient Descent (Black) ')\n \n plt.show()\n \n start_time = timeit.default_timer()\n ibs=self.I_bisection(L,U,minima[1],max_iter)\n print(\" Execution time for Interval bisection Method is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n gss=self.gss(L,U,i,max_iter)\n print(\" Execution time for Golden Section Search is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n pso=self.particle_Swarm(self.func, L, U, 2, max_iter)\n print(\" Execution time for Particle swarm optimization is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n gd=self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)\n print(\" Execution time for Gradient Descent is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n bgd=self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)\n print(\" Execution time for Batch Gradient Descent is\", timeit.default_timer() - start_time,\"s\")\n plt.plot(ibs[1], ibs[2], 'r.')\n plt.text(ibs[1], ibs[2],\"IB\")\n plt.plot(gss[1], gss[2], '.')\n plt.text(gss[1], gss[2],\" GSS\")\n plt.plot(pso[1], pso[2], 'y.')\n plt.text(pso[1], pso[2],\" PSO\")\n plt.plot(gd[1], gd[2], 'g.')\n plt.text(gd[1], gd[2],\" GD \")\n plt.plot(bgd[1],bgd[2], 'k.')\n plt.text(bgd[1], bgd[2],\" Batch_GD\")\n \n plt.xlabel('Value of X')\n plt.ylabel('NUmber of iteration') \n plt.suptitle('Number of iterations vs minimum value of x')\n \n plt.show()","def plotPsCurve(mcoolsPath:list,celltypeNames:list,chroms:list,resolution=100000,title=\"P(s) curve\",plotType=\"interaction\",base=1.1,log_x=True,log_y=True):\n import plotly.express as px\n from IPython.display import Image\n\n #Calculate P(s) data, get a 3 column pd.DataFrame with (bin,resolution,celltype)\n psDataAll = []\n for i in range(len(mcoolsPath)):\n psDataAll.append(compartment.getPsData(mcoolsPath[i],[\"chr\"+str(i+1) for i in range(len(chroms))],resolution=resolution,celltype=celltypeNames[i],base=base)) \n merged = pd.concat(psDataAll)\n\n data = pd.merge(merged,merged.groupby(\"celltype\").sum(),how=\"left\",on=\"celltype\").assign(prob= lambda df: df.aveCount_x/df.aveCount_y)\n\n fig = px.line(x=data[\"bin_x\"]*resolution,y=data[\"prob\"],color=data[\"celltype\"],title=title,log_x=log_x,log_y=log_y).update_layout(template='simple_white')\n fig.update_layout(width=800,height=600)\n fig.update_layout(xaxis_title=\"Genomic Distance(bp)\",\n yaxis_title=\"Contact Probability\")\n if(plotType == \"interaction\"):\n return fig\n else : return Image(fig.to_image(format=\"png\", engine=\"kaleido\"))","def main():\n\n\timport numpy as np\n\timport matplotlib as mpl\n\n\t# Parse arguments\n\targs = cli.parse_args()\n\n\t# Check all files exist\n\tfor filepath in args.files:\n\t\tif not os.path.isfile(filepath):\n\t\t\t_exit('file {} does not exist'.format(filepath))\n\n\t# Use TK interactive backend as it should work on all installations\n\tmpl.use('tkagg')\n\n\t# Turn on interactive plotting (pyplot import must come after call to use())\n\timport matplotlib.pyplot as plt\n\tplt.ion()\n\n\t# Get torsion angle arrays for all input files\n\tarrays = []\n\tfor filepath in args.files:\n\t\twith open(filepath) as fobj:\n\t\t\tchain = list(parse_pdb_chain(fobj))\n\t\t\tarrays.append(torsion_array(chain))\n\n\tangles = np.concatenate(arrays, axis=0)\n\n\t# Make the plot\n\tif args.plottype == 'hexbin':\n\t\thb = ramachandran_hexbin(angles, log=args.log, degrees=not args.radians)\n\t\tif args.cbar:\n\t\t\tplt.colorbar(hb)\n\n\telif args.plottype == 'scatter':\n\t\tramachandran_scatter(angles, degrees=not args.radians)\n\n\telif args.plottype == 'kde':\n\t\t_assert_seaborn_available('kde')\n\t\tramachandran_kdeplot(angles, shade=True, degrees=not args.radians)\n\n\telif args.plottype == 'joint':\n\t\t_assert_seaborn_available('joint')\n\t\tramachandran_jointplot(angles, kind='kde', degrees=not args.radians)\n\n\telse:\n\t\traise ValueError(args.plottype)\n\n\t# Plot title\n\tif len(args.files) == 1:\n\t\tfilename = os.path.basename(args.files[0])\n\telse:\n\t\tfilename = '{} files'.format(len(args.files))\n\n\tplt.gcf().suptitle('{} ({} residues)'.format(filename, angles.shape[0]))\n\n\t# Show the plot, blocking until closed\n\tplt.show(block=True)","def plot_fig43a_looping_TSS(a=10, loglog=True):\n\n fig, ax = plt.subplots(figsize=(default_width, default_height))\n #convert a to basepairs\n a_in_bp = a / ncg.dna_params['lpb']\n links35 = np.tile(35, 44)\n Rlinks = np.array([47, 21, 18, 15, 20, 17])\n Llinks = np.array([245, 30, 26, 15, 23, 35])\n #links to right of methylation site (50 in total)\n Rlinks = np.concatenate((Rlinks, links35))\n #links to left of methylation site (50 in total)\n Llinks = np.concatenate((Llinks, links35))\n #cumulative chain length including burried basepairs\n unwrap = 0\n #plot as positive distance from TSS in bp\n ldna_Rlinks = convert.genomic_length_from_links_unwraps(Rlinks, unwraps=unwrap) #max WLC chain length in bp\n #plot as negative distance from TSS in bp\n ldna_Llinks = -1*convert.genomic_length_from_links_unwraps(Llinks, unwraps=unwrap) #max WLC chain length in bp\n rvals = np.linspace(0.0, 1.0, 1000)\n integral_R = np.load('../deepti/csvs/Bprops/0unwraps/heterogenous/Sarah/Rlinks_1to50nucs/kinkedWLC_greens_Rlinks_50nucs_1000rvals.npy')\n integral_L = np.load('../deepti/csvs/Bprops/0unwraps/heterogenous/Sarah/Llinks_1to50nucs/kinkedWLC_greens_Llinks_50nucs_1000rvals.npy')\n Prob_a_Rlinks_kinked = wlc.prob_R_in_radius_a_given_L(a_in_bp, integral_R, rvals, Rlinks, unwrap)\n Prob_a_Llinks_kinked = wlc.prob_R_in_radius_a_given_L(a_in_bp, integral_L, rvals, Llinks, unwrap)\n #plot in units of nm^(-3)\n ax.plot(ldna_Rlinks, Prob_a_Rlinks_kinked/(ncg.dna_params['lpb']**3), '-o', markersize=3, color=teal_flucts)\n ax.plot(ldna_Llinks, Prob_a_Llinks_kinked/(ncg.dna_params['lpb']**3), '-o', markersize=3, color=teal_flucts)\n plt.xlabel(r'Distance from TSS (bp)')\n plt.ylabel(r'$P_\\mathrm{loop}(a=10\\mathrm{nm}; L)\\;\\;\\;(\\mathrm{nm}^{-3})$')\n plt.subplots_adjust(left=0.16, bottom=0.19, top=0.98, right=0.96)\n plt.yscale('log')\n plt.ylim([10**-3, 10**-0.4])\n plt.xlim([-10000, 10000])\n plt.savefig(f'plots/thesis/fig43a_looping-TSS-yeast.pdf', bbox_inches='tight')","def main():\n # Load data and pre-process it\n path = \"data2.csv\"\n features, labels, df = load_data(path)\n\n # Learning rates - including our own\n learning_rates = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 0.75]\n\n # Num iterations\n num_iterations = [100 for i in range(9)] + [1000]\n\n # Keep track of all final weights for different learning rates\n lines = []\n\n # Compute weights for each learning rate\n for rate, num_iters in zip(learning_rates, num_iterations):\n\n # Get weights from gradient descent and add to weights list\n weights = gradient_descent(features, labels, rate, num_iters)\n lines.append([rate, num_iters] + weights)\n\n # Now write 'lines' to file\n with open('results2.csv', \"w\") as out_file:\n for line in lines:\n out_file.write(\"{}, {}, {}, {}, {} \\n\".format(line[0], line[1],\n line[2], line[3], line[4]))\n out_file.close()\n\n # Select which weights to use for plotting\n index = -1\n\n\n plot_db.visualize_3d(df, lin_reg_weights=lines[index][2:],\n feat1='norm_x1', feat2='norm_x2', labels='label',\n xlim=(-1, 1), ylim=(-1, 1), zlim=(0, 3),\n alpha=learning_rates[index], xlabel='age',\n ylabel='weight', zlabel='height',\n title='')","def plot_results(self):\n\n\n f1, ax1 = plt.subplots()\n h1, = ax1.plot(self.history[\"step\"], self.history[\"trainLoss\"],\\\n \"b-\", label=\"Loss - Train\")\n h2, = ax1.plot(self.history[\"step\"], self.history[\"validLoss\"],\\\n \"b.\", label=\"Loss - Validation\")\n\n ax1.set_ylabel(\"Loss\", color = \"blue\")\n ax1.tick_params(\"y\", color = \"blue\")\n ax1.yaxis.label.set_color(\"blue\")\n ax1.set_xlabel(\"Training Steps [{}]\".format(self.FLAGS.eval_every))\n\n ax2 = ax1.twinx()\n h3, = ax2.plot(self.history[\"step\"], self.history[\"trainAccr\"], \"r-\",\\\n label = \"Accuracy - Train\")\n h4, = ax2.plot(self.history[\"step\"], self.history[\"validAccr\"], \"r.\",\\\n label = \"Accuracy - Validation\")\n\n ax2.set_ylabel(\"Accuracy\", color = \"red\")\n ax2.tick_params(\"y\", color = \"red\")\n ax2.yaxis.label.set_color(\"red\")\n\n hds = [h1,h2,h3,h4]\n lbs = [l.get_label() for l in hds]\n ax1.legend(hds, lbs)\n f1.tight_layout()\n plt.savefig(\"trainingHistory.png\")\n\n plt.close(f1)\n #plt.show()","def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n print \"in here\"\n plt.figure()\n plt.title(title)\n if ylim is not None:\n plt.ylim(*ylim)\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = learning_curve(\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes,scoring=\"accuracy\")\n print(\"now here\")\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n print(\"boom\")\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n print(\"almost there\")\n plt.legend(loc=\"best\")\n return plt","def plot_train_and_valid_curves(ax, train_points, valid_points, learning_rate_updates_epoch, best_per_lr, mode=\"loss\"):\n if mode==\"loss\":\n name = \"Loss\"\n names = \"losses\"\n factor = [1.2, 1.22]\n loc_legend = 1\n elif mode ==\"acc\":\n name = \"Accuracy\"\n names = \"acc\"\n factor = [0.9, 0.88]\n loc_legend = 4\n else:\n print \"Mode not understood. Available modes : 'loss' and 'acc'\"\n return\n\n #ax = plt.subplot(1,1,1)#\n # Plot training and valid loss curves\n ax.plot(np.arange(len(train_points)),train_points, c=\"k\", zorder=1)\n ax.plot(np.arange(len(valid_points)),valid_points, c=\"k\", zorder=1)\n ax.scatter(np.arange(len(train_points)),train_points, c=\"b\", label=\"Train %s\"%names, zorder=2)\n ax.scatter(np.arange(len(valid_points)),valid_points, c=\"r\", label=\"Valid %s\"%names, zorder=2)\n # Plot vertical line when the learning rate was updated\n first = True\n for elem in learning_rate_updates_epoch:\n if first:\n plt.plot([elem-.5,elem-.5], [1.4*valid_points[elem],train_points[elem]*0.6], c=\"k\", label=\"LR updates\", linestyle=\"--\")\n first = False\n else:\n plt.plot([elem-.5,elem-.5], [1.4*valid_points[elem],train_points[elem]*0.6], c=\"k\", linestyle=\"--\")\n # Plot best model in each region\n first = True\n for i,elem in enumerate(best_per_lr):\n if first:\n x = elem[0]\n y = elem[1]\n plt.scatter(x,y, c=\"g\", label=\"Best models\", marker=\"*\", zorder=3, s=100)\n plt.plot([x,x],[y,factor[0]*y], c=\"g\")\n plt.text(x,factor[1]*y, \"Epoch %d\"%(x), fontsize=8)\n first = False\n else:\n x = elem[0]+learning_rate_updates_epoch[i-1]\n y = elem[1]\n plt.scatter(x,y, c=\"g\", marker=\"*\", zorder=3, s=100)\n plt.plot()\n plt.plot([x,x],[y,factor[0]*y], c=\"g\")\n plt.text(x,factor[1]*y, \"Epoch %d\"%(x), fontsize=8)\n # Xlim, Ylim, labels, legend...\n ax.set_ylim([0,1])\n ax.set_xlim([0,len(train_points)+5])\n ax.set_xlabel(\"Epochs\")\n ax.set_ylabel(name)\n handles,labels = ax.get_legend_handles_labels()\n sorted_zip = sorted(zip([2,0,1,3],handles, labels))\n index, handles, labels = zip(*sorted_zip)\n ax.legend(handles,labels, loc=loc_legend, prop={'size':10})","def plot_inv_RV_lit(outpath, fit_slopes, fit_intercepts, fit_stds):\n waves = np.arange(0.8, 4.01, 0.001)\n fig, ax = plt.subplots(figsize=(10, 9))\n\n for i, RV in enumerate([2.5, 3.1, 5.5]):\n # plot the extinction curve from this work\n offset = 0.1 * i\n slopes = interpolate.splev(waves, fit_slopes)\n alav = fit_intercepts(waves) + slopes * (1 / RV - 1 / 3.1)\n (line,) = ax.plot(waves, alav + offset, lw=1.5, label=r\"$R(V) = $\" + str(RV))\n stddev = interpolate.splev(waves, fit_stds)\n color = line.get_color()\n ax.fill_between(\n waves,\n alav + offset - stddev,\n alav + offset + stddev,\n color=color,\n alpha=0.2,\n edgecolor=None,\n )\n\n # plot the literature curves\n styles = [\"--\", \":\"]\n for i, cmodel in enumerate([CCM89, F19]):\n ext_model = cmodel(Rv=RV)\n (indxs,) = np.where(\n np.logical_and(\n 1 / waves >= ext_model.x_range[0], 1 / waves <= ext_model.x_range[1]\n )\n )\n yvals = ext_model(waves[indxs] * u.micron)\n ax.plot(\n waves[indxs],\n yvals + offset,\n lw=1.5,\n color=color,\n ls=styles[i],\n alpha=0.8,\n )\n\n # add text\n ax.text(3.45, 0.03, r\"$R(V) = 2.5$\", fontsize=0.8 * fs, color=\"tab:blue\")\n ax.text(3.45, 0.15, r\"$R(V) = 3.1$\", fontsize=0.8 * fs, color=\"tab:orange\")\n ax.text(3.45, 0.305, r\"$R(V) = 5.5$\", fontsize=0.8 * fs, color=\"tab:green\")\n\n # finalize and save the plot\n ax.set_xlabel(r\"$\\lambda\\ [\\mu m$]\", fontsize=1.2 * fs)\n ax.set_ylabel(r\"$A(\\lambda)/A(V)$ + offset\", fontsize=1.2 * fs)\n ax.set_xlim(0.75, 4.05)\n ax.set_ylim(-0.03, 0.98)\n handles = [\n Line2D([0], [0], color=\"k\", lw=1.5),\n Line2D([0], [0], color=\"k\", lw=1.5, ls=\"--\"),\n Line2D([0], [0], color=\"k\", lw=1.5, ls=\":\"),\n ]\n labels = [\n \"this work\",\n \"Cardelli et al. (1989)\",\n \"Fitzpatrick et al. (2019)\",\n ]\n plt.legend(handles, labels, fontsize=fs)\n plt.savefig(outpath + \"inv_RV_lit.pdf\", bbox_inches=\"tight\")\n\n # also save the plot in log scale\n plt.ylim(0.01, 1)\n plt.yscale(\"log\")\n plt.tight_layout()\n plt.savefig(outpath + \"inv_RV_lit_log.pdf\")","def _plot_curves(plot_file_name, curve_names, metric_sets, number_of_embeddings):\n logger = get_logger('analysis', logging.INFO)\n logger.info('Plot results')\n\n config = load_config(None, join(get_common(), 'config.cfg'))\n plot_width = config.getint('common', 'plot_width')\n fig_width = config.getint('common', 'fig_width')\n fig_height = config.getint('common', 'fig_height')\n #Slice results to only 1-80 clusters\n for i in range(0,len(metric_sets)):\n for j in range(0, len(metric_sets[i])):\n metric_sets[i][j] = metric_sets[i][j][-plot_width:]\n print(len(metric_sets[i][j]))\n\n best_results = [[] for _ in metric_names]\n for m, min_value in enumerate(metric_min_values):\n for results in metric_sets[m]:\n if(metric_min_values[m] == 0):\n best_results[m].append(np.max(results))\n else:\n best_results[m].append(np.min(results))\n\n '''\n This code is used to sort the lines by min mr. Because we now use mutliple metrics and dont sort by a single\n metric, this code is not used anymore, but we keep it for now.\n min_mrs, curve_names, mrs, acps, aris, homogeneity_scores, completeness_scores, number_of_embeddings = \\\n (list(t) for t in\n zip(*sorted(zip(min_mrs, curve_names, mrs, acps, aris, homogeneity_scores, completeness_scores, number_of_embeddings))))\n '''\n\n # How many lines to plot\n number_of_lines = len(curve_names)\n\n # Get various colors needed to plot\n color_map = plt.get_cmap('gist_rainbow')\n colors = [color_map(i) for i in np.linspace(0, 1, number_of_lines)]\n\n #Set fontsize for all plots\n plt.rcParams.update({'font.size': 12})\n\n # Define number of figures\n fig1 = plt.figure(figsize=(fig_width, fig_height))\n\n # Define Plots\n plot_grid = (3, 2)\n\n plots = [None] * len(metric_names)\n\n plots[0] = _add_cluster_subplot(plot_grid, (0, 0), metric_names[0], 1)\n plots[1] = _add_cluster_subplot(plot_grid, (0, 1), metric_names[1], 1)\n plots[2] = _add_cluster_subplot(plot_grid, (1, 0), metric_names[2], 1)\n plots[3] = _add_cluster_subplot(plot_grid, (1, 1), metric_names[3], 1)\n\n #Set the horizontal space between subplots\n plt.subplots_adjust(hspace = 0.3)\n\n # Define curves and their values\n curves = [[] for _ in metric_names]\n\n for m, metric_set in enumerate(metric_sets):\n curves[m] = [plots[m], metric_set]\n\n # Plot all curves\n for index in range(number_of_lines):\n label = curve_names[index]\n for m, metric_name in enumerate(metric_names):\n label = label + '\\n {} {}: {}'.format('Max' if metric_min_values[m]==0 else 'Min', metric_name,\n str(best_results[m][index]))\n color = colors[index]\n number_of_clusters = np.arange(plot_width, 0, -1)\n\n for plot, value in curves:\n plot.plot(number_of_clusters, value[index], color=color, label=label)\n\n # Add legend and save the plot\n fig1.legend(loc='upper center', bbox_to_anchor=(0.5, 0.33), ncol=4)\n #fig1.show()\n fig1.savefig(get_result_png(plot_file_name))\n fig1.savefig(get_result_png(plot_file_name + '.svg'), format='svg')","def plot(profile_likelihood, result):\n\n # Plot log-lp versus scale\n _plot_likelihood_versus_scale(\n profile_likelihood, result, numpy.logspace(-2, 2, 5))\n\n # Plot log-lp versus eta\n _plot_likelihood_versus_eta(\n profile_likelihood, result, numpy.logspace(-2, 2, 5))\n\n # Contour Plot of log-lp function\n _plot_likelihood_versus_eta_scale(profile_likelihood, result)\n\n # Plot first derivative of log likelihood\n _plot_likelihood_der1_eta(profile_likelihood, result)","def _lcResults(self):\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\n noCt = int( self.suffix.split('_')[-1] ) \n \n hdu = fits.open(fitsNnam)\n hdu['LIGHTCURVE'].data['flux'][noCt] = self.getResult(param='Flux')[0]\n hdu['LIGHTCURVE'].data['fluxerr'][noCt] = self.getResult(param='Flux')[1]\n hdu['LIGHTCURVE'].data['index'][noCt] = self.getResult(param='Index')[0]\n hdu['LIGHTCURVE'].data['indexerr'][noCt] = self.getResult(param='Index')[1]\n hdu['LIGHTCURVE'].data['ts'][noCt] = self.getResult(param='TS value')[0]\n hdu['LIGHTCURVE'].data['upperlim'][noCt] = self.getResult(param='upplim')[0] \n hdu['LIGHTCURVE'].data['status'][noCt] = 'done'\n hdu.writeto(fitsNnam, clobber=True)\n hdu.close()","def make_llr_plots(self):\n import matplotlib.pyplot as plt\n plt.rcParams['text.usetex'] = True\n\n outdir = os.path.join(self.outdir, 'LLRDistributions')\n mkdir(outdir)\n\n for injkey in self.values.keys():\n\n data = self.values[injkey]\n metric_type = data['h0_fit_to_h0_fid']['metric_val']['type']\n metric_type_pretty = self.tex_axis_label(metric_type)\n h0_fid_metric = self.fid_values[injkey][\n 'h0_fit_to_%s'%self.labels.dict['data']\n ][\n 'metric_val'\n ]\n h1_fid_metric = self.fid_values[injkey][\n 'h1_fit_to_%s'%self.labels.dict['data']\n ][\n 'metric_val'\n ]\n\n h0_fit_to_h0_fid_metrics = np.array(\n data['h0_fit_to_h0_fid']['metric_val']['vals']\n )\n h1_fit_to_h0_fid_metrics = np.array(\n data['h1_fit_to_h0_fid']['metric_val']['vals']\n )\n h0_fit_to_h1_fid_metrics = np.array(\n data['h0_fit_to_h1_fid']['metric_val']['vals']\n )\n h1_fit_to_h1_fid_metrics = np.array(\n data['h1_fit_to_h1_fid']['metric_val']['vals']\n )\n\n # In the case of likelihood, the maximum metric is the better fit.\n # With chi2 metrics the opposite is true, and so we must multiply\n # everything by -1 in order to apply the same treatment.\n if 'chi2' in metric_type:\n logging.info('Converting chi2 metric to likelihood equivalent.')\n h0_fid_metric *= -1\n h1_fid_metric *= -1\n h0_fit_to_h0_fid_metrics *= -1\n h1_fit_to_h0_fid_metrics *= -1\n h0_fit_to_h1_fid_metrics *= -1\n h1_fit_to_h1_fid_metrics *= -1\n\n if h1_fid_metric < h0_fid_metric:\n bestfit = 'h0'\n altfit = 'h1'\n critical_value = h0_fid_metric-h1_fid_metric\n else:\n bestfit = 'h1'\n altfit = 'h0'\n critical_value = h1_fid_metric-h0_fid_metric\n\n if bestfit == 'h0':\n llrbest = h0_fit_to_h0_fid_metrics - h1_fit_to_h0_fid_metrics\n llralt = h0_fit_to_h1_fid_metrics - h1_fit_to_h1_fid_metrics\n else:\n llrbest = h1_fit_to_h1_fid_metrics - h0_fit_to_h1_fid_metrics\n llralt = h1_fit_to_h0_fid_metrics - h0_fit_to_h0_fid_metrics\n\n minllr = min(min(llrbest), min(llralt))\n maxllr = max(max(llrbest), max(llralt))\n rangellr = maxllr - minllr\n # Special case for low numbers of trials. Here, the plot\n # can't really be interpreted but the numbers printed on\n # it can still be useful, so we need to make something.\n if self.num_trials < 100:\n binning = np.linspace(minllr - 0.1*rangellr,\n maxllr + 0.1*rangellr,\n 10)\n elif self.num_trials < 2000:\n binning = np.linspace(minllr - 0.1*rangellr,\n maxllr + 0.1*rangellr,\n int(self.num_trials/40))\n else:\n binning = np.linspace(minllr - 0.1*rangellr,\n maxllr + 0.1*rangellr,\n 50)\n binwidth = binning[1]-binning[0]\n\n llrbesthist, llrbestbinedges = np.histogram(llrbest, bins=binning)\n llralthist, llraltbinedges = np.histogram(llralt, bins=binning)\n\n llrhistmax = max(max(llrbesthist), max(llralthist))\n\n best_median = np.median(llrbest)\n\n if self.labels.dict['data_name'] == '':\n inj_name = \"data\"\n else:\n inj_name = \"true %s\"%self.tex_axis_label(\n self.labels.dict['data_name']\n )\n best_name = self.labels.dict['%s_name'%bestfit]\n alt_name = self.labels.dict['%s_name'%altfit]\n\n # Calculate p values\n ## First for the preferred hypothesis based on the fiducial fit\n crit_p_value, unc_crit_p_value = self.calc_p_value(\n llrdist=llralt,\n critical_value=critical_value\n )\n ## Then for the alternate hypothesis based on the fiducial fit\n alt_crit_p_value, alt_unc_crit_p_value = self.calc_p_value(\n llrdist=llrbest,\n critical_value=critical_value\n )\n ## Combine these to give a cls value based on arXiv:1407.5052\n cls_value = (1 - alt_crit_p_value) / (1 - crit_p_value)\n unc_cls_value = cls_value * np.sqrt(\n np.power(alt_unc_crit_p_value/alt_crit_p_value, 2.0) + \\\n np.power(unc_crit_p_value/crit_p_value, 2.0)\n )\n ## Then for the preferred hypothesis based on the median. That\n ## is, the case of a median experiment from the distribution\n ## under the preferred hypothesis.\n med_p_value, unc_med_p_value, median_error = self.calc_p_value(\n llrdist=llralt,\n critical_value=best_median,\n median_p_value=True,\n llrbest=llrbest\n )\n\n if metric_type == 'llh':\n plot_title = (r\"\\begin{center}\"\\\n +\"%s %s Event Selection \"%(self.detector,\n self.selection)\\\n +r\"\\\\\"+\" llr Distributions for %s (%i trials)\"%(\n inj_name, self.num_trials)\\\n +r\"\\end{center}\")\n\n else:\n plot_title = (r\"\\begin{center}\"\\\n +\"%s %s Event Selection \"%(self.detector,\n self.selection)\\\n +r\"\\\\\"+\" %s \\\"llr\\\" Distributions for \"\n %(metric_type_pretty)\\\n +\"%s (%i trials)\"%(inj_name,\n self.num_trials)\\\n +r\"\\end{center}\")\n\n # Factor with which to make everything visible\n plot_scaling_factor = 1.55\n\n # In case of median plot, draw both best and alt histograms\n ## Set up the labels for the histograms\n llr_labels = [\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(best_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\"%(best_name) + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name),\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(alt_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\"%(best_name) + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name)\n ]\n self.plot_llr_histograms(\n llrarrays=[llrbest, llralt],\n llrhistmax=llrhistmax,\n binning=binning,\n colors=['r', 'b'],\n labels=llr_labels,\n best_name=best_name,\n alt_name=alt_name,\n critical_value=best_median,\n critical_label=r\"%s Median = $%.4f\\pm%.4f$\"%(\n self.tex_axis_label(best_name),\n best_median,\n median_error),\n critical_height=float(max(llrbesthist))/float(\n plot_scaling_factor*llrhistmax),\n llrhist=llralthist\n )\n plt.legend(loc='upper left')\n plt.title(plot_title)\n # Write the p-value on the plot\n plt.figtext(\n 0.15,\n 0.66,\n r\"$\\mathrm{p}\\left(\\mathcal{H}_{%s}\\right) = %.4f\\pm%.4f$\"%(\n best_name, med_p_value, unc_med_p_value),\n color='k',\n size='xx-large'\n )\n self.save_plot(\n outdir=outdir,\n end='%s_llrDistribution_median_%i_Trials'%(\n metric_type, self.num_trials)\n )\n # Add the extra points if they exist\n if self.extra_points is not None:\n plt.legend(loc='upper left', fontsize=11)\n curleg = plt.gca().get_legend()\n linelist = self.add_extra_points(\n ymax=float(max(llrbesthist))/float(\n plot_scaling_factor*llrhistmax)\n )\n handles, labels = plt.gca().get_legend_handles_labels()\n newhandles = []\n for l, h in zip(labels, handles):\n if l in linelist:\n newhandles.append(h)\n newleg = plt.legend(\n handles=newhandles,\n loc='upper right',\n fontsize=11\n )\n plt.gca().add_artist(newleg)\n plt.gca().add_artist(curleg)\n self.save_plot(\n outdir=outdir,\n end='%s_llrDistribution_median_w_extra_points_%i_Trials'%(\n metric_type, self.num_trials)\n )\n plt.close()\n\n # Make some debugging plots\n ## Set up the labels for the histograms\n llr_labels = [\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(best_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\"%(best_name) + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name),\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(alt_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\"%(best_name) + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name)\n ]\n self.plot_llr_histograms(\n llrarrays=[llrbest, llralt],\n llrhistmax=llrhistmax,\n binning=binning,\n colors=['r', 'b'],\n labels=llr_labels,\n best_name=best_name,\n alt_name=alt_name,\n critical_value=best_median,\n critical_label=r\"%s Median = $%.4f\\pm%.4f$\"%(\n self.tex_axis_label(best_name),\n best_median,\n median_error),\n critical_height=float(max(llrbesthist))/float(\n plot_scaling_factor*llrhistmax),\n llrhist=None\n )\n plt.legend(loc='upper left')\n plt.title(plot_title)\n self.save_plot(\n outdir=outdir,\n end='%s_llrDistribution_median_both_fit_dists_%i_Trials'%(\n metric_type, self.num_trials)\n )\n plt.close()\n ## Set up the labels for the histograms\n llr_labels = [\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(best_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\" + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name),\n ]\n self.plot_llr_histograms(\n llrarrays=[llrbest],\n llrhistmax=llrhistmax,\n binning=binning,\n colors=['r'],\n labels=llr_labels,\n best_name=best_name,\n alt_name=alt_name,\n critical_value=None,\n critical_label=None,\n critical_height=None,\n llrhist=None\n )\n plt.legend(loc='upper left')\n plt.title(plot_title)\n self.save_plot(\n outdir=outdir,\n end='%s_llrDistribution_best_fit_dist_%i_Trials'%(\n metric_type, self.num_trials)\n )\n plt.close()\n ## Set up the labels for the histograms\n llr_labels = [\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(best_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\"%(best_name) + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name),\n ]\n self.plot_llr_histograms(\n llrarrays=[llrbest],\n llrhistmax=llrhistmax,\n binning=binning,\n colors=['r'],\n labels=llr_labels,\n best_name=best_name,\n alt_name=alt_name,\n critical_value=best_median,\n critical_label=r\"%s Median = $%.4f\\pm%.4f$\"%(\n self.tex_axis_label(best_name),\n best_median,\n median_error),\n critical_height=float(max(llrbesthist))/float(\n plot_scaling_factor*llrhistmax),\n llrhist=None\n )\n plt.legend(loc='upper left')\n plt.title(plot_title)\n self.save_plot(\n outdir=outdir,\n end='%s_llrDistribution_median_best_fit_dist_%i_Trials'%(\n metric_type, self.num_trials)\n )\n plt.close()\n ## Set up the labels for the histograms\n llr_labels = [\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(alt_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\"%(best_name) + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name)\n ]\n self.plot_llr_histograms(\n llrarrays=[llralt],\n llrhistmax=llrhistmax,\n binning=binning,\n colors=['b'],\n labels=llr_labels,\n best_name=best_name,\n alt_name=alt_name,\n critical_value=None,\n critical_label=None,\n critical_height=None,\n llrhist=None\n )\n plt.legend(loc='upper left')\n plt.title(plot_title)\n self.save_plot(\n outdir=outdir,\n end='%s_llrDistribution_alt_fit_dist_%i_Trials'%(\n metric_type, self.num_trials)\n )\n plt.close()\n ## Set up the labels for the histograms\n llr_labels = [\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(alt_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\" + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name)\n ]\n self.plot_llr_histograms(\n llrarrays=[llralt],\n llrhistmax=llrhistmax,\n binning=binning,\n colors=['b'],\n labels=llr_labels,\n best_name=best_name,\n alt_name=alt_name,\n critical_value=best_median,\n critical_label=r\"%s Median = $%.4f\\pm%.4f$\"%(\n self.tex_axis_label(best_name),\n best_median,\n median_error),\n critical_height=float(max(llrbesthist))/float(\n plot_scaling_factor*llrhistmax),\n llrhist=llralthist\n )\n plt.legend(loc='upper left')\n plt.title(plot_title)\n # Write the p-value on the plot\n plt.figtext(\n 0.15,\n 0.66,\n r\"$\\mathrm{p}\\left(\\mathcal{H}_{%s}\\right) = %.4f\\pm%.4f$\"%(\n best_name, med_p_value, unc_med_p_value),\n color='k',\n size='xx-large'\n )\n self.save_plot(\n outdir=outdir,\n end='%s_llrDistribution_median_alt_fit_dist_%i_Trials'%(\n metric_type, self.num_trials)\n )\n plt.close()\n\n # In case of critical plot, draw just alt histograms\n ## Set up the label for the histogram\n llr_labels = [\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(alt_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\"%(best_name) + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name)\n ]\n self.plot_llr_histograms(\n llrarrays=[llralt],\n llrhistmax=llrhistmax,\n binning=binning,\n colors=['b'],\n labels=llr_labels,\n best_name=best_name,\n alt_name=alt_name,\n critical_value=critical_value,\n critical_label=r\"Critical Value = %.4f\"%(critical_value),\n critical_height=float(max(llrbesthist))/float(\n plot_scaling_factor*llrhistmax),\n llrhist=llralthist\n )\n plt.legend(loc='upper left')\n plt.title(plot_title)\n # Write the p-value on the plot\n plt.figtext(\n 0.15,\n 0.70,\n r\"$\\mathrm{p}\\left(\\mathcal{H}_{%s}\\right) = %.4f\\pm%.4f$\"%(\n best_name, crit_p_value, unc_crit_p_value),\n color='k',\n size='xx-large'\n )\n self.save_plot(\n outdir=outdir,\n end='%s_llrDistribution_critical_%i_Trials'%(\n metric_type, self.num_trials)\n )\n plt.close()\n\n # Make a second critical plot for the alt hypothesis, so we draw the\n # preferred hypothesis\n ## Set up the label for the histogram\n llr_labels = [\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(best_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\"%(best_name) + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name)\n ]\n self.plot_llr_histograms(\n llrarrays=[llrbest],\n llrhistmax=llrhistmax,\n binning=binning,\n colors=['r'],\n labels=llr_labels,\n best_name=best_name,\n alt_name=alt_name,\n critical_value=critical_value,\n critical_label=r\"Critical Value = %.4f\"%(critical_value),\n critical_height=float(max(llrbesthist))/float(\n plot_scaling_factor*llrhistmax),\n llrhist=llrbesthist\n )\n plt.legend(loc='upper left')\n plt.title(plot_title)\n # Write the p-value on the plot\n plt.figtext(\n 0.15,\n 0.70,\n r\"$\\mathrm{p}\\left(\\mathcal{H}_{%s}\\right) = %.4f\\pm%.4f$\"%(\n alt_name, alt_crit_p_value, alt_unc_crit_p_value),\n color='k',\n size='xx-large'\n )\n self.save_plot(\n outdir=outdir,\n end='%s_llrDistribution_critical_alt_%i_Trials'%(\n metric_type, self.num_trials)\n )\n plt.close()\n\n # Lastly, show both exclusion regions and then the joined cls value\n ## Set up the labels for the histograms\n llr_labels = [\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(best_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\"%(best_name) + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name),\n r\"%s Pseudo-Experiments - \"%(self.tex_axis_label(alt_name)) + \\\n r\"$\\log\\left[\\mathcal{L}\\left(\\mathcal{H}_{%s}\"%(best_name) + \\\n r\"\\right)/\\mathcal{L}\\left(\\mathcal{H}_{%s}\\right)\\right]$\"%(\n alt_name)\n ]\n self.plot_llr_histograms(\n llrarrays=[llrbest, llralt],\n llrhistmax=llrhistmax,\n binning=binning,\n colors=['r', 'b'],\n labels=llr_labels,\n best_name=best_name,\n alt_name=alt_name,\n critical_value=critical_value,\n critical_label=r\"Critical Value = %.4f\"%(critical_value),\n critical_height=float(max(llrbesthist))/float(\n plot_scaling_factor*llrhistmax),\n llrhist=[llrbesthist, llralthist],\n cls=True,\n )\n plt.legend(loc='upper left')\n plt.title(plot_title)\n # Write the p-values on the plot\n plt.figtext(\n 0.50,\n 0.66,\n r\"$\\mathrm{CL}_{s}\\left(\\mathcal{H}_{%s}\"%(best_name) + \\\n r\"\\right)= %.4f\\pm%.4f$\"%(cls_value, unc_cls_value),\n horizontalalignment='center',\n color='k',\n size='xx-large'\n )\n plt.figtext(\n 0.12,\n 0.55,\n r\"$\\mathrm{p}\\left(\\mathcal{H}_{%s}\\right) = %.2f\\pm%.2f$\"%(\n alt_name, alt_crit_p_value, alt_unc_crit_p_value),\n bbox=dict(facecolor='none', edgecolor='red', boxstyle='round'),\n horizontalalignment='left',\n color='k',\n size='x-large'\n )\n plt.figtext(\n 0.88,\n 0.55,\n r\"$\\mathrm{p}\\left(\\mathcal{H}_{%s}\\right) = %.2f\\pm%.2f$\"%(\n best_name, crit_p_value, unc_crit_p_value),\n horizontalalignment='right',\n bbox=dict(facecolor='none', edgecolor='blue', boxstyle='round'),\n color='k',\n size='x-large'\n )\n self.save_plot(\n outdir=outdir,\n end='%s_llrDistribution_CLs_%i_Trials'%(\n metric_type, self.num_trials)\n )\n plt.close()","def plotdFvsLambda1():\n x = numpy.arange(len(df_allk))\n if x[-1]<8:\n fig = pl.figure(figsize = (8,6))\n else:\n fig = pl.figure(figsize = (len(x),6))\n width = 1./(len(P.methods)+1)\n elw = 30*width\n colors = {'TI':'#C45AEC', 'TI-CUBIC':'#33CC33', 'DEXP':'#F87431', 'IEXP':'#FF3030', 'GINS':'#EAC117', 'GDEL':'#347235', 'BAR':'#6698FF', 'UBAR':'#817339', 'RBAR':'#C11B17', 'MBAR':'#F9B7FF'}\n lines = tuple()\n for name in P.methods:\n y = [df_allk[i][name]/P.beta_report for i in x]\n ye = [ddf_allk[i][name]/P.beta_report for i in x]\n line = pl.bar(x+len(lines)*width, y, width, color=colors[name], yerr=ye, lw=0.1*elw, error_kw=dict(elinewidth=elw, ecolor='black', capsize=0.5*elw))\n lines += (line[0],)\n pl.xlabel('States', fontsize=12, color='#151B54')\n pl.ylabel('$\\Delta G$ '+P.units, fontsize=12, color='#151B54')\n pl.xticks(x+0.5*width*len(P.methods), tuple(['%d--%d' % (i, i+1) for i in x]), fontsize=8)\n pl.yticks(fontsize=8)\n pl.xlim(x[0], x[-1]+len(lines)*width)\n ax = pl.gca()\n for dir in ['right', 'top', 'bottom']:\n ax.spines[dir].set_color('none')\n ax.yaxis.set_ticks_position('left')\n for tick in ax.get_xticklines():\n tick.set_visible(False)\n\n leg = pl.legend(lines, tuple(P.methods), loc=3, ncol=2, prop=FP(size=10), fancybox=True)\n leg.get_frame().set_alpha(0.5)\n pl.title('The free energy change breakdown', fontsize = 12)\n pl.savefig(os.path.join(P.output_directory, 'dF_state_long.pdf'), bbox_inches='tight')\n pl.close(fig)\n return","def test_fits_lcurve0(self):\n lcurve_ftools_orig = os.path.join(self.datadir, 'lcurveA.fits')\n\n lcurve_ftools = os.path.join(self.datadir,\n 'lcurve_ftools_lc' +\n HEN_FILE_EXTENSION)\n\n command = \"{0} --outfile {1}\".format(\n os.path.join(self.datadir,\n 'monol_testA_nustar_fpma_ev') + HEN_FILE_EXTENSION,\n os.path.join(self.datadir,\n 'lcurve_lc'))\n hen.lcurve.main(command.split())\n assert os.path.exists(os.path.join(self.datadir,\n 'lcurve_lc') + HEN_FILE_EXTENSION)\n\n command = \"--fits-input {0} --outfile {1}\".format(\n lcurve_ftools_orig,\n lcurve_ftools)\n hen.lcurve.main(command.split())","def main():\n df = prof_conv_bwd_filter()\n df.to_csv(\"prof.cudnnConvBwdFilter.csv\")\n\n \"\"\"visualization, Roofline model\"\"\"\n df = pd.read_csv('prof.cudnnConvBwdFilter.csv', header=0, index_col=0)\n fig = plt.figure(figsize=(10,5))\n ax = fig.add_subplot(1, 1, 1)\n plot_rooline (ax, MACHINE_SPEC, PEAK_PERF, BAND_WIDTH)\n plot_result (ax, df)\n # fig.subplots_adjust(right=0.8)\n plt.subplots_adjust(left=0.1, right=0.6)\n plt.savefig('roofline.png')\n return","def decay_proportion_plot(Lmax=1000, p1=database['K+'], p=75, target_rate=53957518.001):\r\n L_range = np.linspace(0, 1000, 10000)\r\n prop = []\r\n for L in L_range:\r\n prop.append(decay_proportion(L, p1, p, target_rate))\r\n# charac_L = p*c*(p1.tau*1e-3/c)/p1.mass\r\n fig = plt.figure(figsize=[12, 3])\r\n ax = fig.add_subplot(1, 1, 1)\r\n ax.plot(L_range, prop, 'r', lw=2)\r\n ax.set_xlim(0, Lmax)\r\n ax.set_ylim(0)\r\n ax.set_xlabel('Target Distance', fontsize=20)\r\n ax.set_ylabel(r'$K^+$ flux', fontsize=20)\r\n# ax.xaxis.set_major_locator(plt.MultipleLocator(charac_L/4))\r\n# ax.xaxis.set_minor_locator(plt.MultipleLocator(charac_L/20))\r\n# ax.xaxis.set_major_formatter(plt.FuncFormatter(multiple_formatter_non_int(1, charac_L, 'L_{K^+}')))\r\n ax.set_xticks([0])\r\n ax.set_yticks([target_rate])\r\n ax.yaxis.set_major_locator(plt.MultipleLocator(target_rate/1))\r\n ax.yaxis.set_minor_locator(plt.MultipleLocator(target_rate/1))\r\n ax.yaxis.set_major_formatter(plt.FuncFormatter(multiple_formatter_non_int(1, target_rate, 'R_t')))\r\n ax.legend(fontsize=20)\r\n ax.minorticks_off()\r\n# ax.grid()\r\n plt.show()\r\n return","def plot_fig44a_wlc_inhomo_test7():\n\n fig, ax = plt.subplots(figsize=(default_width, default_height))\n #paramters from calculation, all in nm\n a = 5 #looping radius\n DEL = 15.3 #spacing between nucleosomes\n MINUS1NUC = -83.3 #distance between -1 nuc and TSS\n PLUS1NUC = 0\n NNUC = 50 #number of nucleosomes on each side of TSS\n LPB = 0.34 #nm per basepair (as used in calculation)\n\n Llinks = np.concatenate(([245], np.tile(45, 49)))\n Rlinks = np.tile(45, 49)\n #plot as negative distance from TSS in bp\n unwrap=0\n ldna_Llinks = -1*convert.genomic_length_from_links_unwraps(Llinks, unwraps=unwrap)\n ldna_Rlinks = convert.genomic_length_from_links_unwraps(Rlinks, unwraps=unwrap)\n x = np.concatenate((ldna_Llinks[::-1], [0], ldna_Rlinks))\n y = np.loadtxt('csvs/Sarah/pnucsite_test7.txt')\n ax.plot(x, y, '-o', markersize=3, color=teal_flucts)\n plt.xlabel(r'Distance from TSS (bp)')\n plt.ylabel(r'$P_\\mathrm{loop}(a=5\\mathrm{nm}; L)\\;\\;\\;(\\mathrm{nm}^{-3})$')\n plt.subplots_adjust(left=0.16, bottom=0.19, top=0.98, right=0.96)\n plt.yscale('log')\n #plt.ylim([10**-3, 10**-0.4])\n plt.xlim([-10000, 10000])\n plt.savefig(f'plots/thesis/fig44a_wlc-inhomo-looping.pdf', bbox_inches='tight')","def plot_learning_curve(X, y, maxdepth, estimator, plt):\n # create cv training and test scores for various training set sizes\n train_sizes, train_scores, test_scores = learning_curve(estimator,\n X, # feature matrix\n y, # target vector\n cv=10, # number of folds in cross-validation\n scoring='neg_mean_squared_error', # metric\n n_jobs=-1, # use all computer cores,\n train_sizes=np.linspace(0.01, 1.0, 30) # 30 different sizes of the training set\n )\n # create means and standart deviations of training set scores\n train_mean = np.mean(train_scores, axis=1)\n train_std = np.std(train_scores, axis=1)\n\n # create means and standart deviations of test set scores\n test_mean = np.mean(test_scores, axis=1)\n test_std = np.std(test_scores, axis=1)\n\n # draw lines\n plt.plot(train_sizes, train_mean, '--', color='#111111', label=\"Training score\")\n plt.plot(train_sizes, test_mean, color='#111111', label=\"Cross-validation score\")\n\n # draw bands\n plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color=\"#DDDDDD\")\n plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color=\"#f4d0d7\")\n \n # create plot \n plt.title(\"Learning curve\")\n plt.xlabel(\"Training set size\", fontsize=18)\n plt.ylabel(\"mse\", fontsize=18)\n plt.legend(loc=\"best\")\n plt.tight_layout()","def main():\n\n args = arg_parse()\n plot_zcalib(args)","def learning_curve():\n loss = []\n val_loss = []\n data_size = []\n\n x_slid, y_slid = sliding_window_main(x, y)\n x_train, y_train, x_val, y_val, x_test, y_test = data_splitting_main(x_slid, y_slid)\n m_tot = x_train.shape[0]\n\n batch_step = 50\n try:\n for m in range(batch_size, m_tot, batch_step*batch_size):\n print(\"Training: \", m)\n net = create_network()\n history = trainer(net, x_train[:m], y_train[:m], x_val, y_val)\n loss.append(history.history[\"loss\"][-1])\n val_loss.append(history.history[\"val_loss\"][-1])\n data_size.append(m)\n\n print(\"Loss:\", loss[-1])\n print()\n\n finally:\n plt.plot(data_size, loss, label=\"Loss\", marker=\"o\")\n plt.plot(data_size, val_loss, label=\"Val Loss\", marker=\"o\")\n plt.xlabel(\"m\")\n plt.ylabel(\"Losses\")\n plt.title(\"Model Loss\")\n plt.legend()\n plt.savefig(\"img/\" + datetime.now().strftime(\"%y%m%d_%H%M\") + \"_learning_curve.png\")\n plt.show()\n plt.close()\n\n return loss, val_loss","def plot_loss_curves(results):\n loss = results[\"train_loss\"]\n test_loss = results[\"test_loss\"]\n\n accuracy = results[\"train_acc\"]\n test_accuracy = results[\"test_acc\"]\n\n epochs = range(len(results[\"train_loss\"]))\n\n plt.figure(figsize=(15, 7))\n\n # Plot loss\n plt.subplot(1, 2, 1)\n plt.plot(epochs, loss, label=\"train_loss\")\n plt.plot(epochs, test_loss, label=\"test_loss\")\n plt.title(\"Loss\")\n plt.xlabel(\"Epochs\")\n plt.legend()\n\n # Plot accuracy\n plt.subplot(1, 2, 2)\n plt.plot(epochs, accuracy, label=\"train_accuracy\")\n plt.plot(epochs, test_accuracy, label=\"test_accuracy\")\n plt.title(\"Accuracy\")\n plt.xlabel(\"Epochs\")\n plt.legend()","def plot_vis_test(plotfile,pdf_file):\n\t# First some parameters looked up from configfile---------------------------------\n\t\n\tgrbdir = runconf['l2file'][0:10]\n\tpre_tstart = runconf['bkg1start']\n\tpre_tend = runconf['bkg1end']\n\ttrigtime = runconf['trigtime']\n\tgrb_tstart = runconf['transtart']\n\tgrb_tend = runconf['tranend']\n\tpost_tstart = runconf['bkg2start']\n\tpost_tend = runconf['bkg2end']\n\tt_src = grb_tend - grb_tstart \n\tt_tot = (pre_tend-pre_tstart)+(post_tend-post_tstart)\n\tra_tran = runconf['ra']\n\tdec_tran = runconf['dec']\n\tlc_bin = runconf['lc_bin']\n\talpha = runconf['alpha']\n\tbeta = runconf['beta']\n\tE0 = runconf['E0']\n\tA = runconf['A']\n\tsim_scale = t_src\n\tpixbin = int(runconf['pixsize'])\n\tcomp_bin = int(runconf['comp_bin'])\n\ttyp = runconf['typ']\n\n\t# Calling txy to calculate thetax thetay and the coordinates----------------------\n\t\n\tthetax,thetay,x,y,z,t = txy(runconf['mkffile'], trigtime, ra_tran, dec_tran)\n\t\n\t# Plot the 3d visualisation for the position of the transient---------------------\n\tplt.figure()\n\tfig = visualize_3d(grbdir,x,y,z, t, thetax, thetay, grbdir)\t\n\tpdf_file.savefig(fig)\n\t\n\t# Plotting the lightcurves for the four quadrants---------------------------------\n\tfig = plt.figure()\n\tclean_file = fits.open(runconf['infile'])\n\tplt.title('Light curves for '+grbdir + \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n\t\n\tquad0 = clean_file[1].data\n\tdata0,bin_edge = np.histogram(quad0['time'], bins=np.arange(quad0['time'][0],quad0['time'][-1],lc_bin))\n\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data0,label='Quad 0',lw=0.7)\n quad1 = clean_file[2].data\n\tdata1,bin_edge = np.histogram(quad1['time'], bins=np.arange(quad1['time'][0],quad1['time'][-1],lc_bin))\n\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data1,label='Quad 1',lw=0.7) \n\tquad2 = clean_file[3].data\n\tdata2,bin_edge = np.histogram(quad2['time'], bins=np.arange(quad2['time'][0],quad2['time'][-1],lc_bin))\n\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data2,label='Quad 2',lw=0.7)\n quad3 = clean_file[4].data\n data3,bin_edge = np.histogram(quad3['time'], bins=np.arange(quad3['time'][0],quad3['time'][-1],lc_bin))\n plt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data3,label='Quad 3',lw=0.7)\n\tplt.axvspan(grb_tstart,grb_tend,color='blue',alpha=0.1,label='GRB')\n\tplt.axvspan(pre_tstart,pre_tend,color='orange',alpha=0.2)\n\tplt.axvspan(post_tstart,post_tend,color='orange',alpha=0.2,label='Background')\n\tplt.legend(prop={'size':6})\n\tplt.xlim(pre_tstart-100,post_tend+100)\n\tpdf_file.savefig(fig)\n\t\n\t# Calling the sim_dph--------------------------------------------------------------\n\t\n\tgrb_flat,bkgd_flat,grb_dph,bkgd_dph,t_src,t_total = data_bkgd_image(grbdir,pre_tstart,pre_tend,grb_tstart,grb_tend,post_tstart,post_tend)\n\n\tsim_flat,sim_dph,badpix_mask,sim_err_dph = simulated_dph(grbdir,typ,t_src,alpha,beta,E0,A)\n\n\tsrc_dph = grb_dph-bkgd_dph*t_src/t_tot\n\n print \"Total counts in simulated dph: \",(sim_dph).sum()\n print \"Total counts after badpix mask is applied: \",(sim_dph*badpix_mask).sum()\n\tprint \"Excess counts in badpix masked src dph: \",(src_dph*badpix_mask).sum()\n \n\t# Plotting the DPHs before badpix correction---------------------------------------\n\t\n\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n\tplt.suptitle('DPHs before badpix correction for '+grbdir + \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n \t# Sim\n\tim = ax3.imshow(sim_dph,interpolation='none')\n\tax3.set_title('Sim DPH',fontsize=8)\n\tax3.set_xlim(-1,128 - 0.5)\n\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax3.spines['left'].set_position(('data',-0.5))\n\tax3.set_yticklabels([])\n\tax3.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\n\t\n\t # Source \n\tim = ax4.imshow(src_dph,interpolation='none',vmin=0)\n\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\n\tax4.set_xlim(-1,128 -0.5)\n\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax4.spines['left'].set_position(('data',-0.5))\n\tax4.set_yticklabels([])\n\tax4.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\n\n \t# Source + Background\n\tim = ax1.imshow(grb_dph,interpolation='none')\n\tax1.set_title('Src + Bkg DPH',fontsize=8)\n\tax1.set_xlim(-1,128 -0.5)\n\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax1.spines['left'].set_position(('data',-0.5))\n\tax1.set_yticklabels([])\n\tax1.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\n\n \t# Background\n\tim = ax2.imshow(bkgd_dph*t_src/t_total,interpolation='none')\n\tax2.set_title('Bkg DPH',fontsize=8)\n\tax2.set_xlim(-1,128 -0.5)\n\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax2.spines['left'].set_position(('data',-0.5))\n\tax2.set_yticklabels([])\n\tax2.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\n\tf.set_size_inches([6.5,6.5])\n\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\n\t\n\t# Plotting the Badpix mask---------------------------------------------\n\n\tfig = plt.figure()\n\tax = plt.subplot(111)\n\tplt.title('Badpix Mask for '+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n\tim = ax.imshow(badpix_mask,interpolation='none')\n\tax.set_xlim(-9,128 -0.5)\n\tax.axvline(x=-5.,ymin=0,ymax=64,linewidth=5,color='k')\n\tax.spines['left'].set_position(('data',-0.5))\n\tax.xaxis.set_ticks(np.arange(0,128,16))\n\tax.yaxis.set_ticks(np.arange(0,128,16))\n\tfig.colorbar(im,ax=ax,fraction=0.046, pad=0.04)\n\t\n\tpdf_file.savefig(fig) # saves the current figure into a pdf_file page\n\n\t# Plotting badpix masked graphs--------------------------------------------\n\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n \t# Sim\n\tim = ax3.imshow(sim_dph*badpix_mask,interpolation='none')\n\tax3.set_title('Sim DPH',fontsize=8)\n\tax3.set_xlim(-1,128 -0.5)\n\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax3.spines['left'].set_position(('data',-0.5))\n\tax3.set_yticklabels([])\n\tax3.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\n\n\t # Source \n\tim = ax4.imshow(src_dph*badpix_mask,interpolation='none',vmin=0)\n\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\n\tax4.set_xlim(-1,128 -0.5)\n\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax4.spines['left'].set_position(('data',-0.5))\n\tax4.set_yticklabels([])\n\tax4.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\n\n\t # Source + Background\n\tim = ax1.imshow(grb_dph*badpix_mask,interpolation='none')\n\tax1.set_title('Src + Bkg DPH',fontsize=8)\n\tax1.set_xlim(-1,128 -0.5)\n\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax1.spines['left'].set_position(('data',-0.5))\n\tax1.set_yticklabels([])\n\tax1.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\n\t\n\t # Background\n\tim = ax2.imshow(bkgd_dph*badpix_mask*t_src/t_total,interpolation='none')\n\tax2.set_title('Bkg DPH',fontsize=8)\n\tax2.set_xlim(-1,128 -0.5)\n\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax2.spines['left'].set_position(('data',-0.5))\n\tax2.set_yticklabels([])\n\tax2.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\n\tf.set_size_inches([6.5,6.5])\n\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\n\n\t# Plotting badpix masked graphs (Binned) ----------------------------------------------------\n\tfor p in [4,8,16]:\n\t\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n\t\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay)+ \"pixsize=\"+str(p))\n\t\t # Sim\n\t\tim = ax3.imshow(resample(sim_dph*badpix_mask,p),interpolation='none')\n\t\tax3.set_title('Sim DPH',fontsize=8)\n\t\tax3.set_xlim(-1,128/p -0.5)\n\t\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax3.spines['left'].set_position(('data',-0.5))\n\t\tax3.set_yticklabels([])\n\t\tax3.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n\t\tax3.set_xticklabels(np.arange(0,128,16))\n\t\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\n\t\t\n\t\t # Source \n\t\tim = ax4.imshow(resample(src_dph*badpix_mask,p),interpolation='none',vmin=0)\n\t\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\n\t\tax4.set_xlim(-1,128/p -0.5)\n\t\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax4.spines['left'].set_position(('data',-0.5))\n\t\tax4.set_yticklabels([])\n ax4.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n ax4.set_xticklabels(np.arange(0,128,16))\t\t\n\t\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\n\t\t\n\t\t # Source + Background\n\t\tim = ax1.imshow(resample(grb_dph*badpix_mask,p),interpolation='none')\n\t\tax1.set_title('Src + Bkg DPH',fontsize=10)\n\t\tax1.set_xlim(-1,128/p -0.5)\n\t\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax1.spines['left'].set_position(('data',-0.5))\n\t\tax1.set_yticklabels([])\n ax1.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n ax1.set_xticklabels(np.arange(0,128,16))\t\t\n\t\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\n\t\t\n\t\t # Background\n\t\tim = ax2.imshow(resample(bkgd_dph*badpix_mask*t_src/t_total,p),interpolation='none')\n\t\tax2.set_title('Bkg DPH',fontsize=8)\n\t\tax2.set_xlim(-1,128/p -0.5)\n\t\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax2.spines['left'].set_position(('data',-0.5))\n\t\tax2.set_yticklabels([])\n ax2.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n ax2.set_xticklabels(np.arange(0,128,16))\t\t\n\t\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\n\t\tf.set_size_inches([6.5,6.5])\n\t\t\n\t\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\n\n\n\t# Plotting the comparison graphs with equal bins ---------------------------------------\n\tprint \"No. of pixels with zero counts in sim_dph: \",sim_dph[sim_dph==0].size\n\tprint \"No. of pixels with zero counts in grb_dph(no bkg subtration): \",grb_dph[grb_dph==0].size\n\t\n\t# Generating the array for module number ------------------------------------------------\n\tA = ['A'+str(i) for i in range(16)]\n\tB = np.flip(['B'+str(i) for i in range(16)],0)\n\tC = np.flip(['C'+str(i) for i in range(16)],0)\n\tD = ['D'+str(i) for i in range(16)]\n\tquad_a = np.reshape(A,(4,4))\n\tquad_b = np.reshape(B,(4,4))\n\tquad_c = np.reshape(C,(4,4))\n\tquad_d = np.reshape(D,(4,4))\n\tMod_arr = np.ndarray((8,8),dtype='|S3')\n\tMod_arr[:4,:4] = quad_a\n\tMod_arr[:4,4:] = quad_b\n\tMod_arr[4:,4:] = quad_c\n\tMod_arr[4:,:4] = quad_d\n\tMod_names = Mod_arr.flatten()\n\t#print \"Module name array : \",Mod_names\n\t#-----------------------------------------------------------------------------------------\n\t\t\n\tsim_dph = sim_dph*badpix_mask\n\tsim_err_dph = sim_err_dph*badpix_mask\n grb_dph = grb_dph*badpix_mask\n bkgd_dph = bkgd_dph*badpix_mask\n\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\n\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\n\n\tsim_bin = resample(sim_dph,pixbin)\n\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\t\n\tgrb_bin = resample(grb_dph,pixbin)\n\tbkgd_bin = resample(bkgd_dph,pixbin)\n\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\t\n\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\t\n\n\tsim_flat_bin = sim_bin.flatten()\n\tsim_err_flat_bin = sim_err_bin.flatten()\n\tgrb_flat_bin = grb_bin.flatten()\n\tbkgd_flat_bin = bkgd_bin.flatten()\n\tgrb_err_flat_bin = grb_err_bin.flatten()\n\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\n\t\n\n\t # Defining model background and data\n\tmodel = sim_flat_bin\n\tmodel_copy = np.copy(model)\n\tbkgd = bkgd_flat_bin*t_src/t_tot\n\tsrc = grb_flat_bin\n\t\n\tdata = src - bkgd\n\tdata_copy = np.copy(data)\n\t\n\terr_src = grb_err_flat_bin\n\terr_bkgd = bkgd_err_flat_bin\n\terr_model = sim_err_flat_bin\n\terr_model_copy = np.copy(err_model)\n\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\terr_data_copy = np.copy(err_data)\n\t\n\tratio = data/model\n\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\n\t\n\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n\t\n\t # PLotting the comparison plots\n\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\n\t\n\tax1.set_title(\"Comparison between simulated and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f}\".format(tx=thetax,ty=thetay,c=chi_sq))\n\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation\",elinewidth=0.5)\n\tax1.legend()\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\n\tax1.set_ylabel('Counts')\n\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n\n\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\"Ratio = Data/Model\",elinewidth=0.5)\n\tax2.xaxis.set_ticks(np.arange(0,len(data)))\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\n\tax2.tick_params(labelsize=5)\n\tax2.axhline(y=1,linewidth=0.5,color='k')\n\tax2.legend()\n\tax2.set_xlabel('CZT Modules')\n\tax2.set_ylabel('Ratio of counts')\n\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\n\tplt.tight_layout(h_pad=0.0)\n\tf.set_size_inches([6.5,10])\n\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\n\n\t# Plotting comparison graphs with random binning------------------------------\n\t\n sim_flat = sim_dph.flatten()\n\tsim_err_flat = sim_err_dph.flatten()\n grb_flat = grb_dph.flatten()\n bkgd_flat = bkgd_dph.flatten()\n\tsrc_flat = src_dph.flatten()\n\t\n\torder = np.random.permutation(np.arange(0,len(sim_flat)))\n\t\n sim_flat = sim_flat[order]\n\tsim_err_flat = sim_err_flat[order]\n\tgrb_flat = grb_flat[order]\n\tbkgd_flat = bkgd_flat[order]\n\tsrc_flat = src_flat[order]\n\t\n\tprint \"No. of pixels with zero counts in sim_flat: \",sim_flat[sim_flat==0].size\n\tprint \"No. of pixels with zero counts in src_flat: \",src_flat[src_flat==0].size\n\t\n\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\n\tx = np.zeros(len(bins)+2,dtype=np.int64)\n\tx[0] = 0\n\tx[-1] = len(sim_flat)\n\tx[1:-1] = bins\n\t\n\t#print \"The bin edges: \",x # ---------------------------------------------------------------\n\t\n\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\n\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\t\n\tprint \"Total sim_flat_bin : \",sim_flat_bin.sum() #-----------------------------------------\n\t#print \" Max(cumsum) : \",max(np.cumsum(sim_flat)) #-----------------------------------------\n\n # Defining model background and data\n model = sim_flat_bin #avg_flat_bin\n bkgd = bkgd_flat_bin*t_src/t_tot\n src = grb_flat_bin\n\t\n data = src - bkgd\n\n err_src = np.sqrt(src)\n err_bkgd = np.sqrt(bkgd_flat_bin)\n err_model = sim_err_flat_bin\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\t\n\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n # PLotting the comparison plots\n fig = plt.figure()\n plt.title(\"Comparison between simulated and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f} \".format(tx=thetax,ty=thetay,c=chi_sq_new))\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation\",elinewidth=0.5)\n plt.ylabel('Counts')\n\tplt.xlabel('Random Bins')\n\tplt.xticks(np.arange(0,(len(data)),1))\n\tplt.legend()\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\n\n\t# Plotting observed vs predicted counts------------------------------------------------------\n\n\tfig = plt.figure()\n\tplt.title(grbdir + r\" : Observed vs Predicted counts with $\\chi^2$={cs:0.1f}\".format(cs=chi_sq))\n\tplt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\n\tfor i in range(len(model_copy)):\n\t\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\n\tplt.plot(np.arange(-1000,1000),np.arange(-1000,1000),'k',linewidth=0.5)\n\tplt.xlim(min(model_copy)-5,max(model_copy)+5)\n\tplt.ylim(min(data_copy)-5,max(data_copy)+5)\n\tplt.xlabel('Predicted Counts')\n\tplt.ylabel('Observed Counts')\n\tplt.legend()\n\tplt.grid()\n\tpdf_file.savefig(fig)\n\n\t# Scaling the model using curve fit =============================================================== \n\t\n\tparam,pcov = curve_fit(fit_line_int,model_copy,data_copy)\n\tscaling = param[0]\n\tintercept = param[1]\n\t\n\t# Plotting the scaled plots ===================================================================\n\t# Plotting the comparison graphs with equal bins ---------------------------------------\n\n\tsim_dph = sim_dph*badpix_mask\n\tsim_err_dph = sim_err_dph*badpix_mask\n grb_dph = grb_dph*badpix_mask\n bkgd_dph = bkgd_dph*badpix_mask\n\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\n\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\n\n\tsim_bin = resample(sim_dph,pixbin)\n\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\t\n\tgrb_bin = resample(grb_dph,pixbin)\n\tbkgd_bin = resample(bkgd_dph,pixbin)\n\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\t\n\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\t\n\n\tsim_flat_bin = sim_bin.flatten()\n\tsim_err_flat_bin = sim_err_bin.flatten()\n\tgrb_flat_bin = grb_bin.flatten()\n\tbkgd_flat_bin = bkgd_bin.flatten()\n\tgrb_err_flat_bin = grb_err_bin.flatten()\n\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\n\t\n\n\t # Defining model background and data\n\t#model = sim_flat_bin*scaling\n\tmodel = sim_flat_bin*scaling + intercept\n\tbkgd = bkgd_flat_bin*t_src/t_tot\n\tsrc = grb_flat_bin\n\t\n\tdata = src - bkgd\n\t\n\terr_src = grb_err_flat_bin\n\terr_bkgd = bkgd_err_flat_bin\n\t#err_model = sim_err_flat_bin*scaling\n\terr_model = sim_err_flat_bin*scaling\n\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\t\n\tratio = data/model\n\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\n\t\n\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n\t\n\t # PLotting the comparison plots\n\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\n\t\n\tax1.set_title(\"Comparison between simulated (scaled) and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f} \".format(tx=thetax,ty=thetay,c=chi_sq))\n\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation (scaling = {s:0.2f},offset = {o:0.2f})\".format(s=scaling,o=intercept),elinewidth=0.5)\n\tax1.legend()\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\n\tax1.set_ylabel('Counts')\n\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n\t\n\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\"Ratio = Data/Model(scaling = {s:0.2f}, offset={o:0.2f})\".format(s=scaling,o=intercept),elinewidth=0.5)\n\tax2.xaxis.set_ticks(np.arange(0,len(data)))\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\n\tax2.tick_params(labelsize=5)\n\tax2.axhline(y=1,linewidth=0.5,color='k')\n\tax2.legend()\n\tax2.set_xlabel('CZT Modules')\n\tax2.set_ylabel('Ratio of counts')\n\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\n\tplt.tight_layout(h_pad=0.0)\n\tf.set_size_inches([6.5,10])\n\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\n\n\t# Plotting comparison graphs with random binning------------------------------\n\t\n sim_flat = sim_dph.flatten()\n\tsim_err_flat = sim_err_dph.flatten()\n grb_flat = grb_dph.flatten()\n bkgd_flat = bkgd_dph.flatten()\n\tsrc_flat = src_dph.flatten()\n\t\n\torder = np.random.permutation(np.arange(0,len(sim_flat)))\n\t\n sim_flat = sim_flat[order]\n\tsim_err_flat = sim_err_flat[order]\n\tgrb_flat = grb_flat[order]\n\tbkgd_flat = bkgd_flat[order]\n\tsrc_flat = src_flat[order]\n\t\n\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\n\tx = np.zeros(len(bins)+2,dtype=np.int64)\n\tx[0] = 0\n\tx[-1] = len(sim_flat)\n\tx[1:-1] = bins\n\t\n\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\n\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\t\n # Defining model background and data\n #model = sim_flat_bin*scaling\n\tmodel = sim_flat_bin*scaling + intercept\n bkgd = bkgd_flat_bin*t_src/t_tot\n src = grb_flat_bin\n\t\n data = src - bkgd\n\n err_src = np.sqrt(src)\n err_bkgd = np.sqrt(bkgd_flat_bin)\n #err_model = sim_err_flat_bin*scaling\n\terr_model = sim_err_flat_bin*scaling\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\t\n\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n # PLotting the comparison plots\n fig = plt.figure()\n plt.title(\"Comparison between simulated(scaled) and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f} \".format(tx=thetax,ty=thetay,c=chi_sq_new))\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation (scaling = {s:0.2f}, offset = {o:0.2f})\".format(s=scaling,o=intercept),elinewidth=0.5)\n plt.ylabel('Counts')\n\tplt.xlabel('Random Bins')\n\tplt.xticks(np.arange(0,(len(data)),1))\n\tplt.legend()\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\n\n\n\t# Plotting observed vs predicted counts--------------------------------------------------------\n\n\tfig = plt.figure()\n plt.title(grbdir + r\" : Observed vs Predicted counts with $\\chi^2$ = {cs:0.1f}\".format(cs=chi_sq))\n plt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\n\tfor i in range(len(model_copy)):\t\n\t\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\n #plt.plot(np.arange(-1000,1000),fit_line(np.arange(-1000,1000),scaling),'k',linewidth=0.5,label='m = {s:0.2f}'.format(s=scaling))\n\tplt.plot(np.arange(-1000,1000),fit_line_int(np.arange(-1000,1000),scaling,intercept),'k',linewidth=0.5,label='scaling = {s:0.2f}, offset = {i:0.2f}'.format(s=scaling,i=intercept))\n\tplt.plot(np.arange(min(model_copy)-5,max(model_copy)+5),np.ones(len(np.arange(min(model_copy)-5,max(model_copy)+5)))*intercept,'r-',label='intercept',linewidth=0.5)\n plt.xlim(min(model_copy)-5,max(model_copy)+5)\n plt.ylim(min(data_copy)-5,max(data_copy)+5)\n plt.xlabel('Predicted Counts')\n plt.ylabel('Observed Counts')\n\tplt.legend()\n\tplt.grid()\n pdf_file.savefig(fig)\n\t\t\n\tprint \"===============================================================================================\"\n\t\n\treturn","def main():\n # Data processing and parsing\n mp_df = pd.read_csv(\"melting_points.csv\")\n bp_df = pd.read_csv(\"boiling_points.csv\")\n data_processing(mp_df)\n data_processing(bp_df)\n # Plots scatterplots of our variables with the boiling and melting point\n plot_scatter('Mass', 'T_exp', mp_df, 'Molecular Weight', 'Melting Point')\n plot_scatter('Mass', 'T_exp', bp_df, 'Molecular Weight', 'Boiling Point')\n plot_scatter('Atom_counts', 'T_exp', mp_df, 'Number of Atoms',\n 'Melting Point')\n \n # Creates models and prints statistics using simply mass and atom counts\n simple_parameters = ['Mass', 'Atom_counts', 'T_exp']\n model = ml_df(mp_df, simple_parameters, 0.2)\n plot_T(model, simple_parameters, \"simple_melting.png\", 'Melting Point')\n slope_average(mp_df, simple_parameters, 0.2, 100)\n model2 = ml_df(bp_df, simple_parameters, 0.2)\n plot_T(model2, simple_parameters, 'simple_boiling.png', 'Boiling Point')\n slope_average(bp_df, simple_parameters, 0.2, 100)\n # Creates models and prints statistics using more variables\n complex_parameters = [\"Mass\", \"Atom_counts\", \"C\", \"H\", \"Acid\", \"Alcohol\",\n \"Unsaturation\", \"T_exp\"]\n model3 = ml_df(mp_df, complex_parameters, 0.2)\n plot_T(model3, complex_parameters, \"complex_melting.png\", 'Melting Point')\n slope_average(mp_df, complex_parameters, 0.2, 100)\n model4 = ml_df(bp_df, complex_parameters, 0.2)\n plot_T(model4, complex_parameters, \"complex_boiling.png\", 'Boiling Point')\n slope_average(bp_df, complex_parameters, 0.2, 100)\n # Creates models and prints statistics for our model using all available variables\n complex_parameters2 = [\"Mass\", \"Atom_counts\", \"C\", \"H\", 'O', 'N', 'F',\n 'Cl', 'Br', 'I', 'S', 'Si', 'Halide', \"Acid\", \"Alcohol\",\n \"Unsaturation\", \"T_exp\"]\n model = ml_df(mp_df, complex_parameters2, 0.2)\n plot_T(model, complex_parameters2, \"more_complex_melting.png\",\n 'Melting Point')\n slope_average(mp_df, complex_parameters2, 0.2, 100)\n # Tries a few different regressor models\n slope_average(mp_df, complex_parameters2, 0.2, 100, LinearRegression())\n slope_average(bp_df, complex_parameters2, 0.2, 100, RandomForestRegressor())","def test_lcurve_split(self):\n command = '{0} {1} -g'.format(\n os.path.join(self.datadir, 'monol_testA_nustar_fpma_ev' +\n HEN_FILE_EXTENSION),\n os.path.join(self.datadir, 'monol_testB_nustar_fpmb_ev' +\n HEN_FILE_EXTENSION))\n hen.lcurve.main(command.split())\n new_filename = os.path.join(self.datadir,\n 'monol_testA_nustar_fpma_gti000_lc' +\n HEN_FILE_EXTENSION)\n assert os.path.exists(new_filename)\n lc = hen.io.load_lcurve(new_filename)\n gti_to_test = hen.io.load_events(self.first_event_file).gti[0]\n assert np.allclose(gti_to_test, lc.gti)","def main():\n utl.calibrate(False)\n undistort(False)\n edge_detect(False)\n transform(False)\n identify_line(False)\n lane_line(True)","def plot(self) -> None:\n cw_l2_data_list = list(); cw_linf_data_list = list()\n\n for model in self.model_list:\n cw_l2_data_list.append(joblib.load(model + \"/stat/mse-rmse-si-mae-cw_l2_1.pkl\"))\n\n cw_l2_attack = list(zip(self.model_list, cw_l2_data_list))\n\n for model in self.model_list:\n cw_linf_data_list.append(joblib.load(model + \"/stat/mse-rmse-si-mae-cw_inf_1.pkl\"))\n\n cw_linf_attack = list(zip(self.model_list, cw_linf_data_list))\n\n # RMSE v.s. MAE over change budget\n # There will be one graph for each manipulation\n # CW_L2 ATTACK\n for datum in cw_l2_attack:\n ran_color_list = self._random_color_picker(2)\n fig, axis_1 = plt.subplots()\n\n # Generate x_axis\n x_axis = list()\n for key in datum[1]:\n if float(key) not in x_axis:\n x_axis.append(float(key))\n\n x_axis.sort()\n\n # Sort data in datum[1]\n data_dict = self._sort_dict(x_axis, datum[1])\n\n # PLOT RMSE ON AXIS 1\n # Generate y_axis ticks for RMSE\n rmse_values = list()\n for key in data_dict:\n rmse_values.append(data_dict[key][\"rmse\"])\n\n # Generate 10 ticks for the y_axis\n y_axis_ticks = np.linspace(0.0, 0.6, num=10, endpoint=True)\n\n # Plot RMSE\n axis_1.plot(x_axis, rmse_values, color=ran_color_list[0], linestyle=\"solid\")\n axis_1.set_xlabel(\"Perturbation Budget\")\n axis_1.set_ylabel(\"Root Mean Squared Error (RMSE)\", color=ran_color_list[0])\n axis_1.set_yticks(y_axis_ticks)\n \n for tick_label, tick_line in zip(axis_1.get_yticklabels(), axis_1.get_yticklines()):\n tick_label.set_color(ran_color_list[0])\n tick_line.set_color(ran_color_list[0])\n\n # PLOT MAE ON AXIS 2\n axis_2 = axis_1.twinx()\n\n # Generate y-axis ticks for MAE\n mae_values = list()\n for key in data_dict:\n mae_values.append(data_dict[key][\"mae\"])\n\n\n # Plot MAE\n axis_2.plot(x_axis, mae_values, color=ran_color_list[1], linestyle=\"solid\")\n axis_2.set_ylabel(\"Mean Absolute Error (MAE)\", color=ran_color_list[1])\n axis_2.set_yticks(y_axis_ticks)\n \n for tick_label, tick_line in zip(axis_2.get_yticklabels(), axis_2.get_yticklines()):\n tick_label.set_color(ran_color_list[1])\n tick_line.set_color(ran_color_list[1])\n\n model_tag = datum[0].split(\"/\"); model_tag = model_tag[-1]\n plt.savefig(self.save_path + \"/{}_rmse-and-mae-as-perturbation-budget-increases-for-cw_l2-attack-on-model-{}.png\".format(self.plot_name, model_tag), \n bbox_inches=\"tight\")\n plt.close()\n\n # CW_Linf ATTACK\n for datum in cw_linf_attack:\n ran_color_list = self._random_color_picker(2)\n fig, axis_1 = plt.subplots()\n\n # Generate x_axis\n x_axis = list()\n for key in datum[1]:\n if float(key) not in x_axis:\n x_axis.append(float(key))\n\n x_axis.sort()\n\n # Sort data in datum[1]\n data_dict = self._sort_dict(x_axis, datum[1])\n\n # PLOT RMSE ON AXIS 1\n # Generate y_axis ticks for RMSE\n rmse_values = list()\n for key in data_dict:\n rmse_values.append(data_dict[key][\"rmse\"])\n\n # Plot RMSE\n axis_1.plot(x_axis, rmse_values, color=ran_color_list[0], linestyle=\"solid\")\n axis_1.set_xlabel(\"Perturbation Budget\")\n axis_1.set_ylabel(\"Root Mean Squared Error (RMSE)\", color=ran_color_list[0])\n axis_1.set_yticks(y_axis_ticks)\n\n for tick_label, tick_line in zip(axis_1.get_yticklabels(), axis_1.get_yticklines()):\n tick_label.set_color(ran_color_list[0])\n tick_line.set_color(ran_color_list[0])\n\n # PLOT MAE ON AXIS 2\n axis_2 = axis_1.twinx()\n\n # Generate y-axis ticks for MAE\n mae_values = list()\n for key in data_dict:\n mae_values.append(data_dict[key][\"mae\"])\n\n # Plot MAE\n axis_2.plot(x_axis, mae_values, color=ran_color_list[1], linestyle=\"solid\")\n axis_2.set_ylabel(\"Mean Absolute Error (MAE)\", color=ran_color_list[1])\n axis_2.set_yticks(y_axis_ticks)\n \n for tick_label, tick_line in zip(axis_2.get_yticklabels(), axis_2.get_yticklines()):\n tick_label.set_color(ran_color_list[1])\n tick_line.set_color(ran_color_list[1])\n \n model_tag = datum[0].split(\"/\"); model_tag = model_tag[-1]\n plt.savefig(self.save_path + \"/{}_rmse-and-mae-as-perturbation-budget-increases-for-cw_linf-attack-on-model-{}.png\".format(self.plot_name, model_tag),\n bbox_inches=\"tight\")\n plt.close()\n \"RMSE and MAE as Perturbation Budget increases for CW_Linf attack on model {}\".format(model_tag)\n \n # Scattter Index over the change budget\n # All the manipulations will be put on the same graph.\n # CW_L2 ATTACK\n plt.figure()\n plt.xlabel(\"Perturbation Budget\"); plt.ylabel(\"Scatter Index\")\n ran_color_list = self._random_color_picker(len(cw_l2_attack)); i = 0\n\n # Find maximum scatter index value\n scatter_values = list()\n for datum in cw_l2_attack:\n for key in datum[1]:\n scatter_values.append(datum[1][key][\"scatter_index\"])\n\n # Generate y_axis ticks; generate 10 ticks\n y_axis_ticks = np.linspace(0.0, float(Decimal(str(max(scatter_values))) + Decimal(\"0.1\")), num=10, endpoint=True)\n plt.yticks(y_axis_ticks)\n\n # Generate x_axis\n x_axis = list()\n for datum in cw_l2_attack:\n for key in datum[1]:\n if float(key) not in x_axis:\n x_axis.append(float(key))\n\n x_axis.sort()\n\n formal_names = FormalNameMap()\n for datum in cw_l2_attack:\n values = list()\n data_dict = self._sort_dict(x_axis, datum[1])\n for key in data_dict:\n values.append(data_dict[key][\"scatter_index\"])\n\n # Append values to the plot\n line_name = datum[0].split(\"/\"); line_name = line_name[-1]\n formal_name = formal_names.getformalname(line_name) if formal_names.hasname(line_name) else line_name\n if \"vanilla\" in line_name:\n plt.plot(x_axis, values, color=ran_color_list[i], linewidth=3, linestyle=self._random_linestyle(), label=formal_name)\n\n else:\n plt.plot(x_axis, values, color=ran_color_list[i], linestyle=self._random_linestyle(), label=formal_name)\n \n i += 1\n\n plt.legend()\n plt.savefig(self.save_path + \"/{}_scatter-index-as-perturbation-budget-increases-for-cw_l2-attack.png\".format(self.plot_name),\n bbox_inches=\"tight\")\n plt.close()\n\n # CW_Linf ATTACK\n plt.figure()\n plt.xlabel(\"Perturbation Budget\"); plt.ylabel(\"Scatter Index\")\n ran_color_list = self._random_color_picker(len(cw_linf_attack)); i = 0\n\n # Find maximum scatter index value\n scatter_values = list()\n for datum in cw_linf_attack:\n for key in datum[1]:\n scatter_values.append(datum[1][key][\"scatter_index\"])\n\n # Generate y_axis ticks; generate 10 ticks\n y_axis_ticks = np.linspace(0.0, float(Decimal(str(max(scatter_values))) + Decimal(\"0.1\")), num=10, endpoint=True)\n plt.yticks(y_axis_ticks)\n\n # Generate x_axis\n x_axis = list()\n for datum in cw_l2_attack:\n for key in datum[1]:\n if float(key) not in x_axis:\n x_axis.append(float(key))\n\n x_axis.sort()\n\n formal_names = FormalNameMap()\n for datum in cw_linf_attack:\n values = list()\n data_dict = self._sort_dict(x_axis, datum[1])\n for key in data_dict:\n values.append(data_dict[key][\"scatter_index\"])\n\n # Append values to the plot\n line_name = datum[0].split(\"/\"); line_name = line_name[-1]\n formal_name = formal_names.getformalname(line_name) if formal_names.hasname(line_name) else line_name\n if \"vanilla\" in line_name:\n plt.plot(x_axis, values, color=ran_color_list[i], linewidth=3, linestyle=self._random_linestyle(), label=formal_name)\n\n else: \n plt.plot(x_axis, values, color=ran_color_list[i], linestyle=self._random_linestyle(), label=formal_name)\n \n i += 1\n\n plt.legend()\n plt.savefig(self.save_path + \"/{}_scatter-index-as-perturbation-budget-increases-for-cw_linf-attack.png\".format(self.plot_name),\n bbox_inches=\"tight\")\n plt.close()","def main(\n error_band_dir,\n output_dir,\n indep_var,\n ivar_start,\n ivar_stop,\n ivar_step,\n param_list,\n observable_list,\n Lambda_b,\n lambda_mult,\n p_decimal_list,\n orders,\n ignore_orders,\n interaction,\n X_ref_hash,\n prior_set,\n h,\n cbar_lower,\n cbar_upper,\n sigma,\n convention\n ):\n\n color_dict = {\n \"LOp\": plt.get_cmap(\"Greys\"),\n \"LO\": plt.get_cmap(\"Purples\"),\n \"NLO\": plt.get_cmap(\"Oranges\"),\n \"N2LO\": plt.get_cmap(\"Greens\"),\n \"N3LO\": plt.get_cmap(\"Blues\"),\n \"N4LO\": plt.get_cmap(\"Reds\")\n }\n\n fill_transparency = 1\n x = np.arange(ivar_start, ivar_stop, ivar_step)\n\n fig = plt.figure(figsize=(3.4, 3.4))\n ax = fig.add_subplot(1, 1, 1)\n ax.set_zorder(15)\n ax.set_axisbelow(False)\n\n if indep_var == \"theta\":\n param_var = \"energy\"\n indep_var_label = r\"$\\theta$ (deg)\"\n param_var_label = r\"$E_{\\mathrm{lab}}\"\n param_var_units = r\"$ MeV\"\n xmin = 0\n xmax = 180\n else:\n param_var = \"theta\"\n indep_var_label = r\"$E$ (MeV)\"\n param_var_label = r\"$\\theta\"\n param_var_units = r\"^\\circ$\"\n xmin = 0\n xmax = 350\n\n for observable in observable_list:\n for param in param_list:\n if observable == [\"t\", \"t\", \"t\", \"t\"] or \\\n (observable == [\"0\", \"0\", \"0\", \"0\"] and indep_var == \"energy\"):\n ax.set_yscale(\"log\", nonposy='clip')\n\n ax.set_xlabel(indep_var_label)\n # ax.set_ylabel('')\n\n # if indep_var == \"theta\":\n # major_tick_spacing = 60\n # minor_tick_spacing = 20\n # ax.xaxis.set_major_locator(\n # ticker.MultipleLocator(major_tick_spacing))\n # ax.xaxis.set_minor_locator(\n # ticker.MultipleLocator(minor_tick_spacing))\n\n if indep_var == \"energy\":\n major_ticks = np.arange(0, 351, 100)\n # minor_ticks = np.arange(50, 351, 100)\n x_minor_locator = AutoMinorLocator(n=2)\n elif indep_var == \"theta\":\n major_ticks = np.arange(0, 181, 60)\n # minor_ticks = np.arange(0, 181, 20)\n x_minor_locator = AutoMinorLocator(n=3)\n\n ax.xaxis.set_minor_locator(x_minor_locator)\n ax.set_xticks(major_ticks)\n\n # Create the description box\n # text_str = r\"$C_{\" + observable[0] + observable[1] + \\\n # observable[2] + observable[3] + r\"}$\" + \", \" # + \"\\n\"\n text_str = indices_to_observable_name(observable)\n if observable == ['t', 't', 't', 't']:\n text_str += \" (mb)\"\n elif observable == ['0', '0', '0', '0']:\n text_str += \" (mb/sr)\"\n\n # Probably don't include this extra info. Leave for caption.\n #\n # text_str += \", \"\n # if observable != ['t', 't', 't', 't']:\n # text_str += \", \" + param_var_label + r\" = \" + str(param) + param_var_units + \", \" # + \"\\n\"\n # text_str += r\"$\\Lambda_b = \" + str(lambda_mult*Lambda_b) + r\"$ MeV\"\n\n # Don't put in a text box\n #\n # ax.text(.5, .96, text_str,\n # horizontalalignment='center',\n # verticalalignment='top',\n # multialignment='center',\n # transform=ax.transAxes,\n # bbox=dict(facecolor='white', alpha=1, boxstyle='square', pad=.5),\n # zorder=20)\n\n # Don't put in the title\n # plt.title(text_str, fontsize=10)\n\n # Instead use y axis\n ax.set_ylabel(text_str, fontsize=10)\n legend_patches = []\n\n try:\n npwa_name = npwa_filename(observable, param_var, param)\n npwa_file = DataFile().read(os.path.join(\"../npwa_data/\", npwa_name))\n npwa_plot, = ax.plot(npwa_file[0], npwa_file[1],\n color=\"black\", linewidth=1,\n label=\"NPWA\", zorder=10,\n linestyle=\"--\")\n except FileNotFoundError:\n npwa_plot = None\n\n # First get global min/max of all orders\n for i, order in enumerate(orders):\n # obs_name = observable_filename(\n # observable, indep_var, ivar_start, ivar_stop,\n # ivar_step, param_var, param, order)\n # dob_name = dob_filename(p_decimal_list[0], Lambda_b, obs_name)\n dob_name = dob_filename(\n observable, indep_var, ivar_start, ivar_stop,\n ivar_step, param_var, param, order, ignore_orders,\n Lambda_b, lambda_mult, X_ref_hash,\n p_decimal_list[0], prior_set, h, convention, None,\n cbar_lower, cbar_upper, sigma,\n potential_info=None)\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\n if i == 0:\n obs = dob_file[1]\n obs_min = np.min(obs)\n obs_max = np.max(obs)\n else:\n old_obs = obs\n obs = dob_file[1]\n # Probably the worst way to do this.\n obs_min = min(np.min(np.minimum(old_obs, obs)), obs_min)\n obs_max = max(np.max(np.maximum(old_obs, obs)), obs_max)\n\n # Decide the padding above/below the lines\n # This weights values far from 0 more heavily.\n # ymin = obs_min - .25 * abs(obs_min)\n # ymax = obs_max + .25 * abs(obs_max)\n ymin = -1\n ymax = 20\n ax.set_ylim([ymin, ymax])\n # ax.set_xlim([ivar_start, ivar_stop-1])\n ax.set_xlim([xmin, xmax])\n\n # Start layering the plots\n for i, order in enumerate(orders):\n # obs_name = observable_filename(\n # observable, indep_var, ivar_start, ivar_stop,\n # ivar_step, param_var, param, order)\n # dob_name = dob_filename(p_decimal_list[0], Lambda_b, obs_name)\n dob_name = dob_filename(\n observable, indep_var, ivar_start, ivar_stop,\n ivar_step, param_var, param, order, ignore_orders,\n Lambda_b, lambda_mult, X_ref_hash,\n p_decimal_list[0], prior_set, h, convention, None,\n cbar_lower, cbar_upper, sigma,\n potential_info=None)\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\n\n # Plot the lines\n obs = dob_file[1]\n ax.plot(x, obs, color=color_dict[order](.99), zorder=i)\n\n # Plot the error bands\n for band_num, p in enumerate(sorted(p_decimal_list, reverse=True)):\n # dob_name = dob_filename(p, Lambda_b, obs_name)\n dob_name = dob_filename(\n observable, indep_var, ivar_start, ivar_stop,\n ivar_step, param_var, param, order, ignore_orders,\n Lambda_b, lambda_mult, X_ref_hash,\n p, prior_set, h, convention, None,\n cbar_lower, cbar_upper, sigma,\n potential_info=None)\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\n obs_lower = dob_file[2]\n obs_upper = dob_file[3]\n ax.fill_between(\n x, obs_lower, obs_upper,\n facecolor=color_dict[order](\n (band_num + 1) / (len(p_decimal_list) + 1)\n ),\n color=color_dict[order](\n (band_num + 1) / (len(p_decimal_list) + 1)\n ),\n alpha=fill_transparency, interpolate=True, zorder=i)\n\n # Use block patches instead of lines\n # Use innermost \"dark\" color of bands for legend\n # legend_patches.append(\n # mp.patches.Patch(\n # color=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\n # label=order,\n # ))\n legend_patches.append(\n mpatches.Rectangle(\n (1, 1), 0.25, 0.25,\n # color=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\n edgecolor=color_dict[order](.9),\n facecolor=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\n label=order,\n linewidth=1\n ))\n\n if npwa_plot is None:\n my_handles = legend_patches\n handler_dict = dict(zip(my_handles, [HandlerSquare() for i in legend_patches]))\n else:\n my_handles = [npwa_plot, *legend_patches]\n squares = [HandlerSquare() for i in legend_patches]\n line = HandlerLine2D(marker_pad=1, numpoints=None)\n handler_dict = dict(zip(my_handles, [line] + squares))\n\n ax.legend(loc=\"best\", handles=my_handles,\n handler_map=handler_dict,\n handletextpad=.8,\n handlelength=.6,\n fontsize=8)\n\n # Squeeze and save it\n plt.tight_layout()\n # plot_name = plot_obs_error_bands_filename(\n # observable, indep_var, ivar_start, ivar_stop,\n # ivar_step, param_var, param, orders[:i+1],\n # Lambda_b, p_decimal_list)\n plot_name = plot_obs_error_bands_filename(\n observable, indep_var, ivar_start, ivar_stop, ivar_step,\n param_var, param, orders[:i+1], ignore_orders, Lambda_b,\n lambda_mult, X_ref_hash, p_decimal_list,\n prior_set, h, convention, None, cbar_lower, cbar_upper,\n sigma, potential_info=None)\n fig.savefig(os.path.join(output_dir, plot_name), bbox_inches=\"tight\")\n\n call([\"epstopdf\", os.path.join(output_dir, plot_name)])\n call([\"rm\", os.path.join(output_dir, plot_name)])\n\n # Clear the axes for the next observable/parameter.\n plt.cla()","def plotSnr2Density_TT_lines(self):\n # ell values for phi\n #L = np.logspace(np.log10(10.), np.log10(1.e4), 5, 10.)\n #L = np.array([10., 1.e2, 5.e2])\n L = [100.]\n \n # ell values for T\n L1 = np.logspace(np.log10(10.), np.log10(6.e4), 501, 10.)\n\n fig=plt.figure(0)\n ax=fig.add_subplot(111)\n #\n for iL in range(len(L)):\n f = lambda l1: self.snr2DensitySymLowL_TT(L[iL], l1)\n snr2Density = np.array(map(f, L1))\n # expected value if only counting number of modes\n dNLkappadlnl_theory = L1**2 / (np.pi)# * L[iL]**4\n\n # save the values for future use\n data = np.zeros((len(L1), 3))\n data[:,0] = L1.copy()\n data[:,1] = snr2Density\n data[:,2] = dNLkappadlnl_theory\n path = \"./output/cmblensrec/\"+self.CMB.name+\"/dsnr2_dlnl_L\"+intExpForm(L[iL])+\".txt\"\n print \"saving to \"+path\n np.savetxt(path, data)\n \n # normalize both, for plot\n dNLkappadlnl_theory /= np.max(snr2Density)\n snr2Density /= np.max(snr2Density)#[np.where(L1 <= 5.e3)]) # just to rescale in plot\n #ax.plot(L1, snr2Density, lw=2, c=plt.cm.jet(1.*iL/len(L)), label=r'$L_\\phi=$'+str(int(L[iL])))\n ax.plot(L1, snr2Density, lw=2, c='r', label=r'$L_\\phi=$'+str(int(L[iL])))\n ax.plot(L1, dNLkappadlnl_theory, 'r', ls='--', lw=1)\n #\n ax.legend(loc=1)\n ax.set_xscale('log', nonposx='clip')\n ax.set_xlabel(r'$\\ell_T$')\n ax.set_ylabel(r'$d\\text{SNR}(\\phi_L)^2/d\\text{ln}\\ell_T$')\n #ax.set_xlim((1., 5.e3))\n ax.set_ylim((0., 1.1))\n #\n path = \"./figures/cmblensrec/dsnr2_dl_test.pdf\"\n fig.savefig(path, bbox_inches='tight')\n \n plt.show()\n\n return L1, snr2Density","def figure3():\n\n plot_settings = {'y_limits': [-75, -50],\n 'x_limits': None,\n 'y_ticks': [-75, -70, -65, -60, -55, -50],\n 'locator_size': 2.5,\n 'y_label': 'Voltage (mV)',\n 'x_ticks': [],\n 'scale_size': 2,\n 'x_label': \"\",\n 'scale_loc': 3,\n 'figure_name': 'figure_3',\n 'legend': ['0.1 $\\mu$S', '0.125 $\\mu$S', '0.15 $\\mu$S'],\n 'legend_size': 8,\n 'legend_location': 4,\n 'y_on': True}\n line_styles = ['-', 'dotted', '-.']\n\n plt.figure(figsize=(5, 3))\n plt.subplot(1, 2, 1) # Generate subplot 1 (left)\n for ix, g_t_bar in enumerate([0.1, 0.125, 0.15]):\n t, y = solver(6, g_t_bar=g_t_bar, t_start=0.5)\n plt.plot(t, y[:, 0], c='k', linestyle=line_styles[ix])\n alter_figure(plot_settings) # Alter figure for publication\n\n plt.subplot(1, 2, 2) # Generate subplot 2 (right)\n for ix, i_bias_on in enumerate([0, -0.1, -0.2]):\n \"\"\"\n First step (hyperpolarizing)\n \"\"\"\n t1 = 1000\n duration = t1\n\n t, y_hold = solver(t1, duration=duration, i_bias_on=i_bias_on)\n y_0 = y_hold[-1, :] # Create new initial conditions\n\n \"\"\"\n Second step (current pulse)\n \"\"\"\n t1 = 20\n t, y = solver(t1, t_start=0, y_hold=y_0)\n v = y[:, 0]\n\n \"\"\"\n Append the end of the first step onto the second step (for plotting purposes)\n \"\"\"\n len_pre = 100\n t = np.concatenate((np.linspace(-len_pre * np.diff(t)[0], -np.diff(t)[0], len_pre), t))\n v_bar = np.concatenate((y_hold[-len_pre:, 0], v))\n v_bar = v_bar - v_bar[0] + -7.16300325e+01 # Align solution to initial condition of the \"standard simulation\"\n\n plt.plot(t, v_bar, c='k', linestyle=line_styles[ix])\n\n plot_settings['y_ticks'] = []\n plot_settings['y_label'] = \"\"\n plot_settings['x_limits'] = [-1, 20]\n plot_settings['legend'] = ['-72 mV', '-75 mV', '-78 mV']\n plot_settings['scale_size'] = 5\n plot_settings['legend_location'] = 1\n alter_figure(plot_settings, close=True)","def main(args=None):\n opt = _parse_options(args)\n plot_analysis(opt)\n plt.show()","def display1(*args):\n #----------*----------* # unpack\n twiss_func = args[0]\n lat_plot = args[3]\n #-------------------- beta x,y & dispersion x\n s = [twiss_func(i,'s') for i in range(twiss_func.nbpoints)]\n bx = [twiss_func(i,'bx') for i in range(twiss_func.nbpoints)]\n by = [twiss_func(i,'by') for i in range(twiss_func.nbpoints)]\n dx = [twiss_func(i,'dx') for i in range(twiss_func.nbpoints)]\n #-------------------- lattice viseo\n vzero = [0. for i in range(lat_plot.nbpoints)] # zero line\n vis_abszisse = [lat_plot(i,'s') for i in range(lat_plot.nbpoints)]\n vis_ordinate = [lat_plot(i,'viseo') for i in range(lat_plot.nbpoints)]\n #-------------------- figure frame\n width=14; height=7.6\n plt.figure(num=0,figsize=(width,height),facecolor='#eaecef',tight_layout=False)\n\n #-------------------- transverse X\n splot111=plt.subplot(111)\n splot111.set_title('beta functions')\n plt.plot(s,bx, label=r'$\\beta_x$ [m]', color='red', linestyle='-') # beta x\n plt.plot(s,by, label=r'$\\beta_y$ [m]', color='blue', linestyle='-') # beta y\n plt.plot(s,dx, label=r'$\\eta_x$ [m]' , color='green',linestyle='-') # dispersion x\n vscale=splot111.axis()[3]*0.25\n viseoz = [x*vscale for x in vis_ordinate]\n plt.plot(vis_abszisse,viseoz,label='',color='black')\n plt.plot(vis_abszisse,vzero,color='black')\n plt.legend(loc='lower right',fontsize='x-small')","def main():\r\n\t\"\"\"\r\n\tmain() parses arguments to pass to SimPlt\r\n\t\"\"\"\r\n\ttry: b = float(argv[1])\r\n\texcept IndexError: b = 1.0\r\n\t\r\n\ttry: X = float(argv[2])\r\n\texcept IndexError: X = 1.0\r\n\t\t\r\n\tparser = optparse.OptionParser()\r\n\t\r\n\tparser.add_option('-t','--timefac',\r\n dest=\"timefac\",\r\n default=1.0,\r\n type=\"float\",\r\n )\r\n\tparser.add_option('--BW',\r\n dest=\"BW\",\r\n default=False,\r\n action=\"store_true\"\r\n )\r\n\t\t\t\t \r\n\tparser.add_option('-s','--smooth',\r\n dest=\"smooth\",\r\n default=1.0,\r\n type=\"float\",\r\n )\r\n\t\t\t\t \r\n\topt = parser.parse_args()[0]\r\n \t\t\r\n\tLE_SimPlt(b, X, opt.timefac, opt.BW, opt.smooth)\r\n\r\n\treturn","def test_learning_curves():\n\n p = pipeline.Pipeline(\n FX_TRAIN,\n FX_TEST,\n FX_LOOKUP,\n RESULTS_DIR\n )\n\n data = p.learning_curves()","def test_plot1(plot=1, version='scalar'):\n Lx = 10\n Ly = 10\n c = 1.0\n\n def I2(x, y):\n return exp(-(x-Lx/2.0)**2/2.0 -(y-Ly/2.0)**2/2.0)\n def f(x, y, t):\n return 0.0\n def bc(x, y, t):\n return 0.0\n\n I2 = StringFunction('exp(-(x-Lx/2.0)**2/2.0 -(y-Ly/2.0)**2/2.0)',\n independent_variables=('x', 'y'),\n Lx=Lx, Ly=Ly, globals=globals())\n f = StringFunction('0.0', independent_variables=('x', 'y', 't'),\n globals=globals())\n bc = StringFunction('0.0', independent_variables=('x', 'y', 't'),\n globals=globals())\n if plot:\n g = Gnuplot.Gnuplot(persist=1)\n g('set parametric')\n g('set data style lines')\n g('set hidden')\n g('set contour base')\n g('set zrange [-0.7:0.7]') # nice plot...\n \n def action(u, xv, yv, t):\n #print 'action, t=',t,'\\nu=',u, '\\nx=',x, '\\ny=', y\n if plot:\n data = Gnuplot.GridData(u, xv[:,0], yv[0,:], binary=0)\n g.splot(data)\n g('set title \"t=%g\"' % t)\n if plot == 2:\n g.hardcopy(filename='tmp_%020f.ps' % t, enhanced=1, mode='eps',\n color=0, fontname='Times-Roman', fontsize=14)\n time.sleep(1)\n time.sleep(0.2) # pause between frames\n\n implementation = {'ic': version, 'inner': version, 'bc': version}\n nx = 40; ny = 40; tstop = 20 # tstop = 700\n print 'test_plot1:', f, bc, I2\n dt, t_ic, t_inner, t_bc = \\\n solver(I2, f, c, bc, Lx, Ly, nx, ny, 0, tstop,\n user_action=action, implementation=implementation)\n print 'time ic: %s, time scheme: %s, time bc: %s' % (t_ic, t_inner, t_bc)\n time.sleep(3)","def plot_curves():\n lm = np.arange(0, 1.8, .01)\n vm = np.arange(-1.2, 1.2, .01)\n lt = np.arange(0, 1.07, .01)\n plt.subplot(2,1,1)\n plt.plot(lm, force_length_muscle(lm), 'r')\n plt.plot(lm, force_length_parallel(lm), 'g')\n plt.plot(lt, force_length_tendon(lt), 'b')\n plt.legend(('CE', 'PE', 'SE'))\n plt.xlabel('Normalized length')\n plt.ylabel('Force scale factor')\n plt.subplot(2, 1, 2)\n plt.plot(vm, force_velocity_muscle(vm), 'k')\n plt.xlabel('Normalized muscle velocity')\n plt.ylabel('Force scale factor')\n plt.tight_layout()\n plt.show()","def test_fits_lcurve1(self):\n lcurve_ftools = os.path.join(self.datadir,\n 'lcurve_ftools_lc' +\n HEN_FILE_EXTENSION)\n\n lcurve_mp = os.path.join(self.datadir,\n 'lcurve_lc' +\n HEN_FILE_EXTENSION)\n\n lcdata_mp = hen.io.load_data(lcurve_mp)\n lcdata_ftools = hen.io.load_data(lcurve_ftools)\n\n lc_mp = lcdata_mp['counts']\n\n lenmp = len(lc_mp)\n lc_ftools = lcdata_ftools['counts']\n lenftools = len(lc_ftools)\n goodlen = min([lenftools, lenmp])\n\n diff = lc_mp[:goodlen] - lc_ftools[:goodlen]\n\n assert np.all(np.abs(diff) <= 1e-3), \\\n 'Light curve data do not coincide between FITS and HEN'","def _plot_likelihood_der1_eta(profile_likelihood, result):\n\n load_plot_settings()\n\n # Optimal point\n optimal_eta = result['hyperparam']['eta']\n optimal_scale = result['hyperparam']['scale']\n profile_likelihood.mixed_cor.set_scale(optimal_scale)\n\n if (optimal_eta != 0) and (not numpy.isinf(optimal_eta)):\n plot_optimal_eta = True\n else:\n plot_optimal_eta = False\n\n # Specify which portion of eta array be high resolution for plotting\n # in the inset axes\n log_eta_start = -3\n log_eta_end = 3\n\n if plot_optimal_eta:\n log_eta_start_high_res = numpy.floor(numpy.log10(optimal_eta))\n log_eta_end_high_res = log_eta_start_high_res + 2\n\n # Arrays of low and high resolutions of eta\n eta_high_res = numpy.logspace(log_eta_start_high_res,\n log_eta_end_high_res, 100)\n eta_low_res_left = numpy.logspace(log_eta_start,\n log_eta_start_high_res, 50)\n eta_low_res_right = numpy.logspace(log_eta_end_high_res,\n log_eta_end, 20)\n\n # array of eta as a mix of low and high res\n if log_eta_end_high_res >= log_eta_end:\n eta = numpy.r_[eta_low_res_left, eta_high_res]\n else:\n eta = numpy.r_[eta_low_res_left, eta_high_res, eta_low_res_right]\n\n else:\n eta = numpy.logspace(log_eta_start, log_eta_end, 100)\n\n # Compute derivative of L\n dell_deta = numpy.zeros(eta.size)\n for i in range(eta.size):\n dell_deta[i] = profile_likelihood._likelihood_der1_eta(\n profile_likelihood._eta_to_hyperparam(eta[i]))\n\n # Compute upper and lower bound of derivative\n dell_deta_upper_bound, dell_deta_lower_bound = \\\n profile_likelihood.approx.bounds_der1_eta(eta)\n\n # Compute the approximation of first derivative, using both first and\n # second order approximation\n try:\n # eta_high_res might not be defined, depending on plot_optimal_eta\n x = eta_high_res\n except NameError:\n x = numpy.logspace(1, log_eta_end, 100)\n\n # To compute both first and second order approximation relations, compute\n # the second orders first bemuse it also computes the first order.\n appr_maxima_ord2 = profile_likelihood.approx.maximaize_likelihood(degree=2)\n appr_maxima_ord1 = profile_likelihood.approx.maximaize_likelihood(degree=1)\n appr_dell_deta_ord2 = profile_likelihood.approx._likelihood_der1_eta(\n x, degree=2)\n appr_dell_deta_ord1 = profile_likelihood.approx._likelihood_der1_eta(\n x, degree=1)\n\n # Main plot\n fig, ax1 = plt.subplots(figsize=(6, 4.5))\n axes = [ax1]\n\n ax1.semilogx(eta, dell_deta_upper_bound, '--', color='black',\n label='Upper bound')\n ax1.semilogx(eta, dell_deta_lower_bound, '-.', color='black',\n label='Lower bound')\n ax1.semilogx(eta, dell_deta, color='black', label='Exact')\n if plot_optimal_eta:\n ax1.semilogx(optimal_eta, 0, '.', marker='o', markersize=4,\n color='black')\n\n # Min of plot limit\n # ax1.set_yticks(numpy.r_[numpy.arange(-120, 1, 40), 20])\n max_plot = numpy.max(dell_deta)\n max_plot_lim = numpy.ceil(numpy.abs(max_plot)/10.0) * \\\n 10.0*numpy.sign(max_plot)\n\n min_plot_lim1 = -100\n ax1.set_yticks(numpy.array([min_plot_lim1, 0, max_plot_lim]))\n ax1.set_ylim([min_plot_lim1, max_plot_lim])\n ax1.set_xlim([eta[0], eta[-1]])\n ax1.set_xlabel(r'$\\eta$')\n ax1.set_ylabel(r'$\\mathrm{d} \\ell_{\\hat{\\sigma}^2(\\eta)}' +\n r'(\\eta)/\\mathrm{d} \\eta$')\n ax1.set_title('Derivative of Log Marginal Likelihood Function')\n ax1.grid(True)\n # ax1.legend(loc='upper left', frameon=False)\n ax1.patch.set_facecolor('none')\n\n # Inset plot\n if plot_optimal_eta:\n ax2 = plt.axes([0, 0, 1, 1])\n axes.append(ax2)\n\n # Manually set position and relative size of inset axes within ax1\n ip = InsetPosition(ax1, [0.43, 0.39, 0.5, 0.5])\n ax2.set_axes_locator(ip)\n # Mark the region corresponding to the inset axes on ax1 and draw\n # lines in grey linking the two axes.\n\n # Avoid inset mark lines intersect inset axes by setting its anchor\n if log_eta_end > log_eta_end_high_res:\n mark_inset(ax1, ax2, loc1=3, loc2=4, facecolor='none',\n edgecolor='0.5')\n else:\n mark_inset(ax1, ax2, loc1=3, loc2=1, facecolor='none',\n edgecolor='0.5')\n\n ax2.semilogx(eta, numpy.abs(dell_deta_upper_bound), '--',\n color='black')\n ax2.semilogx(eta, numpy.abs(dell_deta_lower_bound), '-.',\n color='black')\n ax2.semilogx(x, appr_dell_deta_ord1,\n label=r'$1^{\\text{st}}$ order approximation',\n color='chocolate')\n ax2.semilogx(x, appr_dell_deta_ord2,\n label=r'$2^{\\text{nd}}$ order approximation',\n color='olivedrab')\n ax2.semilogx(eta_high_res,\n dell_deta[eta_low_res_left.size:\n eta_low_res_left.size+eta_high_res.size],\n color='black')\n ax2.semilogx(optimal_eta, 0, marker='o', markersize=6, linewidth=0,\n color='white', markerfacecolor='black',\n label=r'Exact root at $\\hat{\\eta}_{\\phantom{2}} ' +\n r'= 10^{%0.2f}$' % numpy.log10(optimal_eta))\n\n if appr_maxima_ord1 != []:\n ax2.semilogx(appr_maxima_ord1[-1], 0, marker='o', markersize=6,\n linewidth=0, color='white',\n markerfacecolor='chocolate',\n label=r'Approximated root at $\\hat{\\eta}_1 = ' +\n r'10^{%0.2f}$'\n % numpy.log10(appr_maxima_ord1[-1]))\n\n if appr_maxima_ord2 != []:\n ax2.semilogx(appr_maxima_ord2[-1], 0, marker='o', markersize=6,\n linewidth=0, color='white',\n markerfacecolor='olivedrab',\n label=r'Approximated root at $\\hat{\\eta}_2 = ' +\n r'10^{%0.2f}$'\n % numpy.log10(appr_maxima_ord2[-1]))\n\n ax2.set_xlim([eta_high_res[0], eta_high_res[-1]])\n # plt.setp(ax2.get_yticklabels(), backgroundcolor='white')\n\n # Find suitable range for plot limits\n min_plot = numpy.abs(numpy.min(dell_deta))\n min_plot_base = 10**numpy.floor(numpy.log10(numpy.abs(min_plot)))\n # min_plot_lim = numpy.ceil(min_plot/min_plot_base)*min_plot_base\n min_plot_lim = numpy.ceil(min_plot/min_plot_base + 1.0) * \\\n min_plot_base\n ax2.set_ylim([-min_plot_lim, min_plot_lim])\n ax2.set_yticks([-numpy.abs(min_plot_lim), 0, numpy.abs(min_plot_lim)])\n\n ax2.text(optimal_eta*10**0.03, min_plot_lim*0.05,\n r'$\\hat{\\eta}$' % numpy.log10(optimal_eta),\n horizontalalignment='left', verticalalignment='bottom',\n fontsize=10)\n\n for i in range(len(appr_maxima_ord1)):\n ax2.text(appr_maxima_ord1[i]*10**0.03, min_plot_lim*0.05,\n r'$\\hat{\\eta}_1$' % numpy.log10(optimal_eta),\n horizontalalignment='left', verticalalignment='bottom',\n fontsize=10)\n\n for i in range(len(appr_maxima_ord2)):\n ax2.text(appr_maxima_ord2[i]*10**(-0.14), min_plot_lim*0.05,\n r'$\\hat{\\eta}_2$' % numpy.log10(optimal_eta),\n horizontalalignment='left', verticalalignment='bottom',\n fontsize=10)\n\n # ax2.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))\n ax2.grid(True, axis='y')\n ax2.set_facecolor('oldlace')\n plt.setp(ax2.get_xticklabels(), backgroundcolor='white')\n ax2.tick_params(axis='x', labelsize=10)\n ax2.tick_params(axis='y', labelsize=10)\n\n # ax2.set_yticklabels(ax2.get_yticks(), backgroundcolor='w')\n # ax2.tick_params(axis='y', which='major', pad=0)\n\n handles, labels = [], []\n for ax in axes:\n for h, l in zip(*ax.get_legend_handles_labels()):\n handles.append(h)\n labels.append(l)\n lg = plt.legend(handles, labels, frameon=False, fontsize='small',\n loc='upper left', bbox_to_anchor=(1.2, 1.04))\n\n # Save plots\n plt.tight_layout()\n show_or_save_plot(plt, 'likelihood_derivative',\n transparent_background=False,\n pdf=True, bbox_extra_artists=(lg, ))","def show_learning_curve(self):\n\n # Loop output classes\n for c in range(1,self.n_output_classes):\n # Get data\n x_values = np.array(self.n_class_samples_list[c])\n accuracy = np.array(self.accuracy_list[c])\n precision = np.array(self.precision_list[c])\n recall = np.array(self.recall_list[c])\n F1 = np.array(self.F1_list[c])\n\n # Make plot\n with sns.axes_style(\"ticks\"):\n fig,ax = plt.subplots()\n plt.plot([np.min(x_values),np.max(x_values)],[0.5,0.5],\n color='#777777',linestyle='--')\n plt.plot([np.min(x_values),np.max(x_values)],[0.66,0.66],\n color='#777777',linestyle=':')\n plt.plot([np.min(x_values),np.max(x_values)],[0.8,0.8],\n color='#777777',linestyle=':')\n plt.plot([np.min(x_values),np.max(x_values)],[0.9,0.9],\n color='#777777',linestyle=':')\n\n plt.plot( x_values, accuracy, color='#000000',\n linewidth=1, label='Accuracy' )\n plt.plot( x_values, precision, color='#0000aa',\n linewidth=1, label='Precision' )\n plt.plot( x_values, recall, color='#00aa00',\n linewidth=1, label='Recall' )\n plt.plot( x_values, F1, color='#aa0000',\n linewidth=2, label='F1' )\n\n plt.yticks( [0, 0.5, 0.66, 0.8, 0.9, 1.0],\n ['0','0.5','0.66','0.8','0.9','1.0'], ha='right' )\n plt.xlim(np.max(x_values)*-0.02,np.max(x_values)*1.02)\n plt.ylim(-0.02,1.02)\n plt.xlabel('Number of training samples')\n plt.ylabel('Performance')\n plt.title('Learning curve, class {}'.format(c))\n sns.despine(ax=ax, offset=0, trim=True)\n lgnd = plt.legend(loc=4, ncol=1, frameon=True, fontsize=9)\n lgnd.get_frame().set_facecolor('#ffffff')\n ax.spines['left'].set_bounds(0,1)\n ax.spines['bottom'].set_bounds(np.min(x_values),np.max(x_values))","def main_paper(mode = 'q-diff'):\n mode_name = 'Shared Autonomy'\n if mode== 'override':\n mode_name = 'Overrider'\n\n #trains rl again and fills a q-table\n _, rl_agent = rl_alone(1000, fill_table = True)\n\n #prints the q-table\n for y in range(7):\n for x in range(7):\n state_ind = y*(7)+x\n q = rl_agent.get_q_values(state_ind)\n print('Q-table')\n print(state_ind, [x,y], np.around(q, decimals=5))\n\n in_li = []\n r_li = []\n rp_li=[]\n\n print(\"Begin looping through constraint values\")\n for i in np.arange(0.0, 1.025, 0.025):\n #cooperates and gets results\n rewards, actionsct, rewardsP = grid_human_co(coagent=rl_agent, threshold=i, verbose=False, mode=mode)\n print(\"Threshold: \", i)\n avgInter = np.mean(actionsct, axis=0)\n avgR = np.mean(rewards, axis=0)\n avgRP = np.mean(rewardsP, axis=0)\n in_li.append(avgInter)\n r_li.append(avgR)\n rp_li.append(avgRP)\n print('Avg num of interventions: ', avgInter)\n print('Avg reward: ', avgR)\n print('Avg reward penalized: ', avgRP)\n print()\n\n plt.figure()\n plt.plot(np.arange(0.0, 1.025, 0.025), in_li)\n plt.title(mode_name + ' Interventions')\n plt.xlabel(r\"$\\alpha$\")\n plt.ylabel('Average intervention count')\n plt.show()\n\n plt.figure()\n plt.title(mode_name + ' Returns', fontsize=14)\n plt.xlabel(r\"$\\alpha$\", fontsize=13)\n plt.ylabel('Average return', fontsize=13)\n plt.plot(np.arange(0.0, 1.025, 0.025), r_li,label='Average environment return')\n plt.plot(np.arange(0.0, 1.025, 0.025), rp_li,label=\"Average intervention return\")\n plt.legend(fontsize=9)\n plt.show()","def calcBeamplot(PALC_config, gamma_n, plt_ranges, f, dire_meas_LSA, dire_meas_deg):\n # general\n N = PALC_config.N\n \n########################## SIMULATION SETUP ###############################\n # maximum of discrete mapping points:\n max_points = 10000\n # mesh\n x_range = plt_ranges.p_x\n y_range = plt_ranges.p_y\n pts_x = max_points / (y_range[1]-y_range[0])\n pts_y = max_points / (x_range[1]-x_range[0])\n\n x = np.linspace(x_range[0], x_range[1], num=int(pts_x+1))\n y = np.linspace(y_range[0], y_range[1], num=int(pts_y+1))\n\n X, Y = np.meshgrid(x,y)\n # get vertically array\n x_vert = np.reshape(X, np.size(X))\n y_vert = np.reshape(Y, np.size(Y))\n z_vert = np.array([0])\n\n # reference pressure\n p0 = 2 * 10**(-5)\n # considered frequency\n omega = 2 * np.pi * f\n # initialize driving fct. and output array\n D_opt_LSA = np.ones([N, 1])\n P_LSA = np.zeros([np.shape(x_vert)[0],1], dtype=complex) \n\n # air attenuation\n T = 293.15\n p = 101.325 * 10**(3)\n h = 50\n alpha, c = AirAbsorptionCoefficient(f, T, p, h)\n\n # directivity\n if PALC_config.directivity not in ['Measured Loudspeaker Data']:\n dire_meas_LSA = np.ones([1,1])\n dire_meas_deg = np.ones([1,1])\n \n ######################### SPL CALCULATION #################################\n x_start, y_start, x_stop, y_stop, x_c_n, y_c_n, x_S, y_S = source_pos(gamma_n, PALC_config)\n\n G_LSA_vert = CalcGreenFunctions(x_vert, y_vert, z_vert, x_c_n, y_c_n, 0.82,\\\n PALC_config.directivity, PALC_config.Lambda_y, \\\n gamma_n, c, omega, 1, dire_meas_LSA, \\\n dire_meas_deg, alpha, f, 0 )\n\n P_LSA[:,0] = G_LSA_vert @ D_opt_LSA[:,0]\n\n p_SPL = 20 * np.log10(np.abs(P_LSA) / p0)\n \n p_SPL = np.reshape(p_SPL, np.shape(X))\n\n return p_SPL, X, Y","def plot_ablation_losses():\n\n path1 = os.path.join(path_to_here, '../data/landscape_visualizations/DMSO/original/losses00-54.pickle')\n file = open(path1, 'rb')\n losses_array_1 = pickle.load(file)\n losses_array_1 = losses_array_1[1:, :]\n\n path2 = os.path.join(path_to_here, '../data/landscape_visualizations/DMSO/D_t/losses00-44.pickle')\n file = open(path2, 'rb')\n losses_array_2 = pickle.load(file)\n losses_array_2 = losses_array_2\n\n path3 = os.path.join(path_to_here, '../data/landscape_visualizations/DMSO/D_x_y/losses00-44.pickle')\n file = open(path3, 'rb')\n losses_array_3 = pickle.load(file)\n losses_array_3 = losses_array_3\n\n path4 = os.path.join(path_to_here, '../data/landscape_visualizations/DMSO/homog/losses00-76.pickle')\n file = open(path4, 'rb')\n losses_array_4 = pickle.load(file)\n losses_array_4 = losses_array_4[1:, :]\n\n paths = [path1, path2, path3, path4]\n\n\n\n\n # 'pdf', 'BC', 'pde', 'total', 'norm'\n fig = plt.figure(figsize = (2.1, 1.5))\n\n\n for path, losses_array, label in zip(paths, [losses_array_1, losses_array_2, losses_array_3, losses_array_4], ['D(x, y, t)', 'D(t)', 'D(x, y)', 'D']):\n\n print('shape', losses_array.shape)\n\n losses_base = os.path.basename(path)\n total_losses = losses_array[3, :] # total losses are idx 3 for these runs\n idxs = []\n means = []\n for idx in range(len(total_losses)-200):\n num_half_hrs = int(losses_base[6:8]) + idx*(int(losses_base[9:11])-int(losses_base[6:8]))/len(total_losses)\n if num_half_hrs < 55:\n idxs.append(num_half_hrs)\n means.append(np.mean(total_losses[idx:idx+200]))\n\n\n plt.scatter([i/2 for i in idxs], np.log10(means), s = 0.1, label = label)\n\n plt.ylabel(r'$log_{10}L_{total}$', fontsize = 6, labelpad = 1)\n plt.xlabel('Hours trained', fontsize = 6, labelpad = 1)\n plt.tick_params(axis = 'both', labelsize = 6)\n plt.tight_layout()\n plt.legend(fontsize = 6)\n\n\n\n plt.savefig(path_to_here+'/../outputs/ablation.png', dpi = 1200)","def _show_learning_rate():\n fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6.4 * 2, 4.8))\n\n # Visualize c_prime\n c_prime_list = np.linspace(1, 100, num=11)\n x_label = f\"c'\"\n y_label = \"Minimum Clusters Size\"\n title = \"\"\n\n ax = axes[0]\n x_list = c_prime_list\n\n # MNIST\n y_list = [161, 16, 14, 15, 20, 21, 24, 27, 30, 30, 35]\n ax.plot(x_list, y_list, label=\"MNIST\")\n\n # Fashion MNIST\n y_list = [63, 12, 12, 15, 18, 19, 22, 25, 26, 28, 30]\n ax.plot(x_list, y_list, label=\"Fashion MNIST\")\n\n # 20 news groups\n y_list = [1297, 724, 221, 80, 52, 51, 54, 54, 52, 60, 60]\n ax.plot(x_list, y_list, label=\"Newsgroups\")\n\n ax.set_xlabel(x_label)\n ax.set_ylabel(y_label)\n ax.set_title(title)\n ax.legend()\n ax.set_yscale('log')\n\n # Visualize t0\n t0_list = np.linspace(1, 100, num=11)\n x_label = f\"t0\"\n y_label = \"Minimum Clusters Size\"\n title = \"\"\n\n ax = axes[1]\n x_list = t0_list\n\n # MNIST\n y_list = [16, 16, 16, 16, 16, 17, 16, 16, 16, 16, 16]\n ax.plot(x_list, y_list, label=\"MNIST\")\n\n # Fashion MNIST\n y_list = [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12]\n ax.plot(x_list, y_list, label=\"Fashion MNIST\")\n\n # 20 news groups\n y_list = [765, 765, 767, 772, 772, 773, 789, 789, 793, 796, 799]\n ax.plot(x_list, y_list, label=\"Newsgroups\")\n\n ax.set_xlabel(x_label)\n ax.set_ylabel(y_label)\n ax.set_title(title)\n ax.legend()\n ax.set_yscale('log')\n\n plt.show()","def main():\n\n # choose number of data-points and sample a pair of vectors: the input\n # values and the corresponding target values\n N = 500\n inputs, targets = sample_data(N, arbitrary_function_2, seed=1)\n\n # specify the centres and scale of some rbf basis functions\n default_centres = np.linspace(0,1,21)\n default_scale = 0.03\n default_reg_param = 0.08\n\n # get the cross-validation folds\n num_folds = 4\n folds = create_cv_folds(N, num_folds)\n\n # evaluate then plot the performance of different reg params\n evaluate_reg_param(inputs, targets, folds, default_centres, default_scale)\n # evaluate then plot the performance of different scales\n evaluate_scale(inputs, targets, folds, default_centres, default_reg_param)\n # evaluate then plot the performance of different numbers of basis\n # function centres.\n evaluate_num_centres(\n inputs, targets, folds, default_scale, default_reg_param)\n\n plt.show()","def show_lightcurve(self):\n\n time_array = self.exp_start_times\n\n fig = plt.figure()\n\n if self.transmission_spectroscopy:\n lc_model = self.generate_lightcurves(time_array,\n self.planet.calcTransitDepth())\n plt.ylabel(\"Transit Depth\")\n else:\n lc_model = np.ones_like(time_array)\n plt.ylabel(\"Unit Flux\")\n\n if self._visit_trend:\n trend_model = self._visit_trend.scale_factors\n # have to convert weird model format to flat array\n lc_model = trend_model * lc_model.T[0]\n\n plt.scatter(time_array, lc_model)\n plt.xlabel(\"Time (JD)\")\n plt.title(\"Normalised White Time Series of observation\")\n\n return time_array, lc_model","def runLinAdvec():\n # Courant number \n c = 0.4\n #just run and print, for two different Courant numbers\n main(50, 50, c, displayResults = True)\n main(400, 400, c, displayResults = True)\n print(\"\\n\")\n # run order of convergence tests\n runErrorTests(c, 50, 300, stepNx=50, display=True)\n # run timing tests\n runTimingTests(c, 50, 600, stepNx=50, displayResults=True)\n # check monotonicity\n checkMonotonicity(50, 50, c, displayResults = True)\n # check mass/variance conservation\n checkConservation(200, 200, c, display=True)","def main():\n\t# \"\"\"\n\t# \tMain function of test python module\n\t# \"\"\"\n\t# random.seed(os.urandom(345634)) # initialize random generator\n\t# t = np.linspace(0.0, 24.0, 96.0) # define the time axis of a day, here we use 96 values every quarter of an hour\n\t# # standard load profile -- input\n\t# q = extra.read_slp(t,\n\t# 'Profielen-Elektriciteit-2015-versie-1.00 Folder/profielen Elektriciteit 2015 versie 1.00.csv') # read the sample standard load profile, can be any length, can be resized given a low/high resolution time axis\n\t# q = q / np.sum(q) # normalization of standard load profile\n\t# # process duration\n\t# duration_axis = np.linspace(0.0, 24.0, 96.0)\n\t# (p_d, E_p) = extra.app_time(duration_axis, 10, 2, 0.0,\n\t# 24.0) # function that define the pdf of duration of a process\n\t# # process consumption\n\t# consumption_axis = np.linspace(0.0, 3.5, 96.0)\n\t# (p_k, E_k) = extra.app_consumption(consumption_axis, 10, 2, 0.0,\n\t# 3.5) # function that define the pdf of duration of a process\n\t# # pdf of starting time\n\t# p_t_0 = lpd.infer_t_0(q, p_d, E_k) # computes the pdf of starting time of processes\n\t# p_t_0 = p_t_0 / np.sum(p_t_0) # normalization of the pdf to sum up to zero\n #\n\t# \"\"\"\n\t# 1st Approach, starting time of processes is a discrete propapibility density function\n\t# \"\"\"\n\t# # synthetic profile of D processes\n\t# D = 2000\n\t# synthetic_profile = lpd.synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\n\t# synthetic_profile_1 = lpd.synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\n\t# # expected value of D processes\n\t# q_e_e = lpd.infer_q_e(t, p_t_0, p_d, E_k, D)\n\t# # plot\n\t# plt.step(t, synthetic_profile, \"g-\")\n\t# plt.step(t, q_e_e, \"b--\")\n #\n\t# \"\"\"\n\t# 2nd Approach, starting time of processes is a continuous propapibility density function\n\t# \"\"\"\n\t# # synthetic profile of D processes\n\t# ts, cs = lpd.continous_synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\n\t# plt.step(ts / len(t) * t[-1], cs, where='post', c='r')\n\t# plt.xlim(0, 24.0)\n\t# plt.legend([\"synthetic\", \"expected\", \"continuous\"], loc=0)\n\t# plt.show()\n #\n\t# \"\"\"\n\t# Time discretization\n\t# \"\"\"\n\t# n_intervals = 24 * 1 # discretized in minutes\n\t# discrete_timeaxis = np.linspace(0.0, 24.0, n_intervals + 1)\n\t# discrete_consumption = lpd.signal_discretization(discrete_timeaxis, t, ts, cs)\n\t# plt.step(ts / len(t) * t[-1], cs, where='post', c='r')\n\t# plt.step(discrete_timeaxis, discrete_consumption, where='post', c='k', ls='--', lw=2)\n\t# plt.legend([\"continuous\", \"discretized\"], loc=0)\n\t# plt.show()\n #\n #\n\t# \"\"\"\n\t# Repeated day synthetic profile creation\n\t# \"\"\"\n\t# # synthetic profile of D processes\n\t# D = 2000\n\t# n = 10\n\t# slp = lpd.synthetic_profile_repeated(D, t, p_d, consumption_axis, p_k, p_t_0, n)\n\t# plt.step(range(len(slp)), slp, \"g-\")\n\t# plt.show()\n\tt = np.linspace(0.0, 24.0, 96.0)\n\tload_profile = extra.read_slp(t, 'Profielen-Elektriciteit-2015-versie-1.00 Folder/profielen Elektriciteit 2015 versie 1.00.csv')\n\tslp = synthetic.create_synthetic_load(load_profile, 5.0, 5)\n\tplt.step(range(len(slp)), slp)\n\tplt.show()","def plot_multiLyapunov(systems, mode=2, savefig=True, figname=None):\n if mode == 2:\n print(systems)\n# divnorm = colors.DivergingNorm(vmin=max([np.nanmin(np.nanmax(np.nanmax(system.lyapunov_2, axis=0), axis=0)) for system in systems]), vcenter=0, vmax=max[np.nanmax(system.lyapunov_2) for system in systems])\n if figname == None:\n figname = 'sum_of_first_2_lyapunov'\n \n fig, ax = plt.subplots()\n for system in systems:\n\n lyapunov_2 = system.lyapunov_2\n x = system.x\n y = system.y\n l = system.l\n a = system.a\n\n\n\n plt.contourf(a[0,0,:,:],l[0,0,:,:],np.nanmax(np.nanmax(lyapunov_2, axis=0), axis=0), levels = 100, cmap = 'RdBu_r')\n# , norm=divnorm)\n for i in range(lyapunov_2.shape[0]):\n for j in range(lyapunov_2.shape[1]):\n plt.contour(a[0,0,:,:],l[0,0,:,:],lyapunov_2[i,j], levels = [0,], colors=('k',),alpha=0.1)\n lyap_sum = plt.contour(a[0,0,:,:],l[0,0,:,:],lyapunov_2.max(axis=0).max(axis=0), levels = [0,], colors=('blue',),alpha=1)\n\n# cbar = plt.colorbar()\n plt.plot(wild_chaos[:,0],wild_chaos[:,1],'--r',lw=3)\n plt.title('Sum of the first 2 Lyapunov exponents ')\n plt.ylabel('$\\lambda$')\n plt.xlabel('a')\n# cbar.ax.set_ylabel('Sum of the first 2 Lyapunov exponents')\n\n ax.set_ylim([l.min(),l.max()])\n ax.set_xlim([a.min(),a.max()])\n if savefig:\n plt.savefig(f'images/{figname}.pdf')\n plt.show()","def plot_for_scaling_check(bolo_name):\n\n\n pop_path = \"../Analyse_\" + bolo_name + \"/Populations/Pop_for_scaling/\"\n\n #Load the estimator\n d_est = BDT_fh.open_estimator_file(bolo_name)\n\n #Best estimator for heat: coefficients\n coeff_EC1, coeff_EC2 = float(d_est[\"HEAT\"][:5]), 1 - float(d_est[\"HEAT\"][:5])\n coeff_EIB, coeff_EID = float(d_est[\"FID\"][:5]), 1-float(d_est[\"FID\"][:5])\n\n #Open event files\n data_types = {\"names\": (\"EC1\", \"EC2\", \"EIA\", \"EIB\", \"EIC\", \"EID\"), \"formats\": (\"f\", \"f\", \"f\", \"f\", \"f\", \"f\")}\n\n arr_heatonly = np.loadtxt(pop_path + bolo_name + \"_heatonly_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_all = np.loadtxt(pop_path + bolo_name + \"_all_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_FidGamma = np.loadtxt(pop_path + bolo_name + \"_FidGamma_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S1Gamma = np.loadtxt(pop_path + bolo_name + \"_S1Gamma_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S2Gamma = np.loadtxt(pop_path + bolo_name + \"_S2Gamma_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S1Beta = np.loadtxt(pop_path + bolo_name + \"_S1Beta_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S2Beta = np.loadtxt(pop_path + bolo_name + \"_S2Beta_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S1Pb = np.loadtxt(pop_path + bolo_name + \"_S1Pb_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S2Pb = np.loadtxt(pop_path + bolo_name + \"_S2Pb_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n\n arr_EI_heatonly, arr_EI_all = coeff_EIB*arr_heatonly[\"EIB\"] + coeff_EID*arr_heatonly[\"EID\"], coeff_EIB*arr_all[\"EIB\"] + coeff_EID*arr_all[\"EID\"]\n arr_EC_heatonly, arr_EC_all = coeff_EC1*arr_heatonly[\"EC1\"] + coeff_EC2*arr_heatonly[\"EC2\"], coeff_EC1*arr_all[\"EC1\"] + coeff_EC2*arr_all[\"EC2\"]\n arr_EI_FidGamma, arr_EC_FidGamma = coeff_EIB*arr_FidGamma[\"EIB\"] + coeff_EID*arr_FidGamma[\"EID\"], coeff_EC1*arr_FidGamma[\"EC1\"] + coeff_EC2*arr_FidGamma[\"EC2\"]\n arr_EI_S1Gamma, arr_EI_S2Gamma = coeff_EIB*arr_S1Gamma[\"EIB\"] + coeff_EID*arr_S1Gamma[\"EID\"], coeff_EIB*arr_S2Gamma[\"EIB\"] + coeff_EID*arr_S2Gamma[\"EID\"]\n arr_EC_S1Gamma, arr_EC_S2Gamma = coeff_EC1*arr_S1Gamma[\"EC1\"] + coeff_EC2*arr_S1Gamma[\"EC2\"], coeff_EC1*arr_S2Gamma[\"EC1\"] + coeff_EC2*arr_S2Gamma[\"EC2\"]\n arr_EI_S1Beta, arr_EI_S2Beta = coeff_EIB*arr_S1Beta[\"EIB\"] + coeff_EID*arr_S1Beta[\"EID\"], coeff_EIB*arr_S2Beta[\"EIB\"] + coeff_EID*arr_S2Beta[\"EID\"]\n arr_EC_S1Beta, arr_EC_S2Beta = coeff_EC1*arr_S1Beta[\"EC1\"] + coeff_EC2*arr_S1Beta[\"EC2\"], coeff_EC1*arr_S2Beta[\"EC1\"] + coeff_EC2*arr_S2Beta[\"EC2\"]\n arr_EI_S1Pb, arr_EI_S2Pb = coeff_EIB*arr_S1Pb[\"EIB\"] + coeff_EID*arr_S1Pb[\"EID\"], coeff_EIB*arr_S2Pb[\"EIB\"] + coeff_EID*arr_S2Pb[\"EID\"]\n arr_EC_S1Pb, arr_EC_S2Pb = coeff_EC1*arr_S1Pb[\"EC1\"] + coeff_EC2*arr_S1Pb[\"EC2\"], coeff_EC1*arr_S2Pb[\"EC1\"] + coeff_EC2*arr_S2Pb[\"EC2\"]\n\n lS1Beta, lS2Beta, lS1Pb, lS2Pb = np.where(arr_EC_S1Beta<15), np.where(arr_EC_S2Beta<15), np.where(arr_EC_S1Pb<15), np.where(arr_EC_S2Pb<15)\n lS1Gamma, lS2Gamma, lFidGamma = np.where(arr_EC_S1Gamma<15), np.where(arr_EC_S2Gamma<15), np.where(arr_EC_FidGamma<15)\n lheatonly, lall = np.where(arr_EC_heatonly<15), np.where(arr_EC_all<15)\n\n arr_EI_heatonly, arr_EC_heatonly = arr_EI_heatonly[lheatonly], arr_EC_heatonly[lheatonly]\n arr_EI_all, arr_EC_all = arr_EI_all[lall], arr_EC_all[lall]\n arr_EI_FidGamma, arr_EC_FidGamma = arr_EI_FidGamma[lFidGamma], arr_EC_FidGamma[lFidGamma]\n arr_EI_S1Gamma, arr_EC_S1Gamma = arr_EI_S1Gamma[lS1Gamma], arr_EC_S1Gamma[lS1Gamma]\n arr_EI_S2Gamma, arr_EC_S2Gamma = arr_EI_S2Gamma[lS2Gamma], arr_EC_S2Gamma[lS2Gamma]\n arr_EI_S1Beta, arr_EC_S1Beta = arr_EI_S1Beta[lS1Beta], arr_EC_S1Beta[lS1Beta]\n arr_EI_S2Beta, arr_EC_S2Beta = arr_EI_S2Beta[lS2Beta], arr_EC_S2Beta[lS2Beta]\n arr_EI_S1Pb, arr_EC_S1Pb = arr_EI_S1Pb[lS1Pb], arr_EC_S1Pb[lS1Pb]\n arr_EI_S2Pb, arr_EC_S2Pb = arr_EI_S2Pb[lS2Pb], arr_EC_S2Pb[lS2Pb]\n\n arr_EI_all, arr_EC_all = np.array(arr_EI_all).astype(float), np.array(arr_EC_all).astype(float)\n arr_EI_heatonly, arr_EC_heatonly = np.array(arr_EI_heatonly).astype(float), np.array(arr_EC_heatonly).astype(float)\n arr_EI_FidGamma, arr_EC_FidGamma = np.array(arr_EI_FidGamma).astype(float), np.array(arr_EC_FidGamma).astype(float)\n arr_EI_S1Gamma, arr_EC_S1Gamma = np.array(arr_EI_S1Gamma).astype(float), np.array(arr_EC_S1Gamma).astype(float)\n arr_EI_S2Gamma, arr_EC_S2Gamma = np.array(arr_EI_S2Gamma).astype(float), np.array(arr_EC_S2Gamma).astype(float) \n arr_EI_S1Beta, arr_EC_S1Beta = np.array(arr_EI_S1Beta).astype(float), np.array(arr_EC_S1Beta).astype(float)\n arr_EI_S2Beta, arr_EC_S2Beta = np.array(arr_EI_S2Beta).astype(float), np.array(arr_EC_S2Beta).astype(float)\n arr_EI_S1Pb, arr_EC_S1Pb = np.array(arr_EI_S1Pb).astype(float), np.array(arr_EC_S1Pb).astype(float)\n arr_EI_S2Pb, arr_EC_S2Pb = np.array(arr_EI_S2Pb).astype(float), np.array(arr_EC_S2Pb).astype(float)\n\n\n gr_heatonly = TGraph(len(arr_EI_heatonly), arr_EC_heatonly, arr_EI_heatonly)\n gr_FidGamma, gr_all = TGraph(len(arr_EI_FidGamma), arr_EC_FidGamma, arr_EI_FidGamma), TGraph(len(arr_EI_all), arr_EC_all, arr_EI_all)\n gr_S1Gamma, gr_S2Gamma = TGraph(len(arr_EI_S1Gamma), arr_EC_S1Gamma, arr_EI_S1Gamma), TGraph(len(arr_EI_S2Gamma), arr_EC_S2Gamma, arr_EI_S2Gamma)\n gr_S1Beta, gr_S2Beta = TGraph(len(arr_EI_S1Beta), arr_EC_S1Beta, arr_EI_S1Beta), TGraph(len(arr_EI_S2Beta), arr_EC_S2Beta, arr_EI_S2Beta)\n gr_S1Pb, gr_S2Pb = TGraph(len(arr_EI_S1Pb), arr_EC_S1Pb, arr_EI_S1Pb), TGraph(len(arr_EI_S2Pb), arr_EC_S2Pb, arr_EI_S2Pb)\n\n PyRPl.process_TGraph(gr_all, X_title = \"Heat\", Y_title = \"Ion\", color=kRed, marker_style = 20, marker_size = 0.1)\n PyRPl.process_TGraph(gr_FidGamma, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack), PyRPl.process_TGraph(gr_heatonly, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack)\n PyRPl.process_TGraph(gr_S1Gamma, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack), PyRPl.process_TGraph(gr_S2Gamma, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack)\n PyRPl.process_TGraph(gr_S1Beta, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack), PyRPl.process_TGraph(gr_S2Beta, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack)\n PyRPl.process_TGraph(gr_S1Pb, X_title = \"Heat\", Y_title = \"Ion\", color=kRed), PyRPl.process_TGraph(gr_S2Pb, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack)\n\n list_gr = [gr_all, gr_FidGamma, gr_S1Gamma, gr_S2Gamma, gr_S1Beta, gr_S2Beta, gr_S1Pb, gr_S2Pb, gr_heatonly]\n list_pts = [gr.GetN() for gr in list_gr[1:]]\n print gr_all.GetN(), sum(list_pts)\n h = TH2F(\"h\", \"h\", 100, -5, 15, 100, -5, 15)\n PyRPl.process_TH2(h, X_title = \"Heat\", Y_title = \"Ion\")\n h.Draw()\n for gr in list_gr:\n gr.Draw(\"*same\")\n\n raw_input()\n\n # arr_Q_S1Beta = arr_EI_S1Beta/((1+8./3)*arr_EC_S1Beta - arr_EI_S1Beta*5.5/3)\n # arr_Q_S2Beta = arr_EI_S1Beta/((1+8./3)*arr_EC_S1Beta - arr_EI_S1Beta*5.5/3)\n # arr_Q_S1Pb = arr_EI_S1Pb/((1+8./3)*arr_EC_S1Pb - arr_EI_S1Pb*5.5/3)\n # arr_Q_S2Pb = arr_EI_S2Pb/((1+8./3)*arr_EC_S2Pb - arr_EI_S2Pb*5.5/3)\n \n # gr_QS1Beta, gr_QS2Beta = TGraph(len(arr_Q_S1Beta), arr_EC_S1Beta, arr_Q_S1Beta), TGraph(len(arr_Q_S2Beta), arr_EC_S2Beta, arr_Q_S2Beta)\n # gr_QS1Pb, gr_QS2Pb = TGraph(len(arr_Q_S1Pb), arr_EC_S1Pb, arr_Q_S1Pb), TGraph(len(arr_Q_S2Pb), arr_EC_S2Pb, arr_Q_S2Pb)\n\n\n # PyRPl.process_TGraph(gr_QS1Beta, X_title = \"Heat\", Y_title = \"Q\", color=kOrange-3), PyRPl.process_TGraph(gr_QS2Beta, X_title = \"Heat\", Y_title = \"Q\", color=kBlue)\n # PyRPl.process_TGraph(gr_QS1Pb, X_title = \"Heat\", Y_title = \"Q\", color=kRed), PyRPl.process_TGraph(gr_QS2Pb, X_title = \"Heat\", Y_title = \"Q\", color=kGreen+2)","def get_corner_node_matlab(lcurve, debug=False):\r\n lx, ly = np.zeros(len(lcurve)), np.zeros(len(lcurve))\r\n for lia in range(len(lcurve)):\r\n lx[lia] = lcurve[lia][0]\r\n ly[lia] = lcurve[lia][1]\r\n\r\n import matlab.engine\r\n eng = matlab.engine.start_matlab()\r\n eng.addpath(eng.genpath('../dfg_lowrank/simulation-200820/'))\r\n import io\r\n out = io.StringIO()\r\n err = io.StringIO()\r\n l_opt, info = eng.L_corner(as_matlab(lx), as_matlab(ly), nargout=2, stdout=out, stderr=err)\r\n # print(l_opt)\r\n # print(info)\r\n # print(out.getvalue())\r\n l_opt = int(l_opt)\r\n # k = curvature_lcurve(lx, ly)\r\n # l_opt = np.argmax(k)\r\n # print(l_opt)\r\n if False:\r\n import matplotlib\r\n matplotlib.use(\"Qt4Agg\")\r\n import matplotlib.pyplot as plt\r\n plt.figure()\r\n plt.title(\"L-curve\")\r\n plt.plot([np.log10(lcurve[lia][0]) for lia in range(len(lcurve))],\r\n [np.log10(lcurve[lia][1]) for lia in range(len(lcurve))], '-xb', label=\"L-curve\")\r\n plt.plot(np.log10(lcurve[l_opt][0]), np.log10(lcurve[l_opt][1]), 'or', label=\"optimal value\")\r\n plt.xlabel(\"log(|| Y - UV ||)\")\r\n plt.ylabel(\"log(|| L_U U || + || L_V V ||)\")\r\n plt.show()\r\n\r\n return l_opt","def main():\n args = parse_args()\n\n with open(args.train_details_json, mode='r', encoding='utf-8') as json_f:\n results_dict = json.load(json_f)[-1]\n\n losses_plot = plt.figure()\n plt.plot(range(1, len(results_dict['train_loss']) + 1),\n results_dict['train_loss'])\n plt.plot(range(1, len(results_dict['val_loss']) + 1),\n results_dict['val_loss'])\n plt.plot(range(1, len(results_dict['test_loss']) + 1),\n results_dict['test_loss'])\n plt.legend(['train', 'val', 'test'])\n plt.title(f'loss vs epoch for {args.model} model on {args.dataset} dataset')\n plt.xlabel('epoch')\n plt.ylabel('loss')\n plt.grid(True)\n losses_plot.set_size_inches((8, 8))\n losses_plot.savefig(\n os.path.join(FIGURES_DIR,\n f'{args.dataset}_{args.model}_losses_plot.png'))\n\n accuracies_plot = plt.figure()\n plt.plot(range(1, len(results_dict['train_acc']) + 1),\n results_dict['train_acc'])\n plt.plot(range(1, len(results_dict['val_acc']) + 1),\n results_dict['val_acc'])\n plt.plot(range(1, len(results_dict['test_acc']) + 1),\n results_dict['test_acc'])\n plt.legend(['train', 'val', 'test'])\n plt.title(f'accuracy vs epoch for {args.model} '\n f'model on {args.dataset} dataset')\n plt.xlabel('epoch')\n plt.ylabel('accuracy')\n plt.grid(True)\n accuracies_plot.set_size_inches((8, 8))\n accuracies_plot.savefig(\n os.path.join(FIGURES_DIR,\n f'{args.dataset}_{args.model}_accuracies_plot.png'))","def line_FIR(**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n GR = glo.global_results()\n\n L_line = getattr(GR,'L_'+p.line+'_sun')\n # L_FIR = getattr(GR,'L_FIR_sun')\n\n labs = {'':'SIGAME default: lognormal + power-law, Mach=10',\\\n '_arepoPDF_dim':'AREPO parametric PDF with extinction',\\\n '_arepoPDF':'SIGAME parametric PDF'}\n lab = labs[p.table_ext]\n\n if p.add:\n ax = p.ax\n else:\n fig,ax = plt.subplots(figsize=(8,6))\n\n n = 388\n m = ax.scatter(GR.L_FIR_sun[0:n],L_line[0:n],marker='o',s=20,\\\n c=GR.Zsfr[0:n],label=lab,alpha=0.6,zorder=10)\n\n if p.add_obs:\n add_line_FIR_obs(p.line,ax)\n\n m = ax.scatter(GR.L_FIR_sun/1e6,L_line/1e6,marker='o',s=30,c=GR.Zsfr)\n cbar = plt.colorbar(m,ax=ax)\n cbar.set_label(label='$Z_{\\mathrm{SFR}}$ [Z$_{\\odot}$]',size=15)\n\n ax.set_xscale('log')\n ax.set_yscale('log')\n ax.set_xlabel(getlabel('L_FIR'))\n ax.set_ylabel(getlabel(p.line))\n handles,labels = ax.get_legend_handles_labels()\n handles = np.flip(handles)\n labels = np.flip(labels)\n ax.legend(handles,labels,loc='upper left',fontsize=7)\n if not p.xlim: p.xlim = 10.**np.array([6,13])\n if not p.ylim: \n p.ylim = [np.median(L_line)/1e4,np.median(L_line)*1e4]\n ax.set_xlim(p.xlim)\n ax.set_ylim(p.ylim)\n ax.grid(ls='--')\n\n if p.savefig & (not p.add):\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \n plt.savefig(p.d_plot + 'luminosity/%s_FIR.png' % p.line, format='png', dpi=300) \n\n # plt.close('all')","def main():\n\t#print(scipy.__version__)\n\t#image()\n\t#heat_capacity2()\n\t#hist()\n\t#single_plot()\n\n\t#heat_capacity2()\n\t#single_plot()\n\t#plt.show()\n\t#u0_tc()\n\t#multi_heat_capacity(\"HL_DM_flux5\",True)\n\t#multi_heat_capacity2()\n\t#plot_spin()\n\t#plt.show()\n\theat_capacity2(1,2)\n\t#hist()\n\tplt.show()\n\t#potential()\n\t#plt.show()\n\t#heat_capacity(3,4)\n\t#heat_capacity(5,6)\n\t#heat_capacity(7,8)\n\t#final_spins()\n\t#plot_spin()\n\t#plot_from_csv()\n\t#difference_plot()","def show_solver_history(solver, plot_train=True, plot_val=True, color='black'):\n mpl.rcParams['text.color'] = color\n mpl.rcParams['axes.labelcolor'] = color\n mpl.rcParams['xtick.color'] = color\n mpl.rcParams['ytick.color'] = color\n\n print(\"Stop reason: %s\" % solver.stop_reason)\n print(\"Stop time: %fs\" % solver.training_time_s)\n\n has_gender_loss = np.array(solver.history['val_gender_loss']).sum() > 0.\n has_rec_loss = np.array(solver.history['val_rec_loss']).sum() > 0.\n\n train_loss = np.array(solver.history['train_loss'])\n\n if has_rec_loss:\n train_rec_loss = np.array(solver.history['train_rec_loss'])\n val_rec_loss = np.array(solver.history['val_rec_loss'])\n if has_gender_loss:\n train_gender_loss = np.array(solver.history['train_gender_loss'])\n val_gender_loss = np.array(solver.history['val_gender_loss'])\n\n plt.figure(figsize=(20, 10))\n\n if plot_train:\n if has_rec_loss:\n plt.plot(np.linspace(1, len(train_loss), len(train_rec_loss)),\n train_rec_loss, label='Train Reconstruction loss')\n if has_gender_loss:\n plt.plot(np.linspace(1, len(train_loss), len(train_gender_loss)),\n train_gender_loss, label='ATrain Gender loss')\n\n if plot_val:\n if has_rec_loss:\n plt.plot(np.linspace(1, len(train_loss), len(val_rec_loss)),\n val_rec_loss, label='Val Reconstruction loss')\n if has_gender_loss:\n plt.plot(np.linspace(1, len(train_loss), len(val_gender_loss)),\n val_gender_loss, label='Val Gender loss')\n\n plt.xlabel(\"Iterations\", fontsize=18)\n plt.ylabel(\"Train/Val loss\", fontsize=18)\n plt.xticks(fontsize=14)\n plt.yticks(fontsize=14)\n legend = plt.legend(fontsize=14)\n for text in legend.get_texts():\n text.set_color(\"black\")\n\n plt.show()\n\n if has_rec_loss:\n print(\"Final training reconstruction loss: {}\".format(\n train_rec_loss[-1]))\n print(\"Final validation reconstruction loss: {}\".format(\n val_rec_loss[-1]))\n if has_gender_loss:\n print(\"Final training gender loss: {}\".format(train_gender_loss[-1]))\n print(\"Final validation gender loss: {}\".format(val_gender_loss[-1]))","def main():\n import argparse\n import os\n parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('--dirs', help='List of log directories', nargs='*', default=['./log'])\n parser.add_argument('--num_timesteps', type=int, default=int(10e6))\n parser.add_argument('--xaxis', help='Varible on X-axis', default=X_TIMESTEPS)\n parser.add_argument('--task_name', help='Title of plot', default='Breakout')\n args = parser.parse_args()\n args.dirs = [os.path.abspath(folder) for folder in args.dirs]\n plot_results(args.dirs, args.num_timesteps, args.xaxis, args.task_name)\n plt.show()","def analyze_lnl(lls_dict, xtup, lnL, CL=0.68):\n if len(xtup) == 3:\n NHI_eval, C0_val, C1_val = xtup\n cl3D = cl_interval(lnL, CL=CL) # 1-sigma!\n lls_dict['fit_NHI'] = [NHI_eval[cl3D[0][0]], NHI_eval[cl3D[1][0][0]], NHI_eval[cl3D[1][0][1]]]\n lls_dict['cdict']['best'] = (float(C0_val[cl3D[0][1]]), float(C1_val[cl3D[0][2]]))\n lls_dict['cdict']['fit_val'] = [[float(C0_val[cl3D[0][1]]),\n float(C0_val[cl3D[1][1][0]]),\n float(C0_val[cl3D[1][1][1]])],\n [float(C1_val[cl3D[0][2]]),\n float(C1_val[cl3D[1][2][0]]),\n float(C1_val[cl3D[1][2][1]])]]\n # Return\n return"],"string":"[\n \"def plot():\\n xvals = np.arange(-50, 250, step=0.1)\\n\\n fig = plt.figure()\\n plt.suptitle(\\\"Gaussian with smooth transition to power law\\\")\\n\\n A0vals = [10, 11]\\n avals = [5*10**-3, 10**-3, 5*10**-4]\\n ttvals = [10., 50., 100.]\\n cvals = [-0.1, -0.9, -5./3., -4.]\\n offset = [-30, 0.0, 30]\\n\\n paramvals = [A0vals, avals, ttvals,cvals, offset]\\n titles, labels = return_parameter_names()\\n\\n nplots = len(paramvals)\\n\\n for i in range(nplots):\\n plt.subplot(nplots, 1, i+1)\\n vals = paramvals[i]\\n for j in range(len(vals)):\\n pset = list(default())\\n pset[i] = vals[j]\\n yvals=[]\\n ypower=[]\\n ypeak=[]\\n for x in xvals:\\n yvals.append(fitfunc(x, pset))\\n ypeak.append(logpeak(x,pset))\\n if x > 0:\\n ypower.append(logpowerlaw(x,pset))\\n label = labels[i] + \\\"=\\\"+str(vals[j])\\n plt.plot(xvals, yvals, label = label)\\n\\n plt.title(titles[i])\\n plt.legend()\\n\\n fig.set_size_inches(15, 30)\\n plt.savefig(\\\"graphs/misc/lightcurve_models.pdf\\\")\\n plt.close()\",\n \"def plot_LvsL(modeldata,line1='CIV1548',line2='CIII1908',plotname='./TESTPLOT.pdf',\\n Zgas=False,logU=False,xid=0.3,nh=100,COratio=0.38,Mcutoff=100,\\n logx=False,logy=False,logp1=False,logp2=False,fixxrange=False,fixyrange=False,\\n showobs=None,noobserr=False,verbose=True):\\n NFalse = 0\\n freeparam = []\\n inforstr = \\\"\\\"\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n legenddic = {}\\n legenddic['Zgas'] = r'Z$_\\\\textrm{gas}$'\\n legenddic['logUs'] = r'log$_\\\\textrm{10}$(U)'\\n legenddic['xid'] = r'$\\\\xi_\\\\textrm{d}$'\\n legenddic['nh'] = r'n$_\\\\textrm{H}$ / [cm$^3$]'\\n legenddic['COCOsol'] = r'C/O / [C/O]$_\\\\textrm{sun}$'\\n legenddic['mup'] = r'M$_\\\\textrm{cut IMF}$ / [M$_\\\\textrm{sun}]$'\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not Zgas:\\n Zgasrange = [0.0,1.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' Zgas:vary, '\\n freeparam.append('Zgas')\\n else:\\n Zgasrange = [Zgas-1e-6,Zgas+1e-6]\\n inforstr = inforstr+' '+legenddic['Zgas']+'='+str(Zgas)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not logU:\\n logUrange = [-5.0,0.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' logU:vary, '\\n freeparam.append('logUs')\\n else:\\n logUrange = [logU-0.1,logU+0.1]\\n inforstr = inforstr+' '+legenddic['logUs']+'='+str(logU)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not xid:\\n xidrange = [0.0,0.6]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' xid:vary, '\\n freeparam.append('xid')\\n else:\\n xidrange = [xid-0.01,xid+0.01]\\n inforstr = inforstr+' '+legenddic['xid']+'='+str(xid)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not nh:\\n nhrange = [0.0,1.0e6]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' nH:vary, '\\n freeparam.append('nh')\\n else:\\n nhrange = [nh-1.0,nh+1.0]\\n inforstr = inforstr+' '+legenddic['nh']+'='+str(nh)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not COratio:\\n COratiorange = [0.0,2.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' C/O:vary, '\\n freeparam.append('COCOsol')\\n else:\\n COratiorange = [COratio-0.001,COratio+0.001]\\n inforstr = inforstr+' '+legenddic['COCOsol']+'='+str(COratio)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not Mcutoff:\\n Mcutoffrange = [0.0,400.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' Mcutoff:vary, '\\n freeparam.append('mup')\\n else:\\n Mcutoffrange = [Mcutoff-1.0,Mcutoff+1.0]\\n inforstr = inforstr+' '+legenddic['mup']+'='+str(Mcutoff)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n\\n if NFalse != 2:\\n sys.exit(' Two and only two of the model parameters (Zgas,logU,xid,nh,COratio,Mcutoff) '\\n 'should be set to Flase to define the model grid; however it appears '+str(NFalse)+\\n ' parameters where not set')\\n\\n goodent = np.where( (modeldata['Zgas'] > Zgasrange[0]) & (modeldata['Zgas'] < Zgasrange[1]) &\\n (modeldata['logUs'] > logUrange[0]) & (modeldata['logUs'] < logUrange[1]) &\\n (modeldata['xid'] > xidrange[0]) & (modeldata['xid'] < xidrange[1]) &\\n (modeldata['nh'] > nhrange[0]) & (modeldata['nh'] < nhrange[1]) &\\n (modeldata['COCOsol'] > COratiorange[0]) & (modeldata['COCOsol'] < COratiorange[1]) &\\n (modeldata['mup'] > Mcutoffrange[0]) & (modeldata['mup'] < Mcutoffrange[1]) )\\n\\n Ngoodent = len(goodent[0])\\n\\n if Ngoodent > 1:\\n if verbose: print(' - Getting data for '+str(Ngoodent)+' data points satisfying model selection ')\\n param1 = modeldata[freeparam[0]][goodent]\\n if logp1:\\n param1 = np.log10(param1)\\n\\n param2 = modeldata[freeparam[1]][goodent]\\n if logp2:\\n param2 = np.log10(param2)\\n\\n lum1 = modeldata[line1][goodent]\\n lum2 = modeldata[line2][goodent]\\n else:\\n if verbose: print(' WARNING: Less than 2 model grid points to plot; no output generated')\\n return\\n\\n # - - - - - - - - - - - PLOTTING - - - - - - - - - - -\\n if verbose: print(' - Setting up and generating plot')\\n plotname = plotname\\n fig = plt.figure(figsize=(9, 5))\\n fig.subplots_adjust(wspace=0.1, hspace=0.1,left=0.1, right=0.99, bottom=0.10, top=0.95)\\n Fsize = 10\\n lthick = 1\\n marksize = 3\\n plt.rc('text', usetex=True)\\n plt.rc('font', family='serif',size=Fsize)\\n plt.rc('xtick', labelsize=Fsize)\\n plt.rc('ytick', labelsize=Fsize)\\n plt.clf()\\n plt.ioff()\\n plt.title(inforstr[:-2],fontsize=Fsize)\\n\\n margin = 0.1\\n dx = np.abs(np.max(lum1)-np.min(lum1))\\n dy = np.abs(np.max(lum2)-np.min(lum2))\\n\\n\\n if fixxrange:\\n xrange = fixxrange\\n else:\\n if logx:\\n xrange = [np.min(lum1)-np.min(lum1)/2.,np.max(lum1)+np.max(lum1)/2.]\\n else:\\n xrange = [np.min(lum1)-dx*margin,np.max(lum1)+dx*margin]\\n\\n if fixyrange:\\n yrange = fixyrange\\n else:\\n if logy:\\n yrange = [np.min(lum2)-np.min(lum2)/2.,np.max(lum2)+np.max(lum2)/2.]\\n else:\\n yrange = [np.min(lum2)-dy*margin,np.max(lum2)+dy*margin]\\n\\n # ------------ PARAM1 ------------\\n cmap = plt.cm.get_cmap('winter')\\n cmin = np.min(param1)\\n cmax = np.max(param1)\\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\\n mm = plt.cm.ScalarMappable(cmap=cmap)\\n mm.set_array(cmaparr)\\n cb1 = plt.colorbar(mm)#shrink=0.25\\n\\n pstr1 = legenddic[freeparam[0]]\\n if logp1:\\n pstr1 = r'log$_\\\\textrm{10}$('+pstr1+')'\\n\\n cb1.set_label(pstr1+' (outer circle) - Fixed: black line')\\n\\n for p1 in np.unique(param1):\\n p1col = cmap(colnorm(p1))\\n p1ent = np.where(param1 == p1)\\n\\n plt.plot(lum1[p1ent],lum2[p1ent],'-',lw=lthick, color='k',zorder=1)\\n\\n plt.errorbar(lum1[p1ent],lum2[p1ent],xerr=None,yerr=None,\\n marker='o',lw=0, markersize=marksize*3,\\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\\n\\n # ------------ PARAM2 ------------\\n cmap = plt.cm.get_cmap('spring')\\n cmin = np.min(param2)\\n cmax = np.max(param2)\\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\\n mm = plt.cm.ScalarMappable(cmap=cmap)\\n mm.set_array(cmaparr)\\n cb2 = plt.colorbar(mm)#shrink=0.25\\n\\n pstr2 = legenddic[freeparam[1]]\\n if logp2:\\n pstr2 = 'log10('+pstr2+')'\\n\\n cb2.set_label(pstr2+' (inner circle) - Fixed: gray line')\\n\\n for p2 in np.unique(param2):\\n p2col = cmap(colnorm(p2))\\n p2ent = np.where(param2 == p2)\\n\\n plt.plot(lum1[p2ent],lum2[p2ent],'-',lw=lthick, color='gray',zorder=2)\\n\\n plt.errorbar(lum1[p2ent],lum2[p2ent],xerr=None,yerr=None,\\n marker='o',lw=0, markersize=marksize*1.5,\\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\\n\\n if showobs != None:\\n for ii, objid in enumerate(showobs[:,0]):\\n if (showobs[:,7][ii] > xrange[0]) & (showobs[:,7][ii] < xrange[1]) & \\\\\\n (showobs[:,9][ii] > yrange[0]) & (showobs[:,9][ii] < yrange[1]):\\n\\n if noobserr:\\n obsxerr = None\\n obsyerr = None\\n else:\\n obsxerr = showobs[:,8][ii]\\n obsyerr = showobs[:,10][ii]\\n plt.errorbar(showobs[:,7][ii],showobs[:,9][ii],xerr=obsxerr,yerr=obsyerr,\\n marker='*',lw=lthick, markersize=marksize*2,\\n markerfacecolor='k',ecolor='k',markeredgecolor = 'k',zorder=30)\\n\\n plt.xlabel(r'L$_\\\\textrm{'+line1+r'}$ / [3.826$\\\\times$1e33 erg/s]/[Msun/yr]')\\n plt.ylabel(r'L$_\\\\textrm{'+line2+r'}$ / [3.826$\\\\times$1e33 erg/s]/[Msun/yr]')\\n\\n plt.xlim(xrange)\\n plt.ylim(yrange)\\n\\n if logx:\\n plt.xscale('log')\\n if logy:\\n plt.yscale('log')\\n\\n #--------- LEGEND ---------\\n # plt.errorbar(-1,-1,xerr=None,yerr=None,fmt='o',lw=lthick,ecolor='white', markersize=marksize*2,\\n # markerfacecolor='white',markeredgecolor = 'k',label='Ground-based spec')\\n #\\n # leg = plt.legend(fancybox=True, loc='upper center',prop={'size':Fsize},ncol=1,numpoints=1)\\n # #bbox_to_anchor=(1.25, 1.03)) # add the legend\\n # leg.get_frame().set_alpha(0.7)\\n #--------------------------\\n\\n if verbose: print(' Saving plot to'+plotname)\\n plt.savefig(plotname)\\n plt.clf()\\n plt.close('all')\",\n \"def lCurve(self): \\n\\n # --------------------------------------------------------------------------------------------- #\\n # Read data\\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\\n lcTab = Table.read(fitsNnam)\\n if (self.tstart is not None) and (self.tstop is not None):\\n lcTab = lcTab[ (self.tstart <= lcTab['mjd']) & (lcTab['mjd'] <= self.tstop)]\\n lcTab = lcTab[lcTab['flux'] != -1.] # avoid undone analyses\\n\\n timeMJD = lcTab['mjd']\\n tref = int(np.floor( timeMJD[0] / 100.0)) * 100 # round to lowest hundred\\n timeMJD -= tref\\n ts = lcTab['ts']\\n detect = lcTab['ts'] >= self.tsmin\\n undet = lcTab['ts'] < self.tsmin\\n flux = lcTab['flux'][detect]\\n fluxerr = lcTab['fluxerr'][detect]\\n upperl = lcTab['upperlim'][undet]\\n upperl[upperl == -1.] = 0. # for when it failed\\n scale = 10**int(np.floor(np.log10( np.mean( np.concatenate( (flux, upperl), axis=0) ) ))) \\n\\n # --------------------------------------------------------------------------------------------- #\\n # Plot\\n lcplt = FermiPlot(savepath='', xsize=8.5, ysize=6)\\n lcplt.figname = os.path.join(self.workpath, 'LightCurve.pdf')\\n lcplt.xlabel = r'Time (MJD $-$ {})'.format(tref)\\n lcplt.ylabel = [r'Flux ($10^{%d}$ ph\\\\,cm$^{-2}$\\\\,s$^{-1}$)'%(int(np.log10(scale))), r'TS']\\n lcplt.hline = [None, self.tsmin]\\n deltaY = max(np.concatenate((flux+fluxerr, upperl), axis=0)) - min(np.concatenate((flux-fluxerr, upperl), axis=0))\\n lcplt.ymin = [(min(np.concatenate((flux-fluxerr, upperl-upperl*0.1), axis=0)) - 0.05*deltaY) / scale, min(ts) - 0.05*(max(ts)-min(ts))]\\n lcplt.ymax = [(max(np.concatenate((flux+fluxerr, upperl), axis=0)) + 0.05*deltaY) / scale, max(ts) + 0.05*(max(ts)-min(ts))]\\n deltaX = (timeMJD[-1] + lcTab['mjderr'][-1]) - (timeMJD[0] - lcTab['mjderr'][0]) \\n lcplt.xmin = timeMJD[0] - lcTab['mjderr'][0] - 0.05*deltaX\\n lcplt.xmax = timeMJD[-1] + lcTab['mjderr'][-1] + 0.05*deltaX\\n lcplt.fill = [item for sublist in zip( timeMJD[detect]-lcTab['mjderr'][detect], timeMJD[detect]+lcTab['mjderr'][detect] ) for item in sublist]\\n lcplt.shadecol= self.loran \\n if len(flux) == 0:\\n lcplt.mksize = [2, 2]\\n lcplt.ymode = ['linear', 'linear']\\n lcplt.color = ['gray', 'black']\\n lcplt.prop = [3, 1]\\n lcplt.limit = [True, False]\\n lcplt.multiplot(x = [ timeMJD[undet], timeMJD ],\\n y = [ upperl/scale, ts ],\\n xerr = [ lcTab['mjderr'][undet], lcTab['mjderr']],\\n yerr = [ upperl/scale*0.1, None])\\n else:\\n lcplt.mksize = [2, 2, 2]\\n lcplt.ymode = ['linear', 'linear', 'linear']\\n lcplt.color = ['gray', 'black', 'black']\\n lcplt.prop = [3, 1]\\n lcplt.limit = [[True, False], False]\\n lcplt.multiplot(x = [ [timeMJD[undet], timeMJD[detect]], timeMJD ],\\n y = [ [upperl/scale, flux/scale], ts ],\\n xerr = [ [lcTab['mjderr'][undet], lcTab['mjderr'][detect]], lcTab['mjderr']],\\n yerr = [ [upperl/scale*0.1, fluxerr/scale], None])\\n lcplt.save()\\n\\n print(\\\"\\\\t=== Figure '{}' created ===\\\".format(lcplt.figname)) \\n return\",\n \"def main():\\n error_LU, h_LU = read_2_col_file(\\\"error_LU_data.txt\\\") \\n error, h = read_2_col_file(\\\"error_data.txt\\\")\\n \\n log_h_LU = np.log10(h_LU)\\n log_error_LU = np.log10(error_LU)\\n log_h = np.log10(h)\\n log_error = np.log10(error)\\n\\n plt.plot(log_h_LU, log_error_LU, label=\\\"LU-decomposition\\\")\\n plt.plot(log_h, log_error, label=\\\"general Thomas algo.\\\")\\n plt.legend()\\n plt.title(\\\"Logarithmic plot of relative error \\\")\\n plt.xlabel(\\\"$log_{10}(time \\\\, step)$\\\")\\n plt.ylabel(\\\"$log_{10}(error)$\\\")\\n plt.show()\",\n \"def test_lcurve(self):\\n from astropy.io.fits import Header\\n command = ('{0} -e {1} {2} --safe-interval '\\n '{3} {4} --nproc 2 -b 0.5 -o {5}').format(\\n os.path.join(self.datadir, 'monol_testA_nustar_fpma_ev_calib' +\\n HEN_FILE_EXTENSION),\\n 3, 50, 100, 300,\\n os.path.join(self.datadir, 'monol_testA_E3-50_lc' +\\n HEN_FILE_EXTENSION)\\n )\\n hen.lcurve.main(command.split())\\n\\n new_filename = \\\\\\n os.path.join(os.path.join(self.datadir,\\n 'monol_testA_E3-50_lc' +\\n HEN_FILE_EXTENSION))\\n assert os.path.exists(new_filename)\\n lc = hen.io.load_lcurve(new_filename)\\n assert hasattr(lc, 'header')\\n # Test that the header is correctly conserved\\n Header.fromstring(lc.header)\\n assert hasattr(lc, 'gti')\\n gti_to_test = hen.io.load_events(self.first_event_file).gti\\n assert np.allclose(gti_to_test, lc.gti)\",\n \"def plot_lightcurve(t, lc, lcerr, popt, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params,\\n x = None, y = None, Pns = None, errors = False, binsize = 50,\\n name = None, channel = None, orbit = None, savefile = False, TT_hjd = None,\\n method = 'PLD', color = 'r', scale = None, filext = None, foldext = '',\\n showCuts = False, ncutstarts = None, cutstartTime = None,\\n cutends = False, eclipse = False, extraoutputs = False):\\n # dictionary to save the reduced data, the model, the corrected data, the corrected model\\n plottingdict = {}\\n\\n binsize = int(binsize)\\n\\n if name == None:\\n warnings.warn( \\\"What planetary system are we looking at!? -- setting to 'unknown'\\\" )\\n name = 'unknown'\\n if channel == None:\\n warnings.warn( \\\"What channel are we looking at!? -- setting to 'unknown'\\\" )\\n channel = 'unknown'\\n\\n if method == 'poly':\\n\\n transit, F, ramp = model_poly(popt, t, x, y, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, components = True, eclipse = eclipse)\\n optflux = transit*F*ramp\\n\\n # Correct the lightcurve and bin the data and the optimum values\\n corrected_data = lc / (F*ramp)\\n binned_data = custom_bin(corrected_data, binsize)\\n binned_opt = custom_bin(transit, binsize)\\n binned_times = custom_bin(t, binsize)\\n\\n # Calculate the residuals\\n residuals = lc - optflux\\n rms = np.sqrt(np.sum(residuals**2)/len(residuals))\\n chi2 = chi(popt, t, lc, lcerr, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, x=x, y=y, method = 'poly', eclipse = eclipse)/(len(lc)-len(popt))\\n bic = BIC(popt, t, lc, lcerr, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, x=x, y=y, method = 'poly', eclipse = eclipse)\\n\\n binned_residuals = []\\n for i in range(len(binned_data)):\\n binned_residuals.append(binned_data[i] - binned_opt[i])\\n\\n # Make the plot, 3 plots in one.\\n fig = plt.figure(figsize=(15, 6))\\n #plt.title(\\\"{0}: rms={1}, red_chi2={2}, BIC={3}\\\".format(name, rms, chi2, bic))\\n frame1=fig.add_axes((.1,.6,.8,.4))\\n frame1.axes.get_xaxis().set_visible(False)\\n frame2=fig.add_axes((.1,.2,.8,.4))\\n frame2.axes.get_xaxis().set_visible(False)\\n frame3=fig.add_axes((.1,.0,.8,.2))\\n\\n frame1.plot(t, lc*scale, 'ko', markersize=2, label='Raw data')\\n frame1.plot(t, optflux*scale, color, label='Best fit poly model', linewidth =2)\\n frame1.set_title(\\\"{0} - {1} - {2} lightcurve\\\".format(name, channel, orbit))\\n frame1.set_ylabel(\\\"Raw [e-]\\\")\\n frame1.legend(loc = 'best')\\n\\n\\n frame2.plot(binned_times, binned_data, 'ko', markersize = 4, label='Binned data (x{})'.format(binsize))\\n frame2.plot(binned_times, binned_opt, color = color, label='Best fit transit model')\\n frame2.set_ylabel(\\\"Corrected & Normalised\\\")\\n frame2.legend(loc = 'lower left')\\n frame2.annotate('RMS={0:.3e}\\\\n'.format(rms) + r'$\\\\chi_{red}^2$' + '={0:.3e} \\\\nBIC={1:.3e}'.format(chi2, bic),\\n xy=(0.85, 0.2), xycoords='axes fraction',bbox={'facecolor':color, 'alpha':0.5, 'pad':10})\\n\\n\\n frame3.plot(binned_times,binned_residuals, 'ko', markersize = 4)\\n frame3.axhline(0, color = color)\\n frame3.set_ylabel(\\\"Residuals\\\")\\n\\n plottingdict['Times'] = t\\n plottingdict['Raw Data'] = lc*scale\\n plottingdict['Full Poly Model'] = optflux*scale\\n\\n plottingdict['Corrected Data'] = lc / (F*ramp)\\n plottingdict['Transit Model'] = transit\\n plottingdict['Temporal Ramp'] = ramp\\n plottingdict['Poly'] = F\\n\\n\\n if showCuts:\\n for j in range(ncutstarts):\\n if cutends:\\n frame1.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n frame2.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n frame3.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n else:\\n frame1.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n frame2.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n frame3.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n\\n plt.xlabel(\\\"Time [bjd]\\\")\\n\\n if savefile:\\n plt.savefig(\\\"{5}/PhD/SpitzerTransits/{0}{4}/{0}_{1}_{2}_Poly_lc_{3}.png\\\".format(name, orbit, channel, filext, foldext, os.getenv('HOME')),bbox_inches='tight')\\n plt.close()\\n else:\\n plt.show()\\n plt.close()\\n\\n elif method == 'PLD':\\n\\n DE, pixels, ramp = model_PLD(popt, t, Pns, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, components = True, eclipse = eclipse)\\n\\n optflux = DE + pixels + ramp\\n\\n # Correct the lightcurve and bin the data and the optimum values\\n corrected_data = lc - pixels - ramp\\n binned_data = custom_bin(corrected_data, binsize)\\n binned_opt = custom_bin(DE, binsize)\\n binned_times = custom_bin(t, binsize)\\n\\n # Calculate the residuals\\n residuals = lc - optflux\\n rms = np.sqrt(np.sum(residuals**2)/len(residuals))\\n chi2 = chi(popt, t, lc, lcerr, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, Pns=Pns, method = 'PLD', eclipse = eclipse )/(len(lc)-len(popt))\\n bic = BIC(popt, t, lc, lcerr, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, sys_params, Pns=Pns, method = 'PLD', eclipse = eclipse)\\n\\n binned_residuals = []\\n for i in range(len(binned_data)):\\n binned_residuals.append(binned_data[i] - binned_opt[i])\\n\\n # Make the plot, 3 plots in one.\\n fig = plt.figure(figsize=(15, 6))\\n frame1=fig.add_axes((.1,.6,.8,.4))\\n frame1.axes.get_xaxis().set_visible(False)\\n frame2=fig.add_axes((.1,.2,.8,.4))\\n frame2.axes.get_xaxis().set_visible(False)\\n frame3=fig.add_axes((.1,.0,.8,.2))\\n\\n frame1.plot(t, lc*scale, 'ko', markersize=2, label='Raw data')\\n frame1.plot(t, optflux*scale, color, label='Best fit PLD model', linewidth =2)\\n frame1.set_title(\\\"{0} - {1} - {2} lightcurve\\\".format(name, channel, orbit))\\n frame1.set_ylabel(\\\"Raw [e-]\\\")\\n frame1.legend(loc = 'best')\\n\\n frame2.plot(binned_times, binned_data, 'ko', markersize = 4, label='Binned data (x{})'.format(binsize))\\n frame2.plot(binned_times, binned_opt, color = color, label='Best fit transit model')\\n frame2.set_ylabel(\\\"Corrected & Normalised\\\")\\n frame2.legend(loc = 'lower left')\\n frame2.annotate('RMS={0:.3e}\\\\n'.format(rms) + r'$\\\\chi_{red}^2$' + '={0:.3e} \\\\nBIC={1:.3e}'.format(chi2, bic),\\n xy=(0.85, 0.2), xycoords='axes fraction',bbox={'facecolor':color, 'alpha':0.5, 'pad':10})\\n\\n frame3.plot(binned_times,binned_residuals, 'ko', markersize = 4)\\n frame3.axhline(0, color = color)\\n frame3.set_ylabel(\\\"Residuals\\\")\\n\\n plt.xlabel(\\\"Time [bjd]\\\")\\n\\n plottingdict['Times'] = t\\n plottingdict['Raw Data'] = lc*scale\\n plottingdict['Full PLD Model'] = optflux*scale\\n\\n plottingdict['Corrected Data'] = lc - pixels - ramp\\n plottingdict['Transit Model'] = DE\\n plottingdict['Temporal Ramp'] = ramp\\n plottingdict['PLD'] = pixels\\n\\n if showCuts:\\n for j in range(ncutstarts):\\n if cutends:\\n frame1.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n frame2.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n frame3.axvline(t[-1] - j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n else:\\n frame1.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n frame2.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n frame3.axvline(j*(float(cutstartTime)/(60.*24.)), color = 'k', ls='dashed')\\n\\n if savefile:\\n plt.savefig(\\\"{5}/PhD/SpitzerTransits/{0}{4}/{0}_{1}_{2}_PLD_lc_{3}.png\\\".format(name, orbit, channel,filext,foldext, os.getenv('HOME')),bbox_inches='tight')\\n plt.close()\\n else:\\n plt.show()\\n plt.close()\\n\\n else:\\n warnings.warn( \\\"What model do you want to plot?!\\\" )\\n\\n if extraoutputs:\\n return plottingdict\\n else:\\n pass\",\n \"def plot_results(delaytype):\\n \\n if delaytype == 'Polynomial':\\n # results for ind_obs=graph.indlinks.keys()\\n tolls_collected1 = [[164.76921455176952, 690.919364787199, 1003.9715271443032], [164.76920846294715, 690.9196375266908, 1003.971503332828]]\\n so_costs1 = [[1952.813985915695, 2991.422528084584, 4716.77478205995], [1952.813764068618, 2991.4225756924197, 4716.774942888717]]\\n costs1 = [[1961.4088544768533, 3035.621746689017, 4751.282035585294], [1961.4088092322784, 3035.619283467948, 4751.282202058287]]\\n ue_costs1 = [[1993.9477013089333, 3087.4591784440836, 4889.555908205202], [1993.9532544651581, 3087.4592286945117, 4889.562102247506]]\\n \\n # results for ind_obs=[(36,37,1), (13,14,1), (17,8,1), (24,17,1), (28,22,1), (14,13,1), (17,24,1), (24,40,1), (14,21,1), (16,23,1)]\\n tolls_collected2 = [[165.05685117271955, 354.1634370314711, 1015.9141858070569], [165.0567022829941, 354.1917223859498, 1015.9119098321756]]\\n so_costs2 = [[1952.4102665477528, 2990.2968764530697, 4714.092736177128], [1952.813764068618, 2991.4225756924197, 4716.774942888717]]\\n costs2 = [[1961.0182151023969, 3019.353015234613, 4750.051821659878], [1961.4090940730164, 3020.4668352547183, 4752.675436210774]]\\n ue_costs2 = [[1993.5324364880598, 3086.401670312004, 4886.656461616093], [1993.9532544651581, 3087.4592286945117, 4889.562102247506]]\\n \\n # results for ind_obs=[(17,24,1), (24,40,1), (14,21,1), (16,23,1)]\\n tolls_collected3 = [[122.37203827953705, 369.5962596851687, 993.1260548913893], [124.00144138458633, 375.65796385498777, 1018.6147484597942]]\\n so_costs3 = [[1819.720988976306, 2777.3939885366685, 4677.595258102356], [1952.813764068618, 2991.4225756924197, 4716.774942888717]]\\n costs3 = [[1834.6351015726038, 2802.3016403566235, 4714.102145227426], [1965.8049083549738, 3020.126090534662, 4750.071391283308]]\\n ue_costs3 = [[1872.3492340077084, 2897.9834368843217, 4886.769726452165], [1993.9532544651581, 3087.4592286945117, 4889.562102247506]]\\n \\n # results for ind_obs=[(10,9,1), (19,18,1), (4,5,1), (29,21,1)]\\n tolls_collected4 = [[602.0313412901077, 596.7300601881633, 833.3678073460065], [609.094482012133, 542.8579545671112, 843.1817520139325]]\\n so_costs4 = [[13252.728980891832, 20178.927282098706, 28549.614451166774], [1952.813764068618, 2991.4225756924197, 4716.774942888717]]\\n costs4 = [[13254.342842900576, 20239.30287102026, 28549.614731411177], [1992.3969007442322, 3130.0522527298062, 4805.6728306302175]]\\n ue_costs4 = [[13259.433117552415, 20185.90937054816, 28556.779443740656], [1993.9532544651581, 3087.4592286945117, 4889.562102247506]]\\n \\n \\n if delaytype == 'Hyperbolic':\\n # results for ind_obs=graph.indlinks.keys() \\n tolls_collected1 = [[161.15742640370462, 416.80264009965265, 948.124688583883], [161.4013529462645, 417.25218843773354, 950.1674351402539]]\\n so_costs1 = [[2519.5329969919685, 3679.4257321044365, 5325.940489651684], [2544.920874812245, 3718.292867749051, 5383.190501656683]]\\n costs1 = [[2525.7590340322095, 3679.4261220632625, 5356.8071818038225], [2551.0113714206195, 3718.3028736461647, 5412.310857035818]]\\n ue_costs1 = [[2548.8303153671195, 3736.67165067958, 5444.499035875823], [2574.155371287541, 3775.672784740404, 5503.564654849874]]\\n \\n # results for ind_obs=[(36,37,1), (13,14,1), (17,8,1), (24,17,1), (28,22,1), (14,13,1), (17,24,1), (24,40,1), (14,21,1), (16,23,1)]\\n tolls_collected2 = [[355.56391097372205, 701.3544774585599, 806.6640717434449], [374.34816990991186, 736.3763770517197, 842.0768966826593]]\\n so_costs2 = [[2174.1387842065237, 3306.974167934956, 4969.5560792612605], [2544.920874812245, 3718.292867749051, 5383.190501656683]]\\n costs2 = [[2201.273656230071, 3338.7536674213156, 4988.732654672769], [2564.0153714222406, 3746.115605602468, 5398.046518880896]]\\n ue_costs2 = [[2209.622435372754, 3380.7186533697286, 5104.755602389524], [2574.155371287541, 3775.672784740404, 5503.564654849874]]\\n \\n # results for ind_obs=[(17,24,1), (24,40,1), (14,21,1), (16,23,1)]\\n tolls_collected3 = [[339.37838703976513, 737.9887571945254, 930.0531221139565], [353.28118085588176, 763.3708450755438, 955.644104623869]]\\n so_costs3 = [[2520.1983680076073, 3833.234908857907, 5645.320052639795], [2544.920874812245, 3718.292867749051, 5383.190501656683]]\\n costs3 = [[2536.138622203524, 3862.614483834193, 5668.430527845267], [2562.897499637454, 3750.9322754235955, 5406.402328441233]]\\n ue_costs3 = [[2556.0266644367803, 3903.9758481975805, 5741.545038028959], [2574.155371287541, 3775.672784740404, 5503.564654849874]]\\n \\n # results for ind_obs=[(10,9,1), (19,18,1), (4,5,1), (29,21,1)]\\n tolls_collected4 = [[443.85669649193784, 837.5428841560781, 1380.117295219297], [479.57423528502, 931.2309202734045, 1499.6972922042573]]\\n so_costs4 = [[2158.995768390798, 3843.4202195503713, 7595.092411648515], [2544.920874812245, 3718.292867749051, 5383.190501656683]]\\n costs4 = [[2192.900868595389, 3888.0172745668465, 7626.17585256234], [2575.5748442305207, 3755.844017718783, 5450.304046277645]]\\n ue_costs4 = [[2231.1674179937672, 3999.772800567613, 7867.415688946321], [2574.155371287541, 3775.672784740404, 5503.564654849874]]\\n \\n # effects of toll pricing\\n opacity = 0.4\\n width = 0.2\\n index = np.arange(4)\\n plt.xticks(index, ('all', '10', '4 good', '4 bad') )\\n colors = ['y', 'r', 'b']\\n demands = ['94', '118', '142']\\n for i in [0,1,2]:\\n so = so_costs1[1][i]\\n ue = ue_costs1[1][i]\\n delta = ue-so\\n plt.bar(index-width/2+(i-1)*width,\\n [100.0*(costs1[1][i]-so)/delta, \\n 100.0*(costs2[1][i]-so)/delta, \\n 100.0*(costs3[1][i]-so)/delta, \\n 100.0*(costs4[1][i]-so)/delta],\\n width, alpha=opacity, color=colors[i], label='demand='+demands[i])\\n plt.xlabel('Sets of observed links')\\n plt.ylabel('Relative loss (%)')\\n plt.title('Effects of toll pricing with '+delaytype+' delay function')\\n plt.legend(loc=0)\\n plt.show()\\n \\n # error in predicted UE\\n for i in [0,1,2]:\\n ue = ue_costs1[1][i]\\n plt.bar(index-width/2+(i-1)*width,\\n [100.0*abs(ue_costs1[0][i]-ue)/ue,\\n 100.0*abs(ue_costs2[0][i]-ue)/ue,\\n 100.0*abs(ue_costs3[0][i]-ue)/ue,\\n 100.0*abs(ue_costs4[0][i]-ue)/ue],\\n width, alpha=opacity, color=colors[i], label='demand='+demands[i])\\n plt.xlabel('Sets of observed links')\\n plt.ylabel('Relative error (%)')\\n plt.title('Error in predicted UE with '+delaytype+' delay function')\\n plt.legend(loc=0)\\n plt.show()\\n \\n # error in predicted SO\\n for i in [0,1,2]:\\n so = so_costs1[1][i]\\n plt.bar(index-width/2+(i-1)*width,\\n [100.0*abs(so_costs1[0][i]-so)/so,\\n 100.0*abs(so_costs2[0][i]-so)/so,\\n 100.0*abs(so_costs3[0][i]-so)/so,\\n 100.0*abs(so_costs4[0][i]-so)/so],\\n width, alpha=opacity, color=colors[i], label='demand='+demands[i])\\n plt.xlabel('Sets of observed links')\\n plt.ylabel('Relative error (%)')\\n plt.title('Error in predicted SO with '+delaytype+' delay function')\\n plt.legend(loc=0)\\n plt.show()\\n \\n # error in predicted tolled ue\\n for i in [0,1,2]:\\n plt.bar(index-width/2+(i-1)*width,\\n [100.0*abs(costs1[0][i]-costs1[1][i])/costs1[1][i],\\n 100.0*abs(costs2[0][i]-costs2[1][i])/costs2[1][i],\\n 100.0*abs(costs3[0][i]-costs3[1][i])/costs3[1][i],\\n 100.0*abs(costs4[0][i]-costs4[1][i])/costs4[1][i]],\\n width, alpha=opacity, color=colors[i], label='demand='+demands[i])\\n plt.xlabel('Sets of observed links')\\n plt.ylabel('Relative error (%)')\\n plt.title('Error in predicted tooled UE with '+delaytype+' delay function')\\n plt.legend(loc=0)\\n plt.show()\",\n \"def L_curveTV(f,lam_init = 2.0, q = 0.9):\\n lam = lam_init\\n max_iter = 50\\n residual_list = np.zeros(max_iter)\\n size_list = np.zeros(max_iter)\\n error = np.zeros(max_iter)\\n alt_error = np.zeros(max_iter)\\n \\n for i in range(max_iter): #range(max_iter):\\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc=True, tol = 1.0e-5)\\n #u, _, j = projected_gradient_alt(f,lam, tau = 0.2, tol = 1.0e-4)\\n lam = lam_init * (q ** i)\\n residual_list[i] = np.linalg.norm(u - f)\\n size_list[i] = np.linalg.norm(u)\\n error[i] = np.linalg.norm(u - f) * np.linalg.norm(u)\\n #plt.loglog(residual_list,size_list)\\n #plt.show()\\n opt_idx = np.argmin(error)\\n t = 1.0 / (1.0 + lam_init * (q ** opt_idx))\\n lam = lam_init * (q ** opt_idx)\\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc = True, tol = 1.0e-5)\\n return u, t\",\n \"def test_lpp():\\n \\n tce_filename = \\\"tce0001234567_dvt.fits\\\"\\n \\n #Populate the tce class by reading in a file\\n #At this point a phases array, \\n #and (int) transit_number would get populated.\\n \\n tce = vet.create_tce(file = tce_filename, ext = 1)\\n \\n #This populates the LPP metric fields in this class\\n #It also populates where the plotting data is stored\\n #This function relies on inputs from a precomputed model stored\\n #in something called mapInfo. In theory if someone trains up another \\n #model we could input it here.\\n tce.run_lpp(mapInfo = None)\\n \\n norm_TLpp = tce.lpp.norm\\n raw_TLpp = tce.lpp.raw\\n binned_lpp_transit = tce.lpp.binned_transit\\n \\n plot_out = tce.lpp.plot(filename=\\\"lpp_plot.png\\\")\\n \\n assert norm_TLpp > 0\\n assert norm_TLpp < 10\\n assert len(binned_lpp_transit) > 50\",\n \"def get_l_curve_results(directory, overwrite = False):\\n costs = []\\n tikhs = []\\n regs = []\\n\\n glacier = \\\"helheim\\\"\\n extension = \\\".tar.gz\\\"\\n\\n # If the output hasn't been cached, retrieve it from the giant archives\\n cached_output = os.path.join(directory, \\\"helheim_l_curve.txt\\\")\\n if not os.path.exists(cached_output) or overwrite:\\n for archive_name in [f for f in os.listdir(directory)\\n if (os.path.isfile(os.path.join(directory, f))\\n and\\n f[-len(extension):] == extension)]:\\n start_index = len(glacier) + len(\\\"_lambda-\\\")\\n regularization = float(archive_name[start_index: -len(extension)])\\n cost, tikh = l_curve_point(os.path.join(directory, archive_name),\\n glacier, regularization, 4)\\n costs.append(cost)\\n tikhs.append(tikh)\\n regs.append(regularization)\\n\\n # Save these results to a text file\\n with open(cached_output, 'w') as fid:\\n fid.write(\\\"cost,model_norm,lambda\\\\n\\\")\\n for k in range(len(costs)):\\n fid.write(\\\"{0},{1},{2}\\\\n\\\".format(costs[k], tikhs[k], regs[k]))\\n\\n # Open the cached output\\n with open(cached_output, 'r') as fid:\\n line = fid.readline()\\n line = fid.readline().split('\\\\n')[0]\\n while(line):\\n c, t, r = line.split(',')\\n costs.append(float(c))\\n tikhs.append(float(t))\\n regs.append(float(r))\\n line = fid.readline().split('\\\\n')[0]\\n\\n return costs, tikhs, regs\",\n \"def main():\\n \\n data_base = '/local/duman/SIMULATIONS/many_polymers_5/'\\n save_base = '/usr/users/iff_th2/duman/RolfData/many_polymers_5'\\n \\n \\n ## Index the data\\n# density = [0.08, 0.2, 0.4]\\n# xi_L = [0.05, 0.1, 0.2, 1.0, 2.5, 5.0, 8.0, 16.0]\\n# Pe = [3.0, 7.0, 15.0, 50.0, 150.0, 500.0, 750.0, 1500.0, 4375.0, 8000.0, 10000.0]\\n# kappa = [1.25, 2.5, 5.0, 25.0, 62.5, 125.0, 200.0, 400.0]\\n# fp = [0.0, 0.0048, 0.0112, 0.024, 0.08, 0.24, 0.8, 1.2, 2.4, 7.0]\\n \\n density = [0.2]\\n kappa = [1.25, 2.5, 5.0, 25.0, 62.5, 125.0, 200.0, 400.0]\\n xi_L = [0.05, 0.1, 0.2, 1.0, 2.5, 5.0, 8.0, 16.0]\\n fp = [0.0048, 0.0112, 0.024, 0.08, 0.24, 0.8, 1.2, 2.4, 7.0]\\n Pe = [3.0, 7.0, 15.0, 50.0, 150.0, 500.0, 750.0, 1500.0, 4375.0]\\n \\n ## Create points\\n points = []\\n for i, x in enumerate(xi_L):\\n for j, p in enumerate(Pe):\\n points.append( Phase(x, p, kappa[i], fp[j], 'short') ) \\n \\n for point in points:\\n point.analyse_type()\\n point.set_plot_props()\\n \\n long_xil = [0.05, 0.2, 1.0, 2.5, 16.0]\\n long_pe = [3.0, 150.0, 750.0, 8000.0, 10000.0]\\n long_kappa = [5.0, 20.0, 100.0, 250.0, 1600.0]\\n long_fp = [0.0003, 0.015, 0.075, 0.0, 0.0]\\n long_points = []\\n for i, x in enumerate(long_xil):\\n for j, p in enumerate(long_pe):\\n long_points.append( Phase(x, p, long_kappa[i], long_fp[j], 'long') ) \\n \\n for point in long_points:\\n point.determine_type()\\n point.set_plot_props()\\n \\n plot_data(points, long_points, save_base, xi_L, Pe)\",\n \"def main():\\n df = pd.read_csv('data/ch5_q8_simulation.csv')\\n\\n # Part b\\n plt.figure()\\n plt.scatter(df['x'], df['y'])\\n plt.xlabel('x')\\n plt.ylabel('y')\\n plt.title('Scatterplot y vs. x')\\n plt.savefig('plots/8a.png')\\n\\n # Part c\\n response_var = 'y'\\n pred_vars_lin = ['x']\\n pred_vars_quad = ['x', 'x2']\\n pred_vars_cub = ['x', 'x2', 'x3']\\n pred_vars_quar = ['x', 'x2', 'x3', 'x4']\\n\\n poly_terms = pd.DataFrame({'x2': np.power(df['x'], 2),\\n 'x3': np.power(df['x'], 3),\\n 'x4': np.power(df['x'], 4)})\\n df = pd.concat([df, poly_terms], axis=1)\\n\\n CV_error_lin = loocv(df, response_var, pred_vars_lin)\\n CV_error_quad = loocv(df, response_var, pred_vars_quad)\\n CV_error_cub = loocv(df, response_var, pred_vars_cub)\\n CV_error_quar = loocv(df, response_var, pred_vars_quar)\\n\\n print('Part c')\\n print('CV error (linear) = {:.3f}'.format(CV_error_lin))\\n print('CV error (quadratic) = {:.3f}'.format(CV_error_quad))\\n print('CV error (cubic) = {:.3f}'.format(CV_error_cub))\\n print('CV error (quartic) = {:.3f}'.format(CV_error_quar))\\n\\n # Part d\\n np.random.seed(801)\\n y = np.random.randn(100)\\n x = np.random.randn(100)\\n y = x - 2 * np.power(x, 2) + np.random.randn(100)\\n\\n df = pd.DataFrame({'x': x,\\n 'x2': np.power(x, 2),\\n 'x3': np.power(x, 3),\\n 'x4': np.power(x, 4),\\n 'y': y})\\n\\n CV_error_lin = loocv(df, response_var, pred_vars_lin)\\n CV_error_quad = loocv(df, response_var, pred_vars_quad)\\n CV_error_cub = loocv(df, response_var, pred_vars_cub)\\n CV_error_quar = loocv(df, response_var, pred_vars_quar)\\n\\n print('Part d')\\n print('CV error (linear) = {:.3f}'.format(CV_error_lin))\\n print('CV error (quadratic) = {:.3f}'.format(CV_error_quad))\\n print('CV error (cubic) = {:.3f}'.format(CV_error_cub))\\n print('CV error (quartic) = {:.3f}'.format(CV_error_quar))\\n\\n # Part f\\n model = sm.OLS(df.loc[:, response_var], df.loc[:, pred_vars_quar]).fit()\\n print(model.summary())\",\n \"def llcm_plot():\\n x = np.arange(0, 10, 0.5) # x in [0, 10)\\n y_1 = 2**x\\n y_2 = x**2\\n\\n plt.figure()\\n # specify color, linestyle, and marker w/ keyword argument\\n # plt.plot(x, y_1, label='$2^x$', color='g', linestyle='--', marker='o')\\n plt.plot(x, y_1, 'g--o', label='$2^x$')\\n # plt.plot(x, y_2, label='$x^2$', color='r', linestyle='-', marker='*')\\n plt.plot(x, y_2, 'r-*', label='$x^2$')\\n plt.xlabel('$x$')\\n plt.ylabel('$y$')\\n plt.legend(loc='upper left')\\n plt.title('Line plots w/ Legend, linestyle, color, and marker')\\n plt.show()\\n\\n\\n return None\",\n \"def main(argv):\\n import scipy as sc\\n import scipy.integrate as integrate\\n import matplotlib.pylab as p\\n\\n def dCR_dt(pops, t=0):\\n \\\"\\\"\\\"returns the growth rate of consumer and resource population at any given time step\\\"\\\"\\\"\\n\\n R = pops[0]\\n C = pops[1]\\n K = 10000000\\n dRdt = r * R * (1-(R/K)) - a * R * C\\n dCdt = -z * C + e * a * R * C\\n\\n return sc.array([dRdt, dCdt])\\n\\n #integrate from time point 0 to 15, using 1000 sub-divisions of time\\n t = sc.linspace(0, 15, 1000)\\n\\n def plot(pops):\\n f1 = p.figure()\\n p.plot(t, pops[:,0], 'g-', label='Resource density') # Plot\\n p.plot(t, pops[:,1] , 'b-', label='Consumer density')\\n p.grid()\\n p.legend(loc='best')\\n p.xlabel('Time')\\n p.ylabel('Population density')\\n p.title('Consumer-Resource population dynamics')\\n #p.show()# To display the figure\\n f1.savefig('../Results/LV2_model.pdf')\\n\\n f2 = p.figure()\\n p.plot(pops[:,0], pops[:,1], 'r-', label='Consumer density') # Plot\\n p.grid()\\n p.xlabel('Resource Density')\\n p.ylabel('Consumer density')\\n p.title('Consumer-Resource population dynamics')\\n #p.show()# To display the figure\\n f2.savefig('../Results/LV2_second_model.pdf')\\n\\n return f1,f2\\n\\n if len(argv) < 5:\\n r = 1\\n a = 0.1\\n z = 1.5\\n e = 0.75\\n else:\\n r,a,z,e = float(sys.argv[1]), float(sys.argv[2]), float(sys.argv[3]), float(sys.argv[4])\\n #integrate from time point 0 to 15, using 1000 sub-divisions of time\\n t = sc.linspace(0, 15, 1000)\\n #Set the initial conditions for the two populations\\n R0 = 10\\n C0 = 5\\n RC0 = sc.array([R0, C0])\\n #numerically integrate this system forward from those starting conditions\\n pops, infodict = integrate.odeint(dCR_dt, RC0, t, full_output=True)\\n print(\\\"final Consumer and Resource population values are:\\\", pops[-1]) #prints final values\\n plot(pops)\\n return 0\",\n \"def main():\\n\\tplt.clf()\\n\\taxes = setup_axes()\\n\\tplot_output(axes, \\\"../../simulations/default\\\", \\\"black\\\")\\n\\tplot_output(axes, \\\"../../simulations/yccsr_zero\\\", \\\"crimson\\\")\\n\\tplot_output(axes, \\\"../../simulations/yccsr_linear\\\", \\\"lime\\\")\\n\\tplot_output(axes, \\\"../../simulations/yccsr_1-exp\\\", \\\"deepskyblue\\\")\\n\\tvisuals.plot_track_points_intervals(axes[0],\\n\\t\\tvice.history(\\\"../../simulations/default\\\"), element = \\\"Sr\\\",\\n\\t\\treference = \\\"Fe\\\")\\n\\tplot_legend(axes[1])\\n\\tplt.tight_layout()\\n\\tvisuals.yticklabel_formatter(axes[1])\\n\\tplt.savefig(sys.argv[1])\\n\\tplt.clf()\",\n \"def main():\\n ### read parameters from input file\\n charfile, headerfile, ofname, nfil, nskip, nbin, rmax, ntotal = read_settings()\\n ### loop over the entire trajectory and compute the center of mass, correct for pbc\\n r, pol_corr = compute_polarity_correlation(charfile, headerfile, nfil, nskip, nbin, rmax, ntotal)\\n\\n ### write results to file and generage a plot\\n # generate folder structure\\n os.system('mkdir ' + ofname)\\n # write data of the averaged polarity correlation\\n ofile = open(ofname + '/pol_correlation.data', 'w')\\n ofile.write('polarity correlation function\\\\n\\\\n')\\n ofile.write('r_min\\\\tr_max\\\\tpol_corrlation\\\\n')\\n for i in range(nbin):\\n ofile.write(str(r[i]) + '\\\\t' + str(r[i+1]) + '\\\\t' + str(pol_corr[i]) + '\\\\n')\\n ofile.close()\\n # gen figure of the averaged polarity correlation\\n fig = plt.figure()\\n ax = plt.subplot(111)\\n ax.plot(0.5*(r[:-1]+r[1:]), pol_corr)\\n ax.set_xlabel(r'r [$\\\\sigma]')\\n ax.set_ylabel(r'g_p(r)')\\n ax.set_title('Polarity Correlation Function')\\n plt.savefig(ofname + '/pol_correlation.png')\\n plt.close()\\n return\",\n \"def display4(*args):\\n #-------------------- unpack\\n twiss_func = args[0]\\n cos_like = args[1]\\n sin_like = args[2]\\n lat_plot = args[3]\\n #-------------------- beta x,y & dispersion x\\n s = [twiss_func(i,'s') for i in range(twiss_func.nbpoints)] # Abszisse\\n bx = [twiss_func(i,'bx') for i in range(twiss_func.nbpoints)] # beta x\\n by = [twiss_func(i,'by') for i in range(twiss_func.nbpoints)] # beta y\\n dx = [twiss_func(i,'dx') for i in range(twiss_func.nbpoints)] # dispersion x\\n#-------------------- longitudinal trajectories\\n z1= [cos_like(i,'s') for i in range(cos_like.nbpoints)]\\n cz= [cos_like(i,'cz') for i in range(cos_like.nbpoints)]\\n cdp= [cos_like(i,'cdp') for i in range(cos_like.nbpoints)]\\n\\n z2= [sin_like(i,'s') for i in range(sin_like.nbpoints)]\\n sz= [sin_like(i,'sz') for i in range(sin_like.nbpoints)]\\n sdp= [sin_like(i,'sdp') for i in range(sin_like.nbpoints)]\\n #-------------------- lattice viseo\\n vzero = [0. for i in range(lat_plot.nbpoints)] # zero line\\n vis_abszisse = [lat_plot(i,'s') for i in range(lat_plot.nbpoints)]\\n vis_ordinate = [lat_plot(i,'viseo') for i in range(lat_plot.nbpoints)]\\n #-------------------- figure frame\\n width=14; height=7.6\\n # fighdr = 'lattice version = {}, input file = {}'.format(PARAMS['lattice_version'],PARAMS['input_file'])\\n fig = plt.figure(num=1,figsize=(width,height),facecolor='#eaecef',tight_layout=False)\\n\\n #-------------------- beta functions\\n splot211=plt.subplot(211)\\n splot211.set_title('beta x,y')\\n # mapping box\\n splot211.text(0.01, 1.1, UTIL.FLAGS.get('mapping'),transform=splot211.transAxes,fontsize=8,bbox=dict(boxstyle='round',facecolor='wheat',alpha=0.5),verticalalignment='top')\\n # function plots\\n plt.plot(s,bx, label=r\\\"$\\\\beta$x [m]\\\", color='black', linestyle='-')\\n plt.plot(s,by, label=r\\\"$\\\\beta$y [m]\\\", color='red', linestyle='-')\\n plt.plot(s,dx, label=r'$\\\\eta_x$ [m]' , color='green', linestyle='-') # dispersion x\\n vscale=splot211.axis()[3]*0.25\\n viseoz = [x*vscale for x in vis_ordinate]\\n plt.plot(vis_abszisse,viseoz,label='',color='black')\\n plt.plot(vis_abszisse,vzero,color='green',linestyle='--')\\n # zero line\\n splot211.plot(vis_abszisse,vzero,color='green',linestyle='--')\\n plt.legend(loc='lower right',fontsize='x-small')\\n\\n #-------------------- longitudinal tracks z, dP/P\\n # ax_l = left abszisse\\n ax_l=plt.subplot(212)\\n # ax_l=plt.subplot(10,1,(7,9))\\n ax_l.set_title('synchrotron oscillation')\\n ax_l.set_ylabel(r\\\"z [mm]\\\")\\n ax_l.tick_params(axis='y', colors='green')\\n ax_l.yaxis.label.set_color('green')\\n ax_l.plot(z1,cz,label='C',color='green')\\n ax_l.plot(z2,sz,label='S',color='green',linestyle=':')\\n plt.legend(loc='lower left',fontsize='x-small')\\n # ax_r = right abszisse\\n ax_r = ax_l.twinx()\\n ax_r.set_ylabel(r'$\\\\Delta$p/p [%]')\\n ax_r.tick_params(axis='y', colors='red')\\n ax_r.yaxis.label.set_color('red')\\n ax_r.plot(z2,cdp,label='C',color='red')\\n ax_r.plot(z2,sdp,label='S',color='red',linestyle=':')\\n ax_r.plot(vis_abszisse,vzero,color='red', linestyle='--')\\n plt.legend(loc='lower right',fontsize='x-small')\\n # lattice elements\\n vscale=ax_l.axis()[3]*0.25\\n viseoz = [x*vscale for x in vis_ordinate]\\n ax_l.plot(vis_abszisse,viseoz,label='',color='black')\\n ax_l.plot(vis_abszisse,vzero,color='green',linestyle='--')\",\n \"def test_plot_lin(self):\\n pname = os.path.join(self.datadir, 'monol_testA_E3-50_pds_fit') + \\\\\\n HEN_FILE_EXTENSION\\n cname = os.path.join(self.datadir, 'monol_test_E3-50_cpds_fit') + \\\\\\n HEN_FILE_EXTENSION\\n lname = os.path.join(self.datadir, 'monol_testA_E3-50_lc') + \\\\\\n HEN_FILE_EXTENSION\\n hen.plot.main([pname, cname, lname, '--noplot', '--xlin', '--ylin',\\n '-o', 'dummy.qdp'])\\n hen.plot.main([lname, '--noplot',\\n '--axes', 'time', 'counts', '--xlin', '--ylin',\\n '-o', 'dummy.qdp'])\",\n \"def EPI():\\n TE = np.array([4.22, 33.81, 63.39, 92.98, 122.6, 152.2, 181.7, 211.3, 240.9, 270.5])\\n upper_left = np.array([697.3, 367.0, 217.5, 115.8, 51.8, 23.2, 14.8, 8.7, 6.1, 4.6])\\n center = np.array([1110.2, 907.8, 813.6, 745.2, 692.8, 637.0, 564.9, 521.0, 450.2, 401.6])\\n lower_right = np.array([723.0, 419.2, 224.1, 126.4, 61.8, 32.4, 15.1, 8.8, 3.9, 3.8])\\n upper_center = np.array([782.2, 499.4, 279.5, 154.5, 88.6, 58.2, 43.8, 38.2, 38.2, 36.0])\\n\\n area = [upper_left, center, upper_center, lower_right]\\n colors = [\\\"#1f77b4\\\", \\\"#ff7f0e\\\", \\\"#2ca02c\\\", \\\"#d62728\\\"]\\n name = [\\\"Upper left area\\\", \\\"Center area\\\", \\\"Up center area\\\", \\\"Lower right area\\\"]\\n x_new = np.linspace(4.22, 270.5, 10000)\\n for i, j, k in zip(area, colors, name):\\n popt, _ = curve_fit(M_xy, TE, i, p0=np.array([200, 300]))\\n M0, T2 = popt[0], popt[1]\\n y_new = M_xy(x_new, M0, T2)\\n plt.scatter(TE, i)\\n plt.plot(x_new, y_new, \\\"--\\\", c=j, label=\\\"Fit: %s\\\" % k + f\\\", $T_2$={T2:.2f}\\\")\\n plt.legend(loc=\\\"best\\\")\\n plt.grid()\\n plt.ylabel(\\\"Mean Signal Intensity\\\")\\n plt.xlabel(\\\"TE [ms]\\\")\\n plt.show()\",\n \"def plot(self, noTLS, path_plots, interactive):\\n fig = plt.figure(figsize=(10,12))\\n ax1 = fig.add_subplot(4, 1, 1)\\n ax2 = fig.add_subplot(4, 1, 2)\\n ax3 = fig.add_subplot(4, 2, 5)\\n ax4 = fig.add_subplot(4, 2, 6)\\n ax5 = fig.add_subplot(4, 2, 7)\\n ax6 = fig.add_subplot(4, 2, 8)\\n\\n # First panel: data from each sector\\n colors = self._get_colors(self.nlc)\\n for i, lci in enumerate(self.alllc):\\n p = lci.normalize().remove_outliers(sigma_lower=5.0, sigma_upper=5.0)\\n p.bin(5).scatter(ax=ax1, label='Sector %d' % self.sectors[i], color=colors[i])\\n self.trend.plot(ax=ax1, color='orange', lw=2, label='Trend')\\n ax1.legend(fontsize='small', ncol=4)\\n\\n # Second panel: Detrended light curve\\n self.lc.remove_outliers(sigma_lower=5.0, sigma_upper=5.0).bin(5).scatter(ax=ax2,\\n color='black',\\n label='Detrended')\\n\\n # Third panel: BLS\\n self.BLS.bls.plot(ax=ax3, label='_no_legend_', color='black')\\n mean_SR = np.mean(self.BLS.power)\\n std_SR = np.std(self.BLS.power)\\n best_power = self.BLS.power[np.where(self.BLS.period.value == self.BLS.period_max)[0]]\\n SDE = (best_power - mean_SR)/std_SR\\n ax3.axvline(self.BLS.period_max, alpha=0.4, lw=4)\\n for n in range(2, 10):\\n if n*self.BLS.period_max <= max(self.BLS.period.value):\\n ax3.axvline(n*self.BLS.period_max, alpha=0.4, lw=1, linestyle=\\\"dashed\\\")\\n ax3.axvline(self.BLS.period_max / n, alpha=0.4, lw=1, linestyle=\\\"dashed\\\")\\n sx, ex = ax3.get_xlim()\\n sy, ey = ax3.get_ylim()\\n ax3.text(ex-(ex-sx)/3, ey-(ey-sy)/3,\\n 'P$_{MAX}$ = %.3f d\\\\nT0 = %.2f\\\\nDepth = %.4f\\\\nDuration = %.2f d\\\\nSDE = %.3f' %\\n (self.BLS.period_max, self.BLS.t0_max,\\n self.BLS.depth_max, self.BLS.duration_max, SDE))\\n\\n\\n # Fourth panel: lightcurve folded to the best period from the BLS\\n self.folded.bin(1*self.nlc).scatter(ax=ax4, label='_no_legend_', color='black',\\n marker='.', alpha=0.5)\\n l = max(min(4*self.BLS.duration_max/self.BLS.period_max, 0.5), 0.02)\\n nbins = int(50*0.5/l)\\n r1, dt1 = binningx0dt(self.folded.phase, self.folded.flux, x0=-0.5, nbins=nbins)\\n ax4.plot(r1[::,0], r1[::,1], marker='o', ls='None',\\n color='orange', markersize=5, markeredgecolor='orangered', label='_no_legend_')\\n\\n lc_model = self.BLS.bls.get_transit_model(period=self.BLS.period_max,\\n duration=self.BLS.duration_max,\\n transit_time=self.BLS.t0_max)\\n lc_model_folded = lc_model.fold(self.BLS.period_max, t0=self.BLS.t0_max)\\n ax4.plot(lc_model_folded.phase, lc_model_folded.flux, color='green', lw=2)\\n ax4.set_xlim(-l, l)\\n h = max(lc_model.flux)\\n l = min(lc_model.flux)\\n ax4.set_ylim(l-4.*(h-l), h+5.*(h-l))\\n del lc_model, lc_model_folded, r1, dt1\\n\\n\\n if not noTLS:\\n # Fifth panel: TLS periodogram\\n ax5.axvline(self.tls.period, alpha=0.4, lw=3)\\n ax5.set_xlim(np.min(self.tls.periods), np.max(self.tls.periods))\\n for n in range(2, 10):\\n ax5.axvline(n*self.tls.period, alpha=0.4, lw=1, linestyle=\\\"dashed\\\")\\n ax5.axvline(self.tls.period / n, alpha=0.4, lw=1, linestyle=\\\"dashed\\\")\\n ax5.set_ylabel(r'SDE')\\n ax5.set_xlabel('Period (days)')\\n ax5.plot(self.tls.periods, self.tls.power, color='black', lw=0.5)\\n ax5.set_xlim(0, max(self.tls.periods))\\n\\n period_tls = self.tls.period\\n T0_tls = self.tls.T0\\n depth_tls = self.tls.depth\\n duration_tls = self.tls.duration\\n FAP_tls = self.tls.FAP\\n\\n sx, ex = ax5.get_xlim()\\n sy, ey = ax5.get_ylim()\\n ax5.text(ex-(ex-sx)/3, ey-(ey-sy)/3,\\n 'P$_{MAX}$ = %.3f d\\\\nT0 = %.1f\\\\nDepth = %.4f\\\\nDuration = %.2f d\\\\nFAP = %.4f' %\\n (period_tls, T0_tls, 1.-depth_tls, duration_tls, FAP_tls))\\n\\n # Sixth panel: folded light curve to the best period from the TLS\\n ax6.plot(self.tls.folded_phase, self.tls.folded_y, color='black', marker='.',\\n alpha=0.5, ls='None', markersize=0.7)\\n l = max(min(4*duration_tls/period_tls, 0.5), 0.02)\\n nbins = int(50*0.5/l)\\n r1, dt1 = binningx0dt(self.tls.folded_phase, self.tls.folded_y,\\n x0=0.0, nbins=nbins, useBinCenter=True)\\n ax6.plot(r1[::,0], r1[::,1], marker='o', ls='None', color='orange',\\n markersize=5, markeredgecolor='orangered', label='_no_legend_')\\n ax6.plot(self.tls.model_folded_phase, self.tls.model_folded_model, color='green', lw=2)\\n ax6.set_xlim(0.5-l, 0.5+l)\\n h = max(self.tls.model_folded_model)\\n l = min(self.tls.model_folded_model)\\n ax6.set_ylim(l-4.*(h-l), h+5.*(h-l))\\n ax6.set_xlabel('Phase')\\n ax6.set_ylabel('Relative flux')\\n del r1, dt1\\n\\n fig.subplots_adjust(top=0.98, bottom=0.05, wspace=0.25, left=0.1, right=0.97)\\n fig.savefig(os.path.join(path_plots, 'TIC%d.pdf' % self.TIC))\\n if interactive:\\n plt.show()\\n plt.close('all')\\n del fig\",\n \"def plot_learning_curve(model, X_train, X_test, y_train, y_test):\\n\\n m, train_scores, valid_scores = learning_curve(estimator = model, \\n X = X_train, y = y_train.ravel(), train_sizes = np.linspace(0.1,1.0, 80))\\n\\n train_cv_err = np.mean(train_scores, axis=1)\\n test_cv_err = np.mean(valid_scores, axis=1)\\n tr, = plt.plot(m, train_cv_err)\\n ts, = plt.plot(m, test_cv_err)\\n plt.legend((tr, ts), ('training error', 'test error'), loc = 'best')\\n plt.title('Learning Curve')\\n plt.xlabel('Data Points')\\n plt.ylabel('Accuracy')\",\n \"def plot_LvsL_multiple(line1='CIV1548',line2='CIII1908',line1range=[1e3,1e8],line2range=[1e0,1e8],\\n outputdir='./',verbose=True):\\n modeldata = nm.load_model('combined',verbose=verbose)\\n\\n if verbose: print(' - Putting together permutations of chosen setups for plotting')\\n infodic = {}\\n infodic['Zgas'] = [False,0.0001,0.006,0.040], True\\n infodic['logUs'] = [False,-1.0,-2.5,-4.0] , False\\n infodic['xid'] = [False,0.1,0.3,0.5] , False\\n infodic['nh'] = [False,10,100,1000,10000] , False\\n infodic['CO'] = [False,0.1,0.38,1.4] , False\\n infodic['Mcut'] = [False,100,300] , False\\n\\n variables = [infodic['Zgas'][0],infodic['logUs'][0],infodic['xid'][0],\\n infodic['nh'][0],infodic['CO'][0],infodic['Mcut'][0]]\\n\\n permutations = list(itertools.product(*variables))\\n permutations_with2false = [sublist for sublist in permutations if sublist.count(False) == 2.]\\n Nplots = len(permutations_with2false)\\n\\n if verbose: print(' - With the restriction Nfalse=2 the setup will results in '+str(Nplots)+\\\\\\n ' plots (if model data allows)')\\n if verbose: print(' - These will be saved to the output directory: '+outputdir)\\n for pp, perm in enumerate(permutations_with2false):\\n Zval = perm[0]\\n Uval = perm[1]\\n Xival = perm[2]\\n Nhval = perm[3]\\n COval = perm[4]\\n Mval = perm[5]\\n\\n plotname = outputdir+'NEOGALmodelgrid_Zgas'+str(Zval).replace('.','p')+\\\\\\n '_logU'+str(Uval).replace('.','p')+\\\\\\n '_xid'+str(Xival).replace('.','p')+\\\\\\n '_nH'+str(Nhval).replace('.','p')+\\\\\\n '_CO'+str(COval).replace('.','p')+\\\\\\n '_Mcut'+str(Mval).replace('.','p')+'.pdf'\\n\\n plotname = plotname.replace('False','Free')\\n\\n if verbose:\\n plotno = pp+1\\n infostr = ' - Generating plot '+str(\\\"%.4d\\\" % plotno)+'/'+str(\\\"%.4d\\\" % Nplots)+': '+plotname.split('/')[-1]+' '\\n sys.stdout.write(\\\"%s\\\\r\\\" % infostr)\\n sys.stdout.flush()\\n\\n if not Zval:\\n logp1 = True\\n else:\\n logp1 = False\\n\\n nm.plot_LvsL(modeldata,line1=line1,line2=line2,logx=True,logy=True,logp1=logp1,logp2=False,verbose=False,\\n Zgas=Zval,logU=Uval,xid=Xival,nh=Nhval,COratio=COval,Mcutoff=Mval,\\n fixxrange=line1range,fixyrange=line2range,plotname=plotname)\\n\\n print('\\\\n ... done')\",\n \"def convergence():\\n fig, axes = plt.subplots(nrows=2, figsize=figsize(aspect=1.2))\\n\\n # label names\\n label1 = str(league.lambda1)\\n label2_list = [str(lambda2) for lambda2 in league.lambda2_list]\\n\\n # point spread and point total subplots\\n subplots = [\\n (False, [-0.5, 0.5], league.spreads, 'probability spread > 0.5'),\\n (True, [200.5], league.totals, 'probability total > 200.5'),\\n ]\\n\\n for ax, (commutes, lines, values, ylabel) in zip(axes, subplots):\\n\\n # train margin-dependent Elo model\\n melo = Melo(lines=lines, commutes=commutes, k=1e-4)\\n melo.fit(league.times, league.labels1, league.labels2, values)\\n\\n line = lines[-1]\\n\\n for label2 in label2_list:\\n\\n # evaluation times and labels\\n times = np.arange(league.times.size)[::1000]\\n labels1 = times.size * [label1]\\n labels2 = times.size * [label2]\\n\\n # observed win probability\\n prob = melo.probability(times, labels1, labels2, lines=line)\\n ax.plot(times, prob)\\n\\n # true (analytic) win probability\\n if ax.is_first_row():\\n prob = skellam.sf(line, int(label1), int(label2))\\n ax.axhline(prob, color='k')\\n else:\\n prob = poisson.sf(line, int(label1) + int(label2))\\n ax.axhline(prob, color='k')\\n\\n # axes labels\\n if ax.is_last_row():\\n ax.set_xlabel('Iterations')\\n ax.set_ylabel(ylabel)\\n\\n set_tight(w_pad=.5)\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n description=\\\"Generate bar plots comparing Matlab solvers to RLSO\\\"\\n )\\n parser.add_argument(\\n \\\"--path\\\", type=str, default=None, help=\\\"the path to saved testing data for RLSO\\\"\\n )\\n parser.add_argument(\\n \\\"--trials\\\",\\n type=int,\\n default=1000,\\n help=\\\"Number of test problems to use defaults to 1000\\\",\\n )\\n parser.add_argument(\\n \\\"--savestring\\\",\\n type=str,\\n default=None,\\n help=\\\"Name to save our plot as. Defaults to random int .png\\\",\\n )\\n parser.add_argument(\\n \\\"--titlestring\\\",\\n type=str,\\n default=None,\\n help=\\\"String to append to our plot title. Defaults to None\\\",\\n )\\n parser.add_argument(\\n \\\"--excludeinter\\\",\\n type=bool,\\n default=True,\\n help=\\\"Exclude interior point method form testing. Reasons Convex only, and cannot set starting point. Default True\\\",\\n )\\n\\n args = parser.parse_args()\\n\\n if args.path is None:\\n print(\\\"Path to pickle data needed!\\\")\\n return\\n\\n pickle_savepath = args.path\\n with open(pickle_savepath, \\\"rb\\\") as pickle_file:\\n pickle_tuple = pickle.load(pickle_file)\\n\\n complex_dic = pickle_tuple[4]\\n lastbest = unravel_c_dic_lastbest(complex_dic)\\n Abx0 = generate_Abx0(complex_dic, lastbest)\\n\\n if args.trials is None:\\n trials = 1000\\n else:\\n trials = args.trials\\n if args.savestring is None:\\n save_string = str(np.random.randint(low=1e15, high=9e15)) + \\\".png\\\"\\n else:\\n save_string = args.savestring\\n\\n start = time.time()\\n print(f\\\"start time {start}\\\")\\n\\n solutions = []\\n for tuples in Abx0[:trials]:\\n solutions.append(quad_solve(tuples, printout=False))\\n end = time.time()\\n print(f\\\"Matlab run time: {end - start}\\\")\\n\\n rlso = []\\n active = []\\n inter = []\\n trust = []\\n for [x, y, z, w] in solutions:\\n rlso.append(x)\\n active.append(y)\\n inter.append(z)\\n trust.append(w)\\n title_string = args.titlestring\\n plot_box(\\n rlso,\\n active,\\n inter,\\n trust,\\n title_string,\\n save_string,\\n exclude_inter=args.excludeinter,\\n )\\n\\n return\",\n \"def main() -> None:\\n xlim = L\\n x = np.arange(xlim)\\n xi = np.arange(0, xlim - 1 + STEP, STEP)\\n kappas = sleplet.wavelet_methods.create_kappas(xlim, B, J_MIN)\\n yi = scipy.interpolate.pchip(x, kappas[0])\\n plt.semilogx(xi, yi(xi), label=r\\\"$\\\\Phi_{\\\\ell0}$\\\")\\n for j, k in enumerate(kappas[1:]):\\n yi = scipy.interpolate.pchip(x, k)\\n plt.semilogx(xi, yi(xi), label=rf\\\"$\\\\Psi^{{{j+J_MIN}}}_{{\\\\ell0}}$\\\")\\n plt.xlim(1, xlim)\\n ticks = 2 ** np.arange(np.log2(xlim) + 1, dtype=int)\\n plt.xticks(ticks, ticks)\\n plt.xlabel(r\\\"$\\\\ell$\\\")\\n plt.legend(loc=6)\\n print(f\\\"Opening: axisymmetric_tiling_L{L}\\\")\\n plt.tight_layout()\\n plt.show(block=False)\\n plt.pause(10)\\n plt.close()\",\n \"def lcfig( z=7 ):\\n\\n for modelname in ['z15G','z25G','z40G'] :\\n\\n # initialize a supernova model :\\n snmodel = sncosmo.Model(source=modelname)\\n\\n # Fix the redshift for this instantiation of the model\\n # (NOTE: this does not apply any cosmological dimming. It only\\n # shifts the wavelengths)\\n snmodel.set( z = z )\\n\\n # generate the H band light curve\\n tobs = np.arange( 0, 1000, 10 )\\n M160 = snmodel.bandmag( 'f160w', 'ab', tobs ) # Absolute Magnitude\\n m160 = M160 + cosmo.distmod( z ).value # apparent magnitude\\n\\n pl.plot( tobs, m160 )\\n ax = pl.gca()\\n ax.invert_yaxis()\\n ax.set_xlabel('Time (observer-frame days)')\\n ax.set_ylabel('Apparent Magnitude in F160W')\\n ax.set_xlim( 0, 1000 )\\n ax.set_ylim( 36, 28 )\\n\\n pl.draw()\",\n \"def plot_evaluation(parameters_dict, log_df, settings, evaluation_set_kde, plotname):\\n\\n\\n plots = []\\n\\n\\n ### setup the colors for each component\\n if int(settings['nr_components']) < 3:\\n colors = ['rgb(228,26,28)', 'rgb(55,126,184)']\\n elif int(settings['nr_components']) < 13:\\n colors = np.array(cl.scales[str(settings['nr_components'])]['qual']['Paired'])\\n else:\\n colors = cl.interp(cl.scales['10']['qual']['Paired'], 20)\\n\\n\\n ### set up ab list\\n ab_list = evaluation_set_kde['contact'].keys()\\n\\n\\n\\n\\n ####################### plotting of settings\\n print_to_table = {}\\n for key in sorted(settings.keys()):\\n if key not in ['fold_id_dir','plot_name', 'fixed_parameters', 'threads_proteins', 'qijab_dir',\\n 'debug_mode', 'parameter_file', 'settings_file', 'optimization_log_file', 'braw_dir', 'pdb_dir', 'paramdir',\\n 'mask_sse', 'lambda_w_fix', 'lfactor', 'plotdir', 'psicov_dir', 'contact', 'hessian_pseudocount']:\\n print_to_table[key] = settings[key]\\n\\n print(\\\"Generate settings table...\\\")\\n table_settings_1 = plot_settings_table(print_to_table, 1)\\n table_settings_2 = plot_settings_table(print_to_table, 2)\\n table_settings_3 = plot_settings_table(print_to_table, 3)\\n plots.append(table_settings_1)\\n plots.append(table_settings_2)\\n plots.append(table_settings_3)\\n\\n\\n ####################### negLL and realted plots\\n if 'step' in log_df.columns and 'pass' in log_df.columns:\\n\\n if 'negLL' in log_df.columns:\\n plot_negll = plot_convergence_trace_plotly(log_df,\\n name=['negLL', 'negLL_crossval'],\\n plot_title='neg LL trace for training and cross-val set')\\n plots.append(plot_negll)\\n\\n plot_expfit_negll = plot_exponentialFit_negLL(log_df, plot_title='exponential Fit neg LL')\\n plots.append(plot_expfit_negll)\\n\\n if 'timestamp' in log_df.columns:\\n plot_timestamps = plot_convergence_trace_plotly(log_df,\\n name=['timestamp'],\\n plot_title='time (s) per iteration')\\n plots.append(plot_timestamps)\\n\\n\\n if 'gradient_norm_weights' in log_df.columns:\\n plot_grad_norm_weights = plot_convergence_trace_plotly(log_df,\\n name=['gradient_norm_weights'],\\n plot_title='norm of weight gradients')\\n plots.append(plot_grad_norm_weights)\\n\\n if 'gradient_norm_means' in log_df.columns:\\n plot_grad_norm_means = plot_convergence_trace_plotly(log_df,\\n name=['gradient_norm_means'],\\n plot_title='norm of mean gradients')\\n plots.append(plot_grad_norm_means)\\n\\n if 'gradient_norm_prec' in log_df.columns:\\n plot_grad_norm_prec = plot_convergence_trace_plotly(log_df,\\n name=['gradient_norm_prec'],\\n plot_title='norm of precMat gradients')\\n plots.append(plot_grad_norm_prec)\\n\\n\\n ####################### plotting of parameters\\n print(\\\"Generate distribution of parameters...\\\")\\n\\n #weights\\n weights_dict = {}\\n for component in range(settings['nr_components']):\\n weights_dict['component ' + str(component)] = {\\n 'weights (contact)': parameters_dict[\\\"weight_contact_\\\" + str(component)][0],\\n 'weights (bg)': parameters_dict[\\\"weight_bg_\\\" + str(component)][0]\\n }\\n plot_weights = plot_barplot(\\n weights_dict,\\n 'Distribution of weights',\\n 'component weights',\\n type='group',\\n colors=colors\\n #,plot_out=\\\"/home/vorberg/weights.html\\\"\\n )\\n\\n #mu\\n mu_df = pd.DataFrame.from_dict(dict((k, parameters_dict[k]) for k in sorted(parameters_dict.keys()) if 'mu' in k))\\n plot_means = plot_boxplot(\\n mu_df,\\n 'Distribution of Means',\\n \\\"values of mean parameters\\\",\\n colors=colors\\n #,plot_out=\\\"/home/vorberg/mus.html\\\"\\n )\\n\\n #std deviation\\n prec_df = pd.DataFrame.from_dict(dict((k, parameters_dict[k]) for k in sorted(parameters_dict.keys()) if 'prec' in k))\\n try:\\n std_dev = prec_df.apply(lambda p: np.sqrt(1.0/p))\\n if settings['prec_wrt_L']:\\n std_dev = prec_df.apply(lambda p: np.sqrt(1.0/(p*142))) #in case precision is specified depending on L=142\\n except ZeroDivisionError as e:\\n print(e)\\n std_dev=prec_df\\n\\n std_dev.columns = [column_name.replace(\\\"prec\\\", \\\"std\\\") for column_name in std_dev.columns]\\n plot_stddev = plot_boxplot(\\n std_dev,\\n 'Distribution of std deviations',\\n \\\"values of std deviation parameters\\\",\\n colors=colors\\n #,plot_out=\\\"/home/vorberg/std.html\\\"\\n )\\n\\n\\n plots.append(plot_weights)\\n plots.append(plot_means)\\n plots.append(plot_stddev)\\n\\n ####################### Scatterplot mu vs std dev\\n print(\\\"Generate scatter plot mu vs std...\\\")\\n scatter_dict = {}\\n for component in range(settings['nr_components']):\\n scatter_dict['mu_'+str(component)] = [\\n mu_df['mu_'+str(component)].tolist(),\\n std_dev['std_'+str(component)].tolist(),\\n AB.values()\\n ]\\n plot_mu_vs_stddev = plot_scatter(scatter_dict,\\n 'Mean vs std deviation',\\n 'mean',\\n \\\"std deviation\\\",\\n False,\\n colors\\n #,plot_out=\\\"/home/vorberg/mu_vs_std.html\\\"\\n )\\n\\n plots.append(plot_mu_vs_stddev)\\n\\n\\n ############################################## plotting of gradient norms\\n print(\\\"Generate gradient norms plot...\\\")\\n\\n #gradients for mu\\n mu_grad_dict = {}\\n annotations_dict = {}\\n for component in range(settings['nr_components']):\\n key = 'mu_'+str(component)\\n mu_grad_dict[key] = log_df[key].tolist()[-1]\\n annotations_dict[key] = AB\\n\\n\\n plot_gradient_mu_stats = jitter_plot(mu_grad_dict,\\n 'Distribution of gradients for mean in last iteration',\\n annotations_dict,\\n colors,\\n None)\\n plots.append(plot_gradient_mu_stats)\\n\\n\\n #gradients for precMat\\n precMat_grad_dict = {}\\n annotations_dict = {}\\n for component in range(settings['nr_components']):\\n key = 'prec_'+str(component)\\n precMat_grad_dict['diagPrecMat_'+str(component)] = log_df[key].tolist()[-1]\\n annotations_dict['diagPrecMat_'+str(component)] = AB\\n\\n\\n plot_gradient_precMat_stats = jitter_plot(\\n precMat_grad_dict,\\n 'Distribution of gradients for precMat in last iteration',\\n annotations_dict,\\n colors,\\n None\\n )\\n plots.append(plot_gradient_precMat_stats)\\n\\n ##################################### plotting of gradient trace of a specific ab pair for all components\\n print(\\\"Generate gradient trace plot...\\\")\\n\\n gradient_df = log_df.filter(regex=(\\\"mu_[0-9]*\\\"))\\n plot_gradient_mu_ab_trace = plot_gradient_ab_trace(gradient_df,\\n ab_list,\\n colors\\n )\\n plots.append(plot_gradient_mu_ab_trace)\\n\\n gradient_df = log_df.filter(regex=(\\\"prec_[0-9]*\\\"))\\n plot_gradient_prec_ab_trace = plot_gradient_ab_trace(\\n gradient_df,\\n ab_list,\\n colors\\n )\\n plots.append(plot_gradient_prec_ab_trace)\\n\\n\\n ##################################### plotting of univariate mixtures\\n if len(evaluation_set_kde['contact']) == 0 or len(evaluation_set_kde['bg']) == 0:\\n print \\\"Evaluation set is empty. Cannot plot Mixture Visualization.\\\"\\n else:\\n print(\\\"Generate parameter visualization 1d plots...\\\")\\n plots.append(plot_parameter_visualisation_1d(parameters_dict, evaluation_set_kde, settings, colors, settings['prec_wrt_L']))\\n # plot_parameter_visualisation_1d(parameters_dict, evaluation_set_kde, settings, colors, settings['prec_wrt_L'], plot_out=\\\"/home/vorberg/1d_vis.html\\\")\\n\\n # ------------------------------------------------------------------------------\\n ### define merged plot\\n # ------------------------------------------------------------------------------\\n cols = 3.0\\n rows = int(np.ceil((len(plots)-1) / cols)) + 2\\n subplot_titles = []\\n\\n # set up titles\\n for plot in range(len(plots)-1):\\n subplot_titles.append(plots[plot]['layout']['title'])\\n if len(subplot_titles) < (cols * (rows-2)):\\n for i in range(int((cols * (rows-2))) - len(subplot_titles) ):\\n subplot_titles.append(\\\" \\\")\\n subplot_titles.append(plots[-1]['layout']['title'])\\n\\n\\n # plot all plots as subplots\\n fig = tools.make_subplots(rows=rows,\\n cols=3,\\n specs = [ [{} for col in range(int(cols))] for row in range(rows-2)] + \\\\\\n [[{'rowspan':2, 'colspan': 3}, None, None], [None, None, None]],\\n subplot_titles=tuple(subplot_titles),\\n print_grid=False)\\n\\n\\n\\n\\n for i, plot in enumerate(plots[:-1]):\\n col = i % int(cols)\\n row = (i - col) / int(cols)\\n\\n #add traces to subplot\\n for trace in plot['data']:\\n trace['showlegend']=False\\n fig.append_trace(trace, row + 1, col + 1)\\n\\n # adjust x and y axis for table plotting\\n if 'annotations' in plot['layout'].keys():\\n for cell in plot['layout']['annotations']:\\n cell['yref'] = 'y' + str(i + 1)\\n cell['xref'] = 'x' + str(i + 1)\\n fig['layout']['annotations'] += plot['layout']['annotations']\\n\\n # adjust axis for all plots\\n fig['layout']['xaxis' + str(i + 1)].update(plot['layout']['xaxis1'])\\n fig['layout']['yaxis' + str(i + 1)].update(plot['layout']['yaxis1'])\\n\\n ## add mixture visualisation plot - spans 3 columns\\n for trace in plots[-1]['data']:\\n fig.append_trace(trace, int(rows)-1, 1)\\n fig['layout']['xaxis' + str(int(cols * (rows-2) + 1))].update(plots[-1]['layout']['xaxis1'])\\n fig['layout']['yaxis' + str(int(cols * (rows-2) + 1))].update(plots[-1]['layout']['yaxis1'])\\n\\n #check which plots are visible/invisible according to menu selection\\n trace_visibility_ab = {}\\n for ab in range(len(ab_list)):\\n trace_visibility_ab[ab] = []\\n for i, plot in enumerate(plots):\\n if 'updatemenus' not in plot['layout'].keys():\\n trace_visibility_ab[ab].extend([True] * len(plot['data']))\\n else:\\n trace_visibility_ab[ab].extend(plot['layout']['updatemenus'][0]['buttons'][ab]['args'][1])\\n\\n\\n #use menu of last plot (=vis of mixture) as template for multiplot menu\\n fig['layout']['updatemenus'] = plots[-1]['layout']['updatemenus']\\n for ab in range(len(ab_list)):\\n fig['layout']['updatemenus'][0]['buttons'][ab]['args'][1] = trace_visibility_ab[ab]\\n\\n\\n fig['layout']['legend']['yanchor'] = 'bottom'\\n fig['layout']['legend']['y'] = 0\\n fig['layout']['height'] = rows * 250\\n fig['layout']['font'] = {'size': 18} # set global font size\\n\\n plotly_plot(fig, filename=plotname, auto_open=False)\",\n \"def plot_lc(self, show=False, result = None, **kwargs):\\n for i in ['color', 'ylabel', 'normalize']:\\n try:\\n del kwargs[i]\\n except KeyError:\\n pass\\n\\n ax: Axes = self.lc.scatter()\\n\\n model = result if result is not None and len(result) >0 else self.result\\n\\n if len(model) > 0:\\n params = []\\n\\n for i, j, k in zip(model[self._result.significant==True].amp\\n , model[self._result.significant==True].frequency\\n , model[self._result.significant==True].phase):\\n params.append(i.nominal_value)\\n params.append(j.nominal_value)\\n params.append(k.nominal_value)\\n\\n y = sin_multiple(self.lc.time, *params)\\n ax.plot(self.lc.time, y, color='red', linewidth=1)\\n\\n if show:\\n pl.show()\",\n \"def main():\\n save = False\\n show = True\\n\\n #hd_parameter_plots = HDparameterPlots(save=save)\\n #hd_parameter_plots.flow_parameter_distribution_for_non_lake_cells_for_current_HD_model()\\n #hd_parameter_plots.flow_parameter_distribution_current_HD_model_for_current_HD_model_reprocessed_without_lakes_and_wetlands()\\n #hd_parameter_plots.flow_parameter_distribution_ten_minute_data_from_virna_0k_ALG4_sinkless_no_true_sinks_oceans_lsmask_plus_upscale_rdirs()\\n #hd_parameter_plots.flow_parameter_distribution_ten_minute_data_from_virna_0k_ALG4_sinkless_no_true_sinks_oceans_lsmask_plus_upscale_rdirs_no_tuning()\\n #ice5g_comparison_plots = Ice5GComparisonPlots(save=save)\\n #ice5g_comparison_plots.plotLine()\\n #ice5g_comparison_plots.plotFilled()\\n #ice5g_comparison_plots.plotCombined()\\n #ice5g_comparison_plots.plotCombinedIncludingOceanFloors()\\n #flowmapplot = FlowMapPlots(save)\\n #flowmapplot.FourFlowMapSectionsFromDeglaciation()\\n #flowmapplot.Etopo1FlowMap()\\n #flowmapplot.ICE5G_data_all_points_0k()\\n #flowmapplot.ICE5G_data_all_points_0k_no_sink_filling()\\n #flowmapplot.ICE5G_data_all_points_0k_alg4_two_color()\\n #flowmapplot.ICE5G_data_all_points_21k_alg4_two_color()\\n #flowmapplot.Etopo1FlowMap_two_color()\\n #flowmapplot.Etopo1FlowMap_two_color_directly_upscaled_fields()\\n #flowmapplot.Corrected_HD_Rdirs_FlowMap_two_color()\\n #flowmapplot.ICE5G_data_ALG4_true_sinks_21k_And_ICE5G_data_ALG4_true_sinks_0k_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_Etopo1_ALG4_sinkless_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_Etopo1_ALG4_true_sinks_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_sinkless_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_true_sinks_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_corr_orog_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_no_true_sinks_corr_orog_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_HD_as_data_ALG4_true_sinks_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Upscaled_Rdirs_vs_Directly_Upscaled_fields_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplot.Ten_Minute_Data_from_Virna_data_ALG4_corr_orog_downscaled_lsmask_no_sinks_21k_vs_0k_FlowMap_comparison()\\n #flowmapplot.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n flowmapplotwithcatchment = FlowMapPlotsWithCatchments(save)\\n #flowmapplotwithcatchment.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_virna_data_plus_tarasov_style_orog_corrs_for_both()\\n #flowmapplotwithcatchment.compare_present_day_river_directions_with_catchments_virna_data_with_vs_without_tarasov_style_orog_corrs()\\n #flowmapplotwithcatchment.compare_lgm_river_directions_with_catchments_virna_data_with_vs_without_tarasov_style_orog_corrs()\\n #flowmapplotwithcatchment.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.upscaled_rdirs_with_and_without_tarasov_upscaled_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.\\\\\\n #upscaled_rdirs_with_and_without_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.\\\\\\n #Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.\\\\\\n #Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_glcc_olson_lsmask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_ICE5G_plus_tarasov_style_orog_corrs_for_both()\\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_ICE6G_plus_tarasov_style_orog_corrs_for_both()\\n #flowmapplotwithcatchment.compare_ICE5G_and_ICE6G_with_catchments_tarasov_style_orog_corrs_for_both()\\n #flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs()\\n #flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs_original_ts()\\n flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs_new_ts_10min()\\n outflowplots = OutflowPlots(save)\\n #outflowplots.Compare_Upscaled_Rdirs_vs_Directly_Upscaled_fields_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_as_HD_data_ALG4_sinkless_all_points_0k()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_as_HD_data_ALG4_true_sinks_all_points_0k()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_sinkless_all_points_0k_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_true_sinks_all_points_0k_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_corr_orog_all_points_0k_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_corr_orog_downscaled_ls_mask_all_points_0k_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_Etopo1_ALG4_sinkless_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_Etopo1_ALG4_true_sinks_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_plus_tarasov_upscaled_srtm30_ALG4_corr_orog_0k_directly_upscaled_fields()\\n #outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k()\\n outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k_new_truesinks()\\n #outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k_new_truesinks_individual_rivers()\\n #outflowplots.Compare_ICE5G_with_and_without_tarasov_upscaled_srtm30_ALG4_corr_orog_0k_directly_upscaled_fields()\\n #hd_output_plots = HDOutputPlots()\\n #hd_output_plots.check_water_balance_of_1978_for_constant_forcing_of_0_01()\\n #hd_output_plots.plot_comparison_using_1990_rainfall_data()\\n #hd_output_plots.plot_comparison_using_1990_rainfall_data_adding_back_to_discharge()\\n #coupledrunoutputplots = CoupledRunOutputPlots(save=save)\\n #coupledrunoutputplots.ice6g_rdirs_lgm_run_discharge_plot()\\n #coupledrunoutputplots.extended_present_day_rdirs_lgm_run_discharge_plot()\\n #coupledrunoutputplots.ocean_grid_extended_present_day_rdirs_vs_ice6g_rdirs_lgm_run_discharge_plot()\\n #coupledrunoutputplots.extended_present_day_rdirs_vs_ice6g_rdirs_lgm_echam()\\n #coupledrunoutputplots.extended_present_day_rdirs_vs_ice6g_rdirs_lgm_mpiom_pem()\\n #lake_plots = LakePlots()\\n #lake_plots.plotLakeDepths()\\n #lake_plots.LakeAndRiverMap()\\n #lake_plots.LakeAndRiverMaps()\\n if show:\\n plt.show()\",\n \"def plot_lineratios(modeldata,modeldata2='None',line1='CIV1551',line2='CIII1908',line3='CIV1551',line4='HeII1640',\\n plotname='./TESTPLOT.pdf',Zgas=False,logU=False,xid=0.3,nh=100,COratio=0.38,Mcutoff=100,\\n logx=True,logy=True,logp1=False,logp2=False,fixxrange=False,fixyrange=False,\\n showobs=None,noobserr=False,verbose=True):\\n NFalse = 0\\n freeparam = []\\n inforstr = \\\"\\\"\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n legenddic = {}\\n legenddic['Zgas'] = r'Z$_\\\\textrm{gas}$'\\n legenddic['logUs'] = r'log$_\\\\textrm{10}$(U)'\\n legenddic['xid'] = r'$\\\\xi_\\\\textrm{d}$'\\n legenddic['nh'] = r'n$_\\\\textrm{H}$ / [cm$^3$]'\\n legenddic['COCOsol'] = r'C/O / [C/O]$_\\\\textrm{sun}$'\\n legenddic['mup'] = r'M$_\\\\textrm{cut IMF}$ / [M$_\\\\textrm{sun}]$'\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not Zgas:\\n Zgasrange = [0.0,1.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' Zgas:vary, '\\n freeparam.append('Zgas')\\n else:\\n Zgasrange = [Zgas-1e-6,Zgas+1e-6]\\n inforstr = inforstr+' '+legenddic['Zgas']+'='+str(Zgas)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not logU:\\n logUrange = [-5.0,0.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' logU:vary, '\\n freeparam.append('logUs')\\n else:\\n logUrange = [logU-0.1,logU+0.1]\\n inforstr = inforstr+' '+legenddic['logUs']+'='+str(logU)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not xid:\\n xidrange = [0.0,0.6]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' xid:vary, '\\n freeparam.append('xid')\\n else:\\n xidrange = [xid-0.01,xid+0.01]\\n inforstr = inforstr+' '+legenddic['xid']+'='+str(xid)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not nh:\\n nhrange = [0.0,1.0e6]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' nH:vary, '\\n freeparam.append('nh')\\n else:\\n nhrange = [nh-1.0,nh+1.0]\\n inforstr = inforstr+' '+legenddic['nh']+'='+str(nh)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not COratio:\\n COratiorange = [0.0,2.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' C/O:vary, '\\n freeparam.append('COCOsol')\\n else:\\n COratiorange = [COratio-0.001,COratio+0.001]\\n inforstr = inforstr+' '+legenddic['COCOsol']+'='+str(COratio)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not Mcutoff:\\n Mcutoffrange = [0.0,400.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' Mcutoff:vary, '\\n freeparam.append('mup')\\n else:\\n Mcutoffrange = [Mcutoff-1.0,Mcutoff+1.0]\\n inforstr = inforstr+' '+legenddic['mup']+'='+str(Mcutoff)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n\\n if NFalse != 2:\\n sys.exit(' Two and only two of the model parameters (Zgas,logU,xid,nh,COratio,Mcutoff) '\\n 'should be set to Flase to define the model grid; however it appears '+str(NFalse)+\\n ' parameters where not set')\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n goodent = np.where( (modeldata['Zgas'] > Zgasrange[0]) & (modeldata['Zgas'] < Zgasrange[1]) &\\n (modeldata['logUs'] > logUrange[0]) & (modeldata['logUs'] < logUrange[1]) &\\n (modeldata['xid'] > xidrange[0]) & (modeldata['xid'] < xidrange[1]) &\\n (modeldata['nh'] > nhrange[0]) & (modeldata['nh'] < nhrange[1]) &\\n (modeldata['COCOsol'] > COratiorange[0]) & (modeldata['COCOsol'] < COratiorange[1]) &\\n (modeldata['mup'] > Mcutoffrange[0]) & (modeldata['mup'] < Mcutoffrange[1]) )\\n\\n Ngoodent = len(goodent[0])\\n\\n if Ngoodent > 1:\\n if verbose: print(' - Getting data for '+str(Ngoodent)+' data points satisfying (SFR)model selection ')\\n param1_1 = modeldata[freeparam[0]][goodent]\\n if logp1:\\n param1_1 = np.log10(param1_1)\\n\\n param1_2 = modeldata[freeparam[1]][goodent]\\n if logp2:\\n param1_2 = np.log10(param1_2)\\n\\n ratio1_1 = modeldata[line1][goodent]/modeldata[line2][goodent]\\n ratio1_2 = modeldata[line3][goodent]/modeldata[line4][goodent]\\n else:\\n if verbose: print(' WARNING: Less than 2 (SFR)model grid points to plot; no output generated')\\n return\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if modeldata2 != 'None':\\n goodent2 = np.where( (modeldata2['Zgas'] > Zgasrange[0]) & (modeldata2['Zgas'] < Zgasrange[1]) &\\n (modeldata2['logUs'] > logUrange[0]) & (modeldata2['logUs'] < logUrange[1]) &\\n (modeldata2['xid'] > xidrange[0]) & (modeldata2['xid'] < xidrange[1]) &\\n (modeldata2['nh'] > nhrange[0]) & (modeldata2['nh'] < nhrange[1]) )\\n\\n Ngoodent2 = len(goodent2[0])\\n\\n if Ngoodent > 1:\\n if verbose: print(' - Getting data for '+str(Ngoodent2)+' data points satisfying (AGN)model selection ')\\n param2_1 = modeldata2[freeparam[0]][goodent2]\\n if logp1:\\n param2_1 = np.log10(param2_1)\\n\\n param2_2 = modeldata2[freeparam[1]][goodent2]\\n if logp2:\\n param2_2 = np.log10(param2_2)\\n\\n l2s = ['x','x','x','x'] # line names to use for Feltre+16 file\\n for ll, linestr in enumerate([line1,line2,line3,line4]):\\n if '1908' in linestr:\\n l2 = linestr.replace('1908','1907')\\n else:\\n l2 = linestr\\n\\n l2s[ll] = l2\\n\\n ratio2_1 = modeldata2[l2s[0]][goodent2]/modeldata2[l2s[1]][goodent2]\\n ratio2_2 = modeldata2[l2s[2]][goodent2]/modeldata2[l2s[3]][goodent2]\\n else:\\n if verbose: print(' WARNING: Less than 2 (AGN)model grid points to plot; no output generated')\\n return\\n\\n # - - - - - - - - - - - PLOTTING - - - - - - - - - - -\\n if verbose: print(' - Setting up and generating plot')\\n plotname = plotname\\n fig = plt.figure(figsize=(9, 5))\\n fig.subplots_adjust(wspace=0.1, hspace=0.1,left=0.1, right=0.99, bottom=0.10, top=0.95)\\n Fsize = 10\\n lthick = 1\\n marksize = 3\\n plt.rc('text', usetex=True)\\n plt.rc('font', family='serif',size=Fsize)\\n plt.rc('xtick', labelsize=Fsize)\\n plt.rc('ytick', labelsize=Fsize)\\n plt.clf()\\n plt.ioff()\\n plt.title(inforstr[:-2],fontsize=Fsize)\\n\\n margin = 0.1\\n dx = np.abs(np.max(ratio1_1)-np.min(ratio1_1))\\n dy = np.abs(np.max(ratio1_2)-np.min(ratio1_2))\\n\\n\\n if fixxrange:\\n xrange = fixxrange\\n else:\\n if logx:\\n xrange = [np.min(ratio1_1)-np.min(ratio1_1)/2.,np.max(ratio1_1)+np.max(ratio1_1)/2.]\\n else:\\n xrange = [np.min(ratio1_1)-dx*margin,np.max(ratio1_1)+dx*margin]\\n\\n if fixyrange:\\n yrange = fixyrange\\n else:\\n if logy:\\n yrange = [np.min(ratio1_2)-np.min(ratio1_2)/2.,np.max(ratio1_2)+np.max(ratio1_2)/2.]\\n else:\\n yrange = [np.min(ratio1_2)-dy*margin,np.max(ratio1_2)+dy*margin]\\n\\n # ------------ PARAM1 ------------\\n cmap = plt.cm.get_cmap('winter')\\n cmin = np.min(param1_1)\\n cmax = np.max(param1_1)\\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\\n mm = plt.cm.ScalarMappable(cmap=cmap)\\n mm.set_array(cmaparr)\\n cb1 = plt.colorbar(mm)#shrink=0.25\\n\\n pstr1 = legenddic[freeparam[0]]\\n if logp1:\\n pstr1 = r'log$_\\\\textrm{10}$('+pstr1+')'\\n\\n cb1.set_label(pstr1+' (outer circle) - Fixed: black line')\\n\\n for p1 in np.unique(param1_1):\\n p1col = cmap(colnorm(p1))\\n p1ent = np.where(param1_1 == p1)\\n\\n plt.plot(ratio1_1[p1ent],ratio1_2[p1ent],'-',lw=lthick, color='k',zorder=1)\\n\\n plt.errorbar(ratio1_1[p1ent],ratio1_2[p1ent],xerr=None,yerr=None,\\n marker='o',lw=0, markersize=marksize*3,\\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\\n\\n if modeldata2 is not 'None':\\n p1ent = np.where(param2_1 == p1)\\n\\n plt.plot(ratio2_1[p1ent],ratio2_2[p1ent],'-',lw=lthick, color='k',zorder=1)\\n\\n plt.errorbar(ratio2_1[p1ent],ratio2_2[p1ent],xerr=None,yerr=None,\\n marker='D',lw=0, markersize=marksize*3,\\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\\n\\n\\n # ------------ PARAM2 ------------\\n cmap = plt.cm.get_cmap('spring')\\n cmin = np.min(param1_2)\\n cmax = np.max(param1_2)\\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\\n mm = plt.cm.ScalarMappable(cmap=cmap)\\n mm.set_array(cmaparr)\\n cb2 = plt.colorbar(mm)#shrink=0.25\\n\\n pstr2 = legenddic[freeparam[1]]\\n if logp2:\\n pstr2 = 'log10('+pstr2+')'\\n\\n cb2.set_label(pstr2+' (inner circle) - Fixed: gray line')\\n\\n for p2 in np.unique(param1_2):\\n p2col = cmap(colnorm(p2))\\n p2ent = np.where(param1_2 == p2)\\n\\n plt.plot(ratio1_1[p2ent],ratio1_2[p2ent],'-',lw=lthick, color='gray',zorder=2)\\n\\n plt.errorbar(ratio1_1[p2ent],ratio1_2[p2ent],xerr=None,yerr=None,\\n marker='o',lw=0, markersize=marksize*1.5,\\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\\n\\n if modeldata2 is not 'None':\\n p2ent = np.where(param2_2 == p2)\\n\\n plt.plot(ratio2_1[p2ent],ratio2_2[p2ent],'-',lw=lthick, color='gray',zorder=2)\\n\\n plt.errorbar(ratio2_1[p2ent],ratio2_2[p2ent],xerr=None,yerr=None,\\n marker='D',lw=0, markersize=marksize*1.5,\\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\\n\\n\\n if showobs != None:\\n for ii, objid in enumerate(showobs[:,0]):\\n if (showobs[:,7][ii] > xrange[0]) & (showobs[:,7][ii] < xrange[1]) & \\\\\\n (showobs[:,9][ii] > yrange[0]) & (showobs[:,9][ii] < yrange[1]):\\n\\n if noobserr:\\n obsxerr = None\\n obsyerr = None\\n else:\\n obsxerr = showobs[:,8][ii]\\n obsyerr = showobs[:,10][ii]\\n plt.errorbar(showobs[:,7][ii],showobs[:,9][ii],xerr=obsxerr,yerr=obsyerr,\\n marker='*',lw=lthick, markersize=marksize*2,\\n markerfacecolor='k',ecolor='k',markeredgecolor = 'k',zorder=30)\\n\\n plt.xlabel(line1+'/'+line2)\\n plt.ylabel(line3+'/'+line4)\\n\\n plt.xlim(xrange)\\n plt.ylim(yrange)\\n\\n if logx:\\n plt.xscale('log')\\n if logy:\\n plt.yscale('log')\\n\\n #--------- LEGEND ---------\\n # plt.errorbar(-1,-1,xerr=None,yerr=None,fmt='o',lw=lthick,ecolor='white', markersize=marksize*2,\\n # markerfacecolor='white',markeredgecolor = 'k',label='Ground-based spec')\\n #\\n # leg = plt.legend(fancybox=True, loc='upper center',prop={'size':Fsize},ncol=1,numpoints=1)\\n # #bbox_to_anchor=(1.25, 1.03)) # add the legend\\n # leg.get_frame().set_alpha(0.7)\\n #--------------------------\\n\\n if verbose: print(' Saving plot to'+plotname)\\n plt.savefig(plotname)\\n plt.clf()\\n plt.close('all')\",\n \"def plot_learning_curve(X_train_all, X_val_all, y_train_all, y_val_all, train_sizes, title):\\n\\n errors_df = pd.DataFrame(columns = ['train_size', 'train_acc', 'val_acc'])\\n\\n # Loop through example sizes and get the training and validation error\\n for train_size in train_sizes:\\n # Select Subset of Data\\n X_train = X_train_all[:train_size]\\n X_val = X_val_all[:train_size]\\n y_train = y_train_all[:train_size]\\n y_val = y_val_all[:train_size]\\n\\n # Initialize Model\\n model = svm.SVC(kernel='linear')\\n\\n # Fit model\\n print(f\\\"Training {title} using {train_size} examples\\\")\\n model.fit(X_train, y_train)\\n\\n # Get Predictions \\n train_pred = model.predict(X_train)\\n val_pred = model.predict(X_val)\\n\\n # Get Accuracy Score for X_Train and X_Val\\n errors = pd.DataFrame({\\n 'train_size': [train_size],\\n 'train_acc': [accuracy_score(y_train, train_pred)],\\n 'val_acc': [accuracy_score(y_val, val_pred)]\\n })\\n \\n # Concatenate Dataframes\\n errors_df = pd.concat([errors_df, errors])\\n\\n # Plot Learning Curve\\n fig, ax = plt.subplots()\\n\\n errors_df.plot(x='train_size', y='train_acc',kind='line', ax=ax)\\n errors_df.plot(x='train_size', y='val_acc',kind='line', color='red', ax=ax)\\n\\n ax.set_xlabel(\\\"Training Size\\\")\\n ax.set_ylabel(\\\"Accuracy\\\")\\n ax.set_title(title)\\n\\n # Save Figure\\n plt.savefig('figs/' + title + '_learning_curve.png')\",\n \"def plot_curves_lenstool(label = ' ', courves_file = 'ce.dat', marker = 'm.', \\\\\\n plt_show = False):\\n# x_ca, y_ca = np.loadtxt( courves_file, usecols=(3, 4), unpack=True )\\n# x_cc, y_cc = np.loadtxt( courves_file, usecols=(1, 2), unpack=True )\\n\\n x_cc, y_cc, x_ca, y_ca = \\\\\\n np.loadtxt( courves_file, usecols=(1, 2, 3, 4), unpack=True )\\n\\n plt.figure(1, figsize=(16, 8))\\n\\n plt.subplot(1, 2, 1).set_aspect(1)\\n plt.plot(x_ca, y_ca, marker, linewidth=3, label = label)\\n axis_limit = max( 1.075*max(np.absolute(x_ca)), \\\\\\n 1.075*max(np.absolute(y_ca)) )\\n plt.axis([-axis_limit, axis_limit, -axis_limit, axis_limit])\\n\\n plt.subplot (1, 2, 2).set_aspect(1)\\n plt.plot(x_cc, y_cc, marker, linewidth=3, label = label)\\n axis_limit = max( 1.075*max(np.absolute(x_cc)), \\\\\\n 1.075*max(np.absolute(y_cc)) )\\n plt.axis([-axis_limit, axis_limit, -axis_limit, axis_limit])\\n plt.legend()\\n\\n if plt_show:\\n plt.show()\\n plt.close()\",\n \"def energies():\\n # Hardcoded initial values\\n numsteps = 10000\\n time_max = 1\\n # Running the calculation in the solver class using the velocity verlet method\\n # for better accuracy.\\n verlet = solver(input_matrix, 'verlet', time_max, numsteps)\\n output_matrix, KE, PE, AM = verlet.main()\\n # Creating a simple time axis for plotting\\n x = np.linspace(0, 1, numsteps+1)\\n\\n # Plotting kinetic energy over time\\n plt.figure(1, figsize=(10, 10))\\n plt.plot(x, KE)\\n plt.suptitle('Total kinetic energy in the Earth-Sun system.', fontsize=24)\\n plt.xlabel('time [yr]', fontsize=16)\\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\\n plt.legend(['KE'])\\n\\n # Plotting potential energy over time\\n plt.figure(2, figsize=(10, 10))\\n plt.plot(x, PE)\\n plt.suptitle('Total potential energy in the Earth-Sun system.', fontsize=24)\\n plt.xlabel('time [yr]', fontsize=16)\\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\\n plt.legend(['PE'])\\n\\n # Plotting total energy against time\\n plt.figure(3, figsize=(10, 10))\\n plt.plot(x, PE+KE)\\n plt.suptitle('Total energy in the Earth-Sun system.', fontsize=24)\\n plt.xlabel('time [yr]', fontsize=16)\\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\\n plt.legend(['KE+PE'])\\n\\n # Plotting angular momentum against time. print the amplitude to terminal\\n amplitude = max(AM)-min(AM)\\n print('Amplitude of angular momentum during 1 year: %g[AU²/yr²]' %(amplitude))\\n plt.figure(4, figsize=(10, 10))\\n plt.plot(x, AM)\\n plt.suptitle('Total angular momentum in the Earth-Sun system.', fontsize=24)\\n plt.xlabel('time [yr]', fontsize=16)\\n plt.ylabel('energy [AU²/yr²]', fontsize=16)\\n plt.legend(['AM'])\\n\\n # Plotting the kinetic, potential and total energy against time to see\\n # how great the variations are\\n plt.figure(5, figsize=(10, 10))\\n plt.plot(x, PE, x, KE, x, KE+PE)\\n plt.suptitle('Total energy in the Earth-Sun system.', fontsize=24)\\n plt.xlabel('time [yr]', fontsize=16)\\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\\n plt.legend(['PE', 'KE', 'KE+PE'])\\n plt.show()\",\n \"def prob4():\\n domain = np.linspace(-1, 1, 400)\\n n_array = [2**i for i in range(2,9)]\\n f = lambda x: 1 / (1 + 25*x**2)\\n plt.ion()\\n poly_error = []\\n cheby_error = []\\n for n in n_array:\\n x = np.linspace(-1, 1, n)\\n poly = BarycentricInterpolator(x)\\n poly.set_yi(f(x))\\n poly_error.append(la.norm(f(domain) - poly(domain), ord=np.inf))\\n y = np.array([(1/2)*(2*np.cos(j*np.pi/n)) for j in range(n+1)])\\n cheby = BarycentricInterpolator(y)\\n cheby.set_yi(f(y))\\n cheby_error.append(la.norm(f(domain) - cheby(domain), ord=np.inf))\\n plt.loglog(n_array, poly_error, label=\\\"equally spaced points\\\", basex=2)\\n plt.loglog(n_array, cheby_error, label=\\\"Chebyshev extremal points\\\", basex=2)\\n plt.legend()\\n plt.show()\",\n \"def main():\\n data1, data2 = [], []\\n\\n # Compute results for sizes in the range 0...40,000\\n max_size = 40000\\n # Sizes used are 0**2 = 0, 20**2 = 400, 40**2 = 1600, 60**2 = 3600, \\n # etc. up to 200**2 = 40,000\\n for size in (x**2 for x in range(0, round(sqrt(max_size)) + 1, 20)):\\n compute_time(size, data1, data2)\\n\\n # Create a plotter object\\n plt = Plotter(600, 600, 0, max_size, 0, 120)\\n\\n # Plot the curves\\n plt.pen.width(4)\\n plt.plot_data(data1, \\\"blue\\\")\\n plt.plot_data(data2, \\\"red\\\")\\n\\n # Wait for user interaction\\n plt.interact()\",\n \"def main():\\n\\n print(\\\"Finding maximum clade credibility tree...\\\")\\n find_mcc_tree()\\n print(\\\"Computing clade credibilities...\\\")\\n compute_clade_probabilities()\\n if _CAN_PLOT:\\n print(\\\"Plotting maximum clade credibility tree...\\\")\\n plot_mcc_tree()\\n else:\\n print(\\\"Skipping plotting tree due to lack of PyQt4 support. :(\\\")\\n print(\\\"Computing posterior mean paramter estimates...\\\")\\n ranked_means = utils.write_means(\\\"indoeuropean.log\\\", \\\"parameter_means.csv\\\")\\n print(\\\"Computing ranking correlations...\\\")\\n compute_ranking_correls(ranked_means)\\n print(\\\"Generating LaTeX table...\\\")\\n make_table(ranked_means)\\n print(\\\"Generating rate variation figure...\\\")\\n make_figure(\\\"category_rates.eps\\\")\",\n \"def plot_tuning_curves(self, baseline_rate=10.):\\n x = np.arange(0, 1 + 0.01, 0.01)\\n l0 = self.data['L0']\\n l1 = self.data['L1']\\n y_on = np.exp(np.log(l0) + x * np.log(l1 / l0))\\n y_off = np.exp(np.log(l0) + (1 - x) * np.log(l1 / l0))\\n plt.plot(x, y_on, label='ON')\\n plt.plot(x, y_off, label='OFF')\\n plt.plot(x, baseline_rate + 0 * x, '--')\\n # plt.xlabel('Stimulus intensity')\\n # plt.ylabel('Firing Rate (Hz)')\\n # plt.title('Firing rate as a function \\\\n of Stimulus Intensity')\\n # plt.legend()\",\n \"def pr_curve(model, X_train, y_train, X_test, y_test, train=True):\\n from sklearn.metrics import precision_recall_curve\\n if train==True:\\n ypredTrain = model.predict(X_train) \\n precisions, recalls, thresholds = precision_recall_curve(y_train, ypredTrain)\\n plt.plot(precisions, recalls, linewidth=3, color='r', linestyle='-')\\n plt.rc('xtick', labelsize=10) \\n plt.rc('ytick', labelsize=10) \\n plt.xlabel(\\\"Precision\\\", size=12)\\n plt.ylabel(\\\"Recall\\\", size=12)\\n plt.grid()\\n plt.rcParams['figure.facecolor'] = '#F2F3F4' \\n plt.rcParams['axes.facecolor'] = '#F2F3F4' \\n plt.title(\\\"PR Curve: Precision/Recall Trade-off\\\\n\\\\n(Train)\\\\n\\\", size=14) \\n plt.show()\\n elif train==False:\\n ypredTest = model.predict(X_test)\\n precisions, recalls, thresholds = precision_recall_curve(y_test, ypredTest)\\n plt.plot(precisions, recalls, linewidth=3, color='b', linestyle='-')\\n plt.rc('xtick', labelsize=10) \\n plt.rc('ytick', labelsize=10) \\n plt.xlabel(\\\"Precision\\\", size=12)\\n plt.ylabel(\\\"Recall\\\", size=12)\\n plt.grid()\\n plt.rcParams['figure.facecolor'] = '#F2F3F4' \\n plt.rcParams['axes.facecolor'] = '#F2F3F4'\\n plt.title(\\\"PR Curve: Precision/Recall Trade-off\\\\n\\\\n(Test)\\\\n\\\", size=14)\\n plt.show()\",\n \"def fit_lorentzian(comp_key,p0,time_step,conn,func = fitting.fun_lorentzian,fig=None,wind=3):\\n\\n (fin,) = conn.execute(\\\"select fout from comps where comp_key = ?\\\",(comp_key,)).fetchone()\\n Fin = h5py.File(fin,'r')\\n g = Fin[fd('vanHove',comp_key)]\\n\\n temp = g.attrs['temperature']\\n dtime = g.attrs['dtime']\\n j = int(time_step/dtime)\\n count = g[fd('step',j)]['y']['disp_count'][:]\\n count += g[fd('step',j)]['x']['disp_count'][:]\\n edges = g[fd('step',j)]['y']['disp_edges'][:]\\n\\n \\n edges = np.array([np.mean(edges[j:j+wind]) for j in range(1,len(count)-wind,wind)])\\n count = np.array([np.sum(count[j:j+wind]) for j in range(1,len(count)-wind,wind)])\\n \\n \\n edges = edges[count>30]\\n count = count[count>30]\\n \\n\\n out = fitting.fit_curve(edges,count,p0,func)\\n fig = fitting.display_fit(edges,count,out.beta,func,fig)\\n print out.beta\\n return out,fig\",\n \"def solve_rossler(alpha = 1.0, beta = 1.0, gamma = 1.0, max_time = 10, N = 5):\\n resolution = 72\\n \\n fig1 = plt.figure(dpi = resolution)\\n ax1, ax2, ax3 = fig1.subplots(3, 1)\\n\\n fig = plt.figure()\\n ax = fig.add_axes([0, 0, 1, 1], projection='3d')\\n # ax.axis('off')\\n \\n def rossler_deriv(xi, t0, alpha=alpha, beta = beta, gamma = gamma):\\n \\\"\\\"\\\"Compute the time-derivative of a Lorenz system.\\\"\\\"\\\"\\n \\n xi1, xi2, xi3 = xi\\n \\n xi1_dot = -xi1 - exp(xi3)\\n xi2_dot = xi1 + alpha * xi2\\n xi3_dot = gamma * exp(-xi3) + xi1 - beta\\n \\n return [xi1_dot, xi2_dot, xi3_dot]\\n\\n # Choose random starting points, uniformly distributed from -15 to 15\\n np.random.seed(1)\\n xi0 = -15 + 30 * np.random.random((N, 3))\\n xi0[:,2] = np.log(abs(xi0[:,2]))\\n\\n # Solve for the trajectories\\n t = np.linspace(0, max_time, int(250*max_time))\\n xi_t = np.asarray([integrate.odeint(rossler_deriv, xi0i, t)\\n for xi0i in xi0])\\n \\n # choose a different color for each trajectory\\n colors = plt.cm.viridis(np.linspace(0, 1, N))\\n\\n for i in range(N):\\n xi1, xi2, xi3= xi_t[i,:,:].T\\n \\n ax1.plot(t, xi1, '-', c=colors[i])\\n ax1.set_title('xi1')\\n \\n ax2.plot(t, xi2, '-', c=colors[i])\\n ax2.set_title('xi2')\\n \\n ax3.plot(t, xi3, '-', c=colors[i])\\n ax3.set_title('xi3')\\n \\n lines = ax.plot(xi1, xi2, xi3, '-', c=colors[i])\\n plt.setp(lines, linewidth=2)\\n angle = 104\\n ax.view_init(30, angle)\\n plt.show()\\n\\n return t, xi_t\",\n \"def plot_cv_errors(errors, lambdas , K , path): \\n colors = cycle([\\\"aqua\\\", \\\"black\\\", \\\"blue\\\", \\\"fuchsia\\\", \\\"gray\\\", \\\"green\\\", \\\"lime\\\", \\\"maroon\\\", \\\"navy\\\", \\\"olive\\\", \\\"purple\\\", \\\"red\\\", \\\"silver\\\", \\\"teal\\\", \\\"yellow\\\"])\\n \\n markers = cycle([ \\\".\\\", \\\",\\\", \\\"o\\\", \\\"v\\\" , \\\"^\\\" , \\\">\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\", \\\"8\\\", \\\"s\\\", \\\"p\\\", \\\"*\\\", \\\"h\\\"])\\n \\n \\n for i, data in enumerate(errors):\\n \\n lambda_str = ('%f' % lambdas[i]).rstrip('0').rstrip('.')\\n plt.plot(range(len(data)), data, marker=next(markers), label='$\\\\lambda$ = %s'%lambda_str);\\n \\n plt.ylim(0.975 , 0.99)\\n #plt.xlim(0 , 50)\\n plt.title('ALS-WR Learning Curve, K = %d'% K)\\n plt.xlabel('Number of Epochs');\\n plt.ylabel('RMSE');\\n plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\\n plt.grid()\\n plt.savefig(\\\"../results/\\\"+path)\\n plt.show()\",\n \"def lorenz_data_gen(init_x,init_y,init_z,init_r):\\n # Set initial state x,y,z and parameters r,sigma,b\\n sxin,syin,szin = init_x,init_y,init_z\\n state = np.zeros(3)\\n state[0] = float(sxin); state[1] = float(syin); state[2] = float(szin)\\n \\n \\n r = init_r\\n sigma = 10 # Parameter sigma\\n b = 8./3. # Parameter b\\n param = np.array([r, sigma, b]) # Vector of parameters passed to rka\\n tau = .02 # Timestep from lorenz with n=500\\n #err = 1.e-3 # Error tolerance\\n \\n # Loop over the desired number of steps\\n time = 0\\n nstep = 500\\n # initialize arrays\\n tplot=np.array([]); tauplot=np.array([])\\n xplot=np.array([]); yplot=np.array([]); zplot=np.array([])\\n \\n for istep in range(0,nstep):\\n # Record values for plotting\\n x = state[0]\\n y = state[1] \\n z = state[2]\\n tplot = np.append(tplot,time)\\n tauplot = np.append(tauplot,tau)\\n xplot = np.append(xplot,x)\\n yplot = np.append(yplot,y)\\n zplot = np.append(zplot,z)\\n #if( istep%50 ==0 ):\\n #print('Finished %d steps out of %d '%(istep,nstep))\\n # Find new state using Runge-Kutta4\\n state = rk4(state,time,tau,lorzrk,param)\\n time += tau\\n \\\"\\\"\\\"\\n # Graph the time series x(t)\\n plt.figure(1) \\n plt.clf() # Clear figure 1 window and bring forward\\n plt.plot(tplot,xplot,'-')\\n plt.xlabel('Time'); plt.ylabel('x(t)')\\n plt.title('Lorenz model time series')\\n # plt.show()\\n \\n # Graph the x,y,z phase space trajectory\\n \\n fig=plt.figure(2)\\n ax=p3.Axes3D(fig)\\n ax.plot3D(xplot,yplot,zplot)\\n ax.set_xlabel('x')\\n ax.set_ylabel('y')\\n ax.set_zlabel('z')\\n ax.grid(True)\\n # title('Lorenz model phase space')\\n #ax.set_aspect('equal')\\n plt.show()\\n \\\"\\\"\\\"\\n return xplot,yplot,zplot,tplot\",\n \"def figure2():\\n # sim_data_XPP = pd.read_csv(\\\"XPP.dat\\\", delimiter=\\\" \\\", header=None) # Load the XPP simulation\\n\\n plot_settings = {'y_limits': [-25, 0],\\n 'x_limits': None,\\n 'y_ticks': [-25, -20, -15, -10, -5, 0],\\n 'locator_size': 2.5,\\n 'y_label': 'Current (nA)',\\n 'x_ticks': [],\\n 'scale_size': 0,\\n 'x_label': \\\"\\\",\\n 'scale_loc': 4,\\n 'figure_name': 'figure_2',\\n 'legend': ['I-Na', 'I-NaP'],\\n 'legend_size': 8,\\n 'y_on': True}\\n\\n t, y = solver(100) # Integrate solution\\n t_short = np.where((t >= 8) & (t <= 18))[0] # shorter time bounds for plots A and C\\n v, m, h, m_nap, h_na_p, n, m_t, h_t, m_p, m_n, h_n, z_sk, m_a, h_a, m_h, ca = y[:, ].T # Extract all variables\\n\\n \\\"\\\"\\\"\\n Explicitly calculate all currents: Extra constants duplicated from function dydt to calculate currents\\n \\\"\\\"\\\"\\n g_na_bar = 0.7\\n g_nap_bar = 0.05\\n g_k_bar = 1.3\\n g_p_bar = 0.05\\n g_leak = 0.005\\n g_a_bar = 1.0\\n e_na = 60\\n e_k = -80\\n e_leak = -50\\n e_ca = 40\\n g_t_bar = 0.1\\n g_n_bar = 0.05\\n g_sk_bar = 0.3\\n\\n \\\"\\\"\\\"\\n Calculate currents used in the plot\\n \\\"\\\"\\\"\\n i_na = g_na_bar * (m ** 3) * h * (v - e_na)\\n i_na_p = g_nap_bar * m_nap * h_na_p * (v - e_na)\\n i_k = g_k_bar * (n ** 4) * (v - e_k)\\n i_leak = g_leak * (v - e_leak)\\n i_t = g_t_bar * m_t * h_t * (v - e_ca)\\n i_n = g_n_bar * m_n * h_n * (v - e_ca)\\n i_p = g_p_bar * m_p * (v - e_ca)\\n i_sk = g_sk_bar * (z_sk ** 2) * (v - e_k)\\n i_a = g_a_bar * m_a * h_a * (v - e_k)\\n\\n plt.figure(figsize=(5, 3), dpi=96) # Create figure\\n\\n plt.subplot(2, 2, 1) # Generate subplot 1 (top left)\\n plt.plot(t[t_short], i_na[t_short], 'k-')\\n plt.plot(t[t_short], i_na_p[t_short], c='k', linestyle='dotted')\\n alter_figure(plot_settings) # Alter figure for publication\\n\\n plt.subplot(2, 2, 2) # Generate subplot 2 (top right)\\n plt.plot(t, i_t + i_n + i_p, 'k-')\\n plt.plot(t, i_t, c='k', linestyle='dotted')\\n plt.plot(t, i_p, 'k--')\\n plt.plot(t, i_n, 'k-.')\\n\\n plot_settings['y_limits'] = [-2.5, 0]\\n plot_settings['y_ticks'] = [-2.5, -2, -1.5, -1, -0.5, 0]\\n plot_settings['locator_size'] = 0.25\\n plot_settings['y_label'] = \\\"\\\"\\n plot_settings['legend'] = ['I-Ca', 'I-T', 'I-P', 'I-N']\\n alter_figure(plot_settings) # Alter figure for publication\\n\\n plt.subplot(2, 2, 3) # Generate subplot 3 (bottom left)\\n plt.plot(t[t_short], i_k[t_short], 'k-')\\n plt.plot(t[t_short], i_a[t_short], c='k', linestyle='dotted')\\n plt.plot(t[t_short], i_leak[t_short], 'k-.')\\n\\n plot_settings['y_limits'] = [0, 25]\\n plot_settings['y_ticks'] = [0, 5, 10, 15, 20, 25]\\n plot_settings['locator_size'] = 2.5\\n plot_settings['y_label'] = \\\"Current (nA)\\\"\\n plot_settings['legend'] = ['I-K', 'I-A', 'I-leak']\\n plot_settings['scale_size'] = 2\\n plot_settings['scale_loc'] = 2\\n alter_figure(plot_settings) # Alter figure for publication\\n\\n plt.subplot(2, 2, 4) # Generate subplot 4 (bottom left)\\n\\n plt.plot(t, i_sk, 'k-')\\n # plt.plot(sim_data_XPP[0][900:]-200,sim_data_XPP[34][900:]) # Isk for XPP data\\n\\n plot_settings['y_limits'] = [0, 1]\\n plot_settings['y_ticks'] = [0, 0.2, 0.4, 0.6, 0.8, 1]\\n plot_settings['locator_size'] = 0.2\\n plot_settings['y_label'] = \\\"\\\"\\n plot_settings['legend'] = ['I-SK']\\n plot_settings['scale_size'] = 20\\n plot_settings['scale_loc'] = 2\\n alter_figure(plot_settings, close=True) # Alter figure for publication\",\n \"def main():\\n # Load properties that will be needed\\n store = [Storage.Storage(2), Storage.Storage(4)] \\n pre_energy = [s.get(\\\"free_energy\\\") for s in store]\\n post_energy = [s.get(\\\"post_energy\\\") for s in store]\\n x_range = store[0].get(\\\"x_range\\\")\\n xlocs = np.arange(x_range[0], x_range[1], x_range[2])\\n y_range = store[0].get(\\\"y_range\\\")\\n ylocs = np.arange(y_range[0], y_range[1], y_range[2])\\n # Calculate step size\\n xb2steps = stepsize(pre_energy[0], post_energy[0], xlocs) \\n xb4steps = stepsize(pre_energy[1], post_energy[1], xlocs) \\n # Set up the figure\\n fig = plt.figure(1, figsize=(7.5,2.5)) \\n axe = (fig.add_subplot(1, 2, 1), fig.add_subplot(1, 2, 2))\\n # Plot the results\\n axe[0].plot(ylocs, xb4steps, color='#FF466F', lw=4)\\n axe[1].plot(ylocs, xb2steps, color='#76D753', lw=4)\\n # Annotate the plots\\n axe[0].set_title(\\\"4sXB step size\\\")\\n axe[0].set_xlabel(\\\"Lattice spacing (nm)\\\") \\n axe[0].set_ylabel(\\\"Step size (nm)\\\")\\n axe[0].set_xlim((25.5, 39))\\n axe[0].set_ylim((1, 8))\\n axe[1].set_title(\\\"2sXB step size\\\")\\n axe[1].set_xlabel(\\\"Lattice spacing (nm)\\\") \\n axe[1].set_ylabel(\\\"Step size (nm)\\\")\\n axe[1].set_xlim((25.5, 39))\\n axe[1].set_ylim((1, 8))\\n # Display the plots\\n fig.subplots_adjust(wspace=0.25, hspace=0.48,\\n left=0.08, right=0.98,\\n top=0.85, bottom=0.21)\\n plt.show()\",\n \"def plot(self):\\n \\n \\n x_ibs=[] \\n x_gss=[]\\n y_ibs=[] \\n y_gss=[]\\n x_pso=[]\\n x_bgd=[]\\n y_bgd=[]\\n y_pso=[]\\n x_gd=[]\\n y_gd=[]\\n \\n i=0.0000001\\n \\n # for k in range(1,51):\\n # i= random.uniform(0.00000001, 1)\\n # t_avg_ibs=[]\\n # t_avg_gss=[]\\n # for j in range(1,51):\\n #L=random.randint(-100, 0)\\n #U=random.randint(0, 100)\\n max_iter=self.Max_iter \\n L=self.Lower_bound\\n U=self.Upper_bound\\n \\n minima=self.gss(L,U,i,1000)\\n #print(\\\"minima at X = \\\",minima[1])\\n x_ibs.append(self.I_bisection(L,U,minima[1],max_iter)[0])\\n x_gss.append(self.gss(L,U,i,max_iter)[0])\\n x_pso.append(self.particle_Swarm(self.func, L, U, 2, max_iter)[0])\\n x_gd.append(self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)[0])\\n x_bgd.append(self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)[0])\\n #print(x_pso)\\n for i in x_ibs[0]:\\n #print(self.Func(i)) \\n y_ibs.append(self.Func(i))\\n for i in x_gss[0]:\\n y_gss.append(self.Func(i)) \\n for i in x_pso[0]:\\n y_pso.append(self.Func(i)) \\n for i in x_gd[0]:\\n y_gd.append(self.Func(i)) \\n for i in x_bgd[0]:\\n y_bgd.append(self.Func(i)) \\n #print(y_gss)\\n\\n plt.plot(x_ibs[0], y_ibs, 'r.')\\n plt.plot(x_gss[0], y_gss, '.')\\n plt.plot(x_pso[0], y_pso, 'y.')\\n #plt.plot(x_gd[0], y_gd, 'y.')\\n #plt.plot(x_bgd[0], y_bgd, 'k.')\\n plt.xlabel('x')\\n plt.ylabel('y')\\n \\n plt.suptitle('Interval Bisection Search (Red) vs Golden Section Search (Blue) vs Particle swarm optimization (Green)')\\n #plt.axis([0, 100, 0.00000001, 1]) \\n plt.show()\\n plt.plot(x_gd[0], y_gd, 'r.')\\n plt.plot(x_bgd[0], y_bgd, 'k.')\\n plt.xlabel('x')\\n plt.ylabel('y') \\n plt.suptitle('Gradient Descent (Red) vs Batch Gradient Descent (Black) ')\\n \\n plt.show()\\n \\n start_time = timeit.default_timer()\\n ibs=self.I_bisection(L,U,minima[1],max_iter)\\n print(\\\" Execution time for Interval bisection Method is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n gss=self.gss(L,U,i,max_iter)\\n print(\\\" Execution time for Golden Section Search is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n pso=self.particle_Swarm(self.func, L, U, 2, max_iter)\\n print(\\\" Execution time for Particle swarm optimization is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n gd=self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)\\n print(\\\" Execution time for Gradient Descent is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n bgd=self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)\\n print(\\\" Execution time for Batch Gradient Descent is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n plt.plot(ibs[1], ibs[2], 'r.')\\n plt.text(ibs[1], ibs[2],\\\"IB\\\")\\n plt.plot(gss[1], gss[2], '.')\\n plt.text(gss[1], gss[2],\\\" GSS\\\")\\n plt.plot(pso[1], pso[2], 'y.')\\n plt.text(pso[1], pso[2],\\\" PSO\\\")\\n plt.plot(gd[1], gd[2], 'g.')\\n plt.text(gd[1], gd[2],\\\" GD \\\")\\n plt.plot(bgd[1],bgd[2], 'k.')\\n plt.text(bgd[1], bgd[2],\\\" Batch_GD\\\")\\n \\n plt.xlabel('Value of X')\\n plt.ylabel('NUmber of iteration') \\n plt.suptitle('Number of iterations vs minimum value of x')\\n \\n plt.show()\",\n \"def plotPsCurve(mcoolsPath:list,celltypeNames:list,chroms:list,resolution=100000,title=\\\"P(s) curve\\\",plotType=\\\"interaction\\\",base=1.1,log_x=True,log_y=True):\\n import plotly.express as px\\n from IPython.display import Image\\n\\n #Calculate P(s) data, get a 3 column pd.DataFrame with (bin,resolution,celltype)\\n psDataAll = []\\n for i in range(len(mcoolsPath)):\\n psDataAll.append(compartment.getPsData(mcoolsPath[i],[\\\"chr\\\"+str(i+1) for i in range(len(chroms))],resolution=resolution,celltype=celltypeNames[i],base=base)) \\n merged = pd.concat(psDataAll)\\n\\n data = pd.merge(merged,merged.groupby(\\\"celltype\\\").sum(),how=\\\"left\\\",on=\\\"celltype\\\").assign(prob= lambda df: df.aveCount_x/df.aveCount_y)\\n\\n fig = px.line(x=data[\\\"bin_x\\\"]*resolution,y=data[\\\"prob\\\"],color=data[\\\"celltype\\\"],title=title,log_x=log_x,log_y=log_y).update_layout(template='simple_white')\\n fig.update_layout(width=800,height=600)\\n fig.update_layout(xaxis_title=\\\"Genomic Distance(bp)\\\",\\n yaxis_title=\\\"Contact Probability\\\")\\n if(plotType == \\\"interaction\\\"):\\n return fig\\n else : return Image(fig.to_image(format=\\\"png\\\", engine=\\\"kaleido\\\"))\",\n \"def main():\\n\\n\\timport numpy as np\\n\\timport matplotlib as mpl\\n\\n\\t# Parse arguments\\n\\targs = cli.parse_args()\\n\\n\\t# Check all files exist\\n\\tfor filepath in args.files:\\n\\t\\tif not os.path.isfile(filepath):\\n\\t\\t\\t_exit('file {} does not exist'.format(filepath))\\n\\n\\t# Use TK interactive backend as it should work on all installations\\n\\tmpl.use('tkagg')\\n\\n\\t# Turn on interactive plotting (pyplot import must come after call to use())\\n\\timport matplotlib.pyplot as plt\\n\\tplt.ion()\\n\\n\\t# Get torsion angle arrays for all input files\\n\\tarrays = []\\n\\tfor filepath in args.files:\\n\\t\\twith open(filepath) as fobj:\\n\\t\\t\\tchain = list(parse_pdb_chain(fobj))\\n\\t\\t\\tarrays.append(torsion_array(chain))\\n\\n\\tangles = np.concatenate(arrays, axis=0)\\n\\n\\t# Make the plot\\n\\tif args.plottype == 'hexbin':\\n\\t\\thb = ramachandran_hexbin(angles, log=args.log, degrees=not args.radians)\\n\\t\\tif args.cbar:\\n\\t\\t\\tplt.colorbar(hb)\\n\\n\\telif args.plottype == 'scatter':\\n\\t\\tramachandran_scatter(angles, degrees=not args.radians)\\n\\n\\telif args.plottype == 'kde':\\n\\t\\t_assert_seaborn_available('kde')\\n\\t\\tramachandran_kdeplot(angles, shade=True, degrees=not args.radians)\\n\\n\\telif args.plottype == 'joint':\\n\\t\\t_assert_seaborn_available('joint')\\n\\t\\tramachandran_jointplot(angles, kind='kde', degrees=not args.radians)\\n\\n\\telse:\\n\\t\\traise ValueError(args.plottype)\\n\\n\\t# Plot title\\n\\tif len(args.files) == 1:\\n\\t\\tfilename = os.path.basename(args.files[0])\\n\\telse:\\n\\t\\tfilename = '{} files'.format(len(args.files))\\n\\n\\tplt.gcf().suptitle('{} ({} residues)'.format(filename, angles.shape[0]))\\n\\n\\t# Show the plot, blocking until closed\\n\\tplt.show(block=True)\",\n \"def plot_fig43a_looping_TSS(a=10, loglog=True):\\n\\n fig, ax = plt.subplots(figsize=(default_width, default_height))\\n #convert a to basepairs\\n a_in_bp = a / ncg.dna_params['lpb']\\n links35 = np.tile(35, 44)\\n Rlinks = np.array([47, 21, 18, 15, 20, 17])\\n Llinks = np.array([245, 30, 26, 15, 23, 35])\\n #links to right of methylation site (50 in total)\\n Rlinks = np.concatenate((Rlinks, links35))\\n #links to left of methylation site (50 in total)\\n Llinks = np.concatenate((Llinks, links35))\\n #cumulative chain length including burried basepairs\\n unwrap = 0\\n #plot as positive distance from TSS in bp\\n ldna_Rlinks = convert.genomic_length_from_links_unwraps(Rlinks, unwraps=unwrap) #max WLC chain length in bp\\n #plot as negative distance from TSS in bp\\n ldna_Llinks = -1*convert.genomic_length_from_links_unwraps(Llinks, unwraps=unwrap) #max WLC chain length in bp\\n rvals = np.linspace(0.0, 1.0, 1000)\\n integral_R = np.load('../deepti/csvs/Bprops/0unwraps/heterogenous/Sarah/Rlinks_1to50nucs/kinkedWLC_greens_Rlinks_50nucs_1000rvals.npy')\\n integral_L = np.load('../deepti/csvs/Bprops/0unwraps/heterogenous/Sarah/Llinks_1to50nucs/kinkedWLC_greens_Llinks_50nucs_1000rvals.npy')\\n Prob_a_Rlinks_kinked = wlc.prob_R_in_radius_a_given_L(a_in_bp, integral_R, rvals, Rlinks, unwrap)\\n Prob_a_Llinks_kinked = wlc.prob_R_in_radius_a_given_L(a_in_bp, integral_L, rvals, Llinks, unwrap)\\n #plot in units of nm^(-3)\\n ax.plot(ldna_Rlinks, Prob_a_Rlinks_kinked/(ncg.dna_params['lpb']**3), '-o', markersize=3, color=teal_flucts)\\n ax.plot(ldna_Llinks, Prob_a_Llinks_kinked/(ncg.dna_params['lpb']**3), '-o', markersize=3, color=teal_flucts)\\n plt.xlabel(r'Distance from TSS (bp)')\\n plt.ylabel(r'$P_\\\\mathrm{loop}(a=10\\\\mathrm{nm}; L)\\\\;\\\\;\\\\;(\\\\mathrm{nm}^{-3})$')\\n plt.subplots_adjust(left=0.16, bottom=0.19, top=0.98, right=0.96)\\n plt.yscale('log')\\n plt.ylim([10**-3, 10**-0.4])\\n plt.xlim([-10000, 10000])\\n plt.savefig(f'plots/thesis/fig43a_looping-TSS-yeast.pdf', bbox_inches='tight')\",\n \"def main():\\n # Load data and pre-process it\\n path = \\\"data2.csv\\\"\\n features, labels, df = load_data(path)\\n\\n # Learning rates - including our own\\n learning_rates = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 0.75]\\n\\n # Num iterations\\n num_iterations = [100 for i in range(9)] + [1000]\\n\\n # Keep track of all final weights for different learning rates\\n lines = []\\n\\n # Compute weights for each learning rate\\n for rate, num_iters in zip(learning_rates, num_iterations):\\n\\n # Get weights from gradient descent and add to weights list\\n weights = gradient_descent(features, labels, rate, num_iters)\\n lines.append([rate, num_iters] + weights)\\n\\n # Now write 'lines' to file\\n with open('results2.csv', \\\"w\\\") as out_file:\\n for line in lines:\\n out_file.write(\\\"{}, {}, {}, {}, {} \\\\n\\\".format(line[0], line[1],\\n line[2], line[3], line[4]))\\n out_file.close()\\n\\n # Select which weights to use for plotting\\n index = -1\\n\\n\\n plot_db.visualize_3d(df, lin_reg_weights=lines[index][2:],\\n feat1='norm_x1', feat2='norm_x2', labels='label',\\n xlim=(-1, 1), ylim=(-1, 1), zlim=(0, 3),\\n alpha=learning_rates[index], xlabel='age',\\n ylabel='weight', zlabel='height',\\n title='')\",\n \"def plot_results(self):\\n\\n\\n f1, ax1 = plt.subplots()\\n h1, = ax1.plot(self.history[\\\"step\\\"], self.history[\\\"trainLoss\\\"],\\\\\\n \\\"b-\\\", label=\\\"Loss - Train\\\")\\n h2, = ax1.plot(self.history[\\\"step\\\"], self.history[\\\"validLoss\\\"],\\\\\\n \\\"b.\\\", label=\\\"Loss - Validation\\\")\\n\\n ax1.set_ylabel(\\\"Loss\\\", color = \\\"blue\\\")\\n ax1.tick_params(\\\"y\\\", color = \\\"blue\\\")\\n ax1.yaxis.label.set_color(\\\"blue\\\")\\n ax1.set_xlabel(\\\"Training Steps [{}]\\\".format(self.FLAGS.eval_every))\\n\\n ax2 = ax1.twinx()\\n h3, = ax2.plot(self.history[\\\"step\\\"], self.history[\\\"trainAccr\\\"], \\\"r-\\\",\\\\\\n label = \\\"Accuracy - Train\\\")\\n h4, = ax2.plot(self.history[\\\"step\\\"], self.history[\\\"validAccr\\\"], \\\"r.\\\",\\\\\\n label = \\\"Accuracy - Validation\\\")\\n\\n ax2.set_ylabel(\\\"Accuracy\\\", color = \\\"red\\\")\\n ax2.tick_params(\\\"y\\\", color = \\\"red\\\")\\n ax2.yaxis.label.set_color(\\\"red\\\")\\n\\n hds = [h1,h2,h3,h4]\\n lbs = [l.get_label() for l in hds]\\n ax1.legend(hds, lbs)\\n f1.tight_layout()\\n plt.savefig(\\\"trainingHistory.png\\\")\\n\\n plt.close(f1)\\n #plt.show()\",\n \"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\\n n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\\n print \\\"in here\\\"\\n plt.figure()\\n plt.title(title)\\n if ylim is not None:\\n plt.ylim(*ylim)\\n plt.xlabel(\\\"Training examples\\\")\\n plt.ylabel(\\\"Score\\\")\\n train_sizes, train_scores, test_scores = learning_curve(\\n estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes,scoring=\\\"accuracy\\\")\\n print(\\\"now here\\\")\\n train_scores_mean = np.mean(train_scores, axis=1)\\n train_scores_std = np.std(train_scores, axis=1)\\n test_scores_mean = np.mean(test_scores, axis=1)\\n test_scores_std = np.std(test_scores, axis=1)\\n plt.grid()\\n print(\\\"boom\\\")\\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\\n train_scores_mean + train_scores_std, alpha=0.1,\\n color=\\\"r\\\")\\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\\n test_scores_mean + test_scores_std, alpha=0.1, color=\\\"g\\\")\\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\\\"r\\\",\\n label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\\\"g\\\",\\n label=\\\"Cross-validation score\\\")\\n print(\\\"almost there\\\")\\n plt.legend(loc=\\\"best\\\")\\n return plt\",\n \"def plot_train_and_valid_curves(ax, train_points, valid_points, learning_rate_updates_epoch, best_per_lr, mode=\\\"loss\\\"):\\n if mode==\\\"loss\\\":\\n name = \\\"Loss\\\"\\n names = \\\"losses\\\"\\n factor = [1.2, 1.22]\\n loc_legend = 1\\n elif mode ==\\\"acc\\\":\\n name = \\\"Accuracy\\\"\\n names = \\\"acc\\\"\\n factor = [0.9, 0.88]\\n loc_legend = 4\\n else:\\n print \\\"Mode not understood. Available modes : 'loss' and 'acc'\\\"\\n return\\n\\n #ax = plt.subplot(1,1,1)#\\n # Plot training and valid loss curves\\n ax.plot(np.arange(len(train_points)),train_points, c=\\\"k\\\", zorder=1)\\n ax.plot(np.arange(len(valid_points)),valid_points, c=\\\"k\\\", zorder=1)\\n ax.scatter(np.arange(len(train_points)),train_points, c=\\\"b\\\", label=\\\"Train %s\\\"%names, zorder=2)\\n ax.scatter(np.arange(len(valid_points)),valid_points, c=\\\"r\\\", label=\\\"Valid %s\\\"%names, zorder=2)\\n # Plot vertical line when the learning rate was updated\\n first = True\\n for elem in learning_rate_updates_epoch:\\n if first:\\n plt.plot([elem-.5,elem-.5], [1.4*valid_points[elem],train_points[elem]*0.6], c=\\\"k\\\", label=\\\"LR updates\\\", linestyle=\\\"--\\\")\\n first = False\\n else:\\n plt.plot([elem-.5,elem-.5], [1.4*valid_points[elem],train_points[elem]*0.6], c=\\\"k\\\", linestyle=\\\"--\\\")\\n # Plot best model in each region\\n first = True\\n for i,elem in enumerate(best_per_lr):\\n if first:\\n x = elem[0]\\n y = elem[1]\\n plt.scatter(x,y, c=\\\"g\\\", label=\\\"Best models\\\", marker=\\\"*\\\", zorder=3, s=100)\\n plt.plot([x,x],[y,factor[0]*y], c=\\\"g\\\")\\n plt.text(x,factor[1]*y, \\\"Epoch %d\\\"%(x), fontsize=8)\\n first = False\\n else:\\n x = elem[0]+learning_rate_updates_epoch[i-1]\\n y = elem[1]\\n plt.scatter(x,y, c=\\\"g\\\", marker=\\\"*\\\", zorder=3, s=100)\\n plt.plot()\\n plt.plot([x,x],[y,factor[0]*y], c=\\\"g\\\")\\n plt.text(x,factor[1]*y, \\\"Epoch %d\\\"%(x), fontsize=8)\\n # Xlim, Ylim, labels, legend...\\n ax.set_ylim([0,1])\\n ax.set_xlim([0,len(train_points)+5])\\n ax.set_xlabel(\\\"Epochs\\\")\\n ax.set_ylabel(name)\\n handles,labels = ax.get_legend_handles_labels()\\n sorted_zip = sorted(zip([2,0,1,3],handles, labels))\\n index, handles, labels = zip(*sorted_zip)\\n ax.legend(handles,labels, loc=loc_legend, prop={'size':10})\",\n \"def plot_inv_RV_lit(outpath, fit_slopes, fit_intercepts, fit_stds):\\n waves = np.arange(0.8, 4.01, 0.001)\\n fig, ax = plt.subplots(figsize=(10, 9))\\n\\n for i, RV in enumerate([2.5, 3.1, 5.5]):\\n # plot the extinction curve from this work\\n offset = 0.1 * i\\n slopes = interpolate.splev(waves, fit_slopes)\\n alav = fit_intercepts(waves) + slopes * (1 / RV - 1 / 3.1)\\n (line,) = ax.plot(waves, alav + offset, lw=1.5, label=r\\\"$R(V) = $\\\" + str(RV))\\n stddev = interpolate.splev(waves, fit_stds)\\n color = line.get_color()\\n ax.fill_between(\\n waves,\\n alav + offset - stddev,\\n alav + offset + stddev,\\n color=color,\\n alpha=0.2,\\n edgecolor=None,\\n )\\n\\n # plot the literature curves\\n styles = [\\\"--\\\", \\\":\\\"]\\n for i, cmodel in enumerate([CCM89, F19]):\\n ext_model = cmodel(Rv=RV)\\n (indxs,) = np.where(\\n np.logical_and(\\n 1 / waves >= ext_model.x_range[0], 1 / waves <= ext_model.x_range[1]\\n )\\n )\\n yvals = ext_model(waves[indxs] * u.micron)\\n ax.plot(\\n waves[indxs],\\n yvals + offset,\\n lw=1.5,\\n color=color,\\n ls=styles[i],\\n alpha=0.8,\\n )\\n\\n # add text\\n ax.text(3.45, 0.03, r\\\"$R(V) = 2.5$\\\", fontsize=0.8 * fs, color=\\\"tab:blue\\\")\\n ax.text(3.45, 0.15, r\\\"$R(V) = 3.1$\\\", fontsize=0.8 * fs, color=\\\"tab:orange\\\")\\n ax.text(3.45, 0.305, r\\\"$R(V) = 5.5$\\\", fontsize=0.8 * fs, color=\\\"tab:green\\\")\\n\\n # finalize and save the plot\\n ax.set_xlabel(r\\\"$\\\\lambda\\\\ [\\\\mu m$]\\\", fontsize=1.2 * fs)\\n ax.set_ylabel(r\\\"$A(\\\\lambda)/A(V)$ + offset\\\", fontsize=1.2 * fs)\\n ax.set_xlim(0.75, 4.05)\\n ax.set_ylim(-0.03, 0.98)\\n handles = [\\n Line2D([0], [0], color=\\\"k\\\", lw=1.5),\\n Line2D([0], [0], color=\\\"k\\\", lw=1.5, ls=\\\"--\\\"),\\n Line2D([0], [0], color=\\\"k\\\", lw=1.5, ls=\\\":\\\"),\\n ]\\n labels = [\\n \\\"this work\\\",\\n \\\"Cardelli et al. (1989)\\\",\\n \\\"Fitzpatrick et al. (2019)\\\",\\n ]\\n plt.legend(handles, labels, fontsize=fs)\\n plt.savefig(outpath + \\\"inv_RV_lit.pdf\\\", bbox_inches=\\\"tight\\\")\\n\\n # also save the plot in log scale\\n plt.ylim(0.01, 1)\\n plt.yscale(\\\"log\\\")\\n plt.tight_layout()\\n plt.savefig(outpath + \\\"inv_RV_lit_log.pdf\\\")\",\n \"def _plot_curves(plot_file_name, curve_names, metric_sets, number_of_embeddings):\\n logger = get_logger('analysis', logging.INFO)\\n logger.info('Plot results')\\n\\n config = load_config(None, join(get_common(), 'config.cfg'))\\n plot_width = config.getint('common', 'plot_width')\\n fig_width = config.getint('common', 'fig_width')\\n fig_height = config.getint('common', 'fig_height')\\n #Slice results to only 1-80 clusters\\n for i in range(0,len(metric_sets)):\\n for j in range(0, len(metric_sets[i])):\\n metric_sets[i][j] = metric_sets[i][j][-plot_width:]\\n print(len(metric_sets[i][j]))\\n\\n best_results = [[] for _ in metric_names]\\n for m, min_value in enumerate(metric_min_values):\\n for results in metric_sets[m]:\\n if(metric_min_values[m] == 0):\\n best_results[m].append(np.max(results))\\n else:\\n best_results[m].append(np.min(results))\\n\\n '''\\n This code is used to sort the lines by min mr. Because we now use mutliple metrics and dont sort by a single\\n metric, this code is not used anymore, but we keep it for now.\\n min_mrs, curve_names, mrs, acps, aris, homogeneity_scores, completeness_scores, number_of_embeddings = \\\\\\n (list(t) for t in\\n zip(*sorted(zip(min_mrs, curve_names, mrs, acps, aris, homogeneity_scores, completeness_scores, number_of_embeddings))))\\n '''\\n\\n # How many lines to plot\\n number_of_lines = len(curve_names)\\n\\n # Get various colors needed to plot\\n color_map = plt.get_cmap('gist_rainbow')\\n colors = [color_map(i) for i in np.linspace(0, 1, number_of_lines)]\\n\\n #Set fontsize for all plots\\n plt.rcParams.update({'font.size': 12})\\n\\n # Define number of figures\\n fig1 = plt.figure(figsize=(fig_width, fig_height))\\n\\n # Define Plots\\n plot_grid = (3, 2)\\n\\n plots = [None] * len(metric_names)\\n\\n plots[0] = _add_cluster_subplot(plot_grid, (0, 0), metric_names[0], 1)\\n plots[1] = _add_cluster_subplot(plot_grid, (0, 1), metric_names[1], 1)\\n plots[2] = _add_cluster_subplot(plot_grid, (1, 0), metric_names[2], 1)\\n plots[3] = _add_cluster_subplot(plot_grid, (1, 1), metric_names[3], 1)\\n\\n #Set the horizontal space between subplots\\n plt.subplots_adjust(hspace = 0.3)\\n\\n # Define curves and their values\\n curves = [[] for _ in metric_names]\\n\\n for m, metric_set in enumerate(metric_sets):\\n curves[m] = [plots[m], metric_set]\\n\\n # Plot all curves\\n for index in range(number_of_lines):\\n label = curve_names[index]\\n for m, metric_name in enumerate(metric_names):\\n label = label + '\\\\n {} {}: {}'.format('Max' if metric_min_values[m]==0 else 'Min', metric_name,\\n str(best_results[m][index]))\\n color = colors[index]\\n number_of_clusters = np.arange(plot_width, 0, -1)\\n\\n for plot, value in curves:\\n plot.plot(number_of_clusters, value[index], color=color, label=label)\\n\\n # Add legend and save the plot\\n fig1.legend(loc='upper center', bbox_to_anchor=(0.5, 0.33), ncol=4)\\n #fig1.show()\\n fig1.savefig(get_result_png(plot_file_name))\\n fig1.savefig(get_result_png(plot_file_name + '.svg'), format='svg')\",\n \"def plot(profile_likelihood, result):\\n\\n # Plot log-lp versus scale\\n _plot_likelihood_versus_scale(\\n profile_likelihood, result, numpy.logspace(-2, 2, 5))\\n\\n # Plot log-lp versus eta\\n _plot_likelihood_versus_eta(\\n profile_likelihood, result, numpy.logspace(-2, 2, 5))\\n\\n # Contour Plot of log-lp function\\n _plot_likelihood_versus_eta_scale(profile_likelihood, result)\\n\\n # Plot first derivative of log likelihood\\n _plot_likelihood_der1_eta(profile_likelihood, result)\",\n \"def _lcResults(self):\\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\\n noCt = int( self.suffix.split('_')[-1] ) \\n \\n hdu = fits.open(fitsNnam)\\n hdu['LIGHTCURVE'].data['flux'][noCt] = self.getResult(param='Flux')[0]\\n hdu['LIGHTCURVE'].data['fluxerr'][noCt] = self.getResult(param='Flux')[1]\\n hdu['LIGHTCURVE'].data['index'][noCt] = self.getResult(param='Index')[0]\\n hdu['LIGHTCURVE'].data['indexerr'][noCt] = self.getResult(param='Index')[1]\\n hdu['LIGHTCURVE'].data['ts'][noCt] = self.getResult(param='TS value')[0]\\n hdu['LIGHTCURVE'].data['upperlim'][noCt] = self.getResult(param='upplim')[0] \\n hdu['LIGHTCURVE'].data['status'][noCt] = 'done'\\n hdu.writeto(fitsNnam, clobber=True)\\n hdu.close()\",\n \"def make_llr_plots(self):\\n import matplotlib.pyplot as plt\\n plt.rcParams['text.usetex'] = True\\n\\n outdir = os.path.join(self.outdir, 'LLRDistributions')\\n mkdir(outdir)\\n\\n for injkey in self.values.keys():\\n\\n data = self.values[injkey]\\n metric_type = data['h0_fit_to_h0_fid']['metric_val']['type']\\n metric_type_pretty = self.tex_axis_label(metric_type)\\n h0_fid_metric = self.fid_values[injkey][\\n 'h0_fit_to_%s'%self.labels.dict['data']\\n ][\\n 'metric_val'\\n ]\\n h1_fid_metric = self.fid_values[injkey][\\n 'h1_fit_to_%s'%self.labels.dict['data']\\n ][\\n 'metric_val'\\n ]\\n\\n h0_fit_to_h0_fid_metrics = np.array(\\n data['h0_fit_to_h0_fid']['metric_val']['vals']\\n )\\n h1_fit_to_h0_fid_metrics = np.array(\\n data['h1_fit_to_h0_fid']['metric_val']['vals']\\n )\\n h0_fit_to_h1_fid_metrics = np.array(\\n data['h0_fit_to_h1_fid']['metric_val']['vals']\\n )\\n h1_fit_to_h1_fid_metrics = np.array(\\n data['h1_fit_to_h1_fid']['metric_val']['vals']\\n )\\n\\n # In the case of likelihood, the maximum metric is the better fit.\\n # With chi2 metrics the opposite is true, and so we must multiply\\n # everything by -1 in order to apply the same treatment.\\n if 'chi2' in metric_type:\\n logging.info('Converting chi2 metric to likelihood equivalent.')\\n h0_fid_metric *= -1\\n h1_fid_metric *= -1\\n h0_fit_to_h0_fid_metrics *= -1\\n h1_fit_to_h0_fid_metrics *= -1\\n h0_fit_to_h1_fid_metrics *= -1\\n h1_fit_to_h1_fid_metrics *= -1\\n\\n if h1_fid_metric < h0_fid_metric:\\n bestfit = 'h0'\\n altfit = 'h1'\\n critical_value = h0_fid_metric-h1_fid_metric\\n else:\\n bestfit = 'h1'\\n altfit = 'h0'\\n critical_value = h1_fid_metric-h0_fid_metric\\n\\n if bestfit == 'h0':\\n llrbest = h0_fit_to_h0_fid_metrics - h1_fit_to_h0_fid_metrics\\n llralt = h0_fit_to_h1_fid_metrics - h1_fit_to_h1_fid_metrics\\n else:\\n llrbest = h1_fit_to_h1_fid_metrics - h0_fit_to_h1_fid_metrics\\n llralt = h1_fit_to_h0_fid_metrics - h0_fit_to_h0_fid_metrics\\n\\n minllr = min(min(llrbest), min(llralt))\\n maxllr = max(max(llrbest), max(llralt))\\n rangellr = maxllr - minllr\\n # Special case for low numbers of trials. Here, the plot\\n # can't really be interpreted but the numbers printed on\\n # it can still be useful, so we need to make something.\\n if self.num_trials < 100:\\n binning = np.linspace(minllr - 0.1*rangellr,\\n maxllr + 0.1*rangellr,\\n 10)\\n elif self.num_trials < 2000:\\n binning = np.linspace(minllr - 0.1*rangellr,\\n maxllr + 0.1*rangellr,\\n int(self.num_trials/40))\\n else:\\n binning = np.linspace(minllr - 0.1*rangellr,\\n maxllr + 0.1*rangellr,\\n 50)\\n binwidth = binning[1]-binning[0]\\n\\n llrbesthist, llrbestbinedges = np.histogram(llrbest, bins=binning)\\n llralthist, llraltbinedges = np.histogram(llralt, bins=binning)\\n\\n llrhistmax = max(max(llrbesthist), max(llralthist))\\n\\n best_median = np.median(llrbest)\\n\\n if self.labels.dict['data_name'] == '':\\n inj_name = \\\"data\\\"\\n else:\\n inj_name = \\\"true %s\\\"%self.tex_axis_label(\\n self.labels.dict['data_name']\\n )\\n best_name = self.labels.dict['%s_name'%bestfit]\\n alt_name = self.labels.dict['%s_name'%altfit]\\n\\n # Calculate p values\\n ## First for the preferred hypothesis based on the fiducial fit\\n crit_p_value, unc_crit_p_value = self.calc_p_value(\\n llrdist=llralt,\\n critical_value=critical_value\\n )\\n ## Then for the alternate hypothesis based on the fiducial fit\\n alt_crit_p_value, alt_unc_crit_p_value = self.calc_p_value(\\n llrdist=llrbest,\\n critical_value=critical_value\\n )\\n ## Combine these to give a cls value based on arXiv:1407.5052\\n cls_value = (1 - alt_crit_p_value) / (1 - crit_p_value)\\n unc_cls_value = cls_value * np.sqrt(\\n np.power(alt_unc_crit_p_value/alt_crit_p_value, 2.0) + \\\\\\n np.power(unc_crit_p_value/crit_p_value, 2.0)\\n )\\n ## Then for the preferred hypothesis based on the median. That\\n ## is, the case of a median experiment from the distribution\\n ## under the preferred hypothesis.\\n med_p_value, unc_med_p_value, median_error = self.calc_p_value(\\n llrdist=llralt,\\n critical_value=best_median,\\n median_p_value=True,\\n llrbest=llrbest\\n )\\n\\n if metric_type == 'llh':\\n plot_title = (r\\\"\\\\begin{center}\\\"\\\\\\n +\\\"%s %s Event Selection \\\"%(self.detector,\\n self.selection)\\\\\\n +r\\\"\\\\\\\\\\\"+\\\" llr Distributions for %s (%i trials)\\\"%(\\n inj_name, self.num_trials)\\\\\\n +r\\\"\\\\end{center}\\\")\\n\\n else:\\n plot_title = (r\\\"\\\\begin{center}\\\"\\\\\\n +\\\"%s %s Event Selection \\\"%(self.detector,\\n self.selection)\\\\\\n +r\\\"\\\\\\\\\\\"+\\\" %s \\\\\\\"llr\\\\\\\" Distributions for \\\"\\n %(metric_type_pretty)\\\\\\n +\\\"%s (%i trials)\\\"%(inj_name,\\n self.num_trials)\\\\\\n +r\\\"\\\\end{center}\\\")\\n\\n # Factor with which to make everything visible\\n plot_scaling_factor = 1.55\\n\\n # In case of median plot, draw both best and alt histograms\\n ## Set up the labels for the histograms\\n llr_labels = [\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(best_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\"%(best_name) + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name),\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(alt_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\"%(best_name) + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name)\\n ]\\n self.plot_llr_histograms(\\n llrarrays=[llrbest, llralt],\\n llrhistmax=llrhistmax,\\n binning=binning,\\n colors=['r', 'b'],\\n labels=llr_labels,\\n best_name=best_name,\\n alt_name=alt_name,\\n critical_value=best_median,\\n critical_label=r\\\"%s Median = $%.4f\\\\pm%.4f$\\\"%(\\n self.tex_axis_label(best_name),\\n best_median,\\n median_error),\\n critical_height=float(max(llrbesthist))/float(\\n plot_scaling_factor*llrhistmax),\\n llrhist=llralthist\\n )\\n plt.legend(loc='upper left')\\n plt.title(plot_title)\\n # Write the p-value on the plot\\n plt.figtext(\\n 0.15,\\n 0.66,\\n r\\\"$\\\\mathrm{p}\\\\left(\\\\mathcal{H}_{%s}\\\\right) = %.4f\\\\pm%.4f$\\\"%(\\n best_name, med_p_value, unc_med_p_value),\\n color='k',\\n size='xx-large'\\n )\\n self.save_plot(\\n outdir=outdir,\\n end='%s_llrDistribution_median_%i_Trials'%(\\n metric_type, self.num_trials)\\n )\\n # Add the extra points if they exist\\n if self.extra_points is not None:\\n plt.legend(loc='upper left', fontsize=11)\\n curleg = plt.gca().get_legend()\\n linelist = self.add_extra_points(\\n ymax=float(max(llrbesthist))/float(\\n plot_scaling_factor*llrhistmax)\\n )\\n handles, labels = plt.gca().get_legend_handles_labels()\\n newhandles = []\\n for l, h in zip(labels, handles):\\n if l in linelist:\\n newhandles.append(h)\\n newleg = plt.legend(\\n handles=newhandles,\\n loc='upper right',\\n fontsize=11\\n )\\n plt.gca().add_artist(newleg)\\n plt.gca().add_artist(curleg)\\n self.save_plot(\\n outdir=outdir,\\n end='%s_llrDistribution_median_w_extra_points_%i_Trials'%(\\n metric_type, self.num_trials)\\n )\\n plt.close()\\n\\n # Make some debugging plots\\n ## Set up the labels for the histograms\\n llr_labels = [\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(best_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\"%(best_name) + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name),\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(alt_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\"%(best_name) + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name)\\n ]\\n self.plot_llr_histograms(\\n llrarrays=[llrbest, llralt],\\n llrhistmax=llrhistmax,\\n binning=binning,\\n colors=['r', 'b'],\\n labels=llr_labels,\\n best_name=best_name,\\n alt_name=alt_name,\\n critical_value=best_median,\\n critical_label=r\\\"%s Median = $%.4f\\\\pm%.4f$\\\"%(\\n self.tex_axis_label(best_name),\\n best_median,\\n median_error),\\n critical_height=float(max(llrbesthist))/float(\\n plot_scaling_factor*llrhistmax),\\n llrhist=None\\n )\\n plt.legend(loc='upper left')\\n plt.title(plot_title)\\n self.save_plot(\\n outdir=outdir,\\n end='%s_llrDistribution_median_both_fit_dists_%i_Trials'%(\\n metric_type, self.num_trials)\\n )\\n plt.close()\\n ## Set up the labels for the histograms\\n llr_labels = [\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(best_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\" + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name),\\n ]\\n self.plot_llr_histograms(\\n llrarrays=[llrbest],\\n llrhistmax=llrhistmax,\\n binning=binning,\\n colors=['r'],\\n labels=llr_labels,\\n best_name=best_name,\\n alt_name=alt_name,\\n critical_value=None,\\n critical_label=None,\\n critical_height=None,\\n llrhist=None\\n )\\n plt.legend(loc='upper left')\\n plt.title(plot_title)\\n self.save_plot(\\n outdir=outdir,\\n end='%s_llrDistribution_best_fit_dist_%i_Trials'%(\\n metric_type, self.num_trials)\\n )\\n plt.close()\\n ## Set up the labels for the histograms\\n llr_labels = [\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(best_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\"%(best_name) + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name),\\n ]\\n self.plot_llr_histograms(\\n llrarrays=[llrbest],\\n llrhistmax=llrhistmax,\\n binning=binning,\\n colors=['r'],\\n labels=llr_labels,\\n best_name=best_name,\\n alt_name=alt_name,\\n critical_value=best_median,\\n critical_label=r\\\"%s Median = $%.4f\\\\pm%.4f$\\\"%(\\n self.tex_axis_label(best_name),\\n best_median,\\n median_error),\\n critical_height=float(max(llrbesthist))/float(\\n plot_scaling_factor*llrhistmax),\\n llrhist=None\\n )\\n plt.legend(loc='upper left')\\n plt.title(plot_title)\\n self.save_plot(\\n outdir=outdir,\\n end='%s_llrDistribution_median_best_fit_dist_%i_Trials'%(\\n metric_type, self.num_trials)\\n )\\n plt.close()\\n ## Set up the labels for the histograms\\n llr_labels = [\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(alt_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\"%(best_name) + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name)\\n ]\\n self.plot_llr_histograms(\\n llrarrays=[llralt],\\n llrhistmax=llrhistmax,\\n binning=binning,\\n colors=['b'],\\n labels=llr_labels,\\n best_name=best_name,\\n alt_name=alt_name,\\n critical_value=None,\\n critical_label=None,\\n critical_height=None,\\n llrhist=None\\n )\\n plt.legend(loc='upper left')\\n plt.title(plot_title)\\n self.save_plot(\\n outdir=outdir,\\n end='%s_llrDistribution_alt_fit_dist_%i_Trials'%(\\n metric_type, self.num_trials)\\n )\\n plt.close()\\n ## Set up the labels for the histograms\\n llr_labels = [\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(alt_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\" + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name)\\n ]\\n self.plot_llr_histograms(\\n llrarrays=[llralt],\\n llrhistmax=llrhistmax,\\n binning=binning,\\n colors=['b'],\\n labels=llr_labels,\\n best_name=best_name,\\n alt_name=alt_name,\\n critical_value=best_median,\\n critical_label=r\\\"%s Median = $%.4f\\\\pm%.4f$\\\"%(\\n self.tex_axis_label(best_name),\\n best_median,\\n median_error),\\n critical_height=float(max(llrbesthist))/float(\\n plot_scaling_factor*llrhistmax),\\n llrhist=llralthist\\n )\\n plt.legend(loc='upper left')\\n plt.title(plot_title)\\n # Write the p-value on the plot\\n plt.figtext(\\n 0.15,\\n 0.66,\\n r\\\"$\\\\mathrm{p}\\\\left(\\\\mathcal{H}_{%s}\\\\right) = %.4f\\\\pm%.4f$\\\"%(\\n best_name, med_p_value, unc_med_p_value),\\n color='k',\\n size='xx-large'\\n )\\n self.save_plot(\\n outdir=outdir,\\n end='%s_llrDistribution_median_alt_fit_dist_%i_Trials'%(\\n metric_type, self.num_trials)\\n )\\n plt.close()\\n\\n # In case of critical plot, draw just alt histograms\\n ## Set up the label for the histogram\\n llr_labels = [\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(alt_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\"%(best_name) + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name)\\n ]\\n self.plot_llr_histograms(\\n llrarrays=[llralt],\\n llrhistmax=llrhistmax,\\n binning=binning,\\n colors=['b'],\\n labels=llr_labels,\\n best_name=best_name,\\n alt_name=alt_name,\\n critical_value=critical_value,\\n critical_label=r\\\"Critical Value = %.4f\\\"%(critical_value),\\n critical_height=float(max(llrbesthist))/float(\\n plot_scaling_factor*llrhistmax),\\n llrhist=llralthist\\n )\\n plt.legend(loc='upper left')\\n plt.title(plot_title)\\n # Write the p-value on the plot\\n plt.figtext(\\n 0.15,\\n 0.70,\\n r\\\"$\\\\mathrm{p}\\\\left(\\\\mathcal{H}_{%s}\\\\right) = %.4f\\\\pm%.4f$\\\"%(\\n best_name, crit_p_value, unc_crit_p_value),\\n color='k',\\n size='xx-large'\\n )\\n self.save_plot(\\n outdir=outdir,\\n end='%s_llrDistribution_critical_%i_Trials'%(\\n metric_type, self.num_trials)\\n )\\n plt.close()\\n\\n # Make a second critical plot for the alt hypothesis, so we draw the\\n # preferred hypothesis\\n ## Set up the label for the histogram\\n llr_labels = [\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(best_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\"%(best_name) + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name)\\n ]\\n self.plot_llr_histograms(\\n llrarrays=[llrbest],\\n llrhistmax=llrhistmax,\\n binning=binning,\\n colors=['r'],\\n labels=llr_labels,\\n best_name=best_name,\\n alt_name=alt_name,\\n critical_value=critical_value,\\n critical_label=r\\\"Critical Value = %.4f\\\"%(critical_value),\\n critical_height=float(max(llrbesthist))/float(\\n plot_scaling_factor*llrhistmax),\\n llrhist=llrbesthist\\n )\\n plt.legend(loc='upper left')\\n plt.title(plot_title)\\n # Write the p-value on the plot\\n plt.figtext(\\n 0.15,\\n 0.70,\\n r\\\"$\\\\mathrm{p}\\\\left(\\\\mathcal{H}_{%s}\\\\right) = %.4f\\\\pm%.4f$\\\"%(\\n alt_name, alt_crit_p_value, alt_unc_crit_p_value),\\n color='k',\\n size='xx-large'\\n )\\n self.save_plot(\\n outdir=outdir,\\n end='%s_llrDistribution_critical_alt_%i_Trials'%(\\n metric_type, self.num_trials)\\n )\\n plt.close()\\n\\n # Lastly, show both exclusion regions and then the joined cls value\\n ## Set up the labels for the histograms\\n llr_labels = [\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(best_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\"%(best_name) + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name),\\n r\\\"%s Pseudo-Experiments - \\\"%(self.tex_axis_label(alt_name)) + \\\\\\n r\\\"$\\\\log\\\\left[\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\"%(best_name) + \\\\\\n r\\\"\\\\right)/\\\\mathcal{L}\\\\left(\\\\mathcal{H}_{%s}\\\\right)\\\\right]$\\\"%(\\n alt_name)\\n ]\\n self.plot_llr_histograms(\\n llrarrays=[llrbest, llralt],\\n llrhistmax=llrhistmax,\\n binning=binning,\\n colors=['r', 'b'],\\n labels=llr_labels,\\n best_name=best_name,\\n alt_name=alt_name,\\n critical_value=critical_value,\\n critical_label=r\\\"Critical Value = %.4f\\\"%(critical_value),\\n critical_height=float(max(llrbesthist))/float(\\n plot_scaling_factor*llrhistmax),\\n llrhist=[llrbesthist, llralthist],\\n cls=True,\\n )\\n plt.legend(loc='upper left')\\n plt.title(plot_title)\\n # Write the p-values on the plot\\n plt.figtext(\\n 0.50,\\n 0.66,\\n r\\\"$\\\\mathrm{CL}_{s}\\\\left(\\\\mathcal{H}_{%s}\\\"%(best_name) + \\\\\\n r\\\"\\\\right)= %.4f\\\\pm%.4f$\\\"%(cls_value, unc_cls_value),\\n horizontalalignment='center',\\n color='k',\\n size='xx-large'\\n )\\n plt.figtext(\\n 0.12,\\n 0.55,\\n r\\\"$\\\\mathrm{p}\\\\left(\\\\mathcal{H}_{%s}\\\\right) = %.2f\\\\pm%.2f$\\\"%(\\n alt_name, alt_crit_p_value, alt_unc_crit_p_value),\\n bbox=dict(facecolor='none', edgecolor='red', boxstyle='round'),\\n horizontalalignment='left',\\n color='k',\\n size='x-large'\\n )\\n plt.figtext(\\n 0.88,\\n 0.55,\\n r\\\"$\\\\mathrm{p}\\\\left(\\\\mathcal{H}_{%s}\\\\right) = %.2f\\\\pm%.2f$\\\"%(\\n best_name, crit_p_value, unc_crit_p_value),\\n horizontalalignment='right',\\n bbox=dict(facecolor='none', edgecolor='blue', boxstyle='round'),\\n color='k',\\n size='x-large'\\n )\\n self.save_plot(\\n outdir=outdir,\\n end='%s_llrDistribution_CLs_%i_Trials'%(\\n metric_type, self.num_trials)\\n )\\n plt.close()\",\n \"def plotdFvsLambda1():\\n x = numpy.arange(len(df_allk))\\n if x[-1]<8:\\n fig = pl.figure(figsize = (8,6))\\n else:\\n fig = pl.figure(figsize = (len(x),6))\\n width = 1./(len(P.methods)+1)\\n elw = 30*width\\n colors = {'TI':'#C45AEC', 'TI-CUBIC':'#33CC33', 'DEXP':'#F87431', 'IEXP':'#FF3030', 'GINS':'#EAC117', 'GDEL':'#347235', 'BAR':'#6698FF', 'UBAR':'#817339', 'RBAR':'#C11B17', 'MBAR':'#F9B7FF'}\\n lines = tuple()\\n for name in P.methods:\\n y = [df_allk[i][name]/P.beta_report for i in x]\\n ye = [ddf_allk[i][name]/P.beta_report for i in x]\\n line = pl.bar(x+len(lines)*width, y, width, color=colors[name], yerr=ye, lw=0.1*elw, error_kw=dict(elinewidth=elw, ecolor='black', capsize=0.5*elw))\\n lines += (line[0],)\\n pl.xlabel('States', fontsize=12, color='#151B54')\\n pl.ylabel('$\\\\Delta G$ '+P.units, fontsize=12, color='#151B54')\\n pl.xticks(x+0.5*width*len(P.methods), tuple(['%d--%d' % (i, i+1) for i in x]), fontsize=8)\\n pl.yticks(fontsize=8)\\n pl.xlim(x[0], x[-1]+len(lines)*width)\\n ax = pl.gca()\\n for dir in ['right', 'top', 'bottom']:\\n ax.spines[dir].set_color('none')\\n ax.yaxis.set_ticks_position('left')\\n for tick in ax.get_xticklines():\\n tick.set_visible(False)\\n\\n leg = pl.legend(lines, tuple(P.methods), loc=3, ncol=2, prop=FP(size=10), fancybox=True)\\n leg.get_frame().set_alpha(0.5)\\n pl.title('The free energy change breakdown', fontsize = 12)\\n pl.savefig(os.path.join(P.output_directory, 'dF_state_long.pdf'), bbox_inches='tight')\\n pl.close(fig)\\n return\",\n \"def test_fits_lcurve0(self):\\n lcurve_ftools_orig = os.path.join(self.datadir, 'lcurveA.fits')\\n\\n lcurve_ftools = os.path.join(self.datadir,\\n 'lcurve_ftools_lc' +\\n HEN_FILE_EXTENSION)\\n\\n command = \\\"{0} --outfile {1}\\\".format(\\n os.path.join(self.datadir,\\n 'monol_testA_nustar_fpma_ev') + HEN_FILE_EXTENSION,\\n os.path.join(self.datadir,\\n 'lcurve_lc'))\\n hen.lcurve.main(command.split())\\n assert os.path.exists(os.path.join(self.datadir,\\n 'lcurve_lc') + HEN_FILE_EXTENSION)\\n\\n command = \\\"--fits-input {0} --outfile {1}\\\".format(\\n lcurve_ftools_orig,\\n lcurve_ftools)\\n hen.lcurve.main(command.split())\",\n \"def main():\\n df = prof_conv_bwd_filter()\\n df.to_csv(\\\"prof.cudnnConvBwdFilter.csv\\\")\\n\\n \\\"\\\"\\\"visualization, Roofline model\\\"\\\"\\\"\\n df = pd.read_csv('prof.cudnnConvBwdFilter.csv', header=0, index_col=0)\\n fig = plt.figure(figsize=(10,5))\\n ax = fig.add_subplot(1, 1, 1)\\n plot_rooline (ax, MACHINE_SPEC, PEAK_PERF, BAND_WIDTH)\\n plot_result (ax, df)\\n # fig.subplots_adjust(right=0.8)\\n plt.subplots_adjust(left=0.1, right=0.6)\\n plt.savefig('roofline.png')\\n return\",\n \"def decay_proportion_plot(Lmax=1000, p1=database['K+'], p=75, target_rate=53957518.001):\\r\\n L_range = np.linspace(0, 1000, 10000)\\r\\n prop = []\\r\\n for L in L_range:\\r\\n prop.append(decay_proportion(L, p1, p, target_rate))\\r\\n# charac_L = p*c*(p1.tau*1e-3/c)/p1.mass\\r\\n fig = plt.figure(figsize=[12, 3])\\r\\n ax = fig.add_subplot(1, 1, 1)\\r\\n ax.plot(L_range, prop, 'r', lw=2)\\r\\n ax.set_xlim(0, Lmax)\\r\\n ax.set_ylim(0)\\r\\n ax.set_xlabel('Target Distance', fontsize=20)\\r\\n ax.set_ylabel(r'$K^+$ flux', fontsize=20)\\r\\n# ax.xaxis.set_major_locator(plt.MultipleLocator(charac_L/4))\\r\\n# ax.xaxis.set_minor_locator(plt.MultipleLocator(charac_L/20))\\r\\n# ax.xaxis.set_major_formatter(plt.FuncFormatter(multiple_formatter_non_int(1, charac_L, 'L_{K^+}')))\\r\\n ax.set_xticks([0])\\r\\n ax.set_yticks([target_rate])\\r\\n ax.yaxis.set_major_locator(plt.MultipleLocator(target_rate/1))\\r\\n ax.yaxis.set_minor_locator(plt.MultipleLocator(target_rate/1))\\r\\n ax.yaxis.set_major_formatter(plt.FuncFormatter(multiple_formatter_non_int(1, target_rate, 'R_t')))\\r\\n ax.legend(fontsize=20)\\r\\n ax.minorticks_off()\\r\\n# ax.grid()\\r\\n plt.show()\\r\\n return\",\n \"def plot_fig44a_wlc_inhomo_test7():\\n\\n fig, ax = plt.subplots(figsize=(default_width, default_height))\\n #paramters from calculation, all in nm\\n a = 5 #looping radius\\n DEL = 15.3 #spacing between nucleosomes\\n MINUS1NUC = -83.3 #distance between -1 nuc and TSS\\n PLUS1NUC = 0\\n NNUC = 50 #number of nucleosomes on each side of TSS\\n LPB = 0.34 #nm per basepair (as used in calculation)\\n\\n Llinks = np.concatenate(([245], np.tile(45, 49)))\\n Rlinks = np.tile(45, 49)\\n #plot as negative distance from TSS in bp\\n unwrap=0\\n ldna_Llinks = -1*convert.genomic_length_from_links_unwraps(Llinks, unwraps=unwrap)\\n ldna_Rlinks = convert.genomic_length_from_links_unwraps(Rlinks, unwraps=unwrap)\\n x = np.concatenate((ldna_Llinks[::-1], [0], ldna_Rlinks))\\n y = np.loadtxt('csvs/Sarah/pnucsite_test7.txt')\\n ax.plot(x, y, '-o', markersize=3, color=teal_flucts)\\n plt.xlabel(r'Distance from TSS (bp)')\\n plt.ylabel(r'$P_\\\\mathrm{loop}(a=5\\\\mathrm{nm}; L)\\\\;\\\\;\\\\;(\\\\mathrm{nm}^{-3})$')\\n plt.subplots_adjust(left=0.16, bottom=0.19, top=0.98, right=0.96)\\n plt.yscale('log')\\n #plt.ylim([10**-3, 10**-0.4])\\n plt.xlim([-10000, 10000])\\n plt.savefig(f'plots/thesis/fig44a_wlc-inhomo-looping.pdf', bbox_inches='tight')\",\n \"def plot_learning_curve(X, y, maxdepth, estimator, plt):\\n # create cv training and test scores for various training set sizes\\n train_sizes, train_scores, test_scores = learning_curve(estimator,\\n X, # feature matrix\\n y, # target vector\\n cv=10, # number of folds in cross-validation\\n scoring='neg_mean_squared_error', # metric\\n n_jobs=-1, # use all computer cores,\\n train_sizes=np.linspace(0.01, 1.0, 30) # 30 different sizes of the training set\\n )\\n # create means and standart deviations of training set scores\\n train_mean = np.mean(train_scores, axis=1)\\n train_std = np.std(train_scores, axis=1)\\n\\n # create means and standart deviations of test set scores\\n test_mean = np.mean(test_scores, axis=1)\\n test_std = np.std(test_scores, axis=1)\\n\\n # draw lines\\n plt.plot(train_sizes, train_mean, '--', color='#111111', label=\\\"Training score\\\")\\n plt.plot(train_sizes, test_mean, color='#111111', label=\\\"Cross-validation score\\\")\\n\\n # draw bands\\n plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color=\\\"#DDDDDD\\\")\\n plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color=\\\"#f4d0d7\\\")\\n \\n # create plot \\n plt.title(\\\"Learning curve\\\")\\n plt.xlabel(\\\"Training set size\\\", fontsize=18)\\n plt.ylabel(\\\"mse\\\", fontsize=18)\\n plt.legend(loc=\\\"best\\\")\\n plt.tight_layout()\",\n \"def main():\\n\\n args = arg_parse()\\n plot_zcalib(args)\",\n \"def learning_curve():\\n loss = []\\n val_loss = []\\n data_size = []\\n\\n x_slid, y_slid = sliding_window_main(x, y)\\n x_train, y_train, x_val, y_val, x_test, y_test = data_splitting_main(x_slid, y_slid)\\n m_tot = x_train.shape[0]\\n\\n batch_step = 50\\n try:\\n for m in range(batch_size, m_tot, batch_step*batch_size):\\n print(\\\"Training: \\\", m)\\n net = create_network()\\n history = trainer(net, x_train[:m], y_train[:m], x_val, y_val)\\n loss.append(history.history[\\\"loss\\\"][-1])\\n val_loss.append(history.history[\\\"val_loss\\\"][-1])\\n data_size.append(m)\\n\\n print(\\\"Loss:\\\", loss[-1])\\n print()\\n\\n finally:\\n plt.plot(data_size, loss, label=\\\"Loss\\\", marker=\\\"o\\\")\\n plt.plot(data_size, val_loss, label=\\\"Val Loss\\\", marker=\\\"o\\\")\\n plt.xlabel(\\\"m\\\")\\n plt.ylabel(\\\"Losses\\\")\\n plt.title(\\\"Model Loss\\\")\\n plt.legend()\\n plt.savefig(\\\"img/\\\" + datetime.now().strftime(\\\"%y%m%d_%H%M\\\") + \\\"_learning_curve.png\\\")\\n plt.show()\\n plt.close()\\n\\n return loss, val_loss\",\n \"def plot_loss_curves(results):\\n loss = results[\\\"train_loss\\\"]\\n test_loss = results[\\\"test_loss\\\"]\\n\\n accuracy = results[\\\"train_acc\\\"]\\n test_accuracy = results[\\\"test_acc\\\"]\\n\\n epochs = range(len(results[\\\"train_loss\\\"]))\\n\\n plt.figure(figsize=(15, 7))\\n\\n # Plot loss\\n plt.subplot(1, 2, 1)\\n plt.plot(epochs, loss, label=\\\"train_loss\\\")\\n plt.plot(epochs, test_loss, label=\\\"test_loss\\\")\\n plt.title(\\\"Loss\\\")\\n plt.xlabel(\\\"Epochs\\\")\\n plt.legend()\\n\\n # Plot accuracy\\n plt.subplot(1, 2, 2)\\n plt.plot(epochs, accuracy, label=\\\"train_accuracy\\\")\\n plt.plot(epochs, test_accuracy, label=\\\"test_accuracy\\\")\\n plt.title(\\\"Accuracy\\\")\\n plt.xlabel(\\\"Epochs\\\")\\n plt.legend()\",\n \"def plot_vis_test(plotfile,pdf_file):\\n\\t# First some parameters looked up from configfile---------------------------------\\n\\t\\n\\tgrbdir = runconf['l2file'][0:10]\\n\\tpre_tstart = runconf['bkg1start']\\n\\tpre_tend = runconf['bkg1end']\\n\\ttrigtime = runconf['trigtime']\\n\\tgrb_tstart = runconf['transtart']\\n\\tgrb_tend = runconf['tranend']\\n\\tpost_tstart = runconf['bkg2start']\\n\\tpost_tend = runconf['bkg2end']\\n\\tt_src = grb_tend - grb_tstart \\n\\tt_tot = (pre_tend-pre_tstart)+(post_tend-post_tstart)\\n\\tra_tran = runconf['ra']\\n\\tdec_tran = runconf['dec']\\n\\tlc_bin = runconf['lc_bin']\\n\\talpha = runconf['alpha']\\n\\tbeta = runconf['beta']\\n\\tE0 = runconf['E0']\\n\\tA = runconf['A']\\n\\tsim_scale = t_src\\n\\tpixbin = int(runconf['pixsize'])\\n\\tcomp_bin = int(runconf['comp_bin'])\\n\\ttyp = runconf['typ']\\n\\n\\t# Calling txy to calculate thetax thetay and the coordinates----------------------\\n\\t\\n\\tthetax,thetay,x,y,z,t = txy(runconf['mkffile'], trigtime, ra_tran, dec_tran)\\n\\t\\n\\t# Plot the 3d visualisation for the position of the transient---------------------\\n\\tplt.figure()\\n\\tfig = visualize_3d(grbdir,x,y,z, t, thetax, thetay, grbdir)\\t\\n\\tpdf_file.savefig(fig)\\n\\t\\n\\t# Plotting the lightcurves for the four quadrants---------------------------------\\n\\tfig = plt.figure()\\n\\tclean_file = fits.open(runconf['infile'])\\n\\tplt.title('Light curves for '+grbdir + \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n\\t\\n\\tquad0 = clean_file[1].data\\n\\tdata0,bin_edge = np.histogram(quad0['time'], bins=np.arange(quad0['time'][0],quad0['time'][-1],lc_bin))\\n\\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data0,label='Quad 0',lw=0.7)\\n quad1 = clean_file[2].data\\n\\tdata1,bin_edge = np.histogram(quad1['time'], bins=np.arange(quad1['time'][0],quad1['time'][-1],lc_bin))\\n\\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data1,label='Quad 1',lw=0.7) \\n\\tquad2 = clean_file[3].data\\n\\tdata2,bin_edge = np.histogram(quad2['time'], bins=np.arange(quad2['time'][0],quad2['time'][-1],lc_bin))\\n\\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data2,label='Quad 2',lw=0.7)\\n quad3 = clean_file[4].data\\n data3,bin_edge = np.histogram(quad3['time'], bins=np.arange(quad3['time'][0],quad3['time'][-1],lc_bin))\\n plt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data3,label='Quad 3',lw=0.7)\\n\\tplt.axvspan(grb_tstart,grb_tend,color='blue',alpha=0.1,label='GRB')\\n\\tplt.axvspan(pre_tstart,pre_tend,color='orange',alpha=0.2)\\n\\tplt.axvspan(post_tstart,post_tend,color='orange',alpha=0.2,label='Background')\\n\\tplt.legend(prop={'size':6})\\n\\tplt.xlim(pre_tstart-100,post_tend+100)\\n\\tpdf_file.savefig(fig)\\n\\t\\n\\t# Calling the sim_dph--------------------------------------------------------------\\n\\t\\n\\tgrb_flat,bkgd_flat,grb_dph,bkgd_dph,t_src,t_total = data_bkgd_image(grbdir,pre_tstart,pre_tend,grb_tstart,grb_tend,post_tstart,post_tend)\\n\\n\\tsim_flat,sim_dph,badpix_mask,sim_err_dph = simulated_dph(grbdir,typ,t_src,alpha,beta,E0,A)\\n\\n\\tsrc_dph = grb_dph-bkgd_dph*t_src/t_tot\\n\\n print \\\"Total counts in simulated dph: \\\",(sim_dph).sum()\\n print \\\"Total counts after badpix mask is applied: \\\",(sim_dph*badpix_mask).sum()\\n\\tprint \\\"Excess counts in badpix masked src dph: \\\",(src_dph*badpix_mask).sum()\\n \\n\\t# Plotting the DPHs before badpix correction---------------------------------------\\n\\t\\n\\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\\n\\tplt.suptitle('DPHs before badpix correction for '+grbdir + \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n \\t# Sim\\n\\tim = ax3.imshow(sim_dph,interpolation='none')\\n\\tax3.set_title('Sim DPH',fontsize=8)\\n\\tax3.set_xlim(-1,128 - 0.5)\\n\\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax3.spines['left'].set_position(('data',-0.5))\\n\\tax3.set_yticklabels([])\\n\\tax3.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\\n\\t\\n\\t # Source \\n\\tim = ax4.imshow(src_dph,interpolation='none',vmin=0)\\n\\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\\n\\tax4.set_xlim(-1,128 -0.5)\\n\\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax4.spines['left'].set_position(('data',-0.5))\\n\\tax4.set_yticklabels([])\\n\\tax4.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\\n\\n \\t# Source + Background\\n\\tim = ax1.imshow(grb_dph,interpolation='none')\\n\\tax1.set_title('Src + Bkg DPH',fontsize=8)\\n\\tax1.set_xlim(-1,128 -0.5)\\n\\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax1.spines['left'].set_position(('data',-0.5))\\n\\tax1.set_yticklabels([])\\n\\tax1.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\\n\\n \\t# Background\\n\\tim = ax2.imshow(bkgd_dph*t_src/t_total,interpolation='none')\\n\\tax2.set_title('Bkg DPH',fontsize=8)\\n\\tax2.set_xlim(-1,128 -0.5)\\n\\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax2.spines['left'].set_position(('data',-0.5))\\n\\tax2.set_yticklabels([])\\n\\tax2.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\\n\\tf.set_size_inches([6.5,6.5])\\n\\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\\n\\t\\n\\t# Plotting the Badpix mask---------------------------------------------\\n\\n\\tfig = plt.figure()\\n\\tax = plt.subplot(111)\\n\\tplt.title('Badpix Mask for '+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n\\tim = ax.imshow(badpix_mask,interpolation='none')\\n\\tax.set_xlim(-9,128 -0.5)\\n\\tax.axvline(x=-5.,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax.spines['left'].set_position(('data',-0.5))\\n\\tax.xaxis.set_ticks(np.arange(0,128,16))\\n\\tax.yaxis.set_ticks(np.arange(0,128,16))\\n\\tfig.colorbar(im,ax=ax,fraction=0.046, pad=0.04)\\n\\t\\n\\tpdf_file.savefig(fig) # saves the current figure into a pdf_file page\\n\\n\\t# Plotting badpix masked graphs--------------------------------------------\\n\\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\\n\\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n \\t# Sim\\n\\tim = ax3.imshow(sim_dph*badpix_mask,interpolation='none')\\n\\tax3.set_title('Sim DPH',fontsize=8)\\n\\tax3.set_xlim(-1,128 -0.5)\\n\\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax3.spines['left'].set_position(('data',-0.5))\\n\\tax3.set_yticklabels([])\\n\\tax3.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\\n\\n\\t # Source \\n\\tim = ax4.imshow(src_dph*badpix_mask,interpolation='none',vmin=0)\\n\\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\\n\\tax4.set_xlim(-1,128 -0.5)\\n\\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax4.spines['left'].set_position(('data',-0.5))\\n\\tax4.set_yticklabels([])\\n\\tax4.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\\n\\n\\t # Source + Background\\n\\tim = ax1.imshow(grb_dph*badpix_mask,interpolation='none')\\n\\tax1.set_title('Src + Bkg DPH',fontsize=8)\\n\\tax1.set_xlim(-1,128 -0.5)\\n\\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax1.spines['left'].set_position(('data',-0.5))\\n\\tax1.set_yticklabels([])\\n\\tax1.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\\n\\t\\n\\t # Background\\n\\tim = ax2.imshow(bkgd_dph*badpix_mask*t_src/t_total,interpolation='none')\\n\\tax2.set_title('Bkg DPH',fontsize=8)\\n\\tax2.set_xlim(-1,128 -0.5)\\n\\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax2.spines['left'].set_position(('data',-0.5))\\n\\tax2.set_yticklabels([])\\n\\tax2.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\\n\\tf.set_size_inches([6.5,6.5])\\n\\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\\n\\n\\t# Plotting badpix masked graphs (Binned) ----------------------------------------------------\\n\\tfor p in [4,8,16]:\\n\\t\\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\\n\\t\\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay)+ \\\"pixsize=\\\"+str(p))\\n\\t\\t # Sim\\n\\t\\tim = ax3.imshow(resample(sim_dph*badpix_mask,p),interpolation='none')\\n\\t\\tax3.set_title('Sim DPH',fontsize=8)\\n\\t\\tax3.set_xlim(-1,128/p -0.5)\\n\\t\\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax3.spines['left'].set_position(('data',-0.5))\\n\\t\\tax3.set_yticklabels([])\\n\\t\\tax3.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n\\t\\tax3.set_xticklabels(np.arange(0,128,16))\\n\\t\\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\\n\\t\\t\\n\\t\\t # Source \\n\\t\\tim = ax4.imshow(resample(src_dph*badpix_mask,p),interpolation='none',vmin=0)\\n\\t\\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\\n\\t\\tax4.set_xlim(-1,128/p -0.5)\\n\\t\\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax4.spines['left'].set_position(('data',-0.5))\\n\\t\\tax4.set_yticklabels([])\\n ax4.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n ax4.set_xticklabels(np.arange(0,128,16))\\t\\t\\n\\t\\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\\n\\t\\t\\n\\t\\t # Source + Background\\n\\t\\tim = ax1.imshow(resample(grb_dph*badpix_mask,p),interpolation='none')\\n\\t\\tax1.set_title('Src + Bkg DPH',fontsize=10)\\n\\t\\tax1.set_xlim(-1,128/p -0.5)\\n\\t\\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax1.spines['left'].set_position(('data',-0.5))\\n\\t\\tax1.set_yticklabels([])\\n ax1.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n ax1.set_xticklabels(np.arange(0,128,16))\\t\\t\\n\\t\\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\\n\\t\\t\\n\\t\\t # Background\\n\\t\\tim = ax2.imshow(resample(bkgd_dph*badpix_mask*t_src/t_total,p),interpolation='none')\\n\\t\\tax2.set_title('Bkg DPH',fontsize=8)\\n\\t\\tax2.set_xlim(-1,128/p -0.5)\\n\\t\\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax2.spines['left'].set_position(('data',-0.5))\\n\\t\\tax2.set_yticklabels([])\\n ax2.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n ax2.set_xticklabels(np.arange(0,128,16))\\t\\t\\n\\t\\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\\n\\t\\tf.set_size_inches([6.5,6.5])\\n\\t\\t\\n\\t\\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\\n\\n\\n\\t# Plotting the comparison graphs with equal bins ---------------------------------------\\n\\tprint \\\"No. of pixels with zero counts in sim_dph: \\\",sim_dph[sim_dph==0].size\\n\\tprint \\\"No. of pixels with zero counts in grb_dph(no bkg subtration): \\\",grb_dph[grb_dph==0].size\\n\\t\\n\\t# Generating the array for module number ------------------------------------------------\\n\\tA = ['A'+str(i) for i in range(16)]\\n\\tB = np.flip(['B'+str(i) for i in range(16)],0)\\n\\tC = np.flip(['C'+str(i) for i in range(16)],0)\\n\\tD = ['D'+str(i) for i in range(16)]\\n\\tquad_a = np.reshape(A,(4,4))\\n\\tquad_b = np.reshape(B,(4,4))\\n\\tquad_c = np.reshape(C,(4,4))\\n\\tquad_d = np.reshape(D,(4,4))\\n\\tMod_arr = np.ndarray((8,8),dtype='|S3')\\n\\tMod_arr[:4,:4] = quad_a\\n\\tMod_arr[:4,4:] = quad_b\\n\\tMod_arr[4:,4:] = quad_c\\n\\tMod_arr[4:,:4] = quad_d\\n\\tMod_names = Mod_arr.flatten()\\n\\t#print \\\"Module name array : \\\",Mod_names\\n\\t#-----------------------------------------------------------------------------------------\\n\\t\\t\\n\\tsim_dph = sim_dph*badpix_mask\\n\\tsim_err_dph = sim_err_dph*badpix_mask\\n grb_dph = grb_dph*badpix_mask\\n bkgd_dph = bkgd_dph*badpix_mask\\n\\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\\n\\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\\n\\n\\tsim_bin = resample(sim_dph,pixbin)\\n\\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\\t\\n\\tgrb_bin = resample(grb_dph,pixbin)\\n\\tbkgd_bin = resample(bkgd_dph,pixbin)\\n\\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\\t\\n\\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\\t\\n\\n\\tsim_flat_bin = sim_bin.flatten()\\n\\tsim_err_flat_bin = sim_err_bin.flatten()\\n\\tgrb_flat_bin = grb_bin.flatten()\\n\\tbkgd_flat_bin = bkgd_bin.flatten()\\n\\tgrb_err_flat_bin = grb_err_bin.flatten()\\n\\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\\n\\t\\n\\n\\t # Defining model background and data\\n\\tmodel = sim_flat_bin\\n\\tmodel_copy = np.copy(model)\\n\\tbkgd = bkgd_flat_bin*t_src/t_tot\\n\\tsrc = grb_flat_bin\\n\\t\\n\\tdata = src - bkgd\\n\\tdata_copy = np.copy(data)\\n\\t\\n\\terr_src = grb_err_flat_bin\\n\\terr_bkgd = bkgd_err_flat_bin\\n\\terr_model = sim_err_flat_bin\\n\\terr_model_copy = np.copy(err_model)\\n\\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\terr_data_copy = np.copy(err_data)\\n\\t\\n\\tratio = data/model\\n\\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\\n\\t\\n\\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n\\t\\n\\t # PLotting the comparison plots\\n\\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\\n\\t\\n\\tax1.set_title(\\\"Comparison between simulated and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f}\\\".format(tx=thetax,ty=thetay,c=chi_sq))\\n\\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n\\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation\\\",elinewidth=0.5)\\n\\tax1.legend()\\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\\n\\tax1.set_ylabel('Counts')\\n\\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n\\n\\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\\\"Ratio = Data/Model\\\",elinewidth=0.5)\\n\\tax2.xaxis.set_ticks(np.arange(0,len(data)))\\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\\n\\tax2.tick_params(labelsize=5)\\n\\tax2.axhline(y=1,linewidth=0.5,color='k')\\n\\tax2.legend()\\n\\tax2.set_xlabel('CZT Modules')\\n\\tax2.set_ylabel('Ratio of counts')\\n\\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\\n\\tplt.tight_layout(h_pad=0.0)\\n\\tf.set_size_inches([6.5,10])\\n\\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\\n\\n\\t# Plotting comparison graphs with random binning------------------------------\\n\\t\\n sim_flat = sim_dph.flatten()\\n\\tsim_err_flat = sim_err_dph.flatten()\\n grb_flat = grb_dph.flatten()\\n bkgd_flat = bkgd_dph.flatten()\\n\\tsrc_flat = src_dph.flatten()\\n\\t\\n\\torder = np.random.permutation(np.arange(0,len(sim_flat)))\\n\\t\\n sim_flat = sim_flat[order]\\n\\tsim_err_flat = sim_err_flat[order]\\n\\tgrb_flat = grb_flat[order]\\n\\tbkgd_flat = bkgd_flat[order]\\n\\tsrc_flat = src_flat[order]\\n\\t\\n\\tprint \\\"No. of pixels with zero counts in sim_flat: \\\",sim_flat[sim_flat==0].size\\n\\tprint \\\"No. of pixels with zero counts in src_flat: \\\",src_flat[src_flat==0].size\\n\\t\\n\\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\\n\\tx = np.zeros(len(bins)+2,dtype=np.int64)\\n\\tx[0] = 0\\n\\tx[-1] = len(sim_flat)\\n\\tx[1:-1] = bins\\n\\t\\n\\t#print \\\"The bin edges: \\\",x # ---------------------------------------------------------------\\n\\t\\n\\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\\n\\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\t\\n\\tprint \\\"Total sim_flat_bin : \\\",sim_flat_bin.sum() #-----------------------------------------\\n\\t#print \\\" Max(cumsum) : \\\",max(np.cumsum(sim_flat)) #-----------------------------------------\\n\\n # Defining model background and data\\n model = sim_flat_bin #avg_flat_bin\\n bkgd = bkgd_flat_bin*t_src/t_tot\\n src = grb_flat_bin\\n\\t\\n data = src - bkgd\\n\\n err_src = np.sqrt(src)\\n err_bkgd = np.sqrt(bkgd_flat_bin)\\n err_model = sim_err_flat_bin\\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\t\\n\\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n # PLotting the comparison plots\\n fig = plt.figure()\\n plt.title(\\\"Comparison between simulated and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f} \\\".format(tx=thetax,ty=thetay,c=chi_sq_new))\\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation\\\",elinewidth=0.5)\\n plt.ylabel('Counts')\\n\\tplt.xlabel('Random Bins')\\n\\tplt.xticks(np.arange(0,(len(data)),1))\\n\\tplt.legend()\\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\\n\\n\\t# Plotting observed vs predicted counts------------------------------------------------------\\n\\n\\tfig = plt.figure()\\n\\tplt.title(grbdir + r\\\" : Observed vs Predicted counts with $\\\\chi^2$={cs:0.1f}\\\".format(cs=chi_sq))\\n\\tplt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\\n\\tfor i in range(len(model_copy)):\\n\\t\\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\\n\\tplt.plot(np.arange(-1000,1000),np.arange(-1000,1000),'k',linewidth=0.5)\\n\\tplt.xlim(min(model_copy)-5,max(model_copy)+5)\\n\\tplt.ylim(min(data_copy)-5,max(data_copy)+5)\\n\\tplt.xlabel('Predicted Counts')\\n\\tplt.ylabel('Observed Counts')\\n\\tplt.legend()\\n\\tplt.grid()\\n\\tpdf_file.savefig(fig)\\n\\n\\t# Scaling the model using curve fit =============================================================== \\n\\t\\n\\tparam,pcov = curve_fit(fit_line_int,model_copy,data_copy)\\n\\tscaling = param[0]\\n\\tintercept = param[1]\\n\\t\\n\\t# Plotting the scaled plots ===================================================================\\n\\t# Plotting the comparison graphs with equal bins ---------------------------------------\\n\\n\\tsim_dph = sim_dph*badpix_mask\\n\\tsim_err_dph = sim_err_dph*badpix_mask\\n grb_dph = grb_dph*badpix_mask\\n bkgd_dph = bkgd_dph*badpix_mask\\n\\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\\n\\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\\n\\n\\tsim_bin = resample(sim_dph,pixbin)\\n\\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\\t\\n\\tgrb_bin = resample(grb_dph,pixbin)\\n\\tbkgd_bin = resample(bkgd_dph,pixbin)\\n\\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\\t\\n\\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\\t\\n\\n\\tsim_flat_bin = sim_bin.flatten()\\n\\tsim_err_flat_bin = sim_err_bin.flatten()\\n\\tgrb_flat_bin = grb_bin.flatten()\\n\\tbkgd_flat_bin = bkgd_bin.flatten()\\n\\tgrb_err_flat_bin = grb_err_bin.flatten()\\n\\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\\n\\t\\n\\n\\t # Defining model background and data\\n\\t#model = sim_flat_bin*scaling\\n\\tmodel = sim_flat_bin*scaling + intercept\\n\\tbkgd = bkgd_flat_bin*t_src/t_tot\\n\\tsrc = grb_flat_bin\\n\\t\\n\\tdata = src - bkgd\\n\\t\\n\\terr_src = grb_err_flat_bin\\n\\terr_bkgd = bkgd_err_flat_bin\\n\\t#err_model = sim_err_flat_bin*scaling\\n\\terr_model = sim_err_flat_bin*scaling\\n\\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\t\\n\\tratio = data/model\\n\\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\\n\\t\\n\\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n\\t\\n\\t # PLotting the comparison plots\\n\\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\\n\\t\\n\\tax1.set_title(\\\"Comparison between simulated (scaled) and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f} \\\".format(tx=thetax,ty=thetay,c=chi_sq))\\n\\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n\\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation (scaling = {s:0.2f},offset = {o:0.2f})\\\".format(s=scaling,o=intercept),elinewidth=0.5)\\n\\tax1.legend()\\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\\n\\tax1.set_ylabel('Counts')\\n\\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n\\t\\n\\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\\\"Ratio = Data/Model(scaling = {s:0.2f}, offset={o:0.2f})\\\".format(s=scaling,o=intercept),elinewidth=0.5)\\n\\tax2.xaxis.set_ticks(np.arange(0,len(data)))\\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\\n\\tax2.tick_params(labelsize=5)\\n\\tax2.axhline(y=1,linewidth=0.5,color='k')\\n\\tax2.legend()\\n\\tax2.set_xlabel('CZT Modules')\\n\\tax2.set_ylabel('Ratio of counts')\\n\\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\\n\\tplt.tight_layout(h_pad=0.0)\\n\\tf.set_size_inches([6.5,10])\\n\\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\\n\\n\\t# Plotting comparison graphs with random binning------------------------------\\n\\t\\n sim_flat = sim_dph.flatten()\\n\\tsim_err_flat = sim_err_dph.flatten()\\n grb_flat = grb_dph.flatten()\\n bkgd_flat = bkgd_dph.flatten()\\n\\tsrc_flat = src_dph.flatten()\\n\\t\\n\\torder = np.random.permutation(np.arange(0,len(sim_flat)))\\n\\t\\n sim_flat = sim_flat[order]\\n\\tsim_err_flat = sim_err_flat[order]\\n\\tgrb_flat = grb_flat[order]\\n\\tbkgd_flat = bkgd_flat[order]\\n\\tsrc_flat = src_flat[order]\\n\\t\\n\\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\\n\\tx = np.zeros(len(bins)+2,dtype=np.int64)\\n\\tx[0] = 0\\n\\tx[-1] = len(sim_flat)\\n\\tx[1:-1] = bins\\n\\t\\n\\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\\n\\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\t\\n # Defining model background and data\\n #model = sim_flat_bin*scaling\\n\\tmodel = sim_flat_bin*scaling + intercept\\n bkgd = bkgd_flat_bin*t_src/t_tot\\n src = grb_flat_bin\\n\\t\\n data = src - bkgd\\n\\n err_src = np.sqrt(src)\\n err_bkgd = np.sqrt(bkgd_flat_bin)\\n #err_model = sim_err_flat_bin*scaling\\n\\terr_model = sim_err_flat_bin*scaling\\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\t\\n\\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n # PLotting the comparison plots\\n fig = plt.figure()\\n plt.title(\\\"Comparison between simulated(scaled) and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f} \\\".format(tx=thetax,ty=thetay,c=chi_sq_new))\\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation (scaling = {s:0.2f}, offset = {o:0.2f})\\\".format(s=scaling,o=intercept),elinewidth=0.5)\\n plt.ylabel('Counts')\\n\\tplt.xlabel('Random Bins')\\n\\tplt.xticks(np.arange(0,(len(data)),1))\\n\\tplt.legend()\\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\\n\\n\\n\\t# Plotting observed vs predicted counts--------------------------------------------------------\\n\\n\\tfig = plt.figure()\\n plt.title(grbdir + r\\\" : Observed vs Predicted counts with $\\\\chi^2$ = {cs:0.1f}\\\".format(cs=chi_sq))\\n plt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\\n\\tfor i in range(len(model_copy)):\\t\\n\\t\\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\\n #plt.plot(np.arange(-1000,1000),fit_line(np.arange(-1000,1000),scaling),'k',linewidth=0.5,label='m = {s:0.2f}'.format(s=scaling))\\n\\tplt.plot(np.arange(-1000,1000),fit_line_int(np.arange(-1000,1000),scaling,intercept),'k',linewidth=0.5,label='scaling = {s:0.2f}, offset = {i:0.2f}'.format(s=scaling,i=intercept))\\n\\tplt.plot(np.arange(min(model_copy)-5,max(model_copy)+5),np.ones(len(np.arange(min(model_copy)-5,max(model_copy)+5)))*intercept,'r-',label='intercept',linewidth=0.5)\\n plt.xlim(min(model_copy)-5,max(model_copy)+5)\\n plt.ylim(min(data_copy)-5,max(data_copy)+5)\\n plt.xlabel('Predicted Counts')\\n plt.ylabel('Observed Counts')\\n\\tplt.legend()\\n\\tplt.grid()\\n pdf_file.savefig(fig)\\n\\t\\t\\n\\tprint \\\"===============================================================================================\\\"\\n\\t\\n\\treturn\",\n \"def main():\\n # Data processing and parsing\\n mp_df = pd.read_csv(\\\"melting_points.csv\\\")\\n bp_df = pd.read_csv(\\\"boiling_points.csv\\\")\\n data_processing(mp_df)\\n data_processing(bp_df)\\n # Plots scatterplots of our variables with the boiling and melting point\\n plot_scatter('Mass', 'T_exp', mp_df, 'Molecular Weight', 'Melting Point')\\n plot_scatter('Mass', 'T_exp', bp_df, 'Molecular Weight', 'Boiling Point')\\n plot_scatter('Atom_counts', 'T_exp', mp_df, 'Number of Atoms',\\n 'Melting Point')\\n \\n # Creates models and prints statistics using simply mass and atom counts\\n simple_parameters = ['Mass', 'Atom_counts', 'T_exp']\\n model = ml_df(mp_df, simple_parameters, 0.2)\\n plot_T(model, simple_parameters, \\\"simple_melting.png\\\", 'Melting Point')\\n slope_average(mp_df, simple_parameters, 0.2, 100)\\n model2 = ml_df(bp_df, simple_parameters, 0.2)\\n plot_T(model2, simple_parameters, 'simple_boiling.png', 'Boiling Point')\\n slope_average(bp_df, simple_parameters, 0.2, 100)\\n # Creates models and prints statistics using more variables\\n complex_parameters = [\\\"Mass\\\", \\\"Atom_counts\\\", \\\"C\\\", \\\"H\\\", \\\"Acid\\\", \\\"Alcohol\\\",\\n \\\"Unsaturation\\\", \\\"T_exp\\\"]\\n model3 = ml_df(mp_df, complex_parameters, 0.2)\\n plot_T(model3, complex_parameters, \\\"complex_melting.png\\\", 'Melting Point')\\n slope_average(mp_df, complex_parameters, 0.2, 100)\\n model4 = ml_df(bp_df, complex_parameters, 0.2)\\n plot_T(model4, complex_parameters, \\\"complex_boiling.png\\\", 'Boiling Point')\\n slope_average(bp_df, complex_parameters, 0.2, 100)\\n # Creates models and prints statistics for our model using all available variables\\n complex_parameters2 = [\\\"Mass\\\", \\\"Atom_counts\\\", \\\"C\\\", \\\"H\\\", 'O', 'N', 'F',\\n 'Cl', 'Br', 'I', 'S', 'Si', 'Halide', \\\"Acid\\\", \\\"Alcohol\\\",\\n \\\"Unsaturation\\\", \\\"T_exp\\\"]\\n model = ml_df(mp_df, complex_parameters2, 0.2)\\n plot_T(model, complex_parameters2, \\\"more_complex_melting.png\\\",\\n 'Melting Point')\\n slope_average(mp_df, complex_parameters2, 0.2, 100)\\n # Tries a few different regressor models\\n slope_average(mp_df, complex_parameters2, 0.2, 100, LinearRegression())\\n slope_average(bp_df, complex_parameters2, 0.2, 100, RandomForestRegressor())\",\n \"def test_lcurve_split(self):\\n command = '{0} {1} -g'.format(\\n os.path.join(self.datadir, 'monol_testA_nustar_fpma_ev' +\\n HEN_FILE_EXTENSION),\\n os.path.join(self.datadir, 'monol_testB_nustar_fpmb_ev' +\\n HEN_FILE_EXTENSION))\\n hen.lcurve.main(command.split())\\n new_filename = os.path.join(self.datadir,\\n 'monol_testA_nustar_fpma_gti000_lc' +\\n HEN_FILE_EXTENSION)\\n assert os.path.exists(new_filename)\\n lc = hen.io.load_lcurve(new_filename)\\n gti_to_test = hen.io.load_events(self.first_event_file).gti[0]\\n assert np.allclose(gti_to_test, lc.gti)\",\n \"def main():\\n utl.calibrate(False)\\n undistort(False)\\n edge_detect(False)\\n transform(False)\\n identify_line(False)\\n lane_line(True)\",\n \"def plot(self) -> None:\\n cw_l2_data_list = list(); cw_linf_data_list = list()\\n\\n for model in self.model_list:\\n cw_l2_data_list.append(joblib.load(model + \\\"/stat/mse-rmse-si-mae-cw_l2_1.pkl\\\"))\\n\\n cw_l2_attack = list(zip(self.model_list, cw_l2_data_list))\\n\\n for model in self.model_list:\\n cw_linf_data_list.append(joblib.load(model + \\\"/stat/mse-rmse-si-mae-cw_inf_1.pkl\\\"))\\n\\n cw_linf_attack = list(zip(self.model_list, cw_linf_data_list))\\n\\n # RMSE v.s. MAE over change budget\\n # There will be one graph for each manipulation\\n # CW_L2 ATTACK\\n for datum in cw_l2_attack:\\n ran_color_list = self._random_color_picker(2)\\n fig, axis_1 = plt.subplots()\\n\\n # Generate x_axis\\n x_axis = list()\\n for key in datum[1]:\\n if float(key) not in x_axis:\\n x_axis.append(float(key))\\n\\n x_axis.sort()\\n\\n # Sort data in datum[1]\\n data_dict = self._sort_dict(x_axis, datum[1])\\n\\n # PLOT RMSE ON AXIS 1\\n # Generate y_axis ticks for RMSE\\n rmse_values = list()\\n for key in data_dict:\\n rmse_values.append(data_dict[key][\\\"rmse\\\"])\\n\\n # Generate 10 ticks for the y_axis\\n y_axis_ticks = np.linspace(0.0, 0.6, num=10, endpoint=True)\\n\\n # Plot RMSE\\n axis_1.plot(x_axis, rmse_values, color=ran_color_list[0], linestyle=\\\"solid\\\")\\n axis_1.set_xlabel(\\\"Perturbation Budget\\\")\\n axis_1.set_ylabel(\\\"Root Mean Squared Error (RMSE)\\\", color=ran_color_list[0])\\n axis_1.set_yticks(y_axis_ticks)\\n \\n for tick_label, tick_line in zip(axis_1.get_yticklabels(), axis_1.get_yticklines()):\\n tick_label.set_color(ran_color_list[0])\\n tick_line.set_color(ran_color_list[0])\\n\\n # PLOT MAE ON AXIS 2\\n axis_2 = axis_1.twinx()\\n\\n # Generate y-axis ticks for MAE\\n mae_values = list()\\n for key in data_dict:\\n mae_values.append(data_dict[key][\\\"mae\\\"])\\n\\n\\n # Plot MAE\\n axis_2.plot(x_axis, mae_values, color=ran_color_list[1], linestyle=\\\"solid\\\")\\n axis_2.set_ylabel(\\\"Mean Absolute Error (MAE)\\\", color=ran_color_list[1])\\n axis_2.set_yticks(y_axis_ticks)\\n \\n for tick_label, tick_line in zip(axis_2.get_yticklabels(), axis_2.get_yticklines()):\\n tick_label.set_color(ran_color_list[1])\\n tick_line.set_color(ran_color_list[1])\\n\\n model_tag = datum[0].split(\\\"/\\\"); model_tag = model_tag[-1]\\n plt.savefig(self.save_path + \\\"/{}_rmse-and-mae-as-perturbation-budget-increases-for-cw_l2-attack-on-model-{}.png\\\".format(self.plot_name, model_tag), \\n bbox_inches=\\\"tight\\\")\\n plt.close()\\n\\n # CW_Linf ATTACK\\n for datum in cw_linf_attack:\\n ran_color_list = self._random_color_picker(2)\\n fig, axis_1 = plt.subplots()\\n\\n # Generate x_axis\\n x_axis = list()\\n for key in datum[1]:\\n if float(key) not in x_axis:\\n x_axis.append(float(key))\\n\\n x_axis.sort()\\n\\n # Sort data in datum[1]\\n data_dict = self._sort_dict(x_axis, datum[1])\\n\\n # PLOT RMSE ON AXIS 1\\n # Generate y_axis ticks for RMSE\\n rmse_values = list()\\n for key in data_dict:\\n rmse_values.append(data_dict[key][\\\"rmse\\\"])\\n\\n # Plot RMSE\\n axis_1.plot(x_axis, rmse_values, color=ran_color_list[0], linestyle=\\\"solid\\\")\\n axis_1.set_xlabel(\\\"Perturbation Budget\\\")\\n axis_1.set_ylabel(\\\"Root Mean Squared Error (RMSE)\\\", color=ran_color_list[0])\\n axis_1.set_yticks(y_axis_ticks)\\n\\n for tick_label, tick_line in zip(axis_1.get_yticklabels(), axis_1.get_yticklines()):\\n tick_label.set_color(ran_color_list[0])\\n tick_line.set_color(ran_color_list[0])\\n\\n # PLOT MAE ON AXIS 2\\n axis_2 = axis_1.twinx()\\n\\n # Generate y-axis ticks for MAE\\n mae_values = list()\\n for key in data_dict:\\n mae_values.append(data_dict[key][\\\"mae\\\"])\\n\\n # Plot MAE\\n axis_2.plot(x_axis, mae_values, color=ran_color_list[1], linestyle=\\\"solid\\\")\\n axis_2.set_ylabel(\\\"Mean Absolute Error (MAE)\\\", color=ran_color_list[1])\\n axis_2.set_yticks(y_axis_ticks)\\n \\n for tick_label, tick_line in zip(axis_2.get_yticklabels(), axis_2.get_yticklines()):\\n tick_label.set_color(ran_color_list[1])\\n tick_line.set_color(ran_color_list[1])\\n \\n model_tag = datum[0].split(\\\"/\\\"); model_tag = model_tag[-1]\\n plt.savefig(self.save_path + \\\"/{}_rmse-and-mae-as-perturbation-budget-increases-for-cw_linf-attack-on-model-{}.png\\\".format(self.plot_name, model_tag),\\n bbox_inches=\\\"tight\\\")\\n plt.close()\\n \\\"RMSE and MAE as Perturbation Budget increases for CW_Linf attack on model {}\\\".format(model_tag)\\n \\n # Scattter Index over the change budget\\n # All the manipulations will be put on the same graph.\\n # CW_L2 ATTACK\\n plt.figure()\\n plt.xlabel(\\\"Perturbation Budget\\\"); plt.ylabel(\\\"Scatter Index\\\")\\n ran_color_list = self._random_color_picker(len(cw_l2_attack)); i = 0\\n\\n # Find maximum scatter index value\\n scatter_values = list()\\n for datum in cw_l2_attack:\\n for key in datum[1]:\\n scatter_values.append(datum[1][key][\\\"scatter_index\\\"])\\n\\n # Generate y_axis ticks; generate 10 ticks\\n y_axis_ticks = np.linspace(0.0, float(Decimal(str(max(scatter_values))) + Decimal(\\\"0.1\\\")), num=10, endpoint=True)\\n plt.yticks(y_axis_ticks)\\n\\n # Generate x_axis\\n x_axis = list()\\n for datum in cw_l2_attack:\\n for key in datum[1]:\\n if float(key) not in x_axis:\\n x_axis.append(float(key))\\n\\n x_axis.sort()\\n\\n formal_names = FormalNameMap()\\n for datum in cw_l2_attack:\\n values = list()\\n data_dict = self._sort_dict(x_axis, datum[1])\\n for key in data_dict:\\n values.append(data_dict[key][\\\"scatter_index\\\"])\\n\\n # Append values to the plot\\n line_name = datum[0].split(\\\"/\\\"); line_name = line_name[-1]\\n formal_name = formal_names.getformalname(line_name) if formal_names.hasname(line_name) else line_name\\n if \\\"vanilla\\\" in line_name:\\n plt.plot(x_axis, values, color=ran_color_list[i], linewidth=3, linestyle=self._random_linestyle(), label=formal_name)\\n\\n else:\\n plt.plot(x_axis, values, color=ran_color_list[i], linestyle=self._random_linestyle(), label=formal_name)\\n \\n i += 1\\n\\n plt.legend()\\n plt.savefig(self.save_path + \\\"/{}_scatter-index-as-perturbation-budget-increases-for-cw_l2-attack.png\\\".format(self.plot_name),\\n bbox_inches=\\\"tight\\\")\\n plt.close()\\n\\n # CW_Linf ATTACK\\n plt.figure()\\n plt.xlabel(\\\"Perturbation Budget\\\"); plt.ylabel(\\\"Scatter Index\\\")\\n ran_color_list = self._random_color_picker(len(cw_linf_attack)); i = 0\\n\\n # Find maximum scatter index value\\n scatter_values = list()\\n for datum in cw_linf_attack:\\n for key in datum[1]:\\n scatter_values.append(datum[1][key][\\\"scatter_index\\\"])\\n\\n # Generate y_axis ticks; generate 10 ticks\\n y_axis_ticks = np.linspace(0.0, float(Decimal(str(max(scatter_values))) + Decimal(\\\"0.1\\\")), num=10, endpoint=True)\\n plt.yticks(y_axis_ticks)\\n\\n # Generate x_axis\\n x_axis = list()\\n for datum in cw_l2_attack:\\n for key in datum[1]:\\n if float(key) not in x_axis:\\n x_axis.append(float(key))\\n\\n x_axis.sort()\\n\\n formal_names = FormalNameMap()\\n for datum in cw_linf_attack:\\n values = list()\\n data_dict = self._sort_dict(x_axis, datum[1])\\n for key in data_dict:\\n values.append(data_dict[key][\\\"scatter_index\\\"])\\n\\n # Append values to the plot\\n line_name = datum[0].split(\\\"/\\\"); line_name = line_name[-1]\\n formal_name = formal_names.getformalname(line_name) if formal_names.hasname(line_name) else line_name\\n if \\\"vanilla\\\" in line_name:\\n plt.plot(x_axis, values, color=ran_color_list[i], linewidth=3, linestyle=self._random_linestyle(), label=formal_name)\\n\\n else: \\n plt.plot(x_axis, values, color=ran_color_list[i], linestyle=self._random_linestyle(), label=formal_name)\\n \\n i += 1\\n\\n plt.legend()\\n plt.savefig(self.save_path + \\\"/{}_scatter-index-as-perturbation-budget-increases-for-cw_linf-attack.png\\\".format(self.plot_name),\\n bbox_inches=\\\"tight\\\")\\n plt.close()\",\n \"def main(\\n error_band_dir,\\n output_dir,\\n indep_var,\\n ivar_start,\\n ivar_stop,\\n ivar_step,\\n param_list,\\n observable_list,\\n Lambda_b,\\n lambda_mult,\\n p_decimal_list,\\n orders,\\n ignore_orders,\\n interaction,\\n X_ref_hash,\\n prior_set,\\n h,\\n cbar_lower,\\n cbar_upper,\\n sigma,\\n convention\\n ):\\n\\n color_dict = {\\n \\\"LOp\\\": plt.get_cmap(\\\"Greys\\\"),\\n \\\"LO\\\": plt.get_cmap(\\\"Purples\\\"),\\n \\\"NLO\\\": plt.get_cmap(\\\"Oranges\\\"),\\n \\\"N2LO\\\": plt.get_cmap(\\\"Greens\\\"),\\n \\\"N3LO\\\": plt.get_cmap(\\\"Blues\\\"),\\n \\\"N4LO\\\": plt.get_cmap(\\\"Reds\\\")\\n }\\n\\n fill_transparency = 1\\n x = np.arange(ivar_start, ivar_stop, ivar_step)\\n\\n fig = plt.figure(figsize=(3.4, 3.4))\\n ax = fig.add_subplot(1, 1, 1)\\n ax.set_zorder(15)\\n ax.set_axisbelow(False)\\n\\n if indep_var == \\\"theta\\\":\\n param_var = \\\"energy\\\"\\n indep_var_label = r\\\"$\\\\theta$ (deg)\\\"\\n param_var_label = r\\\"$E_{\\\\mathrm{lab}}\\\"\\n param_var_units = r\\\"$ MeV\\\"\\n xmin = 0\\n xmax = 180\\n else:\\n param_var = \\\"theta\\\"\\n indep_var_label = r\\\"$E$ (MeV)\\\"\\n param_var_label = r\\\"$\\\\theta\\\"\\n param_var_units = r\\\"^\\\\circ$\\\"\\n xmin = 0\\n xmax = 350\\n\\n for observable in observable_list:\\n for param in param_list:\\n if observable == [\\\"t\\\", \\\"t\\\", \\\"t\\\", \\\"t\\\"] or \\\\\\n (observable == [\\\"0\\\", \\\"0\\\", \\\"0\\\", \\\"0\\\"] and indep_var == \\\"energy\\\"):\\n ax.set_yscale(\\\"log\\\", nonposy='clip')\\n\\n ax.set_xlabel(indep_var_label)\\n # ax.set_ylabel('')\\n\\n # if indep_var == \\\"theta\\\":\\n # major_tick_spacing = 60\\n # minor_tick_spacing = 20\\n # ax.xaxis.set_major_locator(\\n # ticker.MultipleLocator(major_tick_spacing))\\n # ax.xaxis.set_minor_locator(\\n # ticker.MultipleLocator(minor_tick_spacing))\\n\\n if indep_var == \\\"energy\\\":\\n major_ticks = np.arange(0, 351, 100)\\n # minor_ticks = np.arange(50, 351, 100)\\n x_minor_locator = AutoMinorLocator(n=2)\\n elif indep_var == \\\"theta\\\":\\n major_ticks = np.arange(0, 181, 60)\\n # minor_ticks = np.arange(0, 181, 20)\\n x_minor_locator = AutoMinorLocator(n=3)\\n\\n ax.xaxis.set_minor_locator(x_minor_locator)\\n ax.set_xticks(major_ticks)\\n\\n # Create the description box\\n # text_str = r\\\"$C_{\\\" + observable[0] + observable[1] + \\\\\\n # observable[2] + observable[3] + r\\\"}$\\\" + \\\", \\\" # + \\\"\\\\n\\\"\\n text_str = indices_to_observable_name(observable)\\n if observable == ['t', 't', 't', 't']:\\n text_str += \\\" (mb)\\\"\\n elif observable == ['0', '0', '0', '0']:\\n text_str += \\\" (mb/sr)\\\"\\n\\n # Probably don't include this extra info. Leave for caption.\\n #\\n # text_str += \\\", \\\"\\n # if observable != ['t', 't', 't', 't']:\\n # text_str += \\\", \\\" + param_var_label + r\\\" = \\\" + str(param) + param_var_units + \\\", \\\" # + \\\"\\\\n\\\"\\n # text_str += r\\\"$\\\\Lambda_b = \\\" + str(lambda_mult*Lambda_b) + r\\\"$ MeV\\\"\\n\\n # Don't put in a text box\\n #\\n # ax.text(.5, .96, text_str,\\n # horizontalalignment='center',\\n # verticalalignment='top',\\n # multialignment='center',\\n # transform=ax.transAxes,\\n # bbox=dict(facecolor='white', alpha=1, boxstyle='square', pad=.5),\\n # zorder=20)\\n\\n # Don't put in the title\\n # plt.title(text_str, fontsize=10)\\n\\n # Instead use y axis\\n ax.set_ylabel(text_str, fontsize=10)\\n legend_patches = []\\n\\n try:\\n npwa_name = npwa_filename(observable, param_var, param)\\n npwa_file = DataFile().read(os.path.join(\\\"../npwa_data/\\\", npwa_name))\\n npwa_plot, = ax.plot(npwa_file[0], npwa_file[1],\\n color=\\\"black\\\", linewidth=1,\\n label=\\\"NPWA\\\", zorder=10,\\n linestyle=\\\"--\\\")\\n except FileNotFoundError:\\n npwa_plot = None\\n\\n # First get global min/max of all orders\\n for i, order in enumerate(orders):\\n # obs_name = observable_filename(\\n # observable, indep_var, ivar_start, ivar_stop,\\n # ivar_step, param_var, param, order)\\n # dob_name = dob_filename(p_decimal_list[0], Lambda_b, obs_name)\\n dob_name = dob_filename(\\n observable, indep_var, ivar_start, ivar_stop,\\n ivar_step, param_var, param, order, ignore_orders,\\n Lambda_b, lambda_mult, X_ref_hash,\\n p_decimal_list[0], prior_set, h, convention, None,\\n cbar_lower, cbar_upper, sigma,\\n potential_info=None)\\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\\n if i == 0:\\n obs = dob_file[1]\\n obs_min = np.min(obs)\\n obs_max = np.max(obs)\\n else:\\n old_obs = obs\\n obs = dob_file[1]\\n # Probably the worst way to do this.\\n obs_min = min(np.min(np.minimum(old_obs, obs)), obs_min)\\n obs_max = max(np.max(np.maximum(old_obs, obs)), obs_max)\\n\\n # Decide the padding above/below the lines\\n # This weights values far from 0 more heavily.\\n # ymin = obs_min - .25 * abs(obs_min)\\n # ymax = obs_max + .25 * abs(obs_max)\\n ymin = -1\\n ymax = 20\\n ax.set_ylim([ymin, ymax])\\n # ax.set_xlim([ivar_start, ivar_stop-1])\\n ax.set_xlim([xmin, xmax])\\n\\n # Start layering the plots\\n for i, order in enumerate(orders):\\n # obs_name = observable_filename(\\n # observable, indep_var, ivar_start, ivar_stop,\\n # ivar_step, param_var, param, order)\\n # dob_name = dob_filename(p_decimal_list[0], Lambda_b, obs_name)\\n dob_name = dob_filename(\\n observable, indep_var, ivar_start, ivar_stop,\\n ivar_step, param_var, param, order, ignore_orders,\\n Lambda_b, lambda_mult, X_ref_hash,\\n p_decimal_list[0], prior_set, h, convention, None,\\n cbar_lower, cbar_upper, sigma,\\n potential_info=None)\\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\\n\\n # Plot the lines\\n obs = dob_file[1]\\n ax.plot(x, obs, color=color_dict[order](.99), zorder=i)\\n\\n # Plot the error bands\\n for band_num, p in enumerate(sorted(p_decimal_list, reverse=True)):\\n # dob_name = dob_filename(p, Lambda_b, obs_name)\\n dob_name = dob_filename(\\n observable, indep_var, ivar_start, ivar_stop,\\n ivar_step, param_var, param, order, ignore_orders,\\n Lambda_b, lambda_mult, X_ref_hash,\\n p, prior_set, h, convention, None,\\n cbar_lower, cbar_upper, sigma,\\n potential_info=None)\\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\\n obs_lower = dob_file[2]\\n obs_upper = dob_file[3]\\n ax.fill_between(\\n x, obs_lower, obs_upper,\\n facecolor=color_dict[order](\\n (band_num + 1) / (len(p_decimal_list) + 1)\\n ),\\n color=color_dict[order](\\n (band_num + 1) / (len(p_decimal_list) + 1)\\n ),\\n alpha=fill_transparency, interpolate=True, zorder=i)\\n\\n # Use block patches instead of lines\\n # Use innermost \\\"dark\\\" color of bands for legend\\n # legend_patches.append(\\n # mp.patches.Patch(\\n # color=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\\n # label=order,\\n # ))\\n legend_patches.append(\\n mpatches.Rectangle(\\n (1, 1), 0.25, 0.25,\\n # color=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\\n edgecolor=color_dict[order](.9),\\n facecolor=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\\n label=order,\\n linewidth=1\\n ))\\n\\n if npwa_plot is None:\\n my_handles = legend_patches\\n handler_dict = dict(zip(my_handles, [HandlerSquare() for i in legend_patches]))\\n else:\\n my_handles = [npwa_plot, *legend_patches]\\n squares = [HandlerSquare() for i in legend_patches]\\n line = HandlerLine2D(marker_pad=1, numpoints=None)\\n handler_dict = dict(zip(my_handles, [line] + squares))\\n\\n ax.legend(loc=\\\"best\\\", handles=my_handles,\\n handler_map=handler_dict,\\n handletextpad=.8,\\n handlelength=.6,\\n fontsize=8)\\n\\n # Squeeze and save it\\n plt.tight_layout()\\n # plot_name = plot_obs_error_bands_filename(\\n # observable, indep_var, ivar_start, ivar_stop,\\n # ivar_step, param_var, param, orders[:i+1],\\n # Lambda_b, p_decimal_list)\\n plot_name = plot_obs_error_bands_filename(\\n observable, indep_var, ivar_start, ivar_stop, ivar_step,\\n param_var, param, orders[:i+1], ignore_orders, Lambda_b,\\n lambda_mult, X_ref_hash, p_decimal_list,\\n prior_set, h, convention, None, cbar_lower, cbar_upper,\\n sigma, potential_info=None)\\n fig.savefig(os.path.join(output_dir, plot_name), bbox_inches=\\\"tight\\\")\\n\\n call([\\\"epstopdf\\\", os.path.join(output_dir, plot_name)])\\n call([\\\"rm\\\", os.path.join(output_dir, plot_name)])\\n\\n # Clear the axes for the next observable/parameter.\\n plt.cla()\",\n \"def plotSnr2Density_TT_lines(self):\\n # ell values for phi\\n #L = np.logspace(np.log10(10.), np.log10(1.e4), 5, 10.)\\n #L = np.array([10., 1.e2, 5.e2])\\n L = [100.]\\n \\n # ell values for T\\n L1 = np.logspace(np.log10(10.), np.log10(6.e4), 501, 10.)\\n\\n fig=plt.figure(0)\\n ax=fig.add_subplot(111)\\n #\\n for iL in range(len(L)):\\n f = lambda l1: self.snr2DensitySymLowL_TT(L[iL], l1)\\n snr2Density = np.array(map(f, L1))\\n # expected value if only counting number of modes\\n dNLkappadlnl_theory = L1**2 / (np.pi)# * L[iL]**4\\n\\n # save the values for future use\\n data = np.zeros((len(L1), 3))\\n data[:,0] = L1.copy()\\n data[:,1] = snr2Density\\n data[:,2] = dNLkappadlnl_theory\\n path = \\\"./output/cmblensrec/\\\"+self.CMB.name+\\\"/dsnr2_dlnl_L\\\"+intExpForm(L[iL])+\\\".txt\\\"\\n print \\\"saving to \\\"+path\\n np.savetxt(path, data)\\n \\n # normalize both, for plot\\n dNLkappadlnl_theory /= np.max(snr2Density)\\n snr2Density /= np.max(snr2Density)#[np.where(L1 <= 5.e3)]) # just to rescale in plot\\n #ax.plot(L1, snr2Density, lw=2, c=plt.cm.jet(1.*iL/len(L)), label=r'$L_\\\\phi=$'+str(int(L[iL])))\\n ax.plot(L1, snr2Density, lw=2, c='r', label=r'$L_\\\\phi=$'+str(int(L[iL])))\\n ax.plot(L1, dNLkappadlnl_theory, 'r', ls='--', lw=1)\\n #\\n ax.legend(loc=1)\\n ax.set_xscale('log', nonposx='clip')\\n ax.set_xlabel(r'$\\\\ell_T$')\\n ax.set_ylabel(r'$d\\\\text{SNR}(\\\\phi_L)^2/d\\\\text{ln}\\\\ell_T$')\\n #ax.set_xlim((1., 5.e3))\\n ax.set_ylim((0., 1.1))\\n #\\n path = \\\"./figures/cmblensrec/dsnr2_dl_test.pdf\\\"\\n fig.savefig(path, bbox_inches='tight')\\n \\n plt.show()\\n\\n return L1, snr2Density\",\n \"def figure3():\\n\\n plot_settings = {'y_limits': [-75, -50],\\n 'x_limits': None,\\n 'y_ticks': [-75, -70, -65, -60, -55, -50],\\n 'locator_size': 2.5,\\n 'y_label': 'Voltage (mV)',\\n 'x_ticks': [],\\n 'scale_size': 2,\\n 'x_label': \\\"\\\",\\n 'scale_loc': 3,\\n 'figure_name': 'figure_3',\\n 'legend': ['0.1 $\\\\mu$S', '0.125 $\\\\mu$S', '0.15 $\\\\mu$S'],\\n 'legend_size': 8,\\n 'legend_location': 4,\\n 'y_on': True}\\n line_styles = ['-', 'dotted', '-.']\\n\\n plt.figure(figsize=(5, 3))\\n plt.subplot(1, 2, 1) # Generate subplot 1 (left)\\n for ix, g_t_bar in enumerate([0.1, 0.125, 0.15]):\\n t, y = solver(6, g_t_bar=g_t_bar, t_start=0.5)\\n plt.plot(t, y[:, 0], c='k', linestyle=line_styles[ix])\\n alter_figure(plot_settings) # Alter figure for publication\\n\\n plt.subplot(1, 2, 2) # Generate subplot 2 (right)\\n for ix, i_bias_on in enumerate([0, -0.1, -0.2]):\\n \\\"\\\"\\\"\\n First step (hyperpolarizing)\\n \\\"\\\"\\\"\\n t1 = 1000\\n duration = t1\\n\\n t, y_hold = solver(t1, duration=duration, i_bias_on=i_bias_on)\\n y_0 = y_hold[-1, :] # Create new initial conditions\\n\\n \\\"\\\"\\\"\\n Second step (current pulse)\\n \\\"\\\"\\\"\\n t1 = 20\\n t, y = solver(t1, t_start=0, y_hold=y_0)\\n v = y[:, 0]\\n\\n \\\"\\\"\\\"\\n Append the end of the first step onto the second step (for plotting purposes)\\n \\\"\\\"\\\"\\n len_pre = 100\\n t = np.concatenate((np.linspace(-len_pre * np.diff(t)[0], -np.diff(t)[0], len_pre), t))\\n v_bar = np.concatenate((y_hold[-len_pre:, 0], v))\\n v_bar = v_bar - v_bar[0] + -7.16300325e+01 # Align solution to initial condition of the \\\"standard simulation\\\"\\n\\n plt.plot(t, v_bar, c='k', linestyle=line_styles[ix])\\n\\n plot_settings['y_ticks'] = []\\n plot_settings['y_label'] = \\\"\\\"\\n plot_settings['x_limits'] = [-1, 20]\\n plot_settings['legend'] = ['-72 mV', '-75 mV', '-78 mV']\\n plot_settings['scale_size'] = 5\\n plot_settings['legend_location'] = 1\\n alter_figure(plot_settings, close=True)\",\n \"def main(args=None):\\n opt = _parse_options(args)\\n plot_analysis(opt)\\n plt.show()\",\n \"def display1(*args):\\n #----------*----------* # unpack\\n twiss_func = args[0]\\n lat_plot = args[3]\\n #-------------------- beta x,y & dispersion x\\n s = [twiss_func(i,'s') for i in range(twiss_func.nbpoints)]\\n bx = [twiss_func(i,'bx') for i in range(twiss_func.nbpoints)]\\n by = [twiss_func(i,'by') for i in range(twiss_func.nbpoints)]\\n dx = [twiss_func(i,'dx') for i in range(twiss_func.nbpoints)]\\n #-------------------- lattice viseo\\n vzero = [0. for i in range(lat_plot.nbpoints)] # zero line\\n vis_abszisse = [lat_plot(i,'s') for i in range(lat_plot.nbpoints)]\\n vis_ordinate = [lat_plot(i,'viseo') for i in range(lat_plot.nbpoints)]\\n #-------------------- figure frame\\n width=14; height=7.6\\n plt.figure(num=0,figsize=(width,height),facecolor='#eaecef',tight_layout=False)\\n\\n #-------------------- transverse X\\n splot111=plt.subplot(111)\\n splot111.set_title('beta functions')\\n plt.plot(s,bx, label=r'$\\\\beta_x$ [m]', color='red', linestyle='-') # beta x\\n plt.plot(s,by, label=r'$\\\\beta_y$ [m]', color='blue', linestyle='-') # beta y\\n plt.plot(s,dx, label=r'$\\\\eta_x$ [m]' , color='green',linestyle='-') # dispersion x\\n vscale=splot111.axis()[3]*0.25\\n viseoz = [x*vscale for x in vis_ordinate]\\n plt.plot(vis_abszisse,viseoz,label='',color='black')\\n plt.plot(vis_abszisse,vzero,color='black')\\n plt.legend(loc='lower right',fontsize='x-small')\",\n \"def main():\\r\\n\\t\\\"\\\"\\\"\\r\\n\\tmain() parses arguments to pass to SimPlt\\r\\n\\t\\\"\\\"\\\"\\r\\n\\ttry: b = float(argv[1])\\r\\n\\texcept IndexError: b = 1.0\\r\\n\\t\\r\\n\\ttry: X = float(argv[2])\\r\\n\\texcept IndexError: X = 1.0\\r\\n\\t\\t\\r\\n\\tparser = optparse.OptionParser()\\r\\n\\t\\r\\n\\tparser.add_option('-t','--timefac',\\r\\n dest=\\\"timefac\\\",\\r\\n default=1.0,\\r\\n type=\\\"float\\\",\\r\\n )\\r\\n\\tparser.add_option('--BW',\\r\\n dest=\\\"BW\\\",\\r\\n default=False,\\r\\n action=\\\"store_true\\\"\\r\\n )\\r\\n\\t\\t\\t\\t \\r\\n\\tparser.add_option('-s','--smooth',\\r\\n dest=\\\"smooth\\\",\\r\\n default=1.0,\\r\\n type=\\\"float\\\",\\r\\n )\\r\\n\\t\\t\\t\\t \\r\\n\\topt = parser.parse_args()[0]\\r\\n \\t\\t\\r\\n\\tLE_SimPlt(b, X, opt.timefac, opt.BW, opt.smooth)\\r\\n\\r\\n\\treturn\",\n \"def test_learning_curves():\\n\\n p = pipeline.Pipeline(\\n FX_TRAIN,\\n FX_TEST,\\n FX_LOOKUP,\\n RESULTS_DIR\\n )\\n\\n data = p.learning_curves()\",\n \"def test_plot1(plot=1, version='scalar'):\\n Lx = 10\\n Ly = 10\\n c = 1.0\\n\\n def I2(x, y):\\n return exp(-(x-Lx/2.0)**2/2.0 -(y-Ly/2.0)**2/2.0)\\n def f(x, y, t):\\n return 0.0\\n def bc(x, y, t):\\n return 0.0\\n\\n I2 = StringFunction('exp(-(x-Lx/2.0)**2/2.0 -(y-Ly/2.0)**2/2.0)',\\n independent_variables=('x', 'y'),\\n Lx=Lx, Ly=Ly, globals=globals())\\n f = StringFunction('0.0', independent_variables=('x', 'y', 't'),\\n globals=globals())\\n bc = StringFunction('0.0', independent_variables=('x', 'y', 't'),\\n globals=globals())\\n if plot:\\n g = Gnuplot.Gnuplot(persist=1)\\n g('set parametric')\\n g('set data style lines')\\n g('set hidden')\\n g('set contour base')\\n g('set zrange [-0.7:0.7]') # nice plot...\\n \\n def action(u, xv, yv, t):\\n #print 'action, t=',t,'\\\\nu=',u, '\\\\nx=',x, '\\\\ny=', y\\n if plot:\\n data = Gnuplot.GridData(u, xv[:,0], yv[0,:], binary=0)\\n g.splot(data)\\n g('set title \\\"t=%g\\\"' % t)\\n if plot == 2:\\n g.hardcopy(filename='tmp_%020f.ps' % t, enhanced=1, mode='eps',\\n color=0, fontname='Times-Roman', fontsize=14)\\n time.sleep(1)\\n time.sleep(0.2) # pause between frames\\n\\n implementation = {'ic': version, 'inner': version, 'bc': version}\\n nx = 40; ny = 40; tstop = 20 # tstop = 700\\n print 'test_plot1:', f, bc, I2\\n dt, t_ic, t_inner, t_bc = \\\\\\n solver(I2, f, c, bc, Lx, Ly, nx, ny, 0, tstop,\\n user_action=action, implementation=implementation)\\n print 'time ic: %s, time scheme: %s, time bc: %s' % (t_ic, t_inner, t_bc)\\n time.sleep(3)\",\n \"def plot_curves():\\n lm = np.arange(0, 1.8, .01)\\n vm = np.arange(-1.2, 1.2, .01)\\n lt = np.arange(0, 1.07, .01)\\n plt.subplot(2,1,1)\\n plt.plot(lm, force_length_muscle(lm), 'r')\\n plt.plot(lm, force_length_parallel(lm), 'g')\\n plt.plot(lt, force_length_tendon(lt), 'b')\\n plt.legend(('CE', 'PE', 'SE'))\\n plt.xlabel('Normalized length')\\n plt.ylabel('Force scale factor')\\n plt.subplot(2, 1, 2)\\n plt.plot(vm, force_velocity_muscle(vm), 'k')\\n plt.xlabel('Normalized muscle velocity')\\n plt.ylabel('Force scale factor')\\n plt.tight_layout()\\n plt.show()\",\n \"def test_fits_lcurve1(self):\\n lcurve_ftools = os.path.join(self.datadir,\\n 'lcurve_ftools_lc' +\\n HEN_FILE_EXTENSION)\\n\\n lcurve_mp = os.path.join(self.datadir,\\n 'lcurve_lc' +\\n HEN_FILE_EXTENSION)\\n\\n lcdata_mp = hen.io.load_data(lcurve_mp)\\n lcdata_ftools = hen.io.load_data(lcurve_ftools)\\n\\n lc_mp = lcdata_mp['counts']\\n\\n lenmp = len(lc_mp)\\n lc_ftools = lcdata_ftools['counts']\\n lenftools = len(lc_ftools)\\n goodlen = min([lenftools, lenmp])\\n\\n diff = lc_mp[:goodlen] - lc_ftools[:goodlen]\\n\\n assert np.all(np.abs(diff) <= 1e-3), \\\\\\n 'Light curve data do not coincide between FITS and HEN'\",\n \"def _plot_likelihood_der1_eta(profile_likelihood, result):\\n\\n load_plot_settings()\\n\\n # Optimal point\\n optimal_eta = result['hyperparam']['eta']\\n optimal_scale = result['hyperparam']['scale']\\n profile_likelihood.mixed_cor.set_scale(optimal_scale)\\n\\n if (optimal_eta != 0) and (not numpy.isinf(optimal_eta)):\\n plot_optimal_eta = True\\n else:\\n plot_optimal_eta = False\\n\\n # Specify which portion of eta array be high resolution for plotting\\n # in the inset axes\\n log_eta_start = -3\\n log_eta_end = 3\\n\\n if plot_optimal_eta:\\n log_eta_start_high_res = numpy.floor(numpy.log10(optimal_eta))\\n log_eta_end_high_res = log_eta_start_high_res + 2\\n\\n # Arrays of low and high resolutions of eta\\n eta_high_res = numpy.logspace(log_eta_start_high_res,\\n log_eta_end_high_res, 100)\\n eta_low_res_left = numpy.logspace(log_eta_start,\\n log_eta_start_high_res, 50)\\n eta_low_res_right = numpy.logspace(log_eta_end_high_res,\\n log_eta_end, 20)\\n\\n # array of eta as a mix of low and high res\\n if log_eta_end_high_res >= log_eta_end:\\n eta = numpy.r_[eta_low_res_left, eta_high_res]\\n else:\\n eta = numpy.r_[eta_low_res_left, eta_high_res, eta_low_res_right]\\n\\n else:\\n eta = numpy.logspace(log_eta_start, log_eta_end, 100)\\n\\n # Compute derivative of L\\n dell_deta = numpy.zeros(eta.size)\\n for i in range(eta.size):\\n dell_deta[i] = profile_likelihood._likelihood_der1_eta(\\n profile_likelihood._eta_to_hyperparam(eta[i]))\\n\\n # Compute upper and lower bound of derivative\\n dell_deta_upper_bound, dell_deta_lower_bound = \\\\\\n profile_likelihood.approx.bounds_der1_eta(eta)\\n\\n # Compute the approximation of first derivative, using both first and\\n # second order approximation\\n try:\\n # eta_high_res might not be defined, depending on plot_optimal_eta\\n x = eta_high_res\\n except NameError:\\n x = numpy.logspace(1, log_eta_end, 100)\\n\\n # To compute both first and second order approximation relations, compute\\n # the second orders first bemuse it also computes the first order.\\n appr_maxima_ord2 = profile_likelihood.approx.maximaize_likelihood(degree=2)\\n appr_maxima_ord1 = profile_likelihood.approx.maximaize_likelihood(degree=1)\\n appr_dell_deta_ord2 = profile_likelihood.approx._likelihood_der1_eta(\\n x, degree=2)\\n appr_dell_deta_ord1 = profile_likelihood.approx._likelihood_der1_eta(\\n x, degree=1)\\n\\n # Main plot\\n fig, ax1 = plt.subplots(figsize=(6, 4.5))\\n axes = [ax1]\\n\\n ax1.semilogx(eta, dell_deta_upper_bound, '--', color='black',\\n label='Upper bound')\\n ax1.semilogx(eta, dell_deta_lower_bound, '-.', color='black',\\n label='Lower bound')\\n ax1.semilogx(eta, dell_deta, color='black', label='Exact')\\n if plot_optimal_eta:\\n ax1.semilogx(optimal_eta, 0, '.', marker='o', markersize=4,\\n color='black')\\n\\n # Min of plot limit\\n # ax1.set_yticks(numpy.r_[numpy.arange(-120, 1, 40), 20])\\n max_plot = numpy.max(dell_deta)\\n max_plot_lim = numpy.ceil(numpy.abs(max_plot)/10.0) * \\\\\\n 10.0*numpy.sign(max_plot)\\n\\n min_plot_lim1 = -100\\n ax1.set_yticks(numpy.array([min_plot_lim1, 0, max_plot_lim]))\\n ax1.set_ylim([min_plot_lim1, max_plot_lim])\\n ax1.set_xlim([eta[0], eta[-1]])\\n ax1.set_xlabel(r'$\\\\eta$')\\n ax1.set_ylabel(r'$\\\\mathrm{d} \\\\ell_{\\\\hat{\\\\sigma}^2(\\\\eta)}' +\\n r'(\\\\eta)/\\\\mathrm{d} \\\\eta$')\\n ax1.set_title('Derivative of Log Marginal Likelihood Function')\\n ax1.grid(True)\\n # ax1.legend(loc='upper left', frameon=False)\\n ax1.patch.set_facecolor('none')\\n\\n # Inset plot\\n if plot_optimal_eta:\\n ax2 = plt.axes([0, 0, 1, 1])\\n axes.append(ax2)\\n\\n # Manually set position and relative size of inset axes within ax1\\n ip = InsetPosition(ax1, [0.43, 0.39, 0.5, 0.5])\\n ax2.set_axes_locator(ip)\\n # Mark the region corresponding to the inset axes on ax1 and draw\\n # lines in grey linking the two axes.\\n\\n # Avoid inset mark lines intersect inset axes by setting its anchor\\n if log_eta_end > log_eta_end_high_res:\\n mark_inset(ax1, ax2, loc1=3, loc2=4, facecolor='none',\\n edgecolor='0.5')\\n else:\\n mark_inset(ax1, ax2, loc1=3, loc2=1, facecolor='none',\\n edgecolor='0.5')\\n\\n ax2.semilogx(eta, numpy.abs(dell_deta_upper_bound), '--',\\n color='black')\\n ax2.semilogx(eta, numpy.abs(dell_deta_lower_bound), '-.',\\n color='black')\\n ax2.semilogx(x, appr_dell_deta_ord1,\\n label=r'$1^{\\\\text{st}}$ order approximation',\\n color='chocolate')\\n ax2.semilogx(x, appr_dell_deta_ord2,\\n label=r'$2^{\\\\text{nd}}$ order approximation',\\n color='olivedrab')\\n ax2.semilogx(eta_high_res,\\n dell_deta[eta_low_res_left.size:\\n eta_low_res_left.size+eta_high_res.size],\\n color='black')\\n ax2.semilogx(optimal_eta, 0, marker='o', markersize=6, linewidth=0,\\n color='white', markerfacecolor='black',\\n label=r'Exact root at $\\\\hat{\\\\eta}_{\\\\phantom{2}} ' +\\n r'= 10^{%0.2f}$' % numpy.log10(optimal_eta))\\n\\n if appr_maxima_ord1 != []:\\n ax2.semilogx(appr_maxima_ord1[-1], 0, marker='o', markersize=6,\\n linewidth=0, color='white',\\n markerfacecolor='chocolate',\\n label=r'Approximated root at $\\\\hat{\\\\eta}_1 = ' +\\n r'10^{%0.2f}$'\\n % numpy.log10(appr_maxima_ord1[-1]))\\n\\n if appr_maxima_ord2 != []:\\n ax2.semilogx(appr_maxima_ord2[-1], 0, marker='o', markersize=6,\\n linewidth=0, color='white',\\n markerfacecolor='olivedrab',\\n label=r'Approximated root at $\\\\hat{\\\\eta}_2 = ' +\\n r'10^{%0.2f}$'\\n % numpy.log10(appr_maxima_ord2[-1]))\\n\\n ax2.set_xlim([eta_high_res[0], eta_high_res[-1]])\\n # plt.setp(ax2.get_yticklabels(), backgroundcolor='white')\\n\\n # Find suitable range for plot limits\\n min_plot = numpy.abs(numpy.min(dell_deta))\\n min_plot_base = 10**numpy.floor(numpy.log10(numpy.abs(min_plot)))\\n # min_plot_lim = numpy.ceil(min_plot/min_plot_base)*min_plot_base\\n min_plot_lim = numpy.ceil(min_plot/min_plot_base + 1.0) * \\\\\\n min_plot_base\\n ax2.set_ylim([-min_plot_lim, min_plot_lim])\\n ax2.set_yticks([-numpy.abs(min_plot_lim), 0, numpy.abs(min_plot_lim)])\\n\\n ax2.text(optimal_eta*10**0.03, min_plot_lim*0.05,\\n r'$\\\\hat{\\\\eta}$' % numpy.log10(optimal_eta),\\n horizontalalignment='left', verticalalignment='bottom',\\n fontsize=10)\\n\\n for i in range(len(appr_maxima_ord1)):\\n ax2.text(appr_maxima_ord1[i]*10**0.03, min_plot_lim*0.05,\\n r'$\\\\hat{\\\\eta}_1$' % numpy.log10(optimal_eta),\\n horizontalalignment='left', verticalalignment='bottom',\\n fontsize=10)\\n\\n for i in range(len(appr_maxima_ord2)):\\n ax2.text(appr_maxima_ord2[i]*10**(-0.14), min_plot_lim*0.05,\\n r'$\\\\hat{\\\\eta}_2$' % numpy.log10(optimal_eta),\\n horizontalalignment='left', verticalalignment='bottom',\\n fontsize=10)\\n\\n # ax2.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))\\n ax2.grid(True, axis='y')\\n ax2.set_facecolor('oldlace')\\n plt.setp(ax2.get_xticklabels(), backgroundcolor='white')\\n ax2.tick_params(axis='x', labelsize=10)\\n ax2.tick_params(axis='y', labelsize=10)\\n\\n # ax2.set_yticklabels(ax2.get_yticks(), backgroundcolor='w')\\n # ax2.tick_params(axis='y', which='major', pad=0)\\n\\n handles, labels = [], []\\n for ax in axes:\\n for h, l in zip(*ax.get_legend_handles_labels()):\\n handles.append(h)\\n labels.append(l)\\n lg = plt.legend(handles, labels, frameon=False, fontsize='small',\\n loc='upper left', bbox_to_anchor=(1.2, 1.04))\\n\\n # Save plots\\n plt.tight_layout()\\n show_or_save_plot(plt, 'likelihood_derivative',\\n transparent_background=False,\\n pdf=True, bbox_extra_artists=(lg, ))\",\n \"def show_learning_curve(self):\\n\\n # Loop output classes\\n for c in range(1,self.n_output_classes):\\n # Get data\\n x_values = np.array(self.n_class_samples_list[c])\\n accuracy = np.array(self.accuracy_list[c])\\n precision = np.array(self.precision_list[c])\\n recall = np.array(self.recall_list[c])\\n F1 = np.array(self.F1_list[c])\\n\\n # Make plot\\n with sns.axes_style(\\\"ticks\\\"):\\n fig,ax = plt.subplots()\\n plt.plot([np.min(x_values),np.max(x_values)],[0.5,0.5],\\n color='#777777',linestyle='--')\\n plt.plot([np.min(x_values),np.max(x_values)],[0.66,0.66],\\n color='#777777',linestyle=':')\\n plt.plot([np.min(x_values),np.max(x_values)],[0.8,0.8],\\n color='#777777',linestyle=':')\\n plt.plot([np.min(x_values),np.max(x_values)],[0.9,0.9],\\n color='#777777',linestyle=':')\\n\\n plt.plot( x_values, accuracy, color='#000000',\\n linewidth=1, label='Accuracy' )\\n plt.plot( x_values, precision, color='#0000aa',\\n linewidth=1, label='Precision' )\\n plt.plot( x_values, recall, color='#00aa00',\\n linewidth=1, label='Recall' )\\n plt.plot( x_values, F1, color='#aa0000',\\n linewidth=2, label='F1' )\\n\\n plt.yticks( [0, 0.5, 0.66, 0.8, 0.9, 1.0],\\n ['0','0.5','0.66','0.8','0.9','1.0'], ha='right' )\\n plt.xlim(np.max(x_values)*-0.02,np.max(x_values)*1.02)\\n plt.ylim(-0.02,1.02)\\n plt.xlabel('Number of training samples')\\n plt.ylabel('Performance')\\n plt.title('Learning curve, class {}'.format(c))\\n sns.despine(ax=ax, offset=0, trim=True)\\n lgnd = plt.legend(loc=4, ncol=1, frameon=True, fontsize=9)\\n lgnd.get_frame().set_facecolor('#ffffff')\\n ax.spines['left'].set_bounds(0,1)\\n ax.spines['bottom'].set_bounds(np.min(x_values),np.max(x_values))\",\n \"def main_paper(mode = 'q-diff'):\\n mode_name = 'Shared Autonomy'\\n if mode== 'override':\\n mode_name = 'Overrider'\\n\\n #trains rl again and fills a q-table\\n _, rl_agent = rl_alone(1000, fill_table = True)\\n\\n #prints the q-table\\n for y in range(7):\\n for x in range(7):\\n state_ind = y*(7)+x\\n q = rl_agent.get_q_values(state_ind)\\n print('Q-table')\\n print(state_ind, [x,y], np.around(q, decimals=5))\\n\\n in_li = []\\n r_li = []\\n rp_li=[]\\n\\n print(\\\"Begin looping through constraint values\\\")\\n for i in np.arange(0.0, 1.025, 0.025):\\n #cooperates and gets results\\n rewards, actionsct, rewardsP = grid_human_co(coagent=rl_agent, threshold=i, verbose=False, mode=mode)\\n print(\\\"Threshold: \\\", i)\\n avgInter = np.mean(actionsct, axis=0)\\n avgR = np.mean(rewards, axis=0)\\n avgRP = np.mean(rewardsP, axis=0)\\n in_li.append(avgInter)\\n r_li.append(avgR)\\n rp_li.append(avgRP)\\n print('Avg num of interventions: ', avgInter)\\n print('Avg reward: ', avgR)\\n print('Avg reward penalized: ', avgRP)\\n print()\\n\\n plt.figure()\\n plt.plot(np.arange(0.0, 1.025, 0.025), in_li)\\n plt.title(mode_name + ' Interventions')\\n plt.xlabel(r\\\"$\\\\alpha$\\\")\\n plt.ylabel('Average intervention count')\\n plt.show()\\n\\n plt.figure()\\n plt.title(mode_name + ' Returns', fontsize=14)\\n plt.xlabel(r\\\"$\\\\alpha$\\\", fontsize=13)\\n plt.ylabel('Average return', fontsize=13)\\n plt.plot(np.arange(0.0, 1.025, 0.025), r_li,label='Average environment return')\\n plt.plot(np.arange(0.0, 1.025, 0.025), rp_li,label=\\\"Average intervention return\\\")\\n plt.legend(fontsize=9)\\n plt.show()\",\n \"def calcBeamplot(PALC_config, gamma_n, plt_ranges, f, dire_meas_LSA, dire_meas_deg):\\n # general\\n N = PALC_config.N\\n \\n########################## SIMULATION SETUP ###############################\\n # maximum of discrete mapping points:\\n max_points = 10000\\n # mesh\\n x_range = plt_ranges.p_x\\n y_range = plt_ranges.p_y\\n pts_x = max_points / (y_range[1]-y_range[0])\\n pts_y = max_points / (x_range[1]-x_range[0])\\n\\n x = np.linspace(x_range[0], x_range[1], num=int(pts_x+1))\\n y = np.linspace(y_range[0], y_range[1], num=int(pts_y+1))\\n\\n X, Y = np.meshgrid(x,y)\\n # get vertically array\\n x_vert = np.reshape(X, np.size(X))\\n y_vert = np.reshape(Y, np.size(Y))\\n z_vert = np.array([0])\\n\\n # reference pressure\\n p0 = 2 * 10**(-5)\\n # considered frequency\\n omega = 2 * np.pi * f\\n # initialize driving fct. and output array\\n D_opt_LSA = np.ones([N, 1])\\n P_LSA = np.zeros([np.shape(x_vert)[0],1], dtype=complex) \\n\\n # air attenuation\\n T = 293.15\\n p = 101.325 * 10**(3)\\n h = 50\\n alpha, c = AirAbsorptionCoefficient(f, T, p, h)\\n\\n # directivity\\n if PALC_config.directivity not in ['Measured Loudspeaker Data']:\\n dire_meas_LSA = np.ones([1,1])\\n dire_meas_deg = np.ones([1,1])\\n \\n ######################### SPL CALCULATION #################################\\n x_start, y_start, x_stop, y_stop, x_c_n, y_c_n, x_S, y_S = source_pos(gamma_n, PALC_config)\\n\\n G_LSA_vert = CalcGreenFunctions(x_vert, y_vert, z_vert, x_c_n, y_c_n, 0.82,\\\\\\n PALC_config.directivity, PALC_config.Lambda_y, \\\\\\n gamma_n, c, omega, 1, dire_meas_LSA, \\\\\\n dire_meas_deg, alpha, f, 0 )\\n\\n P_LSA[:,0] = G_LSA_vert @ D_opt_LSA[:,0]\\n\\n p_SPL = 20 * np.log10(np.abs(P_LSA) / p0)\\n \\n p_SPL = np.reshape(p_SPL, np.shape(X))\\n\\n return p_SPL, X, Y\",\n \"def plot_ablation_losses():\\n\\n path1 = os.path.join(path_to_here, '../data/landscape_visualizations/DMSO/original/losses00-54.pickle')\\n file = open(path1, 'rb')\\n losses_array_1 = pickle.load(file)\\n losses_array_1 = losses_array_1[1:, :]\\n\\n path2 = os.path.join(path_to_here, '../data/landscape_visualizations/DMSO/D_t/losses00-44.pickle')\\n file = open(path2, 'rb')\\n losses_array_2 = pickle.load(file)\\n losses_array_2 = losses_array_2\\n\\n path3 = os.path.join(path_to_here, '../data/landscape_visualizations/DMSO/D_x_y/losses00-44.pickle')\\n file = open(path3, 'rb')\\n losses_array_3 = pickle.load(file)\\n losses_array_3 = losses_array_3\\n\\n path4 = os.path.join(path_to_here, '../data/landscape_visualizations/DMSO/homog/losses00-76.pickle')\\n file = open(path4, 'rb')\\n losses_array_4 = pickle.load(file)\\n losses_array_4 = losses_array_4[1:, :]\\n\\n paths = [path1, path2, path3, path4]\\n\\n\\n\\n\\n # 'pdf', 'BC', 'pde', 'total', 'norm'\\n fig = plt.figure(figsize = (2.1, 1.5))\\n\\n\\n for path, losses_array, label in zip(paths, [losses_array_1, losses_array_2, losses_array_3, losses_array_4], ['D(x, y, t)', 'D(t)', 'D(x, y)', 'D']):\\n\\n print('shape', losses_array.shape)\\n\\n losses_base = os.path.basename(path)\\n total_losses = losses_array[3, :] # total losses are idx 3 for these runs\\n idxs = []\\n means = []\\n for idx in range(len(total_losses)-200):\\n num_half_hrs = int(losses_base[6:8]) + idx*(int(losses_base[9:11])-int(losses_base[6:8]))/len(total_losses)\\n if num_half_hrs < 55:\\n idxs.append(num_half_hrs)\\n means.append(np.mean(total_losses[idx:idx+200]))\\n\\n\\n plt.scatter([i/2 for i in idxs], np.log10(means), s = 0.1, label = label)\\n\\n plt.ylabel(r'$log_{10}L_{total}$', fontsize = 6, labelpad = 1)\\n plt.xlabel('Hours trained', fontsize = 6, labelpad = 1)\\n plt.tick_params(axis = 'both', labelsize = 6)\\n plt.tight_layout()\\n plt.legend(fontsize = 6)\\n\\n\\n\\n plt.savefig(path_to_here+'/../outputs/ablation.png', dpi = 1200)\",\n \"def _show_learning_rate():\\n fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6.4 * 2, 4.8))\\n\\n # Visualize c_prime\\n c_prime_list = np.linspace(1, 100, num=11)\\n x_label = f\\\"c'\\\"\\n y_label = \\\"Minimum Clusters Size\\\"\\n title = \\\"\\\"\\n\\n ax = axes[0]\\n x_list = c_prime_list\\n\\n # MNIST\\n y_list = [161, 16, 14, 15, 20, 21, 24, 27, 30, 30, 35]\\n ax.plot(x_list, y_list, label=\\\"MNIST\\\")\\n\\n # Fashion MNIST\\n y_list = [63, 12, 12, 15, 18, 19, 22, 25, 26, 28, 30]\\n ax.plot(x_list, y_list, label=\\\"Fashion MNIST\\\")\\n\\n # 20 news groups\\n y_list = [1297, 724, 221, 80, 52, 51, 54, 54, 52, 60, 60]\\n ax.plot(x_list, y_list, label=\\\"Newsgroups\\\")\\n\\n ax.set_xlabel(x_label)\\n ax.set_ylabel(y_label)\\n ax.set_title(title)\\n ax.legend()\\n ax.set_yscale('log')\\n\\n # Visualize t0\\n t0_list = np.linspace(1, 100, num=11)\\n x_label = f\\\"t0\\\"\\n y_label = \\\"Minimum Clusters Size\\\"\\n title = \\\"\\\"\\n\\n ax = axes[1]\\n x_list = t0_list\\n\\n # MNIST\\n y_list = [16, 16, 16, 16, 16, 17, 16, 16, 16, 16, 16]\\n ax.plot(x_list, y_list, label=\\\"MNIST\\\")\\n\\n # Fashion MNIST\\n y_list = [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12]\\n ax.plot(x_list, y_list, label=\\\"Fashion MNIST\\\")\\n\\n # 20 news groups\\n y_list = [765, 765, 767, 772, 772, 773, 789, 789, 793, 796, 799]\\n ax.plot(x_list, y_list, label=\\\"Newsgroups\\\")\\n\\n ax.set_xlabel(x_label)\\n ax.set_ylabel(y_label)\\n ax.set_title(title)\\n ax.legend()\\n ax.set_yscale('log')\\n\\n plt.show()\",\n \"def main():\\n\\n # choose number of data-points and sample a pair of vectors: the input\\n # values and the corresponding target values\\n N = 500\\n inputs, targets = sample_data(N, arbitrary_function_2, seed=1)\\n\\n # specify the centres and scale of some rbf basis functions\\n default_centres = np.linspace(0,1,21)\\n default_scale = 0.03\\n default_reg_param = 0.08\\n\\n # get the cross-validation folds\\n num_folds = 4\\n folds = create_cv_folds(N, num_folds)\\n\\n # evaluate then plot the performance of different reg params\\n evaluate_reg_param(inputs, targets, folds, default_centres, default_scale)\\n # evaluate then plot the performance of different scales\\n evaluate_scale(inputs, targets, folds, default_centres, default_reg_param)\\n # evaluate then plot the performance of different numbers of basis\\n # function centres.\\n evaluate_num_centres(\\n inputs, targets, folds, default_scale, default_reg_param)\\n\\n plt.show()\",\n \"def show_lightcurve(self):\\n\\n time_array = self.exp_start_times\\n\\n fig = plt.figure()\\n\\n if self.transmission_spectroscopy:\\n lc_model = self.generate_lightcurves(time_array,\\n self.planet.calcTransitDepth())\\n plt.ylabel(\\\"Transit Depth\\\")\\n else:\\n lc_model = np.ones_like(time_array)\\n plt.ylabel(\\\"Unit Flux\\\")\\n\\n if self._visit_trend:\\n trend_model = self._visit_trend.scale_factors\\n # have to convert weird model format to flat array\\n lc_model = trend_model * lc_model.T[0]\\n\\n plt.scatter(time_array, lc_model)\\n plt.xlabel(\\\"Time (JD)\\\")\\n plt.title(\\\"Normalised White Time Series of observation\\\")\\n\\n return time_array, lc_model\",\n \"def runLinAdvec():\\n # Courant number \\n c = 0.4\\n #just run and print, for two different Courant numbers\\n main(50, 50, c, displayResults = True)\\n main(400, 400, c, displayResults = True)\\n print(\\\"\\\\n\\\")\\n # run order of convergence tests\\n runErrorTests(c, 50, 300, stepNx=50, display=True)\\n # run timing tests\\n runTimingTests(c, 50, 600, stepNx=50, displayResults=True)\\n # check monotonicity\\n checkMonotonicity(50, 50, c, displayResults = True)\\n # check mass/variance conservation\\n checkConservation(200, 200, c, display=True)\",\n \"def main():\\n\\t# \\\"\\\"\\\"\\n\\t# \\tMain function of test python module\\n\\t# \\\"\\\"\\\"\\n\\t# random.seed(os.urandom(345634)) # initialize random generator\\n\\t# t = np.linspace(0.0, 24.0, 96.0) # define the time axis of a day, here we use 96 values every quarter of an hour\\n\\t# # standard load profile -- input\\n\\t# q = extra.read_slp(t,\\n\\t# 'Profielen-Elektriciteit-2015-versie-1.00 Folder/profielen Elektriciteit 2015 versie 1.00.csv') # read the sample standard load profile, can be any length, can be resized given a low/high resolution time axis\\n\\t# q = q / np.sum(q) # normalization of standard load profile\\n\\t# # process duration\\n\\t# duration_axis = np.linspace(0.0, 24.0, 96.0)\\n\\t# (p_d, E_p) = extra.app_time(duration_axis, 10, 2, 0.0,\\n\\t# 24.0) # function that define the pdf of duration of a process\\n\\t# # process consumption\\n\\t# consumption_axis = np.linspace(0.0, 3.5, 96.0)\\n\\t# (p_k, E_k) = extra.app_consumption(consumption_axis, 10, 2, 0.0,\\n\\t# 3.5) # function that define the pdf of duration of a process\\n\\t# # pdf of starting time\\n\\t# p_t_0 = lpd.infer_t_0(q, p_d, E_k) # computes the pdf of starting time of processes\\n\\t# p_t_0 = p_t_0 / np.sum(p_t_0) # normalization of the pdf to sum up to zero\\n #\\n\\t# \\\"\\\"\\\"\\n\\t# 1st Approach, starting time of processes is a discrete propapibility density function\\n\\t# \\\"\\\"\\\"\\n\\t# # synthetic profile of D processes\\n\\t# D = 2000\\n\\t# synthetic_profile = lpd.synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\\n\\t# synthetic_profile_1 = lpd.synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\\n\\t# # expected value of D processes\\n\\t# q_e_e = lpd.infer_q_e(t, p_t_0, p_d, E_k, D)\\n\\t# # plot\\n\\t# plt.step(t, synthetic_profile, \\\"g-\\\")\\n\\t# plt.step(t, q_e_e, \\\"b--\\\")\\n #\\n\\t# \\\"\\\"\\\"\\n\\t# 2nd Approach, starting time of processes is a continuous propapibility density function\\n\\t# \\\"\\\"\\\"\\n\\t# # synthetic profile of D processes\\n\\t# ts, cs = lpd.continous_synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\\n\\t# plt.step(ts / len(t) * t[-1], cs, where='post', c='r')\\n\\t# plt.xlim(0, 24.0)\\n\\t# plt.legend([\\\"synthetic\\\", \\\"expected\\\", \\\"continuous\\\"], loc=0)\\n\\t# plt.show()\\n #\\n\\t# \\\"\\\"\\\"\\n\\t# Time discretization\\n\\t# \\\"\\\"\\\"\\n\\t# n_intervals = 24 * 1 # discretized in minutes\\n\\t# discrete_timeaxis = np.linspace(0.0, 24.0, n_intervals + 1)\\n\\t# discrete_consumption = lpd.signal_discretization(discrete_timeaxis, t, ts, cs)\\n\\t# plt.step(ts / len(t) * t[-1], cs, where='post', c='r')\\n\\t# plt.step(discrete_timeaxis, discrete_consumption, where='post', c='k', ls='--', lw=2)\\n\\t# plt.legend([\\\"continuous\\\", \\\"discretized\\\"], loc=0)\\n\\t# plt.show()\\n #\\n #\\n\\t# \\\"\\\"\\\"\\n\\t# Repeated day synthetic profile creation\\n\\t# \\\"\\\"\\\"\\n\\t# # synthetic profile of D processes\\n\\t# D = 2000\\n\\t# n = 10\\n\\t# slp = lpd.synthetic_profile_repeated(D, t, p_d, consumption_axis, p_k, p_t_0, n)\\n\\t# plt.step(range(len(slp)), slp, \\\"g-\\\")\\n\\t# plt.show()\\n\\tt = np.linspace(0.0, 24.0, 96.0)\\n\\tload_profile = extra.read_slp(t, 'Profielen-Elektriciteit-2015-versie-1.00 Folder/profielen Elektriciteit 2015 versie 1.00.csv')\\n\\tslp = synthetic.create_synthetic_load(load_profile, 5.0, 5)\\n\\tplt.step(range(len(slp)), slp)\\n\\tplt.show()\",\n \"def plot_multiLyapunov(systems, mode=2, savefig=True, figname=None):\\n if mode == 2:\\n print(systems)\\n# divnorm = colors.DivergingNorm(vmin=max([np.nanmin(np.nanmax(np.nanmax(system.lyapunov_2, axis=0), axis=0)) for system in systems]), vcenter=0, vmax=max[np.nanmax(system.lyapunov_2) for system in systems])\\n if figname == None:\\n figname = 'sum_of_first_2_lyapunov'\\n \\n fig, ax = plt.subplots()\\n for system in systems:\\n\\n lyapunov_2 = system.lyapunov_2\\n x = system.x\\n y = system.y\\n l = system.l\\n a = system.a\\n\\n\\n\\n plt.contourf(a[0,0,:,:],l[0,0,:,:],np.nanmax(np.nanmax(lyapunov_2, axis=0), axis=0), levels = 100, cmap = 'RdBu_r')\\n# , norm=divnorm)\\n for i in range(lyapunov_2.shape[0]):\\n for j in range(lyapunov_2.shape[1]):\\n plt.contour(a[0,0,:,:],l[0,0,:,:],lyapunov_2[i,j], levels = [0,], colors=('k',),alpha=0.1)\\n lyap_sum = plt.contour(a[0,0,:,:],l[0,0,:,:],lyapunov_2.max(axis=0).max(axis=0), levels = [0,], colors=('blue',),alpha=1)\\n\\n# cbar = plt.colorbar()\\n plt.plot(wild_chaos[:,0],wild_chaos[:,1],'--r',lw=3)\\n plt.title('Sum of the first 2 Lyapunov exponents ')\\n plt.ylabel('$\\\\lambda$')\\n plt.xlabel('a')\\n# cbar.ax.set_ylabel('Sum of the first 2 Lyapunov exponents')\\n\\n ax.set_ylim([l.min(),l.max()])\\n ax.set_xlim([a.min(),a.max()])\\n if savefig:\\n plt.savefig(f'images/{figname}.pdf')\\n plt.show()\",\n \"def plot_for_scaling_check(bolo_name):\\n\\n\\n pop_path = \\\"../Analyse_\\\" + bolo_name + \\\"/Populations/Pop_for_scaling/\\\"\\n\\n #Load the estimator\\n d_est = BDT_fh.open_estimator_file(bolo_name)\\n\\n #Best estimator for heat: coefficients\\n coeff_EC1, coeff_EC2 = float(d_est[\\\"HEAT\\\"][:5]), 1 - float(d_est[\\\"HEAT\\\"][:5])\\n coeff_EIB, coeff_EID = float(d_est[\\\"FID\\\"][:5]), 1-float(d_est[\\\"FID\\\"][:5])\\n\\n #Open event files\\n data_types = {\\\"names\\\": (\\\"EC1\\\", \\\"EC2\\\", \\\"EIA\\\", \\\"EIB\\\", \\\"EIC\\\", \\\"EID\\\"), \\\"formats\\\": (\\\"f\\\", \\\"f\\\", \\\"f\\\", \\\"f\\\", \\\"f\\\", \\\"f\\\")}\\n\\n arr_heatonly = np.loadtxt(pop_path + bolo_name + \\\"_heatonly_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_all = np.loadtxt(pop_path + bolo_name + \\\"_all_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_FidGamma = np.loadtxt(pop_path + bolo_name + \\\"_FidGamma_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S1Gamma = np.loadtxt(pop_path + bolo_name + \\\"_S1Gamma_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S2Gamma = np.loadtxt(pop_path + bolo_name + \\\"_S2Gamma_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S1Beta = np.loadtxt(pop_path + bolo_name + \\\"_S1Beta_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S2Beta = np.loadtxt(pop_path + bolo_name + \\\"_S2Beta_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S1Pb = np.loadtxt(pop_path + bolo_name + \\\"_S1Pb_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S2Pb = np.loadtxt(pop_path + bolo_name + \\\"_S2Pb_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n\\n arr_EI_heatonly, arr_EI_all = coeff_EIB*arr_heatonly[\\\"EIB\\\"] + coeff_EID*arr_heatonly[\\\"EID\\\"], coeff_EIB*arr_all[\\\"EIB\\\"] + coeff_EID*arr_all[\\\"EID\\\"]\\n arr_EC_heatonly, arr_EC_all = coeff_EC1*arr_heatonly[\\\"EC1\\\"] + coeff_EC2*arr_heatonly[\\\"EC2\\\"], coeff_EC1*arr_all[\\\"EC1\\\"] + coeff_EC2*arr_all[\\\"EC2\\\"]\\n arr_EI_FidGamma, arr_EC_FidGamma = coeff_EIB*arr_FidGamma[\\\"EIB\\\"] + coeff_EID*arr_FidGamma[\\\"EID\\\"], coeff_EC1*arr_FidGamma[\\\"EC1\\\"] + coeff_EC2*arr_FidGamma[\\\"EC2\\\"]\\n arr_EI_S1Gamma, arr_EI_S2Gamma = coeff_EIB*arr_S1Gamma[\\\"EIB\\\"] + coeff_EID*arr_S1Gamma[\\\"EID\\\"], coeff_EIB*arr_S2Gamma[\\\"EIB\\\"] + coeff_EID*arr_S2Gamma[\\\"EID\\\"]\\n arr_EC_S1Gamma, arr_EC_S2Gamma = coeff_EC1*arr_S1Gamma[\\\"EC1\\\"] + coeff_EC2*arr_S1Gamma[\\\"EC2\\\"], coeff_EC1*arr_S2Gamma[\\\"EC1\\\"] + coeff_EC2*arr_S2Gamma[\\\"EC2\\\"]\\n arr_EI_S1Beta, arr_EI_S2Beta = coeff_EIB*arr_S1Beta[\\\"EIB\\\"] + coeff_EID*arr_S1Beta[\\\"EID\\\"], coeff_EIB*arr_S2Beta[\\\"EIB\\\"] + coeff_EID*arr_S2Beta[\\\"EID\\\"]\\n arr_EC_S1Beta, arr_EC_S2Beta = coeff_EC1*arr_S1Beta[\\\"EC1\\\"] + coeff_EC2*arr_S1Beta[\\\"EC2\\\"], coeff_EC1*arr_S2Beta[\\\"EC1\\\"] + coeff_EC2*arr_S2Beta[\\\"EC2\\\"]\\n arr_EI_S1Pb, arr_EI_S2Pb = coeff_EIB*arr_S1Pb[\\\"EIB\\\"] + coeff_EID*arr_S1Pb[\\\"EID\\\"], coeff_EIB*arr_S2Pb[\\\"EIB\\\"] + coeff_EID*arr_S2Pb[\\\"EID\\\"]\\n arr_EC_S1Pb, arr_EC_S2Pb = coeff_EC1*arr_S1Pb[\\\"EC1\\\"] + coeff_EC2*arr_S1Pb[\\\"EC2\\\"], coeff_EC1*arr_S2Pb[\\\"EC1\\\"] + coeff_EC2*arr_S2Pb[\\\"EC2\\\"]\\n\\n lS1Beta, lS2Beta, lS1Pb, lS2Pb = np.where(arr_EC_S1Beta<15), np.where(arr_EC_S2Beta<15), np.where(arr_EC_S1Pb<15), np.where(arr_EC_S2Pb<15)\\n lS1Gamma, lS2Gamma, lFidGamma = np.where(arr_EC_S1Gamma<15), np.where(arr_EC_S2Gamma<15), np.where(arr_EC_FidGamma<15)\\n lheatonly, lall = np.where(arr_EC_heatonly<15), np.where(arr_EC_all<15)\\n\\n arr_EI_heatonly, arr_EC_heatonly = arr_EI_heatonly[lheatonly], arr_EC_heatonly[lheatonly]\\n arr_EI_all, arr_EC_all = arr_EI_all[lall], arr_EC_all[lall]\\n arr_EI_FidGamma, arr_EC_FidGamma = arr_EI_FidGamma[lFidGamma], arr_EC_FidGamma[lFidGamma]\\n arr_EI_S1Gamma, arr_EC_S1Gamma = arr_EI_S1Gamma[lS1Gamma], arr_EC_S1Gamma[lS1Gamma]\\n arr_EI_S2Gamma, arr_EC_S2Gamma = arr_EI_S2Gamma[lS2Gamma], arr_EC_S2Gamma[lS2Gamma]\\n arr_EI_S1Beta, arr_EC_S1Beta = arr_EI_S1Beta[lS1Beta], arr_EC_S1Beta[lS1Beta]\\n arr_EI_S2Beta, arr_EC_S2Beta = arr_EI_S2Beta[lS2Beta], arr_EC_S2Beta[lS2Beta]\\n arr_EI_S1Pb, arr_EC_S1Pb = arr_EI_S1Pb[lS1Pb], arr_EC_S1Pb[lS1Pb]\\n arr_EI_S2Pb, arr_EC_S2Pb = arr_EI_S2Pb[lS2Pb], arr_EC_S2Pb[lS2Pb]\\n\\n arr_EI_all, arr_EC_all = np.array(arr_EI_all).astype(float), np.array(arr_EC_all).astype(float)\\n arr_EI_heatonly, arr_EC_heatonly = np.array(arr_EI_heatonly).astype(float), np.array(arr_EC_heatonly).astype(float)\\n arr_EI_FidGamma, arr_EC_FidGamma = np.array(arr_EI_FidGamma).astype(float), np.array(arr_EC_FidGamma).astype(float)\\n arr_EI_S1Gamma, arr_EC_S1Gamma = np.array(arr_EI_S1Gamma).astype(float), np.array(arr_EC_S1Gamma).astype(float)\\n arr_EI_S2Gamma, arr_EC_S2Gamma = np.array(arr_EI_S2Gamma).astype(float), np.array(arr_EC_S2Gamma).astype(float) \\n arr_EI_S1Beta, arr_EC_S1Beta = np.array(arr_EI_S1Beta).astype(float), np.array(arr_EC_S1Beta).astype(float)\\n arr_EI_S2Beta, arr_EC_S2Beta = np.array(arr_EI_S2Beta).astype(float), np.array(arr_EC_S2Beta).astype(float)\\n arr_EI_S1Pb, arr_EC_S1Pb = np.array(arr_EI_S1Pb).astype(float), np.array(arr_EC_S1Pb).astype(float)\\n arr_EI_S2Pb, arr_EC_S2Pb = np.array(arr_EI_S2Pb).astype(float), np.array(arr_EC_S2Pb).astype(float)\\n\\n\\n gr_heatonly = TGraph(len(arr_EI_heatonly), arr_EC_heatonly, arr_EI_heatonly)\\n gr_FidGamma, gr_all = TGraph(len(arr_EI_FidGamma), arr_EC_FidGamma, arr_EI_FidGamma), TGraph(len(arr_EI_all), arr_EC_all, arr_EI_all)\\n gr_S1Gamma, gr_S2Gamma = TGraph(len(arr_EI_S1Gamma), arr_EC_S1Gamma, arr_EI_S1Gamma), TGraph(len(arr_EI_S2Gamma), arr_EC_S2Gamma, arr_EI_S2Gamma)\\n gr_S1Beta, gr_S2Beta = TGraph(len(arr_EI_S1Beta), arr_EC_S1Beta, arr_EI_S1Beta), TGraph(len(arr_EI_S2Beta), arr_EC_S2Beta, arr_EI_S2Beta)\\n gr_S1Pb, gr_S2Pb = TGraph(len(arr_EI_S1Pb), arr_EC_S1Pb, arr_EI_S1Pb), TGraph(len(arr_EI_S2Pb), arr_EC_S2Pb, arr_EI_S2Pb)\\n\\n PyRPl.process_TGraph(gr_all, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kRed, marker_style = 20, marker_size = 0.1)\\n PyRPl.process_TGraph(gr_FidGamma, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack), PyRPl.process_TGraph(gr_heatonly, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack)\\n PyRPl.process_TGraph(gr_S1Gamma, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack), PyRPl.process_TGraph(gr_S2Gamma, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack)\\n PyRPl.process_TGraph(gr_S1Beta, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack), PyRPl.process_TGraph(gr_S2Beta, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack)\\n PyRPl.process_TGraph(gr_S1Pb, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kRed), PyRPl.process_TGraph(gr_S2Pb, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack)\\n\\n list_gr = [gr_all, gr_FidGamma, gr_S1Gamma, gr_S2Gamma, gr_S1Beta, gr_S2Beta, gr_S1Pb, gr_S2Pb, gr_heatonly]\\n list_pts = [gr.GetN() for gr in list_gr[1:]]\\n print gr_all.GetN(), sum(list_pts)\\n h = TH2F(\\\"h\\\", \\\"h\\\", 100, -5, 15, 100, -5, 15)\\n PyRPl.process_TH2(h, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\")\\n h.Draw()\\n for gr in list_gr:\\n gr.Draw(\\\"*same\\\")\\n\\n raw_input()\\n\\n # arr_Q_S1Beta = arr_EI_S1Beta/((1+8./3)*arr_EC_S1Beta - arr_EI_S1Beta*5.5/3)\\n # arr_Q_S2Beta = arr_EI_S1Beta/((1+8./3)*arr_EC_S1Beta - arr_EI_S1Beta*5.5/3)\\n # arr_Q_S1Pb = arr_EI_S1Pb/((1+8./3)*arr_EC_S1Pb - arr_EI_S1Pb*5.5/3)\\n # arr_Q_S2Pb = arr_EI_S2Pb/((1+8./3)*arr_EC_S2Pb - arr_EI_S2Pb*5.5/3)\\n \\n # gr_QS1Beta, gr_QS2Beta = TGraph(len(arr_Q_S1Beta), arr_EC_S1Beta, arr_Q_S1Beta), TGraph(len(arr_Q_S2Beta), arr_EC_S2Beta, arr_Q_S2Beta)\\n # gr_QS1Pb, gr_QS2Pb = TGraph(len(arr_Q_S1Pb), arr_EC_S1Pb, arr_Q_S1Pb), TGraph(len(arr_Q_S2Pb), arr_EC_S2Pb, arr_Q_S2Pb)\\n\\n\\n # PyRPl.process_TGraph(gr_QS1Beta, X_title = \\\"Heat\\\", Y_title = \\\"Q\\\", color=kOrange-3), PyRPl.process_TGraph(gr_QS2Beta, X_title = \\\"Heat\\\", Y_title = \\\"Q\\\", color=kBlue)\\n # PyRPl.process_TGraph(gr_QS1Pb, X_title = \\\"Heat\\\", Y_title = \\\"Q\\\", color=kRed), PyRPl.process_TGraph(gr_QS2Pb, X_title = \\\"Heat\\\", Y_title = \\\"Q\\\", color=kGreen+2)\",\n \"def get_corner_node_matlab(lcurve, debug=False):\\r\\n lx, ly = np.zeros(len(lcurve)), np.zeros(len(lcurve))\\r\\n for lia in range(len(lcurve)):\\r\\n lx[lia] = lcurve[lia][0]\\r\\n ly[lia] = lcurve[lia][1]\\r\\n\\r\\n import matlab.engine\\r\\n eng = matlab.engine.start_matlab()\\r\\n eng.addpath(eng.genpath('../dfg_lowrank/simulation-200820/'))\\r\\n import io\\r\\n out = io.StringIO()\\r\\n err = io.StringIO()\\r\\n l_opt, info = eng.L_corner(as_matlab(lx), as_matlab(ly), nargout=2, stdout=out, stderr=err)\\r\\n # print(l_opt)\\r\\n # print(info)\\r\\n # print(out.getvalue())\\r\\n l_opt = int(l_opt)\\r\\n # k = curvature_lcurve(lx, ly)\\r\\n # l_opt = np.argmax(k)\\r\\n # print(l_opt)\\r\\n if False:\\r\\n import matplotlib\\r\\n matplotlib.use(\\\"Qt4Agg\\\")\\r\\n import matplotlib.pyplot as plt\\r\\n plt.figure()\\r\\n plt.title(\\\"L-curve\\\")\\r\\n plt.plot([np.log10(lcurve[lia][0]) for lia in range(len(lcurve))],\\r\\n [np.log10(lcurve[lia][1]) for lia in range(len(lcurve))], '-xb', label=\\\"L-curve\\\")\\r\\n plt.plot(np.log10(lcurve[l_opt][0]), np.log10(lcurve[l_opt][1]), 'or', label=\\\"optimal value\\\")\\r\\n plt.xlabel(\\\"log(|| Y - UV ||)\\\")\\r\\n plt.ylabel(\\\"log(|| L_U U || + || L_V V ||)\\\")\\r\\n plt.show()\\r\\n\\r\\n return l_opt\",\n \"def main():\\n args = parse_args()\\n\\n with open(args.train_details_json, mode='r', encoding='utf-8') as json_f:\\n results_dict = json.load(json_f)[-1]\\n\\n losses_plot = plt.figure()\\n plt.plot(range(1, len(results_dict['train_loss']) + 1),\\n results_dict['train_loss'])\\n plt.plot(range(1, len(results_dict['val_loss']) + 1),\\n results_dict['val_loss'])\\n plt.plot(range(1, len(results_dict['test_loss']) + 1),\\n results_dict['test_loss'])\\n plt.legend(['train', 'val', 'test'])\\n plt.title(f'loss vs epoch for {args.model} model on {args.dataset} dataset')\\n plt.xlabel('epoch')\\n plt.ylabel('loss')\\n plt.grid(True)\\n losses_plot.set_size_inches((8, 8))\\n losses_plot.savefig(\\n os.path.join(FIGURES_DIR,\\n f'{args.dataset}_{args.model}_losses_plot.png'))\\n\\n accuracies_plot = plt.figure()\\n plt.plot(range(1, len(results_dict['train_acc']) + 1),\\n results_dict['train_acc'])\\n plt.plot(range(1, len(results_dict['val_acc']) + 1),\\n results_dict['val_acc'])\\n plt.plot(range(1, len(results_dict['test_acc']) + 1),\\n results_dict['test_acc'])\\n plt.legend(['train', 'val', 'test'])\\n plt.title(f'accuracy vs epoch for {args.model} '\\n f'model on {args.dataset} dataset')\\n plt.xlabel('epoch')\\n plt.ylabel('accuracy')\\n plt.grid(True)\\n accuracies_plot.set_size_inches((8, 8))\\n accuracies_plot.savefig(\\n os.path.join(FIGURES_DIR,\\n f'{args.dataset}_{args.model}_accuracies_plot.png'))\",\n \"def line_FIR(**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n GR = glo.global_results()\\n\\n L_line = getattr(GR,'L_'+p.line+'_sun')\\n # L_FIR = getattr(GR,'L_FIR_sun')\\n\\n labs = {'':'SIGAME default: lognormal + power-law, Mach=10',\\\\\\n '_arepoPDF_dim':'AREPO parametric PDF with extinction',\\\\\\n '_arepoPDF':'SIGAME parametric PDF'}\\n lab = labs[p.table_ext]\\n\\n if p.add:\\n ax = p.ax\\n else:\\n fig,ax = plt.subplots(figsize=(8,6))\\n\\n n = 388\\n m = ax.scatter(GR.L_FIR_sun[0:n],L_line[0:n],marker='o',s=20,\\\\\\n c=GR.Zsfr[0:n],label=lab,alpha=0.6,zorder=10)\\n\\n if p.add_obs:\\n add_line_FIR_obs(p.line,ax)\\n\\n m = ax.scatter(GR.L_FIR_sun/1e6,L_line/1e6,marker='o',s=30,c=GR.Zsfr)\\n cbar = plt.colorbar(m,ax=ax)\\n cbar.set_label(label='$Z_{\\\\mathrm{SFR}}$ [Z$_{\\\\odot}$]',size=15)\\n\\n ax.set_xscale('log')\\n ax.set_yscale('log')\\n ax.set_xlabel(getlabel('L_FIR'))\\n ax.set_ylabel(getlabel(p.line))\\n handles,labels = ax.get_legend_handles_labels()\\n handles = np.flip(handles)\\n labels = np.flip(labels)\\n ax.legend(handles,labels,loc='upper left',fontsize=7)\\n if not p.xlim: p.xlim = 10.**np.array([6,13])\\n if not p.ylim: \\n p.ylim = [np.median(L_line)/1e4,np.median(L_line)*1e4]\\n ax.set_xlim(p.xlim)\\n ax.set_ylim(p.ylim)\\n ax.grid(ls='--')\\n\\n if p.savefig & (not p.add):\\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \\n plt.savefig(p.d_plot + 'luminosity/%s_FIR.png' % p.line, format='png', dpi=300) \\n\\n # plt.close('all')\",\n \"def main():\\n\\t#print(scipy.__version__)\\n\\t#image()\\n\\t#heat_capacity2()\\n\\t#hist()\\n\\t#single_plot()\\n\\n\\t#heat_capacity2()\\n\\t#single_plot()\\n\\t#plt.show()\\n\\t#u0_tc()\\n\\t#multi_heat_capacity(\\\"HL_DM_flux5\\\",True)\\n\\t#multi_heat_capacity2()\\n\\t#plot_spin()\\n\\t#plt.show()\\n\\theat_capacity2(1,2)\\n\\t#hist()\\n\\tplt.show()\\n\\t#potential()\\n\\t#plt.show()\\n\\t#heat_capacity(3,4)\\n\\t#heat_capacity(5,6)\\n\\t#heat_capacity(7,8)\\n\\t#final_spins()\\n\\t#plot_spin()\\n\\t#plot_from_csv()\\n\\t#difference_plot()\",\n \"def show_solver_history(solver, plot_train=True, plot_val=True, color='black'):\\n mpl.rcParams['text.color'] = color\\n mpl.rcParams['axes.labelcolor'] = color\\n mpl.rcParams['xtick.color'] = color\\n mpl.rcParams['ytick.color'] = color\\n\\n print(\\\"Stop reason: %s\\\" % solver.stop_reason)\\n print(\\\"Stop time: %fs\\\" % solver.training_time_s)\\n\\n has_gender_loss = np.array(solver.history['val_gender_loss']).sum() > 0.\\n has_rec_loss = np.array(solver.history['val_rec_loss']).sum() > 0.\\n\\n train_loss = np.array(solver.history['train_loss'])\\n\\n if has_rec_loss:\\n train_rec_loss = np.array(solver.history['train_rec_loss'])\\n val_rec_loss = np.array(solver.history['val_rec_loss'])\\n if has_gender_loss:\\n train_gender_loss = np.array(solver.history['train_gender_loss'])\\n val_gender_loss = np.array(solver.history['val_gender_loss'])\\n\\n plt.figure(figsize=(20, 10))\\n\\n if plot_train:\\n if has_rec_loss:\\n plt.plot(np.linspace(1, len(train_loss), len(train_rec_loss)),\\n train_rec_loss, label='Train Reconstruction loss')\\n if has_gender_loss:\\n plt.plot(np.linspace(1, len(train_loss), len(train_gender_loss)),\\n train_gender_loss, label='ATrain Gender loss')\\n\\n if plot_val:\\n if has_rec_loss:\\n plt.plot(np.linspace(1, len(train_loss), len(val_rec_loss)),\\n val_rec_loss, label='Val Reconstruction loss')\\n if has_gender_loss:\\n plt.plot(np.linspace(1, len(train_loss), len(val_gender_loss)),\\n val_gender_loss, label='Val Gender loss')\\n\\n plt.xlabel(\\\"Iterations\\\", fontsize=18)\\n plt.ylabel(\\\"Train/Val loss\\\", fontsize=18)\\n plt.xticks(fontsize=14)\\n plt.yticks(fontsize=14)\\n legend = plt.legend(fontsize=14)\\n for text in legend.get_texts():\\n text.set_color(\\\"black\\\")\\n\\n plt.show()\\n\\n if has_rec_loss:\\n print(\\\"Final training reconstruction loss: {}\\\".format(\\n train_rec_loss[-1]))\\n print(\\\"Final validation reconstruction loss: {}\\\".format(\\n val_rec_loss[-1]))\\n if has_gender_loss:\\n print(\\\"Final training gender loss: {}\\\".format(train_gender_loss[-1]))\\n print(\\\"Final validation gender loss: {}\\\".format(val_gender_loss[-1]))\",\n \"def main():\\n import argparse\\n import os\\n parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\\n parser.add_argument('--dirs', help='List of log directories', nargs='*', default=['./log'])\\n parser.add_argument('--num_timesteps', type=int, default=int(10e6))\\n parser.add_argument('--xaxis', help='Varible on X-axis', default=X_TIMESTEPS)\\n parser.add_argument('--task_name', help='Title of plot', default='Breakout')\\n args = parser.parse_args()\\n args.dirs = [os.path.abspath(folder) for folder in args.dirs]\\n plot_results(args.dirs, args.num_timesteps, args.xaxis, args.task_name)\\n plt.show()\",\n \"def analyze_lnl(lls_dict, xtup, lnL, CL=0.68):\\n if len(xtup) == 3:\\n NHI_eval, C0_val, C1_val = xtup\\n cl3D = cl_interval(lnL, CL=CL) # 1-sigma!\\n lls_dict['fit_NHI'] = [NHI_eval[cl3D[0][0]], NHI_eval[cl3D[1][0][0]], NHI_eval[cl3D[1][0][1]]]\\n lls_dict['cdict']['best'] = (float(C0_val[cl3D[0][1]]), float(C1_val[cl3D[0][2]]))\\n lls_dict['cdict']['fit_val'] = [[float(C0_val[cl3D[0][1]]),\\n float(C0_val[cl3D[1][1][0]]),\\n float(C0_val[cl3D[1][1][1]])],\\n [float(C1_val[cl3D[0][2]]),\\n float(C1_val[cl3D[1][2][0]]),\\n float(C1_val[cl3D[1][2][1]])]]\\n # Return\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70325994","0.6906087","0.6865391","0.6744682","0.6360003","0.6259489","0.62437195","0.6224446","0.6207582","0.61919683","0.619153","0.61545587","0.6145968","0.6132787","0.61294043","0.60725796","0.6056807","0.60417926","0.6017504","0.60079277","0.60035855","0.5962546","0.5954487","0.595359","0.59480333","0.59360623","0.59338903","0.59302133","0.59198064","0.5918835","0.5918281","0.59152836","0.58928794","0.5865147","0.5854578","0.58516675","0.5847087","0.58408093","0.58376527","0.5824081","0.58136904","0.5811072","0.580185","0.5799253","0.57959855","0.57899725","0.57894045","0.5786501","0.5784141","0.5781752","0.5779434","0.577626","0.5768255","0.5766096","0.57638866","0.575835","0.57550704","0.5748555","0.57450503","0.57441115","0.5739241","0.5730977","0.57294595","0.5713095","0.570911","0.57007855","0.5698958","0.56846046","0.5683504","0.56762445","0.5661012","0.5651538","0.56452566","0.56422794","0.5637283","0.5636592","0.56362814","0.5633425","0.5632522","0.56309265","0.56298953","0.5617463","0.56160265","0.56132925","0.5606306","0.5605647","0.55921537","0.5592078","0.55917627","0.55865294","0.55818117","0.5578105","0.55732113","0.5570645","0.55656093","0.5565029","0.55648017","0.55598134","0.55589586","0.55566925"],"string":"[\n \"0.70325994\",\n \"0.6906087\",\n \"0.6865391\",\n \"0.6744682\",\n \"0.6360003\",\n \"0.6259489\",\n \"0.62437195\",\n \"0.6224446\",\n \"0.6207582\",\n \"0.61919683\",\n \"0.619153\",\n \"0.61545587\",\n \"0.6145968\",\n \"0.6132787\",\n \"0.61294043\",\n \"0.60725796\",\n \"0.6056807\",\n \"0.60417926\",\n \"0.6017504\",\n \"0.60079277\",\n \"0.60035855\",\n \"0.5962546\",\n \"0.5954487\",\n \"0.595359\",\n \"0.59480333\",\n \"0.59360623\",\n \"0.59338903\",\n \"0.59302133\",\n \"0.59198064\",\n \"0.5918835\",\n \"0.5918281\",\n \"0.59152836\",\n \"0.58928794\",\n \"0.5865147\",\n \"0.5854578\",\n \"0.58516675\",\n \"0.5847087\",\n \"0.58408093\",\n \"0.58376527\",\n \"0.5824081\",\n \"0.58136904\",\n \"0.5811072\",\n \"0.580185\",\n \"0.5799253\",\n \"0.57959855\",\n \"0.57899725\",\n \"0.57894045\",\n \"0.5786501\",\n \"0.5784141\",\n \"0.5781752\",\n \"0.5779434\",\n \"0.577626\",\n \"0.5768255\",\n \"0.5766096\",\n \"0.57638866\",\n \"0.575835\",\n \"0.57550704\",\n \"0.5748555\",\n \"0.57450503\",\n \"0.57441115\",\n \"0.5739241\",\n \"0.5730977\",\n \"0.57294595\",\n \"0.5713095\",\n \"0.570911\",\n \"0.57007855\",\n \"0.5698958\",\n \"0.56846046\",\n \"0.5683504\",\n \"0.56762445\",\n \"0.5661012\",\n \"0.5651538\",\n \"0.56452566\",\n \"0.56422794\",\n \"0.5637283\",\n \"0.5636592\",\n \"0.56362814\",\n \"0.5633425\",\n \"0.5632522\",\n \"0.56309265\",\n \"0.56298953\",\n \"0.5617463\",\n \"0.56160265\",\n \"0.56132925\",\n \"0.5606306\",\n \"0.5605647\",\n \"0.55921537\",\n \"0.5592078\",\n \"0.55917627\",\n \"0.55865294\",\n \"0.55818117\",\n \"0.5578105\",\n \"0.55732113\",\n \"0.5570645\",\n \"0.55656093\",\n \"0.5565029\",\n \"0.55648017\",\n \"0.55598134\",\n \"0.55589586\",\n \"0.55566925\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6874636"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":78899,"cells":{"query":{"kind":"string","value":"This script runs several inversions for Helheim glacier using different values of the regularization parameter in order to generate the familiar Lcurve plot."},"document":{"kind":"string","value":"def main(argv):\n\n rmin = float(argv[0])\n rmax = float(argv[1])\n nr = int(argv[2])\n\n rs = np.logspace(rmin, rmax, nr)\n\n for n in range(nr):\n r = rs[n]\n log_file_name = \"helheim_lambda-\" + str(r) + \".txt\"\n\n run.main([\"-g\", \"helheim\",\n \"-r\", str(r),\n \"-p\", \"4\",\n \"-i\", \"30\",\n \"-o\", log_file_name])\n\n archive.main([\"-g\", \"helheim\",\n \"-o\", \"helheim_lambda-\" + str(r) + \".tar.gz\",\n \"-x\", log_file_name])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def L_curveTV(f,lam_init = 2.0, q = 0.9):\n lam = lam_init\n max_iter = 50\n residual_list = np.zeros(max_iter)\n size_list = np.zeros(max_iter)\n error = np.zeros(max_iter)\n alt_error = np.zeros(max_iter)\n \n for i in range(max_iter): #range(max_iter):\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc=True, tol = 1.0e-5)\n #u, _, j = projected_gradient_alt(f,lam, tau = 0.2, tol = 1.0e-4)\n lam = lam_init * (q ** i)\n residual_list[i] = np.linalg.norm(u - f)\n size_list[i] = np.linalg.norm(u)\n error[i] = np.linalg.norm(u - f) * np.linalg.norm(u)\n #plt.loglog(residual_list,size_list)\n #plt.show()\n opt_idx = np.argmin(error)\n t = 1.0 / (1.0 + lam_init * (q ** opt_idx))\n lam = lam_init * (q ** opt_idx)\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc = True, tol = 1.0e-5)\n return u, t","def plot():\n xvals = np.arange(-50, 250, step=0.1)\n\n fig = plt.figure()\n plt.suptitle(\"Gaussian with smooth transition to power law\")\n\n A0vals = [10, 11]\n avals = [5*10**-3, 10**-3, 5*10**-4]\n ttvals = [10., 50., 100.]\n cvals = [-0.1, -0.9, -5./3., -4.]\n offset = [-30, 0.0, 30]\n\n paramvals = [A0vals, avals, ttvals,cvals, offset]\n titles, labels = return_parameter_names()\n\n nplots = len(paramvals)\n\n for i in range(nplots):\n plt.subplot(nplots, 1, i+1)\n vals = paramvals[i]\n for j in range(len(vals)):\n pset = list(default())\n pset[i] = vals[j]\n yvals=[]\n ypower=[]\n ypeak=[]\n for x in xvals:\n yvals.append(fitfunc(x, pset))\n ypeak.append(logpeak(x,pset))\n if x > 0:\n ypower.append(logpowerlaw(x,pset))\n label = labels[i] + \"=\"+str(vals[j])\n plt.plot(xvals, yvals, label = label)\n\n plt.title(titles[i])\n plt.legend()\n\n fig.set_size_inches(15, 30)\n plt.savefig(\"graphs/misc/lightcurve_models.pdf\")\n plt.close()","def plot_LvsL(modeldata,line1='CIV1548',line2='CIII1908',plotname='./TESTPLOT.pdf',\n Zgas=False,logU=False,xid=0.3,nh=100,COratio=0.38,Mcutoff=100,\n logx=False,logy=False,logp1=False,logp2=False,fixxrange=False,fixyrange=False,\n showobs=None,noobserr=False,verbose=True):\n NFalse = 0\n freeparam = []\n inforstr = \"\"\n # - - - - - - - - - - - - - - - - - - - - - - - -\n legenddic = {}\n legenddic['Zgas'] = r'Z$_\\textrm{gas}$'\n legenddic['logUs'] = r'log$_\\textrm{10}$(U)'\n legenddic['xid'] = r'$\\xi_\\textrm{d}$'\n legenddic['nh'] = r'n$_\\textrm{H}$ / [cm$^3$]'\n legenddic['COCOsol'] = r'C/O / [C/O]$_\\textrm{sun}$'\n legenddic['mup'] = r'M$_\\textrm{cut IMF}$ / [M$_\\textrm{sun}]$'\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not Zgas:\n Zgasrange = [0.0,1.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' Zgas:vary, '\n freeparam.append('Zgas')\n else:\n Zgasrange = [Zgas-1e-6,Zgas+1e-6]\n inforstr = inforstr+' '+legenddic['Zgas']+'='+str(Zgas)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not logU:\n logUrange = [-5.0,0.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' logU:vary, '\n freeparam.append('logUs')\n else:\n logUrange = [logU-0.1,logU+0.1]\n inforstr = inforstr+' '+legenddic['logUs']+'='+str(logU)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not xid:\n xidrange = [0.0,0.6]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' xid:vary, '\n freeparam.append('xid')\n else:\n xidrange = [xid-0.01,xid+0.01]\n inforstr = inforstr+' '+legenddic['xid']+'='+str(xid)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not nh:\n nhrange = [0.0,1.0e6]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' nH:vary, '\n freeparam.append('nh')\n else:\n nhrange = [nh-1.0,nh+1.0]\n inforstr = inforstr+' '+legenddic['nh']+'='+str(nh)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not COratio:\n COratiorange = [0.0,2.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' C/O:vary, '\n freeparam.append('COCOsol')\n else:\n COratiorange = [COratio-0.001,COratio+0.001]\n inforstr = inforstr+' '+legenddic['COCOsol']+'='+str(COratio)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not Mcutoff:\n Mcutoffrange = [0.0,400.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' Mcutoff:vary, '\n freeparam.append('mup')\n else:\n Mcutoffrange = [Mcutoff-1.0,Mcutoff+1.0]\n inforstr = inforstr+' '+legenddic['mup']+'='+str(Mcutoff)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n\n if NFalse != 2:\n sys.exit(' Two and only two of the model parameters (Zgas,logU,xid,nh,COratio,Mcutoff) '\n 'should be set to Flase to define the model grid; however it appears '+str(NFalse)+\n ' parameters where not set')\n\n goodent = np.where( (modeldata['Zgas'] > Zgasrange[0]) & (modeldata['Zgas'] < Zgasrange[1]) &\n (modeldata['logUs'] > logUrange[0]) & (modeldata['logUs'] < logUrange[1]) &\n (modeldata['xid'] > xidrange[0]) & (modeldata['xid'] < xidrange[1]) &\n (modeldata['nh'] > nhrange[0]) & (modeldata['nh'] < nhrange[1]) &\n (modeldata['COCOsol'] > COratiorange[0]) & (modeldata['COCOsol'] < COratiorange[1]) &\n (modeldata['mup'] > Mcutoffrange[0]) & (modeldata['mup'] < Mcutoffrange[1]) )\n\n Ngoodent = len(goodent[0])\n\n if Ngoodent > 1:\n if verbose: print(' - Getting data for '+str(Ngoodent)+' data points satisfying model selection ')\n param1 = modeldata[freeparam[0]][goodent]\n if logp1:\n param1 = np.log10(param1)\n\n param2 = modeldata[freeparam[1]][goodent]\n if logp2:\n param2 = np.log10(param2)\n\n lum1 = modeldata[line1][goodent]\n lum2 = modeldata[line2][goodent]\n else:\n if verbose: print(' WARNING: Less than 2 model grid points to plot; no output generated')\n return\n\n # - - - - - - - - - - - PLOTTING - - - - - - - - - - -\n if verbose: print(' - Setting up and generating plot')\n plotname = plotname\n fig = plt.figure(figsize=(9, 5))\n fig.subplots_adjust(wspace=0.1, hspace=0.1,left=0.1, right=0.99, bottom=0.10, top=0.95)\n Fsize = 10\n lthick = 1\n marksize = 3\n plt.rc('text', usetex=True)\n plt.rc('font', family='serif',size=Fsize)\n plt.rc('xtick', labelsize=Fsize)\n plt.rc('ytick', labelsize=Fsize)\n plt.clf()\n plt.ioff()\n plt.title(inforstr[:-2],fontsize=Fsize)\n\n margin = 0.1\n dx = np.abs(np.max(lum1)-np.min(lum1))\n dy = np.abs(np.max(lum2)-np.min(lum2))\n\n\n if fixxrange:\n xrange = fixxrange\n else:\n if logx:\n xrange = [np.min(lum1)-np.min(lum1)/2.,np.max(lum1)+np.max(lum1)/2.]\n else:\n xrange = [np.min(lum1)-dx*margin,np.max(lum1)+dx*margin]\n\n if fixyrange:\n yrange = fixyrange\n else:\n if logy:\n yrange = [np.min(lum2)-np.min(lum2)/2.,np.max(lum2)+np.max(lum2)/2.]\n else:\n yrange = [np.min(lum2)-dy*margin,np.max(lum2)+dy*margin]\n\n # ------------ PARAM1 ------------\n cmap = plt.cm.get_cmap('winter')\n cmin = np.min(param1)\n cmax = np.max(param1)\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\n mm = plt.cm.ScalarMappable(cmap=cmap)\n mm.set_array(cmaparr)\n cb1 = plt.colorbar(mm)#shrink=0.25\n\n pstr1 = legenddic[freeparam[0]]\n if logp1:\n pstr1 = r'log$_\\textrm{10}$('+pstr1+')'\n\n cb1.set_label(pstr1+' (outer circle) - Fixed: black line')\n\n for p1 in np.unique(param1):\n p1col = cmap(colnorm(p1))\n p1ent = np.where(param1 == p1)\n\n plt.plot(lum1[p1ent],lum2[p1ent],'-',lw=lthick, color='k',zorder=1)\n\n plt.errorbar(lum1[p1ent],lum2[p1ent],xerr=None,yerr=None,\n marker='o',lw=0, markersize=marksize*3,\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\n\n # ------------ PARAM2 ------------\n cmap = plt.cm.get_cmap('spring')\n cmin = np.min(param2)\n cmax = np.max(param2)\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\n mm = plt.cm.ScalarMappable(cmap=cmap)\n mm.set_array(cmaparr)\n cb2 = plt.colorbar(mm)#shrink=0.25\n\n pstr2 = legenddic[freeparam[1]]\n if logp2:\n pstr2 = 'log10('+pstr2+')'\n\n cb2.set_label(pstr2+' (inner circle) - Fixed: gray line')\n\n for p2 in np.unique(param2):\n p2col = cmap(colnorm(p2))\n p2ent = np.where(param2 == p2)\n\n plt.plot(lum1[p2ent],lum2[p2ent],'-',lw=lthick, color='gray',zorder=2)\n\n plt.errorbar(lum1[p2ent],lum2[p2ent],xerr=None,yerr=None,\n marker='o',lw=0, markersize=marksize*1.5,\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\n\n if showobs != None:\n for ii, objid in enumerate(showobs[:,0]):\n if (showobs[:,7][ii] > xrange[0]) & (showobs[:,7][ii] < xrange[1]) & \\\n (showobs[:,9][ii] > yrange[0]) & (showobs[:,9][ii] < yrange[1]):\n\n if noobserr:\n obsxerr = None\n obsyerr = None\n else:\n obsxerr = showobs[:,8][ii]\n obsyerr = showobs[:,10][ii]\n plt.errorbar(showobs[:,7][ii],showobs[:,9][ii],xerr=obsxerr,yerr=obsyerr,\n marker='*',lw=lthick, markersize=marksize*2,\n markerfacecolor='k',ecolor='k',markeredgecolor = 'k',zorder=30)\n\n plt.xlabel(r'L$_\\textrm{'+line1+r'}$ / [3.826$\\times$1e33 erg/s]/[Msun/yr]')\n plt.ylabel(r'L$_\\textrm{'+line2+r'}$ / [3.826$\\times$1e33 erg/s]/[Msun/yr]')\n\n plt.xlim(xrange)\n plt.ylim(yrange)\n\n if logx:\n plt.xscale('log')\n if logy:\n plt.yscale('log')\n\n #--------- LEGEND ---------\n # plt.errorbar(-1,-1,xerr=None,yerr=None,fmt='o',lw=lthick,ecolor='white', markersize=marksize*2,\n # markerfacecolor='white',markeredgecolor = 'k',label='Ground-based spec')\n #\n # leg = plt.legend(fancybox=True, loc='upper center',prop={'size':Fsize},ncol=1,numpoints=1)\n # #bbox_to_anchor=(1.25, 1.03)) # add the legend\n # leg.get_frame().set_alpha(0.7)\n #--------------------------\n\n if verbose: print(' Saving plot to'+plotname)\n plt.savefig(plotname)\n plt.clf()\n plt.close('all')","def solve_rossler(alpha = 1.0, beta = 1.0, gamma = 1.0, max_time = 10, N = 5):\n resolution = 72\n \n fig1 = plt.figure(dpi = resolution)\n ax1, ax2, ax3 = fig1.subplots(3, 1)\n\n fig = plt.figure()\n ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n # ax.axis('off')\n \n def rossler_deriv(xi, t0, alpha=alpha, beta = beta, gamma = gamma):\n \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n \n xi1, xi2, xi3 = xi\n \n xi1_dot = -xi1 - exp(xi3)\n xi2_dot = xi1 + alpha * xi2\n xi3_dot = gamma * exp(-xi3) + xi1 - beta\n \n return [xi1_dot, xi2_dot, xi3_dot]\n\n # Choose random starting points, uniformly distributed from -15 to 15\n np.random.seed(1)\n xi0 = -15 + 30 * np.random.random((N, 3))\n xi0[:,2] = np.log(abs(xi0[:,2]))\n\n # Solve for the trajectories\n t = np.linspace(0, max_time, int(250*max_time))\n xi_t = np.asarray([integrate.odeint(rossler_deriv, xi0i, t)\n for xi0i in xi0])\n \n # choose a different color for each trajectory\n colors = plt.cm.viridis(np.linspace(0, 1, N))\n\n for i in range(N):\n xi1, xi2, xi3= xi_t[i,:,:].T\n \n ax1.plot(t, xi1, '-', c=colors[i])\n ax1.set_title('xi1')\n \n ax2.plot(t, xi2, '-', c=colors[i])\n ax2.set_title('xi2')\n \n ax3.plot(t, xi3, '-', c=colors[i])\n ax3.set_title('xi3')\n \n lines = ax.plot(xi1, xi2, xi3, '-', c=colors[i])\n plt.setp(lines, linewidth=2)\n angle = 104\n ax.view_init(30, angle)\n plt.show()\n\n return t, xi_t","def compare_CII_w_models(**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n if not p.xlim: p.xlim = [-4,2]\n if not p.ylim: p.ylim = [4,10]\n\n fig,ax = plt.subplots(figsize=(8,6))\n ax.set_ylim(p.ylim)\n ax.set_xlim(p.xlim)\n\n # SIGAME Simba-100 ext ON (default run)\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[1])\n L_line = getattr(GR,'L_[CII]158_sun')\n SFR = getattr(GR,'SFR')\n # Phantom points to close contour lines...\n L_line = np.append(L_line,np.array([1e8,10**9.3]))\n SFR = np.append(SFR,np.array([0.1,10**0.85]))\n lL_line = np.log10(L_line)\n lSFR = np.log10(SFR)\n lSFR = lSFR[L_line > 0]\n lL_line = lL_line[L_line > 0]\n # ax.plot(np.log10(SFR),np.log10(L_line),'o',ms=4,color='midnightblue',\\\n # alpha=0.7,label='SIGAME with Simba-100',zorder=10)\n # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lSFR,lL_line]).T)\n x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j,4:lL_line.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax.contour(x, y, z.reshape(x.shape),colors='forestgreen',levels=6,zorder=10)\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF')\n\n # Select only MS galaxies\n L_line = getattr(GR,'L_[CII]158_sun')\n indices = aux.select_salim18(GR.M_star[L_line > 0],GR.SFR[L_line > 0])\n print('With MS selection criteria: only %i galaxies' % (len(L_line)))\n lSFR = lSFR[indices]\n lL_line = lL_line[indices]\n lSFR = np.append(lSFR,np.array([-1,0.85,1.5]))\n lL_line = np.append(lL_line,np.array([8,9.3,8.4]))\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lSFR,lL_line]).T)\n x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j,4:lL_line.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax.contour(x, y, z.reshape(x.shape),colors='purple',linestyles='dashed',levels=6,zorder=10)\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF (MS)')\n\n\n # SIGAME Simba-100 ext OFF\n # GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[0])\n # L_line = getattr(GR,'L_[CII]158_sun')\n # SFR = getattr(GR,'SFR')\n # # Phantom points to close contour lines...\n # L_line = np.append(L_line,np.array([10.**9.2,1e8]))\n # SFR = np.append(SFR,np.array([0.9,0.1]))\n # lL_line = np.log10(L_line)\n # lSFR = np.log10(SFR)\n # lSFR = lSFR[L_line > 0]\n # lL_line = lL_line[L_line > 0]\n # # ax.plot(np.log10(SFR),np.log10(L_line),'o',ms=4,color='midnightblue',\\\n # # alpha=0.7,label='SIGAME with Simba-100',zorder=10)\n # # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\n # nbins = 100\n # k = kde.gaussian_kde(np.column_stack([lSFR,\\\n # lL_line]).T)\n\n # x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j, \\\n # 4:lL_line.max():nbins*1j]\n # z = k(np.vstack([x.flatten(), y.flatten()]))\n # CS = ax.contour(x, y, z.reshape(x.shape),colors='brown',levels=6,linestyles='dotted')\n # CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF_no_ext')\n\n # SIGAME Simba-25\n # GR = glo.global_results(sim_run=p.sim_runs[0],nGal=p.nGals[0])\n # L_line = getattr(GR,'L_[CII]158_sun')\n # SFR = getattr(GR,'SFR')\n # ax.plot(np.log10(SFR),np.log10(L_line),'^',ms=6,color='darkorchid',alpha=0.7,label='SIGAME with Simba-25')\n\n # Observations in background\n add_line_SFR_obs('[CII]158',L_line,ax,plot_fit=False)\n\n # Popping 2019\n G19 = pd.read_csv(p.d_data + 'models/Popping2019.csv',skiprows=1,sep=' ',\\\n names=['logSFR', 'logLCII', 'log LCII 14th percentile', 'log LCII 86th percentile'])\n ax.plot(G19.logSFR,G19.logLCII,'k-',label='Popping 2019',alpha=0.8)\n ax.fill_between(G19.logSFR,G19['log LCII 14th percentile'].values,\\\n G19['log LCII 86th percentile'].values,color='grey',alpha=0.4)\n\n\n # Padilla 2020\n P20 = pd.read_csv(p.d_data + 'models/DataFig6.csv',skiprows=1,sep=',',\\\n names=['IDSim','GalID','SFR','LCIITotal'])\n P20['logLCII'] = np.log10(P20['LCIITotal'])\n P20['logSFR'] = np.log10(P20['SFR'])\n\n\n colors = ['cyan','orange','midnightblue']\n nbins = 100\n IDSims = ['Ref25','Recal25','Ref100']\n for i,IDSim in enumerate(IDSims):\n # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\n k = kde.gaussian_kde(np.column_stack([P20.logSFR.values[P20.IDSim == IDSim],\\\n P20.logLCII.values[P20.IDSim == IDSim]]).T)\n\n xP20, yP20 = np.mgrid[P20.logSFR.min():P20.logSFR.max():nbins*1j, \\\n 4:P20.logLCII.max():nbins*1j]\n zP20 = k(np.vstack([xP20.flatten(), yP20.flatten()]))\n # To remove weird contour line:\n zP20.reshape(xP20.shape)[(xP20 > -1) & (yP20 < 5.5)] = 1e-5\n zP20.reshape(xP20.shape)[(xP20 < -3)] = 1e-5\n zP20.reshape(xP20.shape)[(xP20 < -2) & (yP20 > 8)] = 1e-5\n zP20.reshape(xP20.shape)[(xP20 > 1) & (yP20 < 7)] = 1e-5\n CS = ax.contour(xP20, yP20, zP20.reshape(xP20.shape),colors=colors[i],levels=5)\n CS.collections[0].set_label('Ramos Padilla 2020 '+IDSim)\n\n\n ax.set_xlabel('log '+getlabel('SFR'))\n ax.set_ylabel('log '+getlabel('[CII]158'))\n handles,labels = ax.get_legend_handles_labels()\n handles = [handles[_] for _ in [8,9,10,11,12,7,0,1,2,3,4,5,6]]\n labels = [labels[_] for _ in [8,9,10,11,12,7,0,1,2,3,4,5,6]]\n plt.legend(handles,labels,fontsize=9,loc='upper left')\n if p.savefig:\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \n plt.savefig(p.d_plot + 'luminosity/CII_SFR_w_models.png', format='png', dpi=300)","def plot_LvsL_multiple(line1='CIV1548',line2='CIII1908',line1range=[1e3,1e8],line2range=[1e0,1e8],\n outputdir='./',verbose=True):\n modeldata = nm.load_model('combined',verbose=verbose)\n\n if verbose: print(' - Putting together permutations of chosen setups for plotting')\n infodic = {}\n infodic['Zgas'] = [False,0.0001,0.006,0.040], True\n infodic['logUs'] = [False,-1.0,-2.5,-4.0] , False\n infodic['xid'] = [False,0.1,0.3,0.5] , False\n infodic['nh'] = [False,10,100,1000,10000] , False\n infodic['CO'] = [False,0.1,0.38,1.4] , False\n infodic['Mcut'] = [False,100,300] , False\n\n variables = [infodic['Zgas'][0],infodic['logUs'][0],infodic['xid'][0],\n infodic['nh'][0],infodic['CO'][0],infodic['Mcut'][0]]\n\n permutations = list(itertools.product(*variables))\n permutations_with2false = [sublist for sublist in permutations if sublist.count(False) == 2.]\n Nplots = len(permutations_with2false)\n\n if verbose: print(' - With the restriction Nfalse=2 the setup will results in '+str(Nplots)+\\\n ' plots (if model data allows)')\n if verbose: print(' - These will be saved to the output directory: '+outputdir)\n for pp, perm in enumerate(permutations_with2false):\n Zval = perm[0]\n Uval = perm[1]\n Xival = perm[2]\n Nhval = perm[3]\n COval = perm[4]\n Mval = perm[5]\n\n plotname = outputdir+'NEOGALmodelgrid_Zgas'+str(Zval).replace('.','p')+\\\n '_logU'+str(Uval).replace('.','p')+\\\n '_xid'+str(Xival).replace('.','p')+\\\n '_nH'+str(Nhval).replace('.','p')+\\\n '_CO'+str(COval).replace('.','p')+\\\n '_Mcut'+str(Mval).replace('.','p')+'.pdf'\n\n plotname = plotname.replace('False','Free')\n\n if verbose:\n plotno = pp+1\n infostr = ' - Generating plot '+str(\"%.4d\" % plotno)+'/'+str(\"%.4d\" % Nplots)+': '+plotname.split('/')[-1]+' '\n sys.stdout.write(\"%s\\r\" % infostr)\n sys.stdout.flush()\n\n if not Zval:\n logp1 = True\n else:\n logp1 = False\n\n nm.plot_LvsL(modeldata,line1=line1,line2=line2,logx=True,logy=True,logp1=logp1,logp2=False,verbose=False,\n Zgas=Zval,logU=Uval,xid=Xival,nh=Nhval,COratio=COval,Mcutoff=Mval,\n fixxrange=line1range,fixyrange=line2range,plotname=plotname)\n\n print('\\n ... done')","def lcfig( z=7 ):\n\n for modelname in ['z15G','z25G','z40G'] :\n\n # initialize a supernova model :\n snmodel = sncosmo.Model(source=modelname)\n\n # Fix the redshift for this instantiation of the model\n # (NOTE: this does not apply any cosmological dimming. It only\n # shifts the wavelengths)\n snmodel.set( z = z )\n\n # generate the H band light curve\n tobs = np.arange( 0, 1000, 10 )\n M160 = snmodel.bandmag( 'f160w', 'ab', tobs ) # Absolute Magnitude\n m160 = M160 + cosmo.distmod( z ).value # apparent magnitude\n\n pl.plot( tobs, m160 )\n ax = pl.gca()\n ax.invert_yaxis()\n ax.set_xlabel('Time (observer-frame days)')\n ax.set_ylabel('Apparent Magnitude in F160W')\n ax.set_xlim( 0, 1000 )\n ax.set_ylim( 36, 28 )\n\n pl.draw()","def plot_convergence(\n optimizers: list = [\"COBYLA\", \"SLSQP\", \"L-BFGS-B\", \"NELDER-MEAD\"],\n g2N: float = 0.2,\n maxit: int = 10000,\n varform: list = [\"ry\"],\n depth: int = 3,\n nrep: int = 10,\n dataprefix: str = \"data/miniBMN\",\n datasuffix: str = \"h5\",\n figprefix: str = \"figures/miniBMN\",\n ht: float = 0.0,\n up: int = 1000,\n):\n # setup parameters\n params = dict()\n params[\"l\"] = str(g2N).replace(\".\", \"\")\n params[\"d\"] = depth\n params[\"v\"] = \"-\".join(varform)\n params[\"m\"] = maxit\n params[\"n\"] = nrep\n params[\"f\"] = dataprefix\n params[\"s\"] = datasuffix\n assert type(optimizers).__name__ == \"list\"\n # collect data\n result = collect_data(optimizers, params)\n # get best runs\n gs = dict()\n for r in optimizers:\n gs[r] = result.loc[r].groupby(\"rep\").apply(min).energy\n gsdf = pd.DataFrame.from_dict(gs, dtype=float)\n print(gsdf.describe().T[[\"min\", \"max\", \"mean\", \"std\"]])\n # Plot\n # select the best runs for each optimizer\n fig, ax = plt.subplots()\n for o in optimizers:\n result.loc[o, gsdf[o].idxmin()].plot(\n x=\"counts\", y=\"energy\", xlim=[0, up], label=o, ax=ax\n )\n ax.axhline(ht, c=\"k\", ls=\"--\", lw=\"2\", label=\"HT\")\n ax.set_xlabel(\"iterations\")\n ax.set_ylabel(\"VQE energy\")\n ax.legend(loc=\"upper right\")\n filename = f\"{figprefix}_l{params['l']}_convergence_{params['v']}_depth{params['d']}_nr{params['n']}_max{params['m']}_xlim{up}\"\n plt.savefig(f\"{filename}.pdf\")\n plt.savefig(f\"{filename}.png\")\n plt.savefig(f\"{filename}.svg\")\n plt.close()","def energies():\n # Hardcoded initial values\n numsteps = 10000\n time_max = 1\n # Running the calculation in the solver class using the velocity verlet method\n # for better accuracy.\n verlet = solver(input_matrix, 'verlet', time_max, numsteps)\n output_matrix, KE, PE, AM = verlet.main()\n # Creating a simple time axis for plotting\n x = np.linspace(0, 1, numsteps+1)\n\n # Plotting kinetic energy over time\n plt.figure(1, figsize=(10, 10))\n plt.plot(x, KE)\n plt.suptitle('Total kinetic energy in the Earth-Sun system.', fontsize=24)\n plt.xlabel('time [yr]', fontsize=16)\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\n plt.legend(['KE'])\n\n # Plotting potential energy over time\n plt.figure(2, figsize=(10, 10))\n plt.plot(x, PE)\n plt.suptitle('Total potential energy in the Earth-Sun system.', fontsize=24)\n plt.xlabel('time [yr]', fontsize=16)\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\n plt.legend(['PE'])\n\n # Plotting total energy against time\n plt.figure(3, figsize=(10, 10))\n plt.plot(x, PE+KE)\n plt.suptitle('Total energy in the Earth-Sun system.', fontsize=24)\n plt.xlabel('time [yr]', fontsize=16)\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\n plt.legend(['KE+PE'])\n\n # Plotting angular momentum against time. print the amplitude to terminal\n amplitude = max(AM)-min(AM)\n print('Amplitude of angular momentum during 1 year: %g[AU²/yr²]' %(amplitude))\n plt.figure(4, figsize=(10, 10))\n plt.plot(x, AM)\n plt.suptitle('Total angular momentum in the Earth-Sun system.', fontsize=24)\n plt.xlabel('time [yr]', fontsize=16)\n plt.ylabel('energy [AU²/yr²]', fontsize=16)\n plt.legend(['AM'])\n\n # Plotting the kinetic, potential and total energy against time to see\n # how great the variations are\n plt.figure(5, figsize=(10, 10))\n plt.plot(x, PE, x, KE, x, KE+PE)\n plt.suptitle('Total energy in the Earth-Sun system.', fontsize=24)\n plt.xlabel('time [yr]', fontsize=16)\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\n plt.legend(['PE', 'KE', 'KE+PE'])\n plt.show()","def main(argv):\n import scipy as sc\n import scipy.integrate as integrate\n import matplotlib.pylab as p\n\n def dCR_dt(pops, t=0):\n \"\"\"returns the growth rate of consumer and resource population at any given time step\"\"\"\n\n R = pops[0]\n C = pops[1]\n K = 10000000\n dRdt = r * R * (1-(R/K)) - a * R * C\n dCdt = -z * C + e * a * R * C\n\n return sc.array([dRdt, dCdt])\n\n #integrate from time point 0 to 15, using 1000 sub-divisions of time\n t = sc.linspace(0, 15, 1000)\n\n def plot(pops):\n f1 = p.figure()\n p.plot(t, pops[:,0], 'g-', label='Resource density') # Plot\n p.plot(t, pops[:,1] , 'b-', label='Consumer density')\n p.grid()\n p.legend(loc='best')\n p.xlabel('Time')\n p.ylabel('Population density')\n p.title('Consumer-Resource population dynamics')\n #p.show()# To display the figure\n f1.savefig('../Results/LV2_model.pdf')\n\n f2 = p.figure()\n p.plot(pops[:,0], pops[:,1], 'r-', label='Consumer density') # Plot\n p.grid()\n p.xlabel('Resource Density')\n p.ylabel('Consumer density')\n p.title('Consumer-Resource population dynamics')\n #p.show()# To display the figure\n f2.savefig('../Results/LV2_second_model.pdf')\n\n return f1,f2\n\n if len(argv) < 5:\n r = 1\n a = 0.1\n z = 1.5\n e = 0.75\n else:\n r,a,z,e = float(sys.argv[1]), float(sys.argv[2]), float(sys.argv[3]), float(sys.argv[4])\n #integrate from time point 0 to 15, using 1000 sub-divisions of time\n t = sc.linspace(0, 15, 1000)\n #Set the initial conditions for the two populations\n R0 = 10\n C0 = 5\n RC0 = sc.array([R0, C0])\n #numerically integrate this system forward from those starting conditions\n pops, infodict = integrate.odeint(dCR_dt, RC0, t, full_output=True)\n print(\"final Consumer and Resource population values are:\", pops[-1]) #prints final values\n plot(pops)\n return 0","def plot_inv_RV_lit(outpath, fit_slopes, fit_intercepts, fit_stds):\n waves = np.arange(0.8, 4.01, 0.001)\n fig, ax = plt.subplots(figsize=(10, 9))\n\n for i, RV in enumerate([2.5, 3.1, 5.5]):\n # plot the extinction curve from this work\n offset = 0.1 * i\n slopes = interpolate.splev(waves, fit_slopes)\n alav = fit_intercepts(waves) + slopes * (1 / RV - 1 / 3.1)\n (line,) = ax.plot(waves, alav + offset, lw=1.5, label=r\"$R(V) = $\" + str(RV))\n stddev = interpolate.splev(waves, fit_stds)\n color = line.get_color()\n ax.fill_between(\n waves,\n alav + offset - stddev,\n alav + offset + stddev,\n color=color,\n alpha=0.2,\n edgecolor=None,\n )\n\n # plot the literature curves\n styles = [\"--\", \":\"]\n for i, cmodel in enumerate([CCM89, F19]):\n ext_model = cmodel(Rv=RV)\n (indxs,) = np.where(\n np.logical_and(\n 1 / waves >= ext_model.x_range[0], 1 / waves <= ext_model.x_range[1]\n )\n )\n yvals = ext_model(waves[indxs] * u.micron)\n ax.plot(\n waves[indxs],\n yvals + offset,\n lw=1.5,\n color=color,\n ls=styles[i],\n alpha=0.8,\n )\n\n # add text\n ax.text(3.45, 0.03, r\"$R(V) = 2.5$\", fontsize=0.8 * fs, color=\"tab:blue\")\n ax.text(3.45, 0.15, r\"$R(V) = 3.1$\", fontsize=0.8 * fs, color=\"tab:orange\")\n ax.text(3.45, 0.305, r\"$R(V) = 5.5$\", fontsize=0.8 * fs, color=\"tab:green\")\n\n # finalize and save the plot\n ax.set_xlabel(r\"$\\lambda\\ [\\mu m$]\", fontsize=1.2 * fs)\n ax.set_ylabel(r\"$A(\\lambda)/A(V)$ + offset\", fontsize=1.2 * fs)\n ax.set_xlim(0.75, 4.05)\n ax.set_ylim(-0.03, 0.98)\n handles = [\n Line2D([0], [0], color=\"k\", lw=1.5),\n Line2D([0], [0], color=\"k\", lw=1.5, ls=\"--\"),\n Line2D([0], [0], color=\"k\", lw=1.5, ls=\":\"),\n ]\n labels = [\n \"this work\",\n \"Cardelli et al. (1989)\",\n \"Fitzpatrick et al. (2019)\",\n ]\n plt.legend(handles, labels, fontsize=fs)\n plt.savefig(outpath + \"inv_RV_lit.pdf\", bbox_inches=\"tight\")\n\n # also save the plot in log scale\n plt.ylim(0.01, 1)\n plt.yscale(\"log\")\n plt.tight_layout()\n plt.savefig(outpath + \"inv_RV_lit_log.pdf\")","def plot(self):\n \n \n x_ibs=[] \n x_gss=[]\n y_ibs=[] \n y_gss=[]\n x_pso=[]\n x_bgd=[]\n y_bgd=[]\n y_pso=[]\n x_gd=[]\n y_gd=[]\n \n i=0.0000001\n \n # for k in range(1,51):\n # i= random.uniform(0.00000001, 1)\n # t_avg_ibs=[]\n # t_avg_gss=[]\n # for j in range(1,51):\n #L=random.randint(-100, 0)\n #U=random.randint(0, 100)\n max_iter=self.Max_iter \n L=self.Lower_bound\n U=self.Upper_bound\n \n minima=self.gss(L,U,i,1000)\n #print(\"minima at X = \",minima[1])\n x_ibs.append(self.I_bisection(L,U,minima[1],max_iter)[0])\n x_gss.append(self.gss(L,U,i,max_iter)[0])\n x_pso.append(self.particle_Swarm(self.func, L, U, 2, max_iter)[0])\n x_gd.append(self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)[0])\n x_bgd.append(self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)[0])\n #print(x_pso)\n for i in x_ibs[0]:\n #print(self.Func(i)) \n y_ibs.append(self.Func(i))\n for i in x_gss[0]:\n y_gss.append(self.Func(i)) \n for i in x_pso[0]:\n y_pso.append(self.Func(i)) \n for i in x_gd[0]:\n y_gd.append(self.Func(i)) \n for i in x_bgd[0]:\n y_bgd.append(self.Func(i)) \n #print(y_gss)\n\n plt.plot(x_ibs[0], y_ibs, 'r.')\n plt.plot(x_gss[0], y_gss, '.')\n plt.plot(x_pso[0], y_pso, 'y.')\n #plt.plot(x_gd[0], y_gd, 'y.')\n #plt.plot(x_bgd[0], y_bgd, 'k.')\n plt.xlabel('x')\n plt.ylabel('y')\n \n plt.suptitle('Interval Bisection Search (Red) vs Golden Section Search (Blue) vs Particle swarm optimization (Green)')\n #plt.axis([0, 100, 0.00000001, 1]) \n plt.show()\n plt.plot(x_gd[0], y_gd, 'r.')\n plt.plot(x_bgd[0], y_bgd, 'k.')\n plt.xlabel('x')\n plt.ylabel('y') \n plt.suptitle('Gradient Descent (Red) vs Batch Gradient Descent (Black) ')\n \n plt.show()\n \n start_time = timeit.default_timer()\n ibs=self.I_bisection(L,U,minima[1],max_iter)\n print(\" Execution time for Interval bisection Method is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n gss=self.gss(L,U,i,max_iter)\n print(\" Execution time for Golden Section Search is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n pso=self.particle_Swarm(self.func, L, U, 2, max_iter)\n print(\" Execution time for Particle swarm optimization is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n gd=self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)\n print(\" Execution time for Gradient Descent is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n bgd=self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)\n print(\" Execution time for Batch Gradient Descent is\", timeit.default_timer() - start_time,\"s\")\n plt.plot(ibs[1], ibs[2], 'r.')\n plt.text(ibs[1], ibs[2],\"IB\")\n plt.plot(gss[1], gss[2], '.')\n plt.text(gss[1], gss[2],\" GSS\")\n plt.plot(pso[1], pso[2], 'y.')\n plt.text(pso[1], pso[2],\" PSO\")\n plt.plot(gd[1], gd[2], 'g.')\n plt.text(gd[1], gd[2],\" GD \")\n plt.plot(bgd[1],bgd[2], 'k.')\n plt.text(bgd[1], bgd[2],\" Batch_GD\")\n \n plt.xlabel('Value of X')\n plt.ylabel('NUmber of iteration') \n plt.suptitle('Number of iterations vs minimum value of x')\n \n plt.show()","def main():\n parser = argparse.ArgumentParser(usage=__doc__)\n parser.add_argument(\"--order\", type=int, default=3, help=\"order of Bessel function\")\n args = parser.parse_args()\n f = lambda x: -special.jv(args.order, x)\n sol = optimize.minimize(f, 1.0)\n x = np.linspace(0, 10, 5000)\n plt.plot(x, special.jv(args.order, x), '-', sol.x, -sol.fun, 'o')\n plt.show() # Displays the image in matplotlib window","def Intgrl(N, R, Zeta1, Zeta2, Za, Zb):\n\n global S12, T11, T12, T22, V11A, V12A, V22A, V11B, V12B, V22B, V1111, V2111, V2121, V2211, V2221, V2222\n\n S12 = 0.0\n T11 = 0.0\n T12 = 0.0\n T22 = 0.0\n V11A = 0.0\n V12A = 0.0\n V22A = 0.0\n V11B = 0.0\n V12B = 0.0\n V22B = 0.0\n V1111 = 0.0\n V2111 = 0.0\n V2121 = 0.0\n V2211 = 0.0\n V2221 = 0.0\n V2222 = 0.0\n\n R2 = R * R\n\n # The coefficients for the contracted Gaussian functions are below\n Coeff = np.array([[1.00000, 0.0000000, 0.000000],\n [0.678914, 0.430129, 0.000000],\n [0.444635, 0.535328, 0.154329]])\n\n Expon = np.array([[0.270950, 0.000000, 0.000000],\n [0.151623, 0.851819, 0.000000],\n [0.109818, 0.405771, 2.227660]])\n D1 = np.zeros([3])\n A1 = np.zeros([3])\n D2 = np.zeros([3])\n A2 = np.zeros([3])\n\n # This loop constructs the contracted Gaussian functions\n for i in range(N):\n A1[i] = Expon[N - 1, i] * (Zeta1 ** 2)\n D1[i] = Coeff[N - 1, i] * ((2.0 * A1[i] / np.pi) ** 0.75)\n A2[i] = Expon[N - 1, i] * (Zeta2 ** 2)\n D2[i] = Coeff[N - 1, i] * ((2.0 * A2[i] / np.pi) ** 0.75)\n\n # Calculate one electron integrals\n # Centre A is first atom centre B is second atom\n # Origin is on second atom\n # V12A - off diagonal nuclear attraction to centre A etc.\n for i in range(N):\n for j in range(N):\n # Rap2 - squared distance between centre A and centre P\n Rap = A2[j] * R / (A1[i] + A2[j])\n Rap2 = Rap ** 2\n Rbp2 = (R - Rap) ** 2\n S12 = S12 + S_int(A1[i], A2[j], R2) * D1[i] * D2[j]\n T11 = T11 + T_int(A1[i], A1[j], 0.0) * D1[i] * D1[j]\n T12 = T12 + T_int(A1[i], A2[j], R2) * D1[i] * D2[j]\n T22 = T22 + T_int(A2[i], A2[j], 0.0) * D2[i] * D2[j]\n V11A = V11A + V_int(A1[i], A1[j], 0.0, 0.0, Za) * D1[i] * D1[j]\n V12A = V12A + V_int(A1[i], A2[j], R2, Rap2, Za) * D1[i] * D2[j]\n V22A = V22A + V_int(A2[i], A2[j], 0.0, R2, Za) * D2[i] * D2[j]\n V11B = V11B + V_int(A1[i], A1[j], 0.0, R2, Zb) * D1[i] * D1[j]\n V12B = V12B + V_int(A1[i], A2[j], R2, Rbp2, Zb) * D1[i] * D2[j]\n V22B = V22B + V_int(A2[i], A2[j], 0.0, 0.0, Zb) * D2[i] * D2[j]\n\n # Calculate two electron integrals\n\n for i in range(N):\n for j in range(N):\n for k in range(N):\n for l in range(N):\n Rap = A2[i] * R / (A2[i] + A1[j])\n Rbp = R - Rap\n Raq = A2[k] * R / (A2[k] + A1[l])\n Rbq = R - Raq\n Rpq = Rap - Raq\n Rap2 = Rap * Rap\n Rbp2 = Rbp * Rbp\n Raq2 = Raq * Raq\n Rbq2 = Rbq * Rbq\n Rpq2 = Rpq * Rpq\n V1111 = V1111 + TwoE(A1[i], A1[j], A1[k], A1[l], 0.0, 0.0, 0.0) * D1[i] * D1[j] * D1[k] * D1[l]\n V2111 = V2111 + TwoE(A2[i], A1[j], A1[k], A1[l], R2, 0.0, Rap2) * D2[i] * D1[j] * D1[k] * D1[l]\n V2121 = V2121 + TwoE(A2[i], A1[j], A2[k], A1[l], R2, R2, Rpq2) * D2[i] * D1[j] * D2[k] * D1[l]\n V2211 = V2211 + TwoE(A2[i], A2[j], A1[k], A1[l], 0.0, 0.0, R2) * D2[i] * D2[j] * D1[k] * D1[l]\n V2221 = V2221 + TwoE(A2[i], A2[j], A2[k], A1[l], 0.0, R2, Rbq2) * D2[i] * D2[j] * D2[k] * D1[l]\n V2222 = V2222 + TwoE(A2[i], A2[j], A2[k], A2[l], 0.0, 0.0, 0.0) * D2[i] * D2[j] * D2[k] * D2[l]\n return","def get_l_curve_results(directory, overwrite = False):\n costs = []\n tikhs = []\n regs = []\n\n glacier = \"helheim\"\n extension = \".tar.gz\"\n\n # If the output hasn't been cached, retrieve it from the giant archives\n cached_output = os.path.join(directory, \"helheim_l_curve.txt\")\n if not os.path.exists(cached_output) or overwrite:\n for archive_name in [f for f in os.listdir(directory)\n if (os.path.isfile(os.path.join(directory, f))\n and\n f[-len(extension):] == extension)]:\n start_index = len(glacier) + len(\"_lambda-\")\n regularization = float(archive_name[start_index: -len(extension)])\n cost, tikh = l_curve_point(os.path.join(directory, archive_name),\n glacier, regularization, 4)\n costs.append(cost)\n tikhs.append(tikh)\n regs.append(regularization)\n\n # Save these results to a text file\n with open(cached_output, 'w') as fid:\n fid.write(\"cost,model_norm,lambda\\n\")\n for k in range(len(costs)):\n fid.write(\"{0},{1},{2}\\n\".format(costs[k], tikhs[k], regs[k]))\n\n # Open the cached output\n with open(cached_output, 'r') as fid:\n line = fid.readline()\n line = fid.readline().split('\\n')[0]\n while(line):\n c, t, r = line.split(',')\n costs.append(float(c))\n tikhs.append(float(t))\n regs.append(float(r))\n line = fid.readline().split('\\n')[0]\n\n return costs, tikhs, regs","def lorenz_data_gen(init_x,init_y,init_z,init_r):\n # Set initial state x,y,z and parameters r,sigma,b\n sxin,syin,szin = init_x,init_y,init_z\n state = np.zeros(3)\n state[0] = float(sxin); state[1] = float(syin); state[2] = float(szin)\n \n \n r = init_r\n sigma = 10 # Parameter sigma\n b = 8./3. # Parameter b\n param = np.array([r, sigma, b]) # Vector of parameters passed to rka\n tau = .02 # Timestep from lorenz with n=500\n #err = 1.e-3 # Error tolerance\n \n # Loop over the desired number of steps\n time = 0\n nstep = 500\n # initialize arrays\n tplot=np.array([]); tauplot=np.array([])\n xplot=np.array([]); yplot=np.array([]); zplot=np.array([])\n \n for istep in range(0,nstep):\n # Record values for plotting\n x = state[0]\n y = state[1] \n z = state[2]\n tplot = np.append(tplot,time)\n tauplot = np.append(tauplot,tau)\n xplot = np.append(xplot,x)\n yplot = np.append(yplot,y)\n zplot = np.append(zplot,z)\n #if( istep%50 ==0 ):\n #print('Finished %d steps out of %d '%(istep,nstep))\n # Find new state using Runge-Kutta4\n state = rk4(state,time,tau,lorzrk,param)\n time += tau\n \"\"\"\n # Graph the time series x(t)\n plt.figure(1) \n plt.clf() # Clear figure 1 window and bring forward\n plt.plot(tplot,xplot,'-')\n plt.xlabel('Time'); plt.ylabel('x(t)')\n plt.title('Lorenz model time series')\n # plt.show()\n \n # Graph the x,y,z phase space trajectory\n \n fig=plt.figure(2)\n ax=p3.Axes3D(fig)\n ax.plot3D(xplot,yplot,zplot)\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('z')\n ax.grid(True)\n # title('Lorenz model phase space')\n #ax.set_aspect('equal')\n plt.show()\n \"\"\"\n return xplot,yplot,zplot,tplot","def main():\n # Load properties that will be needed\n store = [Storage.Storage(2), Storage.Storage(4)] \n pre_energy = [s.get(\"free_energy\") for s in store]\n post_energy = [s.get(\"post_energy\") for s in store]\n x_range = store[0].get(\"x_range\")\n xlocs = np.arange(x_range[0], x_range[1], x_range[2])\n y_range = store[0].get(\"y_range\")\n ylocs = np.arange(y_range[0], y_range[1], y_range[2])\n # Calculate step size\n xb2steps = stepsize(pre_energy[0], post_energy[0], xlocs) \n xb4steps = stepsize(pre_energy[1], post_energy[1], xlocs) \n # Set up the figure\n fig = plt.figure(1, figsize=(7.5,2.5)) \n axe = (fig.add_subplot(1, 2, 1), fig.add_subplot(1, 2, 2))\n # Plot the results\n axe[0].plot(ylocs, xb4steps, color='#FF466F', lw=4)\n axe[1].plot(ylocs, xb2steps, color='#76D753', lw=4)\n # Annotate the plots\n axe[0].set_title(\"4sXB step size\")\n axe[0].set_xlabel(\"Lattice spacing (nm)\") \n axe[0].set_ylabel(\"Step size (nm)\")\n axe[0].set_xlim((25.5, 39))\n axe[0].set_ylim((1, 8))\n axe[1].set_title(\"2sXB step size\")\n axe[1].set_xlabel(\"Lattice spacing (nm)\") \n axe[1].set_ylabel(\"Step size (nm)\")\n axe[1].set_xlim((25.5, 39))\n axe[1].set_ylim((1, 8))\n # Display the plots\n fig.subplots_adjust(wspace=0.25, hspace=0.48,\n left=0.08, right=0.98,\n top=0.85, bottom=0.21)\n plt.show()","def IR():\n s = np.array(\n [2.40774137,2.287696084,2.203613927,2.048710132,1.899829585,1.591776247,\n 2.021218754,2.572949552,3.298381484,3.635993426,3.788266224,3.8307278,3.834208811]\n )\n\n TI = np.array([50, 75, 100, 150, 200, 300, 400, 500, 750, 1000, 1500, 2000, 3000])\n\n comp1 = s * np.array([-159.1,-134.2,-109.1,-64.7,25.0,40.1,88.6,126.8,187.6,219.4,245.4,253.6,256.1])\n comp2 = s * np.array([-368.3,-356.9,-343.8,-318.1,-292.0,-242.5,-199.3,-158.4,-68.8,14.2,131.9,219.5,333.5])\n comp3 = s * np.array([-77.5,-51.9,-29.8,9.9,40.2,85.7,115.4,135.1,160.1,167.6,172.3,171.7,171.8])\n comp4 = s * np.array([-265.0,-240.6,-216.7,-170.5,-128.2,-53.5,9.6,62.3,159.7,223.8,296.5,328.3,346.7])\n comp5 = s * np.array([-346.5,-328.9,-312.1,-278.5,-244.4,-182.3,-128.0,-80.0,30.8,109.3,225.1,299.5,372.2])\n\n comp = [comp1, comp2, comp3, comp4, comp5]\n MSE = []\n colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\"]\n x_new = np.linspace(0, 3000, 10000)\n for i, j, k in zip(comp, colors, np.arange(1, 6)):\n plt.scatter(TI, i, c=j)\n # popt, _ = curve_fit(MZ, TI, i, p0=np.array([200, 220, 300]))\n popt, _ = curve_fit(MZ, TI, i, p0=np.array([300, 220]))\n # M_z0, T1, M0 = popt\n M0, T1 = popt\n y_new = MZ(x_new, *popt)\n plt.plot(x_new, y_new, \"--\", c=j, label=f\"Fit Comp. {k:d} : $T_1$={T1:3.2f}\")\n MSE.append(mean_squared_error(i,y_new[TI]))\n print(MSE)\n print(np.mean(MSE))\n plt.grid()\n plt.legend(loc=\"best\")\n plt.xlabel(\"TI\")\n plt.ylabel(r\"Singal Intensity $M_z$\")\n plt.show()","def test_GBL_tau_inst():\n dz = 0.05\n z = numpy.arange(0., 80. + 1.5*dz, dz)\n\n # Fully ionized H and He\n x_ionH = 1.0\n x_ionHe = 2.0\n\n cosmo = {}\n cosmo['omega_M_0'] = numpy.array([[0.3],[0.6],[1.0]])\n cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']\n cosmo['h'] = 0.65\n cosmo['omega_b_0'] = 0.02 / cosmo['h']**2.\n cosmo['Y_He'] = 0.24\n cd.set_omega_k_0(cosmo)\n\n tau_inst = cr.optical_depth_instant(z, x_ionH=x_ionH, x_ionHe=x_ionHe, \n **cosmo)\n tau_int = cr.integrate_optical_depth(x_ionH, x_ionHe, z, **cosmo)\n\n linestyle = ['-', ':', '--']\n \n pylab.figure()\n pylab.subplot(2,1,1)\n pylab.title(\"Compare to GB&L fig. 1 (astro-ph/9812125v3.)\")\n for i in range(len(linestyle)):\n pylab.plot(z, tau_inst[i], ls=linestyle[i], color='b')\n pylab.plot(z, tau_int[i], ls=linestyle[i], color='r')\n\n pylab.xlim(0,80)\n pylab.ylim(0,1)\n pylab.xlabel(r\"$\\mathrm{z_{ion}}$\")\n pylab.ylabel(r\"$\\tau$\")\n \n pylab.subplot(2,1,2)\n for i in range(len(linestyle)):\n pylab.plot(z, \n 1.e4 * (tau_int[i] - tau_inst[i])/tau_inst[i], \n ls=linestyle[i], color='k')\n diff = (tau_int[i] - tau_inst[i]) / tau_inst[i]\n diff[numpy.isnan(diff)] = 0.0\n print(\"max fractional error in num. int. = %.3g\" % \n numpy.max(numpy.abs(diff))\n )\n ntest.assert_array_less(numpy.abs(diff), \n numpy.zeros(diff.shape) + 2.e-4)\n\n pylab.xlim(0,40)\n pylab.xlabel(r\"$\\mathrm{z_{ion}}$\")\n pylab.ylabel(r\"$\\mathrm{10^4 \\times (num.\\tau - ana.\\tau)/ana.\\tau}$\")","def _run():\n\n temperatures_kelvins = _create_temperature_grid()\n first_derivs_kelvins_pt01 = numpy.gradient(temperatures_kelvins)\n second_derivs_kelvins_pt01 = numpy.gradient(\n numpy.absolute(first_derivs_kelvins_pt01)\n )\n\n this_ratio = (\n numpy.max(temperatures_kelvins) /\n numpy.max(first_derivs_kelvins_pt01)\n )\n\n first_derivs_unitless = first_derivs_kelvins_pt01 * this_ratio\n\n this_ratio = (\n numpy.max(temperatures_kelvins) /\n numpy.max(second_derivs_kelvins_pt01)\n )\n\n second_derivs_unitless = second_derivs_kelvins_pt01 * this_ratio\n\n _, axes_object = pyplot.subplots(\n 1, 1, figsize=(FIGURE_WIDTH_INCHES, FIGURE_HEIGHT_INCHES)\n )\n\n temperature_handle = axes_object.plot(\n temperatures_kelvins, color=TEMPERATURE_COLOUR, linestyle='solid',\n linewidth=SOLID_LINE_WIDTH\n )[0]\n\n second_deriv_handle = axes_object.plot(\n second_derivs_unitless, color=SECOND_DERIV_COLOUR, linestyle='solid',\n linewidth=SOLID_LINE_WIDTH\n )[0]\n\n first_deriv_handle = axes_object.plot(\n first_derivs_unitless, color=FIRST_DERIV_COLOUR, linestyle='dashed',\n linewidth=DASHED_LINE_WIDTH\n )[0]\n\n this_min_index = numpy.argmin(second_derivs_unitless)\n second_derivs_unitless[\n (this_min_index - 10):(this_min_index + 10)\n ] = second_derivs_unitless[this_min_index]\n\n tfp_handle = axes_object.plot(\n -1 * second_derivs_unitless, color=TFP_COLOUR, linestyle='dashed',\n linewidth=DASHED_LINE_WIDTH\n )[0]\n\n axes_object.set_yticks([0])\n axes_object.set_xticks([], [])\n\n x_label_string = r'$x$-coordinate (increasing to the right)'\n axes_object.set_xlabel(x_label_string)\n\n legend_handles = [\n temperature_handle, first_deriv_handle, second_deriv_handle,\n tfp_handle\n ]\n\n legend_strings = [\n TEMPERATURE_LEGEND_STRING, FIRST_DERIV_LEGEND_STRING,\n SECOND_DERIV_LEGEND_STRING, TFP_LEGEND_STRING\n ]\n\n axes_object.legend(legend_handles, legend_strings, loc='lower right')\n\n print 'Saving figure to file: \"{0:s}\"...'.format(OUTPUT_FILE_NAME)\n pyplot.savefig(OUTPUT_FILE_NAME, dpi=FIGURE_RESOLUTION_DPI)\n pyplot.close()","def lCurve(self): \n\n # --------------------------------------------------------------------------------------------- #\n # Read data\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\n lcTab = Table.read(fitsNnam)\n if (self.tstart is not None) and (self.tstop is not None):\n lcTab = lcTab[ (self.tstart <= lcTab['mjd']) & (lcTab['mjd'] <= self.tstop)]\n lcTab = lcTab[lcTab['flux'] != -1.] # avoid undone analyses\n\n timeMJD = lcTab['mjd']\n tref = int(np.floor( timeMJD[0] / 100.0)) * 100 # round to lowest hundred\n timeMJD -= tref\n ts = lcTab['ts']\n detect = lcTab['ts'] >= self.tsmin\n undet = lcTab['ts'] < self.tsmin\n flux = lcTab['flux'][detect]\n fluxerr = lcTab['fluxerr'][detect]\n upperl = lcTab['upperlim'][undet]\n upperl[upperl == -1.] = 0. # for when it failed\n scale = 10**int(np.floor(np.log10( np.mean( np.concatenate( (flux, upperl), axis=0) ) ))) \n\n # --------------------------------------------------------------------------------------------- #\n # Plot\n lcplt = FermiPlot(savepath='', xsize=8.5, ysize=6)\n lcplt.figname = os.path.join(self.workpath, 'LightCurve.pdf')\n lcplt.xlabel = r'Time (MJD $-$ {})'.format(tref)\n lcplt.ylabel = [r'Flux ($10^{%d}$ ph\\,cm$^{-2}$\\,s$^{-1}$)'%(int(np.log10(scale))), r'TS']\n lcplt.hline = [None, self.tsmin]\n deltaY = max(np.concatenate((flux+fluxerr, upperl), axis=0)) - min(np.concatenate((flux-fluxerr, upperl), axis=0))\n lcplt.ymin = [(min(np.concatenate((flux-fluxerr, upperl-upperl*0.1), axis=0)) - 0.05*deltaY) / scale, min(ts) - 0.05*(max(ts)-min(ts))]\n lcplt.ymax = [(max(np.concatenate((flux+fluxerr, upperl), axis=0)) + 0.05*deltaY) / scale, max(ts) + 0.05*(max(ts)-min(ts))]\n deltaX = (timeMJD[-1] + lcTab['mjderr'][-1]) - (timeMJD[0] - lcTab['mjderr'][0]) \n lcplt.xmin = timeMJD[0] - lcTab['mjderr'][0] - 0.05*deltaX\n lcplt.xmax = timeMJD[-1] + lcTab['mjderr'][-1] + 0.05*deltaX\n lcplt.fill = [item for sublist in zip( timeMJD[detect]-lcTab['mjderr'][detect], timeMJD[detect]+lcTab['mjderr'][detect] ) for item in sublist]\n lcplt.shadecol= self.loran \n if len(flux) == 0:\n lcplt.mksize = [2, 2]\n lcplt.ymode = ['linear', 'linear']\n lcplt.color = ['gray', 'black']\n lcplt.prop = [3, 1]\n lcplt.limit = [True, False]\n lcplt.multiplot(x = [ timeMJD[undet], timeMJD ],\n y = [ upperl/scale, ts ],\n xerr = [ lcTab['mjderr'][undet], lcTab['mjderr']],\n yerr = [ upperl/scale*0.1, None])\n else:\n lcplt.mksize = [2, 2, 2]\n lcplt.ymode = ['linear', 'linear', 'linear']\n lcplt.color = ['gray', 'black', 'black']\n lcplt.prop = [3, 1]\n lcplt.limit = [[True, False], False]\n lcplt.multiplot(x = [ [timeMJD[undet], timeMJD[detect]], timeMJD ],\n y = [ [upperl/scale, flux/scale], ts ],\n xerr = [ [lcTab['mjderr'][undet], lcTab['mjderr'][detect]], lcTab['mjderr']],\n yerr = [ [upperl/scale*0.1, fluxerr/scale], None])\n lcplt.save()\n\n print(\"\\t=== Figure '{}' created ===\".format(lcplt.figname)) \n return","def solver(direc):\r\n\r\n # open inputfile\r\n file = 'schroedinger.inp'\r\n fn = os.path.join(direc, file)\r\n inp_all = open(fn, 'r')\r\n\r\n # read parameters from inputfile\r\n lines = inp_all.readlines()\r\n inp_mass = lines[0].split()[0]\r\n mass = float(inp_mass)\r\n inp_xmin = lines[1].split()[0]\r\n xmin = float(inp_xmin)\r\n inp_xmax = lines[1].split()[1]\r\n xmax = float(inp_xmax)\r\n inp_npoint = lines[1].split()[2]\r\n npoint = int(inp_npoint)\r\n inp_firsteigval = lines[2].split()[0]\r\n firsteigval = int(inp_firsteigval)\r\n inp_lasteigval = lines[2].split()[1]\r\n lasteigval = int(inp_lasteigval)\r\n interpoltype = lines[3].split()[0]\r\n inp_nrinterpolpoints = lines[4].split()[0]\r\n nrinterpolpoints = int(inp_nrinterpolpoints)\r\n len_pot = len(lines)-5\r\n xpot = np.zeros(len_pot)\r\n ypot = np.zeros(len_pot)\r\n for ii in range(5, len_pot+5):\r\n xpot[ii - 5] = float(lines[ii].split()[0])\r\n ypot[ii - 5] = float(lines[ii].split()[1])\r\n\r\n # read interpolation type and interpolate the potential\r\n xx = np.linspace(xmin, xmax, npoint)\r\n if interpoltype == 'linear':\r\n pot = np.interp(xx, xpot, ypot)\r\n elif interpoltype == 'polynomial':\r\n degree = int(nrinterpolpoints - 1)\r\n coef = np.polyfit(xpot, ypot, degree)\r\n polf = np.poly1d(coef)\r\n pot = polf(xx)\r\n elif interpoltype == 'cspline':\r\n cubicf = interp1d(xpot, ypot, kind='cubic')\r\n pot = cubicf(xx)\r\n else:\r\n print('interpolation type not found')\r\n sys.exit(1)\r\n\r\n # save x- and y-values for interpolated potential in potential.dat file\r\n potential = np.array([xx, pot])\r\n xypotential = potential.T\r\n np.savetxt(os.path.join(direc, 'potential.dat'), xypotential)\r\n\r\n # formulate matrix-problem for the discretised Schroedinger equation\r\n matrix = np.zeros((npoint, npoint))\r\n delta = abs((xmax-xmin)/(npoint))\r\n aa = 1/(mass*(delta)**2)\r\n for ii in range(1, npoint):\r\n matrix[ii, ii-1] = -aa/2\r\n for ii in range(0, npoint):\r\n matrix[ii, ii] = aa+pot[ii]\r\n for ii in range(0, npoint-1):\r\n matrix[ii, ii+1] = -aa/2\r\n\r\n # compute eigenvalues and eigenvectors\r\n energy, wavefunc = scipy.linalg.eigh(matrix, eigvals=(int(firsteigval-1),\r\n int(lasteigval-1)))\r\n\r\n # normalize wavefunctions\r\n deltavec = delta*np.ones((1, npoint))\r\n wavefunc_sq = wavefunc**2\r\n norm_sq = np.dot(deltavec, wavefunc_sq)\r\n norm = 1/(np.sqrt(norm_sq))\r\n norm_wavefunc = np.dot(wavefunc, np.diag(np.reshape(norm,\r\n (len(energy), ))))\r\n\r\n # save eigenvalues and eigenvectors in energies.dat and wavefuncs.dat files\r\n np.savetxt(os.path.join(direc, 'energies.dat'), energy)\r\n wavefuncs = np.hstack((xx.reshape((npoint, 1)), norm_wavefunc))\r\n np.savetxt(os.path.join(direc, 'wavefuncs.dat'), wavefuncs)\r\n\r\n # compute expectation values and uncertainty for the position\r\n exp_value = np.zeros(lasteigval-firsteigval+1)\r\n exp_value_sq = np.zeros(lasteigval-firsteigval+1)\r\n uncert_x = np.zeros(lasteigval-firsteigval+1)\r\n for ii in range(firsteigval-1, lasteigval):\r\n exp_value[ii] = delta*np.sum(norm_wavefunc[:, ii]**2*xx)\r\n exp_value_sq[ii] = delta*np.sum(norm_wavefunc[:, ii]**2*xx**2)\r\n uncert_x[ii] = np.sqrt(exp_value_sq[ii]-exp_value[ii]**2)\r\n\r\n # save expectation values and uncertainty for the position in expvalues.dat\r\n expvalues = np.array([exp_value, uncert_x])\r\n datexpvalues = expvalues.T\r\n np.savetxt(os.path.join(direc, 'expvalues.dat'), datexpvalues)\r\n\r\n return xypotential, energy","def cloudy_hii_radial_lins(props=['[CII]158'],cols=['b'],ls=['-'],model_name = '',i_model = 0,NIIratio=False,**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n if p.add:\n ax = plt.gca()\n else:\n fig,ax = plt.subplots(figsize=(8,6))\n\n names = ['depth','[CI]610','[CI]370','[CII]158','[OI]63','[OI]145','[OIII]88','[NII]205','[NII]122','CO(1-0)','CO(2-1)','CO(3-2)','CO(4-3)','CO(5-4)']\n\n for prop,col,ls1 in zip(props,cols,ls):\n lins = pd.read_table(p.d_cloudy_test + '%s.lins' % model_name,skiprows=1,names=names)\n # print(lins['depth'].values)\n # print(lins[prop].values)\n # ax.plot(lins['depth'].values/c.pc.to('cm'),lins[prop].values,'o',ms=5,color=col)\n ax.plot(lins['depth'].values/c.pc.to('cm'),lins[prop].values,'-',lw=2,ls=ls1,color=col,label=prop)\n\n ax.set_xlabel('Depth [pc]')\n ax.set_ylabel('ergs/s integrated over sphere')\n ax.legend()\n ax.set_yscale('log')\n\n names = ['it','[CI]610','[CI]370','[CII]158','[OI]63','[OI]145','[OIII]88','[NII]205','[NII]122','CO(1-0)','CO(2-1)','CO(3-2)','CO(4-3)','CO(5-4)']\n\n\n\n if NIIratio:\n ax2 = ax.twinx()\n ax2.plot(lins['depth'].values/c.pc.to('cm'),lins['[NII]122'].values/lins['[NII]205'].values,'o',ms=5,color='k')\n ax2.plot(lins['depth'].values/c.pc.to('cm'),lins['[NII]122'].values/lins['[NII]205'].values,'-',lw=1,ls='-',color='k',label='NII ratio')\n ax2.set_ylim([0,13])\n cloudy_library = clo.library()\n hii_grid = cloudy_library._restore_hii_region_grid()\n print('NII ratio integrated: %.4f' % (10.**hii_grid['[NII]122'][i_model] / 10.**hii_grid['[NII]205'][i_model]))\n print('NII luminosities: ',10.**hii_grid['[NII]122'][i_model],10.**hii_grid['[NII]205'][i_model])\n\n if p.savefig:\n if not os.path.isdir(p.d_plot + 'cloudy/'): os.mkdir(p.d_plot + 'cloudy/') \n plt.savefig(p.d_plot + 'cloudy/%s_lins.png' % (model_name), format='png', dpi=300, facecolor='w')","def CII_vs_CO(**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[1])\n fig,ax1 = plt.subplots()\n L_CII = getattr(GR,'L_[CII]158_sun')\n L_CO = getattr(GR,'L_CO(1-0)_sun')\n Zsfr = getattr(GR,'Zsfr')\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\n sc = ax1.scatter(np.log10(L_CO)-10, np.log10(L_CII)-10, marker='o', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\n vmin=np.log10(0.05), vmax=np.log10(3.1), \\\n s=10, alpha=0.8)#, label='SIGAME 100Mpc_arepoPDF')\n # print('Min Zsfr in Simba sample: ',np.min(Zsfr))\n # print('indices with L_CO < 1e0:')\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\n 4:lL_CII.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='forestgreen',levels=8,zorder=10)\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF')\n\n GR = glo.global_results(sim_run=p.sim_runs[0],nGal=p.nGals[0],grid_ext=p.grid_exts[1])\n L_CII = getattr(GR,'L_[CII]158_sun')\n L_CO = getattr(GR,'L_CO(1-0)_sun')\n Zsfr = getattr(GR,'Zsfr')\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\n lL_CO = np.append(lL_CO,np.array([6.1,5]))\n lL_CII = np.append(lL_CII,np.array([8.9,9.7]))\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\n # s=10, alpha=0.8, label='SIGAME 25Mpc_arepoPDF')\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\n 4:lL_CII.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='deepskyblue',linestyles='dotted',levels=6)\n CS.collections[0].set_label('SIGAME 25Mpc_arepoPDF')\n\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[0])\n L_CII = getattr(GR,'L_[CII]158_sun')\n L_CO = getattr(GR,'L_CO(1-0)_sun')\n Zsfr = getattr(GR,'Zsfr')\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\n lL_CO = np.append(lL_CO,np.array([-2.2,4.7]))\n lL_CII = np.append(lL_CII,np.array([8,9.3]))\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\n # s=10, alpha=0.8, label='SIGAME v3 Simba-%s' % (p.sim_runs[0].replace('_','').replace('Mpc','')))\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\n 4:lL_CII.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='brown',levels=8,zorder=5,linestyles='dashed')\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF_no_ext')\n\n # Observations\n K16 = pd.read_pickle('data/observations/AHIMSA_sample_lit')\n K16_LCII = K16['[CII]158_Lsun']\n K16_LCO = K16['CO(1-0)_Lsun']\n ax1.plot(np.log10(K16_LCO), np.log10(K16_LCII), '>', color='grey', ms=6, fillstyle='none',alpha=0.8, mew=1,zorder=0,\\\n label='Mixed type galaxies [Kamenetzky+16]')\n\n C15 = pd.read_pickle('data/observations/DGS_Cormier_2015')\n C15_LCII = C15['L_[CII]158']\n C15_LCO = C15['L_CO(1-0)']\n C15_Z = C15['Z']\n # L_ul = C15['L_[CII]158'][(C15['L_[CII]158'] < 0) & (C15['L_CO(1-0)'] > 0)]\n # if len(L_ul) > 0:\n # ax1.plot(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul),'s',ms=5,mew=0,color='grey',alpha=0.8)\n # ax1.errorbar(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul), elinewidth=1,\\\n # uplims=np.ones(len(L_ul)),yerr=np.ones(len(L_ul))*1,color='grey',alpha=0.8,lw=0)\n ax1.scatter(np.log10(C15_LCO), np.log10(C15_LCII), marker='+', c=np.log10(C15_Z), cmap='viridis', zorder=0,\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\n s=100, lw=3, alpha=0.8, label='Dwarf galaxies [Cormier+15]')\n\n A17 = pd.read_pickle('data/observations/xCOLD_GASS_Accurso_2017')\n A17 = A17.loc[np.argwhere(A17['L_CO(1-0)'].values > 0).flatten()]\n ax1.scatter(A17['L_CO(1-0)'],A17['L_[CII]158'], marker='d', c=np.log10(A17['Z']), cmap='viridis', zorder=0,\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\n s=50, lw=0, alpha=0.8, label='COLD GASS [Accurso+17]') #c=np.log10(A17['Z']), \n\n CII_obs = np.log10(np.append(K16_LCII.values,C15_LCII.values))\n CO_obs = np.log10(np.append(K16_LCO.values,C15_LCO.values))\n CII_obs = np.append(CII_obs,A17['L_[CII]158'].values)\n CO_obs = np.append(CO_obs,A17['L_CO(1-0)'].values)\n index = np.argwhere((CII_obs > 0) & (CO_obs > 0)).flatten()\n CII_obs = CII_obs[index]\n CO_obs = CO_obs[index]\n\n x = np.linspace(0, 7, 100)\n fit = LinearRegression().fit(CO_obs.reshape(-1, 1),\\\n CII_obs.reshape(-1, 1))\n L_fit = fit.predict(x.reshape(-1, 1))\n ax1.plot(x, L_fit, color='black', linestyle='--', label='Log-linear fit to observations')\n\n ax1.set_ylabel('log ' + getlabel('[CII]158'))\n ax1.set_xlabel('log ' + getlabel('CO(1-0)'))\n plt.colorbar(sc,label=r'log $\\langle$Z$\\rangle_{\\rm SFR}$ [Z$_{\\rm \\odot}$]')\n\n handles, labels = ax1.get_legend_handles_labels()\n print(labels) # labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))\n # handles = [handles[_] for _ in [2,4,3,5,0,6,7,1]]\n # labels = [labels[_] for _ in [2,4,3,5,0,6,7,1]]\n handles = [handles[_] for _ in [2,4,3,5,6,0,1]]\n labels = [labels[_] for _ in [2,4,3,5,6,0,1]]\n plt.legend(handles,labels,loc='lower left',fontsize=10.,frameon=True)\n\n ax1.set_xlim([-3,6.2])\n ax1.set_ylim([4,10])\n\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \n plt.savefig(p.d_plot + 'luminosity/CO_vs_CII%s%s.png' % (p.grid_ext,p.table_ext), dpi=300)","def main():\n\n # Create model_dict from arguments\n model_dict = model_dict_create()\n\n # No. of deviations to consider\n no_of_mags = 50\n dev_list = np.linspace(0.1, 5.0, no_of_mags)\n\n # Load dataset specified in model_dict\n print('Loading data...')\n dataset = model_dict['dataset']\n if (dataset == 'MNIST'):\n X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(model_dict)\n # rd_list = [None, 784, 331, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10]\n rd_list = [None, 331, 100, 80, 60, 40, 20]\n # rd_list = [None,784,100]\n elif dataset == 'GTSRB':\n X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(model_dict)\n rd_list = [1024, 338, 200, 100, 90, 80, 70, 60, 50, 40, 33, 30, 20, 10]\n elif dataset == 'HAR':\n X_train, y_train, X_test, y_test = load_dataset(model_dict)\n # rd_list = [561, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10]\n rd_list = [561]\n X_val = None\n y_val = None\n\n mean = np.mean(X_train, axis=0)\n X_train -= mean\n X_test -= mean\n if (dataset == 'MNIST') or (dataset == 'GTSRB'): X_val -= mean\n\n # fig, ax = plt.subplots(nrows=1, ncols=1)\n\n # for rd in rd_list:\n # model_setup_carlini(model_dict, X_train, y_train, X_test, y_test, X_val, y_val, mean, rd, ax)\n\n partial_carlini = partial(model_setup_carlini, model_dict=model_dict, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test, X_val=X_val, y_val=y_val,\n mean=mean)\n pool=multiprocessing.Pool(processes=8)\n pool.map(partial_carlini,rd_list,1)\n pool.close()\n pool.join()\n\n # dim_red = model_dict['dim_red']\n # plt.legend()\n # plt.savefig('carlini_l2_hist_'+dim_red+'.png')","def main():\n df = pd.read_csv('data/ch5_q8_simulation.csv')\n\n # Part b\n plt.figure()\n plt.scatter(df['x'], df['y'])\n plt.xlabel('x')\n plt.ylabel('y')\n plt.title('Scatterplot y vs. x')\n plt.savefig('plots/8a.png')\n\n # Part c\n response_var = 'y'\n pred_vars_lin = ['x']\n pred_vars_quad = ['x', 'x2']\n pred_vars_cub = ['x', 'x2', 'x3']\n pred_vars_quar = ['x', 'x2', 'x3', 'x4']\n\n poly_terms = pd.DataFrame({'x2': np.power(df['x'], 2),\n 'x3': np.power(df['x'], 3),\n 'x4': np.power(df['x'], 4)})\n df = pd.concat([df, poly_terms], axis=1)\n\n CV_error_lin = loocv(df, response_var, pred_vars_lin)\n CV_error_quad = loocv(df, response_var, pred_vars_quad)\n CV_error_cub = loocv(df, response_var, pred_vars_cub)\n CV_error_quar = loocv(df, response_var, pred_vars_quar)\n\n print('Part c')\n print('CV error (linear) = {:.3f}'.format(CV_error_lin))\n print('CV error (quadratic) = {:.3f}'.format(CV_error_quad))\n print('CV error (cubic) = {:.3f}'.format(CV_error_cub))\n print('CV error (quartic) = {:.3f}'.format(CV_error_quar))\n\n # Part d\n np.random.seed(801)\n y = np.random.randn(100)\n x = np.random.randn(100)\n y = x - 2 * np.power(x, 2) + np.random.randn(100)\n\n df = pd.DataFrame({'x': x,\n 'x2': np.power(x, 2),\n 'x3': np.power(x, 3),\n 'x4': np.power(x, 4),\n 'y': y})\n\n CV_error_lin = loocv(df, response_var, pred_vars_lin)\n CV_error_quad = loocv(df, response_var, pred_vars_quad)\n CV_error_cub = loocv(df, response_var, pred_vars_cub)\n CV_error_quar = loocv(df, response_var, pred_vars_quar)\n\n print('Part d')\n print('CV error (linear) = {:.3f}'.format(CV_error_lin))\n print('CV error (quadratic) = {:.3f}'.format(CV_error_quad))\n print('CV error (cubic) = {:.3f}'.format(CV_error_cub))\n print('CV error (quartic) = {:.3f}'.format(CV_error_quar))\n\n # Part f\n model = sm.OLS(df.loc[:, response_var], df.loc[:, pred_vars_quar]).fit()\n print(model.summary())","def clippingHGMevaluation(input_generator,branches,iden_method,Plot,reference=None):\n for t in range(8,11):\n t = t / 10.0\n thresholds = [-t,t]\n input_signal = input_generator.GetOutput()\n nl_functions = [nlsp.function_factory.hardclip(thresholds),]*branches\n filter_spec_tofind = nlsp.log_bpfilter(branches=branches, input=input_signal)\n ref_nlsystem = nlsp.HammersteinGroupModel_up(input_signal=input_signal,\n nonlinear_functions=nl_functions,\n filter_irs=filter_spec_tofind,\n max_harmonics=range(1,branches+1))\n\n found_filter_spec, nl_functions = iden_method(input_generator,ref_nlsystem.GetOutput(),branches)\n iden_nlsystem = nlsp.HammersteinGroupModel_up(input_signal=input_signal,\n nonlinear_functions=nl_functions,\n filter_irs=found_filter_spec,\n max_harmonics=range(1,branches+1))\n # sine = sumpf.modules.SineWaveGenerator(frequency=5000.0,phase=0.0,samplingrate=input_signal.GetSamplingRate(),length=len(input_signal)).GetSignal()\n sine = sumpf.modules.SweepGenerator(samplingrate=input_signal.GetSamplingRate(),length=len(input_signal)).GetSignal()\n ref_nlsystem.SetInput(sine)\n iden_nlsystem.SetInput(sine)\n if reference is not None:\n reference = nlsp.change_length_signal(reference,length=len(input_signal))\n ref_nlsystem.SetInput(reference)\n iden_nlsystem.SetInput(reference)\n\n if Plot is True:\n plot.relabelandplot(sumpf.modules.FourierTransform(ref_nlsystem.GetOutput()).GetSpectrum(),\"Reference System\",show=False)\n plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem.GetOutput()).GetSpectrum(),\"Identified System\",show=False)\n print \"SNR between Reference and Identified output for symmetric hardclipping HGM(thresholds:%r): %r\" %(thresholds,nlsp.snr(ref_nlsystem.GetOutput(),\n iden_nlsystem.GetOutput()))","def main(\n error_band_dir,\n output_dir,\n indep_var,\n ivar_start,\n ivar_stop,\n ivar_step,\n param_list,\n observable_list,\n Lambda_b,\n lambda_mult,\n p_decimal_list,\n orders,\n ignore_orders,\n interaction,\n X_ref_hash,\n prior_set,\n h,\n cbar_lower,\n cbar_upper,\n sigma,\n convention\n ):\n\n color_dict = {\n \"LOp\": plt.get_cmap(\"Greys\"),\n \"LO\": plt.get_cmap(\"Purples\"),\n \"NLO\": plt.get_cmap(\"Oranges\"),\n \"N2LO\": plt.get_cmap(\"Greens\"),\n \"N3LO\": plt.get_cmap(\"Blues\"),\n \"N4LO\": plt.get_cmap(\"Reds\")\n }\n\n fill_transparency = 1\n x = np.arange(ivar_start, ivar_stop, ivar_step)\n\n fig = plt.figure(figsize=(3.4, 3.4))\n ax = fig.add_subplot(1, 1, 1)\n ax.set_zorder(15)\n ax.set_axisbelow(False)\n\n if indep_var == \"theta\":\n param_var = \"energy\"\n indep_var_label = r\"$\\theta$ (deg)\"\n param_var_label = r\"$E_{\\mathrm{lab}}\"\n param_var_units = r\"$ MeV\"\n xmin = 0\n xmax = 180\n else:\n param_var = \"theta\"\n indep_var_label = r\"$E$ (MeV)\"\n param_var_label = r\"$\\theta\"\n param_var_units = r\"^\\circ$\"\n xmin = 0\n xmax = 350\n\n for observable in observable_list:\n for param in param_list:\n if observable == [\"t\", \"t\", \"t\", \"t\"] or \\\n (observable == [\"0\", \"0\", \"0\", \"0\"] and indep_var == \"energy\"):\n ax.set_yscale(\"log\", nonposy='clip')\n\n ax.set_xlabel(indep_var_label)\n # ax.set_ylabel('')\n\n # if indep_var == \"theta\":\n # major_tick_spacing = 60\n # minor_tick_spacing = 20\n # ax.xaxis.set_major_locator(\n # ticker.MultipleLocator(major_tick_spacing))\n # ax.xaxis.set_minor_locator(\n # ticker.MultipleLocator(minor_tick_spacing))\n\n if indep_var == \"energy\":\n major_ticks = np.arange(0, 351, 100)\n # minor_ticks = np.arange(50, 351, 100)\n x_minor_locator = AutoMinorLocator(n=2)\n elif indep_var == \"theta\":\n major_ticks = np.arange(0, 181, 60)\n # minor_ticks = np.arange(0, 181, 20)\n x_minor_locator = AutoMinorLocator(n=3)\n\n ax.xaxis.set_minor_locator(x_minor_locator)\n ax.set_xticks(major_ticks)\n\n # Create the description box\n # text_str = r\"$C_{\" + observable[0] + observable[1] + \\\n # observable[2] + observable[3] + r\"}$\" + \", \" # + \"\\n\"\n text_str = indices_to_observable_name(observable)\n if observable == ['t', 't', 't', 't']:\n text_str += \" (mb)\"\n elif observable == ['0', '0', '0', '0']:\n text_str += \" (mb/sr)\"\n\n # Probably don't include this extra info. Leave for caption.\n #\n # text_str += \", \"\n # if observable != ['t', 't', 't', 't']:\n # text_str += \", \" + param_var_label + r\" = \" + str(param) + param_var_units + \", \" # + \"\\n\"\n # text_str += r\"$\\Lambda_b = \" + str(lambda_mult*Lambda_b) + r\"$ MeV\"\n\n # Don't put in a text box\n #\n # ax.text(.5, .96, text_str,\n # horizontalalignment='center',\n # verticalalignment='top',\n # multialignment='center',\n # transform=ax.transAxes,\n # bbox=dict(facecolor='white', alpha=1, boxstyle='square', pad=.5),\n # zorder=20)\n\n # Don't put in the title\n # plt.title(text_str, fontsize=10)\n\n # Instead use y axis\n ax.set_ylabel(text_str, fontsize=10)\n legend_patches = []\n\n try:\n npwa_name = npwa_filename(observable, param_var, param)\n npwa_file = DataFile().read(os.path.join(\"../npwa_data/\", npwa_name))\n npwa_plot, = ax.plot(npwa_file[0], npwa_file[1],\n color=\"black\", linewidth=1,\n label=\"NPWA\", zorder=10,\n linestyle=\"--\")\n except FileNotFoundError:\n npwa_plot = None\n\n # First get global min/max of all orders\n for i, order in enumerate(orders):\n # obs_name = observable_filename(\n # observable, indep_var, ivar_start, ivar_stop,\n # ivar_step, param_var, param, order)\n # dob_name = dob_filename(p_decimal_list[0], Lambda_b, obs_name)\n dob_name = dob_filename(\n observable, indep_var, ivar_start, ivar_stop,\n ivar_step, param_var, param, order, ignore_orders,\n Lambda_b, lambda_mult, X_ref_hash,\n p_decimal_list[0], prior_set, h, convention, None,\n cbar_lower, cbar_upper, sigma,\n potential_info=None)\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\n if i == 0:\n obs = dob_file[1]\n obs_min = np.min(obs)\n obs_max = np.max(obs)\n else:\n old_obs = obs\n obs = dob_file[1]\n # Probably the worst way to do this.\n obs_min = min(np.min(np.minimum(old_obs, obs)), obs_min)\n obs_max = max(np.max(np.maximum(old_obs, obs)), obs_max)\n\n # Decide the padding above/below the lines\n # This weights values far from 0 more heavily.\n # ymin = obs_min - .25 * abs(obs_min)\n # ymax = obs_max + .25 * abs(obs_max)\n ymin = -1\n ymax = 20\n ax.set_ylim([ymin, ymax])\n # ax.set_xlim([ivar_start, ivar_stop-1])\n ax.set_xlim([xmin, xmax])\n\n # Start layering the plots\n for i, order in enumerate(orders):\n # obs_name = observable_filename(\n # observable, indep_var, ivar_start, ivar_stop,\n # ivar_step, param_var, param, order)\n # dob_name = dob_filename(p_decimal_list[0], Lambda_b, obs_name)\n dob_name = dob_filename(\n observable, indep_var, ivar_start, ivar_stop,\n ivar_step, param_var, param, order, ignore_orders,\n Lambda_b, lambda_mult, X_ref_hash,\n p_decimal_list[0], prior_set, h, convention, None,\n cbar_lower, cbar_upper, sigma,\n potential_info=None)\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\n\n # Plot the lines\n obs = dob_file[1]\n ax.plot(x, obs, color=color_dict[order](.99), zorder=i)\n\n # Plot the error bands\n for band_num, p in enumerate(sorted(p_decimal_list, reverse=True)):\n # dob_name = dob_filename(p, Lambda_b, obs_name)\n dob_name = dob_filename(\n observable, indep_var, ivar_start, ivar_stop,\n ivar_step, param_var, param, order, ignore_orders,\n Lambda_b, lambda_mult, X_ref_hash,\n p, prior_set, h, convention, None,\n cbar_lower, cbar_upper, sigma,\n potential_info=None)\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\n obs_lower = dob_file[2]\n obs_upper = dob_file[3]\n ax.fill_between(\n x, obs_lower, obs_upper,\n facecolor=color_dict[order](\n (band_num + 1) / (len(p_decimal_list) + 1)\n ),\n color=color_dict[order](\n (band_num + 1) / (len(p_decimal_list) + 1)\n ),\n alpha=fill_transparency, interpolate=True, zorder=i)\n\n # Use block patches instead of lines\n # Use innermost \"dark\" color of bands for legend\n # legend_patches.append(\n # mp.patches.Patch(\n # color=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\n # label=order,\n # ))\n legend_patches.append(\n mpatches.Rectangle(\n (1, 1), 0.25, 0.25,\n # color=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\n edgecolor=color_dict[order](.9),\n facecolor=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\n label=order,\n linewidth=1\n ))\n\n if npwa_plot is None:\n my_handles = legend_patches\n handler_dict = dict(zip(my_handles, [HandlerSquare() for i in legend_patches]))\n else:\n my_handles = [npwa_plot, *legend_patches]\n squares = [HandlerSquare() for i in legend_patches]\n line = HandlerLine2D(marker_pad=1, numpoints=None)\n handler_dict = dict(zip(my_handles, [line] + squares))\n\n ax.legend(loc=\"best\", handles=my_handles,\n handler_map=handler_dict,\n handletextpad=.8,\n handlelength=.6,\n fontsize=8)\n\n # Squeeze and save it\n plt.tight_layout()\n # plot_name = plot_obs_error_bands_filename(\n # observable, indep_var, ivar_start, ivar_stop,\n # ivar_step, param_var, param, orders[:i+1],\n # Lambda_b, p_decimal_list)\n plot_name = plot_obs_error_bands_filename(\n observable, indep_var, ivar_start, ivar_stop, ivar_step,\n param_var, param, orders[:i+1], ignore_orders, Lambda_b,\n lambda_mult, X_ref_hash, p_decimal_list,\n prior_set, h, convention, None, cbar_lower, cbar_upper,\n sigma, potential_info=None)\n fig.savefig(os.path.join(output_dir, plot_name), bbox_inches=\"tight\")\n\n call([\"epstopdf\", os.path.join(output_dir, plot_name)])\n call([\"rm\", os.path.join(output_dir, plot_name)])\n\n # Clear the axes for the next observable/parameter.\n plt.cla()","def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=10000, max_iters=300, optimize=True, plot=True):\r\n\r\n # Contour over a range of length scales and signal/noise ratios.\r\n length_scales = np.linspace(0.1, 60., resolution)\r\n log_SNRs = np.linspace(-3., 4., resolution)\r\n\r\n data = GPy.util.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number)\r\n # data['Y'] = data['Y'][0::2, :]\r\n # data['X'] = data['X'][0::2, :]\r\n\r\n data['Y'] = data['Y'] - np.mean(data['Y'])\r\n\r\n lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)\r\n if plot:\r\n pb.contour(length_scales, log_SNRs, np.exp(lls), 20, cmap=pb.cm.jet)\r\n ax = pb.gca()\r\n pb.xlabel('length scale')\r\n pb.ylabel('log_10 SNR')\r\n\r\n xlim = ax.get_xlim()\r\n ylim = ax.get_ylim()\r\n\r\n # Now run a few optimizations\r\n models = []\r\n optim_point_x = np.empty(2)\r\n optim_point_y = np.empty(2)\r\n np.random.seed(seed=seed)\r\n for i in range(0, model_restarts):\r\n # kern = GPy.kern.rbf(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.))\r\n kern = GPy.kern.rbf(1, variance=np.random.uniform(1e-3, 1), lengthscale=np.random.uniform(5, 50))\r\n\r\n m = GPy.models.GPRegression(data['X'], data['Y'], kernel=kern)\r\n m['noise_variance'] = np.random.uniform(1e-3, 1)\r\n optim_point_x[0] = m['rbf_lengthscale']\r\n optim_point_y[0] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);\r\n\r\n # optimize\r\n if optimize:\r\n m.optimize('scg', xtol=1e-6, ftol=1e-6, max_iters=max_iters)\r\n\r\n optim_point_x[1] = m['rbf_lengthscale']\r\n optim_point_y[1] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);\r\n\r\n if plot:\r\n pb.arrow(optim_point_x[0], optim_point_y[0], optim_point_x[1] - optim_point_x[0], optim_point_y[1] - optim_point_y[0], label=str(i), head_length=1, head_width=0.5, fc='k', ec='k')\r\n models.append(m)\r\n\r\n if plot:\r\n ax.set_xlim(xlim)\r\n ax.set_ylim(ylim)\r\n return m # (models, lls)\r","def plot_lineratios(modeldata,modeldata2='None',line1='CIV1551',line2='CIII1908',line3='CIV1551',line4='HeII1640',\n plotname='./TESTPLOT.pdf',Zgas=False,logU=False,xid=0.3,nh=100,COratio=0.38,Mcutoff=100,\n logx=True,logy=True,logp1=False,logp2=False,fixxrange=False,fixyrange=False,\n showobs=None,noobserr=False,verbose=True):\n NFalse = 0\n freeparam = []\n inforstr = \"\"\n # - - - - - - - - - - - - - - - - - - - - - - - -\n legenddic = {}\n legenddic['Zgas'] = r'Z$_\\textrm{gas}$'\n legenddic['logUs'] = r'log$_\\textrm{10}$(U)'\n legenddic['xid'] = r'$\\xi_\\textrm{d}$'\n legenddic['nh'] = r'n$_\\textrm{H}$ / [cm$^3$]'\n legenddic['COCOsol'] = r'C/O / [C/O]$_\\textrm{sun}$'\n legenddic['mup'] = r'M$_\\textrm{cut IMF}$ / [M$_\\textrm{sun}]$'\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not Zgas:\n Zgasrange = [0.0,1.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' Zgas:vary, '\n freeparam.append('Zgas')\n else:\n Zgasrange = [Zgas-1e-6,Zgas+1e-6]\n inforstr = inforstr+' '+legenddic['Zgas']+'='+str(Zgas)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not logU:\n logUrange = [-5.0,0.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' logU:vary, '\n freeparam.append('logUs')\n else:\n logUrange = [logU-0.1,logU+0.1]\n inforstr = inforstr+' '+legenddic['logUs']+'='+str(logU)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not xid:\n xidrange = [0.0,0.6]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' xid:vary, '\n freeparam.append('xid')\n else:\n xidrange = [xid-0.01,xid+0.01]\n inforstr = inforstr+' '+legenddic['xid']+'='+str(xid)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not nh:\n nhrange = [0.0,1.0e6]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' nH:vary, '\n freeparam.append('nh')\n else:\n nhrange = [nh-1.0,nh+1.0]\n inforstr = inforstr+' '+legenddic['nh']+'='+str(nh)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not COratio:\n COratiorange = [0.0,2.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' C/O:vary, '\n freeparam.append('COCOsol')\n else:\n COratiorange = [COratio-0.001,COratio+0.001]\n inforstr = inforstr+' '+legenddic['COCOsol']+'='+str(COratio)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if not Mcutoff:\n Mcutoffrange = [0.0,400.0]\n NFalse = NFalse + 1.0\n #inforstr = inforstr+' Mcutoff:vary, '\n freeparam.append('mup')\n else:\n Mcutoffrange = [Mcutoff-1.0,Mcutoff+1.0]\n inforstr = inforstr+' '+legenddic['mup']+'='+str(Mcutoff)+', '\n # - - - - - - - - - - - - - - - - - - - - - - - -\n\n if NFalse != 2:\n sys.exit(' Two and only two of the model parameters (Zgas,logU,xid,nh,COratio,Mcutoff) '\n 'should be set to Flase to define the model grid; however it appears '+str(NFalse)+\n ' parameters where not set')\n\n # - - - - - - - - - - - - - - - - - - - - - - - -\n goodent = np.where( (modeldata['Zgas'] > Zgasrange[0]) & (modeldata['Zgas'] < Zgasrange[1]) &\n (modeldata['logUs'] > logUrange[0]) & (modeldata['logUs'] < logUrange[1]) &\n (modeldata['xid'] > xidrange[0]) & (modeldata['xid'] < xidrange[1]) &\n (modeldata['nh'] > nhrange[0]) & (modeldata['nh'] < nhrange[1]) &\n (modeldata['COCOsol'] > COratiorange[0]) & (modeldata['COCOsol'] < COratiorange[1]) &\n (modeldata['mup'] > Mcutoffrange[0]) & (modeldata['mup'] < Mcutoffrange[1]) )\n\n Ngoodent = len(goodent[0])\n\n if Ngoodent > 1:\n if verbose: print(' - Getting data for '+str(Ngoodent)+' data points satisfying (SFR)model selection ')\n param1_1 = modeldata[freeparam[0]][goodent]\n if logp1:\n param1_1 = np.log10(param1_1)\n\n param1_2 = modeldata[freeparam[1]][goodent]\n if logp2:\n param1_2 = np.log10(param1_2)\n\n ratio1_1 = modeldata[line1][goodent]/modeldata[line2][goodent]\n ratio1_2 = modeldata[line3][goodent]/modeldata[line4][goodent]\n else:\n if verbose: print(' WARNING: Less than 2 (SFR)model grid points to plot; no output generated')\n return\n\n # - - - - - - - - - - - - - - - - - - - - - - - -\n if modeldata2 != 'None':\n goodent2 = np.where( (modeldata2['Zgas'] > Zgasrange[0]) & (modeldata2['Zgas'] < Zgasrange[1]) &\n (modeldata2['logUs'] > logUrange[0]) & (modeldata2['logUs'] < logUrange[1]) &\n (modeldata2['xid'] > xidrange[0]) & (modeldata2['xid'] < xidrange[1]) &\n (modeldata2['nh'] > nhrange[0]) & (modeldata2['nh'] < nhrange[1]) )\n\n Ngoodent2 = len(goodent2[0])\n\n if Ngoodent > 1:\n if verbose: print(' - Getting data for '+str(Ngoodent2)+' data points satisfying (AGN)model selection ')\n param2_1 = modeldata2[freeparam[0]][goodent2]\n if logp1:\n param2_1 = np.log10(param2_1)\n\n param2_2 = modeldata2[freeparam[1]][goodent2]\n if logp2:\n param2_2 = np.log10(param2_2)\n\n l2s = ['x','x','x','x'] # line names to use for Feltre+16 file\n for ll, linestr in enumerate([line1,line2,line3,line4]):\n if '1908' in linestr:\n l2 = linestr.replace('1908','1907')\n else:\n l2 = linestr\n\n l2s[ll] = l2\n\n ratio2_1 = modeldata2[l2s[0]][goodent2]/modeldata2[l2s[1]][goodent2]\n ratio2_2 = modeldata2[l2s[2]][goodent2]/modeldata2[l2s[3]][goodent2]\n else:\n if verbose: print(' WARNING: Less than 2 (AGN)model grid points to plot; no output generated')\n return\n\n # - - - - - - - - - - - PLOTTING - - - - - - - - - - -\n if verbose: print(' - Setting up and generating plot')\n plotname = plotname\n fig = plt.figure(figsize=(9, 5))\n fig.subplots_adjust(wspace=0.1, hspace=0.1,left=0.1, right=0.99, bottom=0.10, top=0.95)\n Fsize = 10\n lthick = 1\n marksize = 3\n plt.rc('text', usetex=True)\n plt.rc('font', family='serif',size=Fsize)\n plt.rc('xtick', labelsize=Fsize)\n plt.rc('ytick', labelsize=Fsize)\n plt.clf()\n plt.ioff()\n plt.title(inforstr[:-2],fontsize=Fsize)\n\n margin = 0.1\n dx = np.abs(np.max(ratio1_1)-np.min(ratio1_1))\n dy = np.abs(np.max(ratio1_2)-np.min(ratio1_2))\n\n\n if fixxrange:\n xrange = fixxrange\n else:\n if logx:\n xrange = [np.min(ratio1_1)-np.min(ratio1_1)/2.,np.max(ratio1_1)+np.max(ratio1_1)/2.]\n else:\n xrange = [np.min(ratio1_1)-dx*margin,np.max(ratio1_1)+dx*margin]\n\n if fixyrange:\n yrange = fixyrange\n else:\n if logy:\n yrange = [np.min(ratio1_2)-np.min(ratio1_2)/2.,np.max(ratio1_2)+np.max(ratio1_2)/2.]\n else:\n yrange = [np.min(ratio1_2)-dy*margin,np.max(ratio1_2)+dy*margin]\n\n # ------------ PARAM1 ------------\n cmap = plt.cm.get_cmap('winter')\n cmin = np.min(param1_1)\n cmax = np.max(param1_1)\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\n mm = plt.cm.ScalarMappable(cmap=cmap)\n mm.set_array(cmaparr)\n cb1 = plt.colorbar(mm)#shrink=0.25\n\n pstr1 = legenddic[freeparam[0]]\n if logp1:\n pstr1 = r'log$_\\textrm{10}$('+pstr1+')'\n\n cb1.set_label(pstr1+' (outer circle) - Fixed: black line')\n\n for p1 in np.unique(param1_1):\n p1col = cmap(colnorm(p1))\n p1ent = np.where(param1_1 == p1)\n\n plt.plot(ratio1_1[p1ent],ratio1_2[p1ent],'-',lw=lthick, color='k',zorder=1)\n\n plt.errorbar(ratio1_1[p1ent],ratio1_2[p1ent],xerr=None,yerr=None,\n marker='o',lw=0, markersize=marksize*3,\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\n\n if modeldata2 is not 'None':\n p1ent = np.where(param2_1 == p1)\n\n plt.plot(ratio2_1[p1ent],ratio2_2[p1ent],'-',lw=lthick, color='k',zorder=1)\n\n plt.errorbar(ratio2_1[p1ent],ratio2_2[p1ent],xerr=None,yerr=None,\n marker='D',lw=0, markersize=marksize*3,\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\n\n\n # ------------ PARAM2 ------------\n cmap = plt.cm.get_cmap('spring')\n cmin = np.min(param1_2)\n cmax = np.max(param1_2)\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\n mm = plt.cm.ScalarMappable(cmap=cmap)\n mm.set_array(cmaparr)\n cb2 = plt.colorbar(mm)#shrink=0.25\n\n pstr2 = legenddic[freeparam[1]]\n if logp2:\n pstr2 = 'log10('+pstr2+')'\n\n cb2.set_label(pstr2+' (inner circle) - Fixed: gray line')\n\n for p2 in np.unique(param1_2):\n p2col = cmap(colnorm(p2))\n p2ent = np.where(param1_2 == p2)\n\n plt.plot(ratio1_1[p2ent],ratio1_2[p2ent],'-',lw=lthick, color='gray',zorder=2)\n\n plt.errorbar(ratio1_1[p2ent],ratio1_2[p2ent],xerr=None,yerr=None,\n marker='o',lw=0, markersize=marksize*1.5,\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\n\n if modeldata2 is not 'None':\n p2ent = np.where(param2_2 == p2)\n\n plt.plot(ratio2_1[p2ent],ratio2_2[p2ent],'-',lw=lthick, color='gray',zorder=2)\n\n plt.errorbar(ratio2_1[p2ent],ratio2_2[p2ent],xerr=None,yerr=None,\n marker='D',lw=0, markersize=marksize*1.5,\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\n\n\n if showobs != None:\n for ii, objid in enumerate(showobs[:,0]):\n if (showobs[:,7][ii] > xrange[0]) & (showobs[:,7][ii] < xrange[1]) & \\\n (showobs[:,9][ii] > yrange[0]) & (showobs[:,9][ii] < yrange[1]):\n\n if noobserr:\n obsxerr = None\n obsyerr = None\n else:\n obsxerr = showobs[:,8][ii]\n obsyerr = showobs[:,10][ii]\n plt.errorbar(showobs[:,7][ii],showobs[:,9][ii],xerr=obsxerr,yerr=obsyerr,\n marker='*',lw=lthick, markersize=marksize*2,\n markerfacecolor='k',ecolor='k',markeredgecolor = 'k',zorder=30)\n\n plt.xlabel(line1+'/'+line2)\n plt.ylabel(line3+'/'+line4)\n\n plt.xlim(xrange)\n plt.ylim(yrange)\n\n if logx:\n plt.xscale('log')\n if logy:\n plt.yscale('log')\n\n #--------- LEGEND ---------\n # plt.errorbar(-1,-1,xerr=None,yerr=None,fmt='o',lw=lthick,ecolor='white', markersize=marksize*2,\n # markerfacecolor='white',markeredgecolor = 'k',label='Ground-based spec')\n #\n # leg = plt.legend(fancybox=True, loc='upper center',prop={'size':Fsize},ncol=1,numpoints=1)\n # #bbox_to_anchor=(1.25, 1.03)) # add the legend\n # leg.get_frame().set_alpha(0.7)\n #--------------------------\n\n if verbose: print(' Saving plot to'+plotname)\n plt.savefig(plotname)\n plt.clf()\n plt.close('all')","def main():\n strikes, dips, normals, slip = generate_normal_ss_data(330, 60, n=500, porp=1)\n #strikes, dips, normals, slip = generate_normal_data(330, 60, n=500, porp=10)\n sigma = invert_plane_stress(normals, slip)\n plot(sigma, strikes, dips)\n plt.show()","def EigenvalsPlot(self):\n Hfv = sp.lambdify(('v','w'), self.H)\n v_vals = np.arange(-1.5, 1.5+0.03 ,0.03)\n w_vals = self.t + v_vals # case a \n #w_vals = v_vals # case b \n #w_vals = 2*self.t - v_vals # case c \n #w_vals = -v_vals # case d \n size_v = np.size(v_vals)\n size_aut = self.H.shape[1]\n graph = np.empty([size_v,size_aut]) \n for i in range(0,size_v):\n energies,vectors = la.eig(Hfv(v_vals[i],w_vals[i]))\n energies = np.sort(np.real(energies))\n graph[i] = energies\n graph = np.insert(graph, [0], v_vals.reshape(-1,1), axis = 1) \n graph = np.matrix(graph)\n #with open(self.filetxt, \"w+\") as f:\n # for line in graph:\n # np.savetxt(f,line)\n for i in range(0, size_aut):\n plt.plot(v_vals,graph[:,i+1],'g')\n plt.xlim(-1.5,0.5)\n plt.ylim(-3,3)\n plt.savefig(self.filePNG)\n #plt.savefig(self.filePNG, format='eps', dpi=1200)\n #plt.show()","def varying_lamda(x, y, z, lambda_min, lambda_max, n_lambda, k, save_fig = None, method = 'Ridge', split = True, train = 0.7, seed = 42, max_iter = 1001, l_min = False, plot_indexes = [0,1,2]):\n\n lambdas = np.array([0] + np.logspace(lambda_min, lambda_max, n_lambda).tolist())\n polynomials = np.array(k)\n X, Y = np.meshgrid(lambdas, polynomials)\n MSE = np.zeros(np.shape(X))\n\n j = 0\n for k in polynomials:\n print(k)\n\n model = regression(x, y, z, k = int(k), split = split, train = train, seed = seed)\n if method == 'Ridge':\n model.SVD()\n i = 0\n for lam in lambdas:\n\n if method == 'Ridge':\n beta = model.Ridge(lam = lam)\n elif method == 'Lasso':\n beta = model.Lasso(lam = lam, max_iter = max_iter)\n\n z_tilde = model.z_tilde(beta = beta, X = model.X_test)\n MSE[j, i] = model.MSE(z_tilde = z_tilde, z = model.z_test)\n i += 1\n j += 1\n\n print('Method = ', method)\n lambdas_min = []\n for i in range(len(polynomials)):\n minimum_index = MSE[i].argmin()\n print('Minimum lambda for polynomial %.i: ' %(polynomials[i]), lambdas[minimum_index], MSE[i].min())\n lambdas_min.append(int(minimum_index))\n\n #plt.pcolormesh(lambdas.tolist() + [lambdas[-1] + lambdas[1]], polynomials.tolist() + [polynomials[-1] + 1], MSE)\n #plt.colorbar()\n #plt.show()\n\n plt.title('MSE for the test data with ' + method)\n plt.contourf(lambdas, polynomials, MSE)\n plt.colorbar()\n plt.ylabel('Polynomial order', fontsize = 14)\n plt.xlabel('Lambda', fontsize = 14)\n try:\n plt.savefig(results_dir + save_fig + 'contour' + '.png')\n except:\n pass\n plt.show()\n\n plt.title('MSE for the test data with ' + method)\n plt.plot(lambdas, MSE[plot_indexes[0], :], label = 'k = ' + str(polynomials[plot_indexes[0]]))\n plt.plot(lambdas, MSE[plot_indexes[1], :], label = 'k = ' + str(polynomials[plot_indexes[1]]))\n plt.plot(lambdas, MSE[plot_indexes[2], :], label = 'k = ' + str(polynomials[plot_indexes[2]]))\n if l_min:\n plt.plot(lambdas[lambdas_min[1]], MSE[1, lambdas_min[1]], 'ro', label = 'Lambda min = %.4g' %(lambdas[lambdas_min[1]]))\n else:\n pass\n plt.legend()\n plt.xlabel('Lambda', fontsize = 14)\n plt.ylabel('MSE', fontsize = 14)\n plt.tight_layout()\n try:\n plt.savefig(results_dir + save_fig + '.png')\n except:\n pass\n plt.show()\n return lambdas_min","def main():\n nbin = 60\n nbinM = 100\n \n maxmag = -20\n minmag = -29\n mag = np.linspace(minmag,maxmag,nbinM) \n minz = 0.1\n maxz = 5\n z = np.linspace(minz,maxz,nbin)\n \n minv = 10**1.6\n maxv = 10**2.6\n vel = np.linspace(minv,maxv,nbin)\n \n # writing the script for galfic once and for all\n gl.write_script()\n\n result = [[] for i in range(5)]\n \n for zl in z:\n zrange = z[np.where(z > zl)]\n for zs in zrange:\n for v in vel:\n gl.write_initfile(v,zl,zs)\n os.system('./script_gl > /dev/null 2>&1')\n sigma = gl.analyse_output(mag,zs,zl,v)\n for i in range(nbinM):\n result[0].append(zs)\n result[1].append(zl)\n result[2].append(v)\n result[3].append(mag[i])\n result[4].append(sigma[i])\n\n np.savez('crosssection.npz',x=result)","def test4():\n\t\n\tprint('This takes a while to compute - be patient!')\n\t\n\td = np.linspace(-15000,15000,300)\n\t#Voigt\n\t#p_dict = {'Bfield':700,'rb85frac':1,'Btheta':90*np.pi/180,'lcell':75e-3,'T':84,'Dline':'D2','Elem':'Cs'}\n\tp_dict = {'Bfield':1000,'rb85frac':1,'Btheta':88*np.pi/180,'Bphi':00*np.pi/180,'lcell':75e-3,'T':93,'Dline':'D2','Elem':'Cs'}\n\tpol = np.array([1.0,0.0,0.0])\n\tTVx = get_spectra(d,pol,p_dict,outputs=['I_M45','I_P45','Ix','Iy','S0','Iz'])\n\t\n\tfig2 = plt.figure()\n\tax1a = fig2.add_subplot(411)\n\tax2a = fig2.add_subplot(412,sharex=ax1a)\n\tax3a = fig2.add_subplot(413,sharex=ax1a)\n\tax4a = fig2.add_subplot(414,sharex=ax1a)\n\t\n\tax1a.plot(d,TVx[0],'r',lw=2,label=r'$I_{-45}$')\n\tax2a.plot(d,TVx[1],'b',lw=2,label=r'$I_{+45}$')\n\tax3a.plot(d,TVx[2],'r',lw=2,label=r'$I_x$')\n\tax4a.plot(d,TVx[3],'b',lw=2,label=r'$I_y$')\n\tax4a.plot(d,TVx[0]+TVx[1],'r:',lw=3.5,label=r'$I_{+45}+I_{-45}$')\n\tax4a.plot(d,TVx[2]+TVx[3],'k:',lw=2.5,label=r'$I_x + I_y$')\n\tax4a.plot(d,TVx[4],'g--',lw=1.5,label='$S_0$')\n#\tax4a.plot(d,TVx[5],'c--',lw=2.5,label='$I_z$')\n\t\n\t\n\tax4a.set_xlabel('Detuning (MHz)')\n\tax1a.set_ylabel('I -45')\n\tax2a.set_ylabel('I +45')\n\tax3a.set_ylabel('Ix')\n\tax4a.set_ylabel('Iy')\n\t\n\tax4a.set_xlim(d[0],d[-1]+3000)\n\tax4a.legend(loc=0)\n\t\n\tplt.show()","def hexapodZernikeLinearModel():\n Tfile='/home/jghao/research/decamFocus/psf_withseeing/finerGrid_coeff_matrix/zernike_coeff_finerGrid_training.cp'\n b=p.load(open(Tfile))\n nobs = len(b)\n x = b[:,0]\n y = b[:,1]\n z = b[:,2]\n theta = b[:,3]\n phi = b[:,4]\n fwhm = b[:,5]\n e1 = b[:,6]\n e2 = b[:,7]\n thetax = theta*np.cos(np.deg2rad(phi))\n thetay = theta*np.sin(np.deg2rad(phi))\n \n M22realTrefoil2 = b[:,37] # for x decenter\n M22imagTrefoil1 = b[:,54] \n M22TrefoilXshift = 0.5*(M22realTrefoil2+M22imagTrefoil1)\n\n M22realTrefoil1 = b[:,34] # for y decenter\n M22imagTrefoil2 = b[:,57] \n M22TrefoilYshift = 0.5*(M22realTrefoil1 - M22imagTrefoil2)\n\n M20defocus = b[:,12] # for defocus\n\n M22realComa2 = b[:,36] # for x-tilt\n M22imagComa1 = b[:,55]\n M22ComaXtilt = 0.5*(M22realComa2+M22imagComa1)\n\n M22realComa1 = b[:,35] # for y-tilt\n M22imagComa2 = b[:,56]\n M22ComaYtilt = 0.5*(M22realComa1 - M22imagComa2)\n \n pl.figure(figsize=(21,12))\n pl.subplot(2,3,1)\n t=bp.bin_scatter(M22TrefoilXshift,x,nbins=20,fmt='bo',scatter=True)\n res = linefit(M22TrefoilXshift,x)\n pl.plot(M22TrefoilXshift,M22TrefoilXshift*res[1]+res[0],'r,')\n pl.ylabel('x-decenter')\n pl.xlabel('(M22realTrefoil2+M22imagTrefoil1)/2')\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\n pl.subplot(2,3,2)\n t=bp.bin_scatter(M22TrefoilYshift,y,nbins=20,fmt='bo',scatter=True)\n res = linefit(M22TrefoilYshift,y)\n pl.plot(M22TrefoilYshift,M22TrefoilYshift*res[1]+res[0],'r,')\n pl.ylabel('y-decenter')\n pl.xlabel('(M22realTrefoil1 - M22imagTrefoil2)/2')\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\n pl.subplot(2,3,3)\n t=bp.bin_scatter(M20defocus,z,nbins=20,fmt='bo',scatter=True)\n res = linefit(M20defocus,z)\n pl.plot(M20defocus,M20defocus*res[1]+res[0],'r,')\n pl.ylabel('z-defocus')\n pl.xlabel('M20defocus')\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\n pl.subplot(2,3,4)\n t=bp.bin_scatter(M22ComaXtilt,thetax,nbins=20,fmt='bo',scatter=True)\n res = linefit(M22ComaXtilt,thetax)\n pl.plot(M22ComaXtilt,M22ComaXtilt*res[1]+res[0],'r,')\n pl.ylabel('x-tilt')\n pl.xlabel('(M22realComa2+M22imagComa1)/2')\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\n pl.subplot(2,3,5)\n t=bp.bin_scatter(M22ComaYtilt,thetay,nbins=20,fmt='bo',scatter=True)\n res = linefit(M22ComaYtilt,thetay)\n pl.plot(M22ComaYtilt,M22ComaYtilt*res[1]+res[0],'r,')\n pl.ylabel('y-tilt')\n pl.xlabel('(M22realComa1 - M22imagComa2)/2')\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\n\n pl.close()","def fig_2_11V2(x, y, z, first_poly = 4, complexity = 10, N = 7, method = 'OLS', seed = 42, lam = 0, folds = 5, save_fig = ''):\n errors = np.zeros((4, complexity + 1))\n bias = np.zeros(complexity + 1)\n variance = np.zeros(complexity + 1)\n z_real = FrankeFunction(x, y)\n\n complx = np.arange(first_poly, first_poly + complexity + 1, 1)\n\n MSE = np.zeros(complexity + 1)\n\n for i in range(complexity + 1):\n print(i)\n model = regression(x, y, z, k = first_poly + i, split = False, seed = seed)\n\n for j in range(N):\n _, MSE_R2D2, _, _, _, _ = model.k_cross(fold = folds, method2 = method, lam = lam, random_num = True)\n errors[:, i] += np.mean(MSE_R2D2, axis = 0)\n\n errors /= N\n\n print(errors)\n\n\n plt.title(method + ' Test vs Train error in k-fold with ' + str(folds) + '-folds')\n plt.plot(complx, errors[0], 'go--', label = 'Test', color = 'blue')\n plt.plot(complx, errors[2], 'go--', label = 'Training', color = 'red')\n #plt.ylim([np.min(errors_R2[2]*1.2), np.max(errors_R2[0]*1.2)])\n plt.legend()\n plt.xlabel('Polynomial maximum order', fontsize = 14)\n plt.ylabel('MSE', fontsize = 14)\n plt.savefig(results_dir + 'tradeoff2MSE' + method + save_fig + '.png')\n\n plt.show()\n\n plt.title(method + ' Test vs Train error in k-fold with ' + str(folds) + '-folds')\n plt.xlabel('Polynomial maximum order', fontsize = 14)\n plt.ylabel('R2', fontsize = 14)\n plt.plot(complx, errors[1], 'go--', label = 'Test', color = 'blue')\n plt.plot(complx, errors[3], 'go--', label = 'Training', color = 'red')\n #plt.ylim([np.min(errors_R2[3]*1.2), np.max(errors_R2[1]*1.2)])\n plt.legend()\n plt.tight_layout()\n plt.savefig(results_dir + 'tradeoff2R2' + method + save_fig + '.png')\n plt.show()","def create_lagrit_scripts(visual_mode, ncpu, refine_factor=1, production_mode=True):\n\n # Section 2 : Creates LaGriT script to be run for each polygon\n # Switches to control the LaGriT output\n # Network visualization mode True ouputs the triangulated mesh\n # for each fracture without any refinement. The goal is to visualize\n # the network structure instead of outputing the appropriate values\n # for computation\n\n print(\"Writing LaGriT Control Files\")\n # Go through the list and write out parameter file for each polygon\n # to be an input file for LaGriT\n\n lagrit_input = \"\"\"infile %s \n#LaGriT Script\n# Name the input files that contain the polygons \n# and lines of intersection. \n\ndefine / POLY_FILE / %s \ndefine / LINE_FILE / %s \ndefine / OUTPUT_INTER_ID_SSINT / id_tri_node_CPU%d.list\n\n# Define parameters such as: \n# length scale to refine triangular mesh \n# purturbation distance to break symmetry of refined mesh# \n\n# Read in line and polygon files \nread / POLY_FILE / mo_poly_work\n\"\"\"\n if not visual_mode:\n lagrit_input += \"\"\"\nread / LINE_FILE / mo_line_work \n\"\"\"\n #\n # START: Refine the point distribution\n #\n if (refine_factor > 1):\n lagrit_input += 'extrude / mo_quad_work / mo_line_work / const / H_SCALE8 / volume / 0. 0. 1. \\n'\n if (refine_factor == 2):\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\n'\n\n if (refine_factor == 4):\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\n'\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\n'\n\n if (refine_factor == 8):\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\n'\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\n'\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\n'\n\n lagrit_input += \"\"\" \ngrid2grid / tree_to_fe / mo_quad_work / mo_quad_work \nextract/surfmesh/1,0,0/mo_ext_work/mo_quad_work/external \ncompute / distance_field / mo_ext_work / mo_line_work / dfield \npset / pdel_work / attribute / dfield / 1 0 0 / H_SCALE3 / gt \nrmpoint / pset get pdel_work / inclusive \nrmpoint / compress \n\ncmo / delete / mo_quad_work \ncmo / delete / mo_line_work\ncmo / move / mo_line_work / mo_ext_work \nrmpoint / compress \n\"\"\"\n # END: Refine the point distribution\n #\n lagrit_input += \"\"\"\n## Triangulate Fracture without point addition \ncmo / create / mo_pts / / / triplane \ncopypts / mo_pts / mo_poly_work \ncmo / select / mo_pts \ntriangulate / counterclockwise \n\ncmo / setatt / mo_pts / imt / 1 0 0 / ID \ncmo / setatt / mo_pts / itetclr / 1 0 0 / ID \nresetpts / itp \ncmo / delete / mo_poly_work \ncmo / select / mo_pts \n\n\"\"\"\n if not visual_mode:\n lagrit_input += \"\"\"\n# Creates a Coarse Mesh and then refines it using the distance field from intersections\nmassage / H_SCALE64 / H_EPS / H_EPS\nrecon 0; smooth;recon 0;smooth;recon 0;smooth;recon 0\nresetpts / itp\npset / p_move / attribute / itp / 1 0 0 / 0 / eq\nperturb / pset get p_move / PURTURB64 PURTURB64 0.0\nrecon 0; smooth;recon 0;smooth;recon 0;smooth;recon 0\nsmooth;recon 0;smooth;recon 0;smooth;recon 0\n\nmassage / H_SCALE32 / H_EPS / H_EPS\nresetpts / itp\npset / p_move / attribute / itp / 1 0 0 / 0 / eq\nperturb / pset get p_move / PURTURB32 PURTURB32 0.0\nrecon 0; smooth;recon 0;smooth;recon 0;smooth;recon 0\nsmooth;recon 0;smooth;recon 0;smooth;recon 0\n\nmassage / H_SCALE16 / H_EPS / H_EPS\nresetpts / itp\npset / p_move / attribute / itp / 1 0 0 / 0 / eq\nperturb / pset get p_move / PURTURB16 PURTURB16 0.0\nrecon 0; smooth;recon 0;smooth;recon 0;smooth;recon 0\nsmooth;recon 0;smooth;recon 0;smooth;recon 0\n\nmassage / H_SCALE8 / H_EPS / H_EPS\nresetpts / itp\npset / p_move / attribute / itp / 1 0 0 / 0 / eq\nperturb / pset get p_move / PURTURB8 PURTURB8 0.0\nrecon 0; smooth;recon 0;smooth;recon 0;smooth;recon 0\nsmooth;recon 0;smooth;recon 0;smooth;recon 0\n\ncmo/addatt/ mo_pts /x_four/vdouble/scalar/nnodes \ncmo/addatt/ mo_pts /fac_n/vdouble/scalar/nnodes \n\n# Massage points based on linear function down to h_prime\nmassage2/user_function2.lgi/H_PRIME/fac_n/1.e-5/1.e-5/1 0 0/strictmergelength \n\nassign///maxiter_sm/1 \nsmooth;recon 0;smooth;recon 0;smooth;recon 0\n\nassign///maxiter_sm/10\n\nmassage2/user_function.lgi/H_PRIME/fac_n/1.e-5/1.e-5/1 0 0/strictmergelength \ncmo / DELATT / mo_pts / rf_field_name \n\n# Extrude and excavate the lines of intersection\ncmo / select / mo_line_work \n\nextrude / mo_quad / mo_line_work / const / H_EXTRUDE / volume / 0. 0. 1. \n\"\"\"\n if not production_mode:\n lagrit_input += \"\"\"\ndump / avs / QUAD_FILE / mo_quad \ncmo / delete / mo_quad \nread / QUAD_FILE / mo_quad \n\"\"\"\n else:\n lagrit_input += 'cmo / select / mo_quad \\n'\n\n lagrit_input += \"\"\"\n# Translate extruced lines of intersectino down slightly to excavate \n# nearby points from the mesh \n\ntrans / 1 0 0 / 0. 0. 0. / 0. 0. H_TRANS\nhextotet / 2 / mo_tri / mo_quad \ncmo / delete / mo_quad \naddmesh / excavate / mo_excavate / mo_pts / mo_tri\n\n##### DEBUG #####\n# If meshing fails, uncomment and rerun the script to get tmp meshes, \n# which are otherwise not output \n#dump / avs2 / tmp_tri.inp / mo_tri / 1 1 1 0\n#dump / avs2 / tmp_pts.inp / mo_pts / 1 1 1 0\n#dump / avs2 / tmp_excavate.inp / mo_excavate / 1 1 1 0\n#finish\n#####\n \ncmo / delete / mo_tri \ncmo / delete / mo_pts \n\n# recompute dfield \ncmo / create / mo_final / / / triplane \ncopypts / mo_final / mo_excavate \ncompute / distance_field / mo_final / mo_line_work / dfield \ncmo / printatt / mo_final / dfield / minmax \npset / pdel / attribute dfield / 1,0,0 / lt H_PRIME2 \nrmpoint / pset,get,pdel / inclusive \nrmpoint / compress \n\ncopypts / mo_final / mo_line_work \n\ncmo / select / mo_final \ncmo / setatt / mo_final / imt / 1 0 0 / ID \ncmo / setatt / mo_final / itp / 1 0 0 / 0 \ncmo / setatt / mo_final / itetclr / 1 0 0 / ID \n# cmo / printatt / mo_final / -xyz- / minmax \ntrans/ 1 0 0 / zero / xyz \ncmo / setatt / mo_final / zic / 1 0 0 / 0.0 \ncmo / printatt / mo_final / -xyz- / minmax \nconnect \n\ntrans / 1 0 0 / original / xyz \ncmo / printatt / mo_final / -xyz- / minmax \n\n#cmo / delete / mo_line_work \ncmo / delete / mo_excavate\ncmo / select / mo_final \nresetpts / itp \n\n\"\"\"\n if not production_mode:\n lagrit_input += 'dump / gmv / PRE_FINAL_MASSAGE / mo_final \\n'\n\n lagrit_input += \"\"\"\n## Massage Mesh Away from Intersection \npset / pref / attribute / dfield / 1,0,0 / lt / H_EPS \npset / pregion / attribute / dfield / 1,0,0 / gt / H_SCALE2 \npset / pboundary / attribute / itp / 1,0,0 / eq / 10 \npset / psmooth / not / pregion pref pboundary \n#massage / H_SCALE / 1.e-5 / 1.e-5 / pset get pref / & \n#nosmooth / strictmergelenth\n\nassign///maxiter_sm/1 \n\nsmooth / position / esug / pset get psmooth; recon 0; \nsmooth / position / esug / pset get psmooth; recon 0; \nsmooth / position / esug / pset get psmooth; recon 0; \n\nassign///maxiter_sm/10\n###########################################\n# nodes for Intersection / Mesh Connectivity Check \ncmo / copy / mo_final_check / mo_final\n#\n# Define variables that are hard wired for this part of the workflow\ndefine / MO_TRI_MESH_SSINT / mo_tri_tmp_subset\ndefine / MO_LINE_MESH_SSINT / mo_line_tmp_subset\ndefine / ATT_ID_INTERSECTION_SSINT / b_a\ndefine / ATT_ID_SOURCE_SSINT / id_node_global\ndefine / ATT_ID_SINK_SSINT / id_node_tri\n#\n# Before subsetting the mesh reate a node attribute containing the integer global node number\ncmo / set_id / mo_final_check / node / ATT_ID_SOURCE_SSINT\n#\n# Subset the triangle mesh based on b_a node attribute ne 0\n#\ncmo / select / mo_final_check\npset / pkeep / attribute / ATT_ID_INTERSECTION_SSINT / 1 0 0 / ne / 0\npset / pall / seq / 1 0 0\npset / pdel / not pall pkeep\nrmpoint / pset get pdel / exclusive\nrmpoint / compress\n#\n# Create an integer node attribute in the line mesh to interpolate the triangle node number onto\n# \ncmo / addatt / mo_line_work / ATT_ID_SINK_SSINT / vint / scalar / nnodes\ninterpolate / voronoi / mo_line_work ATT_ID_SINK_SSINT / 1 0 0 / &\n mo_final_check ATT_ID_SOURCE_SSINT\n#\n# Supress AVS output of a bunch of node attributes\n#\ncmo / modatt / mo_line_work / imt / ioflag / l\ncmo / modatt / mo_line_work / itp / ioflag / l\ncmo / modatt / mo_line_work / isn / ioflag / l\ncmo / modatt / mo_line_work / icr / ioflag / l\ncmo / modatt / mo_line_work / a_b / ioflag / l\ncmo / modatt / mo_line_work / b_a / ioflag / l\n#\n# Output list of intersection nodes with the corrosponding node id number from the triangle mesh\n\ndump / avs2 / OUTPUT_INTER_ID_SSINT / mo_line_work / 0 0 2 0\ncmo / delete / mo_line_work\n\ncmo / delete / mo_final_check\n# nodes for intersection check over\n\ncmo / select / mo_final \n\n##### DEBUG\n# write out mesh before it is rotate back into its final location\n# Useful to compare with meshing workflow if something crashes\n#dump / avs2 / tmp_mesh_2D.inp / mo_final / 1 1 1 0 \n##### DEBUG\n# Rotate facture back into original plane \nrotateln / 1 0 0 / nocopy / X1, Y1, Z1 / X2, Y2, Z2 / THETA / 0.,0.,0.,/ \ncmo / printatt / mo_final / -xyz- / minmax \nrecon 1 \n\nresetpts / itp \n\ncmo / addatt / mo_final / unit_area_normal / xyz / vnorm \ncmo / addatt / mo_final / scalar / xnorm ynorm znorm / vnorm \ncmo / DELATT / mo_final / vnorm \n\n\"\"\"\n # Clean up before output to GMV/AVS\n if production_mode:\n lagrit_input += \"\"\"\ncmo / DELATT / mo_final / x_four \ncmo / DELATT / mo_final / fac_n \ncmo / DELATT / mo_final / rf_field_name \ncmo / DELATT / mo_final / xnorm \ncmo / DELATT / mo_final / ynorm \ncmo / DELATT / mo_final / znorm \ncmo / DELATT / mo_final / a_b \ncmo / setatt / mo_final / ipolydat / no \ncmo / modatt / mo_final / icr1 / ioflag / l \ncmo / modatt / mo_final / isn1 / ioflag / l \n \n# Create Family element set\ncmo / addatt / mo_final / family_id / vint / scalar / nelements \ncmo / setatt / mo_final / family_id / 1 0 0 / family\n \n\"\"\"\n lagrit_input += \"\"\"\ndump / OUTFILE_AVS / mo_final\ndump / lagrit / OUTFILE_LG / mo_final\n\"\"\"\n else:\n lagrit_input += \"\"\"\ncmo / setatt / mo_pts / imt / 1 0 0 / ID \ncmo / setatt / mo_pts / itetclr / 1 0 0 / ID \nresetpts / itp \n\ncmo / setatt / mo_line_work / imt / 1 0 0 / ID \ncmo / setatt / mo_line_work / itetclr / 1 0 0 / ID\n\naddmesh / merge / mo_final / mo_pts / mo_line_work \ncmo / delete / mo_pts \ncmo / delete / mo_line_work \n \n# Create Family element set\ncmo / addatt / mo_final / family_id / vint / scalar / nelements \ncmo / setatt / mo_final / family_id / 1 0 0 / family\n\ncmo / select / mo_final \n# Rotate \nrotateln / 1 0 0 / nocopy / X1, Y1, Z1 / X2, Y2, Z2 / THETA / 0.,0.,0.,/ \n\ncmo / printatt / mo_final / -xyz- / minmax \ncmo / modatt / mo_final / icr1 / ioflag / l \ncmo / modatt / mo_final / isn1 / ioflag / l\ndump / lagrit / OUTFILE_LG / mo_final \n\"\"\"\n\n lagrit_input += \"\"\"\nquality \ncmo / delete / mo_final \ncmo / status / brief \nfinish\n\"\"\"\n\n # Create a different Run file for each CPU\n for i in range(1, ncpu + 1):\n file_name = 'mesh_poly_CPU%d.lgi' % i\n f = open(file_name, 'w')\n # Name of parameter Input File\n fparameter_name = 'parameters_CPU%d.mlgi' % i\n fintersection_name = 'intersections_CPU%d.inp' % i\n fpoly_name = 'poly_CPU%d.inp' % i\n parameters = (fparameter_name, fpoly_name, fintersection_name, i)\n f.write(lagrit_input % parameters)\n f.flush()\n f.close()\n print('Writing LaGriT Control Files: Complete')","def main():\n error_LU, h_LU = read_2_col_file(\"error_LU_data.txt\") \n error, h = read_2_col_file(\"error_data.txt\")\n \n log_h_LU = np.log10(h_LU)\n log_error_LU = np.log10(error_LU)\n log_h = np.log10(h)\n log_error = np.log10(error)\n\n plt.plot(log_h_LU, log_error_LU, label=\"LU-decomposition\")\n plt.plot(log_h, log_error, label=\"general Thomas algo.\")\n plt.legend()\n plt.title(\"Logarithmic plot of relative error \")\n plt.xlabel(\"$log_{10}(time \\, step)$\")\n plt.ylabel(\"$log_{10}(error)$\")\n plt.show()","def gVI(g,rBC,lBC,time,npts):\n #Important coeffcients\n global gamma\n gamma = g\n global alpha\n alpha = (gamma+1)/(gamma-1)\n global beta\n beta = (2*gamma)/(gamma-1)\n global epsilon\n epsilon = (2*gamma)/(gamma+1)\n #Boundary conditions\n global lbc\n lbc = lBC\n global rbc\n rbc = rBC\n #Time\n global t\n t = time\n #points\n global numPts\n numPts = npts\n #Speed of sound for states 1 and 5\n global cL\n cL = np.sqrt(gamma*lbc[0]/lbc[1])\n global cR\n cR = np.sqrt(gamma*rbc[0]/rbc[1])","def spore_integrals():\n\n path_tops = os.path.join(path_to_here, '../data/landscape_visualizations/DMSO/{}/{}_hours')\n idx_epochs = [25, 30]\n repeat_names = ['original', 'repeat_a', 'repeat_b']\n pList_data = os.path.join(path_to_here, '../data/embeddings/lims13/lims13_DMSO.pickle')\n pList_data = _load_and_resize_list(pList_data)\n\n\n def _plot_from_idx_repeat(ax, repeat_name, idx_epoch, color, linewidth = 0.6, linestyle = '-'):\n if os.path.isfile(path_tops.format(repeat_name, idx_epoch) + '/p_list_0.pickle'):\n p_list_NN = _load_and_resize_list(path_tops.format(repeat_name, idx_epoch) + '/p_list_0.pickle')\n NN_spore_integrals = [np.sum(i[427:442, 182:196])*(26**2)/(1000**2) for i in p_list_NN]\n ax.plot([1, 2, 3, 4, 5, 6, 7, 8], NN_spore_integrals[1:], c = color, linewidth = linewidth)\n\n\n data_spore_integrals = [np.sum(i[427:442, 182:196])*(26**2)/(1000**2) for i in pList_data]\n\n fig = plt.figure(figsize = (2.3, 1.5))\n for i, idx_epoch in enumerate(idx_epochs):\n ax = fig.add_subplot(len(idx_epochs), 1, i+1)\n ax.plot([1, 2, 3, 4, 5, 6, 7, 8], data_spore_integrals[1:], linewidth = 1, c = 'red')\n if idx_epoch is not idx_epochs[-1]:\n ax.set_xticks([])\n for repeat_name, color in zip(repeat_names, ['blue', 'green', 'orange']):\n _plot_from_idx_repeat(ax = ax, repeat_name = repeat_name, idx_epoch = idx_epoch, color = color)\n if idx_epoch == idx_epochs[-1]:\n _plot_from_idx_repeat(ax = ax, repeat_name = 'original', idx_epoch = 60, color = 'black', linewidth = 1, linestyle = '--')\n\n ax.set_ylabel(r'$\\approx \\int_{spore} \\hat{p}(\\mathbf{x},t)$', fontsize = 6, labelpad = 1)\n ax.set_xlabel('Snapshot', fontsize = 6, labelpad = 1)\n for ax in fig.axes:\n ax.tick_params(axis = 'both', labelsize = 6)\n\n plt.subplots_adjust(hspace = 0)\n plt.tight_layout()\n plt.savefig(path_to_here+'/../outputs/'+'spore_integrals.png', dpi = 1200)","def main():\n\n # choose number of data-points and sample a pair of vectors: the input\n # values and the corresponding target values\n N = 500\n inputs, targets = sample_data(N, arbitrary_function_2, seed=1)\n\n # specify the centres and scale of some rbf basis functions\n default_centres = np.linspace(0,1,21)\n default_scale = 0.03\n default_reg_param = 0.08\n\n # get the cross-validation folds\n num_folds = 4\n folds = create_cv_folds(N, num_folds)\n\n # evaluate then plot the performance of different reg params\n evaluate_reg_param(inputs, targets, folds, default_centres, default_scale)\n # evaluate then plot the performance of different scales\n evaluate_scale(inputs, targets, folds, default_centres, default_reg_param)\n # evaluate then plot the performance of different numbers of basis\n # function centres.\n evaluate_num_centres(\n inputs, targets, folds, default_scale, default_reg_param)\n\n plt.show()","def visualize(I, f, c, bc, Lx, Ly, nx, ny, dt, tstop,\n zmin, zmax, plot=1, version='scalar',\n implementation=\\\n {'ic': 'scalar', 'inner': 'scalar', 'bc': 'scalar'}):\n class Visualizer:\n \"\"\"Action function used by solver.\"\"\"\n def __init__(self, plot=0):\n self.plot = plot\n if self.plot:\n self.g = Gnuplot.Gnuplot(persist=1)\n self.g('set parametric')\n self.g('set data style lines')\n self.g('set hidden')\n self.g('set contour base')\n self.g('set zrange [%g:%g]' % (zmin,zmax))\n\n def __call__(self, u, xv, yv, t):\n if self.plot:\n data = Gnuplot.GridData(u, xv[:,0], yv[0,:], binary=0)\n self.g.splot(data)\n self.g('set title \"t=%g\"' % t)\n if self.plot == 2:\n self.g.hardcopy(filename='tmp_%020f.ps' % t,\n enhanced=1, mode='eps', fontsize=14,\n color=0, fontname='Times-Roman')\n time.sleep(0.8) # pause to finish plot\n\n viz = Visualizer(plot)\n dt, cpu_ic, cpu_inner, cpu_bc = \\\n solver(I, f, c, bc, Lx, Ly, nx, ny, dt, tstop,\n user_action=viz, implementation=implementation)\n time.sleep(3)","def inactivation(self):\n\n def boltzmann(voltage, vhalf, k):\n\n voltage = list(map(lambda x: float(x), voltage))\n exponent = np.exp(np.divide(np.subtract(voltage, vhalf), k))\n return (1/ (exponent + 1))\n\n def find_v_half(current, voltage):\n \"\"\"\n current: array of normalized IV curve\n voltage: array of the test potentials\n \"\"\"\n for i in range(len(voltage)):\n if current.iloc[i] < 0.5:\n continue\n elif current.iloc[i] == 0.5:\n return voltage.iloc[i]\n else:\n volt1 = float(voltage.iloc[i-1])\n volt2 = float(voltage.iloc[i])\n curr1 = float(current.iloc[i-1])\n curr2 = float(current.iloc[i])\n V_half = volt1 + (volt1 - volt2)*(0.5 - curr1)/(curr1 - curr2)\n return V_half\n\n v_half = []\n for i in range(self.n_cols):\n current = self.screened_data.iloc[:,i]\n #print(current)\n v_half.append(find_v_half(current, self.xaxis))\n print('Median: ', np.median(v_half))\n print('Min: ', np.min(v_half))\n print('Max: ', np.max(v_half))\n return 0\n\n initial_guess = [-42, 7]\n popt, pcov = curve_fit(boltzmann, self.xaxis, self.averaged_data, p0= initial_guess)\n plt.plot(self.xaxis, self.averaged_data, label = 'Average of all models')\n plt.plot(boltzmann(self.xaxis, *initial_guess), label = 'Initial Guess')\n plt.plot(self.xaxis, boltzmann(self.xaxis, *popt), label = 'Best Fit')\n plt.xlabel('Voltage (mV)')\n plt.ylabel('Normalized Current')\n plt.title('Steady State Inactivation')\n plt.legend()\n plt.savefig('inactivation_boltzmann_fit.png')\n return popt[0]","def convergence():\n fig, axes = plt.subplots(nrows=2, figsize=figsize(aspect=1.2))\n\n # label names\n label1 = str(league.lambda1)\n label2_list = [str(lambda2) for lambda2 in league.lambda2_list]\n\n # point spread and point total subplots\n subplots = [\n (False, [-0.5, 0.5], league.spreads, 'probability spread > 0.5'),\n (True, [200.5], league.totals, 'probability total > 200.5'),\n ]\n\n for ax, (commutes, lines, values, ylabel) in zip(axes, subplots):\n\n # train margin-dependent Elo model\n melo = Melo(lines=lines, commutes=commutes, k=1e-4)\n melo.fit(league.times, league.labels1, league.labels2, values)\n\n line = lines[-1]\n\n for label2 in label2_list:\n\n # evaluation times and labels\n times = np.arange(league.times.size)[::1000]\n labels1 = times.size * [label1]\n labels2 = times.size * [label2]\n\n # observed win probability\n prob = melo.probability(times, labels1, labels2, lines=line)\n ax.plot(times, prob)\n\n # true (analytic) win probability\n if ax.is_first_row():\n prob = skellam.sf(line, int(label1), int(label2))\n ax.axhline(prob, color='k')\n else:\n prob = poisson.sf(line, int(label1) + int(label2))\n ax.axhline(prob, color='k')\n\n # axes labels\n if ax.is_last_row():\n ax.set_xlabel('Iterations')\n ax.set_ylabel(ylabel)\n\n set_tight(w_pad=.5)","def graphe_gains(res_alea, res_glou, res_glou_e, res_ucb):\n T = np.arange(res_alea.size)\n fig, ax = plt.subplots()\n ax.grid(True)\n plt.xlabel(\"T\")\n plt.ylabel(\"Gain\")\n \n ax.plot(T, res_alea, label = 'aléatoire') \n ax.plot(T, res_glou, label = 'glouton')\n ax.plot(T, res_glou_e, label = 'e-glouton')\n ax.plot(T, res_ucb, label = 'UCB')\n ax.legend(loc = \"upper left\")\n plt.title(\"Gains des 4 algorithmes par rapport à T\")","def display4(*args):\n #-------------------- unpack\n twiss_func = args[0]\n cos_like = args[1]\n sin_like = args[2]\n lat_plot = args[3]\n #-------------------- beta x,y & dispersion x\n s = [twiss_func(i,'s') for i in range(twiss_func.nbpoints)] # Abszisse\n bx = [twiss_func(i,'bx') for i in range(twiss_func.nbpoints)] # beta x\n by = [twiss_func(i,'by') for i in range(twiss_func.nbpoints)] # beta y\n dx = [twiss_func(i,'dx') for i in range(twiss_func.nbpoints)] # dispersion x\n#-------------------- longitudinal trajectories\n z1= [cos_like(i,'s') for i in range(cos_like.nbpoints)]\n cz= [cos_like(i,'cz') for i in range(cos_like.nbpoints)]\n cdp= [cos_like(i,'cdp') for i in range(cos_like.nbpoints)]\n\n z2= [sin_like(i,'s') for i in range(sin_like.nbpoints)]\n sz= [sin_like(i,'sz') for i in range(sin_like.nbpoints)]\n sdp= [sin_like(i,'sdp') for i in range(sin_like.nbpoints)]\n #-------------------- lattice viseo\n vzero = [0. for i in range(lat_plot.nbpoints)] # zero line\n vis_abszisse = [lat_plot(i,'s') for i in range(lat_plot.nbpoints)]\n vis_ordinate = [lat_plot(i,'viseo') for i in range(lat_plot.nbpoints)]\n #-------------------- figure frame\n width=14; height=7.6\n # fighdr = 'lattice version = {}, input file = {}'.format(PARAMS['lattice_version'],PARAMS['input_file'])\n fig = plt.figure(num=1,figsize=(width,height),facecolor='#eaecef',tight_layout=False)\n\n #-------------------- beta functions\n splot211=plt.subplot(211)\n splot211.set_title('beta x,y')\n # mapping box\n splot211.text(0.01, 1.1, UTIL.FLAGS.get('mapping'),transform=splot211.transAxes,fontsize=8,bbox=dict(boxstyle='round',facecolor='wheat',alpha=0.5),verticalalignment='top')\n # function plots\n plt.plot(s,bx, label=r\"$\\beta$x [m]\", color='black', linestyle='-')\n plt.plot(s,by, label=r\"$\\beta$y [m]\", color='red', linestyle='-')\n plt.plot(s,dx, label=r'$\\eta_x$ [m]' , color='green', linestyle='-') # dispersion x\n vscale=splot211.axis()[3]*0.25\n viseoz = [x*vscale for x in vis_ordinate]\n plt.plot(vis_abszisse,viseoz,label='',color='black')\n plt.plot(vis_abszisse,vzero,color='green',linestyle='--')\n # zero line\n splot211.plot(vis_abszisse,vzero,color='green',linestyle='--')\n plt.legend(loc='lower right',fontsize='x-small')\n\n #-------------------- longitudinal tracks z, dP/P\n # ax_l = left abszisse\n ax_l=plt.subplot(212)\n # ax_l=plt.subplot(10,1,(7,9))\n ax_l.set_title('synchrotron oscillation')\n ax_l.set_ylabel(r\"z [mm]\")\n ax_l.tick_params(axis='y', colors='green')\n ax_l.yaxis.label.set_color('green')\n ax_l.plot(z1,cz,label='C',color='green')\n ax_l.plot(z2,sz,label='S',color='green',linestyle=':')\n plt.legend(loc='lower left',fontsize='x-small')\n # ax_r = right abszisse\n ax_r = ax_l.twinx()\n ax_r.set_ylabel(r'$\\Delta$p/p [%]')\n ax_r.tick_params(axis='y', colors='red')\n ax_r.yaxis.label.set_color('red')\n ax_r.plot(z2,cdp,label='C',color='red')\n ax_r.plot(z2,sdp,label='S',color='red',linestyle=':')\n ax_r.plot(vis_abszisse,vzero,color='red', linestyle='--')\n plt.legend(loc='lower right',fontsize='x-small')\n # lattice elements\n vscale=ax_l.axis()[3]*0.25\n viseoz = [x*vscale for x in vis_ordinate]\n ax_l.plot(vis_abszisse,viseoz,label='',color='black')\n ax_l.plot(vis_abszisse,vzero,color='green',linestyle='--')","def task2_extra():\n N = 0\n lam = 0\n Ls = numpy.array([2*L for L in range(1,23)])\n h = 0.01\n tau = 0.000099\n\n iterss = []\n\n for L in Ls:\n a = L // 2\n print(L)\n x = numpy.linspace(-L, L, int(2*L/h) + 1)\n # eps = int(0.1 * len(x))\n\n Vm = V1D(lam, x)\n state = phi(N, x-a)\n\n iters = 0\n while True:\n prob = numpy.abs(state)**2\n mid = int(2*L/h) // 2\n # if max(prob) in prob[mid-eps:mid+eps]:\n if numpy.argmax(prob) <= mid:\n print(iters)\n iterss.append(iters)\n break\n\n state[0] = 0\n state[-1] = 0\n state = implicit_scheme_step(state, tau, h, Vm)\n iters += 1\n\n fig = plt.figure()\n plt.title(\"Iterations of Gaussian travel to center\")\n plt.xlabel(\"$L$\")\n plt.ylabel(\"Time\")\n plt.plot(Ls, tau*numpy.array(iterss))\n plt.show()\n fig.savefig(\"naloga2_iters_of_gaussian_travel.pdf\", bbox_inches=\"tight\")","def test_pass_through_lcoe_args():\n year = 2012\n rev2_points = slice(0, 3)\n res_file = os.path.join(TESTDATADIR, 'wtk/ri_100_wtk_{}.h5'.format(year))\n sam_files = os.path.join(TESTDATADIR, 'SAM/i_windpower_lcoe.json')\n\n output_request = ('cf_mean',\n 'lcoe_fcr',\n 'capital_cost',\n 'fixed_charge_rate',\n 'variable_operating_cost',\n 'fixed_operating_cost')\n\n # run reV 2.0 generation\n gen = Gen.reV_run(tech='windpower', points=rev2_points,\n sam_files=sam_files, res_file=res_file, max_workers=1,\n sites_per_worker=1, fout=None,\n output_request=output_request)\n\n checks = [x in gen.out for x in Gen.LCOE_ARGS]\n assert all(checks)\n assert 'lcoe_fcr' in gen.out\n assert 'cf_mean' in gen.out","def softclippingHGMevaluation(input_generator,branches,iden_method,Plot,reference=None):\n for t in range(8,11):\n t = t / 10.0\n p = 1.0 - t\n input_signal = input_generator.GetOutput()\n nl_functions = [nlsp.function_factory.softclip(power=p),]*branches\n filter_spec_tofind = nlsp.log_bpfilter(branches=branches, input=input_signal)\n ref_nlsystem = nlsp.HammersteinGroupModel_up(input_signal=input_signal,\n nonlinear_functions=nl_functions,\n filter_irs=filter_spec_tofind,\n max_harmonics=range(1,branches+1))\n\n found_filter_spec, nl_functions = iden_method(input_generator,ref_nlsystem.GetOutput(),branches)\n iden_nlsystem = nlsp.HammersteinGroupModel_up(input_signal=input_signal,\n nonlinear_functions=nl_functions,\n filter_irs=found_filter_spec,\n max_harmonics=range(1,branches+1))\n # sine = sumpf.modules.SineWaveGenerator(frequency=5000.0,phase=0.0,samplingrate=input_signal.GetSamplingRate(),length=len(input_signal)).GetSignal()\n sine = sumpf.modules.SweepGenerator(samplingrate=input_signal.GetSamplingRate(),length=len(input_signal)).GetSignal()\n ref_nlsystem.SetInput(sine)\n iden_nlsystem.SetInput(sine)\n if reference is not None:\n reference = nlsp.change_length_signal(reference,length=len(input_signal))\n ref_nlsystem.SetInput(reference)\n iden_nlsystem.SetInput(reference)\n\n if Plot is True:\n plot.relabelandplot(sumpf.modules.FourierTransform(ref_nlsystem.GetOutput()).GetSpectrum(),\"Reference System\",show=False)\n plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem.GetOutput()).GetSpectrum(),\"Identified System\",show=False)\n print \"SNR between Reference and Identified output for symmetric hardclipping HGM(threshold:%r): %r\" %(t,nlsp.snr(ref_nlsystem.GetOutput(),\n iden_nlsystem.GetOutput()))","def plot_for_scaling_check(bolo_name):\n\n\n pop_path = \"../Analyse_\" + bolo_name + \"/Populations/Pop_for_scaling/\"\n\n #Load the estimator\n d_est = BDT_fh.open_estimator_file(bolo_name)\n\n #Best estimator for heat: coefficients\n coeff_EC1, coeff_EC2 = float(d_est[\"HEAT\"][:5]), 1 - float(d_est[\"HEAT\"][:5])\n coeff_EIB, coeff_EID = float(d_est[\"FID\"][:5]), 1-float(d_est[\"FID\"][:5])\n\n #Open event files\n data_types = {\"names\": (\"EC1\", \"EC2\", \"EIA\", \"EIB\", \"EIC\", \"EID\"), \"formats\": (\"f\", \"f\", \"f\", \"f\", \"f\", \"f\")}\n\n arr_heatonly = np.loadtxt(pop_path + bolo_name + \"_heatonly_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_all = np.loadtxt(pop_path + bolo_name + \"_all_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_FidGamma = np.loadtxt(pop_path + bolo_name + \"_FidGamma_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S1Gamma = np.loadtxt(pop_path + bolo_name + \"_S1Gamma_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S2Gamma = np.loadtxt(pop_path + bolo_name + \"_S2Gamma_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S1Beta = np.loadtxt(pop_path + bolo_name + \"_S1Beta_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S2Beta = np.loadtxt(pop_path + bolo_name + \"_S2Beta_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S1Pb = np.loadtxt(pop_path + bolo_name + \"_S1Pb_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n arr_S2Pb = np.loadtxt(pop_path + bolo_name + \"_S2Pb_KTH_cut_full_info.txt\", delimiter=\",\", dtype=data_types)\n\n arr_EI_heatonly, arr_EI_all = coeff_EIB*arr_heatonly[\"EIB\"] + coeff_EID*arr_heatonly[\"EID\"], coeff_EIB*arr_all[\"EIB\"] + coeff_EID*arr_all[\"EID\"]\n arr_EC_heatonly, arr_EC_all = coeff_EC1*arr_heatonly[\"EC1\"] + coeff_EC2*arr_heatonly[\"EC2\"], coeff_EC1*arr_all[\"EC1\"] + coeff_EC2*arr_all[\"EC2\"]\n arr_EI_FidGamma, arr_EC_FidGamma = coeff_EIB*arr_FidGamma[\"EIB\"] + coeff_EID*arr_FidGamma[\"EID\"], coeff_EC1*arr_FidGamma[\"EC1\"] + coeff_EC2*arr_FidGamma[\"EC2\"]\n arr_EI_S1Gamma, arr_EI_S2Gamma = coeff_EIB*arr_S1Gamma[\"EIB\"] + coeff_EID*arr_S1Gamma[\"EID\"], coeff_EIB*arr_S2Gamma[\"EIB\"] + coeff_EID*arr_S2Gamma[\"EID\"]\n arr_EC_S1Gamma, arr_EC_S2Gamma = coeff_EC1*arr_S1Gamma[\"EC1\"] + coeff_EC2*arr_S1Gamma[\"EC2\"], coeff_EC1*arr_S2Gamma[\"EC1\"] + coeff_EC2*arr_S2Gamma[\"EC2\"]\n arr_EI_S1Beta, arr_EI_S2Beta = coeff_EIB*arr_S1Beta[\"EIB\"] + coeff_EID*arr_S1Beta[\"EID\"], coeff_EIB*arr_S2Beta[\"EIB\"] + coeff_EID*arr_S2Beta[\"EID\"]\n arr_EC_S1Beta, arr_EC_S2Beta = coeff_EC1*arr_S1Beta[\"EC1\"] + coeff_EC2*arr_S1Beta[\"EC2\"], coeff_EC1*arr_S2Beta[\"EC1\"] + coeff_EC2*arr_S2Beta[\"EC2\"]\n arr_EI_S1Pb, arr_EI_S2Pb = coeff_EIB*arr_S1Pb[\"EIB\"] + coeff_EID*arr_S1Pb[\"EID\"], coeff_EIB*arr_S2Pb[\"EIB\"] + coeff_EID*arr_S2Pb[\"EID\"]\n arr_EC_S1Pb, arr_EC_S2Pb = coeff_EC1*arr_S1Pb[\"EC1\"] + coeff_EC2*arr_S1Pb[\"EC2\"], coeff_EC1*arr_S2Pb[\"EC1\"] + coeff_EC2*arr_S2Pb[\"EC2\"]\n\n lS1Beta, lS2Beta, lS1Pb, lS2Pb = np.where(arr_EC_S1Beta<15), np.where(arr_EC_S2Beta<15), np.where(arr_EC_S1Pb<15), np.where(arr_EC_S2Pb<15)\n lS1Gamma, lS2Gamma, lFidGamma = np.where(arr_EC_S1Gamma<15), np.where(arr_EC_S2Gamma<15), np.where(arr_EC_FidGamma<15)\n lheatonly, lall = np.where(arr_EC_heatonly<15), np.where(arr_EC_all<15)\n\n arr_EI_heatonly, arr_EC_heatonly = arr_EI_heatonly[lheatonly], arr_EC_heatonly[lheatonly]\n arr_EI_all, arr_EC_all = arr_EI_all[lall], arr_EC_all[lall]\n arr_EI_FidGamma, arr_EC_FidGamma = arr_EI_FidGamma[lFidGamma], arr_EC_FidGamma[lFidGamma]\n arr_EI_S1Gamma, arr_EC_S1Gamma = arr_EI_S1Gamma[lS1Gamma], arr_EC_S1Gamma[lS1Gamma]\n arr_EI_S2Gamma, arr_EC_S2Gamma = arr_EI_S2Gamma[lS2Gamma], arr_EC_S2Gamma[lS2Gamma]\n arr_EI_S1Beta, arr_EC_S1Beta = arr_EI_S1Beta[lS1Beta], arr_EC_S1Beta[lS1Beta]\n arr_EI_S2Beta, arr_EC_S2Beta = arr_EI_S2Beta[lS2Beta], arr_EC_S2Beta[lS2Beta]\n arr_EI_S1Pb, arr_EC_S1Pb = arr_EI_S1Pb[lS1Pb], arr_EC_S1Pb[lS1Pb]\n arr_EI_S2Pb, arr_EC_S2Pb = arr_EI_S2Pb[lS2Pb], arr_EC_S2Pb[lS2Pb]\n\n arr_EI_all, arr_EC_all = np.array(arr_EI_all).astype(float), np.array(arr_EC_all).astype(float)\n arr_EI_heatonly, arr_EC_heatonly = np.array(arr_EI_heatonly).astype(float), np.array(arr_EC_heatonly).astype(float)\n arr_EI_FidGamma, arr_EC_FidGamma = np.array(arr_EI_FidGamma).astype(float), np.array(arr_EC_FidGamma).astype(float)\n arr_EI_S1Gamma, arr_EC_S1Gamma = np.array(arr_EI_S1Gamma).astype(float), np.array(arr_EC_S1Gamma).astype(float)\n arr_EI_S2Gamma, arr_EC_S2Gamma = np.array(arr_EI_S2Gamma).astype(float), np.array(arr_EC_S2Gamma).astype(float) \n arr_EI_S1Beta, arr_EC_S1Beta = np.array(arr_EI_S1Beta).astype(float), np.array(arr_EC_S1Beta).astype(float)\n arr_EI_S2Beta, arr_EC_S2Beta = np.array(arr_EI_S2Beta).astype(float), np.array(arr_EC_S2Beta).astype(float)\n arr_EI_S1Pb, arr_EC_S1Pb = np.array(arr_EI_S1Pb).astype(float), np.array(arr_EC_S1Pb).astype(float)\n arr_EI_S2Pb, arr_EC_S2Pb = np.array(arr_EI_S2Pb).astype(float), np.array(arr_EC_S2Pb).astype(float)\n\n\n gr_heatonly = TGraph(len(arr_EI_heatonly), arr_EC_heatonly, arr_EI_heatonly)\n gr_FidGamma, gr_all = TGraph(len(arr_EI_FidGamma), arr_EC_FidGamma, arr_EI_FidGamma), TGraph(len(arr_EI_all), arr_EC_all, arr_EI_all)\n gr_S1Gamma, gr_S2Gamma = TGraph(len(arr_EI_S1Gamma), arr_EC_S1Gamma, arr_EI_S1Gamma), TGraph(len(arr_EI_S2Gamma), arr_EC_S2Gamma, arr_EI_S2Gamma)\n gr_S1Beta, gr_S2Beta = TGraph(len(arr_EI_S1Beta), arr_EC_S1Beta, arr_EI_S1Beta), TGraph(len(arr_EI_S2Beta), arr_EC_S2Beta, arr_EI_S2Beta)\n gr_S1Pb, gr_S2Pb = TGraph(len(arr_EI_S1Pb), arr_EC_S1Pb, arr_EI_S1Pb), TGraph(len(arr_EI_S2Pb), arr_EC_S2Pb, arr_EI_S2Pb)\n\n PyRPl.process_TGraph(gr_all, X_title = \"Heat\", Y_title = \"Ion\", color=kRed, marker_style = 20, marker_size = 0.1)\n PyRPl.process_TGraph(gr_FidGamma, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack), PyRPl.process_TGraph(gr_heatonly, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack)\n PyRPl.process_TGraph(gr_S1Gamma, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack), PyRPl.process_TGraph(gr_S2Gamma, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack)\n PyRPl.process_TGraph(gr_S1Beta, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack), PyRPl.process_TGraph(gr_S2Beta, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack)\n PyRPl.process_TGraph(gr_S1Pb, X_title = \"Heat\", Y_title = \"Ion\", color=kRed), PyRPl.process_TGraph(gr_S2Pb, X_title = \"Heat\", Y_title = \"Ion\", color=kBlack)\n\n list_gr = [gr_all, gr_FidGamma, gr_S1Gamma, gr_S2Gamma, gr_S1Beta, gr_S2Beta, gr_S1Pb, gr_S2Pb, gr_heatonly]\n list_pts = [gr.GetN() for gr in list_gr[1:]]\n print gr_all.GetN(), sum(list_pts)\n h = TH2F(\"h\", \"h\", 100, -5, 15, 100, -5, 15)\n PyRPl.process_TH2(h, X_title = \"Heat\", Y_title = \"Ion\")\n h.Draw()\n for gr in list_gr:\n gr.Draw(\"*same\")\n\n raw_input()\n\n # arr_Q_S1Beta = arr_EI_S1Beta/((1+8./3)*arr_EC_S1Beta - arr_EI_S1Beta*5.5/3)\n # arr_Q_S2Beta = arr_EI_S1Beta/((1+8./3)*arr_EC_S1Beta - arr_EI_S1Beta*5.5/3)\n # arr_Q_S1Pb = arr_EI_S1Pb/((1+8./3)*arr_EC_S1Pb - arr_EI_S1Pb*5.5/3)\n # arr_Q_S2Pb = arr_EI_S2Pb/((1+8./3)*arr_EC_S2Pb - arr_EI_S2Pb*5.5/3)\n \n # gr_QS1Beta, gr_QS2Beta = TGraph(len(arr_Q_S1Beta), arr_EC_S1Beta, arr_Q_S1Beta), TGraph(len(arr_Q_S2Beta), arr_EC_S2Beta, arr_Q_S2Beta)\n # gr_QS1Pb, gr_QS2Pb = TGraph(len(arr_Q_S1Pb), arr_EC_S1Pb, arr_Q_S1Pb), TGraph(len(arr_Q_S2Pb), arr_EC_S2Pb, arr_Q_S2Pb)\n\n\n # PyRPl.process_TGraph(gr_QS1Beta, X_title = \"Heat\", Y_title = \"Q\", color=kOrange-3), PyRPl.process_TGraph(gr_QS2Beta, X_title = \"Heat\", Y_title = \"Q\", color=kBlue)\n # PyRPl.process_TGraph(gr_QS1Pb, X_title = \"Heat\", Y_title = \"Q\", color=kRed), PyRPl.process_TGraph(gr_QS2Pb, X_title = \"Heat\", Y_title = \"Q\", color=kGreen+2)","def exec(self):\n try:\n xyz = [self.X_0, self.Y_0, self.Z_0]\n for _ in range(self.STEP):\n k_0 = self.__lorenz(xyz)\n k_1 = self.__lorenz([\n x + k * self.DT / 2 for x, k in zip(xyz, k_0)\n ])\n k_2 = self.__lorenz([\n x + k * self.DT / 2 for x, k in zip(xyz, k_1)\n ])\n k_3 = self.__lorenz([\n x + k * self.DT for x, k in zip(xyz, k_2)\n ])\n for i in range(3):\n xyz[i] += (k_0[i] + 2 * k_1[i] + 2 * k_2[i] + k_3[i]) \\\n * self.DT / 6.0\n self.res[i].append(xyz[i])\n self.__plot()\n except Exception as e:\n raise","def main():\n\n args = arg_parse()\n plot_zcalib(args)","def main():\n save = False\n show = True\n\n #hd_parameter_plots = HDparameterPlots(save=save)\n #hd_parameter_plots.flow_parameter_distribution_for_non_lake_cells_for_current_HD_model()\n #hd_parameter_plots.flow_parameter_distribution_current_HD_model_for_current_HD_model_reprocessed_without_lakes_and_wetlands()\n #hd_parameter_plots.flow_parameter_distribution_ten_minute_data_from_virna_0k_ALG4_sinkless_no_true_sinks_oceans_lsmask_plus_upscale_rdirs()\n #hd_parameter_plots.flow_parameter_distribution_ten_minute_data_from_virna_0k_ALG4_sinkless_no_true_sinks_oceans_lsmask_plus_upscale_rdirs_no_tuning()\n #ice5g_comparison_plots = Ice5GComparisonPlots(save=save)\n #ice5g_comparison_plots.plotLine()\n #ice5g_comparison_plots.plotFilled()\n #ice5g_comparison_plots.plotCombined()\n #ice5g_comparison_plots.plotCombinedIncludingOceanFloors()\n #flowmapplot = FlowMapPlots(save)\n #flowmapplot.FourFlowMapSectionsFromDeglaciation()\n #flowmapplot.Etopo1FlowMap()\n #flowmapplot.ICE5G_data_all_points_0k()\n #flowmapplot.ICE5G_data_all_points_0k_no_sink_filling()\n #flowmapplot.ICE5G_data_all_points_0k_alg4_two_color()\n #flowmapplot.ICE5G_data_all_points_21k_alg4_two_color()\n #flowmapplot.Etopo1FlowMap_two_color()\n #flowmapplot.Etopo1FlowMap_two_color_directly_upscaled_fields()\n #flowmapplot.Corrected_HD_Rdirs_FlowMap_two_color()\n #flowmapplot.ICE5G_data_ALG4_true_sinks_21k_And_ICE5G_data_ALG4_true_sinks_0k_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_Etopo1_ALG4_sinkless_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_Etopo1_ALG4_true_sinks_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_sinkless_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_true_sinks_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_corr_orog_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_no_true_sinks_corr_orog_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_HD_as_data_ALG4_true_sinks_0k_directly_upscaled_fields_FlowMap_comparison()\n #flowmapplot.Upscaled_Rdirs_vs_Directly_Upscaled_fields_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplot.Ten_Minute_Data_from_Virna_data_ALG4_corr_orog_downscaled_lsmask_no_sinks_21k_vs_0k_FlowMap_comparison()\n #flowmapplot.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n flowmapplotwithcatchment = FlowMapPlotsWithCatchments(save)\n #flowmapplotwithcatchment.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_virna_data_plus_tarasov_style_orog_corrs_for_both()\n #flowmapplotwithcatchment.compare_present_day_river_directions_with_catchments_virna_data_with_vs_without_tarasov_style_orog_corrs()\n #flowmapplotwithcatchment.compare_lgm_river_directions_with_catchments_virna_data_with_vs_without_tarasov_style_orog_corrs()\n #flowmapplotwithcatchment.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.upscaled_rdirs_with_and_without_tarasov_upscaled_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.\\\n #upscaled_rdirs_with_and_without_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.\\\n #Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.\\\n #Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_glcc_olson_lsmask_0k_FlowMap_comparison()\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_ICE5G_plus_tarasov_style_orog_corrs_for_both()\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_ICE6G_plus_tarasov_style_orog_corrs_for_both()\n #flowmapplotwithcatchment.compare_ICE5G_and_ICE6G_with_catchments_tarasov_style_orog_corrs_for_both()\n #flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs()\n #flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs_original_ts()\n flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs_new_ts_10min()\n outflowplots = OutflowPlots(save)\n #outflowplots.Compare_Upscaled_Rdirs_vs_Directly_Upscaled_fields_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_as_HD_data_ALG4_sinkless_all_points_0k()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_as_HD_data_ALG4_true_sinks_all_points_0k()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_sinkless_all_points_0k_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_true_sinks_all_points_0k_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_corr_orog_all_points_0k_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_corr_orog_downscaled_ls_mask_all_points_0k_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_Etopo1_ALG4_sinkless_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_Etopo1_ALG4_true_sinks_directly_upscaled_fields()\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_plus_tarasov_upscaled_srtm30_ALG4_corr_orog_0k_directly_upscaled_fields()\n #outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k()\n outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k_new_truesinks()\n #outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k_new_truesinks_individual_rivers()\n #outflowplots.Compare_ICE5G_with_and_without_tarasov_upscaled_srtm30_ALG4_corr_orog_0k_directly_upscaled_fields()\n #hd_output_plots = HDOutputPlots()\n #hd_output_plots.check_water_balance_of_1978_for_constant_forcing_of_0_01()\n #hd_output_plots.plot_comparison_using_1990_rainfall_data()\n #hd_output_plots.plot_comparison_using_1990_rainfall_data_adding_back_to_discharge()\n #coupledrunoutputplots = CoupledRunOutputPlots(save=save)\n #coupledrunoutputplots.ice6g_rdirs_lgm_run_discharge_plot()\n #coupledrunoutputplots.extended_present_day_rdirs_lgm_run_discharge_plot()\n #coupledrunoutputplots.ocean_grid_extended_present_day_rdirs_vs_ice6g_rdirs_lgm_run_discharge_plot()\n #coupledrunoutputplots.extended_present_day_rdirs_vs_ice6g_rdirs_lgm_echam()\n #coupledrunoutputplots.extended_present_day_rdirs_vs_ice6g_rdirs_lgm_mpiom_pem()\n #lake_plots = LakePlots()\n #lake_plots.plotLakeDepths()\n #lake_plots.LakeAndRiverMap()\n #lake_plots.LakeAndRiverMaps()\n if show:\n plt.show()","def figure3():\n\n plot_settings = {'y_limits': [-75, -50],\n 'x_limits': None,\n 'y_ticks': [-75, -70, -65, -60, -55, -50],\n 'locator_size': 2.5,\n 'y_label': 'Voltage (mV)',\n 'x_ticks': [],\n 'scale_size': 2,\n 'x_label': \"\",\n 'scale_loc': 3,\n 'figure_name': 'figure_3',\n 'legend': ['0.1 $\\mu$S', '0.125 $\\mu$S', '0.15 $\\mu$S'],\n 'legend_size': 8,\n 'legend_location': 4,\n 'y_on': True}\n line_styles = ['-', 'dotted', '-.']\n\n plt.figure(figsize=(5, 3))\n plt.subplot(1, 2, 1) # Generate subplot 1 (left)\n for ix, g_t_bar in enumerate([0.1, 0.125, 0.15]):\n t, y = solver(6, g_t_bar=g_t_bar, t_start=0.5)\n plt.plot(t, y[:, 0], c='k', linestyle=line_styles[ix])\n alter_figure(plot_settings) # Alter figure for publication\n\n plt.subplot(1, 2, 2) # Generate subplot 2 (right)\n for ix, i_bias_on in enumerate([0, -0.1, -0.2]):\n \"\"\"\n First step (hyperpolarizing)\n \"\"\"\n t1 = 1000\n duration = t1\n\n t, y_hold = solver(t1, duration=duration, i_bias_on=i_bias_on)\n y_0 = y_hold[-1, :] # Create new initial conditions\n\n \"\"\"\n Second step (current pulse)\n \"\"\"\n t1 = 20\n t, y = solver(t1, t_start=0, y_hold=y_0)\n v = y[:, 0]\n\n \"\"\"\n Append the end of the first step onto the second step (for plotting purposes)\n \"\"\"\n len_pre = 100\n t = np.concatenate((np.linspace(-len_pre * np.diff(t)[0], -np.diff(t)[0], len_pre), t))\n v_bar = np.concatenate((y_hold[-len_pre:, 0], v))\n v_bar = v_bar - v_bar[0] + -7.16300325e+01 # Align solution to initial condition of the \"standard simulation\"\n\n plt.plot(t, v_bar, c='k', linestyle=line_styles[ix])\n\n plot_settings['y_ticks'] = []\n plot_settings['y_label'] = \"\"\n plot_settings['x_limits'] = [-1, 20]\n plot_settings['legend'] = ['-72 mV', '-75 mV', '-78 mV']\n plot_settings['scale_size'] = 5\n plot_settings['legend_location'] = 1\n alter_figure(plot_settings, close=True)","def spider(inputfiles, names, type=\"M1\", threshold=0.1, spinwindow=[], Eg_low=0, Eg_high=1e9, Ex_low=0, Ex_high=1e9, scale=2):\n\n Nsp = np.ceil(np.sqrt(len(inputfiles))).astype(int)\n f, ax_list = plt.subplots(Nsp,Nsp,squeeze=False,sharex='col', sharey='row')\n\n for i in range(len(inputfiles)):\n inputfile = inputfiles[i]\n name = names[i]\n ax = ax_list[i%Nsp][int((i-i%Nsp)/Nsp)]\n\n\n levels = read_energy_levels(inputfile)\n Egs = levels[0,0]\n\n Ex_high = min(Ex_high, levels[:,0].max()-Egs)\n Eg_high = min(Eg_high, levels[:,0].max()-Egs)\n \n \n levels_plot_J = []\n levels_plot_Ex = []\n for iEx in range(len(levels[:,0])):\n # print levels[iEx,:]\n J2 = levels[iEx,1]\n par = levels[iEx,2]\n if len(spinwindow) > 0 and not ([J2,par] in spinwindow or [J2-2,par] in spinwindow or [J2+2,par] in spinwindow):\n continue\n Ex = levels[iEx,0]-Egs\n if Ex < Ex_low or Ex > Ex_high:\n continue\n \n levels_plot_J.append(J2/2)\n levels_plot_Ex.append(Ex)\n \n ax.plot(levels_plot_J, levels_plot_Ex, 'o', color='grey', linewidth=0.5)\n ax.set_xlim([levels[:,1].min()/2-1,levels[:,1].max()/2+1])\n ax.set_title(name+r'$\\,E_\\gamma\\in[{:.1f},{:.1f}]$'.format(Eg_low,Eg_high))\n ax.set_ylabel(r'$E_x\\,\\mathrm{[MeV]}$')\n ax.set_xlabel(r'$J\\,\\mathrm{[\\hbar]}$')\n \n \n \n transitions = read_transition_strengths(inputfile, type=type)\n for iEx in range(len(transitions[:,0])):\n J2i = int(transitions[iEx,0])\n pari = int(transitions[iEx,1])\n if len(spinwindow) > 0 and not [J2i,pari] in spinwindow:\n continue\n B = transitions[iEx,7]\n Eg = transitions[iEx,6]\n if B < threshold or Eg<Eg_low or Eg>Eg_high:\n continue\n Ei = transitions[iEx,2]-Egs\n if Ei < Ex_low or Ei > Ex_high:\n continue\n J2f = int(transitions[iEx,3])\n parf = int(transitions[iEx,4])\n Ef = transitions[iEx,5]-Egs\n ax.plot([J2i/2,J2f/2],[Ei,Ef], color='teal', linewidth=(scale*B))\n \n return f, ax_list","def main():\n\t#print(scipy.__version__)\n\t#image()\n\t#heat_capacity2()\n\t#hist()\n\t#single_plot()\n\n\t#heat_capacity2()\n\t#single_plot()\n\t#plt.show()\n\t#u0_tc()\n\t#multi_heat_capacity(\"HL_DM_flux5\",True)\n\t#multi_heat_capacity2()\n\t#plot_spin()\n\t#plt.show()\n\theat_capacity2(1,2)\n\t#hist()\n\tplt.show()\n\t#potential()\n\t#plt.show()\n\t#heat_capacity(3,4)\n\t#heat_capacity(5,6)\n\t#heat_capacity(7,8)\n\t#final_spins()\n\t#plot_spin()\n\t#plot_from_csv()\n\t#difference_plot()","def main() -> None:\n xlim = L\n x = np.arange(xlim)\n xi = np.arange(0, xlim - 1 + STEP, STEP)\n kappas = sleplet.wavelet_methods.create_kappas(xlim, B, J_MIN)\n yi = scipy.interpolate.pchip(x, kappas[0])\n plt.semilogx(xi, yi(xi), label=r\"$\\Phi_{\\ell0}$\")\n for j, k in enumerate(kappas[1:]):\n yi = scipy.interpolate.pchip(x, k)\n plt.semilogx(xi, yi(xi), label=rf\"$\\Psi^{{{j+J_MIN}}}_{{\\ell0}}$\")\n plt.xlim(1, xlim)\n ticks = 2 ** np.arange(np.log2(xlim) + 1, dtype=int)\n plt.xticks(ticks, ticks)\n plt.xlabel(r\"$\\ell$\")\n plt.legend(loc=6)\n print(f\"Opening: axisymmetric_tiling_L{L}\")\n plt.tight_layout()\n plt.show(block=False)\n plt.pause(10)\n plt.close()","def prepare_CvD16_for_M2L_calc(templates_lam_range, verbose=False):\n import glob\n import os\n template_glob=os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*.s100')\n\n vcj_models=sorted(glob.glob(template_glob))\n temp_lamdas, x35, x3, x23, kroupa, flat=np.genfromtxt(vcj_models[-1], unpack=True)\n\n n_ages=7\n n_zs=5\n n_imfs=5\n\n \n\n\n Zs=['m1.5', 'm1.0', 'm0.5', 'p0.0', 'p0.2']\n ages=[1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.5]\n model_imfs_order=['x35', 'x3', 'x23', 'kroupa', 'flat']\n\n t_mask = ((temp_lamdas > templates_lam_range[0]) & (temp_lamdas <templates_lam_range[1]))\n\n\n\n y=x35[t_mask]\n x=temp_lamdas[t_mask]\n\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\n interp=si.interp1d(x, y, fill_value='extrapolate')\n out=interp(new_x)\n\n templates=np.empty((len(out), n_ages, n_zs, n_imfs))\n\n\n\n for a, Z in enumerate(Zs): \n for b, age in enumerate(ages):\n model=glob.glob(os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*{}*{}.ssp.s100'.format(Z, age)))[0]\n if verbose:\n print 'Loading {}'.format(model)\n data=np.genfromtxt(model)\n\n for c, counter in enumerate(reversed(range(1, data.shape[-1]))):\n \n #Interpolate templates onto a uniform wavelength grid and then log-rebin\n y=data[:, counter][t_mask] \n x=temp_lamdas[t_mask]\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\n\n interp=si.interp1d(x, y, fill_value='extrapolate')\n out=interp(new_x) \n\n templates[:, b, a, c]=out\n\n return templates, new_x","def main():\n\n # define bounding box for right VTC\n width = 225\n y_start = 245\n x_start = 50\n bbox = [y_start, x_start, width, width]\n\n # first figure: betas for each predictor\n fig, axes = plt.subplots(figsize=(27.7, 8), ncols=5, nrows=2)\n\n # redefining contrast order to match figures\n contrasts = [\n \"wordVSall\",\n \"bodyVSall\",\n \"adultVSall\",\n \"carVSall\",\n \"corridorVSall\",\n \"numberVSall\",\n \"limbVSall\",\n \"childVSall\",\n \"instrumentVSall\",\n \"corridorVSall\",\n ]\n\n for contrast, ax in zip(contrasts, axes.ravel()):\n image_path = (\n f\"{PATHS['figures_misc']}/figure_4_images/\"\n f\"C1051_20160212_{contrast}_depth_1.png\"\n )\n\n assert os.path.isfile(image_path)\n img = imread(image_path)\n img[np.where(np.sum(img, axis=2) == 0.0)] = 1.0\n\n ax.imshow(img[bbox[0] : bbox[0] + bbox[2], bbox[1] : bbox[1] + bbox[3], :])\n ax.axis(\"off\")\n\n savefig(f\"{PATHS['figures']}/figure_4a.png\")\n plt.close(fig)","def test2():\n\t\n\td = np.linspace(-15000,15000,2500)\n\t#Voigt\n\n\n\t## 700 G, 84 C, Cs, 75mm\n\n\tp_dict = {'Bfield':700,'Btheta':90*np.pi/180,'lcell':75e-3,'T':84,'Dline':'D2','Elem':'Cs'}\n\tpol = 1./np.sqrt(2)*np.array([1.0,1.0,0.0])\n\tTVx = get_spectra(d,pol,p_dict,outputs=['I_M45','I_P45','Ix','Iy','S0','Iz'])\n\t\n\tfig2 = plt.figure()\n\tax1a = fig2.add_subplot(411)\n\tax2a = fig2.add_subplot(412,sharex=ax1a)\n\tax3a = fig2.add_subplot(413,sharex=ax1a)\n\tax4a = fig2.add_subplot(414,sharex=ax1a)\n\t\n\tax1a.plot(d,TVx[0],'r',lw=2,label=r'$I_{-45}$')\n\tax2a.plot(d,TVx[1],'b',lw=2,label=r'$I_{+45}$')\n\tax3a.plot(d,TVx[2],'r',lw=2,label=r'$I_x$')\n\tax4a.plot(d,TVx[3],'b',lw=2,label=r'$I_y$')\n\tax4a.plot(d,TVx[0]+TVx[1],'r:',lw=3.5,label=r'$I_{+45}+I_{-45}$')\n\tax4a.plot(d,TVx[2]+TVx[3],'k:',lw=2.5,label=r'$I_x + I_y$')\n\tax4a.plot(d,TVx[4],'g--',lw=1.5,label='$S_0$')\n\t#\tax4a.plot(d,TVx[5],'c--',lw=2.5,label='$I_z$')\n\t\n\t\n\tax4a.set_xlabel('Detuning (MHz)')\n\tax1a.set_ylabel('I -45')\n\tax2a.set_ylabel('I +45')\n\tax3a.set_ylabel('Ix')\n\tax4a.set_ylabel('Iy')\n\t\n\tax4a.set_xlim(d[0],d[-1]+3000)\n\tax4a.legend(loc=0)\n\t\n\tplt.show()","def test_plot1(plot=1, version='scalar'):\n Lx = 10\n Ly = 10\n c = 1.0\n\n def I2(x, y):\n return exp(-(x-Lx/2.0)**2/2.0 -(y-Ly/2.0)**2/2.0)\n def f(x, y, t):\n return 0.0\n def bc(x, y, t):\n return 0.0\n\n I2 = StringFunction('exp(-(x-Lx/2.0)**2/2.0 -(y-Ly/2.0)**2/2.0)',\n independent_variables=('x', 'y'),\n Lx=Lx, Ly=Ly, globals=globals())\n f = StringFunction('0.0', independent_variables=('x', 'y', 't'),\n globals=globals())\n bc = StringFunction('0.0', independent_variables=('x', 'y', 't'),\n globals=globals())\n if plot:\n g = Gnuplot.Gnuplot(persist=1)\n g('set parametric')\n g('set data style lines')\n g('set hidden')\n g('set contour base')\n g('set zrange [-0.7:0.7]') # nice plot...\n \n def action(u, xv, yv, t):\n #print 'action, t=',t,'\\nu=',u, '\\nx=',x, '\\ny=', y\n if plot:\n data = Gnuplot.GridData(u, xv[:,0], yv[0,:], binary=0)\n g.splot(data)\n g('set title \"t=%g\"' % t)\n if plot == 2:\n g.hardcopy(filename='tmp_%020f.ps' % t, enhanced=1, mode='eps',\n color=0, fontname='Times-Roman', fontsize=14)\n time.sleep(1)\n time.sleep(0.2) # pause between frames\n\n implementation = {'ic': version, 'inner': version, 'bc': version}\n nx = 40; ny = 40; tstop = 20 # tstop = 700\n print 'test_plot1:', f, bc, I2\n dt, t_ic, t_inner, t_bc = \\\n solver(I2, f, c, bc, Lx, Ly, nx, ny, 0, tstop,\n user_action=action, implementation=implementation)\n print 'time ic: %s, time scheme: %s, time bc: %s' % (t_ic, t_inner, t_bc)\n time.sleep(3)","def method_2d(knots,y_n,num):\n cv_iter = 10 # number of iteration for cross-validation \n GSV = np.zeros((cv_iter,cv_iter))\n# tr = np.zeros((cv_iter,cv_iter))\n# fun =np.zeros((cv_iter,cv_iter))\n lam_x = np.linspace(0,0.2,cv_iter)\n lam_y = np.linspace(0,0.2,cv_iter)\n num_knots = len(knots)\n linear_knots = knots[1:num_knots-1]\n num_knots = num_knots-4\n znam = np.zeros((num_knots))\n basis = np.zeros((num,num_knots))\n basis_1 = np.zeros((num,num_knots))\n basis_deriative = np.zeros((num,num_knots))\n basis_deriative_1 = np.zeros((num,num_knots))\n S = np.zeros((num_knots,num_knots,num))\n vs = BsplineVectorSpace(2, knots)\n vs_1 = BsplineVectorSpace(1, linear_knots)\n I_i = np.eye(num_knots)\n for i in xrange(0,num_knots):\n basis[:,i] = vs.basis_der(i,0)(np.linspace(0,1,num))\n basis_deriative[:,i] = vs.basis_der(i,1)(np.linspace(0,1,num))/num\n basis_1[:,i] = vs_1.basis_der(i,0)(np.linspace(0,1,num))\n basis_deriative_1[:,i] = vs_1.basis_der(i,1)(np.linspace(0,1,num))/num\n B = abs(basis_deriative-basis_1)\n S = np.zeros((num_knots,num_knots,num))\n k = np.zeros((num_knots,num_knots,num))\n for i in xrange(num_knots):\n for j in xrange(num_knots):\n S[i,j,:] = B[:,i]*B[:,j]\n k[i,j,:] =basis_deriative_1[:,i] * basis_deriative_1[:,j]\n S_int = np.zeros((num_knots,num_knots))\n k_int = np.zeros((num_knots,num_knots))\n for i in xrange(num_knots):\n for j in xrange(num_knots):\n S_int[i,j] = integrate.trapz(S[i,j,:])\n k_int[i,j] = integrate.trapz(k[i,j,:])\n basis_product = np.kron(basis,basis)\n S_x = np.kron(S_int,I_i)\n S_y = np.kron(I_i,S_int)\n K_x = np.kron(k_int,I_i)\n K_y = np.kron(I_i,k_int)\n for i in xrange(cv_iter):\n for j in xrange(cv_iter):\n influence_matrix = np.dot(np.dot(basis_product,(np.linalg.inv(np.dot(np.transpose(\n basis_product),basis_product)+lam_x[i]*S_x+lam_y[j]*S_y+lam_x[i]* K_x+lam_y[j]*K_y))),np.transpose(basis_product))\n for k in xrange(num_knots):\n znam[k] =(1-influence_matrix[k,k])**2\n tr = np.sum(znam)\n fun = np.sum((y_n-np.dot(influence_matrix,y_n))**2)\n GSV[i,j] =fun/(num*tr)\n print i,j\n a,b = np.unravel_index(GSV.argmin(), GSV.shape)\n# a = np.argmin(np.argmin(GSV,axis = 0))\n# b = np.argmin(np.argmin(GSV,axis = 1))\n lamb_x = lam_x[a]\n lamb_y = lam_y[b]\n print lamb_x,lamb_y\n model_fit = np.dot(np.dot(np.dot(basis_product,(np.linalg.inv(np.dot(np.transpose(\n basis_product),basis_product)+lamb_x*S_x+lamb_y*S_y+lamb_x* K_x+lamb_y*K_y))),np.transpose(basis_product)),y_n)\n return model_fit,GSV","def main():\n # Load data and pre-process it\n path = \"data2.csv\"\n features, labels, df = load_data(path)\n\n # Learning rates - including our own\n learning_rates = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 0.75]\n\n # Num iterations\n num_iterations = [100 for i in range(9)] + [1000]\n\n # Keep track of all final weights for different learning rates\n lines = []\n\n # Compute weights for each learning rate\n for rate, num_iters in zip(learning_rates, num_iterations):\n\n # Get weights from gradient descent and add to weights list\n weights = gradient_descent(features, labels, rate, num_iters)\n lines.append([rate, num_iters] + weights)\n\n # Now write 'lines' to file\n with open('results2.csv', \"w\") as out_file:\n for line in lines:\n out_file.write(\"{}, {}, {}, {}, {} \\n\".format(line[0], line[1],\n line[2], line[3], line[4]))\n out_file.close()\n\n # Select which weights to use for plotting\n index = -1\n\n\n plot_db.visualize_3d(df, lin_reg_weights=lines[index][2:],\n feat1='norm_x1', feat2='norm_x2', labels='label',\n xlim=(-1, 1), ylim=(-1, 1), zlim=(0, 3),\n alpha=learning_rates[index], xlabel='age',\n ylabel='weight', zlabel='height',\n title='')","def VFI(method) :\n iteration=0 # Iteration Counter\n converged = 0 # Convergence Flag|\n \n#----- Initial Settings \n v_update = zeros(n_grid)\n v_func = empty(n_grid)\n k_next_vec = empty(n_grid)\n run_time = empty(2)\n \n def obj(k_next) :\n \"\"\"\n This function is used in value function iteration.\n It represents the objective function to be maximized for one node (state) of current capitals.\n Resulting value is maximized one corresponding to next period's capital as a maximizer. \n Next period's value is computed by interpolation.\n \n Input : k_next (next period's capital)\n \n Output : value_vec (maximized value resulting from choosing optimal capital in the next period)\n \"\"\" \n \n if method==1 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*linear_interp(k_grid,v_update,k_next))\n elif method==2 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*quad_interp(k_grid,v_update,k_next))\n elif method==3 :\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*cubic_interp(k_grid,v_update,k_next))\n \n return value_vec\n\n#----- Value function iteration\n start = time.time() # start time\n while converged==0 :\n index = 0\n for k_current in k_grid :\n k_next = fminbound(obj,k_grid[0],k_grid[-1])\n v_func[index] = (-1) * obj(k_next)\n k_next_vec[index] = k_next\n index = index + 1\n dist = abs(max(v_func - v_update))\n if dist<tol :\n converged = 1\n v_k, g_k = v_func, k_next_vec\n v_update = v_func\n print \"Iteration : \",iteration,\"\",\"Distance : \",dist # convergence process\n iteration = iteration + 1\n v_func = empty(n_grid) \n k_next_vec = empty(n_grid)\n \n end = time.time() # end time\n run_time[0],run_time[1] = runtime_cal(start,end) # total running time\n \n return v_k, g_k, run_time, iteration","def load_hyperparams():\n #Load halo data (encoding='latin1' for Python3)\n with open('../Data/halo_data.pkl', 'rb') as halo_input:\n halo_data = pickle.load(halo_input, encoding='latin1')\n\n #Load interpolator\n with open('../Data/interpolator.pkl', 'rb') as interp:\n vpeak_Mr_interp = pickle.load(interp, encoding='latin1')\n\n #Cosmological params\n cosmo_params = {}\n cosmo_params['omega_b'] = 0.0 \n cosmo_params['omega_m'] = 0.286\n cosmo_params['h'] = 0.7\n\n #hyperparameters\n hparams = {}\n hparams['mpeak_cut'] = 10**7\n hparams['vpeak_cut'] = 10.\n hparams['vmax_cut'] = 9.\n hparams['orphan_radii_cut'] = 300.\n hparams['chi'] = 1.\n hparams['R0'] = 10.0\n hparams['gamma_r'] = 0.0\n hparams['beta'] = 0.\n hparams['O'] = 1.\n hparams['n_realizations'] = 5\n\n #prior hyperparameters\n prior_hparams = {}\n prior_hparams['alpha'] = np.array([-2.,-1.1])\n prior_hparams['sigma_M'] = np.array([0.,2.])\n prior_hparams['M50'] = np.array([7.35,10.85])\n prior_hparams['sigma_mpeak'] = np.array([1e-5,1.])\n prior_hparams['B'] = np.array([1e-5,3.])\n prior_hparams['A'] = np.array([10.,500.])\n prior_hparams['sigma_r'] = np.array([1e-5,2.])\n prior_hparams['n'] = np.array([0.,2.])\n prior_hparams['Mhm'] = np.array([5.,9.])\n\n #Orphan hyperparameters\n orphan_params = {}\n orphan_params['eps'] = 0.01 \n orphan_params['df'] = 1\n\n #Simulation and LMC indices\n sim_indices = {}\n sim_indices['host'] = [0,1]\n sim_indices['LMC'] = [0,0]\n\n return hparams, prior_hparams, cosmo_params, orphan_params, halo_data, sim_indices, vpeak_Mr_interp","def main():\n Nrep = 8 # number of repetition of EM steps\n nm = 3 # number of mixed gaussians.\n ns = 300 # number of samples.\n \n mu, sg, lm, lm_ind, smp, L_true = generate_synthetic_data(nm, ns)\n plt.figure(1, figsize=(5,4))\n plt.clf()\n plot_synthetic_data(smp, mu, sg, lm, lm_ind, nm, ns)\n \n mue, sge, lme = generate_initial_state(nm, ns)\n axi = 0 # subplot number\n plt.figure(2, figsize=(12,9))\n plt.clf()\n for rep in range(Nrep):\n # E-step\n r, L_infer = e_step(smp, mue, sge, lme, nm, ns)\n axi += 1 \n ax = plt.subplot(Nrep/2, 6, axi)\n plot_em_steps(smp, r, mue, sge, lme, nm, ns)\n ax.set_title('E-step : %d' % (rep + 1))\n ax.set_yticklabels([])\n ax.set_xticklabels([])\n ax.set_ylim((-0.1, 0.3))\n\n # M-step\n mue, sge, lme = m_step(smp, r, nm, ns)\n axi += 1 \n ax = plt.subplot(Nrep/2, 6, axi)\n plot_em_steps(smp, r, mue, sge, lme, nm, ns)\n ax.set_title('M-step : %d' % (rep + 1))\n ax.set_yticklabels([])\n ax.set_xticklabels([])\n ax.set_ylim((-0.1, 0.3))\n\n # plot the ground truth for comparison\n axi += 1 \n ax = plt.subplot(Nrep/2, 6, axi)\n plot_synthetic_data(smp, mu, sg, lm, lm_ind, nm, ns)\n ax.set_title('grn_truth')\n ax.set_yticklabels([])\n ax.set_xticklabels([])\n ax.set_ylim((-0.1, 0.3))\n\n print('L_infer = %2.6f , L_true = %2.6f' % (L_infer, L_true))","def plotDistributionWithLimitsRefine(lXs, llYs, lKClassif,out=\"out.png\", title=\"title\", xax=\"xax\", yax=\"yax\",legend=\"\"):\n\n fig = plt.Figure(figsize=(40,20))\n fig.suptitle(title, fontsize=32)\n nbPlots = len(llYs)\n sqrt = int(math.ceil(math.sqrt(nbPlots)))\n ymax = 0.0\n for i,val in enumerate(llYs):\n if lKClassif[i] != \"refine\":\n ymax = max(max(val[0]),ymax)\n ymaxCurrent = max(max(val[2]),ymax)\n ymax = ymax*1.05\n xmax = 147\n gs = gridspec.GridSpec(1,2) \n ax = fig.add_subplot(gs[0])\n gsLimit = gridspec.GridSpecFromSubplotSpec(sqrt,sqrt, subplot_spec=gs[1])\n for i,val in enumerate(llYs):\n if lKClassif[i] != \"refine\":\n ax.plot(lXs,val[0],color=Graphics.lColors[i%25])\n axCurrent = fig.add_subplot(gsLimit[i]) \n axCurrent.fill_between(lXs, val[1], val[2], alpha=0.35, edgecolor='black', facecolor=Graphics.lColors[i%25])\n axCurrent.set_title(\"Cluster K{}, (position: {})\".format(i,lKClassif[i]))\n axCurrent.fill_between(lXs, val[3], val[4], alpha=0.85, edgecolor='darkgray', facecolor='lightgray')\n axCurrent.plot(lXs,val[0],color=Graphics.lColors[i%25])\n axCurrent.set_ylim(0,ymaxCurrent)\n axCurrent.set_xlim(1,xmax)\n axCurrent.text(10, ymaxCurrent*0.90, \"#nucleosomes: {}\".format(legend[i]), fontsize=12)\n axis_font = {'size':'28'}\n ax.set_ylim(0,ymax)\n ax.set_xlim(1,xmax)\n ax.legend([\"K{}\".format(x) for x in range(0,nbPlots)])\n ax.set_title(\"all nucleosomes\", **axis_font)\n ax.set_xlabel(xax, **axis_font)\n ax.set_ylabel(yax, **axis_font)\n ax.tick_params(labelsize=20)\n canvas = FigureCanvasAgg(fig)\n canvas.print_figure(out, dpi=80)","def __init__(self):\n # Set constants\n self.fromHztoeV = 6.58e-16\n self.gramstoeV = 1 / ( 1.78 * 1e-33)\n self.mtoev = 1/(1.97 * 1e-7) \n self.H0 = cosmo.H(0).value * 1e3 / (1e3 * const.kpc.value) #expressed in 1/s\n self.rhocritical = cosmo.critical_density(0).value * self.gramstoeV /(1e-2)**3 # eV/m**3\n self.Om0 = cosmo.Om0 #total matter \n self.OLambda0 = cosmo.Ode0 # cosmological constant\n self.DM0 = self.Om0 - cosmo.Ob0 # dark matter\n self.evtonJoule = 1.60218 * 1e-10 # from eV to nJ\n self.evtoJoule = 1.60218 * 1e-19 # from eV to J\n PSgal1h = np.loadtxt(\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_1h.dat\")\n PSgal2h = np.loadtxt(\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_2h.dat\")\n self.Mpc = 1e3 * const.kpc.value\n self.zmin = 0.001\n self.zmax = 30.001\n self.zbins = 301\n self.h = cosmo.h\n self.z_vect = np.linspace(self.zmin, self.zmax, self.zbins)\n self.k_vect = PSgal1h[:,0]* self.h\n self.Power1h = PSgal1h[:,1:]/(self.h**3)\n self.Power2h = PSgal2h[:,1:]/(self.h**3)\n self.Power = self.Power1h + self.Power2h\n self.Praw_prova1h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power1h))\n self.Praw_prova2h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power2h))\n self.Praw_prova = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power))","def display1(*args):\n #----------*----------* # unpack\n twiss_func = args[0]\n lat_plot = args[3]\n #-------------------- beta x,y & dispersion x\n s = [twiss_func(i,'s') for i in range(twiss_func.nbpoints)]\n bx = [twiss_func(i,'bx') for i in range(twiss_func.nbpoints)]\n by = [twiss_func(i,'by') for i in range(twiss_func.nbpoints)]\n dx = [twiss_func(i,'dx') for i in range(twiss_func.nbpoints)]\n #-------------------- lattice viseo\n vzero = [0. for i in range(lat_plot.nbpoints)] # zero line\n vis_abszisse = [lat_plot(i,'s') for i in range(lat_plot.nbpoints)]\n vis_ordinate = [lat_plot(i,'viseo') for i in range(lat_plot.nbpoints)]\n #-------------------- figure frame\n width=14; height=7.6\n plt.figure(num=0,figsize=(width,height),facecolor='#eaecef',tight_layout=False)\n\n #-------------------- transverse X\n splot111=plt.subplot(111)\n splot111.set_title('beta functions')\n plt.plot(s,bx, label=r'$\\beta_x$ [m]', color='red', linestyle='-') # beta x\n plt.plot(s,by, label=r'$\\beta_y$ [m]', color='blue', linestyle='-') # beta y\n plt.plot(s,dx, label=r'$\\eta_x$ [m]' , color='green',linestyle='-') # dispersion x\n vscale=splot111.axis()[3]*0.25\n viseoz = [x*vscale for x in vis_ordinate]\n plt.plot(vis_abszisse,viseoz,label='',color='black')\n plt.plot(vis_abszisse,vzero,color='black')\n plt.legend(loc='lower right',fontsize='x-small')","def graphe_regrets(rgt_alea, rgt_glou, rgt_glou_e, rgt_ucb):\n T = np.arange(rgt_alea.size)\n fig, ax = plt.subplots()\n ax.grid(True)\n plt.xlabel(\"T\")\n plt.ylabel(\"Regret\")\n \n ax.plot(T, rgt_alea, label = 'aléatoire') \n ax.plot(T, rgt_glou, label = 'glouton')\n ax.plot(T, rgt_glou_e, label = 'e-glouton')\n ax.plot(T, rgt_ucb, label = 'UCB')\n ax.legend(loc = \"upper left\")\n plt.title(\"Regrets des 4 algorithmes par rapport à T\")","def task2_extra2():\n N = 0\n lam = 0\n L = 10\n h = 0.001\n tau = 0.000099\n aa = numpy.array([0.25*a for a in range((L-1)*4)])\n x = numpy.linspace(-L, L, int(2*L/h) + 1)\n Vm = V1D(lam, x)\n # eps=int(0.1*len(x))\n\n iterss = []\n for a in aa:\n print(a)\n state = phi(N, x-a)\n\n iters = 0\n while True:\n prob = numpy.abs(state)**2\n mid = int(2*L/h) // 2\n # if max(prob) in prob[mid-eps:mid+eps]:\n if numpy.argmax(prob) <= mid:\n print(iters)\n iterss.append(iters)\n break\n\n state[0] = 0\n state[-1] = 0\n state = implicit_scheme_step(state, tau, h, Vm)\n iters += 1\n\n fig = plt.figure()\n plt.title(\"Iterations of Gaussian travel to center ($L={}$)\".format(L))\n plt.xlabel(\"$a$\")\n plt.ylabel(\"Time\")\n plt.plot(aa, tau*numpy.array(iterss))\n plt.show()\n fig.savefig(\"naloga2_iters_of_gaussian_travel_fixedL={}.pdf\".format(L), bbox_inches=\"tight\")","def growth_curve(userinputs, filter, catalog):\n logging.info('Running growth curve analysis on {}'.format(catalog))\n # Load the photometry results from the catalog (that is returned by the phot\n # function)\n aper_st, flux_st = np.loadtxt(catalog, unpack=True, usecols=(0,3))\n\n #Growth curve is only done on the ref image so we get the filter from userinp.\n ref_filter = filter\n\n ratio_st = np.empty(len(aper_st))\n\n #number of apertures\n naper = 20\n\n # Calculate the number of stars, make sure it is an integer\n nstar = int(len(aper_st)/naper)\n logging.info('Number of stars used: {}'.format(nstar))\n aper_ind = naper - 1\n\n for k in range(nstar):\n\n for i in range(naper):\n\n ratio_st[i + k*naper] = flux_st[i + k*naper]/flux_st[aper_ind + k*naper]\n\n\n # Find median ratio at each aperture between all the stars and all the clusters\n med_st = np.empty(naper)\n\n for i in range(naper):\n\n med_st[i] = np.median(ratio_st[i::naper])\n\n\n # Plot growth curves\n logging.info('Creating Growth curve plots')\n fig = plt.figure(figsize = (7,7))\n\n aper_x = np.arange(naper) + 1\n\n for i in range(nstar):\n\n ratio_y = ratio_st[i*naper:(i + 1)*naper]\n plt.plot(aper_x, ratio_y, 'y-')\n plt.annotate(str(i + 1), xy=(8.0, ratio_y[7]),\n horizontalalignment='left', verticalalignment='top', fontsize=6)\n\n\n plt.plot(aper_x, med_st, 'r-' , linewidth=4.0)\n plt.hlines(0.5, 0, 20, color='black', linewidth=2, zorder=10)\n plt.vlines(4, 0, 1.1, color='black', linewidth=2, linestyle='dashed', zorder=10)\n plt.vlines(5, 0, 1.1, color='black', linewidth=2, linestyle='dashed', zorder=10)\n plt.vlines(6, 0, 1.1, color='black', linewidth=2, linestyle='dashed', zorder=10)\n\n plt.ylabel('Normalized Flux ' + ref_filter.upper())\n plt.xlabel('Radius (pix)')\n plt.xlim(1,20)\n plt.minorticks_on()\n\n fig.savefig(userinputs['OUTDIR'] + '/plots/plot_growth_curve_{}.pdf'.format(ref_filter))","def plot_zcalib(args):\n\n start_date = args.start_date\n end_date = args.end_date\n\n start_date_dt = dp.parse(start_date) \n end_date_dt = dp.parse(end_date) \n \n min_date = dp.parse(SETTINGS.MIN_START_DATE)\n max_date = dp.parse(SETTINGS.MAX_END_DATE)\n \n if start_date_dt < min_date or end_date_dt > max_date:\n raise ValueError(f'Date must be in range {SETTINGS.MIN_START_DATE} - {SETTINGS.MAX_END_DATE}')\n\n phi_dir = os.path.join(SETTINGS.PHI_DIR)\n img_dir = os.path.join(SETTINGS.Z_CALIB_DIR,'images/')\n if not os.path.exists(img_dir):\n os.makedirs(img_dir)\n filelist1 = glob.glob(phi_dir+\"phiest*\")\n filelist2 = glob.glob(phi_dir+\"phiobs*\")\n filelist1.sort()\n filelist2.sort()\n dates=[]\n ind=[]\n\n if len(filelist1) != len(filelist2):\n raise ValueError(\"Number of phiest and phiobs files does not match!\")\n\n #Loop through files to find the indices of those between the inputted start and end dates\n for f in range(0,len(filelist1)):\n match = re.search(r'\\d{8}',filelist1[f])\n file=match.group()\n file_dt=dp.parse(file)\n if file_dt >= start_date_dt and file_dt <= end_date_dt:\n ind.append(f)\n dates.append(file)\n\n ndays=len(ind)\n print(ndays)\n\n #If the number of elevation angles in the volumes changes over time, then the total number of rays also varies\n #This loop finds the maximum number of rays\n for f in range(0,ndays):\n file=np.load(filelist1[ind[f]])\n if f==0:\n [_,a]=file.shape\n nrays=a\n else:\n [_,a2]=file.shape\n if a2>a:\n nrays=a2\n\n #Number of volumes can vary each day \n nvols=250\n phiest=np.zeros((ndays,nvols,nrays))*np.nan\n phiobs=np.zeros((ndays,nvols,nrays))*np.nan\n good_rays=np.zeros((ndays,nvols))*np.nan\n x=np.zeros((nvols,nrays))*np.nan\n \n #Load each phiest and phiobs data for each day and store into 3D array. \n #Calculate number of good rays for each day/volume\n d=0\n for f in range(0,ndays):\n phiest1 = np.load(filelist1[ind[f]])\n [a,b] = phiest1.shape\n phiest[d,0:a,0:b] = phiest1 \n phiobs1 = np.load(filelist2[ind[f]])\n [a,b] = phiobs1.shape\n phiobs[d,0:a,0:b] = phiobs1\n d=d+1\n \n #Calculate number of good rays in each volume. good_rays(ndays,nvols)\n for j in range(ndays): \n for i in range(nvols):\n good_rays[j,i] = np.nansum(np.isfinite(phiest[j,i,:]))\n\n #bias_each_ray (ndays,nvols,nrays)\n #Calculate a bias/offset for each individual ray\n bias_each_ray = (phiest - phiobs) / phiobs\n \n #Only use volumes with more than 10 good rays for calculation of overall bias.\n ind = good_rays>10\n \n #SINGLE VALUES FOR WHOLE TIME PERIOD\n mean_bias = np.nanmean(bias_each_ray[ind,:])\n mean_bias_db = 10.0*np.log10(1000+mean_bias*1000)-30\n \n median_bias = np.nanmedian(bias_each_ray[ind,:])\n median_bias_db = 10.0*np.log10(1000.0+median_bias*1000.0)-30.0\n \n std = np.nanstd(bias_each_ray[ind,:])\n std_db = 10.0*np.log10(1000.0+std*1000.0)-30.0\n \n #print 'Mean bias = ', mean_bias_db, 'Median bias = ', median_bias_db, 'Standard Deviation = ', std_db\n \n #DAILY VALUES OF BIAS\n mean_bias_each_day=np.zeros(ndays)*np.nan\n median_bias_each_day=np.zeros(ndays)*np.nan\n std_each_day=np.zeros(ndays)*np.nan\n std_error_each_day = np.zeros(ndays)*np.nan\n num_rays_day=np.zeros(ndays)\n \n for day in range(ndays):\n #good_rays has shape (days,vols)\n #find index for volumes with more than 10 good rays\n ind = good_rays[day,:]>10\n #find all rays on each day within these volumes\n bias_one_day = bias_each_ray[day,ind,:].flatten()\n ind2 = np.isfinite(bias_one_day) \n if np.sum(ind2)>0:\n std_error_each_day[day] = scipy.stats.sem(bias_one_day[ind2])\n mean_bias_each_day[day] = np.nanmean(bias_one_day)\n median_bias_each_day[day] = np.nanmedian(bias_one_day)\n std_each_day[day] = np.nanstd(bias_one_day)\n \n #Number of rays for each day\n num_rays_day[day] = np.sum(np.isfinite(bias_one_day))\n\n #Convert to dB \n mean_bias_each_day_db = 10.0*np.log10(1000.0+mean_bias_each_day*1000.0)-30.0\n median_bias_each_day_db = 10.0*np.log10(1000.0+median_bias_each_day*1000.0)-30.0\n std_each_day_db = 10.0*np.log10(1000.0+std_each_day*1000.0)-30.0\n std_error_each_day_db = 10.0*np.log10(1000.0+std_error_each_day*1000.0)-30.0\n \n #Put data into dataframe\n time = pd.to_datetime(dates, format = '%Y%m%d')\n data = pd.DataFrame({'Mean Bias' : mean_bias_each_day_db, 'Median Bias' : median_bias_each_day_db, \n 'Standard Error' : std_error_each_day_db, 'Standard Deviation' : std_each_day_db}, \n index=time) \n \n #Make plot \n fig, ax1 = plt.subplots(figsize=(15,8)) \n plt.errorbar(data.index, mean_bias_each_day_db, std_error_each_day_db, \n color='black',fmt='o',markersize='4', elinewidth=2,capsize=4)\n plt.plot([start_date_dt, end_date_dt],[median_bias_db,median_bias_db],'r-',\n label=\"Median Bias = %s\" % round(median_bias_db,2))\n plt.plot([start_date_dt, end_date_dt],[mean_bias_db,mean_bias_db],'g', \n label=\"Mean Bias = %s\" % round(mean_bias_db,2))\n plt.plot([start_date_dt, end_date_dt],[mean_bias_db+std_db*2,mean_bias_db+std_db*2],'g--',\n label=\"Standard Deviation = %s\" % round(std_db,2))\n plt.plot([start_date_dt, end_date_dt],[mean_bias_db-std_db*2,mean_bias_db-std_db*2],'g--')\n \n plt.plot(data.index, median_bias_each_day_db,'rx')\n \n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%y'))\n plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=1))\n plt.gca().xaxis.set_minor_locator(mdates.WeekdayLocator(interval=1))\n plt.xlim(start_date_dt,end_date_dt)\n\n plt.xticks(rotation=90)\n plt.xlabel('Time',{'fontsize':18})\n plt.ylabel('Z Bias (dBZ)',{'fontsize':18})\n plt.yticks(size=18)\n plt.xticks(size=18)\n plt.grid()\n plt.legend(loc=0,fontsize=18)\n \n #If you want to overlay number of rays for each data point then uncomment these lines.\n #May need some tweaking to get the yaxis scale correct for the data you are plotting. \n# ax2=ax1.twinx()\n# ax2.set_ylim(0,20000)\n# ax2.plot(data.index, num_rays_day,'bx-')\n# ax2.set_yticks([5000, 10000])\n# ax2.set_yticks([1000, 2000, 3000, 4000, 7500],minor=True)\n# plt.ylabel('Total number of Rays',{'fontsize':18})\n# plt.yticks(size=18)\n# plt.xlim(start_date_dt,end_date_dt)\n\n #Save the plot\n imgname = f'{img_dir}/Z_calibration_{start_date}_{end_date}.png'\n plt.tight_layout()\n plt.savefig(imgname,dpi=150)","def reconstruct(self, lam = 0.0001, display=False):\n \n self.history = {}\n \n x = utils.initImage(self.data, self.fov, self.scales[0], self.pulse_ft)\n \n if display:\n plt.ion()\n plt.show()\n self.__fig, self.__axs = plt.subplots(1, 2)\n plt.subplots_adjust(wspace=-0.1)\n self.__display(x, np.zeros_like(x), 'Initialization')\n \n self.history['init'] = x\n \n for scale in self.scales:\n\n x = utils.upscaleImage(x, self.fov, scale, self.pulse)\n \n gammas = (utils.ftVectors(self.data['bi_uvcoord1'], self.fov, \n scale, self.pulse_ft),\n utils.ftVectors(self.data['bi_uvcoord2'], self.fov, \n scale, self.pulse_ft),\n utils.ftVectors(self.data['bi_uvcoord3'], self.fov, \n scale, self.pulse_ft))\n \n for beta in self.betas:\n \n # (a) solve for Z while keeping x constant\n Z = utils.mostLikelyPatches(x, beta, self.data, \n self.patch_size, self.gmm)\n \n # (b) solve for x while keeping Z constant\n x = utils.taylorExpansion(x, Z, beta, self.data, gammas,\n self.patch_size, lam=lam)\n \n if display:\n self.__axs[0].clear()\n self.__axs[1].clear()\n self.__display(x, Z, 'Scale: ' + str(scale) + '\\n' +\\\n r'$\\beta$: ' + str(beta))\n self.history[scale] = x\n \n if display:\n plt.ioff()\n \n self.res = np.rot90(utils.upscaleImage(x, self.fov, \n self.naxis, self.pulse),2)\n return self.res","def main():\n \n data_base = '/local/duman/SIMULATIONS/many_polymers_5/'\n save_base = '/usr/users/iff_th2/duman/RolfData/many_polymers_5'\n \n \n ## Index the data\n# density = [0.08, 0.2, 0.4]\n# xi_L = [0.05, 0.1, 0.2, 1.0, 2.5, 5.0, 8.0, 16.0]\n# Pe = [3.0, 7.0, 15.0, 50.0, 150.0, 500.0, 750.0, 1500.0, 4375.0, 8000.0, 10000.0]\n# kappa = [1.25, 2.5, 5.0, 25.0, 62.5, 125.0, 200.0, 400.0]\n# fp = [0.0, 0.0048, 0.0112, 0.024, 0.08, 0.24, 0.8, 1.2, 2.4, 7.0]\n \n density = [0.2]\n kappa = [1.25, 2.5, 5.0, 25.0, 62.5, 125.0, 200.0, 400.0]\n xi_L = [0.05, 0.1, 0.2, 1.0, 2.5, 5.0, 8.0, 16.0]\n fp = [0.0048, 0.0112, 0.024, 0.08, 0.24, 0.8, 1.2, 2.4, 7.0]\n Pe = [3.0, 7.0, 15.0, 50.0, 150.0, 500.0, 750.0, 1500.0, 4375.0]\n \n ## Create points\n points = []\n for i, x in enumerate(xi_L):\n for j, p in enumerate(Pe):\n points.append( Phase(x, p, kappa[i], fp[j], 'short') ) \n \n for point in points:\n point.analyse_type()\n point.set_plot_props()\n \n long_xil = [0.05, 0.2, 1.0, 2.5, 16.0]\n long_pe = [3.0, 150.0, 750.0, 8000.0, 10000.0]\n long_kappa = [5.0, 20.0, 100.0, 250.0, 1600.0]\n long_fp = [0.0003, 0.015, 0.075, 0.0, 0.0]\n long_points = []\n for i, x in enumerate(long_xil):\n for j, p in enumerate(long_pe):\n long_points.append( Phase(x, p, long_kappa[i], long_fp[j], 'long') ) \n \n for point in long_points:\n point.determine_type()\n point.set_plot_props()\n \n plot_data(points, long_points, save_base, xi_L, Pe)","def fit_lorentzian(comp_key,p0,time_step,conn,func = fitting.fun_lorentzian,fig=None,wind=3):\n\n (fin,) = conn.execute(\"select fout from comps where comp_key = ?\",(comp_key,)).fetchone()\n Fin = h5py.File(fin,'r')\n g = Fin[fd('vanHove',comp_key)]\n\n temp = g.attrs['temperature']\n dtime = g.attrs['dtime']\n j = int(time_step/dtime)\n count = g[fd('step',j)]['y']['disp_count'][:]\n count += g[fd('step',j)]['x']['disp_count'][:]\n edges = g[fd('step',j)]['y']['disp_edges'][:]\n\n \n edges = np.array([np.mean(edges[j:j+wind]) for j in range(1,len(count)-wind,wind)])\n count = np.array([np.sum(count[j:j+wind]) for j in range(1,len(count)-wind,wind)])\n \n \n edges = edges[count>30]\n count = count[count>30]\n \n\n out = fitting.fit_curve(edges,count,p0,func)\n fig = fitting.display_fit(edges,count,out.beta,func,fig)\n print out.beta\n return out,fig","def plot_fig44a_wlc_inhomo_test7():\n\n fig, ax = plt.subplots(figsize=(default_width, default_height))\n #paramters from calculation, all in nm\n a = 5 #looping radius\n DEL = 15.3 #spacing between nucleosomes\n MINUS1NUC = -83.3 #distance between -1 nuc and TSS\n PLUS1NUC = 0\n NNUC = 50 #number of nucleosomes on each side of TSS\n LPB = 0.34 #nm per basepair (as used in calculation)\n\n Llinks = np.concatenate(([245], np.tile(45, 49)))\n Rlinks = np.tile(45, 49)\n #plot as negative distance from TSS in bp\n unwrap=0\n ldna_Llinks = -1*convert.genomic_length_from_links_unwraps(Llinks, unwraps=unwrap)\n ldna_Rlinks = convert.genomic_length_from_links_unwraps(Rlinks, unwraps=unwrap)\n x = np.concatenate((ldna_Llinks[::-1], [0], ldna_Rlinks))\n y = np.loadtxt('csvs/Sarah/pnucsite_test7.txt')\n ax.plot(x, y, '-o', markersize=3, color=teal_flucts)\n plt.xlabel(r'Distance from TSS (bp)')\n plt.ylabel(r'$P_\\mathrm{loop}(a=5\\mathrm{nm}; L)\\;\\;\\;(\\mathrm{nm}^{-3})$')\n plt.subplots_adjust(left=0.16, bottom=0.19, top=0.98, right=0.96)\n plt.yscale('log')\n #plt.ylim([10**-3, 10**-0.4])\n plt.xlim([-10000, 10000])\n plt.savefig(f'plots/thesis/fig44a_wlc-inhomo-looping.pdf', bbox_inches='tight')","def main(gage_id: str, inputs_path: str = '', outputs_dir: str = '', data_type: str = 'Q', version: str = '2_2', \r\n max_cl: float = 0.99, adj_flows: float = 1.0, round_decimals: int = 1, verbose: bool = True, \r\n display_plots: bool = True, extrapolate: bool = True, exclude_tails: bool = True) -> pd.DataFrame:\r\n standard_aep = [0.9, 0.5, 0.2, 0.1, 0.05, 0.02, 0.01, 0.005, 0.002, 0.001, 5E-04, 2E-04, 1E-04, 5E-05, 2E-05, 1E-05,\r\n 5E-06, 2E-06, 1E-06] \r\n\r\n inputs_path = pl.Path(inputs_path)\r\n if inputs_path.suffix == '':\r\n if verbose:\r\n print('Specified inputs_path is to a directory, identifying .rpt files:')\r\n ssp_results = list_ssp_files(inputs_path, max_cl, verbose) \r\n df = make_ssp_table(ssp_results, version) \r\n elif inputs_path.suffix == '.csv':\r\n df = pd.read_csv(inputs_path, index_col = 0)\r\n assert df.index.name == 'AEP', ('The first column of the specified csv must be titled \"AEP\" and contain annual '\r\n 'exceedance probabilites.')\r\n if verbose:\r\n print('Specified inputs_path is to a csv, loaded table.\\n')\r\n else:\r\n raise NameError('The specified inputs_path is not a directory of .rpt files or a path to a csv file as '\r\n 'expected.')\r\n if data_type not in ['Q', 'P']:\r\n raise ValueError('The specified data_type may only be \"Q\" or \"P\" corresponding to discharge or precipitation, '\r\n 'respectively') \r\n df = monotonic_test(df, adj_flows, verbose)\r\n cl = list(map(float, df.columns)) \r\n clz = zvar(cl) \r\n aep = df.index \r\n aepz = zvar(aep) \r\n data = np.log10(df) \r\n q = binq(data)\r\n res = interp_aep(data, q, clz, aepz, extrapolate) \r\n restrans = zvar_inv(res, cl)\r\n aepm = mean_aep(restrans, exclude_tails) \r\n aepmz = zvar(aepm) \r\n standard_aepz = zvar(standard_aep) \r\n table = ffc_summary(standard_aep, standard_aepz) \r\n table['Q_Mean_cfs'] = np.round(10**table['AEPz'].apply(interp_q(aepmz, q)), round_decimals)\r\n table['Q_Median_cfs'] = np.round(10**table['AEPz'].apply(interp_q(aepz, np.array(data['0.5']))), round_decimals)\r\n mean_curve_table = table.copy().drop(columns=['AEPz'])\r\n if display_plots: \r\n plot_ssp_meanmedffc(table, gage_id, data_type) \r\n if data_type == 'P':\r\n mean_curve_table.rename(columns = {'Q_Mean_cfs': 'P_Mean_in', 'Q_Median_cfs': 'P_Median_in'}, inplace = True)\r\n if verbose:\r\n display(mean_curve_table.head(2)) \r\n if outputs_dir!='':\r\n make_directories([outputs_dir], verbose)\r\n mean_curve_table.to_csv(pl.Path(outputs_dir)/f'MeanCurve_{gage_id}.csv') \r\n return mean_curve_table","def localization_full(Gint, focal_genes, \n num_reps = 200,\n method = 'LCC', \n print_counter = False,\n label = 'focal genes',\n line_height = 0.1,\n legend_loc = 'upper left'):\n \n numedges_list, numedges_rand, LCC_list, LCC_rand = localization(Gint, focal_genes, num_reps, \n sample_frac = 1, \n method = method, \n plot = False,\n print_counter = print_counter)\n if method == 'numedges':\n analysis_list = numedges_list\n analysis_rand = numedges_rand\n title = 'number of edges'\n else:\n analysis_list = LCC_list\n analysis_rand = LCC_rand\n title = 'largest connected component'\n\n # plot distributions for non-sampled case\n fig, ax = plt.subplots(figsize = (12, 7))\n sns.set_style('white') \n plt.vlines(np.mean(analysis_list), ymin = 0, ymax = line_height, color = 'r', lw = 2, label = label)\n sns.kdeplot(analysis_rand, ax = ax, color = 'k', lw = 2, alpha = 0.5, shade = True, label = 'random')\n plt.legend(loc = legend_loc, fontsize = 12)\n plt.ylabel('frequency', fontsize = 16)\n plt.xlabel(title, fontsize = 16)\n\n # print the z-score and fdr\n analysis_z = (np.mean(analysis_list) - np.mean(analysis_rand))/float(np.std(analysis_rand))\n\n print(1 - ndtr(analysis_z))\n\n plt.title('permutation p = ' + str(1 - ndtr(analysis_z)))\n \n return numedges_list, numedges_rand, LCC_list, LCC_rand","def plot_curves_lenstool(label = ' ', courves_file = 'ce.dat', marker = 'm.', \\\n plt_show = False):\n# x_ca, y_ca = np.loadtxt( courves_file, usecols=(3, 4), unpack=True )\n# x_cc, y_cc = np.loadtxt( courves_file, usecols=(1, 2), unpack=True )\n\n x_cc, y_cc, x_ca, y_ca = \\\n np.loadtxt( courves_file, usecols=(1, 2, 3, 4), unpack=True )\n\n plt.figure(1, figsize=(16, 8))\n\n plt.subplot(1, 2, 1).set_aspect(1)\n plt.plot(x_ca, y_ca, marker, linewidth=3, label = label)\n axis_limit = max( 1.075*max(np.absolute(x_ca)), \\\n 1.075*max(np.absolute(y_ca)) )\n plt.axis([-axis_limit, axis_limit, -axis_limit, axis_limit])\n\n plt.subplot (1, 2, 2).set_aspect(1)\n plt.plot(x_cc, y_cc, marker, linewidth=3, label = label)\n axis_limit = max( 1.075*max(np.absolute(x_cc)), \\\n 1.075*max(np.absolute(y_cc)) )\n plt.axis([-axis_limit, axis_limit, -axis_limit, axis_limit])\n plt.legend()\n\n if plt_show:\n plt.show()\n plt.close()","def figure4():\n\n plot_settings = {'y_limits': [-80, -50],\n 'x_limits': None,\n 'y_ticks': [-80, -70, -60, -50],\n 'locator_size': 5,\n 'y_label': 'Voltage (mV)',\n 'x_ticks': [],\n 'scale_size': 20,\n 'x_label': \"\",\n 'scale_loc': 4,\n 'figure_name': 'figure_4',\n 'legend': ['control', 'apamin'],\n 'legend_size': 8,\n 'y_on': True}\n line_styles = ['-', 'dotted']\n\n plt.figure(figsize=(5, 3), dpi=96)\n\n plt.subplot(2, 1, 1) # Generate figure 1 (top)\n for ix, g_sk_bar in enumerate([0.3, 0]):\n t, y = solver(100, g_sk_bar=g_sk_bar)\n plt.plot(t, y[:, 0], c='k', linestyle=line_styles[ix])\n alter_figure(plot_settings) # Alter figure for publication\n\n plt.subplot(2, 1, 2)\n t1 = 1200\n t, y = solver(t1, t_start=50, duration=t1, i_bias_on=0.33, g_sk_bar=0.03)\n plt.plot(t, y[:, 0], 'k-')\n\n plot_settings['y_limits'] = [-100, 30]\n plot_settings['x_limits'] = [0, t1]\n plot_settings['y_ticks'] = [-80, -60, -40, -20, 0, 20]\n plot_settings['locator_size'] = 10\n plot_settings['scale_size'] = 100\n plot_settings['legend'] = None\n alter_figure(plot_settings, close=True) # Alter plot for publication","def main():\r\n graphPerformance = False # Built in graphing ability, currently not functional, but mechanism is in place.\r\n trainData = \"2_1000_0_1600_0_0_CV_0_Train.txt\"\r\n testData = \"2_1000_0_1600_0_0_CV_0_Test.txt\"\r\n outProg = \"GH_GALE_ProgressTrack\"\r\n outPop = \"GH_GALE_PopulationOut\"\r\n bitLength = 1 # This implementation is not yet set up to handle other rule representations, or bit encoding lengths.\r\n CVpartitions = 10\r\n trackCycles = 1\r\n \r\n iterInput = '5.10.20' \r\n xdim = 10\r\n ydim = 10\r\n dist = 2\r\n wild = 0.75\r\n prune = 1\r\n \r\n #Figure out the iteration stops for evaluation, and the max iterations.\r\n iterList = iterInput.split('.')\r\n for i in range(len(iterList)):\r\n iterList[i] = int(iterList[i])\r\n lastIter = iterList[len(iterList)-1] \r\n\r\n #Sets up up algorithm to be run.\r\n GALEConstants.setConstants(prune, wild)\r\n e = GALE_Environment(trainData,testData,bitLength)\r\n sampleSize = e.getNrSamples()\r\n gale = GALE(e, outProg, outPop, bitLength, CVpartitions, graphPerformance, xdim, ydim, dist)\r\n \r\n #Set some GALE parameters.\r\n if trackCycles == 'Default':\r\n gale.setTrackingIterations(sampleSize)\r\n else:\r\n gale.setTrackingIterations(trackCycles) \r\n gale.setNumberOfTrials(lastIter, iterList) \r\n \r\n #Run the GALE Algorithm \r\n gale.runGALE()","def prob4():\n domain = np.linspace(-1, 1, 400)\n n_array = [2**i for i in range(2,9)]\n f = lambda x: 1 / (1 + 25*x**2)\n plt.ion()\n poly_error = []\n cheby_error = []\n for n in n_array:\n x = np.linspace(-1, 1, n)\n poly = BarycentricInterpolator(x)\n poly.set_yi(f(x))\n poly_error.append(la.norm(f(domain) - poly(domain), ord=np.inf))\n y = np.array([(1/2)*(2*np.cos(j*np.pi/n)) for j in range(n+1)])\n cheby = BarycentricInterpolator(y)\n cheby.set_yi(f(y))\n cheby_error.append(la.norm(f(domain) - cheby(domain), ord=np.inf))\n plt.loglog(n_array, poly_error, label=\"equally spaced points\", basex=2)\n plt.loglog(n_array, cheby_error, label=\"Chebyshev extremal points\", basex=2)\n plt.legend()\n plt.show()","def plot_ri(ri_FW, G_FW, lims=None, compare=True, path=None):\n nplots=len(ri_FW[0].keys())\n ncols=3\n nrows=int(np.ceil(nplots/ncols))\n _, axes = plt.subplots(nrows,ncols, figsize=(13, 5*nrows))\n ri_iter=dict()\n i=0\n j=0\n\n #ri_iter is the ri that has been prescribed as an argument to the optimization \n for n in ri_FW[0].keys():\n ri_iter[n]=[]\n\n for ri in ri_FW:\n for n in ri.keys():\n ri_iter[n].append(ri[n])\n \n #compute the net rebalancing flow at each node\n #those are the real net flows that occur on the graph\n if compare:\n real_ri=dict()\n G_ri=dict()\n for n in G_FW[0].nodes():\n real_ri[n]=np.zeros((len(ri_FW),))\n G_ri[n]=np.zeros((len(ri_FW),))\n for i in range(len(G_FW)):\n G=G_FW[i]\n for e in G.edges():\n if e[1]!='R' and not e[1].endswith('_p'):\n real_ri[e[0]][i]-=G[e[0]][e[1]]['f_r']\n real_ri[e[1]][i]+=G[e[0]][e[1]]['f_r']\n for n in G.nodes():\n G_ri[n][i]=G.nodes[n]['ri']\n i=0\n for n in ri_iter.keys():\n \n if j==ncols:\n j=0\n i+=1\n if i==nrows:\n i=0\n axes[i,j].plot(np.arange(1,len(ri_iter[n]),1),ri_iter[n][:-1], 'o-', label='Goal. ri_t')\n if compare:\n axes[i,j].plot(np.arange(1,len(ri_iter[n]),1),real_ri[n][1:], 'o--', label='net flows')\n axes[i,j].plot(np.arange(1,len(ri_iter[n]),1),real_ri[n][1:]-ri_iter[n][:-1], 'o--', label='diff')\n\n # axes[i,j].plot(G_ri[n],'o--', label='G_ri')\n axes[i,j].grid(True)\n axes[i,j].set_xlabel('Iteration #')\n axes[i,j].set_title(' node : ' + str(n))\n axes[i,j].set_xlim(lims)\n axes[i,j].set_xticks(np.arange(1,len(ri_iter[n]),1))\n axes[i,j].legend()\n # axes[i,j].set_ylim([np.min(ri_iter[n][-5:])*.95 , np.max(ri_iter[n][-5:])*1.05])\n j+=1\n \n if path!=None:\n plt.savefig(os.path.join(path,'ri.png'))\n \n plt.close()\n return","def main():\n lake_drivers = Dynamic_Lake_Drivers()\n #lake_drivers.prepare_orography_ICE5G_0k_uncorrected()\n #lake_drivers.prepare_orography_ICE5G_0k_corrected()\n #lake_drivers.prepare_orography_ICE6G_21k_corrected()\n #lake_drivers.prepare_river_directions_with_depressions_from_glac1D()\n #lake_drivers.evaluate_glac1D_ts1900_basins()\n #import time\n # start = time.time()\n #lake_drivers.evaluate_ICE6G_lgm_basins()\n # end = time.time()\n # print(end - start)\n #lake_drivers.prepare_basins_from_glac1D()\n #lake_drivers.extract_lake_volumes_from_glac1D_basins()\n #lake_drivers.connect_catchments_for_glac1D()\n lake_drivers.connect_catchments_for_transient_run()\n #lake_drivers.extract_volumes_for_transient_run()\n #lake_drivers.add_10min_rmouth_to_transient_data()\n #lake_drivers.expand_transient_data_catchments_to_include_rmouth()\n #lake_drivers.remove_no_data_values_from_upscaled_MERIT_correction_set()\n #lake_drivers.remove_disconnected_points_from_slm()","def analyze(argv):\n costs, tikhs, regs = get_l_curve_results(argv[0])\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n plt.scatter(tikhs, costs)\n for k in range(len(regs)):\n exponent = math.log(regs[k], 10)\n ax.annotate(r'$10^{{{0:.2f}}}$'.format(exponent),\n xy = (tikhs[k], costs[k]),\n xytext = (0, 30),\n textcoords = 'offset points',\n rotation = 45,\n fontsize = 16)\n plt.xlabel(r'Model norm (MPa $\\cdot$ m / a)', fontsize = 16)\n plt.ylabel(r'Cost function (MPa $\\cdot$ m${}^3$ / a)', fontsize = 16)\n plt.xlim(0, 1.15 * np.max(tikhs))\n plt.ylim(0.95 * np.min(costs), 1.05 * np.max(costs))\n plt.show()\n\n return regs, tikhs, costs","def AGN_SB_diagnostic(**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n GR = glo.global_results()\n\n fig,ax = plt.subplots()\n L_CII = getattr(GR,'L_[CII]158_sun')\n\n # x = getattr(GR,'L_[CII]158_sun')/getattr(GR,'L_[NII]122_sun')\n x = getattr(GR,'L_[OIV]25_sun')/getattr(GR,'L_[OIII]88_sun')\n y = getattr(GR,'L_[NeIII]15_sun')/getattr(GR,'L_[NeII]12_sun')\n sc = ax.scatter(x,y,marker='o',s=3,alpha=0.6,c=np.log10(getattr(GR,'SFR')))\n ax.set_xscale('log')\n ax.set_yscale('log')\n ax.set_xlim([8e-5,200])\n ax.set_ylim([0.02,150])\n # ax.set_xlabel('[CII]$_{158}$/[NII]$_{122}$')\n plt.colorbar(sc,label='log(SFR)')\n ax.set_xlabel('[OIV]$_{25.9}$/[OIII]$_{88}$')\n ax.set_ylabel('[NeIII]$_{15.6}$/[NeII]$_{12.8}$')\n if p.savefig:\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \n plt.savefig(p.d_plot + 'luminosity/ratio_%s' % ratio_name,dpi=300)","def plotEnergiesOpt(monthlyData, optIdx):\n \n \n dummyRange = np.asarray(range(len(optIdx)))\n \n fig = plt.figure(figsize=(16, 8))\n \n plt.suptitle('Energy Comparison')\n ax1 = plt.subplot(1,1,1)\n plt.plot(monthlyData['H'][optIdx, dummyRange], label = 'H', color='r')\n plt.plot(monthlyData['C'][optIdx, dummyRange], label = 'C', color='b')\n plt.plot(monthlyData['L'][optIdx, dummyRange], label = 'L', color='g')\n plt.plot(monthlyData['PV'][optIdx, dummyRange], label = 'PV', color='c')\n plt.plot(monthlyData['E_HCL'][optIdx, dummyRange], label = 'HCL', color='m')\n plt.plot(monthlyData['E_tot'][optIdx, dummyRange], label = 'E_tot', color='k')\n plt.ylabel('Energy [kWh]')\n plt.xlim(0,288)\n\n# plt.legend()\n \n majorLocator = MultipleLocator(24)\n majorFormatter = FormatStrFormatter('%d')\n minorLocator = MultipleLocator(4)\n minorFormatter = FormatStrFormatter('%d')\n\n ax1.xaxis.set_major_locator(majorLocator)\n ax1.xaxis.set_major_formatter(majorFormatter)\n ax1.xaxis.set_minor_locator(minorLocator)\n# ax1.xaxis.set_minor_formatter(minorFormatter)\n plt.grid(True, which=u'major')\n \n # Shrink current axis by 20%\n box = ax1.get_position()\n ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n \n # Put a legend to the right of the current axis\n ax1.legend(loc='upper left', bbox_to_anchor=(1, 1.05))\n# \n\n plt.xticks(range(0,288,24),('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'))\n# ax2 = plt.subplot(2,1,2, sharex=ax1)\n# plt.plot(multiplier*monthlyData[energyType][indices['H'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for H', color='r')\n# plt.plot(multiplier*monthlyData[energyType][indices['C'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for C', color='b')\n# plt.plot(multiplier*monthlyData[energyType][indices['L'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for L', color='g')\n# plt.plot(multiplier*monthlyData[energyType][indices['PV'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for PV', color='c')\n# plt.plot(multiplier*monthlyData[energyType][indices['E_HCL'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for HCL', color='m')\n# plt.plot(multiplier*monthlyData[energyType][indices['E_tot'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for E_tot', color='k')\n# plt.plot(multiplier*monthlyData[energyType][indices['45'],:]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'fixed at 45 deg', color='y')\n# plt.ylabel('Energy Difference [kWh]')\n# plt.legend()\n#\n# ax2.xaxis.set_major_locator(majorLocator)\n# ax2.xaxis.set_major_formatter(majorFormatter)\n# ax2.xaxis.set_minor_locator(minorLocator)\n## ax2.xaxis.set_minor_formatter(minorFormatter)\n# plt.grid(True, which='both')\n# \n return fig","def ISM_effs(line,**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n GR = glo.global_results()\n\n L_tot = getattr(GR,'L_%s_sun' % line)\n L_H2 = getattr(GR,'L_%s_H2_sun' % line)\n L_HI = getattr(GR,'L_%s_HI_sun' % line)\n L_HII = getattr(GR,'L_%s_HII_sun' % line)\n M_HII = getattr(GR,'M_HII')\n M_HI = getattr(GR,'M_HI')\n M_H2 = getattr(GR,'M_H2')\n M_gas = getattr(GR,'M_gas')\n\n fig, ax = plt.subplots(figsize=(8, 6 ),facecolor='w')\n x=ax.hist(np.log10(L_tot[L_tot > 0]/M_gas[L_tot > 0]),bins=100,alpha=0.6,label='tot',color='grey')\n x=ax.hist(np.log10(L_H2[L_H2 > 0]/M_H2[L_H2 > 0]),bins=100,alpha=0.6,label='H2',color='r')\n x=ax.hist(np.log10(L_HI[L_HI > 0]/M_HI[L_HI > 0]),bins=100,alpha=0.8,label='HI',color='orange')\n x=ax.hist(np.log10(L_HII[L_HII > 0]/M_HII[L_HII > 0]),bins=100,alpha=0.5,label='HII',color='b')\n ax.set_xlabel('log efficiency of %s [L$_{\\odot}$/M$_{\\odot}$]' % line)\n ax.set_ylabel('Number of galaxies')\n plt.legend()\n if p.savefig:\n plt.savefig(p.d_plot + 'luminosity/ISM_effs_%s.png' % line)","def plot_ebtel(data_directory,data_file,**kwargs):\n \n #Load the data\n data = np.loadtxt(data_directory+data_file)\n #Slice the array to get the vectors we want\n time = data[:,0]\n temp = data[:,1]\n dens = data[:,2]\n temp_apex = data[:,5]\n dens_apex = data[:,6]\n heat = data[:,10]\n \n #Set up the figure\n fig,axes = plt.subplots(3,1,figsize=(12,8))\n #Set the fontsize\n fs = 16\n\n #Plot the heating\n axes[0].plot(time,heat)\n axes[0].set_ylabel(r'$h$ (erg cm$^{-3}$ s$^{-1}$)',fontsize=fs)\n axes[0].set_title(r'EBTEL-C Parameters',fontsize=fs)\n axes[0].set_xlim([time[0],time[-1]])\n axes[0].locator_params(nbins=5)\n axes[0].ticklabel_format(axis='y', style='sci', scilimits=(-2,2) )\n #Plot the temperatures\n axes[1].plot(time,temp/10**6,label=r'$T$')\n axes[1].plot(time,temp_apex/10**6,'r--',label=r'$T_a$')\n axes[1].set_ylabel(r'$T$ (MK)',fontsize=fs)\n axes[1].legend(loc=1)\n axes[1].set_xlim([time[0],time[-1]])\n axes[1].locator_params(nbins=5)\n #Plot the densities\n axes[2].plot(time,dens/10**8,label=r'$n$')\n axes[2].plot(time,dens_apex/10**8,'r--',label=r'$n_a$')\n axes[2].set_xlabel(r'$t$ (s)',fontsize=fs)\n axes[2].set_ylabel(r'$n$ ($10^{8}$ cm$^{-3}$)',fontsize=fs)\n axes[2].set_xlim([time[0],time[-1]])\n axes[2].locator_params(nbins=5)\n axes[2].legend(loc=1)\n\n #Check if output filename is specified\n if 'print_fig_filename' in kwargs:\n plt.savefig(kwargs['print_fig_filename'],format='eps',dpi=1000)\n else:\n plt.show()","def run(datadir, outdir, real_data_fnames, MT_green_func_fnames, single_force_green_func_fnames, data_labels, inversion_type, num_samples, comparison_metric, nlloc_hyp_filename, perform_normallised_waveform_inversion=True, compare_all_waveforms_simultaneously=False, cut_phase_start_vals=[], cut_phase_length=0, plot_switch=False, num_processors=1, set_pre_time_shift_values_to_zero_switch=True, only_save_non_zero_solns_switch=False, return_absolute_similarity_values_switch=False, invert_for_ratio_of_multiple_media_greens_func_switch=False, green_func_fnames_split_index=0, green_func_phase_labels=[], invert_for_relative_magnitudes_switch=False, rel_exp_mag_range=[1.0,1.0], auto_shift_for_best_fit=True, manual_indices_time_shift_MT=[], manual_indices_time_shift_SF=[]):\n # Do some initial checks:\n if auto_shift_for_best_fit:\n manual_indices_time_shift_MT = np.zeros(len(real_data_fnames))\n manual_indices_time_shift_SF = np.zeros(len(real_data_fnames))\n else:\n if (len(manual_indices_time_shift_MT)==0) and (len(manual_indices_time_shift_SF)==0):\n print(\"Error. Because <auto_shift_for_best_fit> is set to false, manual_indices_time_shift_MT and or manual_indices_time_shift_SF are not specified. Exiting.\")\n sys.exit()\n\n # Run specific multi medium inversion, if specified:\n if invert_for_ratio_of_multiple_media_greens_func_switch:\n # For muliple media greens functions:\n run_multi_medium_inversion(datadir, outdir, real_data_fnames, MT_green_func_fnames, single_force_green_func_fnames, data_labels, inversion_type, perform_normallised_waveform_inversion, compare_all_waveforms_simultaneously, num_samples, comparison_metric, manual_indices_time_shift_MT, manual_indices_time_shift_SF, nlloc_hyp_filename, cut_phase_start_vals=cut_phase_start_vals, cut_phase_length=cut_phase_length, plot_switch=plot_switch, num_processors=num_processors, set_pre_time_shift_values_to_zero_switch=set_pre_time_shift_values_to_zero_switch, only_save_non_zero_solns_switch=only_save_non_zero_solns_switch, return_absolute_similarity_values_switch=return_absolute_similarity_values_switch, invert_for_ratio_of_multiple_media_greens_func_switch=invert_for_ratio_of_multiple_media_greens_func_switch, green_func_fnames_split_index=green_func_fnames_split_index, green_func_phase_labels=green_func_phase_labels, auto_shift_for_best_fit=auto_shift_for_best_fit)\n else:\n # Run for normal, single set of greens functions:\n \n # Load input data (completely, for specific inversion type):\n real_data_array, green_func_array = get_overall_real_and_green_func_data(datadir, real_data_fnames, MT_green_func_fnames, single_force_green_func_fnames, inversion_type, manual_indices_time_shift_MT=manual_indices_time_shift_MT, manual_indices_time_shift_SF=manual_indices_time_shift_SF, cut_phase_start_vals=cut_phase_start_vals, cut_phase_length=cut_phase_length, set_pre_time_shift_values_to_zero_switch=set_pre_time_shift_values_to_zero_switch)\n \n # Perform the inversion:\n M = perform_inversion(real_data_array, green_func_array)\n M_amplitude = ((np.sum(M**2))**0.5)\n\n # And get forward model synthetic waveform result:\n synth_forward_model_result_array = forward_model(green_func_array, M)\n \n # And plot the results:\n if plot_switch:\n plot_specific_forward_model_result(real_data_array, synth_forward_model_result_array, data_labels, plot_title=\"Initial theoretical inversion solution\", perform_normallised_waveform_inversion=perform_normallised_waveform_inversion)\n \n # And save least squares output:\n # Set output arrays to equal least squares output: \n MTs = M\n similarity_curr_sample, shift_idxs = compare_synth_to_real_waveforms(real_data_array, synth_forward_model_result_array, comparison_metric, perform_normallised_waveform_inversion, compare_all_waveforms_simultaneously, auto_shift_for_best_fit) \n MTp = np.array([similarity_curr_sample])\n # And save data to MTFIT style file:\n outdir_least_squares = outdir+\"/least_squares_result\"\n os.system(\"mkdir -p \"+outdir_least_squares)\n save_to_MTFIT_style_file(MTs, MTp, nlloc_hyp_filename, inversion_type, outdir_least_squares, shift_idxs=shift_idxs) # Saves pickled dictionary containing data from inversion\n # And save most likely solution and real data waveforms to file:\n if inversion_type == \"DC_single_force_couple\" or inversion_type == \"DC_single_force_no_coupling\" or inversion_type == \"DC_crack_couple\" or inversion_type == \"single_force_crack_no_coupling\":\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:-1, np.where(MTp==np.max(MTp))[0][0]])\n else:\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:, np.where(MTp==np.max(MTp))[0][0]])\n # And get shift associated with most likely model:\n if len(shift_idxs) > 0:\n shift_idxs_most_likely_result = np.array(shift_idxs)\n else:\n shift_idxs_most_likely_result = []\n # And save:\n save_specific_waveforms_to_file(real_data_array, synth_forward_model_most_likely_result_array, data_labels, nlloc_hyp_filename, inversion_type, outdir_least_squares, shift_idxs=shift_idxs_most_likely_result, normallise_data=perform_normallised_waveform_inversion)\n \n # And do Monte Carlo random sampling to obtain PDF of moment tensor:\n MTs, MTp, MTp_absolute, shift_idxs_all_samples = perform_monte_carlo_sampled_waveform_inversion(real_data_array, green_func_array, num_samples, M_amplitude=M_amplitude,inversion_type=inversion_type, comparison_metric=comparison_metric, perform_normallised_waveform_inversion=perform_normallised_waveform_inversion, compare_all_waveforms_simultaneously=compare_all_waveforms_simultaneously, num_processors=num_processors, return_absolute_similarity_values_switch=return_absolute_similarity_values_switch, invert_for_relative_magnitudes_switch=invert_for_relative_magnitudes_switch, rel_exp_mag_range=rel_exp_mag_range, auto_shift_for_best_fit=auto_shift_for_best_fit)\n \n # Check that probability of output is non-zero:\n if math.isnan(MTp[0]):\n print(\"Error: Sum of probabilities is equal to zero - therefore no adiquate solution could be found and inversion is terminating.\")\n sys.exit()\n \n # Remove zero probability values if specified:\n if only_save_non_zero_solns_switch:\n MTp, MTs = remove_zero_prob_results(MTp, MTs)\n \n # And plot most likely solution:\n if plot_switch:\n if inversion_type == \"DC_single_force_couple\" or inversion_type == \"DC_single_force_no_coupling\" or inversion_type == \"DC_crack_couple\" or inversion_type == \"single_force_crack_no_coupling\":\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:-1, np.where(MTp==np.max(MTp))[0][0]])\n else:\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:, np.where(MTp==np.max(MTp))[0][0]])\n plot_specific_forward_model_result(real_data_array, synth_forward_model_most_likely_result_array, data_labels, plot_title=\"Most likely Monte Carlo sampled solution\", perform_normallised_waveform_inversion=perform_normallised_waveform_inversion)\n print(\"Most likely solution:\", MTs[:,np.where(MTp==np.max(MTp))[0][0]])\n \n # And save data to MTFIT style file:\n save_to_MTFIT_style_file(MTs, MTp, nlloc_hyp_filename, inversion_type, outdir, MTp_absolute=MTp_absolute, shift_idxs=shift_idxs_all_samples) # Saves pickled dictionary containing data from inversion\n # And save most likely solution and real data waveforms to file:\n if inversion_type == \"DC_single_force_couple\" or inversion_type == \"DC_single_force_no_coupling\" or inversion_type == \"DC_crack_couple\" or inversion_type == \"single_force_crack_no_coupling\":\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:-1, np.where(MTp==np.max(MTp))[0][0]])\n else:\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:, np.where(MTp==np.max(MTp))[0][0]])\n # And get shift associated with most likely model:\n if len(shift_idxs_all_samples) > 0:\n shift_idxs_most_likely_result = np.array(shift_idxs_all_samples)[0, np.where(MTp==np.max(MTp))[0][0]]\n else:\n shift_idxs_most_likely_result = []\n # And save:\n save_specific_waveforms_to_file(real_data_array, synth_forward_model_most_likely_result_array, data_labels, nlloc_hyp_filename, inversion_type, outdir, shift_idxs=shift_idxs_most_likely_result, normallise_data=perform_normallised_waveform_inversion)\n\n print(\"Finished\")","def main():\n\n #for row_start in np.arange(0, 128):\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_row'+str(row_start).zfill(3), Imin=12, Imax=136)\n\n Marcov_Chain_MLE(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '', 160.0, [90.0, 70.0, 50.0, 30.0], 0.0, 0.5)\n plt.show()\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_all')\n\n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAsource_VBdrain', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaS-VbD_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, forward' , 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaD-VbS_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, reversed', 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \n\n #hist_IDS_VGS(0, 14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Hist-IDSAT_MLC-rv1-01020304_reverse-read_', range(0, 128), 'MLC programming {2ms, 10ms, 40ms, 200ms} pulses, VGS=1.8, VDS=2.0 for level=1-2-3-4\\nhistogram of read-IDSAT (VGS=VDS=0.8V)', 0, 150, 0, 150, 1000)\n #\n #t_label = []\n #for t in np.arange(0, 0.002*(71) + 0.0001, 0.002):\n # t_label.append(str(t))\n #\n ##MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [21], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row-21', Imin=82, Imax=142)\n\n #for row_start in np.arange(0, 128):\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row_'+str(row_start).zfill(3), Imin=80, Imax=142)\n\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01', Imin=80, Imax=142)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01', 20, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle0102', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle0102', 20, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle010203', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle010203', 20, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01020304', 40, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01020304', 10, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 50, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01', 20, 125, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 50, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle0102', 20, 125, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 50, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle010203', 20, 125, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 40, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01020304', 10, 125, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 40, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\n\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\n\n\n # (L, Nfin, VT_flavor, Nrow, Imax)\n col_list = [(36, 1, 'ULVT', 32 , 60 ), (36, 1, 'LVT', 32 , 50 ), (36, 1, 'SVT', 32 , 45 ),\n (36, 1, 'ULVT', 128, 60 ), (36, 1, 'LVT', 128, 50 ), (36, 1, 'SVT', 128, 45 ),\n (20, 1, 'ULVT', 32 , 75 ), (20, 1, 'LVT', 32 , 60 ), (20, 1, 'SVT', 32 , 50 ),\n (20, 1, 'ULVT', 128, 75 ), (20, 1, 'LVT', 128, 60 ), (20, 1, 'SVT', 128, 50 ),\n (16, 1, 'ULVT', 32 , 80 ), (16, 1, 'LVT', 32 , 65 ), (16, 1, 'SVT', 32 , 60 ),\n (16, 1, 'ULVT', 128, 80 ), (16, 1, 'LVT', 128, 65 ), (16, 1, 'SVT', 128, 60 ),\n (36, 2, 'ULVT', 32 , 115), (36, 2, 'LVT', 32 , 95 ), (36, 2, 'SVT', 32 , 85 ),\n (36, 2, 'ULVT', 128, 115), (36, 2, 'LVT', 128, 95 ), (36, 2, 'SVT', 128, 85 ), \n (20, 2, 'ULVT', 32 , 135), (20, 2, 'LVT', 32 , 115), (20, 2, 'SVT', 32 , 100),\n (20, 2, 'ULVT', 128, 135), (20, 2, 'LVT', 128, 120), (20, 2, 'SVT', 128, 100),\n (16, 2, 'ULVT', 32 , 150), (16, 2, 'LVT', 32 , 125), (16, 2, 'SVT', 32 , 115),\n (16, 2, 'ULVT', 128, 150), (16, 2, 'LVT', 128, 125), (16, 2, 'SVT', 128, 115)]\n\n #MLC_IDSAT_algorithm_rv1(11, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [135+20], [0.2], 1, np.arange(0, 0.01*16+0.0001, 0.01), '', ['../Data/chip11/MLC_programming_Chip11_Col21_2msPULSE_VG1p8_VD2p4_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_rv1_cycle01_EfficientPython')\n\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', '0.9-1.2-1.5-1.8', 2.4, 128, range(0, 128), [59+16, 72+40, 80+31, 68+23], [0.2, 0.2, 0.2, 0.2], 4, [0, 15, 15.1, 37.5, 37.6, 59.8, 59.9, 78.1], ['0', '15', '', '37.4', '', '59.6', '', '77.8'], ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG-0p9-1p2-1p5-1p8_VD2p4', '_rv1_cycle01020304')\n\n t_ratio_lst = [(0, 0.17), (0.16, 0.34), (0.33, 0.505), (0.495, 0.67), (0.66, 0.84), (0.83, 1)]\n\n #t_label = []\n #for t in np.arange(0, 0.2*(59+16) + 0.0001, 0.2):\n # t_label.append(str(t))\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(0, 128), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(59+16), t_ratio[1]*0.2*(59+16)])\n # segment += 1\n\n #t_label = []\n #for t in np.arange(0, 0.2*(72+40) + 0.0001, 0.2):\n # t_label.append(str(0.2*(59+16) + t))\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(0, 128), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(72+40), t_ratio[1]*0.2*(72+40)])\n # segment += 1\n\n\n #t_label = []\n #for t in np.arange(0, 0.2*(80+31) + 0.0001, 0.2):\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + t))\n ##MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(0, 128), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(80+31), t_ratio[1]*0.2*(80+31)])\n # segment += 1\n\n\n #t_label = []\n #for t in np.arange(0, 0.2*(68+23) + 0.0001, 0.2):\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + 0.2*(80+31) + t))\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(68+23), t_ratio[1]*0.2*(68+23)])\n # segment += 1\n\n\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 38, 112)\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle010203', 16, 110)\n\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle010203', 44, 133)\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle010203', 14, 133)\n\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle010203', 50, 135)\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 20, 140)\n\n\n\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 38, 112)\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle01', 16, 110)\n\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle01', 44, 133)\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle01', 14, 133)\n\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle01', 50, 135)\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 20, 140)\n\n\n\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 38, 112)\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle0102', 16, 110)\n # \n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle0102', 44, 133)\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle0102', 14, 133)\n # \n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle0102', 50, 135)\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 20, 140)","def main():\n print_banner()\n params = read_steering()\n s, x, y, cur, theta = build_kinoshita()\n s, x, y, cur, theta = read_centerline(s, x, y, cur, theta)\n s, x, y, cur, theta = extend_centerline(s, x, y, cur, theta)\n for t in range(TSTEPS+1):\n cur, theta = tan2curv(s, x, y)\n cur_ori = np.copy(cur)\n cur = filter_curvature(cur, t)\n cur_flt = np.copy(cur)\n cur = lag(s, cur, t)\n cur_lag = np.copy(cur)\n beck_bed = build_beck(cur, s, t)\n allxyz = offset_all(x, y, beck_bed, t)\n if t == 0:\n write_xyz_file(allxyz)\n write_mesh_file(allxyz, beck_bed)\n oxbowxList, oxbowyList = [], []\n centerlinexList, centerlineyList = [], []\n if np.mod(t, GPRINT) == 0:\n centerlinexList.append(x)\n centerlineyList.append(y)\n mf.make_figure(x, y, allxyz, cur_ori, cur_flt, cur_lag, s, beck_bed,\n params, t, oxbowxList, oxbowyList, centerlinexList, centerlineyList)\n if t == TSTEPS:\n break\n s, x, y = migration(s, x, y, cur_flt, cur_lag, theta, t)\n s, x, y, oxbowx, oxbowy, found_cutoff = cutoff(s, x, y)\n s, x, y = smooth_centerline(x, y)\n s, x, y, cur, theta = resample_centerline(s, x, y)\n if found_cutoff:\n oxbowxList.append(oxbowx)\n oxbowyList.append(oxbowy)\n make_gif()\n job_done()","def gelplot_imshow(distances, bandwidths, intensities, lanes, names,\n gel_len, wellx, welly, wellsep, res, cursor_ovr,\n back_col, band_col, well_col, noise, Itol, title,\n FWTM, show=True):\n nlanes = len(lanes)\n gel_width = sum(wellx) + (nlanes+1)*wellsep # cm\n res = res.to('px/cm')\n pxl_x = int(round(gel_width * res))\n pxl_y = int(round(gel_len * res))\n centers = [(l+1)*wellsep + sum(wellx[:l]) + 0.5*wellx[l]\n for l in xrange(nlanes)]\n rgb_arr = np.zeros(shape=(pxl_y, pxl_x, 3), dtype=np.float32)\n bandlengths = wellx\n # Paint the bands\n for i in xrange(nlanes):\n distXmid = centers[i]\n pxlXmid = distXmid * res\n bandlength = bandlengths[i]\n from_x = int(round((distXmid - bandlength/2.0) * res))\n to_x = int(round((distXmid + bandlength/2.0) * res))\n for j in xrange(len(lanes[i])):\n distYmid = distances[i][j]\n pxlYmid = int(round(distYmid * res))\n bandwidth = bandwidths[i][j] # w=FWHM or w=FWTM ???\n if FWTM:\n FWHM = Gauss_FWHM(bandwidth)\n else:\n FWHM = bandwidth\n std_dev = Gauss_dev(FWHM)\n maxI = intensities[i][j]\n midI = Gaussian(distYmid, maxI, distYmid, std_dev)\n if pxlYmid < len(rgb_arr): # band within gel frontiers\n rgb_arr[pxlYmid, from_x:to_x] += midI\n bckwdYstop = False if pxlYmid > 0 else True\n forwdYstop = False if pxlYmid < len(rgb_arr)-1 else True\n pxlYbck = pxlYmid-1\n pxlYfor = pxlYmid+1\n while not bckwdYstop or not forwdYstop:\n if not bckwdYstop:\n distYbck = Q_(pxlYbck,'px')/res\n bckYI = Gaussian(distYbck, maxI, distYmid, std_dev)\n if pxlYbck < len(rgb_arr):\n rgb_arr[pxlYbck, from_x:to_x] += bckYI\n pxlYbck -= 1\n if bckYI <= Itol or pxlYbck == -1:\n bckwdYstop = True\n if not forwdYstop:\n distYfor = Q_(pxlYfor,'px')/res\n forYI = Gaussian(distYfor, maxI, distYmid, std_dev)\n rgb_arr[pxlYfor, from_x:to_x] += forYI\n pxlYfor += 1\n if forYI <= Itol or pxlYfor == pxl_y:\n forwdYstop = True\n # Background color\n if noise is None or noise <= 0:\n rgb_arr += back_col\n else:\n bckg = np.random.normal(back_col, noise,\n (len(rgb_arr), len(rgb_arr[0])))\n rgb_arr += bckg[:,:,np.newaxis]\n # Saturation\n rgb_arr[rgb_arr > 1] = 1\n rgb_arr[rgb_arr < 0] = 0\n #bands_arr = np.ma.masked_where(rgb_arr == back_col, rgb_arr) #############\n bands_arr = rgb_arr\n # Plot\n gel_len = gel_len.magnitude\n gel_width = gel_width.magnitude\n wellx = wellx.magnitude\n welly = welly.magnitude\n wellsep = wellsep.magnitude\n centers = [c.magnitude for c in centers]\n bandlengths = bandlengths.magnitude\n bandwidths = [[bw.magnitude for bw in bwlane] for bwlane in bandwidths]\n fig = plt.figure()\n ax1 = fig.add_subplot(111, axisbg=str(back_col))\n ax1.xaxis.tick_top()\n ax1.yaxis.set_ticks_position('left')\n ax1.spines['left'].set_position(('outward', 8))\n ax1.spines['left'].set_bounds(0, gel_len)\n ax1.spines['right'].set_visible(False)\n ax1.spines['bottom'].set_visible(False)\n ax1.spines['top'].set_visible(False)\n ax1.spines['right'].set_color(str(back_col))\n ax1.spines['bottom'].set_color(str(back_col))\n ax1.xaxis.set_label_position('top')\n plt.xticks(centers, names)\n majorLocator = FixedLocator(range(int(gel_len+1)))\n minorLocator = FixedLocator([j/10.0 for k in range(0, int(gel_len+1)*10, 10)\n for j in range(1+k, 10+k, 1)])\n ax1.yaxis.set_major_locator(majorLocator)\n ax1.yaxis.set_minor_locator(minorLocator)\n ax1.tick_params(axis='x', which='both', top='off')\n bands_plt = ax1.imshow(bands_arr, extent=[0, gel_width, gel_len, 0],\n interpolation='none')\n # Draw wells\n for i in xrange(nlanes):\n ctr = centers[i]\n wx = wellx[i]\n wy = welly[i]\n ax1.fill_between(x=[ctr-wx/2, ctr+wx/2], y1=[0,0],\n y2=[-wy, -wy], color=str(well_col))\n # Invisible rectangles overlapping the bands for datacursor to detect\n bands = []\n for i in xrange(nlanes):\n bandlength = bandlengths[i]\n center = centers[i]\n x = center - bandlength/2.0\n for j in xrange(len(lanes[i])):\n dna_frag = lanes[i][j]\n bandwidth = bandwidths[i][j]\n dist = distances[i][j].magnitude\n y = dist - bandwidth/2.0\n band = plt.Rectangle((x,y), bandlength, bandwidth, fc='r',\n alpha=0, label='{} bp'.format(len(dna_frag)))\n plt.gca().add_patch(band)\n bands.append(band)\n plt.ylim(gel_len, -max(welly))\n xlim = sum(wellx) + (nlanes+1)*wellsep\n plt.xlim(0, xlim)\n plt.ylabel('Distance (cm)')\n plt.xlabel('Lanes')\n bbox_args = dict(boxstyle='round,pad=0.6', fc='none')\n an1 = plt.annotate(title, xy=(0,0),\n xytext=(xlim+0.4, (gel_len+max(welly))/2.0),\n va=\"center\", bbox=bbox_args)\n an1.draggable()\n plt.gca().set_aspect('equal', adjustable='box')\n cursor_args = dict(display='multiple',\n draggable=True,\n hover = False,\n bbox=dict(fc='white'),\n arrowprops=dict(arrowstyle='simple',\n fc='white', alpha=0.5),\n xytext=(15, -15),\n formatter='{label}'.format)\n if cursor_ovr:\n for key in cursor_ovr:\n cursor_args[key] = cursor_ovr[key]\n if cursor_args['hover'] == True: cursor_args['display'] = 'single'\n datacursor(bands, **cursor_args)\n #fig.savefig('example.png', dpi=300)\n if show: plt.show()\n return plt","def optimize(self, acceleration=True, plot=False, fileExtension=None):\n \n x = np.zeros(self.nUnknowns+self.nLambda)\n x[0:self.nUnknowns] = self.physicalToTransformed(self.initial)\n\n chi2 = 1e10\n chi2Old = 1e20\n relchi2 = np.abs((chi2 - chi2Old) / chi2Old)\n xnew = np.copy(x)\n \n loop = 0 \n loopInt = 0\n \n lambdaLM = 1e-3\n chi2Best = 1e10\n chi2Old = 1e10\n nWorstChi2 = 0\n\n # dChi2Old = 0\n\n self.chi2 = []\n self.l0 = []\n self.l1 = []\n \n while ((relchi2 > 1e-6) & (loop < 20) & (nWorstChi2 < 8)):\n\n chi2, chi2NW, dChi2, ddChi2, stokes = self.computeFunctionAndGradient(x[0:self.nUnknowns], x[self.nUnknowns:])\n\n chi2Old = np.copy(chi2) \n \n H = 0.5 * ddChi2 \n H += np.diag(lambdaLM * np.diag(H))\n gradF = 0.5 * dChi2\n\n# First deal with the Hazel part\n U, w, VT = np.linalg.svd(H[0:self.nUnknowns,0:self.nUnknowns], full_matrices=True)\n\n wmax = np.max(w)\n wInv = 1.0 / w\n wInv[w < 1e-6*wmax] = 0.0\n\n# xnew = xold - H^-1 * grad F\n deltaxnew = -VT.T.dot(np.diag(wInv)).dot(U.T).dot(gradF[0:self.nUnknowns])\n xnew[0:self.nUnknowns] = x[0:self.nUnknowns] + deltaxnew\n \n chi2, chi2NW, stokes = self.meritFunction(xnew[0:self.nUnknowns], xnew[self.nUnknowns:])\n \n if ((loop + 1) % 5 == 0):\n thr = self.lambdaL1 \n thr = self.lambdaL1 + 500.*self.lambdaL1*np.exp(-loop)\n print(thr)\n\n if (self.wavelet == 'iuwt'):\n tmp = (self.obs[0,:] - stokes[0,:])[:,None]\n if (self.innerIterations == 1):\n res = ps.sparse.iuwt_decomposition(tmp, self.nLevelsIUWT, 0, True)\n res[0][np.abs(res[0]) < thr] = 0.0 \n xnew[self.nUnknowns:] = ps.sparse.iuwt_recomposition(res[0], 0, res[1])[:,0]\n else: \n xnew[self.nUnknowns:] = ps.sparse.proxes.prox_l1General(tmp, self.forwardIUWT, self.backwardIUWT, \n thr, threshold=self.thresholdIUWT, verbose=False)[:,0]\n\n if (self.wavelet == 'wavelet'):\n xnew[self.nUnknowns:] = ps.sparse.proxes.prox_l1General(self.obs[0,:] - stokes[0,:], self.wavedec, self.waverec, thr, threshold='hard', verbose=False)\n\n xnew[self.nUnknowns:] /= (1.0+xnew[self.nUnknowns])\n xnew[self.nUnknowns:] = upLimitThr(xnew[self.nUnknowns:], 0.0)\n\n #x = np.copy(xnew)\n\n chi2, chi2NW, stokes = self.meritFunction(xnew[0:self.nUnknowns], xnew[self.nUnknowns:])\n \n if (chi2NW < chi2Best):\n if (lambdaLM >= 1e4):\n lambdaLM /= 100.0\n elif ((lambdaLM >= 1e-4) or (lambdaLM < 1e4)):\n lambdaLM /= 10.0\n elif(lambdaLM < 1e-4):\n lambdaLM /= 5.0\n if (lambdaLM < 1e-6):\n lambdaLM = 1e-6\n\n chi2Best = np.copy(chi2NW)\n x = np.copy(xnew)\n nWorstChi2 = 0\n else:\n if (lambdaLM > 1e4):\n lambdaLM *= 100.0\n elif ((lambdaLM >= 1e-4) or (lambdaLM < 1e4)):\n lambdaLM *= 10.0\n elif(lambdaLM < 1e-4):\n lambdaLM *= 5.0\n nWorstChi2 += 1\n\n relchi2 = np.abs((chi2 - chi2Old) / chi2Old)\n l1Norm = np.linalg.norm(x[self.nUnknowns:], 1)\n\n if (self.wavelet == 'iuwt'):\n tmp = self.forwardIUWT(xnew[self.nUnknowns:][:,None])\n l0Norm = np.sum(np.abs(tmp) > 1e-6)\n if (self.wavelet == 'wavelet'): \n l0Norm = np.sum(np.abs(self.wavedec(x[self.nUnknowns:])) > 1e-10)\n \n print(\"Iteration {0} - chi2={1:10.4f} - l1={2} - l0={3} - relchi2={4} - lambda={5}\".format(loop, chi2NW, l1Norm, l0Norm, relchi2, lambdaLM))\n self.printNodes(x[0:self.nUnknowns])\n\n self.chi2.append(chi2NW)\n self.l0.append(l0Norm)\n self.l1.append(l1Norm)\n\n \n loop += 1\n\n xPhysical = self.transformedToPhysical(x[0:self.nNodesTotal])\n nodes = self.vector2Nodes(xPhysical)\n stokes, cont, atmosNew = ps.radtran.synthLTENodes(self.referenceAtmos, nodes)\n stokes /= self.contHSRA\n\n sys = x[self.nUnknowns:]\n\n np.savez( \"results/lte_{0}_lambda_{1}_inner_{2}.npz\".format(self.wavelet,fileExtension,self.innerIterations), self.obs, stokes, sys, self.chi2, x, self.wavelength, \n self.l1, self.l0, self.maskChi2)\n\n\n # pl.close('all')\n\n \n # f, ax = pl.subplots(nrows=2, ncols=2, figsize=(12,9))\n # ax = ax.flatten()\n # labelStokes = ['I/Ic','Q/Ic','U/Ic','V/Ic']\n # ax[0].plot(self.obs[0,:], label='obs')\n # ax[0].plot(stokes[0,:] + sys, label='stokes+sys')\n # ax[0].plot(1.0+sys, label='sys')\n # ax[0].plot(stokes[0,:], label='stokes')\n\n # ax[0].legend()\n\n # ax[1].plot(self.obs[0,:] / (1.0+sys))\n # ax[1].plot(stokes[0,:])\n \n # pl.tight_layout()\n if (plot):\n pl.savefig('/scratch/Dropbox/CONGRESOS/2015/Hinode9/code/systematicsExampleWithFit.png')\n\n \n print(\"--------\")\n print(\"l1 norm of systematics : {0}\".format(np.linalg.norm(x[self.nUnknowns:], 1)))\n\n return x","def prob4():\n print \"Lab pdf is wrong here\"\n t=0.0\n t1=[]\n t2=[]\n for i in xrange(5,12):\n A=diag_dom(2**i,2**(i+1))\n b=np.random.random((2**i,))\n \n t=time.time()\n gauss_seidel(A,b,maxiters=1000)\n t1+=[time.time()-t]\n \n t=time.time()\n la.solve(A,b)\n t2+=[time.time()-t]\n\n dom=2**np.arange(5,12) \n plt.loglog(dom,t1,'b.-',basey=2,basex=2,lw=2, ms=2, label=\"Gauss-Seidel Approximation Time\")\n plt.loglog(dom,t2,'r.-',basey=2,basex=2,lw=2, ms=2, label=\"la.solve(A,b)\")\n plt.xlabel(\"Matrix size nxn\")\n plt.ylabel(\"Time to Find Approximation\")\n plt.legend(loc=\"upper left\")\n plt.title(\"Temporal Complexity of Gauss-Seidel vs la.solve(A,b)\", fontsize=18)\n plt.show()","def P_AI_Rocky(in_dict):\n # START\n fs = 16\n plt.rc('font', size=fs)\n fig = plt.figure(figsize=(14,12))\n ds = nc.Dataset(in_dict['fn'])\n\n # PLOT CODE\n aa = [-122.8, -122.54, 47.92, 48.22]\n import cmocean\n cmap = cmocean.cm.balance\n # cmap = 'RdYlBu_r'\n\n from warnings import filterwarnings\n filterwarnings('ignore') # skip some warning messages\n \n # plot Code\n \n # calculate divergence and vorticity\n uu = ds['u'][0, -1, :, :]\n vv = ds['v'][0, -1, :, :]\n u = zfun.fillit(uu)\n v = zfun.fillit(vv)\n u[np.isnan(u)] = 0\n v[np.isnan(v)] = 0\n \n G = zrfun.get_basic_info(in_dict['fn'], only_G=True)\n \n dive = ((np.diff(u, axis=1)/G['DX'][:, 1:-1])[1:-1, :]\n + (np.diff(v, axis = 0)/G['DY'][1:-1, :])[:, 1:-1])\n #dive[G['mask_rho'][1:-1,1:-1]==False] = np.nan\n \n vort = np.diff(v, axis=1)/G['DX'][1:,1:] - np.diff(u, axis=0)/G['DY'][1:,1:]\n #vort[G['mask_rho'][1:,1:]==False] = np.nan\n \n scl = 2e-3\n \n # panel 1\n ax = fig.add_subplot(121)\n # cs = plt.pcolormesh(G['lon_psi'], G['lat_psi'], dive/scl, cmap=cmap,\n # vmin=-1, vmax=1)\n cs = plt.pcolormesh(G['lon_rho'][1:-1,1:-1], G['lat_rho'][1:-1,1:-1], dive/scl, cmap=cmap,\n vmin=-1, vmax=1, shading='gouraud')\n tstr = (r'Surface Divergence (%0.1e $s^{-1}$)' % (scl))\n #pfun.add_bathy_contours(ax, ds, txt=True)\n pfun.add_coast(ax)\n ax.axis(aa)\n pfun.dar(ax)\n ax.set_xlabel('Longitude')\n ax.set_ylabel('Latitude')\n ax.set_title(tstr)\n pfun.add_info(ax, in_dict['fn'])\n ax.set_xticks([-122.8, -122.7, -122.6])\n ax.set_yticks([48, 48.1, 48.2])\n #\n # panel 2\n ax = fig.add_subplot(122)\n # cs = plt.pcolormesh(G['lon_rho'], G['lat_rho'], vort/scl, cmap=cmap,\n # vmin=-1, vmax=1)\n cs = plt.pcolormesh(G['lon_psi'], G['lat_psi'], vort/scl, cmap=cmap,\n vmin=-1, vmax=1, shading='gouraud')\n tstr = (r'Surface Vorticity (%0.1e $s^{-1}$)' % (scl))\n ax.set_xticks([-122.8, -122.7, -122.6])\n ax.set_yticks([])\n #fig.colorbar(cs)\n \n # Inset colorbar\n from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n cbaxes = inset_axes(ax, width=\"4%\", height=\"40%\", loc='lower left')\n fig.colorbar(cs, cax=cbaxes, orientation='vertical')\n \n #pfun.add_bathy_contours(ax, ds)\n pfun.add_coast(ax)\n ax.axis(aa)\n pfun.dar(ax)\n ax.set_xlabel('Longitude')\n ax.set_title(tstr) \n \n #fig.tight_layout()\n # FINISH\n ds.close()\n if len(in_dict['fn_out']) > 0:\n plt.savefig(in_dict['fn_out'])\n plt.close()\n else:\n plt.show()\n plt.rcdefaults()","def main() -> None:\n region = sleplet.slepian.Region(mask_name=\"africa\")\n slepian = sleplet.slepian_methods.choose_slepian_method(L, region)\n africa = sleplet.functions.SlepianAfrica(L, region=region, smoothing=SMOOTHING)\n\n # perform reconstruction\n f = sleplet.slepian_methods.slepian_inverse(africa.coefficients, L, slepian)\n\n # plot\n name = f\"africa_slepian_reconstruction_L{L}\"\n sleplet.plotting.PlotSphere(\n f,\n L,\n name,\n normalise=NORMALISE,\n region=slepian.region,\n ).execute()","def cgls_Ploak_Ribiere(self, iterations = None):\n if self.iters == 0 :\n self.r = - self.df(self.x)\n self.r_old = self.r.copy()\n self.d = self.r.copy()\n self.delta_new = np.sum(self.r**2)\n self.delta_0 = self.delta_new.copy()\n # \n if iterations == None :\n iterations = self.imax\n #\n for i in range(iterations):\n #\n # perform a line search of f along d\n self.x, status = self.line_search(self.x, self.d)\n # \n self.r = - self.df(self.x)\n delta_old = self.delta_new\n delta_mid = np.sum(self.r * self.r_old)\n self.r_old = self.r.copy()\n self.delta_new = np.sum(self.r**2)\n #\n # Polak-Ribiere formula\n beta = (self.delta_new - delta_mid)/ delta_old\n #\n self.d = self.r + beta * self.d\n #\n # reset the algorithm \n if (self.iters % 50 == 0) or (status == False) or beta <= 0.0 :\n self.d = self.r\n else :\n self.d = self.r + beta * self.d\n #\n # calculate the error\n self.errors.append(self.f(self.x))\n self.iters = self.iters + 1\n if self.iters > self.imax or (self.errors[-1] < self.e_tol):\n break\n #\n #\n return self.x","def comp_vel(p1=database['K+'], p2=database['pi+'], p3=database['p+'], pmin=0, pmax=80):\r\n p_range = np.linspace(pmin, pmax, 1000)\r\n m1 = p1.mass\r\n m2 = p2.mass\r\n m3 = p3.mass\r\n v1, v2, v3 = [], [], []\r\n for p in p_range:\r\n v1.append(c*beta(p, m1))\r\n v2.append(c*beta(p, m2))\r\n v3.append(c*beta(p, m3))\r\n fig = plt.figure(figsize=[10, 5])\r\n ax = fig.add_subplot(1, 1, 1)\r\n p1_name = r'K$^+$'\r\n p2_name = r'$\\pi^+$'\r\n p3_name = r'p$^+$'\r\n ax.plot(p_range, v1, 'r', label=p1_name)\r\n ax.plot(p_range, v2, 'b', label=p2_name)\r\n ax.plot(p_range, v3, 'g', label=p3_name)\r\n ax.set_xlabel('p / GeV', fontsize=20)\r\n ax.set_ylabel(r'v / $ms^{-1}$', fontsize=20)\r\n ax.axvline(75, color='k', label='p = 75 GeV')\r\n ax.set_xticks(np.arange(pmin, pmax+1, 1))\r\n ax.set_xticklabels(np.arange(pmin, pmax+1, 1))\r\n ax.grid()\r\n ax.minorticks_on()\r\n ax.set_xlim(pmin, pmax)\r\n# ax.set_ylim(np.min(v1+v2))\r\n ax.legend(fontsize=20)\r\n plt.show\r\n return"],"string":"[\n \"def L_curveTV(f,lam_init = 2.0, q = 0.9):\\n lam = lam_init\\n max_iter = 50\\n residual_list = np.zeros(max_iter)\\n size_list = np.zeros(max_iter)\\n error = np.zeros(max_iter)\\n alt_error = np.zeros(max_iter)\\n \\n for i in range(max_iter): #range(max_iter):\\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc=True, tol = 1.0e-5)\\n #u, _, j = projected_gradient_alt(f,lam, tau = 0.2, tol = 1.0e-4)\\n lam = lam_init * (q ** i)\\n residual_list[i] = np.linalg.norm(u - f)\\n size_list[i] = np.linalg.norm(u)\\n error[i] = np.linalg.norm(u - f) * np.linalg.norm(u)\\n #plt.loglog(residual_list,size_list)\\n #plt.show()\\n opt_idx = np.argmin(error)\\n t = 1.0 / (1.0 + lam_init * (q ** opt_idx))\\n lam = lam_init * (q ** opt_idx)\\n u = ChambollePock_denoise(f,lam, tau = 0.5, sig = 0.25, acc = True, tol = 1.0e-5)\\n return u, t\",\n \"def plot():\\n xvals = np.arange(-50, 250, step=0.1)\\n\\n fig = plt.figure()\\n plt.suptitle(\\\"Gaussian with smooth transition to power law\\\")\\n\\n A0vals = [10, 11]\\n avals = [5*10**-3, 10**-3, 5*10**-4]\\n ttvals = [10., 50., 100.]\\n cvals = [-0.1, -0.9, -5./3., -4.]\\n offset = [-30, 0.0, 30]\\n\\n paramvals = [A0vals, avals, ttvals,cvals, offset]\\n titles, labels = return_parameter_names()\\n\\n nplots = len(paramvals)\\n\\n for i in range(nplots):\\n plt.subplot(nplots, 1, i+1)\\n vals = paramvals[i]\\n for j in range(len(vals)):\\n pset = list(default())\\n pset[i] = vals[j]\\n yvals=[]\\n ypower=[]\\n ypeak=[]\\n for x in xvals:\\n yvals.append(fitfunc(x, pset))\\n ypeak.append(logpeak(x,pset))\\n if x > 0:\\n ypower.append(logpowerlaw(x,pset))\\n label = labels[i] + \\\"=\\\"+str(vals[j])\\n plt.plot(xvals, yvals, label = label)\\n\\n plt.title(titles[i])\\n plt.legend()\\n\\n fig.set_size_inches(15, 30)\\n plt.savefig(\\\"graphs/misc/lightcurve_models.pdf\\\")\\n plt.close()\",\n \"def plot_LvsL(modeldata,line1='CIV1548',line2='CIII1908',plotname='./TESTPLOT.pdf',\\n Zgas=False,logU=False,xid=0.3,nh=100,COratio=0.38,Mcutoff=100,\\n logx=False,logy=False,logp1=False,logp2=False,fixxrange=False,fixyrange=False,\\n showobs=None,noobserr=False,verbose=True):\\n NFalse = 0\\n freeparam = []\\n inforstr = \\\"\\\"\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n legenddic = {}\\n legenddic['Zgas'] = r'Z$_\\\\textrm{gas}$'\\n legenddic['logUs'] = r'log$_\\\\textrm{10}$(U)'\\n legenddic['xid'] = r'$\\\\xi_\\\\textrm{d}$'\\n legenddic['nh'] = r'n$_\\\\textrm{H}$ / [cm$^3$]'\\n legenddic['COCOsol'] = r'C/O / [C/O]$_\\\\textrm{sun}$'\\n legenddic['mup'] = r'M$_\\\\textrm{cut IMF}$ / [M$_\\\\textrm{sun}]$'\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not Zgas:\\n Zgasrange = [0.0,1.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' Zgas:vary, '\\n freeparam.append('Zgas')\\n else:\\n Zgasrange = [Zgas-1e-6,Zgas+1e-6]\\n inforstr = inforstr+' '+legenddic['Zgas']+'='+str(Zgas)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not logU:\\n logUrange = [-5.0,0.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' logU:vary, '\\n freeparam.append('logUs')\\n else:\\n logUrange = [logU-0.1,logU+0.1]\\n inforstr = inforstr+' '+legenddic['logUs']+'='+str(logU)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not xid:\\n xidrange = [0.0,0.6]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' xid:vary, '\\n freeparam.append('xid')\\n else:\\n xidrange = [xid-0.01,xid+0.01]\\n inforstr = inforstr+' '+legenddic['xid']+'='+str(xid)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not nh:\\n nhrange = [0.0,1.0e6]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' nH:vary, '\\n freeparam.append('nh')\\n else:\\n nhrange = [nh-1.0,nh+1.0]\\n inforstr = inforstr+' '+legenddic['nh']+'='+str(nh)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not COratio:\\n COratiorange = [0.0,2.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' C/O:vary, '\\n freeparam.append('COCOsol')\\n else:\\n COratiorange = [COratio-0.001,COratio+0.001]\\n inforstr = inforstr+' '+legenddic['COCOsol']+'='+str(COratio)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not Mcutoff:\\n Mcutoffrange = [0.0,400.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' Mcutoff:vary, '\\n freeparam.append('mup')\\n else:\\n Mcutoffrange = [Mcutoff-1.0,Mcutoff+1.0]\\n inforstr = inforstr+' '+legenddic['mup']+'='+str(Mcutoff)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n\\n if NFalse != 2:\\n sys.exit(' Two and only two of the model parameters (Zgas,logU,xid,nh,COratio,Mcutoff) '\\n 'should be set to Flase to define the model grid; however it appears '+str(NFalse)+\\n ' parameters where not set')\\n\\n goodent = np.where( (modeldata['Zgas'] > Zgasrange[0]) & (modeldata['Zgas'] < Zgasrange[1]) &\\n (modeldata['logUs'] > logUrange[0]) & (modeldata['logUs'] < logUrange[1]) &\\n (modeldata['xid'] > xidrange[0]) & (modeldata['xid'] < xidrange[1]) &\\n (modeldata['nh'] > nhrange[0]) & (modeldata['nh'] < nhrange[1]) &\\n (modeldata['COCOsol'] > COratiorange[0]) & (modeldata['COCOsol'] < COratiorange[1]) &\\n (modeldata['mup'] > Mcutoffrange[0]) & (modeldata['mup'] < Mcutoffrange[1]) )\\n\\n Ngoodent = len(goodent[0])\\n\\n if Ngoodent > 1:\\n if verbose: print(' - Getting data for '+str(Ngoodent)+' data points satisfying model selection ')\\n param1 = modeldata[freeparam[0]][goodent]\\n if logp1:\\n param1 = np.log10(param1)\\n\\n param2 = modeldata[freeparam[1]][goodent]\\n if logp2:\\n param2 = np.log10(param2)\\n\\n lum1 = modeldata[line1][goodent]\\n lum2 = modeldata[line2][goodent]\\n else:\\n if verbose: print(' WARNING: Less than 2 model grid points to plot; no output generated')\\n return\\n\\n # - - - - - - - - - - - PLOTTING - - - - - - - - - - -\\n if verbose: print(' - Setting up and generating plot')\\n plotname = plotname\\n fig = plt.figure(figsize=(9, 5))\\n fig.subplots_adjust(wspace=0.1, hspace=0.1,left=0.1, right=0.99, bottom=0.10, top=0.95)\\n Fsize = 10\\n lthick = 1\\n marksize = 3\\n plt.rc('text', usetex=True)\\n plt.rc('font', family='serif',size=Fsize)\\n plt.rc('xtick', labelsize=Fsize)\\n plt.rc('ytick', labelsize=Fsize)\\n plt.clf()\\n plt.ioff()\\n plt.title(inforstr[:-2],fontsize=Fsize)\\n\\n margin = 0.1\\n dx = np.abs(np.max(lum1)-np.min(lum1))\\n dy = np.abs(np.max(lum2)-np.min(lum2))\\n\\n\\n if fixxrange:\\n xrange = fixxrange\\n else:\\n if logx:\\n xrange = [np.min(lum1)-np.min(lum1)/2.,np.max(lum1)+np.max(lum1)/2.]\\n else:\\n xrange = [np.min(lum1)-dx*margin,np.max(lum1)+dx*margin]\\n\\n if fixyrange:\\n yrange = fixyrange\\n else:\\n if logy:\\n yrange = [np.min(lum2)-np.min(lum2)/2.,np.max(lum2)+np.max(lum2)/2.]\\n else:\\n yrange = [np.min(lum2)-dy*margin,np.max(lum2)+dy*margin]\\n\\n # ------------ PARAM1 ------------\\n cmap = plt.cm.get_cmap('winter')\\n cmin = np.min(param1)\\n cmax = np.max(param1)\\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\\n mm = plt.cm.ScalarMappable(cmap=cmap)\\n mm.set_array(cmaparr)\\n cb1 = plt.colorbar(mm)#shrink=0.25\\n\\n pstr1 = legenddic[freeparam[0]]\\n if logp1:\\n pstr1 = r'log$_\\\\textrm{10}$('+pstr1+')'\\n\\n cb1.set_label(pstr1+' (outer circle) - Fixed: black line')\\n\\n for p1 in np.unique(param1):\\n p1col = cmap(colnorm(p1))\\n p1ent = np.where(param1 == p1)\\n\\n plt.plot(lum1[p1ent],lum2[p1ent],'-',lw=lthick, color='k',zorder=1)\\n\\n plt.errorbar(lum1[p1ent],lum2[p1ent],xerr=None,yerr=None,\\n marker='o',lw=0, markersize=marksize*3,\\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\\n\\n # ------------ PARAM2 ------------\\n cmap = plt.cm.get_cmap('spring')\\n cmin = np.min(param2)\\n cmax = np.max(param2)\\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\\n mm = plt.cm.ScalarMappable(cmap=cmap)\\n mm.set_array(cmaparr)\\n cb2 = plt.colorbar(mm)#shrink=0.25\\n\\n pstr2 = legenddic[freeparam[1]]\\n if logp2:\\n pstr2 = 'log10('+pstr2+')'\\n\\n cb2.set_label(pstr2+' (inner circle) - Fixed: gray line')\\n\\n for p2 in np.unique(param2):\\n p2col = cmap(colnorm(p2))\\n p2ent = np.where(param2 == p2)\\n\\n plt.plot(lum1[p2ent],lum2[p2ent],'-',lw=lthick, color='gray',zorder=2)\\n\\n plt.errorbar(lum1[p2ent],lum2[p2ent],xerr=None,yerr=None,\\n marker='o',lw=0, markersize=marksize*1.5,\\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\\n\\n if showobs != None:\\n for ii, objid in enumerate(showobs[:,0]):\\n if (showobs[:,7][ii] > xrange[0]) & (showobs[:,7][ii] < xrange[1]) & \\\\\\n (showobs[:,9][ii] > yrange[0]) & (showobs[:,9][ii] < yrange[1]):\\n\\n if noobserr:\\n obsxerr = None\\n obsyerr = None\\n else:\\n obsxerr = showobs[:,8][ii]\\n obsyerr = showobs[:,10][ii]\\n plt.errorbar(showobs[:,7][ii],showobs[:,9][ii],xerr=obsxerr,yerr=obsyerr,\\n marker='*',lw=lthick, markersize=marksize*2,\\n markerfacecolor='k',ecolor='k',markeredgecolor = 'k',zorder=30)\\n\\n plt.xlabel(r'L$_\\\\textrm{'+line1+r'}$ / [3.826$\\\\times$1e33 erg/s]/[Msun/yr]')\\n plt.ylabel(r'L$_\\\\textrm{'+line2+r'}$ / [3.826$\\\\times$1e33 erg/s]/[Msun/yr]')\\n\\n plt.xlim(xrange)\\n plt.ylim(yrange)\\n\\n if logx:\\n plt.xscale('log')\\n if logy:\\n plt.yscale('log')\\n\\n #--------- LEGEND ---------\\n # plt.errorbar(-1,-1,xerr=None,yerr=None,fmt='o',lw=lthick,ecolor='white', markersize=marksize*2,\\n # markerfacecolor='white',markeredgecolor = 'k',label='Ground-based spec')\\n #\\n # leg = plt.legend(fancybox=True, loc='upper center',prop={'size':Fsize},ncol=1,numpoints=1)\\n # #bbox_to_anchor=(1.25, 1.03)) # add the legend\\n # leg.get_frame().set_alpha(0.7)\\n #--------------------------\\n\\n if verbose: print(' Saving plot to'+plotname)\\n plt.savefig(plotname)\\n plt.clf()\\n plt.close('all')\",\n \"def solve_rossler(alpha = 1.0, beta = 1.0, gamma = 1.0, max_time = 10, N = 5):\\n resolution = 72\\n \\n fig1 = plt.figure(dpi = resolution)\\n ax1, ax2, ax3 = fig1.subplots(3, 1)\\n\\n fig = plt.figure()\\n ax = fig.add_axes([0, 0, 1, 1], projection='3d')\\n # ax.axis('off')\\n \\n def rossler_deriv(xi, t0, alpha=alpha, beta = beta, gamma = gamma):\\n \\\"\\\"\\\"Compute the time-derivative of a Lorenz system.\\\"\\\"\\\"\\n \\n xi1, xi2, xi3 = xi\\n \\n xi1_dot = -xi1 - exp(xi3)\\n xi2_dot = xi1 + alpha * xi2\\n xi3_dot = gamma * exp(-xi3) + xi1 - beta\\n \\n return [xi1_dot, xi2_dot, xi3_dot]\\n\\n # Choose random starting points, uniformly distributed from -15 to 15\\n np.random.seed(1)\\n xi0 = -15 + 30 * np.random.random((N, 3))\\n xi0[:,2] = np.log(abs(xi0[:,2]))\\n\\n # Solve for the trajectories\\n t = np.linspace(0, max_time, int(250*max_time))\\n xi_t = np.asarray([integrate.odeint(rossler_deriv, xi0i, t)\\n for xi0i in xi0])\\n \\n # choose a different color for each trajectory\\n colors = plt.cm.viridis(np.linspace(0, 1, N))\\n\\n for i in range(N):\\n xi1, xi2, xi3= xi_t[i,:,:].T\\n \\n ax1.plot(t, xi1, '-', c=colors[i])\\n ax1.set_title('xi1')\\n \\n ax2.plot(t, xi2, '-', c=colors[i])\\n ax2.set_title('xi2')\\n \\n ax3.plot(t, xi3, '-', c=colors[i])\\n ax3.set_title('xi3')\\n \\n lines = ax.plot(xi1, xi2, xi3, '-', c=colors[i])\\n plt.setp(lines, linewidth=2)\\n angle = 104\\n ax.view_init(30, angle)\\n plt.show()\\n\\n return t, xi_t\",\n \"def compare_CII_w_models(**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n if not p.xlim: p.xlim = [-4,2]\\n if not p.ylim: p.ylim = [4,10]\\n\\n fig,ax = plt.subplots(figsize=(8,6))\\n ax.set_ylim(p.ylim)\\n ax.set_xlim(p.xlim)\\n\\n # SIGAME Simba-100 ext ON (default run)\\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[1])\\n L_line = getattr(GR,'L_[CII]158_sun')\\n SFR = getattr(GR,'SFR')\\n # Phantom points to close contour lines...\\n L_line = np.append(L_line,np.array([1e8,10**9.3]))\\n SFR = np.append(SFR,np.array([0.1,10**0.85]))\\n lL_line = np.log10(L_line)\\n lSFR = np.log10(SFR)\\n lSFR = lSFR[L_line > 0]\\n lL_line = lL_line[L_line > 0]\\n # ax.plot(np.log10(SFR),np.log10(L_line),'o',ms=4,color='midnightblue',\\\\\\n # alpha=0.7,label='SIGAME with Simba-100',zorder=10)\\n # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lSFR,lL_line]).T)\\n x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j,4:lL_line.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax.contour(x, y, z.reshape(x.shape),colors='forestgreen',levels=6,zorder=10)\\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF')\\n\\n # Select only MS galaxies\\n L_line = getattr(GR,'L_[CII]158_sun')\\n indices = aux.select_salim18(GR.M_star[L_line > 0],GR.SFR[L_line > 0])\\n print('With MS selection criteria: only %i galaxies' % (len(L_line)))\\n lSFR = lSFR[indices]\\n lL_line = lL_line[indices]\\n lSFR = np.append(lSFR,np.array([-1,0.85,1.5]))\\n lL_line = np.append(lL_line,np.array([8,9.3,8.4]))\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lSFR,lL_line]).T)\\n x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j,4:lL_line.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax.contour(x, y, z.reshape(x.shape),colors='purple',linestyles='dashed',levels=6,zorder=10)\\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF (MS)')\\n\\n\\n # SIGAME Simba-100 ext OFF\\n # GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[0])\\n # L_line = getattr(GR,'L_[CII]158_sun')\\n # SFR = getattr(GR,'SFR')\\n # # Phantom points to close contour lines...\\n # L_line = np.append(L_line,np.array([10.**9.2,1e8]))\\n # SFR = np.append(SFR,np.array([0.9,0.1]))\\n # lL_line = np.log10(L_line)\\n # lSFR = np.log10(SFR)\\n # lSFR = lSFR[L_line > 0]\\n # lL_line = lL_line[L_line > 0]\\n # # ax.plot(np.log10(SFR),np.log10(L_line),'o',ms=4,color='midnightblue',\\\\\\n # # alpha=0.7,label='SIGAME with Simba-100',zorder=10)\\n # # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\\n # nbins = 100\\n # k = kde.gaussian_kde(np.column_stack([lSFR,\\\\\\n # lL_line]).T)\\n\\n # x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j, \\\\\\n # 4:lL_line.max():nbins*1j]\\n # z = k(np.vstack([x.flatten(), y.flatten()]))\\n # CS = ax.contour(x, y, z.reshape(x.shape),colors='brown',levels=6,linestyles='dotted')\\n # CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF_no_ext')\\n\\n # SIGAME Simba-25\\n # GR = glo.global_results(sim_run=p.sim_runs[0],nGal=p.nGals[0])\\n # L_line = getattr(GR,'L_[CII]158_sun')\\n # SFR = getattr(GR,'SFR')\\n # ax.plot(np.log10(SFR),np.log10(L_line),'^',ms=6,color='darkorchid',alpha=0.7,label='SIGAME with Simba-25')\\n\\n # Observations in background\\n add_line_SFR_obs('[CII]158',L_line,ax,plot_fit=False)\\n\\n # Popping 2019\\n G19 = pd.read_csv(p.d_data + 'models/Popping2019.csv',skiprows=1,sep=' ',\\\\\\n names=['logSFR', 'logLCII', 'log LCII 14th percentile', 'log LCII 86th percentile'])\\n ax.plot(G19.logSFR,G19.logLCII,'k-',label='Popping 2019',alpha=0.8)\\n ax.fill_between(G19.logSFR,G19['log LCII 14th percentile'].values,\\\\\\n G19['log LCII 86th percentile'].values,color='grey',alpha=0.4)\\n\\n\\n # Padilla 2020\\n P20 = pd.read_csv(p.d_data + 'models/DataFig6.csv',skiprows=1,sep=',',\\\\\\n names=['IDSim','GalID','SFR','LCIITotal'])\\n P20['logLCII'] = np.log10(P20['LCIITotal'])\\n P20['logSFR'] = np.log10(P20['SFR'])\\n\\n\\n colors = ['cyan','orange','midnightblue']\\n nbins = 100\\n IDSims = ['Ref25','Recal25','Ref100']\\n for i,IDSim in enumerate(IDSims):\\n # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\\n k = kde.gaussian_kde(np.column_stack([P20.logSFR.values[P20.IDSim == IDSim],\\\\\\n P20.logLCII.values[P20.IDSim == IDSim]]).T)\\n\\n xP20, yP20 = np.mgrid[P20.logSFR.min():P20.logSFR.max():nbins*1j, \\\\\\n 4:P20.logLCII.max():nbins*1j]\\n zP20 = k(np.vstack([xP20.flatten(), yP20.flatten()]))\\n # To remove weird contour line:\\n zP20.reshape(xP20.shape)[(xP20 > -1) & (yP20 < 5.5)] = 1e-5\\n zP20.reshape(xP20.shape)[(xP20 < -3)] = 1e-5\\n zP20.reshape(xP20.shape)[(xP20 < -2) & (yP20 > 8)] = 1e-5\\n zP20.reshape(xP20.shape)[(xP20 > 1) & (yP20 < 7)] = 1e-5\\n CS = ax.contour(xP20, yP20, zP20.reshape(xP20.shape),colors=colors[i],levels=5)\\n CS.collections[0].set_label('Ramos Padilla 2020 '+IDSim)\\n\\n\\n ax.set_xlabel('log '+getlabel('SFR'))\\n ax.set_ylabel('log '+getlabel('[CII]158'))\\n handles,labels = ax.get_legend_handles_labels()\\n handles = [handles[_] for _ in [8,9,10,11,12,7,0,1,2,3,4,5,6]]\\n labels = [labels[_] for _ in [8,9,10,11,12,7,0,1,2,3,4,5,6]]\\n plt.legend(handles,labels,fontsize=9,loc='upper left')\\n if p.savefig:\\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \\n plt.savefig(p.d_plot + 'luminosity/CII_SFR_w_models.png', format='png', dpi=300)\",\n \"def plot_LvsL_multiple(line1='CIV1548',line2='CIII1908',line1range=[1e3,1e8],line2range=[1e0,1e8],\\n outputdir='./',verbose=True):\\n modeldata = nm.load_model('combined',verbose=verbose)\\n\\n if verbose: print(' - Putting together permutations of chosen setups for plotting')\\n infodic = {}\\n infodic['Zgas'] = [False,0.0001,0.006,0.040], True\\n infodic['logUs'] = [False,-1.0,-2.5,-4.0] , False\\n infodic['xid'] = [False,0.1,0.3,0.5] , False\\n infodic['nh'] = [False,10,100,1000,10000] , False\\n infodic['CO'] = [False,0.1,0.38,1.4] , False\\n infodic['Mcut'] = [False,100,300] , False\\n\\n variables = [infodic['Zgas'][0],infodic['logUs'][0],infodic['xid'][0],\\n infodic['nh'][0],infodic['CO'][0],infodic['Mcut'][0]]\\n\\n permutations = list(itertools.product(*variables))\\n permutations_with2false = [sublist for sublist in permutations if sublist.count(False) == 2.]\\n Nplots = len(permutations_with2false)\\n\\n if verbose: print(' - With the restriction Nfalse=2 the setup will results in '+str(Nplots)+\\\\\\n ' plots (if model data allows)')\\n if verbose: print(' - These will be saved to the output directory: '+outputdir)\\n for pp, perm in enumerate(permutations_with2false):\\n Zval = perm[0]\\n Uval = perm[1]\\n Xival = perm[2]\\n Nhval = perm[3]\\n COval = perm[4]\\n Mval = perm[5]\\n\\n plotname = outputdir+'NEOGALmodelgrid_Zgas'+str(Zval).replace('.','p')+\\\\\\n '_logU'+str(Uval).replace('.','p')+\\\\\\n '_xid'+str(Xival).replace('.','p')+\\\\\\n '_nH'+str(Nhval).replace('.','p')+\\\\\\n '_CO'+str(COval).replace('.','p')+\\\\\\n '_Mcut'+str(Mval).replace('.','p')+'.pdf'\\n\\n plotname = plotname.replace('False','Free')\\n\\n if verbose:\\n plotno = pp+1\\n infostr = ' - Generating plot '+str(\\\"%.4d\\\" % plotno)+'/'+str(\\\"%.4d\\\" % Nplots)+': '+plotname.split('/')[-1]+' '\\n sys.stdout.write(\\\"%s\\\\r\\\" % infostr)\\n sys.stdout.flush()\\n\\n if not Zval:\\n logp1 = True\\n else:\\n logp1 = False\\n\\n nm.plot_LvsL(modeldata,line1=line1,line2=line2,logx=True,logy=True,logp1=logp1,logp2=False,verbose=False,\\n Zgas=Zval,logU=Uval,xid=Xival,nh=Nhval,COratio=COval,Mcutoff=Mval,\\n fixxrange=line1range,fixyrange=line2range,plotname=plotname)\\n\\n print('\\\\n ... done')\",\n \"def lcfig( z=7 ):\\n\\n for modelname in ['z15G','z25G','z40G'] :\\n\\n # initialize a supernova model :\\n snmodel = sncosmo.Model(source=modelname)\\n\\n # Fix the redshift for this instantiation of the model\\n # (NOTE: this does not apply any cosmological dimming. It only\\n # shifts the wavelengths)\\n snmodel.set( z = z )\\n\\n # generate the H band light curve\\n tobs = np.arange( 0, 1000, 10 )\\n M160 = snmodel.bandmag( 'f160w', 'ab', tobs ) # Absolute Magnitude\\n m160 = M160 + cosmo.distmod( z ).value # apparent magnitude\\n\\n pl.plot( tobs, m160 )\\n ax = pl.gca()\\n ax.invert_yaxis()\\n ax.set_xlabel('Time (observer-frame days)')\\n ax.set_ylabel('Apparent Magnitude in F160W')\\n ax.set_xlim( 0, 1000 )\\n ax.set_ylim( 36, 28 )\\n\\n pl.draw()\",\n \"def plot_convergence(\\n optimizers: list = [\\\"COBYLA\\\", \\\"SLSQP\\\", \\\"L-BFGS-B\\\", \\\"NELDER-MEAD\\\"],\\n g2N: float = 0.2,\\n maxit: int = 10000,\\n varform: list = [\\\"ry\\\"],\\n depth: int = 3,\\n nrep: int = 10,\\n dataprefix: str = \\\"data/miniBMN\\\",\\n datasuffix: str = \\\"h5\\\",\\n figprefix: str = \\\"figures/miniBMN\\\",\\n ht: float = 0.0,\\n up: int = 1000,\\n):\\n # setup parameters\\n params = dict()\\n params[\\\"l\\\"] = str(g2N).replace(\\\".\\\", \\\"\\\")\\n params[\\\"d\\\"] = depth\\n params[\\\"v\\\"] = \\\"-\\\".join(varform)\\n params[\\\"m\\\"] = maxit\\n params[\\\"n\\\"] = nrep\\n params[\\\"f\\\"] = dataprefix\\n params[\\\"s\\\"] = datasuffix\\n assert type(optimizers).__name__ == \\\"list\\\"\\n # collect data\\n result = collect_data(optimizers, params)\\n # get best runs\\n gs = dict()\\n for r in optimizers:\\n gs[r] = result.loc[r].groupby(\\\"rep\\\").apply(min).energy\\n gsdf = pd.DataFrame.from_dict(gs, dtype=float)\\n print(gsdf.describe().T[[\\\"min\\\", \\\"max\\\", \\\"mean\\\", \\\"std\\\"]])\\n # Plot\\n # select the best runs for each optimizer\\n fig, ax = plt.subplots()\\n for o in optimizers:\\n result.loc[o, gsdf[o].idxmin()].plot(\\n x=\\\"counts\\\", y=\\\"energy\\\", xlim=[0, up], label=o, ax=ax\\n )\\n ax.axhline(ht, c=\\\"k\\\", ls=\\\"--\\\", lw=\\\"2\\\", label=\\\"HT\\\")\\n ax.set_xlabel(\\\"iterations\\\")\\n ax.set_ylabel(\\\"VQE energy\\\")\\n ax.legend(loc=\\\"upper right\\\")\\n filename = f\\\"{figprefix}_l{params['l']}_convergence_{params['v']}_depth{params['d']}_nr{params['n']}_max{params['m']}_xlim{up}\\\"\\n plt.savefig(f\\\"{filename}.pdf\\\")\\n plt.savefig(f\\\"{filename}.png\\\")\\n plt.savefig(f\\\"{filename}.svg\\\")\\n plt.close()\",\n \"def energies():\\n # Hardcoded initial values\\n numsteps = 10000\\n time_max = 1\\n # Running the calculation in the solver class using the velocity verlet method\\n # for better accuracy.\\n verlet = solver(input_matrix, 'verlet', time_max, numsteps)\\n output_matrix, KE, PE, AM = verlet.main()\\n # Creating a simple time axis for plotting\\n x = np.linspace(0, 1, numsteps+1)\\n\\n # Plotting kinetic energy over time\\n plt.figure(1, figsize=(10, 10))\\n plt.plot(x, KE)\\n plt.suptitle('Total kinetic energy in the Earth-Sun system.', fontsize=24)\\n plt.xlabel('time [yr]', fontsize=16)\\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\\n plt.legend(['KE'])\\n\\n # Plotting potential energy over time\\n plt.figure(2, figsize=(10, 10))\\n plt.plot(x, PE)\\n plt.suptitle('Total potential energy in the Earth-Sun system.', fontsize=24)\\n plt.xlabel('time [yr]', fontsize=16)\\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\\n plt.legend(['PE'])\\n\\n # Plotting total energy against time\\n plt.figure(3, figsize=(10, 10))\\n plt.plot(x, PE+KE)\\n plt.suptitle('Total energy in the Earth-Sun system.', fontsize=24)\\n plt.xlabel('time [yr]', fontsize=16)\\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\\n plt.legend(['KE+PE'])\\n\\n # Plotting angular momentum against time. print the amplitude to terminal\\n amplitude = max(AM)-min(AM)\\n print('Amplitude of angular momentum during 1 year: %g[AU²/yr²]' %(amplitude))\\n plt.figure(4, figsize=(10, 10))\\n plt.plot(x, AM)\\n plt.suptitle('Total angular momentum in the Earth-Sun system.', fontsize=24)\\n plt.xlabel('time [yr]', fontsize=16)\\n plt.ylabel('energy [AU²/yr²]', fontsize=16)\\n plt.legend(['AM'])\\n\\n # Plotting the kinetic, potential and total energy against time to see\\n # how great the variations are\\n plt.figure(5, figsize=(10, 10))\\n plt.plot(x, PE, x, KE, x, KE+PE)\\n plt.suptitle('Total energy in the Earth-Sun system.', fontsize=24)\\n plt.xlabel('time [yr]', fontsize=16)\\n plt.ylabel('energy [AU²*kg/yr²]', fontsize=16)\\n plt.legend(['PE', 'KE', 'KE+PE'])\\n plt.show()\",\n \"def main(argv):\\n import scipy as sc\\n import scipy.integrate as integrate\\n import matplotlib.pylab as p\\n\\n def dCR_dt(pops, t=0):\\n \\\"\\\"\\\"returns the growth rate of consumer and resource population at any given time step\\\"\\\"\\\"\\n\\n R = pops[0]\\n C = pops[1]\\n K = 10000000\\n dRdt = r * R * (1-(R/K)) - a * R * C\\n dCdt = -z * C + e * a * R * C\\n\\n return sc.array([dRdt, dCdt])\\n\\n #integrate from time point 0 to 15, using 1000 sub-divisions of time\\n t = sc.linspace(0, 15, 1000)\\n\\n def plot(pops):\\n f1 = p.figure()\\n p.plot(t, pops[:,0], 'g-', label='Resource density') # Plot\\n p.plot(t, pops[:,1] , 'b-', label='Consumer density')\\n p.grid()\\n p.legend(loc='best')\\n p.xlabel('Time')\\n p.ylabel('Population density')\\n p.title('Consumer-Resource population dynamics')\\n #p.show()# To display the figure\\n f1.savefig('../Results/LV2_model.pdf')\\n\\n f2 = p.figure()\\n p.plot(pops[:,0], pops[:,1], 'r-', label='Consumer density') # Plot\\n p.grid()\\n p.xlabel('Resource Density')\\n p.ylabel('Consumer density')\\n p.title('Consumer-Resource population dynamics')\\n #p.show()# To display the figure\\n f2.savefig('../Results/LV2_second_model.pdf')\\n\\n return f1,f2\\n\\n if len(argv) < 5:\\n r = 1\\n a = 0.1\\n z = 1.5\\n e = 0.75\\n else:\\n r,a,z,e = float(sys.argv[1]), float(sys.argv[2]), float(sys.argv[3]), float(sys.argv[4])\\n #integrate from time point 0 to 15, using 1000 sub-divisions of time\\n t = sc.linspace(0, 15, 1000)\\n #Set the initial conditions for the two populations\\n R0 = 10\\n C0 = 5\\n RC0 = sc.array([R0, C0])\\n #numerically integrate this system forward from those starting conditions\\n pops, infodict = integrate.odeint(dCR_dt, RC0, t, full_output=True)\\n print(\\\"final Consumer and Resource population values are:\\\", pops[-1]) #prints final values\\n plot(pops)\\n return 0\",\n \"def plot_inv_RV_lit(outpath, fit_slopes, fit_intercepts, fit_stds):\\n waves = np.arange(0.8, 4.01, 0.001)\\n fig, ax = plt.subplots(figsize=(10, 9))\\n\\n for i, RV in enumerate([2.5, 3.1, 5.5]):\\n # plot the extinction curve from this work\\n offset = 0.1 * i\\n slopes = interpolate.splev(waves, fit_slopes)\\n alav = fit_intercepts(waves) + slopes * (1 / RV - 1 / 3.1)\\n (line,) = ax.plot(waves, alav + offset, lw=1.5, label=r\\\"$R(V) = $\\\" + str(RV))\\n stddev = interpolate.splev(waves, fit_stds)\\n color = line.get_color()\\n ax.fill_between(\\n waves,\\n alav + offset - stddev,\\n alav + offset + stddev,\\n color=color,\\n alpha=0.2,\\n edgecolor=None,\\n )\\n\\n # plot the literature curves\\n styles = [\\\"--\\\", \\\":\\\"]\\n for i, cmodel in enumerate([CCM89, F19]):\\n ext_model = cmodel(Rv=RV)\\n (indxs,) = np.where(\\n np.logical_and(\\n 1 / waves >= ext_model.x_range[0], 1 / waves <= ext_model.x_range[1]\\n )\\n )\\n yvals = ext_model(waves[indxs] * u.micron)\\n ax.plot(\\n waves[indxs],\\n yvals + offset,\\n lw=1.5,\\n color=color,\\n ls=styles[i],\\n alpha=0.8,\\n )\\n\\n # add text\\n ax.text(3.45, 0.03, r\\\"$R(V) = 2.5$\\\", fontsize=0.8 * fs, color=\\\"tab:blue\\\")\\n ax.text(3.45, 0.15, r\\\"$R(V) = 3.1$\\\", fontsize=0.8 * fs, color=\\\"tab:orange\\\")\\n ax.text(3.45, 0.305, r\\\"$R(V) = 5.5$\\\", fontsize=0.8 * fs, color=\\\"tab:green\\\")\\n\\n # finalize and save the plot\\n ax.set_xlabel(r\\\"$\\\\lambda\\\\ [\\\\mu m$]\\\", fontsize=1.2 * fs)\\n ax.set_ylabel(r\\\"$A(\\\\lambda)/A(V)$ + offset\\\", fontsize=1.2 * fs)\\n ax.set_xlim(0.75, 4.05)\\n ax.set_ylim(-0.03, 0.98)\\n handles = [\\n Line2D([0], [0], color=\\\"k\\\", lw=1.5),\\n Line2D([0], [0], color=\\\"k\\\", lw=1.5, ls=\\\"--\\\"),\\n Line2D([0], [0], color=\\\"k\\\", lw=1.5, ls=\\\":\\\"),\\n ]\\n labels = [\\n \\\"this work\\\",\\n \\\"Cardelli et al. (1989)\\\",\\n \\\"Fitzpatrick et al. (2019)\\\",\\n ]\\n plt.legend(handles, labels, fontsize=fs)\\n plt.savefig(outpath + \\\"inv_RV_lit.pdf\\\", bbox_inches=\\\"tight\\\")\\n\\n # also save the plot in log scale\\n plt.ylim(0.01, 1)\\n plt.yscale(\\\"log\\\")\\n plt.tight_layout()\\n plt.savefig(outpath + \\\"inv_RV_lit_log.pdf\\\")\",\n \"def plot(self):\\n \\n \\n x_ibs=[] \\n x_gss=[]\\n y_ibs=[] \\n y_gss=[]\\n x_pso=[]\\n x_bgd=[]\\n y_bgd=[]\\n y_pso=[]\\n x_gd=[]\\n y_gd=[]\\n \\n i=0.0000001\\n \\n # for k in range(1,51):\\n # i= random.uniform(0.00000001, 1)\\n # t_avg_ibs=[]\\n # t_avg_gss=[]\\n # for j in range(1,51):\\n #L=random.randint(-100, 0)\\n #U=random.randint(0, 100)\\n max_iter=self.Max_iter \\n L=self.Lower_bound\\n U=self.Upper_bound\\n \\n minima=self.gss(L,U,i,1000)\\n #print(\\\"minima at X = \\\",minima[1])\\n x_ibs.append(self.I_bisection(L,U,minima[1],max_iter)[0])\\n x_gss.append(self.gss(L,U,i,max_iter)[0])\\n x_pso.append(self.particle_Swarm(self.func, L, U, 2, max_iter)[0])\\n x_gd.append(self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)[0])\\n x_bgd.append(self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)[0])\\n #print(x_pso)\\n for i in x_ibs[0]:\\n #print(self.Func(i)) \\n y_ibs.append(self.Func(i))\\n for i in x_gss[0]:\\n y_gss.append(self.Func(i)) \\n for i in x_pso[0]:\\n y_pso.append(self.Func(i)) \\n for i in x_gd[0]:\\n y_gd.append(self.Func(i)) \\n for i in x_bgd[0]:\\n y_bgd.append(self.Func(i)) \\n #print(y_gss)\\n\\n plt.plot(x_ibs[0], y_ibs, 'r.')\\n plt.plot(x_gss[0], y_gss, '.')\\n plt.plot(x_pso[0], y_pso, 'y.')\\n #plt.plot(x_gd[0], y_gd, 'y.')\\n #plt.plot(x_bgd[0], y_bgd, 'k.')\\n plt.xlabel('x')\\n plt.ylabel('y')\\n \\n plt.suptitle('Interval Bisection Search (Red) vs Golden Section Search (Blue) vs Particle swarm optimization (Green)')\\n #plt.axis([0, 100, 0.00000001, 1]) \\n plt.show()\\n plt.plot(x_gd[0], y_gd, 'r.')\\n plt.plot(x_bgd[0], y_bgd, 'k.')\\n plt.xlabel('x')\\n plt.ylabel('y') \\n plt.suptitle('Gradient Descent (Red) vs Batch Gradient Descent (Black) ')\\n \\n plt.show()\\n \\n start_time = timeit.default_timer()\\n ibs=self.I_bisection(L,U,minima[1],max_iter)\\n print(\\\" Execution time for Interval bisection Method is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n gss=self.gss(L,U,i,max_iter)\\n print(\\\" Execution time for Golden Section Search is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n pso=self.particle_Swarm(self.func, L, U, 2, max_iter)\\n print(\\\" Execution time for Particle swarm optimization is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n gd=self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)\\n print(\\\" Execution time for Gradient Descent is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n bgd=self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)\\n print(\\\" Execution time for Batch Gradient Descent is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n plt.plot(ibs[1], ibs[2], 'r.')\\n plt.text(ibs[1], ibs[2],\\\"IB\\\")\\n plt.plot(gss[1], gss[2], '.')\\n plt.text(gss[1], gss[2],\\\" GSS\\\")\\n plt.plot(pso[1], pso[2], 'y.')\\n plt.text(pso[1], pso[2],\\\" PSO\\\")\\n plt.plot(gd[1], gd[2], 'g.')\\n plt.text(gd[1], gd[2],\\\" GD \\\")\\n plt.plot(bgd[1],bgd[2], 'k.')\\n plt.text(bgd[1], bgd[2],\\\" Batch_GD\\\")\\n \\n plt.xlabel('Value of X')\\n plt.ylabel('NUmber of iteration') \\n plt.suptitle('Number of iterations vs minimum value of x')\\n \\n plt.show()\",\n \"def main():\\n parser = argparse.ArgumentParser(usage=__doc__)\\n parser.add_argument(\\\"--order\\\", type=int, default=3, help=\\\"order of Bessel function\\\")\\n args = parser.parse_args()\\n f = lambda x: -special.jv(args.order, x)\\n sol = optimize.minimize(f, 1.0)\\n x = np.linspace(0, 10, 5000)\\n plt.plot(x, special.jv(args.order, x), '-', sol.x, -sol.fun, 'o')\\n plt.show() # Displays the image in matplotlib window\",\n \"def Intgrl(N, R, Zeta1, Zeta2, Za, Zb):\\n\\n global S12, T11, T12, T22, V11A, V12A, V22A, V11B, V12B, V22B, V1111, V2111, V2121, V2211, V2221, V2222\\n\\n S12 = 0.0\\n T11 = 0.0\\n T12 = 0.0\\n T22 = 0.0\\n V11A = 0.0\\n V12A = 0.0\\n V22A = 0.0\\n V11B = 0.0\\n V12B = 0.0\\n V22B = 0.0\\n V1111 = 0.0\\n V2111 = 0.0\\n V2121 = 0.0\\n V2211 = 0.0\\n V2221 = 0.0\\n V2222 = 0.0\\n\\n R2 = R * R\\n\\n # The coefficients for the contracted Gaussian functions are below\\n Coeff = np.array([[1.00000, 0.0000000, 0.000000],\\n [0.678914, 0.430129, 0.000000],\\n [0.444635, 0.535328, 0.154329]])\\n\\n Expon = np.array([[0.270950, 0.000000, 0.000000],\\n [0.151623, 0.851819, 0.000000],\\n [0.109818, 0.405771, 2.227660]])\\n D1 = np.zeros([3])\\n A1 = np.zeros([3])\\n D2 = np.zeros([3])\\n A2 = np.zeros([3])\\n\\n # This loop constructs the contracted Gaussian functions\\n for i in range(N):\\n A1[i] = Expon[N - 1, i] * (Zeta1 ** 2)\\n D1[i] = Coeff[N - 1, i] * ((2.0 * A1[i] / np.pi) ** 0.75)\\n A2[i] = Expon[N - 1, i] * (Zeta2 ** 2)\\n D2[i] = Coeff[N - 1, i] * ((2.0 * A2[i] / np.pi) ** 0.75)\\n\\n # Calculate one electron integrals\\n # Centre A is first atom centre B is second atom\\n # Origin is on second atom\\n # V12A - off diagonal nuclear attraction to centre A etc.\\n for i in range(N):\\n for j in range(N):\\n # Rap2 - squared distance between centre A and centre P\\n Rap = A2[j] * R / (A1[i] + A2[j])\\n Rap2 = Rap ** 2\\n Rbp2 = (R - Rap) ** 2\\n S12 = S12 + S_int(A1[i], A2[j], R2) * D1[i] * D2[j]\\n T11 = T11 + T_int(A1[i], A1[j], 0.0) * D1[i] * D1[j]\\n T12 = T12 + T_int(A1[i], A2[j], R2) * D1[i] * D2[j]\\n T22 = T22 + T_int(A2[i], A2[j], 0.0) * D2[i] * D2[j]\\n V11A = V11A + V_int(A1[i], A1[j], 0.0, 0.0, Za) * D1[i] * D1[j]\\n V12A = V12A + V_int(A1[i], A2[j], R2, Rap2, Za) * D1[i] * D2[j]\\n V22A = V22A + V_int(A2[i], A2[j], 0.0, R2, Za) * D2[i] * D2[j]\\n V11B = V11B + V_int(A1[i], A1[j], 0.0, R2, Zb) * D1[i] * D1[j]\\n V12B = V12B + V_int(A1[i], A2[j], R2, Rbp2, Zb) * D1[i] * D2[j]\\n V22B = V22B + V_int(A2[i], A2[j], 0.0, 0.0, Zb) * D2[i] * D2[j]\\n\\n # Calculate two electron integrals\\n\\n for i in range(N):\\n for j in range(N):\\n for k in range(N):\\n for l in range(N):\\n Rap = A2[i] * R / (A2[i] + A1[j])\\n Rbp = R - Rap\\n Raq = A2[k] * R / (A2[k] + A1[l])\\n Rbq = R - Raq\\n Rpq = Rap - Raq\\n Rap2 = Rap * Rap\\n Rbp2 = Rbp * Rbp\\n Raq2 = Raq * Raq\\n Rbq2 = Rbq * Rbq\\n Rpq2 = Rpq * Rpq\\n V1111 = V1111 + TwoE(A1[i], A1[j], A1[k], A1[l], 0.0, 0.0, 0.0) * D1[i] * D1[j] * D1[k] * D1[l]\\n V2111 = V2111 + TwoE(A2[i], A1[j], A1[k], A1[l], R2, 0.0, Rap2) * D2[i] * D1[j] * D1[k] * D1[l]\\n V2121 = V2121 + TwoE(A2[i], A1[j], A2[k], A1[l], R2, R2, Rpq2) * D2[i] * D1[j] * D2[k] * D1[l]\\n V2211 = V2211 + TwoE(A2[i], A2[j], A1[k], A1[l], 0.0, 0.0, R2) * D2[i] * D2[j] * D1[k] * D1[l]\\n V2221 = V2221 + TwoE(A2[i], A2[j], A2[k], A1[l], 0.0, R2, Rbq2) * D2[i] * D2[j] * D2[k] * D1[l]\\n V2222 = V2222 + TwoE(A2[i], A2[j], A2[k], A2[l], 0.0, 0.0, 0.0) * D2[i] * D2[j] * D2[k] * D2[l]\\n return\",\n \"def get_l_curve_results(directory, overwrite = False):\\n costs = []\\n tikhs = []\\n regs = []\\n\\n glacier = \\\"helheim\\\"\\n extension = \\\".tar.gz\\\"\\n\\n # If the output hasn't been cached, retrieve it from the giant archives\\n cached_output = os.path.join(directory, \\\"helheim_l_curve.txt\\\")\\n if not os.path.exists(cached_output) or overwrite:\\n for archive_name in [f for f in os.listdir(directory)\\n if (os.path.isfile(os.path.join(directory, f))\\n and\\n f[-len(extension):] == extension)]:\\n start_index = len(glacier) + len(\\\"_lambda-\\\")\\n regularization = float(archive_name[start_index: -len(extension)])\\n cost, tikh = l_curve_point(os.path.join(directory, archive_name),\\n glacier, regularization, 4)\\n costs.append(cost)\\n tikhs.append(tikh)\\n regs.append(regularization)\\n\\n # Save these results to a text file\\n with open(cached_output, 'w') as fid:\\n fid.write(\\\"cost,model_norm,lambda\\\\n\\\")\\n for k in range(len(costs)):\\n fid.write(\\\"{0},{1},{2}\\\\n\\\".format(costs[k], tikhs[k], regs[k]))\\n\\n # Open the cached output\\n with open(cached_output, 'r') as fid:\\n line = fid.readline()\\n line = fid.readline().split('\\\\n')[0]\\n while(line):\\n c, t, r = line.split(',')\\n costs.append(float(c))\\n tikhs.append(float(t))\\n regs.append(float(r))\\n line = fid.readline().split('\\\\n')[0]\\n\\n return costs, tikhs, regs\",\n \"def lorenz_data_gen(init_x,init_y,init_z,init_r):\\n # Set initial state x,y,z and parameters r,sigma,b\\n sxin,syin,szin = init_x,init_y,init_z\\n state = np.zeros(3)\\n state[0] = float(sxin); state[1] = float(syin); state[2] = float(szin)\\n \\n \\n r = init_r\\n sigma = 10 # Parameter sigma\\n b = 8./3. # Parameter b\\n param = np.array([r, sigma, b]) # Vector of parameters passed to rka\\n tau = .02 # Timestep from lorenz with n=500\\n #err = 1.e-3 # Error tolerance\\n \\n # Loop over the desired number of steps\\n time = 0\\n nstep = 500\\n # initialize arrays\\n tplot=np.array([]); tauplot=np.array([])\\n xplot=np.array([]); yplot=np.array([]); zplot=np.array([])\\n \\n for istep in range(0,nstep):\\n # Record values for plotting\\n x = state[0]\\n y = state[1] \\n z = state[2]\\n tplot = np.append(tplot,time)\\n tauplot = np.append(tauplot,tau)\\n xplot = np.append(xplot,x)\\n yplot = np.append(yplot,y)\\n zplot = np.append(zplot,z)\\n #if( istep%50 ==0 ):\\n #print('Finished %d steps out of %d '%(istep,nstep))\\n # Find new state using Runge-Kutta4\\n state = rk4(state,time,tau,lorzrk,param)\\n time += tau\\n \\\"\\\"\\\"\\n # Graph the time series x(t)\\n plt.figure(1) \\n plt.clf() # Clear figure 1 window and bring forward\\n plt.plot(tplot,xplot,'-')\\n plt.xlabel('Time'); plt.ylabel('x(t)')\\n plt.title('Lorenz model time series')\\n # plt.show()\\n \\n # Graph the x,y,z phase space trajectory\\n \\n fig=plt.figure(2)\\n ax=p3.Axes3D(fig)\\n ax.plot3D(xplot,yplot,zplot)\\n ax.set_xlabel('x')\\n ax.set_ylabel('y')\\n ax.set_zlabel('z')\\n ax.grid(True)\\n # title('Lorenz model phase space')\\n #ax.set_aspect('equal')\\n plt.show()\\n \\\"\\\"\\\"\\n return xplot,yplot,zplot,tplot\",\n \"def main():\\n # Load properties that will be needed\\n store = [Storage.Storage(2), Storage.Storage(4)] \\n pre_energy = [s.get(\\\"free_energy\\\") for s in store]\\n post_energy = [s.get(\\\"post_energy\\\") for s in store]\\n x_range = store[0].get(\\\"x_range\\\")\\n xlocs = np.arange(x_range[0], x_range[1], x_range[2])\\n y_range = store[0].get(\\\"y_range\\\")\\n ylocs = np.arange(y_range[0], y_range[1], y_range[2])\\n # Calculate step size\\n xb2steps = stepsize(pre_energy[0], post_energy[0], xlocs) \\n xb4steps = stepsize(pre_energy[1], post_energy[1], xlocs) \\n # Set up the figure\\n fig = plt.figure(1, figsize=(7.5,2.5)) \\n axe = (fig.add_subplot(1, 2, 1), fig.add_subplot(1, 2, 2))\\n # Plot the results\\n axe[0].plot(ylocs, xb4steps, color='#FF466F', lw=4)\\n axe[1].plot(ylocs, xb2steps, color='#76D753', lw=4)\\n # Annotate the plots\\n axe[0].set_title(\\\"4sXB step size\\\")\\n axe[0].set_xlabel(\\\"Lattice spacing (nm)\\\") \\n axe[0].set_ylabel(\\\"Step size (nm)\\\")\\n axe[0].set_xlim((25.5, 39))\\n axe[0].set_ylim((1, 8))\\n axe[1].set_title(\\\"2sXB step size\\\")\\n axe[1].set_xlabel(\\\"Lattice spacing (nm)\\\") \\n axe[1].set_ylabel(\\\"Step size (nm)\\\")\\n axe[1].set_xlim((25.5, 39))\\n axe[1].set_ylim((1, 8))\\n # Display the plots\\n fig.subplots_adjust(wspace=0.25, hspace=0.48,\\n left=0.08, right=0.98,\\n top=0.85, bottom=0.21)\\n plt.show()\",\n \"def IR():\\n s = np.array(\\n [2.40774137,2.287696084,2.203613927,2.048710132,1.899829585,1.591776247,\\n 2.021218754,2.572949552,3.298381484,3.635993426,3.788266224,3.8307278,3.834208811]\\n )\\n\\n TI = np.array([50, 75, 100, 150, 200, 300, 400, 500, 750, 1000, 1500, 2000, 3000])\\n\\n comp1 = s * np.array([-159.1,-134.2,-109.1,-64.7,25.0,40.1,88.6,126.8,187.6,219.4,245.4,253.6,256.1])\\n comp2 = s * np.array([-368.3,-356.9,-343.8,-318.1,-292.0,-242.5,-199.3,-158.4,-68.8,14.2,131.9,219.5,333.5])\\n comp3 = s * np.array([-77.5,-51.9,-29.8,9.9,40.2,85.7,115.4,135.1,160.1,167.6,172.3,171.7,171.8])\\n comp4 = s * np.array([-265.0,-240.6,-216.7,-170.5,-128.2,-53.5,9.6,62.3,159.7,223.8,296.5,328.3,346.7])\\n comp5 = s * np.array([-346.5,-328.9,-312.1,-278.5,-244.4,-182.3,-128.0,-80.0,30.8,109.3,225.1,299.5,372.2])\\n\\n comp = [comp1, comp2, comp3, comp4, comp5]\\n MSE = []\\n colors = [\\\"#1f77b4\\\", \\\"#ff7f0e\\\", \\\"#2ca02c\\\", \\\"#d62728\\\", \\\"#9467bd\\\"]\\n x_new = np.linspace(0, 3000, 10000)\\n for i, j, k in zip(comp, colors, np.arange(1, 6)):\\n plt.scatter(TI, i, c=j)\\n # popt, _ = curve_fit(MZ, TI, i, p0=np.array([200, 220, 300]))\\n popt, _ = curve_fit(MZ, TI, i, p0=np.array([300, 220]))\\n # M_z0, T1, M0 = popt\\n M0, T1 = popt\\n y_new = MZ(x_new, *popt)\\n plt.plot(x_new, y_new, \\\"--\\\", c=j, label=f\\\"Fit Comp. {k:d} : $T_1$={T1:3.2f}\\\")\\n MSE.append(mean_squared_error(i,y_new[TI]))\\n print(MSE)\\n print(np.mean(MSE))\\n plt.grid()\\n plt.legend(loc=\\\"best\\\")\\n plt.xlabel(\\\"TI\\\")\\n plt.ylabel(r\\\"Singal Intensity $M_z$\\\")\\n plt.show()\",\n \"def test_GBL_tau_inst():\\n dz = 0.05\\n z = numpy.arange(0., 80. + 1.5*dz, dz)\\n\\n # Fully ionized H and He\\n x_ionH = 1.0\\n x_ionHe = 2.0\\n\\n cosmo = {}\\n cosmo['omega_M_0'] = numpy.array([[0.3],[0.6],[1.0]])\\n cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']\\n cosmo['h'] = 0.65\\n cosmo['omega_b_0'] = 0.02 / cosmo['h']**2.\\n cosmo['Y_He'] = 0.24\\n cd.set_omega_k_0(cosmo)\\n\\n tau_inst = cr.optical_depth_instant(z, x_ionH=x_ionH, x_ionHe=x_ionHe, \\n **cosmo)\\n tau_int = cr.integrate_optical_depth(x_ionH, x_ionHe, z, **cosmo)\\n\\n linestyle = ['-', ':', '--']\\n \\n pylab.figure()\\n pylab.subplot(2,1,1)\\n pylab.title(\\\"Compare to GB&L fig. 1 (astro-ph/9812125v3.)\\\")\\n for i in range(len(linestyle)):\\n pylab.plot(z, tau_inst[i], ls=linestyle[i], color='b')\\n pylab.plot(z, tau_int[i], ls=linestyle[i], color='r')\\n\\n pylab.xlim(0,80)\\n pylab.ylim(0,1)\\n pylab.xlabel(r\\\"$\\\\mathrm{z_{ion}}$\\\")\\n pylab.ylabel(r\\\"$\\\\tau$\\\")\\n \\n pylab.subplot(2,1,2)\\n for i in range(len(linestyle)):\\n pylab.plot(z, \\n 1.e4 * (tau_int[i] - tau_inst[i])/tau_inst[i], \\n ls=linestyle[i], color='k')\\n diff = (tau_int[i] - tau_inst[i]) / tau_inst[i]\\n diff[numpy.isnan(diff)] = 0.0\\n print(\\\"max fractional error in num. int. = %.3g\\\" % \\n numpy.max(numpy.abs(diff))\\n )\\n ntest.assert_array_less(numpy.abs(diff), \\n numpy.zeros(diff.shape) + 2.e-4)\\n\\n pylab.xlim(0,40)\\n pylab.xlabel(r\\\"$\\\\mathrm{z_{ion}}$\\\")\\n pylab.ylabel(r\\\"$\\\\mathrm{10^4 \\\\times (num.\\\\tau - ana.\\\\tau)/ana.\\\\tau}$\\\")\",\n \"def _run():\\n\\n temperatures_kelvins = _create_temperature_grid()\\n first_derivs_kelvins_pt01 = numpy.gradient(temperatures_kelvins)\\n second_derivs_kelvins_pt01 = numpy.gradient(\\n numpy.absolute(first_derivs_kelvins_pt01)\\n )\\n\\n this_ratio = (\\n numpy.max(temperatures_kelvins) /\\n numpy.max(first_derivs_kelvins_pt01)\\n )\\n\\n first_derivs_unitless = first_derivs_kelvins_pt01 * this_ratio\\n\\n this_ratio = (\\n numpy.max(temperatures_kelvins) /\\n numpy.max(second_derivs_kelvins_pt01)\\n )\\n\\n second_derivs_unitless = second_derivs_kelvins_pt01 * this_ratio\\n\\n _, axes_object = pyplot.subplots(\\n 1, 1, figsize=(FIGURE_WIDTH_INCHES, FIGURE_HEIGHT_INCHES)\\n )\\n\\n temperature_handle = axes_object.plot(\\n temperatures_kelvins, color=TEMPERATURE_COLOUR, linestyle='solid',\\n linewidth=SOLID_LINE_WIDTH\\n )[0]\\n\\n second_deriv_handle = axes_object.plot(\\n second_derivs_unitless, color=SECOND_DERIV_COLOUR, linestyle='solid',\\n linewidth=SOLID_LINE_WIDTH\\n )[0]\\n\\n first_deriv_handle = axes_object.plot(\\n first_derivs_unitless, color=FIRST_DERIV_COLOUR, linestyle='dashed',\\n linewidth=DASHED_LINE_WIDTH\\n )[0]\\n\\n this_min_index = numpy.argmin(second_derivs_unitless)\\n second_derivs_unitless[\\n (this_min_index - 10):(this_min_index + 10)\\n ] = second_derivs_unitless[this_min_index]\\n\\n tfp_handle = axes_object.plot(\\n -1 * second_derivs_unitless, color=TFP_COLOUR, linestyle='dashed',\\n linewidth=DASHED_LINE_WIDTH\\n )[0]\\n\\n axes_object.set_yticks([0])\\n axes_object.set_xticks([], [])\\n\\n x_label_string = r'$x$-coordinate (increasing to the right)'\\n axes_object.set_xlabel(x_label_string)\\n\\n legend_handles = [\\n temperature_handle, first_deriv_handle, second_deriv_handle,\\n tfp_handle\\n ]\\n\\n legend_strings = [\\n TEMPERATURE_LEGEND_STRING, FIRST_DERIV_LEGEND_STRING,\\n SECOND_DERIV_LEGEND_STRING, TFP_LEGEND_STRING\\n ]\\n\\n axes_object.legend(legend_handles, legend_strings, loc='lower right')\\n\\n print 'Saving figure to file: \\\"{0:s}\\\"...'.format(OUTPUT_FILE_NAME)\\n pyplot.savefig(OUTPUT_FILE_NAME, dpi=FIGURE_RESOLUTION_DPI)\\n pyplot.close()\",\n \"def lCurve(self): \\n\\n # --------------------------------------------------------------------------------------------- #\\n # Read data\\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\\n lcTab = Table.read(fitsNnam)\\n if (self.tstart is not None) and (self.tstop is not None):\\n lcTab = lcTab[ (self.tstart <= lcTab['mjd']) & (lcTab['mjd'] <= self.tstop)]\\n lcTab = lcTab[lcTab['flux'] != -1.] # avoid undone analyses\\n\\n timeMJD = lcTab['mjd']\\n tref = int(np.floor( timeMJD[0] / 100.0)) * 100 # round to lowest hundred\\n timeMJD -= tref\\n ts = lcTab['ts']\\n detect = lcTab['ts'] >= self.tsmin\\n undet = lcTab['ts'] < self.tsmin\\n flux = lcTab['flux'][detect]\\n fluxerr = lcTab['fluxerr'][detect]\\n upperl = lcTab['upperlim'][undet]\\n upperl[upperl == -1.] = 0. # for when it failed\\n scale = 10**int(np.floor(np.log10( np.mean( np.concatenate( (flux, upperl), axis=0) ) ))) \\n\\n # --------------------------------------------------------------------------------------------- #\\n # Plot\\n lcplt = FermiPlot(savepath='', xsize=8.5, ysize=6)\\n lcplt.figname = os.path.join(self.workpath, 'LightCurve.pdf')\\n lcplt.xlabel = r'Time (MJD $-$ {})'.format(tref)\\n lcplt.ylabel = [r'Flux ($10^{%d}$ ph\\\\,cm$^{-2}$\\\\,s$^{-1}$)'%(int(np.log10(scale))), r'TS']\\n lcplt.hline = [None, self.tsmin]\\n deltaY = max(np.concatenate((flux+fluxerr, upperl), axis=0)) - min(np.concatenate((flux-fluxerr, upperl), axis=0))\\n lcplt.ymin = [(min(np.concatenate((flux-fluxerr, upperl-upperl*0.1), axis=0)) - 0.05*deltaY) / scale, min(ts) - 0.05*(max(ts)-min(ts))]\\n lcplt.ymax = [(max(np.concatenate((flux+fluxerr, upperl), axis=0)) + 0.05*deltaY) / scale, max(ts) + 0.05*(max(ts)-min(ts))]\\n deltaX = (timeMJD[-1] + lcTab['mjderr'][-1]) - (timeMJD[0] - lcTab['mjderr'][0]) \\n lcplt.xmin = timeMJD[0] - lcTab['mjderr'][0] - 0.05*deltaX\\n lcplt.xmax = timeMJD[-1] + lcTab['mjderr'][-1] + 0.05*deltaX\\n lcplt.fill = [item for sublist in zip( timeMJD[detect]-lcTab['mjderr'][detect], timeMJD[detect]+lcTab['mjderr'][detect] ) for item in sublist]\\n lcplt.shadecol= self.loran \\n if len(flux) == 0:\\n lcplt.mksize = [2, 2]\\n lcplt.ymode = ['linear', 'linear']\\n lcplt.color = ['gray', 'black']\\n lcplt.prop = [3, 1]\\n lcplt.limit = [True, False]\\n lcplt.multiplot(x = [ timeMJD[undet], timeMJD ],\\n y = [ upperl/scale, ts ],\\n xerr = [ lcTab['mjderr'][undet], lcTab['mjderr']],\\n yerr = [ upperl/scale*0.1, None])\\n else:\\n lcplt.mksize = [2, 2, 2]\\n lcplt.ymode = ['linear', 'linear', 'linear']\\n lcplt.color = ['gray', 'black', 'black']\\n lcplt.prop = [3, 1]\\n lcplt.limit = [[True, False], False]\\n lcplt.multiplot(x = [ [timeMJD[undet], timeMJD[detect]], timeMJD ],\\n y = [ [upperl/scale, flux/scale], ts ],\\n xerr = [ [lcTab['mjderr'][undet], lcTab['mjderr'][detect]], lcTab['mjderr']],\\n yerr = [ [upperl/scale*0.1, fluxerr/scale], None])\\n lcplt.save()\\n\\n print(\\\"\\\\t=== Figure '{}' created ===\\\".format(lcplt.figname)) \\n return\",\n \"def solver(direc):\\r\\n\\r\\n # open inputfile\\r\\n file = 'schroedinger.inp'\\r\\n fn = os.path.join(direc, file)\\r\\n inp_all = open(fn, 'r')\\r\\n\\r\\n # read parameters from inputfile\\r\\n lines = inp_all.readlines()\\r\\n inp_mass = lines[0].split()[0]\\r\\n mass = float(inp_mass)\\r\\n inp_xmin = lines[1].split()[0]\\r\\n xmin = float(inp_xmin)\\r\\n inp_xmax = lines[1].split()[1]\\r\\n xmax = float(inp_xmax)\\r\\n inp_npoint = lines[1].split()[2]\\r\\n npoint = int(inp_npoint)\\r\\n inp_firsteigval = lines[2].split()[0]\\r\\n firsteigval = int(inp_firsteigval)\\r\\n inp_lasteigval = lines[2].split()[1]\\r\\n lasteigval = int(inp_lasteigval)\\r\\n interpoltype = lines[3].split()[0]\\r\\n inp_nrinterpolpoints = lines[4].split()[0]\\r\\n nrinterpolpoints = int(inp_nrinterpolpoints)\\r\\n len_pot = len(lines)-5\\r\\n xpot = np.zeros(len_pot)\\r\\n ypot = np.zeros(len_pot)\\r\\n for ii in range(5, len_pot+5):\\r\\n xpot[ii - 5] = float(lines[ii].split()[0])\\r\\n ypot[ii - 5] = float(lines[ii].split()[1])\\r\\n\\r\\n # read interpolation type and interpolate the potential\\r\\n xx = np.linspace(xmin, xmax, npoint)\\r\\n if interpoltype == 'linear':\\r\\n pot = np.interp(xx, xpot, ypot)\\r\\n elif interpoltype == 'polynomial':\\r\\n degree = int(nrinterpolpoints - 1)\\r\\n coef = np.polyfit(xpot, ypot, degree)\\r\\n polf = np.poly1d(coef)\\r\\n pot = polf(xx)\\r\\n elif interpoltype == 'cspline':\\r\\n cubicf = interp1d(xpot, ypot, kind='cubic')\\r\\n pot = cubicf(xx)\\r\\n else:\\r\\n print('interpolation type not found')\\r\\n sys.exit(1)\\r\\n\\r\\n # save x- and y-values for interpolated potential in potential.dat file\\r\\n potential = np.array([xx, pot])\\r\\n xypotential = potential.T\\r\\n np.savetxt(os.path.join(direc, 'potential.dat'), xypotential)\\r\\n\\r\\n # formulate matrix-problem for the discretised Schroedinger equation\\r\\n matrix = np.zeros((npoint, npoint))\\r\\n delta = abs((xmax-xmin)/(npoint))\\r\\n aa = 1/(mass*(delta)**2)\\r\\n for ii in range(1, npoint):\\r\\n matrix[ii, ii-1] = -aa/2\\r\\n for ii in range(0, npoint):\\r\\n matrix[ii, ii] = aa+pot[ii]\\r\\n for ii in range(0, npoint-1):\\r\\n matrix[ii, ii+1] = -aa/2\\r\\n\\r\\n # compute eigenvalues and eigenvectors\\r\\n energy, wavefunc = scipy.linalg.eigh(matrix, eigvals=(int(firsteigval-1),\\r\\n int(lasteigval-1)))\\r\\n\\r\\n # normalize wavefunctions\\r\\n deltavec = delta*np.ones((1, npoint))\\r\\n wavefunc_sq = wavefunc**2\\r\\n norm_sq = np.dot(deltavec, wavefunc_sq)\\r\\n norm = 1/(np.sqrt(norm_sq))\\r\\n norm_wavefunc = np.dot(wavefunc, np.diag(np.reshape(norm,\\r\\n (len(energy), ))))\\r\\n\\r\\n # save eigenvalues and eigenvectors in energies.dat and wavefuncs.dat files\\r\\n np.savetxt(os.path.join(direc, 'energies.dat'), energy)\\r\\n wavefuncs = np.hstack((xx.reshape((npoint, 1)), norm_wavefunc))\\r\\n np.savetxt(os.path.join(direc, 'wavefuncs.dat'), wavefuncs)\\r\\n\\r\\n # compute expectation values and uncertainty for the position\\r\\n exp_value = np.zeros(lasteigval-firsteigval+1)\\r\\n exp_value_sq = np.zeros(lasteigval-firsteigval+1)\\r\\n uncert_x = np.zeros(lasteigval-firsteigval+1)\\r\\n for ii in range(firsteigval-1, lasteigval):\\r\\n exp_value[ii] = delta*np.sum(norm_wavefunc[:, ii]**2*xx)\\r\\n exp_value_sq[ii] = delta*np.sum(norm_wavefunc[:, ii]**2*xx**2)\\r\\n uncert_x[ii] = np.sqrt(exp_value_sq[ii]-exp_value[ii]**2)\\r\\n\\r\\n # save expectation values and uncertainty for the position in expvalues.dat\\r\\n expvalues = np.array([exp_value, uncert_x])\\r\\n datexpvalues = expvalues.T\\r\\n np.savetxt(os.path.join(direc, 'expvalues.dat'), datexpvalues)\\r\\n\\r\\n return xypotential, energy\",\n \"def cloudy_hii_radial_lins(props=['[CII]158'],cols=['b'],ls=['-'],model_name = '',i_model = 0,NIIratio=False,**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n if p.add:\\n ax = plt.gca()\\n else:\\n fig,ax = plt.subplots(figsize=(8,6))\\n\\n names = ['depth','[CI]610','[CI]370','[CII]158','[OI]63','[OI]145','[OIII]88','[NII]205','[NII]122','CO(1-0)','CO(2-1)','CO(3-2)','CO(4-3)','CO(5-4)']\\n\\n for prop,col,ls1 in zip(props,cols,ls):\\n lins = pd.read_table(p.d_cloudy_test + '%s.lins' % model_name,skiprows=1,names=names)\\n # print(lins['depth'].values)\\n # print(lins[prop].values)\\n # ax.plot(lins['depth'].values/c.pc.to('cm'),lins[prop].values,'o',ms=5,color=col)\\n ax.plot(lins['depth'].values/c.pc.to('cm'),lins[prop].values,'-',lw=2,ls=ls1,color=col,label=prop)\\n\\n ax.set_xlabel('Depth [pc]')\\n ax.set_ylabel('ergs/s integrated over sphere')\\n ax.legend()\\n ax.set_yscale('log')\\n\\n names = ['it','[CI]610','[CI]370','[CII]158','[OI]63','[OI]145','[OIII]88','[NII]205','[NII]122','CO(1-0)','CO(2-1)','CO(3-2)','CO(4-3)','CO(5-4)']\\n\\n\\n\\n if NIIratio:\\n ax2 = ax.twinx()\\n ax2.plot(lins['depth'].values/c.pc.to('cm'),lins['[NII]122'].values/lins['[NII]205'].values,'o',ms=5,color='k')\\n ax2.plot(lins['depth'].values/c.pc.to('cm'),lins['[NII]122'].values/lins['[NII]205'].values,'-',lw=1,ls='-',color='k',label='NII ratio')\\n ax2.set_ylim([0,13])\\n cloudy_library = clo.library()\\n hii_grid = cloudy_library._restore_hii_region_grid()\\n print('NII ratio integrated: %.4f' % (10.**hii_grid['[NII]122'][i_model] / 10.**hii_grid['[NII]205'][i_model]))\\n print('NII luminosities: ',10.**hii_grid['[NII]122'][i_model],10.**hii_grid['[NII]205'][i_model])\\n\\n if p.savefig:\\n if not os.path.isdir(p.d_plot + 'cloudy/'): os.mkdir(p.d_plot + 'cloudy/') \\n plt.savefig(p.d_plot + 'cloudy/%s_lins.png' % (model_name), format='png', dpi=300, facecolor='w')\",\n \"def CII_vs_CO(**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[1])\\n fig,ax1 = plt.subplots()\\n L_CII = getattr(GR,'L_[CII]158_sun')\\n L_CO = getattr(GR,'L_CO(1-0)_sun')\\n Zsfr = getattr(GR,'Zsfr')\\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \\n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\\n sc = ax1.scatter(np.log10(L_CO)-10, np.log10(L_CII)-10, marker='o', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\\\\n vmin=np.log10(0.05), vmax=np.log10(3.1), \\\\\\n s=10, alpha=0.8)#, label='SIGAME 100Mpc_arepoPDF')\\n # print('Min Zsfr in Simba sample: ',np.min(Zsfr))\\n # print('indices with L_CO < 1e0:')\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\\\\n 4:lL_CII.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='forestgreen',levels=8,zorder=10)\\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF')\\n\\n GR = glo.global_results(sim_run=p.sim_runs[0],nGal=p.nGals[0],grid_ext=p.grid_exts[1])\\n L_CII = getattr(GR,'L_[CII]158_sun')\\n L_CO = getattr(GR,'L_CO(1-0)_sun')\\n Zsfr = getattr(GR,'Zsfr')\\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \\n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\\n lL_CO = np.append(lL_CO,np.array([6.1,5]))\\n lL_CII = np.append(lL_CII,np.array([8.9,9.7]))\\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\\\\n # s=10, alpha=0.8, label='SIGAME 25Mpc_arepoPDF')\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\\\\n 4:lL_CII.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='deepskyblue',linestyles='dotted',levels=6)\\n CS.collections[0].set_label('SIGAME 25Mpc_arepoPDF')\\n\\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[0])\\n L_CII = getattr(GR,'L_[CII]158_sun')\\n L_CO = getattr(GR,'L_CO(1-0)_sun')\\n Zsfr = getattr(GR,'Zsfr')\\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \\n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\\n lL_CO = np.append(lL_CO,np.array([-2.2,4.7]))\\n lL_CII = np.append(lL_CII,np.array([8,9.3]))\\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\\\\n # s=10, alpha=0.8, label='SIGAME v3 Simba-%s' % (p.sim_runs[0].replace('_','').replace('Mpc','')))\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\\\\n 4:lL_CII.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='brown',levels=8,zorder=5,linestyles='dashed')\\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF_no_ext')\\n\\n # Observations\\n K16 = pd.read_pickle('data/observations/AHIMSA_sample_lit')\\n K16_LCII = K16['[CII]158_Lsun']\\n K16_LCO = K16['CO(1-0)_Lsun']\\n ax1.plot(np.log10(K16_LCO), np.log10(K16_LCII), '>', color='grey', ms=6, fillstyle='none',alpha=0.8, mew=1,zorder=0,\\\\\\n label='Mixed type galaxies [Kamenetzky+16]')\\n\\n C15 = pd.read_pickle('data/observations/DGS_Cormier_2015')\\n C15_LCII = C15['L_[CII]158']\\n C15_LCO = C15['L_CO(1-0)']\\n C15_Z = C15['Z']\\n # L_ul = C15['L_[CII]158'][(C15['L_[CII]158'] < 0) & (C15['L_CO(1-0)'] > 0)]\\n # if len(L_ul) > 0:\\n # ax1.plot(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul),'s',ms=5,mew=0,color='grey',alpha=0.8)\\n # ax1.errorbar(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul), elinewidth=1,\\\\\\n # uplims=np.ones(len(L_ul)),yerr=np.ones(len(L_ul))*1,color='grey',alpha=0.8,lw=0)\\n ax1.scatter(np.log10(C15_LCO), np.log10(C15_LCII), marker='+', c=np.log10(C15_Z), cmap='viridis', zorder=0,\\\\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\\\\n s=100, lw=3, alpha=0.8, label='Dwarf galaxies [Cormier+15]')\\n\\n A17 = pd.read_pickle('data/observations/xCOLD_GASS_Accurso_2017')\\n A17 = A17.loc[np.argwhere(A17['L_CO(1-0)'].values > 0).flatten()]\\n ax1.scatter(A17['L_CO(1-0)'],A17['L_[CII]158'], marker='d', c=np.log10(A17['Z']), cmap='viridis', zorder=0,\\\\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\\\\n s=50, lw=0, alpha=0.8, label='COLD GASS [Accurso+17]') #c=np.log10(A17['Z']), \\n\\n CII_obs = np.log10(np.append(K16_LCII.values,C15_LCII.values))\\n CO_obs = np.log10(np.append(K16_LCO.values,C15_LCO.values))\\n CII_obs = np.append(CII_obs,A17['L_[CII]158'].values)\\n CO_obs = np.append(CO_obs,A17['L_CO(1-0)'].values)\\n index = np.argwhere((CII_obs > 0) & (CO_obs > 0)).flatten()\\n CII_obs = CII_obs[index]\\n CO_obs = CO_obs[index]\\n\\n x = np.linspace(0, 7, 100)\\n fit = LinearRegression().fit(CO_obs.reshape(-1, 1),\\\\\\n CII_obs.reshape(-1, 1))\\n L_fit = fit.predict(x.reshape(-1, 1))\\n ax1.plot(x, L_fit, color='black', linestyle='--', label='Log-linear fit to observations')\\n\\n ax1.set_ylabel('log ' + getlabel('[CII]158'))\\n ax1.set_xlabel('log ' + getlabel('CO(1-0)'))\\n plt.colorbar(sc,label=r'log $\\\\langle$Z$\\\\rangle_{\\\\rm SFR}$ [Z$_{\\\\rm \\\\odot}$]')\\n\\n handles, labels = ax1.get_legend_handles_labels()\\n print(labels) # labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))\\n # handles = [handles[_] for _ in [2,4,3,5,0,6,7,1]]\\n # labels = [labels[_] for _ in [2,4,3,5,0,6,7,1]]\\n handles = [handles[_] for _ in [2,4,3,5,6,0,1]]\\n labels = [labels[_] for _ in [2,4,3,5,6,0,1]]\\n plt.legend(handles,labels,loc='lower left',fontsize=10.,frameon=True)\\n\\n ax1.set_xlim([-3,6.2])\\n ax1.set_ylim([4,10])\\n\\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \\n plt.savefig(p.d_plot + 'luminosity/CO_vs_CII%s%s.png' % (p.grid_ext,p.table_ext), dpi=300)\",\n \"def main():\\n\\n # Create model_dict from arguments\\n model_dict = model_dict_create()\\n\\n # No. of deviations to consider\\n no_of_mags = 50\\n dev_list = np.linspace(0.1, 5.0, no_of_mags)\\n\\n # Load dataset specified in model_dict\\n print('Loading data...')\\n dataset = model_dict['dataset']\\n if (dataset == 'MNIST'):\\n X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(model_dict)\\n # rd_list = [None, 784, 331, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10]\\n rd_list = [None, 331, 100, 80, 60, 40, 20]\\n # rd_list = [None,784,100]\\n elif dataset == 'GTSRB':\\n X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(model_dict)\\n rd_list = [1024, 338, 200, 100, 90, 80, 70, 60, 50, 40, 33, 30, 20, 10]\\n elif dataset == 'HAR':\\n X_train, y_train, X_test, y_test = load_dataset(model_dict)\\n # rd_list = [561, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10]\\n rd_list = [561]\\n X_val = None\\n y_val = None\\n\\n mean = np.mean(X_train, axis=0)\\n X_train -= mean\\n X_test -= mean\\n if (dataset == 'MNIST') or (dataset == 'GTSRB'): X_val -= mean\\n\\n # fig, ax = plt.subplots(nrows=1, ncols=1)\\n\\n # for rd in rd_list:\\n # model_setup_carlini(model_dict, X_train, y_train, X_test, y_test, X_val, y_val, mean, rd, ax)\\n\\n partial_carlini = partial(model_setup_carlini, model_dict=model_dict, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test, X_val=X_val, y_val=y_val,\\n mean=mean)\\n pool=multiprocessing.Pool(processes=8)\\n pool.map(partial_carlini,rd_list,1)\\n pool.close()\\n pool.join()\\n\\n # dim_red = model_dict['dim_red']\\n # plt.legend()\\n # plt.savefig('carlini_l2_hist_'+dim_red+'.png')\",\n \"def main():\\n df = pd.read_csv('data/ch5_q8_simulation.csv')\\n\\n # Part b\\n plt.figure()\\n plt.scatter(df['x'], df['y'])\\n plt.xlabel('x')\\n plt.ylabel('y')\\n plt.title('Scatterplot y vs. x')\\n plt.savefig('plots/8a.png')\\n\\n # Part c\\n response_var = 'y'\\n pred_vars_lin = ['x']\\n pred_vars_quad = ['x', 'x2']\\n pred_vars_cub = ['x', 'x2', 'x3']\\n pred_vars_quar = ['x', 'x2', 'x3', 'x4']\\n\\n poly_terms = pd.DataFrame({'x2': np.power(df['x'], 2),\\n 'x3': np.power(df['x'], 3),\\n 'x4': np.power(df['x'], 4)})\\n df = pd.concat([df, poly_terms], axis=1)\\n\\n CV_error_lin = loocv(df, response_var, pred_vars_lin)\\n CV_error_quad = loocv(df, response_var, pred_vars_quad)\\n CV_error_cub = loocv(df, response_var, pred_vars_cub)\\n CV_error_quar = loocv(df, response_var, pred_vars_quar)\\n\\n print('Part c')\\n print('CV error (linear) = {:.3f}'.format(CV_error_lin))\\n print('CV error (quadratic) = {:.3f}'.format(CV_error_quad))\\n print('CV error (cubic) = {:.3f}'.format(CV_error_cub))\\n print('CV error (quartic) = {:.3f}'.format(CV_error_quar))\\n\\n # Part d\\n np.random.seed(801)\\n y = np.random.randn(100)\\n x = np.random.randn(100)\\n y = x - 2 * np.power(x, 2) + np.random.randn(100)\\n\\n df = pd.DataFrame({'x': x,\\n 'x2': np.power(x, 2),\\n 'x3': np.power(x, 3),\\n 'x4': np.power(x, 4),\\n 'y': y})\\n\\n CV_error_lin = loocv(df, response_var, pred_vars_lin)\\n CV_error_quad = loocv(df, response_var, pred_vars_quad)\\n CV_error_cub = loocv(df, response_var, pred_vars_cub)\\n CV_error_quar = loocv(df, response_var, pred_vars_quar)\\n\\n print('Part d')\\n print('CV error (linear) = {:.3f}'.format(CV_error_lin))\\n print('CV error (quadratic) = {:.3f}'.format(CV_error_quad))\\n print('CV error (cubic) = {:.3f}'.format(CV_error_cub))\\n print('CV error (quartic) = {:.3f}'.format(CV_error_quar))\\n\\n # Part f\\n model = sm.OLS(df.loc[:, response_var], df.loc[:, pred_vars_quar]).fit()\\n print(model.summary())\",\n \"def clippingHGMevaluation(input_generator,branches,iden_method,Plot,reference=None):\\n for t in range(8,11):\\n t = t / 10.0\\n thresholds = [-t,t]\\n input_signal = input_generator.GetOutput()\\n nl_functions = [nlsp.function_factory.hardclip(thresholds),]*branches\\n filter_spec_tofind = nlsp.log_bpfilter(branches=branches, input=input_signal)\\n ref_nlsystem = nlsp.HammersteinGroupModel_up(input_signal=input_signal,\\n nonlinear_functions=nl_functions,\\n filter_irs=filter_spec_tofind,\\n max_harmonics=range(1,branches+1))\\n\\n found_filter_spec, nl_functions = iden_method(input_generator,ref_nlsystem.GetOutput(),branches)\\n iden_nlsystem = nlsp.HammersteinGroupModel_up(input_signal=input_signal,\\n nonlinear_functions=nl_functions,\\n filter_irs=found_filter_spec,\\n max_harmonics=range(1,branches+1))\\n # sine = sumpf.modules.SineWaveGenerator(frequency=5000.0,phase=0.0,samplingrate=input_signal.GetSamplingRate(),length=len(input_signal)).GetSignal()\\n sine = sumpf.modules.SweepGenerator(samplingrate=input_signal.GetSamplingRate(),length=len(input_signal)).GetSignal()\\n ref_nlsystem.SetInput(sine)\\n iden_nlsystem.SetInput(sine)\\n if reference is not None:\\n reference = nlsp.change_length_signal(reference,length=len(input_signal))\\n ref_nlsystem.SetInput(reference)\\n iden_nlsystem.SetInput(reference)\\n\\n if Plot is True:\\n plot.relabelandplot(sumpf.modules.FourierTransform(ref_nlsystem.GetOutput()).GetSpectrum(),\\\"Reference System\\\",show=False)\\n plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem.GetOutput()).GetSpectrum(),\\\"Identified System\\\",show=False)\\n print \\\"SNR between Reference and Identified output for symmetric hardclipping HGM(thresholds:%r): %r\\\" %(thresholds,nlsp.snr(ref_nlsystem.GetOutput(),\\n iden_nlsystem.GetOutput()))\",\n \"def main(\\n error_band_dir,\\n output_dir,\\n indep_var,\\n ivar_start,\\n ivar_stop,\\n ivar_step,\\n param_list,\\n observable_list,\\n Lambda_b,\\n lambda_mult,\\n p_decimal_list,\\n orders,\\n ignore_orders,\\n interaction,\\n X_ref_hash,\\n prior_set,\\n h,\\n cbar_lower,\\n cbar_upper,\\n sigma,\\n convention\\n ):\\n\\n color_dict = {\\n \\\"LOp\\\": plt.get_cmap(\\\"Greys\\\"),\\n \\\"LO\\\": plt.get_cmap(\\\"Purples\\\"),\\n \\\"NLO\\\": plt.get_cmap(\\\"Oranges\\\"),\\n \\\"N2LO\\\": plt.get_cmap(\\\"Greens\\\"),\\n \\\"N3LO\\\": plt.get_cmap(\\\"Blues\\\"),\\n \\\"N4LO\\\": plt.get_cmap(\\\"Reds\\\")\\n }\\n\\n fill_transparency = 1\\n x = np.arange(ivar_start, ivar_stop, ivar_step)\\n\\n fig = plt.figure(figsize=(3.4, 3.4))\\n ax = fig.add_subplot(1, 1, 1)\\n ax.set_zorder(15)\\n ax.set_axisbelow(False)\\n\\n if indep_var == \\\"theta\\\":\\n param_var = \\\"energy\\\"\\n indep_var_label = r\\\"$\\\\theta$ (deg)\\\"\\n param_var_label = r\\\"$E_{\\\\mathrm{lab}}\\\"\\n param_var_units = r\\\"$ MeV\\\"\\n xmin = 0\\n xmax = 180\\n else:\\n param_var = \\\"theta\\\"\\n indep_var_label = r\\\"$E$ (MeV)\\\"\\n param_var_label = r\\\"$\\\\theta\\\"\\n param_var_units = r\\\"^\\\\circ$\\\"\\n xmin = 0\\n xmax = 350\\n\\n for observable in observable_list:\\n for param in param_list:\\n if observable == [\\\"t\\\", \\\"t\\\", \\\"t\\\", \\\"t\\\"] or \\\\\\n (observable == [\\\"0\\\", \\\"0\\\", \\\"0\\\", \\\"0\\\"] and indep_var == \\\"energy\\\"):\\n ax.set_yscale(\\\"log\\\", nonposy='clip')\\n\\n ax.set_xlabel(indep_var_label)\\n # ax.set_ylabel('')\\n\\n # if indep_var == \\\"theta\\\":\\n # major_tick_spacing = 60\\n # minor_tick_spacing = 20\\n # ax.xaxis.set_major_locator(\\n # ticker.MultipleLocator(major_tick_spacing))\\n # ax.xaxis.set_minor_locator(\\n # ticker.MultipleLocator(minor_tick_spacing))\\n\\n if indep_var == \\\"energy\\\":\\n major_ticks = np.arange(0, 351, 100)\\n # minor_ticks = np.arange(50, 351, 100)\\n x_minor_locator = AutoMinorLocator(n=2)\\n elif indep_var == \\\"theta\\\":\\n major_ticks = np.arange(0, 181, 60)\\n # minor_ticks = np.arange(0, 181, 20)\\n x_minor_locator = AutoMinorLocator(n=3)\\n\\n ax.xaxis.set_minor_locator(x_minor_locator)\\n ax.set_xticks(major_ticks)\\n\\n # Create the description box\\n # text_str = r\\\"$C_{\\\" + observable[0] + observable[1] + \\\\\\n # observable[2] + observable[3] + r\\\"}$\\\" + \\\", \\\" # + \\\"\\\\n\\\"\\n text_str = indices_to_observable_name(observable)\\n if observable == ['t', 't', 't', 't']:\\n text_str += \\\" (mb)\\\"\\n elif observable == ['0', '0', '0', '0']:\\n text_str += \\\" (mb/sr)\\\"\\n\\n # Probably don't include this extra info. Leave for caption.\\n #\\n # text_str += \\\", \\\"\\n # if observable != ['t', 't', 't', 't']:\\n # text_str += \\\", \\\" + param_var_label + r\\\" = \\\" + str(param) + param_var_units + \\\", \\\" # + \\\"\\\\n\\\"\\n # text_str += r\\\"$\\\\Lambda_b = \\\" + str(lambda_mult*Lambda_b) + r\\\"$ MeV\\\"\\n\\n # Don't put in a text box\\n #\\n # ax.text(.5, .96, text_str,\\n # horizontalalignment='center',\\n # verticalalignment='top',\\n # multialignment='center',\\n # transform=ax.transAxes,\\n # bbox=dict(facecolor='white', alpha=1, boxstyle='square', pad=.5),\\n # zorder=20)\\n\\n # Don't put in the title\\n # plt.title(text_str, fontsize=10)\\n\\n # Instead use y axis\\n ax.set_ylabel(text_str, fontsize=10)\\n legend_patches = []\\n\\n try:\\n npwa_name = npwa_filename(observable, param_var, param)\\n npwa_file = DataFile().read(os.path.join(\\\"../npwa_data/\\\", npwa_name))\\n npwa_plot, = ax.plot(npwa_file[0], npwa_file[1],\\n color=\\\"black\\\", linewidth=1,\\n label=\\\"NPWA\\\", zorder=10,\\n linestyle=\\\"--\\\")\\n except FileNotFoundError:\\n npwa_plot = None\\n\\n # First get global min/max of all orders\\n for i, order in enumerate(orders):\\n # obs_name = observable_filename(\\n # observable, indep_var, ivar_start, ivar_stop,\\n # ivar_step, param_var, param, order)\\n # dob_name = dob_filename(p_decimal_list[0], Lambda_b, obs_name)\\n dob_name = dob_filename(\\n observable, indep_var, ivar_start, ivar_stop,\\n ivar_step, param_var, param, order, ignore_orders,\\n Lambda_b, lambda_mult, X_ref_hash,\\n p_decimal_list[0], prior_set, h, convention, None,\\n cbar_lower, cbar_upper, sigma,\\n potential_info=None)\\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\\n if i == 0:\\n obs = dob_file[1]\\n obs_min = np.min(obs)\\n obs_max = np.max(obs)\\n else:\\n old_obs = obs\\n obs = dob_file[1]\\n # Probably the worst way to do this.\\n obs_min = min(np.min(np.minimum(old_obs, obs)), obs_min)\\n obs_max = max(np.max(np.maximum(old_obs, obs)), obs_max)\\n\\n # Decide the padding above/below the lines\\n # This weights values far from 0 more heavily.\\n # ymin = obs_min - .25 * abs(obs_min)\\n # ymax = obs_max + .25 * abs(obs_max)\\n ymin = -1\\n ymax = 20\\n ax.set_ylim([ymin, ymax])\\n # ax.set_xlim([ivar_start, ivar_stop-1])\\n ax.set_xlim([xmin, xmax])\\n\\n # Start layering the plots\\n for i, order in enumerate(orders):\\n # obs_name = observable_filename(\\n # observable, indep_var, ivar_start, ivar_stop,\\n # ivar_step, param_var, param, order)\\n # dob_name = dob_filename(p_decimal_list[0], Lambda_b, obs_name)\\n dob_name = dob_filename(\\n observable, indep_var, ivar_start, ivar_stop,\\n ivar_step, param_var, param, order, ignore_orders,\\n Lambda_b, lambda_mult, X_ref_hash,\\n p_decimal_list[0], prior_set, h, convention, None,\\n cbar_lower, cbar_upper, sigma,\\n potential_info=None)\\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\\n\\n # Plot the lines\\n obs = dob_file[1]\\n ax.plot(x, obs, color=color_dict[order](.99), zorder=i)\\n\\n # Plot the error bands\\n for band_num, p in enumerate(sorted(p_decimal_list, reverse=True)):\\n # dob_name = dob_filename(p, Lambda_b, obs_name)\\n dob_name = dob_filename(\\n observable, indep_var, ivar_start, ivar_stop,\\n ivar_step, param_var, param, order, ignore_orders,\\n Lambda_b, lambda_mult, X_ref_hash,\\n p, prior_set, h, convention, None,\\n cbar_lower, cbar_upper, sigma,\\n potential_info=None)\\n dob_file = DataFile().read(os.path.join(error_band_dir, dob_name))\\n obs_lower = dob_file[2]\\n obs_upper = dob_file[3]\\n ax.fill_between(\\n x, obs_lower, obs_upper,\\n facecolor=color_dict[order](\\n (band_num + 1) / (len(p_decimal_list) + 1)\\n ),\\n color=color_dict[order](\\n (band_num + 1) / (len(p_decimal_list) + 1)\\n ),\\n alpha=fill_transparency, interpolate=True, zorder=i)\\n\\n # Use block patches instead of lines\\n # Use innermost \\\"dark\\\" color of bands for legend\\n # legend_patches.append(\\n # mp.patches.Patch(\\n # color=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\\n # label=order,\\n # ))\\n legend_patches.append(\\n mpatches.Rectangle(\\n (1, 1), 0.25, 0.25,\\n # color=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\\n edgecolor=color_dict[order](.9),\\n facecolor=color_dict[order](len(p_decimal_list) / (len(p_decimal_list) + 1)),\\n label=order,\\n linewidth=1\\n ))\\n\\n if npwa_plot is None:\\n my_handles = legend_patches\\n handler_dict = dict(zip(my_handles, [HandlerSquare() for i in legend_patches]))\\n else:\\n my_handles = [npwa_plot, *legend_patches]\\n squares = [HandlerSquare() for i in legend_patches]\\n line = HandlerLine2D(marker_pad=1, numpoints=None)\\n handler_dict = dict(zip(my_handles, [line] + squares))\\n\\n ax.legend(loc=\\\"best\\\", handles=my_handles,\\n handler_map=handler_dict,\\n handletextpad=.8,\\n handlelength=.6,\\n fontsize=8)\\n\\n # Squeeze and save it\\n plt.tight_layout()\\n # plot_name = plot_obs_error_bands_filename(\\n # observable, indep_var, ivar_start, ivar_stop,\\n # ivar_step, param_var, param, orders[:i+1],\\n # Lambda_b, p_decimal_list)\\n plot_name = plot_obs_error_bands_filename(\\n observable, indep_var, ivar_start, ivar_stop, ivar_step,\\n param_var, param, orders[:i+1], ignore_orders, Lambda_b,\\n lambda_mult, X_ref_hash, p_decimal_list,\\n prior_set, h, convention, None, cbar_lower, cbar_upper,\\n sigma, potential_info=None)\\n fig.savefig(os.path.join(output_dir, plot_name), bbox_inches=\\\"tight\\\")\\n\\n call([\\\"epstopdf\\\", os.path.join(output_dir, plot_name)])\\n call([\\\"rm\\\", os.path.join(output_dir, plot_name)])\\n\\n # Clear the axes for the next observable/parameter.\\n plt.cla()\",\n \"def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=10000, max_iters=300, optimize=True, plot=True):\\r\\n\\r\\n # Contour over a range of length scales and signal/noise ratios.\\r\\n length_scales = np.linspace(0.1, 60., resolution)\\r\\n log_SNRs = np.linspace(-3., 4., resolution)\\r\\n\\r\\n data = GPy.util.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number)\\r\\n # data['Y'] = data['Y'][0::2, :]\\r\\n # data['X'] = data['X'][0::2, :]\\r\\n\\r\\n data['Y'] = data['Y'] - np.mean(data['Y'])\\r\\n\\r\\n lls = GPy.examples.regression._contour_data(data, length_scales, log_SNRs, GPy.kern.rbf)\\r\\n if plot:\\r\\n pb.contour(length_scales, log_SNRs, np.exp(lls), 20, cmap=pb.cm.jet)\\r\\n ax = pb.gca()\\r\\n pb.xlabel('length scale')\\r\\n pb.ylabel('log_10 SNR')\\r\\n\\r\\n xlim = ax.get_xlim()\\r\\n ylim = ax.get_ylim()\\r\\n\\r\\n # Now run a few optimizations\\r\\n models = []\\r\\n optim_point_x = np.empty(2)\\r\\n optim_point_y = np.empty(2)\\r\\n np.random.seed(seed=seed)\\r\\n for i in range(0, model_restarts):\\r\\n # kern = GPy.kern.rbf(1, variance=np.random.exponential(1.), lengthscale=np.random.exponential(50.))\\r\\n kern = GPy.kern.rbf(1, variance=np.random.uniform(1e-3, 1), lengthscale=np.random.uniform(5, 50))\\r\\n\\r\\n m = GPy.models.GPRegression(data['X'], data['Y'], kernel=kern)\\r\\n m['noise_variance'] = np.random.uniform(1e-3, 1)\\r\\n optim_point_x[0] = m['rbf_lengthscale']\\r\\n optim_point_y[0] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);\\r\\n\\r\\n # optimize\\r\\n if optimize:\\r\\n m.optimize('scg', xtol=1e-6, ftol=1e-6, max_iters=max_iters)\\r\\n\\r\\n optim_point_x[1] = m['rbf_lengthscale']\\r\\n optim_point_y[1] = np.log10(m['rbf_variance']) - np.log10(m['noise_variance']);\\r\\n\\r\\n if plot:\\r\\n pb.arrow(optim_point_x[0], optim_point_y[0], optim_point_x[1] - optim_point_x[0], optim_point_y[1] - optim_point_y[0], label=str(i), head_length=1, head_width=0.5, fc='k', ec='k')\\r\\n models.append(m)\\r\\n\\r\\n if plot:\\r\\n ax.set_xlim(xlim)\\r\\n ax.set_ylim(ylim)\\r\\n return m # (models, lls)\\r\",\n \"def plot_lineratios(modeldata,modeldata2='None',line1='CIV1551',line2='CIII1908',line3='CIV1551',line4='HeII1640',\\n plotname='./TESTPLOT.pdf',Zgas=False,logU=False,xid=0.3,nh=100,COratio=0.38,Mcutoff=100,\\n logx=True,logy=True,logp1=False,logp2=False,fixxrange=False,fixyrange=False,\\n showobs=None,noobserr=False,verbose=True):\\n NFalse = 0\\n freeparam = []\\n inforstr = \\\"\\\"\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n legenddic = {}\\n legenddic['Zgas'] = r'Z$_\\\\textrm{gas}$'\\n legenddic['logUs'] = r'log$_\\\\textrm{10}$(U)'\\n legenddic['xid'] = r'$\\\\xi_\\\\textrm{d}$'\\n legenddic['nh'] = r'n$_\\\\textrm{H}$ / [cm$^3$]'\\n legenddic['COCOsol'] = r'C/O / [C/O]$_\\\\textrm{sun}$'\\n legenddic['mup'] = r'M$_\\\\textrm{cut IMF}$ / [M$_\\\\textrm{sun}]$'\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not Zgas:\\n Zgasrange = [0.0,1.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' Zgas:vary, '\\n freeparam.append('Zgas')\\n else:\\n Zgasrange = [Zgas-1e-6,Zgas+1e-6]\\n inforstr = inforstr+' '+legenddic['Zgas']+'='+str(Zgas)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not logU:\\n logUrange = [-5.0,0.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' logU:vary, '\\n freeparam.append('logUs')\\n else:\\n logUrange = [logU-0.1,logU+0.1]\\n inforstr = inforstr+' '+legenddic['logUs']+'='+str(logU)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not xid:\\n xidrange = [0.0,0.6]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' xid:vary, '\\n freeparam.append('xid')\\n else:\\n xidrange = [xid-0.01,xid+0.01]\\n inforstr = inforstr+' '+legenddic['xid']+'='+str(xid)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not nh:\\n nhrange = [0.0,1.0e6]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' nH:vary, '\\n freeparam.append('nh')\\n else:\\n nhrange = [nh-1.0,nh+1.0]\\n inforstr = inforstr+' '+legenddic['nh']+'='+str(nh)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not COratio:\\n COratiorange = [0.0,2.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' C/O:vary, '\\n freeparam.append('COCOsol')\\n else:\\n COratiorange = [COratio-0.001,COratio+0.001]\\n inforstr = inforstr+' '+legenddic['COCOsol']+'='+str(COratio)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if not Mcutoff:\\n Mcutoffrange = [0.0,400.0]\\n NFalse = NFalse + 1.0\\n #inforstr = inforstr+' Mcutoff:vary, '\\n freeparam.append('mup')\\n else:\\n Mcutoffrange = [Mcutoff-1.0,Mcutoff+1.0]\\n inforstr = inforstr+' '+legenddic['mup']+'='+str(Mcutoff)+', '\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n\\n if NFalse != 2:\\n sys.exit(' Two and only two of the model parameters (Zgas,logU,xid,nh,COratio,Mcutoff) '\\n 'should be set to Flase to define the model grid; however it appears '+str(NFalse)+\\n ' parameters where not set')\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n goodent = np.where( (modeldata['Zgas'] > Zgasrange[0]) & (modeldata['Zgas'] < Zgasrange[1]) &\\n (modeldata['logUs'] > logUrange[0]) & (modeldata['logUs'] < logUrange[1]) &\\n (modeldata['xid'] > xidrange[0]) & (modeldata['xid'] < xidrange[1]) &\\n (modeldata['nh'] > nhrange[0]) & (modeldata['nh'] < nhrange[1]) &\\n (modeldata['COCOsol'] > COratiorange[0]) & (modeldata['COCOsol'] < COratiorange[1]) &\\n (modeldata['mup'] > Mcutoffrange[0]) & (modeldata['mup'] < Mcutoffrange[1]) )\\n\\n Ngoodent = len(goodent[0])\\n\\n if Ngoodent > 1:\\n if verbose: print(' - Getting data for '+str(Ngoodent)+' data points satisfying (SFR)model selection ')\\n param1_1 = modeldata[freeparam[0]][goodent]\\n if logp1:\\n param1_1 = np.log10(param1_1)\\n\\n param1_2 = modeldata[freeparam[1]][goodent]\\n if logp2:\\n param1_2 = np.log10(param1_2)\\n\\n ratio1_1 = modeldata[line1][goodent]/modeldata[line2][goodent]\\n ratio1_2 = modeldata[line3][goodent]/modeldata[line4][goodent]\\n else:\\n if verbose: print(' WARNING: Less than 2 (SFR)model grid points to plot; no output generated')\\n return\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - -\\n if modeldata2 != 'None':\\n goodent2 = np.where( (modeldata2['Zgas'] > Zgasrange[0]) & (modeldata2['Zgas'] < Zgasrange[1]) &\\n (modeldata2['logUs'] > logUrange[0]) & (modeldata2['logUs'] < logUrange[1]) &\\n (modeldata2['xid'] > xidrange[0]) & (modeldata2['xid'] < xidrange[1]) &\\n (modeldata2['nh'] > nhrange[0]) & (modeldata2['nh'] < nhrange[1]) )\\n\\n Ngoodent2 = len(goodent2[0])\\n\\n if Ngoodent > 1:\\n if verbose: print(' - Getting data for '+str(Ngoodent2)+' data points satisfying (AGN)model selection ')\\n param2_1 = modeldata2[freeparam[0]][goodent2]\\n if logp1:\\n param2_1 = np.log10(param2_1)\\n\\n param2_2 = modeldata2[freeparam[1]][goodent2]\\n if logp2:\\n param2_2 = np.log10(param2_2)\\n\\n l2s = ['x','x','x','x'] # line names to use for Feltre+16 file\\n for ll, linestr in enumerate([line1,line2,line3,line4]):\\n if '1908' in linestr:\\n l2 = linestr.replace('1908','1907')\\n else:\\n l2 = linestr\\n\\n l2s[ll] = l2\\n\\n ratio2_1 = modeldata2[l2s[0]][goodent2]/modeldata2[l2s[1]][goodent2]\\n ratio2_2 = modeldata2[l2s[2]][goodent2]/modeldata2[l2s[3]][goodent2]\\n else:\\n if verbose: print(' WARNING: Less than 2 (AGN)model grid points to plot; no output generated')\\n return\\n\\n # - - - - - - - - - - - PLOTTING - - - - - - - - - - -\\n if verbose: print(' - Setting up and generating plot')\\n plotname = plotname\\n fig = plt.figure(figsize=(9, 5))\\n fig.subplots_adjust(wspace=0.1, hspace=0.1,left=0.1, right=0.99, bottom=0.10, top=0.95)\\n Fsize = 10\\n lthick = 1\\n marksize = 3\\n plt.rc('text', usetex=True)\\n plt.rc('font', family='serif',size=Fsize)\\n plt.rc('xtick', labelsize=Fsize)\\n plt.rc('ytick', labelsize=Fsize)\\n plt.clf()\\n plt.ioff()\\n plt.title(inforstr[:-2],fontsize=Fsize)\\n\\n margin = 0.1\\n dx = np.abs(np.max(ratio1_1)-np.min(ratio1_1))\\n dy = np.abs(np.max(ratio1_2)-np.min(ratio1_2))\\n\\n\\n if fixxrange:\\n xrange = fixxrange\\n else:\\n if logx:\\n xrange = [np.min(ratio1_1)-np.min(ratio1_1)/2.,np.max(ratio1_1)+np.max(ratio1_1)/2.]\\n else:\\n xrange = [np.min(ratio1_1)-dx*margin,np.max(ratio1_1)+dx*margin]\\n\\n if fixyrange:\\n yrange = fixyrange\\n else:\\n if logy:\\n yrange = [np.min(ratio1_2)-np.min(ratio1_2)/2.,np.max(ratio1_2)+np.max(ratio1_2)/2.]\\n else:\\n yrange = [np.min(ratio1_2)-dy*margin,np.max(ratio1_2)+dy*margin]\\n\\n # ------------ PARAM1 ------------\\n cmap = plt.cm.get_cmap('winter')\\n cmin = np.min(param1_1)\\n cmax = np.max(param1_1)\\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\\n mm = plt.cm.ScalarMappable(cmap=cmap)\\n mm.set_array(cmaparr)\\n cb1 = plt.colorbar(mm)#shrink=0.25\\n\\n pstr1 = legenddic[freeparam[0]]\\n if logp1:\\n pstr1 = r'log$_\\\\textrm{10}$('+pstr1+')'\\n\\n cb1.set_label(pstr1+' (outer circle) - Fixed: black line')\\n\\n for p1 in np.unique(param1_1):\\n p1col = cmap(colnorm(p1))\\n p1ent = np.where(param1_1 == p1)\\n\\n plt.plot(ratio1_1[p1ent],ratio1_2[p1ent],'-',lw=lthick, color='k',zorder=1)\\n\\n plt.errorbar(ratio1_1[p1ent],ratio1_2[p1ent],xerr=None,yerr=None,\\n marker='o',lw=0, markersize=marksize*3,\\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\\n\\n if modeldata2 is not 'None':\\n p1ent = np.where(param2_1 == p1)\\n\\n plt.plot(ratio2_1[p1ent],ratio2_2[p1ent],'-',lw=lthick, color='k',zorder=1)\\n\\n plt.errorbar(ratio2_1[p1ent],ratio2_2[p1ent],xerr=None,yerr=None,\\n marker='D',lw=0, markersize=marksize*3,\\n markerfacecolor=p1col,ecolor=p1col,markeredgecolor = 'k',zorder=10)\\n\\n\\n # ------------ PARAM2 ------------\\n cmap = plt.cm.get_cmap('spring')\\n cmin = np.min(param1_2)\\n cmax = np.max(param1_2)\\n colnorm = matplotlib.colors.Normalize(vmin=cmin,vmax=cmax)\\n cmaparr = np.linspace(cmin, cmax, 30) #cmax-cmin)\\n mm = plt.cm.ScalarMappable(cmap=cmap)\\n mm.set_array(cmaparr)\\n cb2 = plt.colorbar(mm)#shrink=0.25\\n\\n pstr2 = legenddic[freeparam[1]]\\n if logp2:\\n pstr2 = 'log10('+pstr2+')'\\n\\n cb2.set_label(pstr2+' (inner circle) - Fixed: gray line')\\n\\n for p2 in np.unique(param1_2):\\n p2col = cmap(colnorm(p2))\\n p2ent = np.where(param1_2 == p2)\\n\\n plt.plot(ratio1_1[p2ent],ratio1_2[p2ent],'-',lw=lthick, color='gray',zorder=2)\\n\\n plt.errorbar(ratio1_1[p2ent],ratio1_2[p2ent],xerr=None,yerr=None,\\n marker='o',lw=0, markersize=marksize*1.5,\\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\\n\\n if modeldata2 is not 'None':\\n p2ent = np.where(param2_2 == p2)\\n\\n plt.plot(ratio2_1[p2ent],ratio2_2[p2ent],'-',lw=lthick, color='gray',zorder=2)\\n\\n plt.errorbar(ratio2_1[p2ent],ratio2_2[p2ent],xerr=None,yerr=None,\\n marker='D',lw=0, markersize=marksize*1.5,\\n markerfacecolor=p2col,ecolor=p2col,markeredgecolor = 'k',zorder=20)\\n\\n\\n if showobs != None:\\n for ii, objid in enumerate(showobs[:,0]):\\n if (showobs[:,7][ii] > xrange[0]) & (showobs[:,7][ii] < xrange[1]) & \\\\\\n (showobs[:,9][ii] > yrange[0]) & (showobs[:,9][ii] < yrange[1]):\\n\\n if noobserr:\\n obsxerr = None\\n obsyerr = None\\n else:\\n obsxerr = showobs[:,8][ii]\\n obsyerr = showobs[:,10][ii]\\n plt.errorbar(showobs[:,7][ii],showobs[:,9][ii],xerr=obsxerr,yerr=obsyerr,\\n marker='*',lw=lthick, markersize=marksize*2,\\n markerfacecolor='k',ecolor='k',markeredgecolor = 'k',zorder=30)\\n\\n plt.xlabel(line1+'/'+line2)\\n plt.ylabel(line3+'/'+line4)\\n\\n plt.xlim(xrange)\\n plt.ylim(yrange)\\n\\n if logx:\\n plt.xscale('log')\\n if logy:\\n plt.yscale('log')\\n\\n #--------- LEGEND ---------\\n # plt.errorbar(-1,-1,xerr=None,yerr=None,fmt='o',lw=lthick,ecolor='white', markersize=marksize*2,\\n # markerfacecolor='white',markeredgecolor = 'k',label='Ground-based spec')\\n #\\n # leg = plt.legend(fancybox=True, loc='upper center',prop={'size':Fsize},ncol=1,numpoints=1)\\n # #bbox_to_anchor=(1.25, 1.03)) # add the legend\\n # leg.get_frame().set_alpha(0.7)\\n #--------------------------\\n\\n if verbose: print(' Saving plot to'+plotname)\\n plt.savefig(plotname)\\n plt.clf()\\n plt.close('all')\",\n \"def main():\\n strikes, dips, normals, slip = generate_normal_ss_data(330, 60, n=500, porp=1)\\n #strikes, dips, normals, slip = generate_normal_data(330, 60, n=500, porp=10)\\n sigma = invert_plane_stress(normals, slip)\\n plot(sigma, strikes, dips)\\n plt.show()\",\n \"def EigenvalsPlot(self):\\n Hfv = sp.lambdify(('v','w'), self.H)\\n v_vals = np.arange(-1.5, 1.5+0.03 ,0.03)\\n w_vals = self.t + v_vals # case a \\n #w_vals = v_vals # case b \\n #w_vals = 2*self.t - v_vals # case c \\n #w_vals = -v_vals # case d \\n size_v = np.size(v_vals)\\n size_aut = self.H.shape[1]\\n graph = np.empty([size_v,size_aut]) \\n for i in range(0,size_v):\\n energies,vectors = la.eig(Hfv(v_vals[i],w_vals[i]))\\n energies = np.sort(np.real(energies))\\n graph[i] = energies\\n graph = np.insert(graph, [0], v_vals.reshape(-1,1), axis = 1) \\n graph = np.matrix(graph)\\n #with open(self.filetxt, \\\"w+\\\") as f:\\n # for line in graph:\\n # np.savetxt(f,line)\\n for i in range(0, size_aut):\\n plt.plot(v_vals,graph[:,i+1],'g')\\n plt.xlim(-1.5,0.5)\\n plt.ylim(-3,3)\\n plt.savefig(self.filePNG)\\n #plt.savefig(self.filePNG, format='eps', dpi=1200)\\n #plt.show()\",\n \"def varying_lamda(x, y, z, lambda_min, lambda_max, n_lambda, k, save_fig = None, method = 'Ridge', split = True, train = 0.7, seed = 42, max_iter = 1001, l_min = False, plot_indexes = [0,1,2]):\\n\\n lambdas = np.array([0] + np.logspace(lambda_min, lambda_max, n_lambda).tolist())\\n polynomials = np.array(k)\\n X, Y = np.meshgrid(lambdas, polynomials)\\n MSE = np.zeros(np.shape(X))\\n\\n j = 0\\n for k in polynomials:\\n print(k)\\n\\n model = regression(x, y, z, k = int(k), split = split, train = train, seed = seed)\\n if method == 'Ridge':\\n model.SVD()\\n i = 0\\n for lam in lambdas:\\n\\n if method == 'Ridge':\\n beta = model.Ridge(lam = lam)\\n elif method == 'Lasso':\\n beta = model.Lasso(lam = lam, max_iter = max_iter)\\n\\n z_tilde = model.z_tilde(beta = beta, X = model.X_test)\\n MSE[j, i] = model.MSE(z_tilde = z_tilde, z = model.z_test)\\n i += 1\\n j += 1\\n\\n print('Method = ', method)\\n lambdas_min = []\\n for i in range(len(polynomials)):\\n minimum_index = MSE[i].argmin()\\n print('Minimum lambda for polynomial %.i: ' %(polynomials[i]), lambdas[minimum_index], MSE[i].min())\\n lambdas_min.append(int(minimum_index))\\n\\n #plt.pcolormesh(lambdas.tolist() + [lambdas[-1] + lambdas[1]], polynomials.tolist() + [polynomials[-1] + 1], MSE)\\n #plt.colorbar()\\n #plt.show()\\n\\n plt.title('MSE for the test data with ' + method)\\n plt.contourf(lambdas, polynomials, MSE)\\n plt.colorbar()\\n plt.ylabel('Polynomial order', fontsize = 14)\\n plt.xlabel('Lambda', fontsize = 14)\\n try:\\n plt.savefig(results_dir + save_fig + 'contour' + '.png')\\n except:\\n pass\\n plt.show()\\n\\n plt.title('MSE for the test data with ' + method)\\n plt.plot(lambdas, MSE[plot_indexes[0], :], label = 'k = ' + str(polynomials[plot_indexes[0]]))\\n plt.plot(lambdas, MSE[plot_indexes[1], :], label = 'k = ' + str(polynomials[plot_indexes[1]]))\\n plt.plot(lambdas, MSE[plot_indexes[2], :], label = 'k = ' + str(polynomials[plot_indexes[2]]))\\n if l_min:\\n plt.plot(lambdas[lambdas_min[1]], MSE[1, lambdas_min[1]], 'ro', label = 'Lambda min = %.4g' %(lambdas[lambdas_min[1]]))\\n else:\\n pass\\n plt.legend()\\n plt.xlabel('Lambda', fontsize = 14)\\n plt.ylabel('MSE', fontsize = 14)\\n plt.tight_layout()\\n try:\\n plt.savefig(results_dir + save_fig + '.png')\\n except:\\n pass\\n plt.show()\\n return lambdas_min\",\n \"def main():\\n nbin = 60\\n nbinM = 100\\n \\n maxmag = -20\\n minmag = -29\\n mag = np.linspace(minmag,maxmag,nbinM) \\n minz = 0.1\\n maxz = 5\\n z = np.linspace(minz,maxz,nbin)\\n \\n minv = 10**1.6\\n maxv = 10**2.6\\n vel = np.linspace(minv,maxv,nbin)\\n \\n # writing the script for galfic once and for all\\n gl.write_script()\\n\\n result = [[] for i in range(5)]\\n \\n for zl in z:\\n zrange = z[np.where(z > zl)]\\n for zs in zrange:\\n for v in vel:\\n gl.write_initfile(v,zl,zs)\\n os.system('./script_gl > /dev/null 2>&1')\\n sigma = gl.analyse_output(mag,zs,zl,v)\\n for i in range(nbinM):\\n result[0].append(zs)\\n result[1].append(zl)\\n result[2].append(v)\\n result[3].append(mag[i])\\n result[4].append(sigma[i])\\n\\n np.savez('crosssection.npz',x=result)\",\n \"def test4():\\n\\t\\n\\tprint('This takes a while to compute - be patient!')\\n\\t\\n\\td = np.linspace(-15000,15000,300)\\n\\t#Voigt\\n\\t#p_dict = {'Bfield':700,'rb85frac':1,'Btheta':90*np.pi/180,'lcell':75e-3,'T':84,'Dline':'D2','Elem':'Cs'}\\n\\tp_dict = {'Bfield':1000,'rb85frac':1,'Btheta':88*np.pi/180,'Bphi':00*np.pi/180,'lcell':75e-3,'T':93,'Dline':'D2','Elem':'Cs'}\\n\\tpol = np.array([1.0,0.0,0.0])\\n\\tTVx = get_spectra(d,pol,p_dict,outputs=['I_M45','I_P45','Ix','Iy','S0','Iz'])\\n\\t\\n\\tfig2 = plt.figure()\\n\\tax1a = fig2.add_subplot(411)\\n\\tax2a = fig2.add_subplot(412,sharex=ax1a)\\n\\tax3a = fig2.add_subplot(413,sharex=ax1a)\\n\\tax4a = fig2.add_subplot(414,sharex=ax1a)\\n\\t\\n\\tax1a.plot(d,TVx[0],'r',lw=2,label=r'$I_{-45}$')\\n\\tax2a.plot(d,TVx[1],'b',lw=2,label=r'$I_{+45}$')\\n\\tax3a.plot(d,TVx[2],'r',lw=2,label=r'$I_x$')\\n\\tax4a.plot(d,TVx[3],'b',lw=2,label=r'$I_y$')\\n\\tax4a.plot(d,TVx[0]+TVx[1],'r:',lw=3.5,label=r'$I_{+45}+I_{-45}$')\\n\\tax4a.plot(d,TVx[2]+TVx[3],'k:',lw=2.5,label=r'$I_x + I_y$')\\n\\tax4a.plot(d,TVx[4],'g--',lw=1.5,label='$S_0$')\\n#\\tax4a.plot(d,TVx[5],'c--',lw=2.5,label='$I_z$')\\n\\t\\n\\t\\n\\tax4a.set_xlabel('Detuning (MHz)')\\n\\tax1a.set_ylabel('I -45')\\n\\tax2a.set_ylabel('I +45')\\n\\tax3a.set_ylabel('Ix')\\n\\tax4a.set_ylabel('Iy')\\n\\t\\n\\tax4a.set_xlim(d[0],d[-1]+3000)\\n\\tax4a.legend(loc=0)\\n\\t\\n\\tplt.show()\",\n \"def hexapodZernikeLinearModel():\\n Tfile='/home/jghao/research/decamFocus/psf_withseeing/finerGrid_coeff_matrix/zernike_coeff_finerGrid_training.cp'\\n b=p.load(open(Tfile))\\n nobs = len(b)\\n x = b[:,0]\\n y = b[:,1]\\n z = b[:,2]\\n theta = b[:,3]\\n phi = b[:,4]\\n fwhm = b[:,5]\\n e1 = b[:,6]\\n e2 = b[:,7]\\n thetax = theta*np.cos(np.deg2rad(phi))\\n thetay = theta*np.sin(np.deg2rad(phi))\\n \\n M22realTrefoil2 = b[:,37] # for x decenter\\n M22imagTrefoil1 = b[:,54] \\n M22TrefoilXshift = 0.5*(M22realTrefoil2+M22imagTrefoil1)\\n\\n M22realTrefoil1 = b[:,34] # for y decenter\\n M22imagTrefoil2 = b[:,57] \\n M22TrefoilYshift = 0.5*(M22realTrefoil1 - M22imagTrefoil2)\\n\\n M20defocus = b[:,12] # for defocus\\n\\n M22realComa2 = b[:,36] # for x-tilt\\n M22imagComa1 = b[:,55]\\n M22ComaXtilt = 0.5*(M22realComa2+M22imagComa1)\\n\\n M22realComa1 = b[:,35] # for y-tilt\\n M22imagComa2 = b[:,56]\\n M22ComaYtilt = 0.5*(M22realComa1 - M22imagComa2)\\n \\n pl.figure(figsize=(21,12))\\n pl.subplot(2,3,1)\\n t=bp.bin_scatter(M22TrefoilXshift,x,nbins=20,fmt='bo',scatter=True)\\n res = linefit(M22TrefoilXshift,x)\\n pl.plot(M22TrefoilXshift,M22TrefoilXshift*res[1]+res[0],'r,')\\n pl.ylabel('x-decenter')\\n pl.xlabel('(M22realTrefoil2+M22imagTrefoil1)/2')\\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\\n pl.subplot(2,3,2)\\n t=bp.bin_scatter(M22TrefoilYshift,y,nbins=20,fmt='bo',scatter=True)\\n res = linefit(M22TrefoilYshift,y)\\n pl.plot(M22TrefoilYshift,M22TrefoilYshift*res[1]+res[0],'r,')\\n pl.ylabel('y-decenter')\\n pl.xlabel('(M22realTrefoil1 - M22imagTrefoil2)/2')\\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\\n pl.subplot(2,3,3)\\n t=bp.bin_scatter(M20defocus,z,nbins=20,fmt='bo',scatter=True)\\n res = linefit(M20defocus,z)\\n pl.plot(M20defocus,M20defocus*res[1]+res[0],'r,')\\n pl.ylabel('z-defocus')\\n pl.xlabel('M20defocus')\\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\\n pl.subplot(2,3,4)\\n t=bp.bin_scatter(M22ComaXtilt,thetax,nbins=20,fmt='bo',scatter=True)\\n res = linefit(M22ComaXtilt,thetax)\\n pl.plot(M22ComaXtilt,M22ComaXtilt*res[1]+res[0],'r,')\\n pl.ylabel('x-tilt')\\n pl.xlabel('(M22realComa2+M22imagComa1)/2')\\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\\n pl.subplot(2,3,5)\\n t=bp.bin_scatter(M22ComaYtilt,thetay,nbins=20,fmt='bo',scatter=True)\\n res = linefit(M22ComaYtilt,thetay)\\n pl.plot(M22ComaYtilt,M22ComaYtilt*res[1]+res[0],'r,')\\n pl.ylabel('y-tilt')\\n pl.xlabel('(M22realComa1 - M22imagComa2)/2')\\n pl.title('slope: '+str(round(res[1],4))+' Intercept: '+str(round(res[0],4)))\\n\\n pl.close()\",\n \"def fig_2_11V2(x, y, z, first_poly = 4, complexity = 10, N = 7, method = 'OLS', seed = 42, lam = 0, folds = 5, save_fig = ''):\\n errors = np.zeros((4, complexity + 1))\\n bias = np.zeros(complexity + 1)\\n variance = np.zeros(complexity + 1)\\n z_real = FrankeFunction(x, y)\\n\\n complx = np.arange(first_poly, first_poly + complexity + 1, 1)\\n\\n MSE = np.zeros(complexity + 1)\\n\\n for i in range(complexity + 1):\\n print(i)\\n model = regression(x, y, z, k = first_poly + i, split = False, seed = seed)\\n\\n for j in range(N):\\n _, MSE_R2D2, _, _, _, _ = model.k_cross(fold = folds, method2 = method, lam = lam, random_num = True)\\n errors[:, i] += np.mean(MSE_R2D2, axis = 0)\\n\\n errors /= N\\n\\n print(errors)\\n\\n\\n plt.title(method + ' Test vs Train error in k-fold with ' + str(folds) + '-folds')\\n plt.plot(complx, errors[0], 'go--', label = 'Test', color = 'blue')\\n plt.plot(complx, errors[2], 'go--', label = 'Training', color = 'red')\\n #plt.ylim([np.min(errors_R2[2]*1.2), np.max(errors_R2[0]*1.2)])\\n plt.legend()\\n plt.xlabel('Polynomial maximum order', fontsize = 14)\\n plt.ylabel('MSE', fontsize = 14)\\n plt.savefig(results_dir + 'tradeoff2MSE' + method + save_fig + '.png')\\n\\n plt.show()\\n\\n plt.title(method + ' Test vs Train error in k-fold with ' + str(folds) + '-folds')\\n plt.xlabel('Polynomial maximum order', fontsize = 14)\\n plt.ylabel('R2', fontsize = 14)\\n plt.plot(complx, errors[1], 'go--', label = 'Test', color = 'blue')\\n plt.plot(complx, errors[3], 'go--', label = 'Training', color = 'red')\\n #plt.ylim([np.min(errors_R2[3]*1.2), np.max(errors_R2[1]*1.2)])\\n plt.legend()\\n plt.tight_layout()\\n plt.savefig(results_dir + 'tradeoff2R2' + method + save_fig + '.png')\\n plt.show()\",\n \"def create_lagrit_scripts(visual_mode, ncpu, refine_factor=1, production_mode=True):\\n\\n # Section 2 : Creates LaGriT script to be run for each polygon\\n # Switches to control the LaGriT output\\n # Network visualization mode True ouputs the triangulated mesh\\n # for each fracture without any refinement. The goal is to visualize\\n # the network structure instead of outputing the appropriate values\\n # for computation\\n\\n print(\\\"Writing LaGriT Control Files\\\")\\n # Go through the list and write out parameter file for each polygon\\n # to be an input file for LaGriT\\n\\n lagrit_input = \\\"\\\"\\\"infile %s \\n#LaGriT Script\\n# Name the input files that contain the polygons \\n# and lines of intersection. \\n\\ndefine / POLY_FILE / %s \\ndefine / LINE_FILE / %s \\ndefine / OUTPUT_INTER_ID_SSINT / id_tri_node_CPU%d.list\\n\\n# Define parameters such as: \\n# length scale to refine triangular mesh \\n# purturbation distance to break symmetry of refined mesh# \\n\\n# Read in line and polygon files \\nread / POLY_FILE / mo_poly_work\\n\\\"\\\"\\\"\\n if not visual_mode:\\n lagrit_input += \\\"\\\"\\\"\\nread / LINE_FILE / mo_line_work \\n\\\"\\\"\\\"\\n #\\n # START: Refine the point distribution\\n #\\n if (refine_factor > 1):\\n lagrit_input += 'extrude / mo_quad_work / mo_line_work / const / H_SCALE8 / volume / 0. 0. 1. \\\\n'\\n if (refine_factor == 2):\\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\\\n'\\n\\n if (refine_factor == 4):\\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\\\n'\\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\\\n'\\n\\n if (refine_factor == 8):\\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\\\n'\\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\\\n'\\n lagrit_input += 'refine/constant/imt1/linear/element/1 0 0 /-1.,0.,0./inclusive amr 2 \\\\n'\\n\\n lagrit_input += \\\"\\\"\\\" \\ngrid2grid / tree_to_fe / mo_quad_work / mo_quad_work \\nextract/surfmesh/1,0,0/mo_ext_work/mo_quad_work/external \\ncompute / distance_field / mo_ext_work / mo_line_work / dfield \\npset / pdel_work / attribute / dfield / 1 0 0 / H_SCALE3 / gt \\nrmpoint / pset get pdel_work / inclusive \\nrmpoint / compress \\n\\ncmo / delete / mo_quad_work \\ncmo / delete / mo_line_work\\ncmo / move / mo_line_work / mo_ext_work \\nrmpoint / compress \\n\\\"\\\"\\\"\\n # END: Refine the point distribution\\n #\\n lagrit_input += \\\"\\\"\\\"\\n## Triangulate Fracture without point addition \\ncmo / create / mo_pts / / / triplane \\ncopypts / mo_pts / mo_poly_work \\ncmo / select / mo_pts \\ntriangulate / counterclockwise \\n\\ncmo / setatt / mo_pts / imt / 1 0 0 / ID \\ncmo / setatt / mo_pts / itetclr / 1 0 0 / ID \\nresetpts / itp \\ncmo / delete / mo_poly_work \\ncmo / select / mo_pts \\n\\n\\\"\\\"\\\"\\n if not visual_mode:\\n lagrit_input += \\\"\\\"\\\"\\n# Creates a Coarse Mesh and then refines it using the distance field from intersections\\nmassage / H_SCALE64 / H_EPS / H_EPS\\nrecon 0; smooth;recon 0;smooth;recon 0;smooth;recon 0\\nresetpts / itp\\npset / p_move / attribute / itp / 1 0 0 / 0 / eq\\nperturb / pset get p_move / PURTURB64 PURTURB64 0.0\\nrecon 0; smooth;recon 0;smooth;recon 0;smooth;recon 0\\nsmooth;recon 0;smooth;recon 0;smooth;recon 0\\n\\nmassage / H_SCALE32 / H_EPS / H_EPS\\nresetpts / itp\\npset / p_move / attribute / itp / 1 0 0 / 0 / eq\\nperturb / pset get p_move / PURTURB32 PURTURB32 0.0\\nrecon 0; smooth;recon 0;smooth;recon 0;smooth;recon 0\\nsmooth;recon 0;smooth;recon 0;smooth;recon 0\\n\\nmassage / H_SCALE16 / H_EPS / H_EPS\\nresetpts / itp\\npset / p_move / attribute / itp / 1 0 0 / 0 / eq\\nperturb / pset get p_move / PURTURB16 PURTURB16 0.0\\nrecon 0; smooth;recon 0;smooth;recon 0;smooth;recon 0\\nsmooth;recon 0;smooth;recon 0;smooth;recon 0\\n\\nmassage / H_SCALE8 / H_EPS / H_EPS\\nresetpts / itp\\npset / p_move / attribute / itp / 1 0 0 / 0 / eq\\nperturb / pset get p_move / PURTURB8 PURTURB8 0.0\\nrecon 0; smooth;recon 0;smooth;recon 0;smooth;recon 0\\nsmooth;recon 0;smooth;recon 0;smooth;recon 0\\n\\ncmo/addatt/ mo_pts /x_four/vdouble/scalar/nnodes \\ncmo/addatt/ mo_pts /fac_n/vdouble/scalar/nnodes \\n\\n# Massage points based on linear function down to h_prime\\nmassage2/user_function2.lgi/H_PRIME/fac_n/1.e-5/1.e-5/1 0 0/strictmergelength \\n\\nassign///maxiter_sm/1 \\nsmooth;recon 0;smooth;recon 0;smooth;recon 0\\n\\nassign///maxiter_sm/10\\n\\nmassage2/user_function.lgi/H_PRIME/fac_n/1.e-5/1.e-5/1 0 0/strictmergelength \\ncmo / DELATT / mo_pts / rf_field_name \\n\\n# Extrude and excavate the lines of intersection\\ncmo / select / mo_line_work \\n\\nextrude / mo_quad / mo_line_work / const / H_EXTRUDE / volume / 0. 0. 1. \\n\\\"\\\"\\\"\\n if not production_mode:\\n lagrit_input += \\\"\\\"\\\"\\ndump / avs / QUAD_FILE / mo_quad \\ncmo / delete / mo_quad \\nread / QUAD_FILE / mo_quad \\n\\\"\\\"\\\"\\n else:\\n lagrit_input += 'cmo / select / mo_quad \\\\n'\\n\\n lagrit_input += \\\"\\\"\\\"\\n# Translate extruced lines of intersectino down slightly to excavate \\n# nearby points from the mesh \\n\\ntrans / 1 0 0 / 0. 0. 0. / 0. 0. H_TRANS\\nhextotet / 2 / mo_tri / mo_quad \\ncmo / delete / mo_quad \\naddmesh / excavate / mo_excavate / mo_pts / mo_tri\\n\\n##### DEBUG #####\\n# If meshing fails, uncomment and rerun the script to get tmp meshes, \\n# which are otherwise not output \\n#dump / avs2 / tmp_tri.inp / mo_tri / 1 1 1 0\\n#dump / avs2 / tmp_pts.inp / mo_pts / 1 1 1 0\\n#dump / avs2 / tmp_excavate.inp / mo_excavate / 1 1 1 0\\n#finish\\n#####\\n \\ncmo / delete / mo_tri \\ncmo / delete / mo_pts \\n\\n# recompute dfield \\ncmo / create / mo_final / / / triplane \\ncopypts / mo_final / mo_excavate \\ncompute / distance_field / mo_final / mo_line_work / dfield \\ncmo / printatt / mo_final / dfield / minmax \\npset / pdel / attribute dfield / 1,0,0 / lt H_PRIME2 \\nrmpoint / pset,get,pdel / inclusive \\nrmpoint / compress \\n\\ncopypts / mo_final / mo_line_work \\n\\ncmo / select / mo_final \\ncmo / setatt / mo_final / imt / 1 0 0 / ID \\ncmo / setatt / mo_final / itp / 1 0 0 / 0 \\ncmo / setatt / mo_final / itetclr / 1 0 0 / ID \\n# cmo / printatt / mo_final / -xyz- / minmax \\ntrans/ 1 0 0 / zero / xyz \\ncmo / setatt / mo_final / zic / 1 0 0 / 0.0 \\ncmo / printatt / mo_final / -xyz- / minmax \\nconnect \\n\\ntrans / 1 0 0 / original / xyz \\ncmo / printatt / mo_final / -xyz- / minmax \\n\\n#cmo / delete / mo_line_work \\ncmo / delete / mo_excavate\\ncmo / select / mo_final \\nresetpts / itp \\n\\n\\\"\\\"\\\"\\n if not production_mode:\\n lagrit_input += 'dump / gmv / PRE_FINAL_MASSAGE / mo_final \\\\n'\\n\\n lagrit_input += \\\"\\\"\\\"\\n## Massage Mesh Away from Intersection \\npset / pref / attribute / dfield / 1,0,0 / lt / H_EPS \\npset / pregion / attribute / dfield / 1,0,0 / gt / H_SCALE2 \\npset / pboundary / attribute / itp / 1,0,0 / eq / 10 \\npset / psmooth / not / pregion pref pboundary \\n#massage / H_SCALE / 1.e-5 / 1.e-5 / pset get pref / & \\n#nosmooth / strictmergelenth\\n\\nassign///maxiter_sm/1 \\n\\nsmooth / position / esug / pset get psmooth; recon 0; \\nsmooth / position / esug / pset get psmooth; recon 0; \\nsmooth / position / esug / pset get psmooth; recon 0; \\n\\nassign///maxiter_sm/10\\n###########################################\\n# nodes for Intersection / Mesh Connectivity Check \\ncmo / copy / mo_final_check / mo_final\\n#\\n# Define variables that are hard wired for this part of the workflow\\ndefine / MO_TRI_MESH_SSINT / mo_tri_tmp_subset\\ndefine / MO_LINE_MESH_SSINT / mo_line_tmp_subset\\ndefine / ATT_ID_INTERSECTION_SSINT / b_a\\ndefine / ATT_ID_SOURCE_SSINT / id_node_global\\ndefine / ATT_ID_SINK_SSINT / id_node_tri\\n#\\n# Before subsetting the mesh reate a node attribute containing the integer global node number\\ncmo / set_id / mo_final_check / node / ATT_ID_SOURCE_SSINT\\n#\\n# Subset the triangle mesh based on b_a node attribute ne 0\\n#\\ncmo / select / mo_final_check\\npset / pkeep / attribute / ATT_ID_INTERSECTION_SSINT / 1 0 0 / ne / 0\\npset / pall / seq / 1 0 0\\npset / pdel / not pall pkeep\\nrmpoint / pset get pdel / exclusive\\nrmpoint / compress\\n#\\n# Create an integer node attribute in the line mesh to interpolate the triangle node number onto\\n# \\ncmo / addatt / mo_line_work / ATT_ID_SINK_SSINT / vint / scalar / nnodes\\ninterpolate / voronoi / mo_line_work ATT_ID_SINK_SSINT / 1 0 0 / &\\n mo_final_check ATT_ID_SOURCE_SSINT\\n#\\n# Supress AVS output of a bunch of node attributes\\n#\\ncmo / modatt / mo_line_work / imt / ioflag / l\\ncmo / modatt / mo_line_work / itp / ioflag / l\\ncmo / modatt / mo_line_work / isn / ioflag / l\\ncmo / modatt / mo_line_work / icr / ioflag / l\\ncmo / modatt / mo_line_work / a_b / ioflag / l\\ncmo / modatt / mo_line_work / b_a / ioflag / l\\n#\\n# Output list of intersection nodes with the corrosponding node id number from the triangle mesh\\n\\ndump / avs2 / OUTPUT_INTER_ID_SSINT / mo_line_work / 0 0 2 0\\ncmo / delete / mo_line_work\\n\\ncmo / delete / mo_final_check\\n# nodes for intersection check over\\n\\ncmo / select / mo_final \\n\\n##### DEBUG\\n# write out mesh before it is rotate back into its final location\\n# Useful to compare with meshing workflow if something crashes\\n#dump / avs2 / tmp_mesh_2D.inp / mo_final / 1 1 1 0 \\n##### DEBUG\\n# Rotate facture back into original plane \\nrotateln / 1 0 0 / nocopy / X1, Y1, Z1 / X2, Y2, Z2 / THETA / 0.,0.,0.,/ \\ncmo / printatt / mo_final / -xyz- / minmax \\nrecon 1 \\n\\nresetpts / itp \\n\\ncmo / addatt / mo_final / unit_area_normal / xyz / vnorm \\ncmo / addatt / mo_final / scalar / xnorm ynorm znorm / vnorm \\ncmo / DELATT / mo_final / vnorm \\n\\n\\\"\\\"\\\"\\n # Clean up before output to GMV/AVS\\n if production_mode:\\n lagrit_input += \\\"\\\"\\\"\\ncmo / DELATT / mo_final / x_four \\ncmo / DELATT / mo_final / fac_n \\ncmo / DELATT / mo_final / rf_field_name \\ncmo / DELATT / mo_final / xnorm \\ncmo / DELATT / mo_final / ynorm \\ncmo / DELATT / mo_final / znorm \\ncmo / DELATT / mo_final / a_b \\ncmo / setatt / mo_final / ipolydat / no \\ncmo / modatt / mo_final / icr1 / ioflag / l \\ncmo / modatt / mo_final / isn1 / ioflag / l \\n \\n# Create Family element set\\ncmo / addatt / mo_final / family_id / vint / scalar / nelements \\ncmo / setatt / mo_final / family_id / 1 0 0 / family\\n \\n\\\"\\\"\\\"\\n lagrit_input += \\\"\\\"\\\"\\ndump / OUTFILE_AVS / mo_final\\ndump / lagrit / OUTFILE_LG / mo_final\\n\\\"\\\"\\\"\\n else:\\n lagrit_input += \\\"\\\"\\\"\\ncmo / setatt / mo_pts / imt / 1 0 0 / ID \\ncmo / setatt / mo_pts / itetclr / 1 0 0 / ID \\nresetpts / itp \\n\\ncmo / setatt / mo_line_work / imt / 1 0 0 / ID \\ncmo / setatt / mo_line_work / itetclr / 1 0 0 / ID\\n\\naddmesh / merge / mo_final / mo_pts / mo_line_work \\ncmo / delete / mo_pts \\ncmo / delete / mo_line_work \\n \\n# Create Family element set\\ncmo / addatt / mo_final / family_id / vint / scalar / nelements \\ncmo / setatt / mo_final / family_id / 1 0 0 / family\\n\\ncmo / select / mo_final \\n# Rotate \\nrotateln / 1 0 0 / nocopy / X1, Y1, Z1 / X2, Y2, Z2 / THETA / 0.,0.,0.,/ \\n\\ncmo / printatt / mo_final / -xyz- / minmax \\ncmo / modatt / mo_final / icr1 / ioflag / l \\ncmo / modatt / mo_final / isn1 / ioflag / l\\ndump / lagrit / OUTFILE_LG / mo_final \\n\\\"\\\"\\\"\\n\\n lagrit_input += \\\"\\\"\\\"\\nquality \\ncmo / delete / mo_final \\ncmo / status / brief \\nfinish\\n\\\"\\\"\\\"\\n\\n # Create a different Run file for each CPU\\n for i in range(1, ncpu + 1):\\n file_name = 'mesh_poly_CPU%d.lgi' % i\\n f = open(file_name, 'w')\\n # Name of parameter Input File\\n fparameter_name = 'parameters_CPU%d.mlgi' % i\\n fintersection_name = 'intersections_CPU%d.inp' % i\\n fpoly_name = 'poly_CPU%d.inp' % i\\n parameters = (fparameter_name, fpoly_name, fintersection_name, i)\\n f.write(lagrit_input % parameters)\\n f.flush()\\n f.close()\\n print('Writing LaGriT Control Files: Complete')\",\n \"def main():\\n error_LU, h_LU = read_2_col_file(\\\"error_LU_data.txt\\\") \\n error, h = read_2_col_file(\\\"error_data.txt\\\")\\n \\n log_h_LU = np.log10(h_LU)\\n log_error_LU = np.log10(error_LU)\\n log_h = np.log10(h)\\n log_error = np.log10(error)\\n\\n plt.plot(log_h_LU, log_error_LU, label=\\\"LU-decomposition\\\")\\n plt.plot(log_h, log_error, label=\\\"general Thomas algo.\\\")\\n plt.legend()\\n plt.title(\\\"Logarithmic plot of relative error \\\")\\n plt.xlabel(\\\"$log_{10}(time \\\\, step)$\\\")\\n plt.ylabel(\\\"$log_{10}(error)$\\\")\\n plt.show()\",\n \"def gVI(g,rBC,lBC,time,npts):\\n #Important coeffcients\\n global gamma\\n gamma = g\\n global alpha\\n alpha = (gamma+1)/(gamma-1)\\n global beta\\n beta = (2*gamma)/(gamma-1)\\n global epsilon\\n epsilon = (2*gamma)/(gamma+1)\\n #Boundary conditions\\n global lbc\\n lbc = lBC\\n global rbc\\n rbc = rBC\\n #Time\\n global t\\n t = time\\n #points\\n global numPts\\n numPts = npts\\n #Speed of sound for states 1 and 5\\n global cL\\n cL = np.sqrt(gamma*lbc[0]/lbc[1])\\n global cR\\n cR = np.sqrt(gamma*rbc[0]/rbc[1])\",\n \"def spore_integrals():\\n\\n path_tops = os.path.join(path_to_here, '../data/landscape_visualizations/DMSO/{}/{}_hours')\\n idx_epochs = [25, 30]\\n repeat_names = ['original', 'repeat_a', 'repeat_b']\\n pList_data = os.path.join(path_to_here, '../data/embeddings/lims13/lims13_DMSO.pickle')\\n pList_data = _load_and_resize_list(pList_data)\\n\\n\\n def _plot_from_idx_repeat(ax, repeat_name, idx_epoch, color, linewidth = 0.6, linestyle = '-'):\\n if os.path.isfile(path_tops.format(repeat_name, idx_epoch) + '/p_list_0.pickle'):\\n p_list_NN = _load_and_resize_list(path_tops.format(repeat_name, idx_epoch) + '/p_list_0.pickle')\\n NN_spore_integrals = [np.sum(i[427:442, 182:196])*(26**2)/(1000**2) for i in p_list_NN]\\n ax.plot([1, 2, 3, 4, 5, 6, 7, 8], NN_spore_integrals[1:], c = color, linewidth = linewidth)\\n\\n\\n data_spore_integrals = [np.sum(i[427:442, 182:196])*(26**2)/(1000**2) for i in pList_data]\\n\\n fig = plt.figure(figsize = (2.3, 1.5))\\n for i, idx_epoch in enumerate(idx_epochs):\\n ax = fig.add_subplot(len(idx_epochs), 1, i+1)\\n ax.plot([1, 2, 3, 4, 5, 6, 7, 8], data_spore_integrals[1:], linewidth = 1, c = 'red')\\n if idx_epoch is not idx_epochs[-1]:\\n ax.set_xticks([])\\n for repeat_name, color in zip(repeat_names, ['blue', 'green', 'orange']):\\n _plot_from_idx_repeat(ax = ax, repeat_name = repeat_name, idx_epoch = idx_epoch, color = color)\\n if idx_epoch == idx_epochs[-1]:\\n _plot_from_idx_repeat(ax = ax, repeat_name = 'original', idx_epoch = 60, color = 'black', linewidth = 1, linestyle = '--')\\n\\n ax.set_ylabel(r'$\\\\approx \\\\int_{spore} \\\\hat{p}(\\\\mathbf{x},t)$', fontsize = 6, labelpad = 1)\\n ax.set_xlabel('Snapshot', fontsize = 6, labelpad = 1)\\n for ax in fig.axes:\\n ax.tick_params(axis = 'both', labelsize = 6)\\n\\n plt.subplots_adjust(hspace = 0)\\n plt.tight_layout()\\n plt.savefig(path_to_here+'/../outputs/'+'spore_integrals.png', dpi = 1200)\",\n \"def main():\\n\\n # choose number of data-points and sample a pair of vectors: the input\\n # values and the corresponding target values\\n N = 500\\n inputs, targets = sample_data(N, arbitrary_function_2, seed=1)\\n\\n # specify the centres and scale of some rbf basis functions\\n default_centres = np.linspace(0,1,21)\\n default_scale = 0.03\\n default_reg_param = 0.08\\n\\n # get the cross-validation folds\\n num_folds = 4\\n folds = create_cv_folds(N, num_folds)\\n\\n # evaluate then plot the performance of different reg params\\n evaluate_reg_param(inputs, targets, folds, default_centres, default_scale)\\n # evaluate then plot the performance of different scales\\n evaluate_scale(inputs, targets, folds, default_centres, default_reg_param)\\n # evaluate then plot the performance of different numbers of basis\\n # function centres.\\n evaluate_num_centres(\\n inputs, targets, folds, default_scale, default_reg_param)\\n\\n plt.show()\",\n \"def visualize(I, f, c, bc, Lx, Ly, nx, ny, dt, tstop,\\n zmin, zmax, plot=1, version='scalar',\\n implementation=\\\\\\n {'ic': 'scalar', 'inner': 'scalar', 'bc': 'scalar'}):\\n class Visualizer:\\n \\\"\\\"\\\"Action function used by solver.\\\"\\\"\\\"\\n def __init__(self, plot=0):\\n self.plot = plot\\n if self.plot:\\n self.g = Gnuplot.Gnuplot(persist=1)\\n self.g('set parametric')\\n self.g('set data style lines')\\n self.g('set hidden')\\n self.g('set contour base')\\n self.g('set zrange [%g:%g]' % (zmin,zmax))\\n\\n def __call__(self, u, xv, yv, t):\\n if self.plot:\\n data = Gnuplot.GridData(u, xv[:,0], yv[0,:], binary=0)\\n self.g.splot(data)\\n self.g('set title \\\"t=%g\\\"' % t)\\n if self.plot == 2:\\n self.g.hardcopy(filename='tmp_%020f.ps' % t,\\n enhanced=1, mode='eps', fontsize=14,\\n color=0, fontname='Times-Roman')\\n time.sleep(0.8) # pause to finish plot\\n\\n viz = Visualizer(plot)\\n dt, cpu_ic, cpu_inner, cpu_bc = \\\\\\n solver(I, f, c, bc, Lx, Ly, nx, ny, dt, tstop,\\n user_action=viz, implementation=implementation)\\n time.sleep(3)\",\n \"def inactivation(self):\\n\\n def boltzmann(voltage, vhalf, k):\\n\\n voltage = list(map(lambda x: float(x), voltage))\\n exponent = np.exp(np.divide(np.subtract(voltage, vhalf), k))\\n return (1/ (exponent + 1))\\n\\n def find_v_half(current, voltage):\\n \\\"\\\"\\\"\\n current: array of normalized IV curve\\n voltage: array of the test potentials\\n \\\"\\\"\\\"\\n for i in range(len(voltage)):\\n if current.iloc[i] < 0.5:\\n continue\\n elif current.iloc[i] == 0.5:\\n return voltage.iloc[i]\\n else:\\n volt1 = float(voltage.iloc[i-1])\\n volt2 = float(voltage.iloc[i])\\n curr1 = float(current.iloc[i-1])\\n curr2 = float(current.iloc[i])\\n V_half = volt1 + (volt1 - volt2)*(0.5 - curr1)/(curr1 - curr2)\\n return V_half\\n\\n v_half = []\\n for i in range(self.n_cols):\\n current = self.screened_data.iloc[:,i]\\n #print(current)\\n v_half.append(find_v_half(current, self.xaxis))\\n print('Median: ', np.median(v_half))\\n print('Min: ', np.min(v_half))\\n print('Max: ', np.max(v_half))\\n return 0\\n\\n initial_guess = [-42, 7]\\n popt, pcov = curve_fit(boltzmann, self.xaxis, self.averaged_data, p0= initial_guess)\\n plt.plot(self.xaxis, self.averaged_data, label = 'Average of all models')\\n plt.plot(boltzmann(self.xaxis, *initial_guess), label = 'Initial Guess')\\n plt.plot(self.xaxis, boltzmann(self.xaxis, *popt), label = 'Best Fit')\\n plt.xlabel('Voltage (mV)')\\n plt.ylabel('Normalized Current')\\n plt.title('Steady State Inactivation')\\n plt.legend()\\n plt.savefig('inactivation_boltzmann_fit.png')\\n return popt[0]\",\n \"def convergence():\\n fig, axes = plt.subplots(nrows=2, figsize=figsize(aspect=1.2))\\n\\n # label names\\n label1 = str(league.lambda1)\\n label2_list = [str(lambda2) for lambda2 in league.lambda2_list]\\n\\n # point spread and point total subplots\\n subplots = [\\n (False, [-0.5, 0.5], league.spreads, 'probability spread > 0.5'),\\n (True, [200.5], league.totals, 'probability total > 200.5'),\\n ]\\n\\n for ax, (commutes, lines, values, ylabel) in zip(axes, subplots):\\n\\n # train margin-dependent Elo model\\n melo = Melo(lines=lines, commutes=commutes, k=1e-4)\\n melo.fit(league.times, league.labels1, league.labels2, values)\\n\\n line = lines[-1]\\n\\n for label2 in label2_list:\\n\\n # evaluation times and labels\\n times = np.arange(league.times.size)[::1000]\\n labels1 = times.size * [label1]\\n labels2 = times.size * [label2]\\n\\n # observed win probability\\n prob = melo.probability(times, labels1, labels2, lines=line)\\n ax.plot(times, prob)\\n\\n # true (analytic) win probability\\n if ax.is_first_row():\\n prob = skellam.sf(line, int(label1), int(label2))\\n ax.axhline(prob, color='k')\\n else:\\n prob = poisson.sf(line, int(label1) + int(label2))\\n ax.axhline(prob, color='k')\\n\\n # axes labels\\n if ax.is_last_row():\\n ax.set_xlabel('Iterations')\\n ax.set_ylabel(ylabel)\\n\\n set_tight(w_pad=.5)\",\n \"def graphe_gains(res_alea, res_glou, res_glou_e, res_ucb):\\n T = np.arange(res_alea.size)\\n fig, ax = plt.subplots()\\n ax.grid(True)\\n plt.xlabel(\\\"T\\\")\\n plt.ylabel(\\\"Gain\\\")\\n \\n ax.plot(T, res_alea, label = 'aléatoire') \\n ax.plot(T, res_glou, label = 'glouton')\\n ax.plot(T, res_glou_e, label = 'e-glouton')\\n ax.plot(T, res_ucb, label = 'UCB')\\n ax.legend(loc = \\\"upper left\\\")\\n plt.title(\\\"Gains des 4 algorithmes par rapport à T\\\")\",\n \"def display4(*args):\\n #-------------------- unpack\\n twiss_func = args[0]\\n cos_like = args[1]\\n sin_like = args[2]\\n lat_plot = args[3]\\n #-------------------- beta x,y & dispersion x\\n s = [twiss_func(i,'s') for i in range(twiss_func.nbpoints)] # Abszisse\\n bx = [twiss_func(i,'bx') for i in range(twiss_func.nbpoints)] # beta x\\n by = [twiss_func(i,'by') for i in range(twiss_func.nbpoints)] # beta y\\n dx = [twiss_func(i,'dx') for i in range(twiss_func.nbpoints)] # dispersion x\\n#-------------------- longitudinal trajectories\\n z1= [cos_like(i,'s') for i in range(cos_like.nbpoints)]\\n cz= [cos_like(i,'cz') for i in range(cos_like.nbpoints)]\\n cdp= [cos_like(i,'cdp') for i in range(cos_like.nbpoints)]\\n\\n z2= [sin_like(i,'s') for i in range(sin_like.nbpoints)]\\n sz= [sin_like(i,'sz') for i in range(sin_like.nbpoints)]\\n sdp= [sin_like(i,'sdp') for i in range(sin_like.nbpoints)]\\n #-------------------- lattice viseo\\n vzero = [0. for i in range(lat_plot.nbpoints)] # zero line\\n vis_abszisse = [lat_plot(i,'s') for i in range(lat_plot.nbpoints)]\\n vis_ordinate = [lat_plot(i,'viseo') for i in range(lat_plot.nbpoints)]\\n #-------------------- figure frame\\n width=14; height=7.6\\n # fighdr = 'lattice version = {}, input file = {}'.format(PARAMS['lattice_version'],PARAMS['input_file'])\\n fig = plt.figure(num=1,figsize=(width,height),facecolor='#eaecef',tight_layout=False)\\n\\n #-------------------- beta functions\\n splot211=plt.subplot(211)\\n splot211.set_title('beta x,y')\\n # mapping box\\n splot211.text(0.01, 1.1, UTIL.FLAGS.get('mapping'),transform=splot211.transAxes,fontsize=8,bbox=dict(boxstyle='round',facecolor='wheat',alpha=0.5),verticalalignment='top')\\n # function plots\\n plt.plot(s,bx, label=r\\\"$\\\\beta$x [m]\\\", color='black', linestyle='-')\\n plt.plot(s,by, label=r\\\"$\\\\beta$y [m]\\\", color='red', linestyle='-')\\n plt.plot(s,dx, label=r'$\\\\eta_x$ [m]' , color='green', linestyle='-') # dispersion x\\n vscale=splot211.axis()[3]*0.25\\n viseoz = [x*vscale for x in vis_ordinate]\\n plt.plot(vis_abszisse,viseoz,label='',color='black')\\n plt.plot(vis_abszisse,vzero,color='green',linestyle='--')\\n # zero line\\n splot211.plot(vis_abszisse,vzero,color='green',linestyle='--')\\n plt.legend(loc='lower right',fontsize='x-small')\\n\\n #-------------------- longitudinal tracks z, dP/P\\n # ax_l = left abszisse\\n ax_l=plt.subplot(212)\\n # ax_l=plt.subplot(10,1,(7,9))\\n ax_l.set_title('synchrotron oscillation')\\n ax_l.set_ylabel(r\\\"z [mm]\\\")\\n ax_l.tick_params(axis='y', colors='green')\\n ax_l.yaxis.label.set_color('green')\\n ax_l.plot(z1,cz,label='C',color='green')\\n ax_l.plot(z2,sz,label='S',color='green',linestyle=':')\\n plt.legend(loc='lower left',fontsize='x-small')\\n # ax_r = right abszisse\\n ax_r = ax_l.twinx()\\n ax_r.set_ylabel(r'$\\\\Delta$p/p [%]')\\n ax_r.tick_params(axis='y', colors='red')\\n ax_r.yaxis.label.set_color('red')\\n ax_r.plot(z2,cdp,label='C',color='red')\\n ax_r.plot(z2,sdp,label='S',color='red',linestyle=':')\\n ax_r.plot(vis_abszisse,vzero,color='red', linestyle='--')\\n plt.legend(loc='lower right',fontsize='x-small')\\n # lattice elements\\n vscale=ax_l.axis()[3]*0.25\\n viseoz = [x*vscale for x in vis_ordinate]\\n ax_l.plot(vis_abszisse,viseoz,label='',color='black')\\n ax_l.plot(vis_abszisse,vzero,color='green',linestyle='--')\",\n \"def task2_extra():\\n N = 0\\n lam = 0\\n Ls = numpy.array([2*L for L in range(1,23)])\\n h = 0.01\\n tau = 0.000099\\n\\n iterss = []\\n\\n for L in Ls:\\n a = L // 2\\n print(L)\\n x = numpy.linspace(-L, L, int(2*L/h) + 1)\\n # eps = int(0.1 * len(x))\\n\\n Vm = V1D(lam, x)\\n state = phi(N, x-a)\\n\\n iters = 0\\n while True:\\n prob = numpy.abs(state)**2\\n mid = int(2*L/h) // 2\\n # if max(prob) in prob[mid-eps:mid+eps]:\\n if numpy.argmax(prob) <= mid:\\n print(iters)\\n iterss.append(iters)\\n break\\n\\n state[0] = 0\\n state[-1] = 0\\n state = implicit_scheme_step(state, tau, h, Vm)\\n iters += 1\\n\\n fig = plt.figure()\\n plt.title(\\\"Iterations of Gaussian travel to center\\\")\\n plt.xlabel(\\\"$L$\\\")\\n plt.ylabel(\\\"Time\\\")\\n plt.plot(Ls, tau*numpy.array(iterss))\\n plt.show()\\n fig.savefig(\\\"naloga2_iters_of_gaussian_travel.pdf\\\", bbox_inches=\\\"tight\\\")\",\n \"def test_pass_through_lcoe_args():\\n year = 2012\\n rev2_points = slice(0, 3)\\n res_file = os.path.join(TESTDATADIR, 'wtk/ri_100_wtk_{}.h5'.format(year))\\n sam_files = os.path.join(TESTDATADIR, 'SAM/i_windpower_lcoe.json')\\n\\n output_request = ('cf_mean',\\n 'lcoe_fcr',\\n 'capital_cost',\\n 'fixed_charge_rate',\\n 'variable_operating_cost',\\n 'fixed_operating_cost')\\n\\n # run reV 2.0 generation\\n gen = Gen.reV_run(tech='windpower', points=rev2_points,\\n sam_files=sam_files, res_file=res_file, max_workers=1,\\n sites_per_worker=1, fout=None,\\n output_request=output_request)\\n\\n checks = [x in gen.out for x in Gen.LCOE_ARGS]\\n assert all(checks)\\n assert 'lcoe_fcr' in gen.out\\n assert 'cf_mean' in gen.out\",\n \"def softclippingHGMevaluation(input_generator,branches,iden_method,Plot,reference=None):\\n for t in range(8,11):\\n t = t / 10.0\\n p = 1.0 - t\\n input_signal = input_generator.GetOutput()\\n nl_functions = [nlsp.function_factory.softclip(power=p),]*branches\\n filter_spec_tofind = nlsp.log_bpfilter(branches=branches, input=input_signal)\\n ref_nlsystem = nlsp.HammersteinGroupModel_up(input_signal=input_signal,\\n nonlinear_functions=nl_functions,\\n filter_irs=filter_spec_tofind,\\n max_harmonics=range(1,branches+1))\\n\\n found_filter_spec, nl_functions = iden_method(input_generator,ref_nlsystem.GetOutput(),branches)\\n iden_nlsystem = nlsp.HammersteinGroupModel_up(input_signal=input_signal,\\n nonlinear_functions=nl_functions,\\n filter_irs=found_filter_spec,\\n max_harmonics=range(1,branches+1))\\n # sine = sumpf.modules.SineWaveGenerator(frequency=5000.0,phase=0.0,samplingrate=input_signal.GetSamplingRate(),length=len(input_signal)).GetSignal()\\n sine = sumpf.modules.SweepGenerator(samplingrate=input_signal.GetSamplingRate(),length=len(input_signal)).GetSignal()\\n ref_nlsystem.SetInput(sine)\\n iden_nlsystem.SetInput(sine)\\n if reference is not None:\\n reference = nlsp.change_length_signal(reference,length=len(input_signal))\\n ref_nlsystem.SetInput(reference)\\n iden_nlsystem.SetInput(reference)\\n\\n if Plot is True:\\n plot.relabelandplot(sumpf.modules.FourierTransform(ref_nlsystem.GetOutput()).GetSpectrum(),\\\"Reference System\\\",show=False)\\n plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem.GetOutput()).GetSpectrum(),\\\"Identified System\\\",show=False)\\n print \\\"SNR between Reference and Identified output for symmetric hardclipping HGM(threshold:%r): %r\\\" %(t,nlsp.snr(ref_nlsystem.GetOutput(),\\n iden_nlsystem.GetOutput()))\",\n \"def plot_for_scaling_check(bolo_name):\\n\\n\\n pop_path = \\\"../Analyse_\\\" + bolo_name + \\\"/Populations/Pop_for_scaling/\\\"\\n\\n #Load the estimator\\n d_est = BDT_fh.open_estimator_file(bolo_name)\\n\\n #Best estimator for heat: coefficients\\n coeff_EC1, coeff_EC2 = float(d_est[\\\"HEAT\\\"][:5]), 1 - float(d_est[\\\"HEAT\\\"][:5])\\n coeff_EIB, coeff_EID = float(d_est[\\\"FID\\\"][:5]), 1-float(d_est[\\\"FID\\\"][:5])\\n\\n #Open event files\\n data_types = {\\\"names\\\": (\\\"EC1\\\", \\\"EC2\\\", \\\"EIA\\\", \\\"EIB\\\", \\\"EIC\\\", \\\"EID\\\"), \\\"formats\\\": (\\\"f\\\", \\\"f\\\", \\\"f\\\", \\\"f\\\", \\\"f\\\", \\\"f\\\")}\\n\\n arr_heatonly = np.loadtxt(pop_path + bolo_name + \\\"_heatonly_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_all = np.loadtxt(pop_path + bolo_name + \\\"_all_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_FidGamma = np.loadtxt(pop_path + bolo_name + \\\"_FidGamma_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S1Gamma = np.loadtxt(pop_path + bolo_name + \\\"_S1Gamma_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S2Gamma = np.loadtxt(pop_path + bolo_name + \\\"_S2Gamma_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S1Beta = np.loadtxt(pop_path + bolo_name + \\\"_S1Beta_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S2Beta = np.loadtxt(pop_path + bolo_name + \\\"_S2Beta_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S1Pb = np.loadtxt(pop_path + bolo_name + \\\"_S1Pb_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n arr_S2Pb = np.loadtxt(pop_path + bolo_name + \\\"_S2Pb_KTH_cut_full_info.txt\\\", delimiter=\\\",\\\", dtype=data_types)\\n\\n arr_EI_heatonly, arr_EI_all = coeff_EIB*arr_heatonly[\\\"EIB\\\"] + coeff_EID*arr_heatonly[\\\"EID\\\"], coeff_EIB*arr_all[\\\"EIB\\\"] + coeff_EID*arr_all[\\\"EID\\\"]\\n arr_EC_heatonly, arr_EC_all = coeff_EC1*arr_heatonly[\\\"EC1\\\"] + coeff_EC2*arr_heatonly[\\\"EC2\\\"], coeff_EC1*arr_all[\\\"EC1\\\"] + coeff_EC2*arr_all[\\\"EC2\\\"]\\n arr_EI_FidGamma, arr_EC_FidGamma = coeff_EIB*arr_FidGamma[\\\"EIB\\\"] + coeff_EID*arr_FidGamma[\\\"EID\\\"], coeff_EC1*arr_FidGamma[\\\"EC1\\\"] + coeff_EC2*arr_FidGamma[\\\"EC2\\\"]\\n arr_EI_S1Gamma, arr_EI_S2Gamma = coeff_EIB*arr_S1Gamma[\\\"EIB\\\"] + coeff_EID*arr_S1Gamma[\\\"EID\\\"], coeff_EIB*arr_S2Gamma[\\\"EIB\\\"] + coeff_EID*arr_S2Gamma[\\\"EID\\\"]\\n arr_EC_S1Gamma, arr_EC_S2Gamma = coeff_EC1*arr_S1Gamma[\\\"EC1\\\"] + coeff_EC2*arr_S1Gamma[\\\"EC2\\\"], coeff_EC1*arr_S2Gamma[\\\"EC1\\\"] + coeff_EC2*arr_S2Gamma[\\\"EC2\\\"]\\n arr_EI_S1Beta, arr_EI_S2Beta = coeff_EIB*arr_S1Beta[\\\"EIB\\\"] + coeff_EID*arr_S1Beta[\\\"EID\\\"], coeff_EIB*arr_S2Beta[\\\"EIB\\\"] + coeff_EID*arr_S2Beta[\\\"EID\\\"]\\n arr_EC_S1Beta, arr_EC_S2Beta = coeff_EC1*arr_S1Beta[\\\"EC1\\\"] + coeff_EC2*arr_S1Beta[\\\"EC2\\\"], coeff_EC1*arr_S2Beta[\\\"EC1\\\"] + coeff_EC2*arr_S2Beta[\\\"EC2\\\"]\\n arr_EI_S1Pb, arr_EI_S2Pb = coeff_EIB*arr_S1Pb[\\\"EIB\\\"] + coeff_EID*arr_S1Pb[\\\"EID\\\"], coeff_EIB*arr_S2Pb[\\\"EIB\\\"] + coeff_EID*arr_S2Pb[\\\"EID\\\"]\\n arr_EC_S1Pb, arr_EC_S2Pb = coeff_EC1*arr_S1Pb[\\\"EC1\\\"] + coeff_EC2*arr_S1Pb[\\\"EC2\\\"], coeff_EC1*arr_S2Pb[\\\"EC1\\\"] + coeff_EC2*arr_S2Pb[\\\"EC2\\\"]\\n\\n lS1Beta, lS2Beta, lS1Pb, lS2Pb = np.where(arr_EC_S1Beta<15), np.where(arr_EC_S2Beta<15), np.where(arr_EC_S1Pb<15), np.where(arr_EC_S2Pb<15)\\n lS1Gamma, lS2Gamma, lFidGamma = np.where(arr_EC_S1Gamma<15), np.where(arr_EC_S2Gamma<15), np.where(arr_EC_FidGamma<15)\\n lheatonly, lall = np.where(arr_EC_heatonly<15), np.where(arr_EC_all<15)\\n\\n arr_EI_heatonly, arr_EC_heatonly = arr_EI_heatonly[lheatonly], arr_EC_heatonly[lheatonly]\\n arr_EI_all, arr_EC_all = arr_EI_all[lall], arr_EC_all[lall]\\n arr_EI_FidGamma, arr_EC_FidGamma = arr_EI_FidGamma[lFidGamma], arr_EC_FidGamma[lFidGamma]\\n arr_EI_S1Gamma, arr_EC_S1Gamma = arr_EI_S1Gamma[lS1Gamma], arr_EC_S1Gamma[lS1Gamma]\\n arr_EI_S2Gamma, arr_EC_S2Gamma = arr_EI_S2Gamma[lS2Gamma], arr_EC_S2Gamma[lS2Gamma]\\n arr_EI_S1Beta, arr_EC_S1Beta = arr_EI_S1Beta[lS1Beta], arr_EC_S1Beta[lS1Beta]\\n arr_EI_S2Beta, arr_EC_S2Beta = arr_EI_S2Beta[lS2Beta], arr_EC_S2Beta[lS2Beta]\\n arr_EI_S1Pb, arr_EC_S1Pb = arr_EI_S1Pb[lS1Pb], arr_EC_S1Pb[lS1Pb]\\n arr_EI_S2Pb, arr_EC_S2Pb = arr_EI_S2Pb[lS2Pb], arr_EC_S2Pb[lS2Pb]\\n\\n arr_EI_all, arr_EC_all = np.array(arr_EI_all).astype(float), np.array(arr_EC_all).astype(float)\\n arr_EI_heatonly, arr_EC_heatonly = np.array(arr_EI_heatonly).astype(float), np.array(arr_EC_heatonly).astype(float)\\n arr_EI_FidGamma, arr_EC_FidGamma = np.array(arr_EI_FidGamma).astype(float), np.array(arr_EC_FidGamma).astype(float)\\n arr_EI_S1Gamma, arr_EC_S1Gamma = np.array(arr_EI_S1Gamma).astype(float), np.array(arr_EC_S1Gamma).astype(float)\\n arr_EI_S2Gamma, arr_EC_S2Gamma = np.array(arr_EI_S2Gamma).astype(float), np.array(arr_EC_S2Gamma).astype(float) \\n arr_EI_S1Beta, arr_EC_S1Beta = np.array(arr_EI_S1Beta).astype(float), np.array(arr_EC_S1Beta).astype(float)\\n arr_EI_S2Beta, arr_EC_S2Beta = np.array(arr_EI_S2Beta).astype(float), np.array(arr_EC_S2Beta).astype(float)\\n arr_EI_S1Pb, arr_EC_S1Pb = np.array(arr_EI_S1Pb).astype(float), np.array(arr_EC_S1Pb).astype(float)\\n arr_EI_S2Pb, arr_EC_S2Pb = np.array(arr_EI_S2Pb).astype(float), np.array(arr_EC_S2Pb).astype(float)\\n\\n\\n gr_heatonly = TGraph(len(arr_EI_heatonly), arr_EC_heatonly, arr_EI_heatonly)\\n gr_FidGamma, gr_all = TGraph(len(arr_EI_FidGamma), arr_EC_FidGamma, arr_EI_FidGamma), TGraph(len(arr_EI_all), arr_EC_all, arr_EI_all)\\n gr_S1Gamma, gr_S2Gamma = TGraph(len(arr_EI_S1Gamma), arr_EC_S1Gamma, arr_EI_S1Gamma), TGraph(len(arr_EI_S2Gamma), arr_EC_S2Gamma, arr_EI_S2Gamma)\\n gr_S1Beta, gr_S2Beta = TGraph(len(arr_EI_S1Beta), arr_EC_S1Beta, arr_EI_S1Beta), TGraph(len(arr_EI_S2Beta), arr_EC_S2Beta, arr_EI_S2Beta)\\n gr_S1Pb, gr_S2Pb = TGraph(len(arr_EI_S1Pb), arr_EC_S1Pb, arr_EI_S1Pb), TGraph(len(arr_EI_S2Pb), arr_EC_S2Pb, arr_EI_S2Pb)\\n\\n PyRPl.process_TGraph(gr_all, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kRed, marker_style = 20, marker_size = 0.1)\\n PyRPl.process_TGraph(gr_FidGamma, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack), PyRPl.process_TGraph(gr_heatonly, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack)\\n PyRPl.process_TGraph(gr_S1Gamma, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack), PyRPl.process_TGraph(gr_S2Gamma, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack)\\n PyRPl.process_TGraph(gr_S1Beta, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack), PyRPl.process_TGraph(gr_S2Beta, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack)\\n PyRPl.process_TGraph(gr_S1Pb, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kRed), PyRPl.process_TGraph(gr_S2Pb, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\", color=kBlack)\\n\\n list_gr = [gr_all, gr_FidGamma, gr_S1Gamma, gr_S2Gamma, gr_S1Beta, gr_S2Beta, gr_S1Pb, gr_S2Pb, gr_heatonly]\\n list_pts = [gr.GetN() for gr in list_gr[1:]]\\n print gr_all.GetN(), sum(list_pts)\\n h = TH2F(\\\"h\\\", \\\"h\\\", 100, -5, 15, 100, -5, 15)\\n PyRPl.process_TH2(h, X_title = \\\"Heat\\\", Y_title = \\\"Ion\\\")\\n h.Draw()\\n for gr in list_gr:\\n gr.Draw(\\\"*same\\\")\\n\\n raw_input()\\n\\n # arr_Q_S1Beta = arr_EI_S1Beta/((1+8./3)*arr_EC_S1Beta - arr_EI_S1Beta*5.5/3)\\n # arr_Q_S2Beta = arr_EI_S1Beta/((1+8./3)*arr_EC_S1Beta - arr_EI_S1Beta*5.5/3)\\n # arr_Q_S1Pb = arr_EI_S1Pb/((1+8./3)*arr_EC_S1Pb - arr_EI_S1Pb*5.5/3)\\n # arr_Q_S2Pb = arr_EI_S2Pb/((1+8./3)*arr_EC_S2Pb - arr_EI_S2Pb*5.5/3)\\n \\n # gr_QS1Beta, gr_QS2Beta = TGraph(len(arr_Q_S1Beta), arr_EC_S1Beta, arr_Q_S1Beta), TGraph(len(arr_Q_S2Beta), arr_EC_S2Beta, arr_Q_S2Beta)\\n # gr_QS1Pb, gr_QS2Pb = TGraph(len(arr_Q_S1Pb), arr_EC_S1Pb, arr_Q_S1Pb), TGraph(len(arr_Q_S2Pb), arr_EC_S2Pb, arr_Q_S2Pb)\\n\\n\\n # PyRPl.process_TGraph(gr_QS1Beta, X_title = \\\"Heat\\\", Y_title = \\\"Q\\\", color=kOrange-3), PyRPl.process_TGraph(gr_QS2Beta, X_title = \\\"Heat\\\", Y_title = \\\"Q\\\", color=kBlue)\\n # PyRPl.process_TGraph(gr_QS1Pb, X_title = \\\"Heat\\\", Y_title = \\\"Q\\\", color=kRed), PyRPl.process_TGraph(gr_QS2Pb, X_title = \\\"Heat\\\", Y_title = \\\"Q\\\", color=kGreen+2)\",\n \"def exec(self):\\n try:\\n xyz = [self.X_0, self.Y_0, self.Z_0]\\n for _ in range(self.STEP):\\n k_0 = self.__lorenz(xyz)\\n k_1 = self.__lorenz([\\n x + k * self.DT / 2 for x, k in zip(xyz, k_0)\\n ])\\n k_2 = self.__lorenz([\\n x + k * self.DT / 2 for x, k in zip(xyz, k_1)\\n ])\\n k_3 = self.__lorenz([\\n x + k * self.DT for x, k in zip(xyz, k_2)\\n ])\\n for i in range(3):\\n xyz[i] += (k_0[i] + 2 * k_1[i] + 2 * k_2[i] + k_3[i]) \\\\\\n * self.DT / 6.0\\n self.res[i].append(xyz[i])\\n self.__plot()\\n except Exception as e:\\n raise\",\n \"def main():\\n\\n args = arg_parse()\\n plot_zcalib(args)\",\n \"def main():\\n save = False\\n show = True\\n\\n #hd_parameter_plots = HDparameterPlots(save=save)\\n #hd_parameter_plots.flow_parameter_distribution_for_non_lake_cells_for_current_HD_model()\\n #hd_parameter_plots.flow_parameter_distribution_current_HD_model_for_current_HD_model_reprocessed_without_lakes_and_wetlands()\\n #hd_parameter_plots.flow_parameter_distribution_ten_minute_data_from_virna_0k_ALG4_sinkless_no_true_sinks_oceans_lsmask_plus_upscale_rdirs()\\n #hd_parameter_plots.flow_parameter_distribution_ten_minute_data_from_virna_0k_ALG4_sinkless_no_true_sinks_oceans_lsmask_plus_upscale_rdirs_no_tuning()\\n #ice5g_comparison_plots = Ice5GComparisonPlots(save=save)\\n #ice5g_comparison_plots.plotLine()\\n #ice5g_comparison_plots.plotFilled()\\n #ice5g_comparison_plots.plotCombined()\\n #ice5g_comparison_plots.plotCombinedIncludingOceanFloors()\\n #flowmapplot = FlowMapPlots(save)\\n #flowmapplot.FourFlowMapSectionsFromDeglaciation()\\n #flowmapplot.Etopo1FlowMap()\\n #flowmapplot.ICE5G_data_all_points_0k()\\n #flowmapplot.ICE5G_data_all_points_0k_no_sink_filling()\\n #flowmapplot.ICE5G_data_all_points_0k_alg4_two_color()\\n #flowmapplot.ICE5G_data_all_points_21k_alg4_two_color()\\n #flowmapplot.Etopo1FlowMap_two_color()\\n #flowmapplot.Etopo1FlowMap_two_color_directly_upscaled_fields()\\n #flowmapplot.Corrected_HD_Rdirs_FlowMap_two_color()\\n #flowmapplot.ICE5G_data_ALG4_true_sinks_21k_And_ICE5G_data_ALG4_true_sinks_0k_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_Etopo1_ALG4_sinkless_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_Etopo1_ALG4_true_sinks_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_sinkless_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_true_sinks_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_corr_orog_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_data_ALG4_no_true_sinks_corr_orog_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Corrected_HD_Rdirs_And_ICE5G_HD_as_data_ALG4_true_sinks_0k_directly_upscaled_fields_FlowMap_comparison()\\n #flowmapplot.Upscaled_Rdirs_vs_Directly_Upscaled_fields_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplot.Ten_Minute_Data_from_Virna_data_ALG4_corr_orog_downscaled_lsmask_no_sinks_21k_vs_0k_FlowMap_comparison()\\n #flowmapplot.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n flowmapplotwithcatchment = FlowMapPlotsWithCatchments(save)\\n #flowmapplotwithcatchment.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_virna_data_plus_tarasov_style_orog_corrs_for_both()\\n #flowmapplotwithcatchment.compare_present_day_river_directions_with_catchments_virna_data_with_vs_without_tarasov_style_orog_corrs()\\n #flowmapplotwithcatchment.compare_lgm_river_directions_with_catchments_virna_data_with_vs_without_tarasov_style_orog_corrs()\\n #flowmapplotwithcatchment.Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.upscaled_rdirs_with_and_without_tarasov_upscaled_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.\\\\\\n #upscaled_rdirs_with_and_without_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.\\\\\\n #Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_downscaled_ls_mask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.\\\\\\n #Upscaled_Rdirs_vs_Corrected_HD_Rdirs_tarasov_upscaled_north_america_only_data_ALG4_corr_orog_glcc_olson_lsmask_0k_FlowMap_comparison()\\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_ICE5G_plus_tarasov_style_orog_corrs_for_both()\\n #flowmapplotwithcatchment.compare_present_day_and_lgm_river_directions_with_catchments_ICE6G_plus_tarasov_style_orog_corrs_for_both()\\n #flowmapplotwithcatchment.compare_ICE5G_and_ICE6G_with_catchments_tarasov_style_orog_corrs_for_both()\\n #flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs()\\n #flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs_original_ts()\\n flowmapplotwithcatchment.compare_river_directions_with_dynriver_corrs_and_MERIThydro_derived_corrs_new_ts_10min()\\n outflowplots = OutflowPlots(save)\\n #outflowplots.Compare_Upscaled_Rdirs_vs_Directly_Upscaled_fields_ICE5G_data_ALG4_corr_orog_downscaled_ls_mask_0k()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_as_HD_data_ALG4_sinkless_all_points_0k()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_as_HD_data_ALG4_true_sinks_all_points_0k()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_sinkless_all_points_0k_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_true_sinks_all_points_0k_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_corr_orog_all_points_0k_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_ALG4_corr_orog_downscaled_ls_mask_all_points_0k_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_Etopo1_ALG4_sinkless_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_Etopo1_ALG4_true_sinks_directly_upscaled_fields()\\n #outflowplots.Compare_Corrected_HD_Rdirs_And_ICE5G_plus_tarasov_upscaled_srtm30_ALG4_corr_orog_0k_directly_upscaled_fields()\\n #outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k()\\n outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k_new_truesinks()\\n #outflowplots.Compare_Original_Corrections_vs_Upscaled_MERIT_DEM_0k_new_truesinks_individual_rivers()\\n #outflowplots.Compare_ICE5G_with_and_without_tarasov_upscaled_srtm30_ALG4_corr_orog_0k_directly_upscaled_fields()\\n #hd_output_plots = HDOutputPlots()\\n #hd_output_plots.check_water_balance_of_1978_for_constant_forcing_of_0_01()\\n #hd_output_plots.plot_comparison_using_1990_rainfall_data()\\n #hd_output_plots.plot_comparison_using_1990_rainfall_data_adding_back_to_discharge()\\n #coupledrunoutputplots = CoupledRunOutputPlots(save=save)\\n #coupledrunoutputplots.ice6g_rdirs_lgm_run_discharge_plot()\\n #coupledrunoutputplots.extended_present_day_rdirs_lgm_run_discharge_plot()\\n #coupledrunoutputplots.ocean_grid_extended_present_day_rdirs_vs_ice6g_rdirs_lgm_run_discharge_plot()\\n #coupledrunoutputplots.extended_present_day_rdirs_vs_ice6g_rdirs_lgm_echam()\\n #coupledrunoutputplots.extended_present_day_rdirs_vs_ice6g_rdirs_lgm_mpiom_pem()\\n #lake_plots = LakePlots()\\n #lake_plots.plotLakeDepths()\\n #lake_plots.LakeAndRiverMap()\\n #lake_plots.LakeAndRiverMaps()\\n if show:\\n plt.show()\",\n \"def figure3():\\n\\n plot_settings = {'y_limits': [-75, -50],\\n 'x_limits': None,\\n 'y_ticks': [-75, -70, -65, -60, -55, -50],\\n 'locator_size': 2.5,\\n 'y_label': 'Voltage (mV)',\\n 'x_ticks': [],\\n 'scale_size': 2,\\n 'x_label': \\\"\\\",\\n 'scale_loc': 3,\\n 'figure_name': 'figure_3',\\n 'legend': ['0.1 $\\\\mu$S', '0.125 $\\\\mu$S', '0.15 $\\\\mu$S'],\\n 'legend_size': 8,\\n 'legend_location': 4,\\n 'y_on': True}\\n line_styles = ['-', 'dotted', '-.']\\n\\n plt.figure(figsize=(5, 3))\\n plt.subplot(1, 2, 1) # Generate subplot 1 (left)\\n for ix, g_t_bar in enumerate([0.1, 0.125, 0.15]):\\n t, y = solver(6, g_t_bar=g_t_bar, t_start=0.5)\\n plt.plot(t, y[:, 0], c='k', linestyle=line_styles[ix])\\n alter_figure(plot_settings) # Alter figure for publication\\n\\n plt.subplot(1, 2, 2) # Generate subplot 2 (right)\\n for ix, i_bias_on in enumerate([0, -0.1, -0.2]):\\n \\\"\\\"\\\"\\n First step (hyperpolarizing)\\n \\\"\\\"\\\"\\n t1 = 1000\\n duration = t1\\n\\n t, y_hold = solver(t1, duration=duration, i_bias_on=i_bias_on)\\n y_0 = y_hold[-1, :] # Create new initial conditions\\n\\n \\\"\\\"\\\"\\n Second step (current pulse)\\n \\\"\\\"\\\"\\n t1 = 20\\n t, y = solver(t1, t_start=0, y_hold=y_0)\\n v = y[:, 0]\\n\\n \\\"\\\"\\\"\\n Append the end of the first step onto the second step (for plotting purposes)\\n \\\"\\\"\\\"\\n len_pre = 100\\n t = np.concatenate((np.linspace(-len_pre * np.diff(t)[0], -np.diff(t)[0], len_pre), t))\\n v_bar = np.concatenate((y_hold[-len_pre:, 0], v))\\n v_bar = v_bar - v_bar[0] + -7.16300325e+01 # Align solution to initial condition of the \\\"standard simulation\\\"\\n\\n plt.plot(t, v_bar, c='k', linestyle=line_styles[ix])\\n\\n plot_settings['y_ticks'] = []\\n plot_settings['y_label'] = \\\"\\\"\\n plot_settings['x_limits'] = [-1, 20]\\n plot_settings['legend'] = ['-72 mV', '-75 mV', '-78 mV']\\n plot_settings['scale_size'] = 5\\n plot_settings['legend_location'] = 1\\n alter_figure(plot_settings, close=True)\",\n \"def spider(inputfiles, names, type=\\\"M1\\\", threshold=0.1, spinwindow=[], Eg_low=0, Eg_high=1e9, Ex_low=0, Ex_high=1e9, scale=2):\\n\\n Nsp = np.ceil(np.sqrt(len(inputfiles))).astype(int)\\n f, ax_list = plt.subplots(Nsp,Nsp,squeeze=False,sharex='col', sharey='row')\\n\\n for i in range(len(inputfiles)):\\n inputfile = inputfiles[i]\\n name = names[i]\\n ax = ax_list[i%Nsp][int((i-i%Nsp)/Nsp)]\\n\\n\\n levels = read_energy_levels(inputfile)\\n Egs = levels[0,0]\\n\\n Ex_high = min(Ex_high, levels[:,0].max()-Egs)\\n Eg_high = min(Eg_high, levels[:,0].max()-Egs)\\n \\n \\n levels_plot_J = []\\n levels_plot_Ex = []\\n for iEx in range(len(levels[:,0])):\\n # print levels[iEx,:]\\n J2 = levels[iEx,1]\\n par = levels[iEx,2]\\n if len(spinwindow) > 0 and not ([J2,par] in spinwindow or [J2-2,par] in spinwindow or [J2+2,par] in spinwindow):\\n continue\\n Ex = levels[iEx,0]-Egs\\n if Ex < Ex_low or Ex > Ex_high:\\n continue\\n \\n levels_plot_J.append(J2/2)\\n levels_plot_Ex.append(Ex)\\n \\n ax.plot(levels_plot_J, levels_plot_Ex, 'o', color='grey', linewidth=0.5)\\n ax.set_xlim([levels[:,1].min()/2-1,levels[:,1].max()/2+1])\\n ax.set_title(name+r'$\\\\,E_\\\\gamma\\\\in[{:.1f},{:.1f}]$'.format(Eg_low,Eg_high))\\n ax.set_ylabel(r'$E_x\\\\,\\\\mathrm{[MeV]}$')\\n ax.set_xlabel(r'$J\\\\,\\\\mathrm{[\\\\hbar]}$')\\n \\n \\n \\n transitions = read_transition_strengths(inputfile, type=type)\\n for iEx in range(len(transitions[:,0])):\\n J2i = int(transitions[iEx,0])\\n pari = int(transitions[iEx,1])\\n if len(spinwindow) > 0 and not [J2i,pari] in spinwindow:\\n continue\\n B = transitions[iEx,7]\\n Eg = transitions[iEx,6]\\n if B < threshold or Eg<Eg_low or Eg>Eg_high:\\n continue\\n Ei = transitions[iEx,2]-Egs\\n if Ei < Ex_low or Ei > Ex_high:\\n continue\\n J2f = int(transitions[iEx,3])\\n parf = int(transitions[iEx,4])\\n Ef = transitions[iEx,5]-Egs\\n ax.plot([J2i/2,J2f/2],[Ei,Ef], color='teal', linewidth=(scale*B))\\n \\n return f, ax_list\",\n \"def main():\\n\\t#print(scipy.__version__)\\n\\t#image()\\n\\t#heat_capacity2()\\n\\t#hist()\\n\\t#single_plot()\\n\\n\\t#heat_capacity2()\\n\\t#single_plot()\\n\\t#plt.show()\\n\\t#u0_tc()\\n\\t#multi_heat_capacity(\\\"HL_DM_flux5\\\",True)\\n\\t#multi_heat_capacity2()\\n\\t#plot_spin()\\n\\t#plt.show()\\n\\theat_capacity2(1,2)\\n\\t#hist()\\n\\tplt.show()\\n\\t#potential()\\n\\t#plt.show()\\n\\t#heat_capacity(3,4)\\n\\t#heat_capacity(5,6)\\n\\t#heat_capacity(7,8)\\n\\t#final_spins()\\n\\t#plot_spin()\\n\\t#plot_from_csv()\\n\\t#difference_plot()\",\n \"def main() -> None:\\n xlim = L\\n x = np.arange(xlim)\\n xi = np.arange(0, xlim - 1 + STEP, STEP)\\n kappas = sleplet.wavelet_methods.create_kappas(xlim, B, J_MIN)\\n yi = scipy.interpolate.pchip(x, kappas[0])\\n plt.semilogx(xi, yi(xi), label=r\\\"$\\\\Phi_{\\\\ell0}$\\\")\\n for j, k in enumerate(kappas[1:]):\\n yi = scipy.interpolate.pchip(x, k)\\n plt.semilogx(xi, yi(xi), label=rf\\\"$\\\\Psi^{{{j+J_MIN}}}_{{\\\\ell0}}$\\\")\\n plt.xlim(1, xlim)\\n ticks = 2 ** np.arange(np.log2(xlim) + 1, dtype=int)\\n plt.xticks(ticks, ticks)\\n plt.xlabel(r\\\"$\\\\ell$\\\")\\n plt.legend(loc=6)\\n print(f\\\"Opening: axisymmetric_tiling_L{L}\\\")\\n plt.tight_layout()\\n plt.show(block=False)\\n plt.pause(10)\\n plt.close()\",\n \"def prepare_CvD16_for_M2L_calc(templates_lam_range, verbose=False):\\n import glob\\n import os\\n template_glob=os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*.s100')\\n\\n vcj_models=sorted(glob.glob(template_glob))\\n temp_lamdas, x35, x3, x23, kroupa, flat=np.genfromtxt(vcj_models[-1], unpack=True)\\n\\n n_ages=7\\n n_zs=5\\n n_imfs=5\\n\\n \\n\\n\\n Zs=['m1.5', 'm1.0', 'm0.5', 'p0.0', 'p0.2']\\n ages=[1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.5]\\n model_imfs_order=['x35', 'x3', 'x23', 'kroupa', 'flat']\\n\\n t_mask = ((temp_lamdas > templates_lam_range[0]) & (temp_lamdas <templates_lam_range[1]))\\n\\n\\n\\n y=x35[t_mask]\\n x=temp_lamdas[t_mask]\\n\\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\\n interp=si.interp1d(x, y, fill_value='extrapolate')\\n out=interp(new_x)\\n\\n templates=np.empty((len(out), n_ages, n_zs, n_imfs))\\n\\n\\n\\n for a, Z in enumerate(Zs): \\n for b, age in enumerate(ages):\\n model=glob.glob(os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*{}*{}.ssp.s100'.format(Z, age)))[0]\\n if verbose:\\n print 'Loading {}'.format(model)\\n data=np.genfromtxt(model)\\n\\n for c, counter in enumerate(reversed(range(1, data.shape[-1]))):\\n \\n #Interpolate templates onto a uniform wavelength grid and then log-rebin\\n y=data[:, counter][t_mask] \\n x=temp_lamdas[t_mask]\\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\\n\\n interp=si.interp1d(x, y, fill_value='extrapolate')\\n out=interp(new_x) \\n\\n templates[:, b, a, c]=out\\n\\n return templates, new_x\",\n \"def main():\\n\\n # define bounding box for right VTC\\n width = 225\\n y_start = 245\\n x_start = 50\\n bbox = [y_start, x_start, width, width]\\n\\n # first figure: betas for each predictor\\n fig, axes = plt.subplots(figsize=(27.7, 8), ncols=5, nrows=2)\\n\\n # redefining contrast order to match figures\\n contrasts = [\\n \\\"wordVSall\\\",\\n \\\"bodyVSall\\\",\\n \\\"adultVSall\\\",\\n \\\"carVSall\\\",\\n \\\"corridorVSall\\\",\\n \\\"numberVSall\\\",\\n \\\"limbVSall\\\",\\n \\\"childVSall\\\",\\n \\\"instrumentVSall\\\",\\n \\\"corridorVSall\\\",\\n ]\\n\\n for contrast, ax in zip(contrasts, axes.ravel()):\\n image_path = (\\n f\\\"{PATHS['figures_misc']}/figure_4_images/\\\"\\n f\\\"C1051_20160212_{contrast}_depth_1.png\\\"\\n )\\n\\n assert os.path.isfile(image_path)\\n img = imread(image_path)\\n img[np.where(np.sum(img, axis=2) == 0.0)] = 1.0\\n\\n ax.imshow(img[bbox[0] : bbox[0] + bbox[2], bbox[1] : bbox[1] + bbox[3], :])\\n ax.axis(\\\"off\\\")\\n\\n savefig(f\\\"{PATHS['figures']}/figure_4a.png\\\")\\n plt.close(fig)\",\n \"def test2():\\n\\t\\n\\td = np.linspace(-15000,15000,2500)\\n\\t#Voigt\\n\\n\\n\\t## 700 G, 84 C, Cs, 75mm\\n\\n\\tp_dict = {'Bfield':700,'Btheta':90*np.pi/180,'lcell':75e-3,'T':84,'Dline':'D2','Elem':'Cs'}\\n\\tpol = 1./np.sqrt(2)*np.array([1.0,1.0,0.0])\\n\\tTVx = get_spectra(d,pol,p_dict,outputs=['I_M45','I_P45','Ix','Iy','S0','Iz'])\\n\\t\\n\\tfig2 = plt.figure()\\n\\tax1a = fig2.add_subplot(411)\\n\\tax2a = fig2.add_subplot(412,sharex=ax1a)\\n\\tax3a = fig2.add_subplot(413,sharex=ax1a)\\n\\tax4a = fig2.add_subplot(414,sharex=ax1a)\\n\\t\\n\\tax1a.plot(d,TVx[0],'r',lw=2,label=r'$I_{-45}$')\\n\\tax2a.plot(d,TVx[1],'b',lw=2,label=r'$I_{+45}$')\\n\\tax3a.plot(d,TVx[2],'r',lw=2,label=r'$I_x$')\\n\\tax4a.plot(d,TVx[3],'b',lw=2,label=r'$I_y$')\\n\\tax4a.plot(d,TVx[0]+TVx[1],'r:',lw=3.5,label=r'$I_{+45}+I_{-45}$')\\n\\tax4a.plot(d,TVx[2]+TVx[3],'k:',lw=2.5,label=r'$I_x + I_y$')\\n\\tax4a.plot(d,TVx[4],'g--',lw=1.5,label='$S_0$')\\n\\t#\\tax4a.plot(d,TVx[5],'c--',lw=2.5,label='$I_z$')\\n\\t\\n\\t\\n\\tax4a.set_xlabel('Detuning (MHz)')\\n\\tax1a.set_ylabel('I -45')\\n\\tax2a.set_ylabel('I +45')\\n\\tax3a.set_ylabel('Ix')\\n\\tax4a.set_ylabel('Iy')\\n\\t\\n\\tax4a.set_xlim(d[0],d[-1]+3000)\\n\\tax4a.legend(loc=0)\\n\\t\\n\\tplt.show()\",\n \"def test_plot1(plot=1, version='scalar'):\\n Lx = 10\\n Ly = 10\\n c = 1.0\\n\\n def I2(x, y):\\n return exp(-(x-Lx/2.0)**2/2.0 -(y-Ly/2.0)**2/2.0)\\n def f(x, y, t):\\n return 0.0\\n def bc(x, y, t):\\n return 0.0\\n\\n I2 = StringFunction('exp(-(x-Lx/2.0)**2/2.0 -(y-Ly/2.0)**2/2.0)',\\n independent_variables=('x', 'y'),\\n Lx=Lx, Ly=Ly, globals=globals())\\n f = StringFunction('0.0', independent_variables=('x', 'y', 't'),\\n globals=globals())\\n bc = StringFunction('0.0', independent_variables=('x', 'y', 't'),\\n globals=globals())\\n if plot:\\n g = Gnuplot.Gnuplot(persist=1)\\n g('set parametric')\\n g('set data style lines')\\n g('set hidden')\\n g('set contour base')\\n g('set zrange [-0.7:0.7]') # nice plot...\\n \\n def action(u, xv, yv, t):\\n #print 'action, t=',t,'\\\\nu=',u, '\\\\nx=',x, '\\\\ny=', y\\n if plot:\\n data = Gnuplot.GridData(u, xv[:,0], yv[0,:], binary=0)\\n g.splot(data)\\n g('set title \\\"t=%g\\\"' % t)\\n if plot == 2:\\n g.hardcopy(filename='tmp_%020f.ps' % t, enhanced=1, mode='eps',\\n color=0, fontname='Times-Roman', fontsize=14)\\n time.sleep(1)\\n time.sleep(0.2) # pause between frames\\n\\n implementation = {'ic': version, 'inner': version, 'bc': version}\\n nx = 40; ny = 40; tstop = 20 # tstop = 700\\n print 'test_plot1:', f, bc, I2\\n dt, t_ic, t_inner, t_bc = \\\\\\n solver(I2, f, c, bc, Lx, Ly, nx, ny, 0, tstop,\\n user_action=action, implementation=implementation)\\n print 'time ic: %s, time scheme: %s, time bc: %s' % (t_ic, t_inner, t_bc)\\n time.sleep(3)\",\n \"def method_2d(knots,y_n,num):\\n cv_iter = 10 # number of iteration for cross-validation \\n GSV = np.zeros((cv_iter,cv_iter))\\n# tr = np.zeros((cv_iter,cv_iter))\\n# fun =np.zeros((cv_iter,cv_iter))\\n lam_x = np.linspace(0,0.2,cv_iter)\\n lam_y = np.linspace(0,0.2,cv_iter)\\n num_knots = len(knots)\\n linear_knots = knots[1:num_knots-1]\\n num_knots = num_knots-4\\n znam = np.zeros((num_knots))\\n basis = np.zeros((num,num_knots))\\n basis_1 = np.zeros((num,num_knots))\\n basis_deriative = np.zeros((num,num_knots))\\n basis_deriative_1 = np.zeros((num,num_knots))\\n S = np.zeros((num_knots,num_knots,num))\\n vs = BsplineVectorSpace(2, knots)\\n vs_1 = BsplineVectorSpace(1, linear_knots)\\n I_i = np.eye(num_knots)\\n for i in xrange(0,num_knots):\\n basis[:,i] = vs.basis_der(i,0)(np.linspace(0,1,num))\\n basis_deriative[:,i] = vs.basis_der(i,1)(np.linspace(0,1,num))/num\\n basis_1[:,i] = vs_1.basis_der(i,0)(np.linspace(0,1,num))\\n basis_deriative_1[:,i] = vs_1.basis_der(i,1)(np.linspace(0,1,num))/num\\n B = abs(basis_deriative-basis_1)\\n S = np.zeros((num_knots,num_knots,num))\\n k = np.zeros((num_knots,num_knots,num))\\n for i in xrange(num_knots):\\n for j in xrange(num_knots):\\n S[i,j,:] = B[:,i]*B[:,j]\\n k[i,j,:] =basis_deriative_1[:,i] * basis_deriative_1[:,j]\\n S_int = np.zeros((num_knots,num_knots))\\n k_int = np.zeros((num_knots,num_knots))\\n for i in xrange(num_knots):\\n for j in xrange(num_knots):\\n S_int[i,j] = integrate.trapz(S[i,j,:])\\n k_int[i,j] = integrate.trapz(k[i,j,:])\\n basis_product = np.kron(basis,basis)\\n S_x = np.kron(S_int,I_i)\\n S_y = np.kron(I_i,S_int)\\n K_x = np.kron(k_int,I_i)\\n K_y = np.kron(I_i,k_int)\\n for i in xrange(cv_iter):\\n for j in xrange(cv_iter):\\n influence_matrix = np.dot(np.dot(basis_product,(np.linalg.inv(np.dot(np.transpose(\\n basis_product),basis_product)+lam_x[i]*S_x+lam_y[j]*S_y+lam_x[i]* K_x+lam_y[j]*K_y))),np.transpose(basis_product))\\n for k in xrange(num_knots):\\n znam[k] =(1-influence_matrix[k,k])**2\\n tr = np.sum(znam)\\n fun = np.sum((y_n-np.dot(influence_matrix,y_n))**2)\\n GSV[i,j] =fun/(num*tr)\\n print i,j\\n a,b = np.unravel_index(GSV.argmin(), GSV.shape)\\n# a = np.argmin(np.argmin(GSV,axis = 0))\\n# b = np.argmin(np.argmin(GSV,axis = 1))\\n lamb_x = lam_x[a]\\n lamb_y = lam_y[b]\\n print lamb_x,lamb_y\\n model_fit = np.dot(np.dot(np.dot(basis_product,(np.linalg.inv(np.dot(np.transpose(\\n basis_product),basis_product)+lamb_x*S_x+lamb_y*S_y+lamb_x* K_x+lamb_y*K_y))),np.transpose(basis_product)),y_n)\\n return model_fit,GSV\",\n \"def main():\\n # Load data and pre-process it\\n path = \\\"data2.csv\\\"\\n features, labels, df = load_data(path)\\n\\n # Learning rates - including our own\\n learning_rates = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 0.75]\\n\\n # Num iterations\\n num_iterations = [100 for i in range(9)] + [1000]\\n\\n # Keep track of all final weights for different learning rates\\n lines = []\\n\\n # Compute weights for each learning rate\\n for rate, num_iters in zip(learning_rates, num_iterations):\\n\\n # Get weights from gradient descent and add to weights list\\n weights = gradient_descent(features, labels, rate, num_iters)\\n lines.append([rate, num_iters] + weights)\\n\\n # Now write 'lines' to file\\n with open('results2.csv', \\\"w\\\") as out_file:\\n for line in lines:\\n out_file.write(\\\"{}, {}, {}, {}, {} \\\\n\\\".format(line[0], line[1],\\n line[2], line[3], line[4]))\\n out_file.close()\\n\\n # Select which weights to use for plotting\\n index = -1\\n\\n\\n plot_db.visualize_3d(df, lin_reg_weights=lines[index][2:],\\n feat1='norm_x1', feat2='norm_x2', labels='label',\\n xlim=(-1, 1), ylim=(-1, 1), zlim=(0, 3),\\n alpha=learning_rates[index], xlabel='age',\\n ylabel='weight', zlabel='height',\\n title='')\",\n \"def VFI(method) :\\n iteration=0 # Iteration Counter\\n converged = 0 # Convergence Flag|\\n \\n#----- Initial Settings \\n v_update = zeros(n_grid)\\n v_func = empty(n_grid)\\n k_next_vec = empty(n_grid)\\n run_time = empty(2)\\n \\n def obj(k_next) :\\n \\\"\\\"\\\"\\n This function is used in value function iteration.\\n It represents the objective function to be maximized for one node (state) of current capitals.\\n Resulting value is maximized one corresponding to next period's capital as a maximizer. \\n Next period's value is computed by interpolation.\\n \\n Input : k_next (next period's capital)\\n \\n Output : value_vec (maximized value resulting from choosing optimal capital in the next period)\\n \\\"\\\"\\\" \\n \\n if method==1 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*linear_interp(k_grid,v_update,k_next))\\n elif method==2 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*quad_interp(k_grid,v_update,k_next))\\n elif method==3 :\\n value_vec = -1 * (log(A*k_current**(alpha) - k_next) + beta*cubic_interp(k_grid,v_update,k_next))\\n \\n return value_vec\\n\\n#----- Value function iteration\\n start = time.time() # start time\\n while converged==0 :\\n index = 0\\n for k_current in k_grid :\\n k_next = fminbound(obj,k_grid[0],k_grid[-1])\\n v_func[index] = (-1) * obj(k_next)\\n k_next_vec[index] = k_next\\n index = index + 1\\n dist = abs(max(v_func - v_update))\\n if dist<tol :\\n converged = 1\\n v_k, g_k = v_func, k_next_vec\\n v_update = v_func\\n print \\\"Iteration : \\\",iteration,\\\"\\\",\\\"Distance : \\\",dist # convergence process\\n iteration = iteration + 1\\n v_func = empty(n_grid) \\n k_next_vec = empty(n_grid)\\n \\n end = time.time() # end time\\n run_time[0],run_time[1] = runtime_cal(start,end) # total running time\\n \\n return v_k, g_k, run_time, iteration\",\n \"def load_hyperparams():\\n #Load halo data (encoding='latin1' for Python3)\\n with open('../Data/halo_data.pkl', 'rb') as halo_input:\\n halo_data = pickle.load(halo_input, encoding='latin1')\\n\\n #Load interpolator\\n with open('../Data/interpolator.pkl', 'rb') as interp:\\n vpeak_Mr_interp = pickle.load(interp, encoding='latin1')\\n\\n #Cosmological params\\n cosmo_params = {}\\n cosmo_params['omega_b'] = 0.0 \\n cosmo_params['omega_m'] = 0.286\\n cosmo_params['h'] = 0.7\\n\\n #hyperparameters\\n hparams = {}\\n hparams['mpeak_cut'] = 10**7\\n hparams['vpeak_cut'] = 10.\\n hparams['vmax_cut'] = 9.\\n hparams['orphan_radii_cut'] = 300.\\n hparams['chi'] = 1.\\n hparams['R0'] = 10.0\\n hparams['gamma_r'] = 0.0\\n hparams['beta'] = 0.\\n hparams['O'] = 1.\\n hparams['n_realizations'] = 5\\n\\n #prior hyperparameters\\n prior_hparams = {}\\n prior_hparams['alpha'] = np.array([-2.,-1.1])\\n prior_hparams['sigma_M'] = np.array([0.,2.])\\n prior_hparams['M50'] = np.array([7.35,10.85])\\n prior_hparams['sigma_mpeak'] = np.array([1e-5,1.])\\n prior_hparams['B'] = np.array([1e-5,3.])\\n prior_hparams['A'] = np.array([10.,500.])\\n prior_hparams['sigma_r'] = np.array([1e-5,2.])\\n prior_hparams['n'] = np.array([0.,2.])\\n prior_hparams['Mhm'] = np.array([5.,9.])\\n\\n #Orphan hyperparameters\\n orphan_params = {}\\n orphan_params['eps'] = 0.01 \\n orphan_params['df'] = 1\\n\\n #Simulation and LMC indices\\n sim_indices = {}\\n sim_indices['host'] = [0,1]\\n sim_indices['LMC'] = [0,0]\\n\\n return hparams, prior_hparams, cosmo_params, orphan_params, halo_data, sim_indices, vpeak_Mr_interp\",\n \"def main():\\n Nrep = 8 # number of repetition of EM steps\\n nm = 3 # number of mixed gaussians.\\n ns = 300 # number of samples.\\n \\n mu, sg, lm, lm_ind, smp, L_true = generate_synthetic_data(nm, ns)\\n plt.figure(1, figsize=(5,4))\\n plt.clf()\\n plot_synthetic_data(smp, mu, sg, lm, lm_ind, nm, ns)\\n \\n mue, sge, lme = generate_initial_state(nm, ns)\\n axi = 0 # subplot number\\n plt.figure(2, figsize=(12,9))\\n plt.clf()\\n for rep in range(Nrep):\\n # E-step\\n r, L_infer = e_step(smp, mue, sge, lme, nm, ns)\\n axi += 1 \\n ax = plt.subplot(Nrep/2, 6, axi)\\n plot_em_steps(smp, r, mue, sge, lme, nm, ns)\\n ax.set_title('E-step : %d' % (rep + 1))\\n ax.set_yticklabels([])\\n ax.set_xticklabels([])\\n ax.set_ylim((-0.1, 0.3))\\n\\n # M-step\\n mue, sge, lme = m_step(smp, r, nm, ns)\\n axi += 1 \\n ax = plt.subplot(Nrep/2, 6, axi)\\n plot_em_steps(smp, r, mue, sge, lme, nm, ns)\\n ax.set_title('M-step : %d' % (rep + 1))\\n ax.set_yticklabels([])\\n ax.set_xticklabels([])\\n ax.set_ylim((-0.1, 0.3))\\n\\n # plot the ground truth for comparison\\n axi += 1 \\n ax = plt.subplot(Nrep/2, 6, axi)\\n plot_synthetic_data(smp, mu, sg, lm, lm_ind, nm, ns)\\n ax.set_title('grn_truth')\\n ax.set_yticklabels([])\\n ax.set_xticklabels([])\\n ax.set_ylim((-0.1, 0.3))\\n\\n print('L_infer = %2.6f , L_true = %2.6f' % (L_infer, L_true))\",\n \"def plotDistributionWithLimitsRefine(lXs, llYs, lKClassif,out=\\\"out.png\\\", title=\\\"title\\\", xax=\\\"xax\\\", yax=\\\"yax\\\",legend=\\\"\\\"):\\n\\n fig = plt.Figure(figsize=(40,20))\\n fig.suptitle(title, fontsize=32)\\n nbPlots = len(llYs)\\n sqrt = int(math.ceil(math.sqrt(nbPlots)))\\n ymax = 0.0\\n for i,val in enumerate(llYs):\\n if lKClassif[i] != \\\"refine\\\":\\n ymax = max(max(val[0]),ymax)\\n ymaxCurrent = max(max(val[2]),ymax)\\n ymax = ymax*1.05\\n xmax = 147\\n gs = gridspec.GridSpec(1,2) \\n ax = fig.add_subplot(gs[0])\\n gsLimit = gridspec.GridSpecFromSubplotSpec(sqrt,sqrt, subplot_spec=gs[1])\\n for i,val in enumerate(llYs):\\n if lKClassif[i] != \\\"refine\\\":\\n ax.plot(lXs,val[0],color=Graphics.lColors[i%25])\\n axCurrent = fig.add_subplot(gsLimit[i]) \\n axCurrent.fill_between(lXs, val[1], val[2], alpha=0.35, edgecolor='black', facecolor=Graphics.lColors[i%25])\\n axCurrent.set_title(\\\"Cluster K{}, (position: {})\\\".format(i,lKClassif[i]))\\n axCurrent.fill_between(lXs, val[3], val[4], alpha=0.85, edgecolor='darkgray', facecolor='lightgray')\\n axCurrent.plot(lXs,val[0],color=Graphics.lColors[i%25])\\n axCurrent.set_ylim(0,ymaxCurrent)\\n axCurrent.set_xlim(1,xmax)\\n axCurrent.text(10, ymaxCurrent*0.90, \\\"#nucleosomes: {}\\\".format(legend[i]), fontsize=12)\\n axis_font = {'size':'28'}\\n ax.set_ylim(0,ymax)\\n ax.set_xlim(1,xmax)\\n ax.legend([\\\"K{}\\\".format(x) for x in range(0,nbPlots)])\\n ax.set_title(\\\"all nucleosomes\\\", **axis_font)\\n ax.set_xlabel(xax, **axis_font)\\n ax.set_ylabel(yax, **axis_font)\\n ax.tick_params(labelsize=20)\\n canvas = FigureCanvasAgg(fig)\\n canvas.print_figure(out, dpi=80)\",\n \"def __init__(self):\\n # Set constants\\n self.fromHztoeV = 6.58e-16\\n self.gramstoeV = 1 / ( 1.78 * 1e-33)\\n self.mtoev = 1/(1.97 * 1e-7) \\n self.H0 = cosmo.H(0).value * 1e3 / (1e3 * const.kpc.value) #expressed in 1/s\\n self.rhocritical = cosmo.critical_density(0).value * self.gramstoeV /(1e-2)**3 # eV/m**3\\n self.Om0 = cosmo.Om0 #total matter \\n self.OLambda0 = cosmo.Ode0 # cosmological constant\\n self.DM0 = self.Om0 - cosmo.Ob0 # dark matter\\n self.evtonJoule = 1.60218 * 1e-10 # from eV to nJ\\n self.evtoJoule = 1.60218 * 1e-19 # from eV to J\\n PSgal1h = np.loadtxt(\\\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_1h.dat\\\")\\n PSgal2h = np.loadtxt(\\\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_2h.dat\\\")\\n self.Mpc = 1e3 * const.kpc.value\\n self.zmin = 0.001\\n self.zmax = 30.001\\n self.zbins = 301\\n self.h = cosmo.h\\n self.z_vect = np.linspace(self.zmin, self.zmax, self.zbins)\\n self.k_vect = PSgal1h[:,0]* self.h\\n self.Power1h = PSgal1h[:,1:]/(self.h**3)\\n self.Power2h = PSgal2h[:,1:]/(self.h**3)\\n self.Power = self.Power1h + self.Power2h\\n self.Praw_prova1h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power1h))\\n self.Praw_prova2h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power2h))\\n self.Praw_prova = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power))\",\n \"def display1(*args):\\n #----------*----------* # unpack\\n twiss_func = args[0]\\n lat_plot = args[3]\\n #-------------------- beta x,y & dispersion x\\n s = [twiss_func(i,'s') for i in range(twiss_func.nbpoints)]\\n bx = [twiss_func(i,'bx') for i in range(twiss_func.nbpoints)]\\n by = [twiss_func(i,'by') for i in range(twiss_func.nbpoints)]\\n dx = [twiss_func(i,'dx') for i in range(twiss_func.nbpoints)]\\n #-------------------- lattice viseo\\n vzero = [0. for i in range(lat_plot.nbpoints)] # zero line\\n vis_abszisse = [lat_plot(i,'s') for i in range(lat_plot.nbpoints)]\\n vis_ordinate = [lat_plot(i,'viseo') for i in range(lat_plot.nbpoints)]\\n #-------------------- figure frame\\n width=14; height=7.6\\n plt.figure(num=0,figsize=(width,height),facecolor='#eaecef',tight_layout=False)\\n\\n #-------------------- transverse X\\n splot111=plt.subplot(111)\\n splot111.set_title('beta functions')\\n plt.plot(s,bx, label=r'$\\\\beta_x$ [m]', color='red', linestyle='-') # beta x\\n plt.plot(s,by, label=r'$\\\\beta_y$ [m]', color='blue', linestyle='-') # beta y\\n plt.plot(s,dx, label=r'$\\\\eta_x$ [m]' , color='green',linestyle='-') # dispersion x\\n vscale=splot111.axis()[3]*0.25\\n viseoz = [x*vscale for x in vis_ordinate]\\n plt.plot(vis_abszisse,viseoz,label='',color='black')\\n plt.plot(vis_abszisse,vzero,color='black')\\n plt.legend(loc='lower right',fontsize='x-small')\",\n \"def graphe_regrets(rgt_alea, rgt_glou, rgt_glou_e, rgt_ucb):\\n T = np.arange(rgt_alea.size)\\n fig, ax = plt.subplots()\\n ax.grid(True)\\n plt.xlabel(\\\"T\\\")\\n plt.ylabel(\\\"Regret\\\")\\n \\n ax.plot(T, rgt_alea, label = 'aléatoire') \\n ax.plot(T, rgt_glou, label = 'glouton')\\n ax.plot(T, rgt_glou_e, label = 'e-glouton')\\n ax.plot(T, rgt_ucb, label = 'UCB')\\n ax.legend(loc = \\\"upper left\\\")\\n plt.title(\\\"Regrets des 4 algorithmes par rapport à T\\\")\",\n \"def task2_extra2():\\n N = 0\\n lam = 0\\n L = 10\\n h = 0.001\\n tau = 0.000099\\n aa = numpy.array([0.25*a for a in range((L-1)*4)])\\n x = numpy.linspace(-L, L, int(2*L/h) + 1)\\n Vm = V1D(lam, x)\\n # eps=int(0.1*len(x))\\n\\n iterss = []\\n for a in aa:\\n print(a)\\n state = phi(N, x-a)\\n\\n iters = 0\\n while True:\\n prob = numpy.abs(state)**2\\n mid = int(2*L/h) // 2\\n # if max(prob) in prob[mid-eps:mid+eps]:\\n if numpy.argmax(prob) <= mid:\\n print(iters)\\n iterss.append(iters)\\n break\\n\\n state[0] = 0\\n state[-1] = 0\\n state = implicit_scheme_step(state, tau, h, Vm)\\n iters += 1\\n\\n fig = plt.figure()\\n plt.title(\\\"Iterations of Gaussian travel to center ($L={}$)\\\".format(L))\\n plt.xlabel(\\\"$a$\\\")\\n plt.ylabel(\\\"Time\\\")\\n plt.plot(aa, tau*numpy.array(iterss))\\n plt.show()\\n fig.savefig(\\\"naloga2_iters_of_gaussian_travel_fixedL={}.pdf\\\".format(L), bbox_inches=\\\"tight\\\")\",\n \"def growth_curve(userinputs, filter, catalog):\\n logging.info('Running growth curve analysis on {}'.format(catalog))\\n # Load the photometry results from the catalog (that is returned by the phot\\n # function)\\n aper_st, flux_st = np.loadtxt(catalog, unpack=True, usecols=(0,3))\\n\\n #Growth curve is only done on the ref image so we get the filter from userinp.\\n ref_filter = filter\\n\\n ratio_st = np.empty(len(aper_st))\\n\\n #number of apertures\\n naper = 20\\n\\n # Calculate the number of stars, make sure it is an integer\\n nstar = int(len(aper_st)/naper)\\n logging.info('Number of stars used: {}'.format(nstar))\\n aper_ind = naper - 1\\n\\n for k in range(nstar):\\n\\n for i in range(naper):\\n\\n ratio_st[i + k*naper] = flux_st[i + k*naper]/flux_st[aper_ind + k*naper]\\n\\n\\n # Find median ratio at each aperture between all the stars and all the clusters\\n med_st = np.empty(naper)\\n\\n for i in range(naper):\\n\\n med_st[i] = np.median(ratio_st[i::naper])\\n\\n\\n # Plot growth curves\\n logging.info('Creating Growth curve plots')\\n fig = plt.figure(figsize = (7,7))\\n\\n aper_x = np.arange(naper) + 1\\n\\n for i in range(nstar):\\n\\n ratio_y = ratio_st[i*naper:(i + 1)*naper]\\n plt.plot(aper_x, ratio_y, 'y-')\\n plt.annotate(str(i + 1), xy=(8.0, ratio_y[7]),\\n horizontalalignment='left', verticalalignment='top', fontsize=6)\\n\\n\\n plt.plot(aper_x, med_st, 'r-' , linewidth=4.0)\\n plt.hlines(0.5, 0, 20, color='black', linewidth=2, zorder=10)\\n plt.vlines(4, 0, 1.1, color='black', linewidth=2, linestyle='dashed', zorder=10)\\n plt.vlines(5, 0, 1.1, color='black', linewidth=2, linestyle='dashed', zorder=10)\\n plt.vlines(6, 0, 1.1, color='black', linewidth=2, linestyle='dashed', zorder=10)\\n\\n plt.ylabel('Normalized Flux ' + ref_filter.upper())\\n plt.xlabel('Radius (pix)')\\n plt.xlim(1,20)\\n plt.minorticks_on()\\n\\n fig.savefig(userinputs['OUTDIR'] + '/plots/plot_growth_curve_{}.pdf'.format(ref_filter))\",\n \"def plot_zcalib(args):\\n\\n start_date = args.start_date\\n end_date = args.end_date\\n\\n start_date_dt = dp.parse(start_date) \\n end_date_dt = dp.parse(end_date) \\n \\n min_date = dp.parse(SETTINGS.MIN_START_DATE)\\n max_date = dp.parse(SETTINGS.MAX_END_DATE)\\n \\n if start_date_dt < min_date or end_date_dt > max_date:\\n raise ValueError(f'Date must be in range {SETTINGS.MIN_START_DATE} - {SETTINGS.MAX_END_DATE}')\\n\\n phi_dir = os.path.join(SETTINGS.PHI_DIR)\\n img_dir = os.path.join(SETTINGS.Z_CALIB_DIR,'images/')\\n if not os.path.exists(img_dir):\\n os.makedirs(img_dir)\\n filelist1 = glob.glob(phi_dir+\\\"phiest*\\\")\\n filelist2 = glob.glob(phi_dir+\\\"phiobs*\\\")\\n filelist1.sort()\\n filelist2.sort()\\n dates=[]\\n ind=[]\\n\\n if len(filelist1) != len(filelist2):\\n raise ValueError(\\\"Number of phiest and phiobs files does not match!\\\")\\n\\n #Loop through files to find the indices of those between the inputted start and end dates\\n for f in range(0,len(filelist1)):\\n match = re.search(r'\\\\d{8}',filelist1[f])\\n file=match.group()\\n file_dt=dp.parse(file)\\n if file_dt >= start_date_dt and file_dt <= end_date_dt:\\n ind.append(f)\\n dates.append(file)\\n\\n ndays=len(ind)\\n print(ndays)\\n\\n #If the number of elevation angles in the volumes changes over time, then the total number of rays also varies\\n #This loop finds the maximum number of rays\\n for f in range(0,ndays):\\n file=np.load(filelist1[ind[f]])\\n if f==0:\\n [_,a]=file.shape\\n nrays=a\\n else:\\n [_,a2]=file.shape\\n if a2>a:\\n nrays=a2\\n\\n #Number of volumes can vary each day \\n nvols=250\\n phiest=np.zeros((ndays,nvols,nrays))*np.nan\\n phiobs=np.zeros((ndays,nvols,nrays))*np.nan\\n good_rays=np.zeros((ndays,nvols))*np.nan\\n x=np.zeros((nvols,nrays))*np.nan\\n \\n #Load each phiest and phiobs data for each day and store into 3D array. \\n #Calculate number of good rays for each day/volume\\n d=0\\n for f in range(0,ndays):\\n phiest1 = np.load(filelist1[ind[f]])\\n [a,b] = phiest1.shape\\n phiest[d,0:a,0:b] = phiest1 \\n phiobs1 = np.load(filelist2[ind[f]])\\n [a,b] = phiobs1.shape\\n phiobs[d,0:a,0:b] = phiobs1\\n d=d+1\\n \\n #Calculate number of good rays in each volume. good_rays(ndays,nvols)\\n for j in range(ndays): \\n for i in range(nvols):\\n good_rays[j,i] = np.nansum(np.isfinite(phiest[j,i,:]))\\n\\n #bias_each_ray (ndays,nvols,nrays)\\n #Calculate a bias/offset for each individual ray\\n bias_each_ray = (phiest - phiobs) / phiobs\\n \\n #Only use volumes with more than 10 good rays for calculation of overall bias.\\n ind = good_rays>10\\n \\n #SINGLE VALUES FOR WHOLE TIME PERIOD\\n mean_bias = np.nanmean(bias_each_ray[ind,:])\\n mean_bias_db = 10.0*np.log10(1000+mean_bias*1000)-30\\n \\n median_bias = np.nanmedian(bias_each_ray[ind,:])\\n median_bias_db = 10.0*np.log10(1000.0+median_bias*1000.0)-30.0\\n \\n std = np.nanstd(bias_each_ray[ind,:])\\n std_db = 10.0*np.log10(1000.0+std*1000.0)-30.0\\n \\n #print 'Mean bias = ', mean_bias_db, 'Median bias = ', median_bias_db, 'Standard Deviation = ', std_db\\n \\n #DAILY VALUES OF BIAS\\n mean_bias_each_day=np.zeros(ndays)*np.nan\\n median_bias_each_day=np.zeros(ndays)*np.nan\\n std_each_day=np.zeros(ndays)*np.nan\\n std_error_each_day = np.zeros(ndays)*np.nan\\n num_rays_day=np.zeros(ndays)\\n \\n for day in range(ndays):\\n #good_rays has shape (days,vols)\\n #find index for volumes with more than 10 good rays\\n ind = good_rays[day,:]>10\\n #find all rays on each day within these volumes\\n bias_one_day = bias_each_ray[day,ind,:].flatten()\\n ind2 = np.isfinite(bias_one_day) \\n if np.sum(ind2)>0:\\n std_error_each_day[day] = scipy.stats.sem(bias_one_day[ind2])\\n mean_bias_each_day[day] = np.nanmean(bias_one_day)\\n median_bias_each_day[day] = np.nanmedian(bias_one_day)\\n std_each_day[day] = np.nanstd(bias_one_day)\\n \\n #Number of rays for each day\\n num_rays_day[day] = np.sum(np.isfinite(bias_one_day))\\n\\n #Convert to dB \\n mean_bias_each_day_db = 10.0*np.log10(1000.0+mean_bias_each_day*1000.0)-30.0\\n median_bias_each_day_db = 10.0*np.log10(1000.0+median_bias_each_day*1000.0)-30.0\\n std_each_day_db = 10.0*np.log10(1000.0+std_each_day*1000.0)-30.0\\n std_error_each_day_db = 10.0*np.log10(1000.0+std_error_each_day*1000.0)-30.0\\n \\n #Put data into dataframe\\n time = pd.to_datetime(dates, format = '%Y%m%d')\\n data = pd.DataFrame({'Mean Bias' : mean_bias_each_day_db, 'Median Bias' : median_bias_each_day_db, \\n 'Standard Error' : std_error_each_day_db, 'Standard Deviation' : std_each_day_db}, \\n index=time) \\n \\n #Make plot \\n fig, ax1 = plt.subplots(figsize=(15,8)) \\n plt.errorbar(data.index, mean_bias_each_day_db, std_error_each_day_db, \\n color='black',fmt='o',markersize='4', elinewidth=2,capsize=4)\\n plt.plot([start_date_dt, end_date_dt],[median_bias_db,median_bias_db],'r-',\\n label=\\\"Median Bias = %s\\\" % round(median_bias_db,2))\\n plt.plot([start_date_dt, end_date_dt],[mean_bias_db,mean_bias_db],'g', \\n label=\\\"Mean Bias = %s\\\" % round(mean_bias_db,2))\\n plt.plot([start_date_dt, end_date_dt],[mean_bias_db+std_db*2,mean_bias_db+std_db*2],'g--',\\n label=\\\"Standard Deviation = %s\\\" % round(std_db,2))\\n plt.plot([start_date_dt, end_date_dt],[mean_bias_db-std_db*2,mean_bias_db-std_db*2],'g--')\\n \\n plt.plot(data.index, median_bias_each_day_db,'rx')\\n \\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%y'))\\n plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=1))\\n plt.gca().xaxis.set_minor_locator(mdates.WeekdayLocator(interval=1))\\n plt.xlim(start_date_dt,end_date_dt)\\n\\n plt.xticks(rotation=90)\\n plt.xlabel('Time',{'fontsize':18})\\n plt.ylabel('Z Bias (dBZ)',{'fontsize':18})\\n plt.yticks(size=18)\\n plt.xticks(size=18)\\n plt.grid()\\n plt.legend(loc=0,fontsize=18)\\n \\n #If you want to overlay number of rays for each data point then uncomment these lines.\\n #May need some tweaking to get the yaxis scale correct for the data you are plotting. \\n# ax2=ax1.twinx()\\n# ax2.set_ylim(0,20000)\\n# ax2.plot(data.index, num_rays_day,'bx-')\\n# ax2.set_yticks([5000, 10000])\\n# ax2.set_yticks([1000, 2000, 3000, 4000, 7500],minor=True)\\n# plt.ylabel('Total number of Rays',{'fontsize':18})\\n# plt.yticks(size=18)\\n# plt.xlim(start_date_dt,end_date_dt)\\n\\n #Save the plot\\n imgname = f'{img_dir}/Z_calibration_{start_date}_{end_date}.png'\\n plt.tight_layout()\\n plt.savefig(imgname,dpi=150)\",\n \"def reconstruct(self, lam = 0.0001, display=False):\\n \\n self.history = {}\\n \\n x = utils.initImage(self.data, self.fov, self.scales[0], self.pulse_ft)\\n \\n if display:\\n plt.ion()\\n plt.show()\\n self.__fig, self.__axs = plt.subplots(1, 2)\\n plt.subplots_adjust(wspace=-0.1)\\n self.__display(x, np.zeros_like(x), 'Initialization')\\n \\n self.history['init'] = x\\n \\n for scale in self.scales:\\n\\n x = utils.upscaleImage(x, self.fov, scale, self.pulse)\\n \\n gammas = (utils.ftVectors(self.data['bi_uvcoord1'], self.fov, \\n scale, self.pulse_ft),\\n utils.ftVectors(self.data['bi_uvcoord2'], self.fov, \\n scale, self.pulse_ft),\\n utils.ftVectors(self.data['bi_uvcoord3'], self.fov, \\n scale, self.pulse_ft))\\n \\n for beta in self.betas:\\n \\n # (a) solve for Z while keeping x constant\\n Z = utils.mostLikelyPatches(x, beta, self.data, \\n self.patch_size, self.gmm)\\n \\n # (b) solve for x while keeping Z constant\\n x = utils.taylorExpansion(x, Z, beta, self.data, gammas,\\n self.patch_size, lam=lam)\\n \\n if display:\\n self.__axs[0].clear()\\n self.__axs[1].clear()\\n self.__display(x, Z, 'Scale: ' + str(scale) + '\\\\n' +\\\\\\n r'$\\\\beta$: ' + str(beta))\\n self.history[scale] = x\\n \\n if display:\\n plt.ioff()\\n \\n self.res = np.rot90(utils.upscaleImage(x, self.fov, \\n self.naxis, self.pulse),2)\\n return self.res\",\n \"def main():\\n \\n data_base = '/local/duman/SIMULATIONS/many_polymers_5/'\\n save_base = '/usr/users/iff_th2/duman/RolfData/many_polymers_5'\\n \\n \\n ## Index the data\\n# density = [0.08, 0.2, 0.4]\\n# xi_L = [0.05, 0.1, 0.2, 1.0, 2.5, 5.0, 8.0, 16.0]\\n# Pe = [3.0, 7.0, 15.0, 50.0, 150.0, 500.0, 750.0, 1500.0, 4375.0, 8000.0, 10000.0]\\n# kappa = [1.25, 2.5, 5.0, 25.0, 62.5, 125.0, 200.0, 400.0]\\n# fp = [0.0, 0.0048, 0.0112, 0.024, 0.08, 0.24, 0.8, 1.2, 2.4, 7.0]\\n \\n density = [0.2]\\n kappa = [1.25, 2.5, 5.0, 25.0, 62.5, 125.0, 200.0, 400.0]\\n xi_L = [0.05, 0.1, 0.2, 1.0, 2.5, 5.0, 8.0, 16.0]\\n fp = [0.0048, 0.0112, 0.024, 0.08, 0.24, 0.8, 1.2, 2.4, 7.0]\\n Pe = [3.0, 7.0, 15.0, 50.0, 150.0, 500.0, 750.0, 1500.0, 4375.0]\\n \\n ## Create points\\n points = []\\n for i, x in enumerate(xi_L):\\n for j, p in enumerate(Pe):\\n points.append( Phase(x, p, kappa[i], fp[j], 'short') ) \\n \\n for point in points:\\n point.analyse_type()\\n point.set_plot_props()\\n \\n long_xil = [0.05, 0.2, 1.0, 2.5, 16.0]\\n long_pe = [3.0, 150.0, 750.0, 8000.0, 10000.0]\\n long_kappa = [5.0, 20.0, 100.0, 250.0, 1600.0]\\n long_fp = [0.0003, 0.015, 0.075, 0.0, 0.0]\\n long_points = []\\n for i, x in enumerate(long_xil):\\n for j, p in enumerate(long_pe):\\n long_points.append( Phase(x, p, long_kappa[i], long_fp[j], 'long') ) \\n \\n for point in long_points:\\n point.determine_type()\\n point.set_plot_props()\\n \\n plot_data(points, long_points, save_base, xi_L, Pe)\",\n \"def fit_lorentzian(comp_key,p0,time_step,conn,func = fitting.fun_lorentzian,fig=None,wind=3):\\n\\n (fin,) = conn.execute(\\\"select fout from comps where comp_key = ?\\\",(comp_key,)).fetchone()\\n Fin = h5py.File(fin,'r')\\n g = Fin[fd('vanHove',comp_key)]\\n\\n temp = g.attrs['temperature']\\n dtime = g.attrs['dtime']\\n j = int(time_step/dtime)\\n count = g[fd('step',j)]['y']['disp_count'][:]\\n count += g[fd('step',j)]['x']['disp_count'][:]\\n edges = g[fd('step',j)]['y']['disp_edges'][:]\\n\\n \\n edges = np.array([np.mean(edges[j:j+wind]) for j in range(1,len(count)-wind,wind)])\\n count = np.array([np.sum(count[j:j+wind]) for j in range(1,len(count)-wind,wind)])\\n \\n \\n edges = edges[count>30]\\n count = count[count>30]\\n \\n\\n out = fitting.fit_curve(edges,count,p0,func)\\n fig = fitting.display_fit(edges,count,out.beta,func,fig)\\n print out.beta\\n return out,fig\",\n \"def plot_fig44a_wlc_inhomo_test7():\\n\\n fig, ax = plt.subplots(figsize=(default_width, default_height))\\n #paramters from calculation, all in nm\\n a = 5 #looping radius\\n DEL = 15.3 #spacing between nucleosomes\\n MINUS1NUC = -83.3 #distance between -1 nuc and TSS\\n PLUS1NUC = 0\\n NNUC = 50 #number of nucleosomes on each side of TSS\\n LPB = 0.34 #nm per basepair (as used in calculation)\\n\\n Llinks = np.concatenate(([245], np.tile(45, 49)))\\n Rlinks = np.tile(45, 49)\\n #plot as negative distance from TSS in bp\\n unwrap=0\\n ldna_Llinks = -1*convert.genomic_length_from_links_unwraps(Llinks, unwraps=unwrap)\\n ldna_Rlinks = convert.genomic_length_from_links_unwraps(Rlinks, unwraps=unwrap)\\n x = np.concatenate((ldna_Llinks[::-1], [0], ldna_Rlinks))\\n y = np.loadtxt('csvs/Sarah/pnucsite_test7.txt')\\n ax.plot(x, y, '-o', markersize=3, color=teal_flucts)\\n plt.xlabel(r'Distance from TSS (bp)')\\n plt.ylabel(r'$P_\\\\mathrm{loop}(a=5\\\\mathrm{nm}; L)\\\\;\\\\;\\\\;(\\\\mathrm{nm}^{-3})$')\\n plt.subplots_adjust(left=0.16, bottom=0.19, top=0.98, right=0.96)\\n plt.yscale('log')\\n #plt.ylim([10**-3, 10**-0.4])\\n plt.xlim([-10000, 10000])\\n plt.savefig(f'plots/thesis/fig44a_wlc-inhomo-looping.pdf', bbox_inches='tight')\",\n \"def main(gage_id: str, inputs_path: str = '', outputs_dir: str = '', data_type: str = 'Q', version: str = '2_2', \\r\\n max_cl: float = 0.99, adj_flows: float = 1.0, round_decimals: int = 1, verbose: bool = True, \\r\\n display_plots: bool = True, extrapolate: bool = True, exclude_tails: bool = True) -> pd.DataFrame:\\r\\n standard_aep = [0.9, 0.5, 0.2, 0.1, 0.05, 0.02, 0.01, 0.005, 0.002, 0.001, 5E-04, 2E-04, 1E-04, 5E-05, 2E-05, 1E-05,\\r\\n 5E-06, 2E-06, 1E-06] \\r\\n\\r\\n inputs_path = pl.Path(inputs_path)\\r\\n if inputs_path.suffix == '':\\r\\n if verbose:\\r\\n print('Specified inputs_path is to a directory, identifying .rpt files:')\\r\\n ssp_results = list_ssp_files(inputs_path, max_cl, verbose) \\r\\n df = make_ssp_table(ssp_results, version) \\r\\n elif inputs_path.suffix == '.csv':\\r\\n df = pd.read_csv(inputs_path, index_col = 0)\\r\\n assert df.index.name == 'AEP', ('The first column of the specified csv must be titled \\\"AEP\\\" and contain annual '\\r\\n 'exceedance probabilites.')\\r\\n if verbose:\\r\\n print('Specified inputs_path is to a csv, loaded table.\\\\n')\\r\\n else:\\r\\n raise NameError('The specified inputs_path is not a directory of .rpt files or a path to a csv file as '\\r\\n 'expected.')\\r\\n if data_type not in ['Q', 'P']:\\r\\n raise ValueError('The specified data_type may only be \\\"Q\\\" or \\\"P\\\" corresponding to discharge or precipitation, '\\r\\n 'respectively') \\r\\n df = monotonic_test(df, adj_flows, verbose)\\r\\n cl = list(map(float, df.columns)) \\r\\n clz = zvar(cl) \\r\\n aep = df.index \\r\\n aepz = zvar(aep) \\r\\n data = np.log10(df) \\r\\n q = binq(data)\\r\\n res = interp_aep(data, q, clz, aepz, extrapolate) \\r\\n restrans = zvar_inv(res, cl)\\r\\n aepm = mean_aep(restrans, exclude_tails) \\r\\n aepmz = zvar(aepm) \\r\\n standard_aepz = zvar(standard_aep) \\r\\n table = ffc_summary(standard_aep, standard_aepz) \\r\\n table['Q_Mean_cfs'] = np.round(10**table['AEPz'].apply(interp_q(aepmz, q)), round_decimals)\\r\\n table['Q_Median_cfs'] = np.round(10**table['AEPz'].apply(interp_q(aepz, np.array(data['0.5']))), round_decimals)\\r\\n mean_curve_table = table.copy().drop(columns=['AEPz'])\\r\\n if display_plots: \\r\\n plot_ssp_meanmedffc(table, gage_id, data_type) \\r\\n if data_type == 'P':\\r\\n mean_curve_table.rename(columns = {'Q_Mean_cfs': 'P_Mean_in', 'Q_Median_cfs': 'P_Median_in'}, inplace = True)\\r\\n if verbose:\\r\\n display(mean_curve_table.head(2)) \\r\\n if outputs_dir!='':\\r\\n make_directories([outputs_dir], verbose)\\r\\n mean_curve_table.to_csv(pl.Path(outputs_dir)/f'MeanCurve_{gage_id}.csv') \\r\\n return mean_curve_table\",\n \"def localization_full(Gint, focal_genes, \\n num_reps = 200,\\n method = 'LCC', \\n print_counter = False,\\n label = 'focal genes',\\n line_height = 0.1,\\n legend_loc = 'upper left'):\\n \\n numedges_list, numedges_rand, LCC_list, LCC_rand = localization(Gint, focal_genes, num_reps, \\n sample_frac = 1, \\n method = method, \\n plot = False,\\n print_counter = print_counter)\\n if method == 'numedges':\\n analysis_list = numedges_list\\n analysis_rand = numedges_rand\\n title = 'number of edges'\\n else:\\n analysis_list = LCC_list\\n analysis_rand = LCC_rand\\n title = 'largest connected component'\\n\\n # plot distributions for non-sampled case\\n fig, ax = plt.subplots(figsize = (12, 7))\\n sns.set_style('white') \\n plt.vlines(np.mean(analysis_list), ymin = 0, ymax = line_height, color = 'r', lw = 2, label = label)\\n sns.kdeplot(analysis_rand, ax = ax, color = 'k', lw = 2, alpha = 0.5, shade = True, label = 'random')\\n plt.legend(loc = legend_loc, fontsize = 12)\\n plt.ylabel('frequency', fontsize = 16)\\n plt.xlabel(title, fontsize = 16)\\n\\n # print the z-score and fdr\\n analysis_z = (np.mean(analysis_list) - np.mean(analysis_rand))/float(np.std(analysis_rand))\\n\\n print(1 - ndtr(analysis_z))\\n\\n plt.title('permutation p = ' + str(1 - ndtr(analysis_z)))\\n \\n return numedges_list, numedges_rand, LCC_list, LCC_rand\",\n \"def plot_curves_lenstool(label = ' ', courves_file = 'ce.dat', marker = 'm.', \\\\\\n plt_show = False):\\n# x_ca, y_ca = np.loadtxt( courves_file, usecols=(3, 4), unpack=True )\\n# x_cc, y_cc = np.loadtxt( courves_file, usecols=(1, 2), unpack=True )\\n\\n x_cc, y_cc, x_ca, y_ca = \\\\\\n np.loadtxt( courves_file, usecols=(1, 2, 3, 4), unpack=True )\\n\\n plt.figure(1, figsize=(16, 8))\\n\\n plt.subplot(1, 2, 1).set_aspect(1)\\n plt.plot(x_ca, y_ca, marker, linewidth=3, label = label)\\n axis_limit = max( 1.075*max(np.absolute(x_ca)), \\\\\\n 1.075*max(np.absolute(y_ca)) )\\n plt.axis([-axis_limit, axis_limit, -axis_limit, axis_limit])\\n\\n plt.subplot (1, 2, 2).set_aspect(1)\\n plt.plot(x_cc, y_cc, marker, linewidth=3, label = label)\\n axis_limit = max( 1.075*max(np.absolute(x_cc)), \\\\\\n 1.075*max(np.absolute(y_cc)) )\\n plt.axis([-axis_limit, axis_limit, -axis_limit, axis_limit])\\n plt.legend()\\n\\n if plt_show:\\n plt.show()\\n plt.close()\",\n \"def figure4():\\n\\n plot_settings = {'y_limits': [-80, -50],\\n 'x_limits': None,\\n 'y_ticks': [-80, -70, -60, -50],\\n 'locator_size': 5,\\n 'y_label': 'Voltage (mV)',\\n 'x_ticks': [],\\n 'scale_size': 20,\\n 'x_label': \\\"\\\",\\n 'scale_loc': 4,\\n 'figure_name': 'figure_4',\\n 'legend': ['control', 'apamin'],\\n 'legend_size': 8,\\n 'y_on': True}\\n line_styles = ['-', 'dotted']\\n\\n plt.figure(figsize=(5, 3), dpi=96)\\n\\n plt.subplot(2, 1, 1) # Generate figure 1 (top)\\n for ix, g_sk_bar in enumerate([0.3, 0]):\\n t, y = solver(100, g_sk_bar=g_sk_bar)\\n plt.plot(t, y[:, 0], c='k', linestyle=line_styles[ix])\\n alter_figure(plot_settings) # Alter figure for publication\\n\\n plt.subplot(2, 1, 2)\\n t1 = 1200\\n t, y = solver(t1, t_start=50, duration=t1, i_bias_on=0.33, g_sk_bar=0.03)\\n plt.plot(t, y[:, 0], 'k-')\\n\\n plot_settings['y_limits'] = [-100, 30]\\n plot_settings['x_limits'] = [0, t1]\\n plot_settings['y_ticks'] = [-80, -60, -40, -20, 0, 20]\\n plot_settings['locator_size'] = 10\\n plot_settings['scale_size'] = 100\\n plot_settings['legend'] = None\\n alter_figure(plot_settings, close=True) # Alter plot for publication\",\n \"def main():\\r\\n graphPerformance = False # Built in graphing ability, currently not functional, but mechanism is in place.\\r\\n trainData = \\\"2_1000_0_1600_0_0_CV_0_Train.txt\\\"\\r\\n testData = \\\"2_1000_0_1600_0_0_CV_0_Test.txt\\\"\\r\\n outProg = \\\"GH_GALE_ProgressTrack\\\"\\r\\n outPop = \\\"GH_GALE_PopulationOut\\\"\\r\\n bitLength = 1 # This implementation is not yet set up to handle other rule representations, or bit encoding lengths.\\r\\n CVpartitions = 10\\r\\n trackCycles = 1\\r\\n \\r\\n iterInput = '5.10.20' \\r\\n xdim = 10\\r\\n ydim = 10\\r\\n dist = 2\\r\\n wild = 0.75\\r\\n prune = 1\\r\\n \\r\\n #Figure out the iteration stops for evaluation, and the max iterations.\\r\\n iterList = iterInput.split('.')\\r\\n for i in range(len(iterList)):\\r\\n iterList[i] = int(iterList[i])\\r\\n lastIter = iterList[len(iterList)-1] \\r\\n\\r\\n #Sets up up algorithm to be run.\\r\\n GALEConstants.setConstants(prune, wild)\\r\\n e = GALE_Environment(trainData,testData,bitLength)\\r\\n sampleSize = e.getNrSamples()\\r\\n gale = GALE(e, outProg, outPop, bitLength, CVpartitions, graphPerformance, xdim, ydim, dist)\\r\\n \\r\\n #Set some GALE parameters.\\r\\n if trackCycles == 'Default':\\r\\n gale.setTrackingIterations(sampleSize)\\r\\n else:\\r\\n gale.setTrackingIterations(trackCycles) \\r\\n gale.setNumberOfTrials(lastIter, iterList) \\r\\n \\r\\n #Run the GALE Algorithm \\r\\n gale.runGALE()\",\n \"def prob4():\\n domain = np.linspace(-1, 1, 400)\\n n_array = [2**i for i in range(2,9)]\\n f = lambda x: 1 / (1 + 25*x**2)\\n plt.ion()\\n poly_error = []\\n cheby_error = []\\n for n in n_array:\\n x = np.linspace(-1, 1, n)\\n poly = BarycentricInterpolator(x)\\n poly.set_yi(f(x))\\n poly_error.append(la.norm(f(domain) - poly(domain), ord=np.inf))\\n y = np.array([(1/2)*(2*np.cos(j*np.pi/n)) for j in range(n+1)])\\n cheby = BarycentricInterpolator(y)\\n cheby.set_yi(f(y))\\n cheby_error.append(la.norm(f(domain) - cheby(domain), ord=np.inf))\\n plt.loglog(n_array, poly_error, label=\\\"equally spaced points\\\", basex=2)\\n plt.loglog(n_array, cheby_error, label=\\\"Chebyshev extremal points\\\", basex=2)\\n plt.legend()\\n plt.show()\",\n \"def plot_ri(ri_FW, G_FW, lims=None, compare=True, path=None):\\n nplots=len(ri_FW[0].keys())\\n ncols=3\\n nrows=int(np.ceil(nplots/ncols))\\n _, axes = plt.subplots(nrows,ncols, figsize=(13, 5*nrows))\\n ri_iter=dict()\\n i=0\\n j=0\\n\\n #ri_iter is the ri that has been prescribed as an argument to the optimization \\n for n in ri_FW[0].keys():\\n ri_iter[n]=[]\\n\\n for ri in ri_FW:\\n for n in ri.keys():\\n ri_iter[n].append(ri[n])\\n \\n #compute the net rebalancing flow at each node\\n #those are the real net flows that occur on the graph\\n if compare:\\n real_ri=dict()\\n G_ri=dict()\\n for n in G_FW[0].nodes():\\n real_ri[n]=np.zeros((len(ri_FW),))\\n G_ri[n]=np.zeros((len(ri_FW),))\\n for i in range(len(G_FW)):\\n G=G_FW[i]\\n for e in G.edges():\\n if e[1]!='R' and not e[1].endswith('_p'):\\n real_ri[e[0]][i]-=G[e[0]][e[1]]['f_r']\\n real_ri[e[1]][i]+=G[e[0]][e[1]]['f_r']\\n for n in G.nodes():\\n G_ri[n][i]=G.nodes[n]['ri']\\n i=0\\n for n in ri_iter.keys():\\n \\n if j==ncols:\\n j=0\\n i+=1\\n if i==nrows:\\n i=0\\n axes[i,j].plot(np.arange(1,len(ri_iter[n]),1),ri_iter[n][:-1], 'o-', label='Goal. ri_t')\\n if compare:\\n axes[i,j].plot(np.arange(1,len(ri_iter[n]),1),real_ri[n][1:], 'o--', label='net flows')\\n axes[i,j].plot(np.arange(1,len(ri_iter[n]),1),real_ri[n][1:]-ri_iter[n][:-1], 'o--', label='diff')\\n\\n # axes[i,j].plot(G_ri[n],'o--', label='G_ri')\\n axes[i,j].grid(True)\\n axes[i,j].set_xlabel('Iteration #')\\n axes[i,j].set_title(' node : ' + str(n))\\n axes[i,j].set_xlim(lims)\\n axes[i,j].set_xticks(np.arange(1,len(ri_iter[n]),1))\\n axes[i,j].legend()\\n # axes[i,j].set_ylim([np.min(ri_iter[n][-5:])*.95 , np.max(ri_iter[n][-5:])*1.05])\\n j+=1\\n \\n if path!=None:\\n plt.savefig(os.path.join(path,'ri.png'))\\n \\n plt.close()\\n return\",\n \"def main():\\n lake_drivers = Dynamic_Lake_Drivers()\\n #lake_drivers.prepare_orography_ICE5G_0k_uncorrected()\\n #lake_drivers.prepare_orography_ICE5G_0k_corrected()\\n #lake_drivers.prepare_orography_ICE6G_21k_corrected()\\n #lake_drivers.prepare_river_directions_with_depressions_from_glac1D()\\n #lake_drivers.evaluate_glac1D_ts1900_basins()\\n #import time\\n # start = time.time()\\n #lake_drivers.evaluate_ICE6G_lgm_basins()\\n # end = time.time()\\n # print(end - start)\\n #lake_drivers.prepare_basins_from_glac1D()\\n #lake_drivers.extract_lake_volumes_from_glac1D_basins()\\n #lake_drivers.connect_catchments_for_glac1D()\\n lake_drivers.connect_catchments_for_transient_run()\\n #lake_drivers.extract_volumes_for_transient_run()\\n #lake_drivers.add_10min_rmouth_to_transient_data()\\n #lake_drivers.expand_transient_data_catchments_to_include_rmouth()\\n #lake_drivers.remove_no_data_values_from_upscaled_MERIT_correction_set()\\n #lake_drivers.remove_disconnected_points_from_slm()\",\n \"def analyze(argv):\\n costs, tikhs, regs = get_l_curve_results(argv[0])\\n\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n plt.scatter(tikhs, costs)\\n for k in range(len(regs)):\\n exponent = math.log(regs[k], 10)\\n ax.annotate(r'$10^{{{0:.2f}}}$'.format(exponent),\\n xy = (tikhs[k], costs[k]),\\n xytext = (0, 30),\\n textcoords = 'offset points',\\n rotation = 45,\\n fontsize = 16)\\n plt.xlabel(r'Model norm (MPa $\\\\cdot$ m / a)', fontsize = 16)\\n plt.ylabel(r'Cost function (MPa $\\\\cdot$ m${}^3$ / a)', fontsize = 16)\\n plt.xlim(0, 1.15 * np.max(tikhs))\\n plt.ylim(0.95 * np.min(costs), 1.05 * np.max(costs))\\n plt.show()\\n\\n return regs, tikhs, costs\",\n \"def AGN_SB_diagnostic(**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n GR = glo.global_results()\\n\\n fig,ax = plt.subplots()\\n L_CII = getattr(GR,'L_[CII]158_sun')\\n\\n # x = getattr(GR,'L_[CII]158_sun')/getattr(GR,'L_[NII]122_sun')\\n x = getattr(GR,'L_[OIV]25_sun')/getattr(GR,'L_[OIII]88_sun')\\n y = getattr(GR,'L_[NeIII]15_sun')/getattr(GR,'L_[NeII]12_sun')\\n sc = ax.scatter(x,y,marker='o',s=3,alpha=0.6,c=np.log10(getattr(GR,'SFR')))\\n ax.set_xscale('log')\\n ax.set_yscale('log')\\n ax.set_xlim([8e-5,200])\\n ax.set_ylim([0.02,150])\\n # ax.set_xlabel('[CII]$_{158}$/[NII]$_{122}$')\\n plt.colorbar(sc,label='log(SFR)')\\n ax.set_xlabel('[OIV]$_{25.9}$/[OIII]$_{88}$')\\n ax.set_ylabel('[NeIII]$_{15.6}$/[NeII]$_{12.8}$')\\n if p.savefig:\\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \\n plt.savefig(p.d_plot + 'luminosity/ratio_%s' % ratio_name,dpi=300)\",\n \"def plotEnergiesOpt(monthlyData, optIdx):\\n \\n \\n dummyRange = np.asarray(range(len(optIdx)))\\n \\n fig = plt.figure(figsize=(16, 8))\\n \\n plt.suptitle('Energy Comparison')\\n ax1 = plt.subplot(1,1,1)\\n plt.plot(monthlyData['H'][optIdx, dummyRange], label = 'H', color='r')\\n plt.plot(monthlyData['C'][optIdx, dummyRange], label = 'C', color='b')\\n plt.plot(monthlyData['L'][optIdx, dummyRange], label = 'L', color='g')\\n plt.plot(monthlyData['PV'][optIdx, dummyRange], label = 'PV', color='c')\\n plt.plot(monthlyData['E_HCL'][optIdx, dummyRange], label = 'HCL', color='m')\\n plt.plot(monthlyData['E_tot'][optIdx, dummyRange], label = 'E_tot', color='k')\\n plt.ylabel('Energy [kWh]')\\n plt.xlim(0,288)\\n\\n# plt.legend()\\n \\n majorLocator = MultipleLocator(24)\\n majorFormatter = FormatStrFormatter('%d')\\n minorLocator = MultipleLocator(4)\\n minorFormatter = FormatStrFormatter('%d')\\n\\n ax1.xaxis.set_major_locator(majorLocator)\\n ax1.xaxis.set_major_formatter(majorFormatter)\\n ax1.xaxis.set_minor_locator(minorLocator)\\n# ax1.xaxis.set_minor_formatter(minorFormatter)\\n plt.grid(True, which=u'major')\\n \\n # Shrink current axis by 20%\\n box = ax1.get_position()\\n ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])\\n \\n # Put a legend to the right of the current axis\\n ax1.legend(loc='upper left', bbox_to_anchor=(1, 1.05))\\n# \\n\\n plt.xticks(range(0,288,24),('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'))\\n# ax2 = plt.subplot(2,1,2, sharex=ax1)\\n# plt.plot(multiplier*monthlyData[energyType][indices['H'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for H', color='r')\\n# plt.plot(multiplier*monthlyData[energyType][indices['C'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for C', color='b')\\n# plt.plot(multiplier*monthlyData[energyType][indices['L'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for L', color='g')\\n# plt.plot(multiplier*monthlyData[energyType][indices['PV'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for PV', color='c')\\n# plt.plot(multiplier*monthlyData[energyType][indices['E_HCL'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for HCL', color='m')\\n# plt.plot(multiplier*monthlyData[energyType][indices['E_tot'], dummyRange]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'optimized for E_tot', color='k')\\n# plt.plot(multiplier*monthlyData[energyType][indices['45'],:]-multiplier*monthlyData[energyType][indices[energyType], dummyRange], label = 'fixed at 45 deg', color='y')\\n# plt.ylabel('Energy Difference [kWh]')\\n# plt.legend()\\n#\\n# ax2.xaxis.set_major_locator(majorLocator)\\n# ax2.xaxis.set_major_formatter(majorFormatter)\\n# ax2.xaxis.set_minor_locator(minorLocator)\\n## ax2.xaxis.set_minor_formatter(minorFormatter)\\n# plt.grid(True, which='both')\\n# \\n return fig\",\n \"def ISM_effs(line,**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n GR = glo.global_results()\\n\\n L_tot = getattr(GR,'L_%s_sun' % line)\\n L_H2 = getattr(GR,'L_%s_H2_sun' % line)\\n L_HI = getattr(GR,'L_%s_HI_sun' % line)\\n L_HII = getattr(GR,'L_%s_HII_sun' % line)\\n M_HII = getattr(GR,'M_HII')\\n M_HI = getattr(GR,'M_HI')\\n M_H2 = getattr(GR,'M_H2')\\n M_gas = getattr(GR,'M_gas')\\n\\n fig, ax = plt.subplots(figsize=(8, 6 ),facecolor='w')\\n x=ax.hist(np.log10(L_tot[L_tot > 0]/M_gas[L_tot > 0]),bins=100,alpha=0.6,label='tot',color='grey')\\n x=ax.hist(np.log10(L_H2[L_H2 > 0]/M_H2[L_H2 > 0]),bins=100,alpha=0.6,label='H2',color='r')\\n x=ax.hist(np.log10(L_HI[L_HI > 0]/M_HI[L_HI > 0]),bins=100,alpha=0.8,label='HI',color='orange')\\n x=ax.hist(np.log10(L_HII[L_HII > 0]/M_HII[L_HII > 0]),bins=100,alpha=0.5,label='HII',color='b')\\n ax.set_xlabel('log efficiency of %s [L$_{\\\\odot}$/M$_{\\\\odot}$]' % line)\\n ax.set_ylabel('Number of galaxies')\\n plt.legend()\\n if p.savefig:\\n plt.savefig(p.d_plot + 'luminosity/ISM_effs_%s.png' % line)\",\n \"def plot_ebtel(data_directory,data_file,**kwargs):\\n \\n #Load the data\\n data = np.loadtxt(data_directory+data_file)\\n #Slice the array to get the vectors we want\\n time = data[:,0]\\n temp = data[:,1]\\n dens = data[:,2]\\n temp_apex = data[:,5]\\n dens_apex = data[:,6]\\n heat = data[:,10]\\n \\n #Set up the figure\\n fig,axes = plt.subplots(3,1,figsize=(12,8))\\n #Set the fontsize\\n fs = 16\\n\\n #Plot the heating\\n axes[0].plot(time,heat)\\n axes[0].set_ylabel(r'$h$ (erg cm$^{-3}$ s$^{-1}$)',fontsize=fs)\\n axes[0].set_title(r'EBTEL-C Parameters',fontsize=fs)\\n axes[0].set_xlim([time[0],time[-1]])\\n axes[0].locator_params(nbins=5)\\n axes[0].ticklabel_format(axis='y', style='sci', scilimits=(-2,2) )\\n #Plot the temperatures\\n axes[1].plot(time,temp/10**6,label=r'$T$')\\n axes[1].plot(time,temp_apex/10**6,'r--',label=r'$T_a$')\\n axes[1].set_ylabel(r'$T$ (MK)',fontsize=fs)\\n axes[1].legend(loc=1)\\n axes[1].set_xlim([time[0],time[-1]])\\n axes[1].locator_params(nbins=5)\\n #Plot the densities\\n axes[2].plot(time,dens/10**8,label=r'$n$')\\n axes[2].plot(time,dens_apex/10**8,'r--',label=r'$n_a$')\\n axes[2].set_xlabel(r'$t$ (s)',fontsize=fs)\\n axes[2].set_ylabel(r'$n$ ($10^{8}$ cm$^{-3}$)',fontsize=fs)\\n axes[2].set_xlim([time[0],time[-1]])\\n axes[2].locator_params(nbins=5)\\n axes[2].legend(loc=1)\\n\\n #Check if output filename is specified\\n if 'print_fig_filename' in kwargs:\\n plt.savefig(kwargs['print_fig_filename'],format='eps',dpi=1000)\\n else:\\n plt.show()\",\n \"def run(datadir, outdir, real_data_fnames, MT_green_func_fnames, single_force_green_func_fnames, data_labels, inversion_type, num_samples, comparison_metric, nlloc_hyp_filename, perform_normallised_waveform_inversion=True, compare_all_waveforms_simultaneously=False, cut_phase_start_vals=[], cut_phase_length=0, plot_switch=False, num_processors=1, set_pre_time_shift_values_to_zero_switch=True, only_save_non_zero_solns_switch=False, return_absolute_similarity_values_switch=False, invert_for_ratio_of_multiple_media_greens_func_switch=False, green_func_fnames_split_index=0, green_func_phase_labels=[], invert_for_relative_magnitudes_switch=False, rel_exp_mag_range=[1.0,1.0], auto_shift_for_best_fit=True, manual_indices_time_shift_MT=[], manual_indices_time_shift_SF=[]):\\n # Do some initial checks:\\n if auto_shift_for_best_fit:\\n manual_indices_time_shift_MT = np.zeros(len(real_data_fnames))\\n manual_indices_time_shift_SF = np.zeros(len(real_data_fnames))\\n else:\\n if (len(manual_indices_time_shift_MT)==0) and (len(manual_indices_time_shift_SF)==0):\\n print(\\\"Error. Because <auto_shift_for_best_fit> is set to false, manual_indices_time_shift_MT and or manual_indices_time_shift_SF are not specified. Exiting.\\\")\\n sys.exit()\\n\\n # Run specific multi medium inversion, if specified:\\n if invert_for_ratio_of_multiple_media_greens_func_switch:\\n # For muliple media greens functions:\\n run_multi_medium_inversion(datadir, outdir, real_data_fnames, MT_green_func_fnames, single_force_green_func_fnames, data_labels, inversion_type, perform_normallised_waveform_inversion, compare_all_waveforms_simultaneously, num_samples, comparison_metric, manual_indices_time_shift_MT, manual_indices_time_shift_SF, nlloc_hyp_filename, cut_phase_start_vals=cut_phase_start_vals, cut_phase_length=cut_phase_length, plot_switch=plot_switch, num_processors=num_processors, set_pre_time_shift_values_to_zero_switch=set_pre_time_shift_values_to_zero_switch, only_save_non_zero_solns_switch=only_save_non_zero_solns_switch, return_absolute_similarity_values_switch=return_absolute_similarity_values_switch, invert_for_ratio_of_multiple_media_greens_func_switch=invert_for_ratio_of_multiple_media_greens_func_switch, green_func_fnames_split_index=green_func_fnames_split_index, green_func_phase_labels=green_func_phase_labels, auto_shift_for_best_fit=auto_shift_for_best_fit)\\n else:\\n # Run for normal, single set of greens functions:\\n \\n # Load input data (completely, for specific inversion type):\\n real_data_array, green_func_array = get_overall_real_and_green_func_data(datadir, real_data_fnames, MT_green_func_fnames, single_force_green_func_fnames, inversion_type, manual_indices_time_shift_MT=manual_indices_time_shift_MT, manual_indices_time_shift_SF=manual_indices_time_shift_SF, cut_phase_start_vals=cut_phase_start_vals, cut_phase_length=cut_phase_length, set_pre_time_shift_values_to_zero_switch=set_pre_time_shift_values_to_zero_switch)\\n \\n # Perform the inversion:\\n M = perform_inversion(real_data_array, green_func_array)\\n M_amplitude = ((np.sum(M**2))**0.5)\\n\\n # And get forward model synthetic waveform result:\\n synth_forward_model_result_array = forward_model(green_func_array, M)\\n \\n # And plot the results:\\n if plot_switch:\\n plot_specific_forward_model_result(real_data_array, synth_forward_model_result_array, data_labels, plot_title=\\\"Initial theoretical inversion solution\\\", perform_normallised_waveform_inversion=perform_normallised_waveform_inversion)\\n \\n # And save least squares output:\\n # Set output arrays to equal least squares output: \\n MTs = M\\n similarity_curr_sample, shift_idxs = compare_synth_to_real_waveforms(real_data_array, synth_forward_model_result_array, comparison_metric, perform_normallised_waveform_inversion, compare_all_waveforms_simultaneously, auto_shift_for_best_fit) \\n MTp = np.array([similarity_curr_sample])\\n # And save data to MTFIT style file:\\n outdir_least_squares = outdir+\\\"/least_squares_result\\\"\\n os.system(\\\"mkdir -p \\\"+outdir_least_squares)\\n save_to_MTFIT_style_file(MTs, MTp, nlloc_hyp_filename, inversion_type, outdir_least_squares, shift_idxs=shift_idxs) # Saves pickled dictionary containing data from inversion\\n # And save most likely solution and real data waveforms to file:\\n if inversion_type == \\\"DC_single_force_couple\\\" or inversion_type == \\\"DC_single_force_no_coupling\\\" or inversion_type == \\\"DC_crack_couple\\\" or inversion_type == \\\"single_force_crack_no_coupling\\\":\\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:-1, np.where(MTp==np.max(MTp))[0][0]])\\n else:\\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:, np.where(MTp==np.max(MTp))[0][0]])\\n # And get shift associated with most likely model:\\n if len(shift_idxs) > 0:\\n shift_idxs_most_likely_result = np.array(shift_idxs)\\n else:\\n shift_idxs_most_likely_result = []\\n # And save:\\n save_specific_waveforms_to_file(real_data_array, synth_forward_model_most_likely_result_array, data_labels, nlloc_hyp_filename, inversion_type, outdir_least_squares, shift_idxs=shift_idxs_most_likely_result, normallise_data=perform_normallised_waveform_inversion)\\n \\n # And do Monte Carlo random sampling to obtain PDF of moment tensor:\\n MTs, MTp, MTp_absolute, shift_idxs_all_samples = perform_monte_carlo_sampled_waveform_inversion(real_data_array, green_func_array, num_samples, M_amplitude=M_amplitude,inversion_type=inversion_type, comparison_metric=comparison_metric, perform_normallised_waveform_inversion=perform_normallised_waveform_inversion, compare_all_waveforms_simultaneously=compare_all_waveforms_simultaneously, num_processors=num_processors, return_absolute_similarity_values_switch=return_absolute_similarity_values_switch, invert_for_relative_magnitudes_switch=invert_for_relative_magnitudes_switch, rel_exp_mag_range=rel_exp_mag_range, auto_shift_for_best_fit=auto_shift_for_best_fit)\\n \\n # Check that probability of output is non-zero:\\n if math.isnan(MTp[0]):\\n print(\\\"Error: Sum of probabilities is equal to zero - therefore no adiquate solution could be found and inversion is terminating.\\\")\\n sys.exit()\\n \\n # Remove zero probability values if specified:\\n if only_save_non_zero_solns_switch:\\n MTp, MTs = remove_zero_prob_results(MTp, MTs)\\n \\n # And plot most likely solution:\\n if plot_switch:\\n if inversion_type == \\\"DC_single_force_couple\\\" or inversion_type == \\\"DC_single_force_no_coupling\\\" or inversion_type == \\\"DC_crack_couple\\\" or inversion_type == \\\"single_force_crack_no_coupling\\\":\\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:-1, np.where(MTp==np.max(MTp))[0][0]])\\n else:\\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:, np.where(MTp==np.max(MTp))[0][0]])\\n plot_specific_forward_model_result(real_data_array, synth_forward_model_most_likely_result_array, data_labels, plot_title=\\\"Most likely Monte Carlo sampled solution\\\", perform_normallised_waveform_inversion=perform_normallised_waveform_inversion)\\n print(\\\"Most likely solution:\\\", MTs[:,np.where(MTp==np.max(MTp))[0][0]])\\n \\n # And save data to MTFIT style file:\\n save_to_MTFIT_style_file(MTs, MTp, nlloc_hyp_filename, inversion_type, outdir, MTp_absolute=MTp_absolute, shift_idxs=shift_idxs_all_samples) # Saves pickled dictionary containing data from inversion\\n # And save most likely solution and real data waveforms to file:\\n if inversion_type == \\\"DC_single_force_couple\\\" or inversion_type == \\\"DC_single_force_no_coupling\\\" or inversion_type == \\\"DC_crack_couple\\\" or inversion_type == \\\"single_force_crack_no_coupling\\\":\\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:-1, np.where(MTp==np.max(MTp))[0][0]])\\n else:\\n synth_forward_model_most_likely_result_array = forward_model(green_func_array, MTs[:, np.where(MTp==np.max(MTp))[0][0]])\\n # And get shift associated with most likely model:\\n if len(shift_idxs_all_samples) > 0:\\n shift_idxs_most_likely_result = np.array(shift_idxs_all_samples)[0, np.where(MTp==np.max(MTp))[0][0]]\\n else:\\n shift_idxs_most_likely_result = []\\n # And save:\\n save_specific_waveforms_to_file(real_data_array, synth_forward_model_most_likely_result_array, data_labels, nlloc_hyp_filename, inversion_type, outdir, shift_idxs=shift_idxs_most_likely_result, normallise_data=perform_normallised_waveform_inversion)\\n\\n print(\\\"Finished\\\")\",\n \"def main():\\n\\n #for row_start in np.arange(0, 128):\\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_row'+str(row_start).zfill(3), Imin=12, Imax=136)\\n\\n Marcov_Chain_MLE(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '', 160.0, [90.0, 70.0, 50.0, 30.0], 0.0, 0.5)\\n plt.show()\\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_all')\\n\\n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAsource_VBdrain', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaS-VbD_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, forward' , 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \\n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaD-VbS_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, reversed', 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \\n\\n #hist_IDS_VGS(0, 14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Hist-IDSAT_MLC-rv1-01020304_reverse-read_', range(0, 128), 'MLC programming {2ms, 10ms, 40ms, 200ms} pulses, VGS=1.8, VDS=2.0 for level=1-2-3-4\\\\nhistogram of read-IDSAT (VGS=VDS=0.8V)', 0, 150, 0, 150, 1000)\\n #\\n #t_label = []\\n #for t in np.arange(0, 0.002*(71) + 0.0001, 0.002):\\n # t_label.append(str(t))\\n #\\n ##MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [21], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row-21', Imin=82, Imax=142)\\n\\n #for row_start in np.arange(0, 128):\\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row_'+str(row_start).zfill(3), Imin=80, Imax=142)\\n\\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01', Imin=80, Imax=142)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01', 20, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle0102', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle0102', 20, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle010203', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle010203', 20, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01020304', 40, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01020304', 10, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 50, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01', 20, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 50, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle0102', 20, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 50, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle010203', 20, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 40, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01020304', 10, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 40, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\\n\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\\n\\n\\n # (L, Nfin, VT_flavor, Nrow, Imax)\\n col_list = [(36, 1, 'ULVT', 32 , 60 ), (36, 1, 'LVT', 32 , 50 ), (36, 1, 'SVT', 32 , 45 ),\\n (36, 1, 'ULVT', 128, 60 ), (36, 1, 'LVT', 128, 50 ), (36, 1, 'SVT', 128, 45 ),\\n (20, 1, 'ULVT', 32 , 75 ), (20, 1, 'LVT', 32 , 60 ), (20, 1, 'SVT', 32 , 50 ),\\n (20, 1, 'ULVT', 128, 75 ), (20, 1, 'LVT', 128, 60 ), (20, 1, 'SVT', 128, 50 ),\\n (16, 1, 'ULVT', 32 , 80 ), (16, 1, 'LVT', 32 , 65 ), (16, 1, 'SVT', 32 , 60 ),\\n (16, 1, 'ULVT', 128, 80 ), (16, 1, 'LVT', 128, 65 ), (16, 1, 'SVT', 128, 60 ),\\n (36, 2, 'ULVT', 32 , 115), (36, 2, 'LVT', 32 , 95 ), (36, 2, 'SVT', 32 , 85 ),\\n (36, 2, 'ULVT', 128, 115), (36, 2, 'LVT', 128, 95 ), (36, 2, 'SVT', 128, 85 ), \\n (20, 2, 'ULVT', 32 , 135), (20, 2, 'LVT', 32 , 115), (20, 2, 'SVT', 32 , 100),\\n (20, 2, 'ULVT', 128, 135), (20, 2, 'LVT', 128, 120), (20, 2, 'SVT', 128, 100),\\n (16, 2, 'ULVT', 32 , 150), (16, 2, 'LVT', 32 , 125), (16, 2, 'SVT', 32 , 115),\\n (16, 2, 'ULVT', 128, 150), (16, 2, 'LVT', 128, 125), (16, 2, 'SVT', 128, 115)]\\n\\n #MLC_IDSAT_algorithm_rv1(11, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [135+20], [0.2], 1, np.arange(0, 0.01*16+0.0001, 0.01), '', ['../Data/chip11/MLC_programming_Chip11_Col21_2msPULSE_VG1p8_VD2p4_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_rv1_cycle01_EfficientPython')\\n\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', '0.9-1.2-1.5-1.8', 2.4, 128, range(0, 128), [59+16, 72+40, 80+31, 68+23], [0.2, 0.2, 0.2, 0.2], 4, [0, 15, 15.1, 37.5, 37.6, 59.8, 59.9, 78.1], ['0', '15', '', '37.4', '', '59.6', '', '77.8'], ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG-0p9-1p2-1p5-1p8_VD2p4', '_rv1_cycle01020304')\\n\\n t_ratio_lst = [(0, 0.17), (0.16, 0.34), (0.33, 0.505), (0.495, 0.67), (0.66, 0.84), (0.83, 1)]\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(59+16) + 0.0001, 0.2):\\n # t_label.append(str(t))\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(0, 128), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(59+16), t_ratio[1]*0.2*(59+16)])\\n # segment += 1\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(72+40) + 0.0001, 0.2):\\n # t_label.append(str(0.2*(59+16) + t))\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(0, 128), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(72+40), t_ratio[1]*0.2*(72+40)])\\n # segment += 1\\n\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(80+31) + 0.0001, 0.2):\\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + t))\\n ##MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(0, 128), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(80+31), t_ratio[1]*0.2*(80+31)])\\n # segment += 1\\n\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(68+23) + 0.0001, 0.2):\\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + 0.2*(80+31) + t))\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(68+23), t_ratio[1]*0.2*(68+23)])\\n # segment += 1\\n\\n\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 38, 112)\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle010203', 16, 110)\\n\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle010203', 44, 133)\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle010203', 14, 133)\\n\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle010203', 50, 135)\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 20, 140)\\n\\n\\n\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 38, 112)\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle01', 16, 110)\\n\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle01', 44, 133)\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle01', 14, 133)\\n\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle01', 50, 135)\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 20, 140)\\n\\n\\n\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 38, 112)\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle0102', 16, 110)\\n # \\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle0102', 44, 133)\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle0102', 14, 133)\\n # \\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle0102', 50, 135)\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 20, 140)\",\n \"def main():\\n print_banner()\\n params = read_steering()\\n s, x, y, cur, theta = build_kinoshita()\\n s, x, y, cur, theta = read_centerline(s, x, y, cur, theta)\\n s, x, y, cur, theta = extend_centerline(s, x, y, cur, theta)\\n for t in range(TSTEPS+1):\\n cur, theta = tan2curv(s, x, y)\\n cur_ori = np.copy(cur)\\n cur = filter_curvature(cur, t)\\n cur_flt = np.copy(cur)\\n cur = lag(s, cur, t)\\n cur_lag = np.copy(cur)\\n beck_bed = build_beck(cur, s, t)\\n allxyz = offset_all(x, y, beck_bed, t)\\n if t == 0:\\n write_xyz_file(allxyz)\\n write_mesh_file(allxyz, beck_bed)\\n oxbowxList, oxbowyList = [], []\\n centerlinexList, centerlineyList = [], []\\n if np.mod(t, GPRINT) == 0:\\n centerlinexList.append(x)\\n centerlineyList.append(y)\\n mf.make_figure(x, y, allxyz, cur_ori, cur_flt, cur_lag, s, beck_bed,\\n params, t, oxbowxList, oxbowyList, centerlinexList, centerlineyList)\\n if t == TSTEPS:\\n break\\n s, x, y = migration(s, x, y, cur_flt, cur_lag, theta, t)\\n s, x, y, oxbowx, oxbowy, found_cutoff = cutoff(s, x, y)\\n s, x, y = smooth_centerline(x, y)\\n s, x, y, cur, theta = resample_centerline(s, x, y)\\n if found_cutoff:\\n oxbowxList.append(oxbowx)\\n oxbowyList.append(oxbowy)\\n make_gif()\\n job_done()\",\n \"def gelplot_imshow(distances, bandwidths, intensities, lanes, names,\\n gel_len, wellx, welly, wellsep, res, cursor_ovr,\\n back_col, band_col, well_col, noise, Itol, title,\\n FWTM, show=True):\\n nlanes = len(lanes)\\n gel_width = sum(wellx) + (nlanes+1)*wellsep # cm\\n res = res.to('px/cm')\\n pxl_x = int(round(gel_width * res))\\n pxl_y = int(round(gel_len * res))\\n centers = [(l+1)*wellsep + sum(wellx[:l]) + 0.5*wellx[l]\\n for l in xrange(nlanes)]\\n rgb_arr = np.zeros(shape=(pxl_y, pxl_x, 3), dtype=np.float32)\\n bandlengths = wellx\\n # Paint the bands\\n for i in xrange(nlanes):\\n distXmid = centers[i]\\n pxlXmid = distXmid * res\\n bandlength = bandlengths[i]\\n from_x = int(round((distXmid - bandlength/2.0) * res))\\n to_x = int(round((distXmid + bandlength/2.0) * res))\\n for j in xrange(len(lanes[i])):\\n distYmid = distances[i][j]\\n pxlYmid = int(round(distYmid * res))\\n bandwidth = bandwidths[i][j] # w=FWHM or w=FWTM ???\\n if FWTM:\\n FWHM = Gauss_FWHM(bandwidth)\\n else:\\n FWHM = bandwidth\\n std_dev = Gauss_dev(FWHM)\\n maxI = intensities[i][j]\\n midI = Gaussian(distYmid, maxI, distYmid, std_dev)\\n if pxlYmid < len(rgb_arr): # band within gel frontiers\\n rgb_arr[pxlYmid, from_x:to_x] += midI\\n bckwdYstop = False if pxlYmid > 0 else True\\n forwdYstop = False if pxlYmid < len(rgb_arr)-1 else True\\n pxlYbck = pxlYmid-1\\n pxlYfor = pxlYmid+1\\n while not bckwdYstop or not forwdYstop:\\n if not bckwdYstop:\\n distYbck = Q_(pxlYbck,'px')/res\\n bckYI = Gaussian(distYbck, maxI, distYmid, std_dev)\\n if pxlYbck < len(rgb_arr):\\n rgb_arr[pxlYbck, from_x:to_x] += bckYI\\n pxlYbck -= 1\\n if bckYI <= Itol or pxlYbck == -1:\\n bckwdYstop = True\\n if not forwdYstop:\\n distYfor = Q_(pxlYfor,'px')/res\\n forYI = Gaussian(distYfor, maxI, distYmid, std_dev)\\n rgb_arr[pxlYfor, from_x:to_x] += forYI\\n pxlYfor += 1\\n if forYI <= Itol or pxlYfor == pxl_y:\\n forwdYstop = True\\n # Background color\\n if noise is None or noise <= 0:\\n rgb_arr += back_col\\n else:\\n bckg = np.random.normal(back_col, noise,\\n (len(rgb_arr), len(rgb_arr[0])))\\n rgb_arr += bckg[:,:,np.newaxis]\\n # Saturation\\n rgb_arr[rgb_arr > 1] = 1\\n rgb_arr[rgb_arr < 0] = 0\\n #bands_arr = np.ma.masked_where(rgb_arr == back_col, rgb_arr) #############\\n bands_arr = rgb_arr\\n # Plot\\n gel_len = gel_len.magnitude\\n gel_width = gel_width.magnitude\\n wellx = wellx.magnitude\\n welly = welly.magnitude\\n wellsep = wellsep.magnitude\\n centers = [c.magnitude for c in centers]\\n bandlengths = bandlengths.magnitude\\n bandwidths = [[bw.magnitude for bw in bwlane] for bwlane in bandwidths]\\n fig = plt.figure()\\n ax1 = fig.add_subplot(111, axisbg=str(back_col))\\n ax1.xaxis.tick_top()\\n ax1.yaxis.set_ticks_position('left')\\n ax1.spines['left'].set_position(('outward', 8))\\n ax1.spines['left'].set_bounds(0, gel_len)\\n ax1.spines['right'].set_visible(False)\\n ax1.spines['bottom'].set_visible(False)\\n ax1.spines['top'].set_visible(False)\\n ax1.spines['right'].set_color(str(back_col))\\n ax1.spines['bottom'].set_color(str(back_col))\\n ax1.xaxis.set_label_position('top')\\n plt.xticks(centers, names)\\n majorLocator = FixedLocator(range(int(gel_len+1)))\\n minorLocator = FixedLocator([j/10.0 for k in range(0, int(gel_len+1)*10, 10)\\n for j in range(1+k, 10+k, 1)])\\n ax1.yaxis.set_major_locator(majorLocator)\\n ax1.yaxis.set_minor_locator(minorLocator)\\n ax1.tick_params(axis='x', which='both', top='off')\\n bands_plt = ax1.imshow(bands_arr, extent=[0, gel_width, gel_len, 0],\\n interpolation='none')\\n # Draw wells\\n for i in xrange(nlanes):\\n ctr = centers[i]\\n wx = wellx[i]\\n wy = welly[i]\\n ax1.fill_between(x=[ctr-wx/2, ctr+wx/2], y1=[0,0],\\n y2=[-wy, -wy], color=str(well_col))\\n # Invisible rectangles overlapping the bands for datacursor to detect\\n bands = []\\n for i in xrange(nlanes):\\n bandlength = bandlengths[i]\\n center = centers[i]\\n x = center - bandlength/2.0\\n for j in xrange(len(lanes[i])):\\n dna_frag = lanes[i][j]\\n bandwidth = bandwidths[i][j]\\n dist = distances[i][j].magnitude\\n y = dist - bandwidth/2.0\\n band = plt.Rectangle((x,y), bandlength, bandwidth, fc='r',\\n alpha=0, label='{} bp'.format(len(dna_frag)))\\n plt.gca().add_patch(band)\\n bands.append(band)\\n plt.ylim(gel_len, -max(welly))\\n xlim = sum(wellx) + (nlanes+1)*wellsep\\n plt.xlim(0, xlim)\\n plt.ylabel('Distance (cm)')\\n plt.xlabel('Lanes')\\n bbox_args = dict(boxstyle='round,pad=0.6', fc='none')\\n an1 = plt.annotate(title, xy=(0,0),\\n xytext=(xlim+0.4, (gel_len+max(welly))/2.0),\\n va=\\\"center\\\", bbox=bbox_args)\\n an1.draggable()\\n plt.gca().set_aspect('equal', adjustable='box')\\n cursor_args = dict(display='multiple',\\n draggable=True,\\n hover = False,\\n bbox=dict(fc='white'),\\n arrowprops=dict(arrowstyle='simple',\\n fc='white', alpha=0.5),\\n xytext=(15, -15),\\n formatter='{label}'.format)\\n if cursor_ovr:\\n for key in cursor_ovr:\\n cursor_args[key] = cursor_ovr[key]\\n if cursor_args['hover'] == True: cursor_args['display'] = 'single'\\n datacursor(bands, **cursor_args)\\n #fig.savefig('example.png', dpi=300)\\n if show: plt.show()\\n return plt\",\n \"def optimize(self, acceleration=True, plot=False, fileExtension=None):\\n \\n x = np.zeros(self.nUnknowns+self.nLambda)\\n x[0:self.nUnknowns] = self.physicalToTransformed(self.initial)\\n\\n chi2 = 1e10\\n chi2Old = 1e20\\n relchi2 = np.abs((chi2 - chi2Old) / chi2Old)\\n xnew = np.copy(x)\\n \\n loop = 0 \\n loopInt = 0\\n \\n lambdaLM = 1e-3\\n chi2Best = 1e10\\n chi2Old = 1e10\\n nWorstChi2 = 0\\n\\n # dChi2Old = 0\\n\\n self.chi2 = []\\n self.l0 = []\\n self.l1 = []\\n \\n while ((relchi2 > 1e-6) & (loop < 20) & (nWorstChi2 < 8)):\\n\\n chi2, chi2NW, dChi2, ddChi2, stokes = self.computeFunctionAndGradient(x[0:self.nUnknowns], x[self.nUnknowns:])\\n\\n chi2Old = np.copy(chi2) \\n \\n H = 0.5 * ddChi2 \\n H += np.diag(lambdaLM * np.diag(H))\\n gradF = 0.5 * dChi2\\n\\n# First deal with the Hazel part\\n U, w, VT = np.linalg.svd(H[0:self.nUnknowns,0:self.nUnknowns], full_matrices=True)\\n\\n wmax = np.max(w)\\n wInv = 1.0 / w\\n wInv[w < 1e-6*wmax] = 0.0\\n\\n# xnew = xold - H^-1 * grad F\\n deltaxnew = -VT.T.dot(np.diag(wInv)).dot(U.T).dot(gradF[0:self.nUnknowns])\\n xnew[0:self.nUnknowns] = x[0:self.nUnknowns] + deltaxnew\\n \\n chi2, chi2NW, stokes = self.meritFunction(xnew[0:self.nUnknowns], xnew[self.nUnknowns:])\\n \\n if ((loop + 1) % 5 == 0):\\n thr = self.lambdaL1 \\n thr = self.lambdaL1 + 500.*self.lambdaL1*np.exp(-loop)\\n print(thr)\\n\\n if (self.wavelet == 'iuwt'):\\n tmp = (self.obs[0,:] - stokes[0,:])[:,None]\\n if (self.innerIterations == 1):\\n res = ps.sparse.iuwt_decomposition(tmp, self.nLevelsIUWT, 0, True)\\n res[0][np.abs(res[0]) < thr] = 0.0 \\n xnew[self.nUnknowns:] = ps.sparse.iuwt_recomposition(res[0], 0, res[1])[:,0]\\n else: \\n xnew[self.nUnknowns:] = ps.sparse.proxes.prox_l1General(tmp, self.forwardIUWT, self.backwardIUWT, \\n thr, threshold=self.thresholdIUWT, verbose=False)[:,0]\\n\\n if (self.wavelet == 'wavelet'):\\n xnew[self.nUnknowns:] = ps.sparse.proxes.prox_l1General(self.obs[0,:] - stokes[0,:], self.wavedec, self.waverec, thr, threshold='hard', verbose=False)\\n\\n xnew[self.nUnknowns:] /= (1.0+xnew[self.nUnknowns])\\n xnew[self.nUnknowns:] = upLimitThr(xnew[self.nUnknowns:], 0.0)\\n\\n #x = np.copy(xnew)\\n\\n chi2, chi2NW, stokes = self.meritFunction(xnew[0:self.nUnknowns], xnew[self.nUnknowns:])\\n \\n if (chi2NW < chi2Best):\\n if (lambdaLM >= 1e4):\\n lambdaLM /= 100.0\\n elif ((lambdaLM >= 1e-4) or (lambdaLM < 1e4)):\\n lambdaLM /= 10.0\\n elif(lambdaLM < 1e-4):\\n lambdaLM /= 5.0\\n if (lambdaLM < 1e-6):\\n lambdaLM = 1e-6\\n\\n chi2Best = np.copy(chi2NW)\\n x = np.copy(xnew)\\n nWorstChi2 = 0\\n else:\\n if (lambdaLM > 1e4):\\n lambdaLM *= 100.0\\n elif ((lambdaLM >= 1e-4) or (lambdaLM < 1e4)):\\n lambdaLM *= 10.0\\n elif(lambdaLM < 1e-4):\\n lambdaLM *= 5.0\\n nWorstChi2 += 1\\n\\n relchi2 = np.abs((chi2 - chi2Old) / chi2Old)\\n l1Norm = np.linalg.norm(x[self.nUnknowns:], 1)\\n\\n if (self.wavelet == 'iuwt'):\\n tmp = self.forwardIUWT(xnew[self.nUnknowns:][:,None])\\n l0Norm = np.sum(np.abs(tmp) > 1e-6)\\n if (self.wavelet == 'wavelet'): \\n l0Norm = np.sum(np.abs(self.wavedec(x[self.nUnknowns:])) > 1e-10)\\n \\n print(\\\"Iteration {0} - chi2={1:10.4f} - l1={2} - l0={3} - relchi2={4} - lambda={5}\\\".format(loop, chi2NW, l1Norm, l0Norm, relchi2, lambdaLM))\\n self.printNodes(x[0:self.nUnknowns])\\n\\n self.chi2.append(chi2NW)\\n self.l0.append(l0Norm)\\n self.l1.append(l1Norm)\\n\\n \\n loop += 1\\n\\n xPhysical = self.transformedToPhysical(x[0:self.nNodesTotal])\\n nodes = self.vector2Nodes(xPhysical)\\n stokes, cont, atmosNew = ps.radtran.synthLTENodes(self.referenceAtmos, nodes)\\n stokes /= self.contHSRA\\n\\n sys = x[self.nUnknowns:]\\n\\n np.savez( \\\"results/lte_{0}_lambda_{1}_inner_{2}.npz\\\".format(self.wavelet,fileExtension,self.innerIterations), self.obs, stokes, sys, self.chi2, x, self.wavelength, \\n self.l1, self.l0, self.maskChi2)\\n\\n\\n # pl.close('all')\\n\\n \\n # f, ax = pl.subplots(nrows=2, ncols=2, figsize=(12,9))\\n # ax = ax.flatten()\\n # labelStokes = ['I/Ic','Q/Ic','U/Ic','V/Ic']\\n # ax[0].plot(self.obs[0,:], label='obs')\\n # ax[0].plot(stokes[0,:] + sys, label='stokes+sys')\\n # ax[0].plot(1.0+sys, label='sys')\\n # ax[0].plot(stokes[0,:], label='stokes')\\n\\n # ax[0].legend()\\n\\n # ax[1].plot(self.obs[0,:] / (1.0+sys))\\n # ax[1].plot(stokes[0,:])\\n \\n # pl.tight_layout()\\n if (plot):\\n pl.savefig('/scratch/Dropbox/CONGRESOS/2015/Hinode9/code/systematicsExampleWithFit.png')\\n\\n \\n print(\\\"--------\\\")\\n print(\\\"l1 norm of systematics : {0}\\\".format(np.linalg.norm(x[self.nUnknowns:], 1)))\\n\\n return x\",\n \"def prob4():\\n print \\\"Lab pdf is wrong here\\\"\\n t=0.0\\n t1=[]\\n t2=[]\\n for i in xrange(5,12):\\n A=diag_dom(2**i,2**(i+1))\\n b=np.random.random((2**i,))\\n \\n t=time.time()\\n gauss_seidel(A,b,maxiters=1000)\\n t1+=[time.time()-t]\\n \\n t=time.time()\\n la.solve(A,b)\\n t2+=[time.time()-t]\\n\\n dom=2**np.arange(5,12) \\n plt.loglog(dom,t1,'b.-',basey=2,basex=2,lw=2, ms=2, label=\\\"Gauss-Seidel Approximation Time\\\")\\n plt.loglog(dom,t2,'r.-',basey=2,basex=2,lw=2, ms=2, label=\\\"la.solve(A,b)\\\")\\n plt.xlabel(\\\"Matrix size nxn\\\")\\n plt.ylabel(\\\"Time to Find Approximation\\\")\\n plt.legend(loc=\\\"upper left\\\")\\n plt.title(\\\"Temporal Complexity of Gauss-Seidel vs la.solve(A,b)\\\", fontsize=18)\\n plt.show()\",\n \"def P_AI_Rocky(in_dict):\\n # START\\n fs = 16\\n plt.rc('font', size=fs)\\n fig = plt.figure(figsize=(14,12))\\n ds = nc.Dataset(in_dict['fn'])\\n\\n # PLOT CODE\\n aa = [-122.8, -122.54, 47.92, 48.22]\\n import cmocean\\n cmap = cmocean.cm.balance\\n # cmap = 'RdYlBu_r'\\n\\n from warnings import filterwarnings\\n filterwarnings('ignore') # skip some warning messages\\n \\n # plot Code\\n \\n # calculate divergence and vorticity\\n uu = ds['u'][0, -1, :, :]\\n vv = ds['v'][0, -1, :, :]\\n u = zfun.fillit(uu)\\n v = zfun.fillit(vv)\\n u[np.isnan(u)] = 0\\n v[np.isnan(v)] = 0\\n \\n G = zrfun.get_basic_info(in_dict['fn'], only_G=True)\\n \\n dive = ((np.diff(u, axis=1)/G['DX'][:, 1:-1])[1:-1, :]\\n + (np.diff(v, axis = 0)/G['DY'][1:-1, :])[:, 1:-1])\\n #dive[G['mask_rho'][1:-1,1:-1]==False] = np.nan\\n \\n vort = np.diff(v, axis=1)/G['DX'][1:,1:] - np.diff(u, axis=0)/G['DY'][1:,1:]\\n #vort[G['mask_rho'][1:,1:]==False] = np.nan\\n \\n scl = 2e-3\\n \\n # panel 1\\n ax = fig.add_subplot(121)\\n # cs = plt.pcolormesh(G['lon_psi'], G['lat_psi'], dive/scl, cmap=cmap,\\n # vmin=-1, vmax=1)\\n cs = plt.pcolormesh(G['lon_rho'][1:-1,1:-1], G['lat_rho'][1:-1,1:-1], dive/scl, cmap=cmap,\\n vmin=-1, vmax=1, shading='gouraud')\\n tstr = (r'Surface Divergence (%0.1e $s^{-1}$)' % (scl))\\n #pfun.add_bathy_contours(ax, ds, txt=True)\\n pfun.add_coast(ax)\\n ax.axis(aa)\\n pfun.dar(ax)\\n ax.set_xlabel('Longitude')\\n ax.set_ylabel('Latitude')\\n ax.set_title(tstr)\\n pfun.add_info(ax, in_dict['fn'])\\n ax.set_xticks([-122.8, -122.7, -122.6])\\n ax.set_yticks([48, 48.1, 48.2])\\n #\\n # panel 2\\n ax = fig.add_subplot(122)\\n # cs = plt.pcolormesh(G['lon_rho'], G['lat_rho'], vort/scl, cmap=cmap,\\n # vmin=-1, vmax=1)\\n cs = plt.pcolormesh(G['lon_psi'], G['lat_psi'], vort/scl, cmap=cmap,\\n vmin=-1, vmax=1, shading='gouraud')\\n tstr = (r'Surface Vorticity (%0.1e $s^{-1}$)' % (scl))\\n ax.set_xticks([-122.8, -122.7, -122.6])\\n ax.set_yticks([])\\n #fig.colorbar(cs)\\n \\n # Inset colorbar\\n from mpl_toolkits.axes_grid1.inset_locator import inset_axes\\n cbaxes = inset_axes(ax, width=\\\"4%\\\", height=\\\"40%\\\", loc='lower left')\\n fig.colorbar(cs, cax=cbaxes, orientation='vertical')\\n \\n #pfun.add_bathy_contours(ax, ds)\\n pfun.add_coast(ax)\\n ax.axis(aa)\\n pfun.dar(ax)\\n ax.set_xlabel('Longitude')\\n ax.set_title(tstr) \\n \\n #fig.tight_layout()\\n # FINISH\\n ds.close()\\n if len(in_dict['fn_out']) > 0:\\n plt.savefig(in_dict['fn_out'])\\n plt.close()\\n else:\\n plt.show()\\n plt.rcdefaults()\",\n \"def main() -> None:\\n region = sleplet.slepian.Region(mask_name=\\\"africa\\\")\\n slepian = sleplet.slepian_methods.choose_slepian_method(L, region)\\n africa = sleplet.functions.SlepianAfrica(L, region=region, smoothing=SMOOTHING)\\n\\n # perform reconstruction\\n f = sleplet.slepian_methods.slepian_inverse(africa.coefficients, L, slepian)\\n\\n # plot\\n name = f\\\"africa_slepian_reconstruction_L{L}\\\"\\n sleplet.plotting.PlotSphere(\\n f,\\n L,\\n name,\\n normalise=NORMALISE,\\n region=slepian.region,\\n ).execute()\",\n \"def cgls_Ploak_Ribiere(self, iterations = None):\\n if self.iters == 0 :\\n self.r = - self.df(self.x)\\n self.r_old = self.r.copy()\\n self.d = self.r.copy()\\n self.delta_new = np.sum(self.r**2)\\n self.delta_0 = self.delta_new.copy()\\n # \\n if iterations == None :\\n iterations = self.imax\\n #\\n for i in range(iterations):\\n #\\n # perform a line search of f along d\\n self.x, status = self.line_search(self.x, self.d)\\n # \\n self.r = - self.df(self.x)\\n delta_old = self.delta_new\\n delta_mid = np.sum(self.r * self.r_old)\\n self.r_old = self.r.copy()\\n self.delta_new = np.sum(self.r**2)\\n #\\n # Polak-Ribiere formula\\n beta = (self.delta_new - delta_mid)/ delta_old\\n #\\n self.d = self.r + beta * self.d\\n #\\n # reset the algorithm \\n if (self.iters % 50 == 0) or (status == False) or beta <= 0.0 :\\n self.d = self.r\\n else :\\n self.d = self.r + beta * self.d\\n #\\n # calculate the error\\n self.errors.append(self.f(self.x))\\n self.iters = self.iters + 1\\n if self.iters > self.imax or (self.errors[-1] < self.e_tol):\\n break\\n #\\n #\\n return self.x\",\n \"def comp_vel(p1=database['K+'], p2=database['pi+'], p3=database['p+'], pmin=0, pmax=80):\\r\\n p_range = np.linspace(pmin, pmax, 1000)\\r\\n m1 = p1.mass\\r\\n m2 = p2.mass\\r\\n m3 = p3.mass\\r\\n v1, v2, v3 = [], [], []\\r\\n for p in p_range:\\r\\n v1.append(c*beta(p, m1))\\r\\n v2.append(c*beta(p, m2))\\r\\n v3.append(c*beta(p, m3))\\r\\n fig = plt.figure(figsize=[10, 5])\\r\\n ax = fig.add_subplot(1, 1, 1)\\r\\n p1_name = r'K$^+$'\\r\\n p2_name = r'$\\\\pi^+$'\\r\\n p3_name = r'p$^+$'\\r\\n ax.plot(p_range, v1, 'r', label=p1_name)\\r\\n ax.plot(p_range, v2, 'b', label=p2_name)\\r\\n ax.plot(p_range, v3, 'g', label=p3_name)\\r\\n ax.set_xlabel('p / GeV', fontsize=20)\\r\\n ax.set_ylabel(r'v / $ms^{-1}$', fontsize=20)\\r\\n ax.axvline(75, color='k', label='p = 75 GeV')\\r\\n ax.set_xticks(np.arange(pmin, pmax+1, 1))\\r\\n ax.set_xticklabels(np.arange(pmin, pmax+1, 1))\\r\\n ax.grid()\\r\\n ax.minorticks_on()\\r\\n ax.set_xlim(pmin, pmax)\\r\\n# ax.set_ylim(np.min(v1+v2))\\r\\n ax.legend(fontsize=20)\\r\\n plt.show\\r\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62898856","0.62495583","0.62437105","0.6178935","0.60789984","0.59641284","0.5952815","0.59437585","0.5928498","0.58640605","0.58632755","0.58602977","0.58514357","0.5839851","0.58129305","0.5812024","0.5801944","0.57913727","0.57665944","0.57651573","0.57607657","0.5738283","0.5722176","0.57193786","0.5710724","0.5702077","0.56800455","0.5653122","0.56439614","0.56413764","0.5640366","0.56224626","0.5600789","0.5595545","0.5591348","0.5553986","0.555348","0.55528706","0.55513626","0.5547745","0.5541795","0.5540215","0.5534558","0.5515776","0.55056334","0.5504904","0.5504135","0.55023307","0.5493978","0.5488765","0.5487188","0.54740375","0.54722667","0.54708123","0.5464526","0.54604346","0.5453375","0.5451181","0.5432337","0.5421085","0.5413737","0.5405757","0.5401832","0.5397742","0.5396486","0.53955215","0.53856474","0.53819925","0.538172","0.5379059","0.536849","0.53681964","0.53623974","0.5359938","0.53561926","0.5354222","0.5349605","0.5346016","0.5343854","0.5340401","0.53371584","0.53352666","0.5328862","0.5328356","0.5327075","0.5310109","0.5309558","0.53011566","0.5300764","0.5300669","0.5297699","0.52956015","0.52907926","0.52874196","0.52870643","0.5279585","0.52719986","0.5265952","0.52658874","0.52638656","0.5263835"],"string":"[\n \"0.62898856\",\n \"0.62495583\",\n \"0.62437105\",\n \"0.6178935\",\n \"0.60789984\",\n \"0.59641284\",\n \"0.5952815\",\n \"0.59437585\",\n \"0.5928498\",\n \"0.58640605\",\n \"0.58632755\",\n \"0.58602977\",\n \"0.58514357\",\n \"0.5839851\",\n \"0.58129305\",\n \"0.5812024\",\n \"0.5801944\",\n \"0.57913727\",\n \"0.57665944\",\n \"0.57651573\",\n \"0.57607657\",\n \"0.5738283\",\n \"0.5722176\",\n \"0.57193786\",\n \"0.5710724\",\n \"0.5702077\",\n \"0.56800455\",\n \"0.5653122\",\n \"0.56439614\",\n \"0.56413764\",\n \"0.5640366\",\n \"0.56224626\",\n \"0.5600789\",\n \"0.5595545\",\n \"0.5591348\",\n \"0.5553986\",\n \"0.555348\",\n \"0.55528706\",\n \"0.55513626\",\n \"0.5547745\",\n \"0.5541795\",\n \"0.5540215\",\n \"0.5534558\",\n \"0.5515776\",\n \"0.55056334\",\n \"0.5504904\",\n \"0.5504135\",\n \"0.55023307\",\n \"0.5493978\",\n \"0.5488765\",\n \"0.5487188\",\n \"0.54740375\",\n \"0.54722667\",\n \"0.54708123\",\n \"0.5464526\",\n \"0.54604346\",\n \"0.5453375\",\n \"0.5451181\",\n \"0.5432337\",\n \"0.5421085\",\n \"0.5413737\",\n \"0.5405757\",\n \"0.5401832\",\n \"0.5397742\",\n \"0.5396486\",\n \"0.53955215\",\n \"0.53856474\",\n \"0.53819925\",\n \"0.538172\",\n \"0.5379059\",\n \"0.536849\",\n \"0.53681964\",\n \"0.53623974\",\n \"0.5359938\",\n \"0.53561926\",\n \"0.5354222\",\n \"0.5349605\",\n \"0.5346016\",\n \"0.5343854\",\n \"0.5340401\",\n \"0.53371584\",\n \"0.53352666\",\n \"0.5328862\",\n \"0.5328356\",\n \"0.5327075\",\n \"0.5310109\",\n \"0.5309558\",\n \"0.53011566\",\n \"0.5300764\",\n \"0.5300669\",\n \"0.5297699\",\n \"0.52956015\",\n \"0.52907926\",\n \"0.52874196\",\n \"0.52870643\",\n \"0.5279585\",\n \"0.52719986\",\n \"0.5265952\",\n \"0.52658874\",\n \"0.52638656\",\n \"0.5263835\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":788,"numItemsPerPage":100,"numTotalItems":95772,"offset":78800,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjMwMTc1NywianRpIjoiNDFlNmU0YjQtZTc3Ny00NjYzLWIxZmMtYzkzMGM0NzhjNTIyIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYzMDUzNTcsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.YyfIY2wvJAVb4CxH2UcmRMJxPeliAVPpA7x8uESg_2Fj02jskzx7VyY48KfPZb2azRzc2n8Ngh93D3hstMf3CA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Returns the generator of | def ImageFrom(self, *args):
return _BRepAlgo.BRepAlgo_Image_ImageFrom(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generator(self):\n return [None, 1]",
"def __iter__(self):\n return self.new_generator()",
"def __iter__(self):\n yield from self.gen",
"def generator(self):\n return self._generator",
"def next(self):\n return next(self.gen)",
"def __next__(self):\n\t\treturn next(... | [
"0.79404163",
"0.7918392",
"0.7738084",
"0.76406306",
"0.74970204",
"0.7315077",
"0.7311586",
"0.7281729",
"0.7196769",
"0.7107632",
"0.7102853",
"0.7102853",
"0.7102853",
"0.7066889",
"0.70628047",
"0.7060039",
"0.705205",
"0.7013537",
"0.6943162",
"0.6943162",
"0.6941401",
... | 0.0 | -1 |
Returns the upper generator of | def Root(self, *args):
return _BRepAlgo.BRepAlgo_Image_Root(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generator(self):\n return [None, 1]",
"def upper(self):\n return asarray(upper(self))",
"def __reversed__(self): \n yield from self._traverse_backward(self.root)",
"def test_generator_upward(narrow_power_range):\n with patch('random.randint', side_effect=lambda a,b: 1):\n ... | [
"0.60364664",
"0.5688385",
"0.56213117",
"0.5604861",
"0.55862796",
"0.5522919",
"0.5509888",
"0.5479019",
"0.5426594",
"0.5424152",
"0.54210746",
"0.5410417",
"0.54048795",
"0.5371009",
"0.5363992",
"0.5357496",
"0.5340855",
"0.53278434",
"0.53191775",
"0.5314349",
"0.530818... | 0.0 | -1 |
Returns the Image of . Returns in the list if HasImage(S) is false. | def Image(self, *args):
return _BRepAlgo.BRepAlgo_Image_Image(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def image(images):\n return images[0]",
"def image_list(self):\n return self._image_list",
"def imageList(self):\n return self.__imageList",
"def get_image():\n return models.Image.objects.all()[0]",
"def image(self, name=None):\n return self.find(self.images(), name=name)",
... | [
"0.65314615",
"0.6324996",
"0.6283613",
"0.62743634",
"0.62229705",
"0.6198889",
"0.6184821",
"0.61594623",
"0.61504596",
"0.6045783",
"0.60433507",
"0.60303265",
"0.60279834",
"0.6024575",
"0.5979277",
"0.59509367",
"0.5948533",
"0.59441626",
"0.59319085",
"0.5929545",
"0.59... | 0.0 | -1 |
Stores in the images of images of...images of . contains only if HasImage(S) is false. | def LastImage(self, *args):
return _BRepAlgo.BRepAlgo_Image_LastImage(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def images_exist(self):\n pass",
"def is_new_based_on_imgs(soup):\n\n \n \n prev_hashes = get_prev_img_hashes()\n temp_hashes = get_temp_img_hashes(soup)\n\n if len(temp_hashes.difference(prev_hashes))>0:\n print(\"new, based on images\")\n return True\n else:\n retu... | [
"0.64222836",
"0.60871553",
"0.5958527",
"0.5944521",
"0.57835066",
"0.5639918",
"0.5607725",
"0.56000996",
"0.55904555",
"0.5569224",
"0.5565882",
"0.55637634",
"0.55468184",
"0.5535983",
"0.55163836",
"0.550949",
"0.5457397",
"0.5457057",
"0.54569876",
"0.54168576",
"0.5387... | 0.0 | -1 |
Keeps only the link between roots and lastimage. | def Compact(self, *args):
return _BRepAlgo.BRepAlgo_Image_Compact(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rememberRootURL():",
"def _links(self):\n if not self.is_root:\n return None\n if self._link_storage is None:\n self.arbor._build_attr(\"_link_storage\", self)\n return self._link_storage",
"def image_reference(self, image_id):\n info = self.image_info[imag... | [
"0.5610798",
"0.54741144",
"0.5473307",
"0.54716057",
"0.5461998",
"0.5272428",
"0.52661407",
"0.5263526",
"0.5223144",
"0.51833904",
"0.5129498",
"0.51124483",
"0.51074564",
"0.5052281",
"0.5049906",
"0.5035831",
"0.50332713",
"0.50332713",
"0.5017148",
"0.49999708",
"0.4963... | 0.0 | -1 |
Init with the set of edges must have pcurves on . | def Init(self, *args):
return _BRepAlgo.BRepAlgo_Loop_Init(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.delCurves = []\n return",
"def draw_edges():\n\n def bezier(p0, p1, p2, **kwargs):\n x0, y0 = p0\n x1, y1 = p1\n x2, y2 = p2\n xb = [\n (1 - t) ** 2 * x0 + 2 * t * (1 - t) * x1 + t *... | [
"0.61682373",
"0.6118325",
"0.60639524",
"0.59735173",
"0.59690154",
"0.5952848",
"0.5878638",
"0.5873813",
"0.5852583",
"0.58372134",
"0.57928324",
"0.5787087",
"0.577534",
"0.5755702",
"0.57443535",
"0.57016903",
"0.5666402",
"0.5664702",
"0.56506795",
"0.56304246",
"0.5628... | 0.0 | -1 |
Add E with . will be copied and trim by vertices in . | def AddEdge(self, *args):
return _BRepAlgo.BRepAlgo_Loop_AddEdge(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_edge(self, e):\n v, w = e\n self[v][w] = e\n self[w][v] = e",
"def CopyReplaceVertices(self, *args):\n return _ShapeBuild.ShapeBuild_Edge_CopyReplaceVertices(self, *args)",
"def edgeSplit(self, e, newCoords=None):\n assert (e.pFace is None or e.pFace.containsEdge(e)) ... | [
"0.6130455",
"0.58633107",
"0.5817108",
"0.58140856",
"0.55439997",
"0.5529",
"0.54320157",
"0.54156584",
"0.5411694",
"0.53584874",
"0.5344284",
"0.5317272",
"0.51816463",
"0.51699406",
"0.51607704",
"0.51530313",
"0.51142377",
"0.5096206",
"0.509287",
"0.5092844",
"0.508864... | 0.0 | -1 |
Add as const edge, E can be in the result. | def AddConstEdge(self, *args):
return _BRepAlgo.BRepAlgo_Loop_AddConstEdge(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addEdge(self, e):\n v = e.either()\n w = e.other(v)\n self._validateVertex(v)\n self._validateVertex(w)\n self._adj[v].add(e)\n self._adj[w].add(e)\n self._E += 1",
"def add_edge(self, e):\n a, b = e\n self[a][b] = e\n self[b][a] = e",
"def add_edge(self, e):\n ... | [
"0.7211913",
"0.6675955",
"0.667085",
"0.6334199",
"0.62683904",
"0.6137948",
"0.6120605",
"0.6069564",
"0.60568464",
"0.6040104",
"0.60051143",
"0.5988388",
"0.59750146",
"0.5960292",
"0.5952103",
"0.5939984",
"0.5931788",
"0.5870777",
"0.58619887",
"0.5810366",
"0.579694",
... | 0.6939654 | 1 |
Add as a set of const edges. | def AddConstEdges(self, *args):
return _BRepAlgo.BRepAlgo_Loop_AddConstEdges(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AddConstEdge(self, *args):\n return _BRepAlgo.BRepAlgo_Loop_AddConstEdge(self, *args)",
"def addEdges(self, edges):\n for edge in edges:\n self.addEdge(edge[0], edge[1], edge[2])",
"def add_all_edges(self):\n for n1 in self.vertices():\n for n2 in self.vertices():\n ... | [
"0.7046306",
"0.64266396",
"0.63983893",
"0.6357126",
"0.6321411",
"0.62738585",
"0.6187922",
"0.60680056",
"0.59887",
"0.5985752",
"0.597097",
"0.5963551",
"0.596135",
"0.59137535",
"0.58819777",
"0.5788639",
"0.5775192",
"0.5719451",
"0.56963676",
"0.5694593",
"0.56856745",... | 0.7485751 | 0 |
Cut the edge in several edges on the vertices. | def CutEdge(self, *args):
return _BRepAlgo.BRepAlgo_Loop_CutEdge(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cut_edge_based_on_other_edge(bm, edge, other_edge, cut_direction):\n new_edges = []\n new_verts = []\n target_faces = []\n for v in edge.verts:\n split_point = v.co + cut_direction\n e, new_vert = split_edge_at_point_from_closest_vert(other_edge, v, split_point)\n e = bmesh.ops... | [
"0.62837875",
"0.6094797",
"0.6000174",
"0.59297794",
"0.59142405",
"0.5832908",
"0.5784323",
"0.5767214",
"0.57564753",
"0.57304054",
"0.57289046",
"0.5636046",
"0.5623915",
"0.5606259",
"0.55841815",
"0.55755126",
"0.55632216",
"0.5559399",
"0.5559359",
"0.55513865",
"0.551... | 0.7193409 | 0 |
Returns the list of wires performed. can be an empty list. | def NewWires(self, *args):
return _BRepAlgo.BRepAlgo_Loop_NewWires(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wires(self):\n return [o.wires for o in self.obs]",
"def wires(self) -> Iterator[TopoDS_Wire]:\n return map(Wire, self._top_exp.wires())",
"def get_all_wifi(self):\n return list(self.collection.find({\"sensor_type\": \"wifi\"}, {\"_id\": False})) # Return a list",
"def wires(self):\... | [
"0.7884733",
"0.70887345",
"0.7003496",
"0.68727016",
"0.68129",
"0.68129",
"0.65752876",
"0.6518975",
"0.6518975",
"0.6518975",
"0.642296",
"0.64077765",
"0.6341834",
"0.63046914",
"0.6294988",
"0.625506",
"0.6237706",
"0.62376195",
"0.61910653",
"0.6157411",
"0.6106702",
... | 0.5504076 | 74 |
Build faces from the wires result. | def WiresToFaces(self, *args):
return _BRepAlgo.BRepAlgo_Loop_WiresToFaces(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_faces(self):\n for i, j in combinations(self.gens, 2):\n c0 = self.triangle_verts[self.vertex_at_mirrors(i, j)]\n f0 = []\n m = self.cox_mat[i][j]\n H = (i, j)\n type = 0\n if self.active[i] and self.active[j]:\n type =... | [
"0.6333746",
"0.6220341",
"0.584095",
"0.58127135",
"0.58002806",
"0.5661341",
"0.5653663",
"0.5646849",
"0.5539003",
"0.54848516",
"0.5479848",
"0.5455054",
"0.54154086",
"0.5395386",
"0.5383307",
"0.53753495",
"0.5358957",
"0.53468597",
"0.531035",
"0.5270992",
"0.5233197",... | 0.5459415 | 11 |
Returns the list of new edges built from an edge it can be an empty list. | def NewEdges(self, *args):
return _BRepAlgo.BRepAlgo_Loop_NewEdges(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_edge_list(self):\n return [(edge.value, edge.node_from.value, edge.node_to.value) for edge in self.edges]",
"def get_edges(self):\n try:\n temp = self.edges\n except:\n temp = []\n return temp",
"def _create_edge_ist(self) -> EdgeList:\r\n return... | [
"0.7429224",
"0.72771645",
"0.7195488",
"0.71634096",
"0.7082809",
"0.7026056",
"0.7021033",
"0.700611",
"0.6959626",
"0.6926502",
"0.6926502",
"0.6926502",
"0.69073725",
"0.68655765",
"0.6854366",
"0.68357295",
"0.676993",
"0.6733669",
"0.6700197",
"0.66938275",
"0.66740346"... | 0.63346565 | 56 |
Returns the datamap of vertices with their substitutes. | def GetVerticesForSubstitute(self, *args):
return _BRepAlgo.BRepAlgo_Loop_GetVerticesForSubstitute(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vert_ind_as_val(input_vertices):\n\tvertex_map = {}\n\tfor element in input_vertices:\n\t\tvertex_map[element] = element\n\treturn vertex_map",
"def get_vertices(self):\n return str(self.vert_dict.keys())",
"def obtener_vertices(self):\n return list(self.vertices.keys())",
"def get_vertices... | [
"0.58614796",
"0.5618814",
"0.5495101",
"0.53759533",
"0.5374195",
"0.53642493",
"0.531116",
"0.52988595",
"0.52684796",
"0.5238554",
"0.5228671",
"0.52214265",
"0.52214265",
"0.52214265",
"0.52155685",
"0.52054477",
"0.5196186",
"0.5187754",
"0.517995",
"0.5175423",
"0.51742... | 0.53150946 | 6 |
Add an edge or a wire to the list of shape to project | def Add(self, *args):
return _BRepAlgo.BRepAlgo_NormalProjection_Add(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_edge(self, e):\n v, w = e\n self[v][w] = e\n self[w][v] = e",
"def addEdge(self,u,v,w):\r\n self.graph.append([u,v,w])",
"def add_shape(self, shape):\n\n if isinstance(shape, Shape):\n self.shapes.append(shape)\n else:\n raise TypeError",
... | [
"0.59437746",
"0.5856103",
"0.57332313",
"0.57171166",
"0.5682549",
"0.56806207",
"0.5659431",
"0.56438947",
"0.5620642",
"0.56052876",
"0.5600976",
"0.5597647",
"0.55774224",
"0.5562483",
"0.55554557",
"0.5527331",
"0.5526795",
"0.54983586",
"0.5486293",
"0.54818237",
"0.544... | 0.0 | -1 |
Set the parameters used for computation Tol3d is the requiered tolerance between the 3d projected curve and its 2d representation InternalContinuity is the order of constraints used for approximation. MaxDeg and MaxSeg are the maximum degree and the maximum number of segment for BSpline resulting of an approximation. | def SetParams(self, *args):
return _BRepAlgo.BRepAlgo_NormalProjection_SetParams(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Tol3d(self, *args):\n return _Adaptor3d.Adaptor3d_TopolTool_Tol3d(self, *args)",
"def get_params3D(ima, xform = \"xform.align3d\"):\n\tt = ima.get_attr(xform)\n\td = t.get_params(\"spider\")\n\treturn d[\"phi\"],d[\"theta\"],d[\"psi\"],d[\"tx\"],d[\"ty\"],d[\"tz\"],d[\"mirror\"],d[\"scale\"]",
"def... | [
"0.6417356",
"0.5619551",
"0.5473504",
"0.5384897",
"0.53774697",
"0.53138775",
"0.5278674",
"0.52696157",
"0.5244743",
"0.5212177",
"0.52039826",
"0.5202426",
"0.51852477",
"0.5131529",
"0.5122184",
"0.5051622",
"0.50500596",
"0.5048082",
"0.5037627",
"0.5023085",
"0.5011095... | 0.0 | -1 |
Set the parameters used for computation in their default values | def SetDefaultParams(self, *args):
return _BRepAlgo.BRepAlgo_NormalProjection_SetDefaultParams(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_default_parameters(self):\n super().set_default_parameters()",
"def _default_parameters(cls) -> Options:\n params = super()._default_parameters()\n params.main_axes = None\n params.i_means = None\n params.q_means = None\n params.scales = None\n\n return pa... | [
"0.7848651",
"0.731754",
"0.7247119",
"0.7064335",
"0.70568067",
"0.7053067",
"0.7039655",
"0.70384187",
"0.70343184",
"0.70059544",
"0.6982248",
"0.69581896",
"0.692335",
"0.69154316",
"0.69147325",
"0.6892796",
"0.6870813",
"0.68666285",
"0.6856961",
"0.68546677",
"0.684576... | 0.6385395 | 95 |
Sets the maximum distance between target shape and shape to project. If this condition is not satisfied then corresponding part of solution is discarded. if MaxDist < 0 then this method does not affect the algorithm | def SetMaxDistance(self, *args):
return _BRepAlgo.BRepAlgo_NormalProjection_SetMaxDistance(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SetMaxTolerance(self, *args):\n return _ShapeUpgrade.ShapeUpgrade_ShapeDivide_SetMaxTolerance(self, *args)",
"def SetMaxTolerance(self, *args):\n return _ShapeUpgrade.ShapeUpgrade_Tool_SetMaxTolerance(self, *args)",
"def target_mode(self):\r\n if not self.in_target_mode:\r\n ... | [
"0.5773515",
"0.55397105",
"0.537085",
"0.53070605",
"0.5186212",
"0.5132834",
"0.51288295",
"0.5102438",
"0.50803",
"0.503841",
"0.49928033",
"0.4985218",
"0.4962091",
"0.49406153",
"0.49362886",
"0.4915126",
"0.4903148",
"0.4903148",
"0.48885536",
"0.4883879",
"0.48513916",... | 0.63957596 | 0 |
if With3d = Standard_False the 3dcurve is not computed the initial 3dcurve is kept to build the resulting edges. | def Compute3d(self, *args):
return _BRepAlgo.BRepAlgo_NormalProjection_Compute3d(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def D3(self, *args):\n return _Adaptor3d.Adaptor3d_Curve_D3(self, *args)",
"def RemoveCurve3d(self, *args):\n return _ShapeBuild.ShapeBuild_Edge_RemoveCurve3d(self, *args)",
"def D3(*args):\n return _Geom2dLProp.Geom2dLProp_Curve2dTool_D3(*args)",
"def Has3d(self, *args):\n return... | [
"0.69803053",
"0.69790787",
"0.6296154",
"0.6218713",
"0.62031525",
"0.61601573",
"0.6110127",
"0.61080956",
"0.60679334",
"0.5979571",
"0.5960561",
"0.5953705",
"0.591507",
"0.58673805",
"0.5858085",
"0.58526415",
"0.5824958",
"0.5807383",
"0.5793229",
"0.57730055",
"0.57436... | 0.5199893 | 50 |
Manage limitation of projected edges. | def SetLimit(self, *args):
return _BRepAlgo.BRepAlgo_NormalProjection_SetLimit(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_limits(self):\n S = self.structure\n self.limit_edges = []\n for u in S:\n # direct successors are fully known\n for v in (suc for suc in S.successors(u) if suc):\n S[u][v][\"upper_limit\"] = S[u][v][DIST]\n S[u][v][\"lower_limit\"] =... | [
"0.5725529",
"0.5669141",
"0.5660197",
"0.5653452",
"0.54620993",
"0.5417658",
"0.5376966",
"0.5368331",
"0.5293482",
"0.5276614",
"0.5274168",
"0.52734935",
"0.5241691",
"0.52151877",
"0.5212338",
"0.5209439",
"0.51887906",
"0.51676625",
"0.51416695",
"0.5139888",
"0.5139888... | 0.5382936 | 6 |
Builds the result as a compound. | def Build(self, *args):
return _BRepAlgo.BRepAlgo_NormalProjection_Build(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build(c):",
"def compound(self):\n return self._compound",
"def build(_):",
"def build():",
"def compose(self):\r\n return_lib = self.other\r\n return_lib.update({\r\n 'ID' : self.id,\r\n 'Name' : self.name,\r\n 'Alias' : self.alias,\r\n ... | [
"0.59723455",
"0.5771552",
"0.56610304",
"0.5645296",
"0.5584837",
"0.55202204",
"0.5478838",
"0.5463801",
"0.5402597",
"0.536524",
"0.52879417",
"0.52709746",
"0.52184033",
"0.5178762",
"0.51352626",
"0.5100086",
"0.5092237",
"0.5087989",
"0.5081667",
"0.5074326",
"0.5060444... | 0.0 | -1 |
For a resulting edge, returns the corresponding initial edge. | def Ancestor(self, *args):
return _BRepAlgo.BRepAlgo_NormalProjection_Ancestor(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_graph_first_edge(g):\n return g[\"edgeSet\"][0] if 'edgeSet' in g and g[\"edgeSet\"] else {}",
"def reverse(edge):\n return Edge(orig=edge.dest, dest=edge.orig, orig_id=edge.dest_id, dest_id=edge.orig_id)",
"def get_edge(self, edge):\n if len(edge) != 2:\n raise TypeError\n ... | [
"0.58714134",
"0.57738036",
"0.5592109",
"0.5563187",
"0.5553413",
"0.5516781",
"0.5451425",
"0.5447719",
"0.5327945",
"0.5284508",
"0.52071446",
"0.5197241",
"0.51811385",
"0.5129491",
"0.5120517",
"0.50300336",
"0.5022962",
"0.4997377",
"0.49945867",
"0.49932507",
"0.497378... | 0.0 | -1 |
For a projected edge, returns the corresponding initial face. | def Couple(self, *args):
return _BRepAlgo.BRepAlgo_NormalProjection_Couple(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def faces_from_edge(self, edge):\n assert isinstance(edge, Edge)\n return map(Face, self._top_exp.faces_from_edge(edge.topods_shape()))",
"def face(self):\n\n return self.faceup",
"def get_face(self, image):\n face = self.__detect_face(image)[0]\n x1, y1, x2, y2, _, _ = face.... | [
"0.6326266",
"0.6022131",
"0.59142417",
"0.5786397",
"0.56976974",
"0.56215537",
"0.56146854",
"0.56094706",
"0.5577278",
"0.5577278",
"0.55683047",
"0.5539707",
"0.55011696",
"0.54858553",
"0.5437727",
"0.5425079",
"0.5405903",
"0.5399007",
"0.5397073",
"0.5339341",
"0.53367... | 0.0 | -1 |
Returns the list of shapes generated from the shape . | def Generated(self, *args):
return _BRepAlgo.BRepAlgo_NormalProjection_Generated(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shapes(self):\n return [load_node(item) for item in self.get_attribute('shapes')]",
"def shapes(self):\n return self.container['shapes']",
"def getshapes(self):\n return sorted(self._shapes.keys())",
"def type_shapes(self):\n return self._type_shapes",
"def shape(self):",
"def... | [
"0.78419596",
"0.75871074",
"0.7399741",
"0.7292827",
"0.71652555",
"0.71652555",
"0.70627165",
"0.7032713",
"0.6962173",
"0.6954566",
"0.6947017",
"0.69395155",
"0.69376546",
"0.69070655",
"0.6901026",
"0.68835175",
"0.68499553",
"0.68491197",
"0.6842416",
"0.68217",
"0.6818... | 0.0 | -1 |
build the result as a list of wire if possible in a first returns a wire only if there is only a wire. | def BuildWire(self, *args):
return _BRepAlgo.BRepAlgo_NormalProjection_BuildWire(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_merged_all_node(lst: List[IntermediateStation]) -> Optional[IntermediateStation]:\n return next(filter(attrgetter(\"merged_all\"), lst), None)",
"def build_wires(self) -> List[Segment]:\n segments = [Segment(self.ORIGIN, self.ORIGIN.move_to(self._diagram[0]))]\n for step in self._diagram... | [
"0.53298795",
"0.53151935",
"0.51846665",
"0.50938267",
"0.49160537",
"0.48856074",
"0.48807946",
"0.48639023",
"0.48458937",
"0.48451367",
"0.4844374",
"0.4827442",
"0.48249894",
"0.48172957",
"0.4809209",
"0.48054534",
"0.47912017",
"0.47908917",
"0.47884992",
"0.47829214",
... | 0.46463734 | 34 |
Remove the non valid part of an offsetshape 1 Remove all the free boundary and the faces connex to such edges. 2 Remove all the shapes not valid in the result (according to the side of offseting) in this verion only the first point is implemented. | def Deboucle3D(*args):
return _BRepAlgo.BRepAlgo_Tool_Deboucle3D(*args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_dangling_points(xy, NL, KL, BL, check=False):\n dangles = np.where(~KL.any(axis=1))[0]\n if len(dangles) > 0:\n print 'le: remove_dangling_points: Removing dangling points: dangles = ', dangles\n if check:\n plt.plot(xy[:, 0], xy[:, 1], 'b.')\n for ii in range(l... | [
"0.58785754",
"0.5857711",
"0.57484597",
"0.5721556",
"0.5695583",
"0.5563021",
"0.5516007",
"0.5485433",
"0.54782903",
"0.5448066",
"0.5389216",
"0.5384667",
"0.53840625",
"0.5364062",
"0.53361607",
"0.531742",
"0.53164166",
"0.5309057",
"0.5307305",
"0.52896863",
"0.5269481... | 0.0 | -1 |
Remove the non valid part of an offsetshape 1 Remove all the free boundary and the faces connex to such edges. 2 Remove all the shapes not valid in the result (according to the side of offseting) in this verion only the first point is implemented. | def BRepAlgo_Tool_Deboucle3D(*args):
return _BRepAlgo.BRepAlgo_Tool_Deboucle3D(*args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_dangling_points(xy, NL, KL, BL, check=False):\n dangles = np.where(~KL.any(axis=1))[0]\n if len(dangles) > 0:\n print 'le: remove_dangling_points: Removing dangling points: dangles = ', dangles\n if check:\n plt.plot(xy[:, 0], xy[:, 1], 'b.')\n for ii in range(l... | [
"0.58785754",
"0.5857711",
"0.57484597",
"0.5721556",
"0.5695583",
"0.5563021",
"0.5516007",
"0.5485433",
"0.54782903",
"0.5448066",
"0.5389216",
"0.5384667",
"0.53840625",
"0.5364062",
"0.53361607",
"0.531742",
"0.53164166",
"0.5309057",
"0.5307305",
"0.52896863",
"0.5269481... | 0.0 | -1 |
Constructs the common part of shapes S1 and S2. | def __init__(self, *args):
_BRepAlgo.BRepAlgo_Common_swiginit(self,_BRepAlgo.new_BRepAlgo_Common(*args)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def blendshape(shape0:str, shape1:str, mix, points) -> core.BpfInterface:\n X, Y, kws = util.parseargs(*points)\n a = makebpf(shape0, X, Y)\n b = makebpf(shape1, X, Y)\n return core.blend(a, b, mix)",
"def convolved_shape(shape1, shape2, mode, axis):\n\n m, n = shape1[axis], shape2[axis]\n p, q... | [
"0.58307207",
"0.57843333",
"0.5770231",
"0.5759107",
"0.5725605",
"0.5708924",
"0.5704088",
"0.5677647",
"0.5672065",
"0.56407833",
"0.5633823",
"0.5530289",
"0.55280584",
"0.5514282",
"0.55104434",
"0.550918",
"0.54873985",
"0.54839",
"0.5462472",
"0.5439261",
"0.54316914",... | 0.0 | -1 |
Cuts the shape S2 from the shape S1. | def __init__(self, *args):
_BRepAlgo.BRepAlgo_Cut_swiginit(self,_BRepAlgo.new_BRepAlgo_Cut(*args)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cut_both(self):\n return TCut(self._return_if('_cut_both'))",
"def to_other(self, c2, cut=False):\n\n p = self.c.p\n\n p_v = p.v # p may be invalid by the time we want to use it\n\n c2, nd = self.unl_to_pos(c2, p)\n\n if c2 is None:\n return\n\n # in case... | [
"0.5555149",
"0.54492426",
"0.53406155",
"0.5334083",
"0.53154165",
"0.53094304",
"0.5263163",
"0.52425534",
"0.5156484",
"0.5151237",
"0.50962096",
"0.50957835",
"0.5075516",
"0.5050481",
"0.5041012",
"0.504091",
"0.50189376",
"0.49802697",
"0.4973997",
"0.49733466",
"0.4969... | 0.0 | -1 |
Fuse S1 and S2. | def __init__(self, *args):
_BRepAlgo.BRepAlgo_Fuse_swiginit(self,_BRepAlgo.new_BRepAlgo_Fuse(*args)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fuse(track1, track2):\n if track1.fused or track2.fused:\n return False\n matched_box_num = 0\n if track1.end_fid - P['th_track_fuse_len'] + 1 < track2.start_fid or len(track1) < P['th_track_fuse_len']\\\n or track1.start_fid > track2.start_fi... | [
"0.59770054",
"0.58343977",
"0.57405406",
"0.5701181",
"0.56660146",
"0.5445767",
"0.5419253",
"0.5412659",
"0.5324222",
"0.5311835",
"0.5299016",
"0.5295709",
"0.5295709",
"0.5295709",
"0.52313334",
"0.5201415",
"0.5173906",
"0.5159949",
"0.51541173",
"0.5140248",
"0.5135577... | 0.4677158 | 81 |
Initializes the first part | def Init1(self, *args):
return _BRepAlgo.BRepAlgo_Section_Init1(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize(self):\n\t\tpass",
"def initialise(self):",
"def initialize(self):\r\n pass",
"def initialize(self):\r\n pass",
"def initialize(self):\n pass",
"def initialize(self):\n pass",
"def initialize(self):\n pass",
"def initialize(self):\n pass",
"d... | [
"0.66732645",
"0.66727394",
"0.662304",
"0.662304",
"0.65748346",
"0.65748346",
"0.65748346",
"0.65748346",
"0.65748346",
"0.65494424",
"0.65494424",
"0.65494424",
"0.65494424",
"0.6547686",
"0.6547686",
"0.6547686",
"0.6547686",
"0.6547686",
"0.6547686",
"0.6547686",
"0.6547... | 0.6182043 | 42 |
Indicates if the Pcurve must be (or not) performed on first part. | def ComputePCurveOn1(self, *args):
return _BRepAlgo.BRepAlgo_Section_ComputePCurveOn1(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isSetCurve(self):\n return _libsbml.ReactionGlyph_isSetCurve(self)",
"def isSetCurve(self):\n return _libsbml.GeneralGlyph_isSetCurve(self)",
"def isSetCurve(self):\n return _libsbml.ReferenceGlyph_isSetCurve(self)",
"def isSetCurve(self):\n return _libsbml.SpeciesReferenceGly... | [
"0.66216546",
"0.6554725",
"0.64873916",
"0.63525736",
"0.6299431",
"0.6262032",
"0.61106616",
"0.6055835",
"0.6034955",
"0.60219824",
"0.5987621",
"0.59857976",
"0.59857976",
"0.59446955",
"0.5892998",
"0.5862073",
"0.5846161",
"0.5800989",
"0.5759518",
"0.57186365",
"0.5702... | 0.0 | -1 |
Define options for the computation of further intersections which will be performed by the function Build in this framework. By default, no parametric 2D curve (pcurve) is defined for the elementary edges of the result. If ComputePCurve1 equals true, further computations performed in this framework with the function Bu... | def ComputePCurveOn2(self, *args):
return _BRepAlgo.BRepAlgo_Section_ComputePCurveOn2(self, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ComputePCurveOn1(self, *args):\n return _BRepAlgo.BRepAlgo_Section_ComputePCurveOn1(self, *args)",
"def PricingAddCurves(builder, curves):\n return AddCurves(builder, curves)",
"def TransformPCurve(self, *args):\n return _ShapeBuild.ShapeBuild_Edge_TransformPCurve(self, *args)",
"def cur... | [
"0.63747",
"0.6279916",
"0.5839161",
"0.5615367",
"0.5537828",
"0.5535059",
"0.54384214",
"0.53751516",
"0.53667706",
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"0.52979684",
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"0.5162741",
"0.5156271",
"0.51324844",
"0.51028144",
"0.5075227",
"0.5058656",
"0.50237304... | 0.6815839 | 0 |
Upload a file to an S3 bucket | def upload_file(file_name, object_name=None, bucket = BUCKET_NAME):
# Upload the file
s3_client = boto3.client('s3')
try:
response = s3_client.upload_file(file_name, bucket, object_name) if type(file_name) == str else s3_client.upload_fileobj(file_name, BUCKET_NAME, object_name)
except ClientEr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _upload_s3(self, filename, bucket, objectKey):\n return s3_client.upload_file(filename, bucket, objectKey)",
"def upload(filename, bucket):\n print(\"Uploading {} to S3\".format(filename.lower().replace('_', '-')))\n url = \"https://s3.ca-central-1.amazonaws.com/{}/{}\".format(bucket,\n ... | [
"0.87235487",
"0.86594856",
"0.8603333",
"0.851218",
"0.85043377",
"0.84550345",
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"0.7964086",
"0.7959839",
"0.7955395",
"0.7954518",
"0.7954518... | 0.7805529 | 31 |
Make the variable for the problem. Use the function self.new_cnf_code(step, name, object) to make whatever codes for CNF variables you need to make your control knowledge for the Logistics problem. You can make variables which mean anything if you can think of constraints to make that enforce that meaning. As an exampl... | def make_control_knowledge_variables(self, horizon):
# You might want to save your variables here, or feel free to make as
# many data structures as you need to keep track of them.
self.control_fluent_codes = {}
""" *** YOUR CODE HERE *** """
# DID NOT DEFINE ANY EXTRA VARIABL... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.name = ''\n self.variables = []\n self.assumptions = []\n self.guarantees = []",
"def variabilize(self):\n if self.nvars>=0:\n pass #already done\n else:\n varTab = syt.SymbolTable()\n def convertArgs(args):\n ... | [
"0.56510055",
"0.5419212",
"0.5407943",
"0.52857524",
"0.52654815",
"0.52528906",
"0.52507615",
"0.5224148",
"0.51912344",
"0.5181718",
"0.51804876",
"0.51642627",
"0.51620805",
"0.513914",
"0.5131809",
"0.5116504",
"0.5077894",
"0.50545853",
"0.5040229",
"0.5036804",
"0.5015... | 0.62422013 | 0 |
This is where you should make your control knowledge clauses. These clauses should have the type "control". (LogisticsEncoding, int) > None | def make_control_knowledge(self, horizon):
""" *** YOUR CODE HERE *** """
# ADD_RULE1_COUNT = 0
# ADD_RULE2_COUNT = 0
# ADD_RULE3_COUNT = 0
close = list()
far = list()
for g in self.problem.goal:
for p in self.problem.propositions:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def control_law( self, inputs ):",
"def make_control_knowledge_variables(self, horizon):\n # You might want to save your variables here, or feel free to make as\n # many data structures as you need to keep track of them.\n\n self.control_fluent_codes = {}\n\n \"\"\" *** YOUR CODE HERE... | [
"0.54791707",
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"0.49444517",
"0.49334157",
"0.4919567",
"0.48492172",
"0.481751",
"0... | 0.50455076 | 9 |
Make an encoding of self.problem for the given horizon. For this encoding, we have broken this method up into a number of submethods that you need to implement. (LogisticsEncoding, int, str, str) > None | def encode(self, horizon, exec_semantics, plangraph_constraints):
super().encode(horizon, exec_semantics, plangraph_constraints)
self.make_control_knowledge_variables(horizon)
self.make_control_knowledge(horizon) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encoder(ne, nj):\n #contrainte sup les equipes ne peuvent pas s'affronter elles-meme\n contrainte = ''\n for e in range(ne):\n for j in range(nj):\n contrainte += str(-codage(ne,nj,j,e,e))+' 0\\n'\n return contrainte+encoderC1(ne, nj) +'\\n'+ encoderC2(ne, nj)+'\\n'+ \\\n ... | [
"0.49800134",
"0.49576762",
"0.49297398",
"0.48456433",
"0.48439774",
"0.47990614",
"0.4773234",
"0.4755465",
"0.46668708",
"0.46619892",
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"0.46022564",
"0.45864835",
"0.45528847",
"0.4546904",
"0.45393407",
"0.45337522",
"0.4525348",
"0.... | 0.5831212 | 0 |
Get the configuration entries for each participant. | def get_participants(n_stk, n_man):
stakeholders_hds = [bip32.BIP32.from_seed(os.urandom(32))
for _ in range(n_stk)]
cosigners_hds = [bip32.BIP32.from_seed(os.urandom(32))
for _ in range(n_stk)]
stakeholders = [
{
"xpub": stakeholders_hds[i].g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_config(self):\n\n return {section: self.sections[section].get_values() for section in self.sections}",
"def gather_configs(self):\n configs = []\n for what in self.order:\n for key in self.plugins[what]:\n mgr = self.plugins[what][key]\n c = m... | [
"0.66029215",
"0.64097726",
"0.62155825",
"0.60910946",
"0.60910946",
"0.60891306",
"0.60192245",
"0.598684",
"0.5980571",
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"0.5848187",
"0.58464956",
"0.58260256",
"0.58018756",
"0.5791864",
"0.574... | 0.0 | -1 |
Read a JSON object | def _readobj(self, sock):
buff = b""
while True:
n_to_read = max(2048, len(buff))
chunk = sock.recv(n_to_read)
buff += chunk
if len(chunk) != n_to_read:
print("Got: {}", buff)
return json.loads(buff) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_json(self, *args, **kwargs):\n with self.open('rb') as f:\n return json.load(f, *args, **kwargs)",
"def read_json(self, key):\n return json.loads(self.get_object(key))",
"def read_json(self):\n self._fopen.seek(self._json_start, 0)\n return json.loads(self._fopen... | [
"0.77058166",
"0.7668772",
"0.76047844",
"0.74460083",
"0.73519",
"0.7323888",
"0.73185146",
"0.7238177",
"0.7204975",
"0.7164135",
"0.71494013",
"0.71494013",
"0.71410316",
"0.7094965",
"0.7094596",
"0.702092",
"0.702092",
"0.6998307",
"0.69916403",
"0.6924644",
"0.6902423",... | 0.0 | -1 |
Intercept any call that is not explicitly defined and call . We might still want to define the actual methods in the subclasses for documentation purposes. | def __getattr__(self, name):
name = name.replace('_', '-')
def wrapper(*args, **kwargs):
if len(args) != 0 and len(kwargs) != 0:
raise RpcError(name, {}, "Cannot mix positional and non-positional arguments")
elif len(args) != 0:
return self.call(n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __subclasshook__(self, *args, **kwargs): # real signature unknown\n pass",
"def __subclasshook__(self, *args, **kwargs): # real signature unknown\n pass",
"def __subclasshook__(self, *args, **kwargs): # real signature unknown\n pass",
"def __subclasshook__(self, *args, **kwargs): # r... | [
"0.679027",
"0.679027",
"0.679027",
"0.679027",
"0.679027",
"0.679027",
"0.679027",
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"0.679027",
"0.679027",
"0.679027",
"0.679027",
"0.679027",
"0.67902... | 0.0 | -1 |
Start the underlying process and start monitoring it. | def start(self, stdin=None, stdout=None, stderr=None):
logging.debug("Starting '%s'", " ".join(self.cmd_line))
self.proc = subprocess.Popen(self.cmd_line,
stdin=stdin,
stdout=stdout if stdout
e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _StartMonitoringProcess(self, process):\n if process is None:\n raise ValueError('Missing process.')\n\n pid = process.pid\n\n if pid in self._process_information_per_pid:\n raise KeyError(\n 'Already monitoring process (PID: {0:d}).'.format(pid))\n\n if pid in self._rpc_clients_... | [
"0.7643342",
"0.7508366",
"0.73010033",
"0.72515625",
"0.7217871",
"0.70760834",
"0.70511764",
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"0.6815982",
"0.67911863",
"0.675352",
"0.6741543",
"0.6711454",
"0.669930... | 0.0 | -1 |
Kill process without giving it warning. | def kill(self):
self.proc.kill()
self.proc.wait()
self.thread.join() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def processKill(uPid):\n return processTerminate(uPid);",
"def kill(self):\r\n try:\r\n if self.process:\r\n self.process.kill()\r\n self.process.wait()\r\n except WindowsError:\r\n # kill may not be available under windows environment\r\n ... | [
"0.7896664",
"0.770771",
"0.7687021",
"0.7505421",
"0.74414134",
"0.7414322",
"0.7326775",
"0.7304749",
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"0.7012978",
"0.70051336",
"0.69897634",
"0.6925199",
"0.69224155",
"0.6908679... | 0.6804302 | 31 |
Tail the stdout of the process and remember it. Stores the lines of output produced by the process in self.logs and signals that a new line was read so that it can be picked up by consumers. | def tail(self):
for line in iter(self.proc.stdout.readline, ''):
if len(line) == 0:
break
if self.log_filter(line.decode('ASCII')):
continue
if self.verbose:
logging.debug(f"{self.prefix}: {line.decode().rstrip()}")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _stdout_reader(self):\n self._is_launched.wait()\n stdout_iterator = iter(self._popen.stdout.readline, b\"\")\n for line in stdout_iterator:\n self._log(\"raw\", \"stdout : {0}\".format(line.strip()))\n self.stdout_queue.put_nowait(line.strip())\n self.stdout_q... | [
"0.7018636",
"0.68938357",
"0.64648557",
"0.63040227",
"0.61803836",
"0.611249",
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"0.56633466",
"0.56391853",
"0.5608646",
"0.5590531",
"0.555853... | 0.8187086 | 0 |
Look for `regex` in the logs. | def is_in_log(self, regex, start=0):
ex = re.compile(regex)
for l in self.logs[start:]:
if ex.search(l):
logging.debug("Found '%s' in logs", regex)
return l
logging.debug("Did not find '%s' in logs", regex)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def matchLogAgainstRegex(self, log):\n\n matchedString = ''\n\n if not log:\n # no log provided\n raise ValueError('no log provided')\n\n # try regex matching\n regexMatches = self.compiledRegex.findall(log)\n\n # get specified match based on idx num\n ... | [
"0.6812222",
"0.6396781",
"0.6323904",
"0.6270402",
"0.6225342",
"0.61722404",
"0.60080373",
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"0.5873692",
"0.58668035",
"0.5854508",
"0.58435524",
"0.581534"... | 0.68955874 | 0 |
Look for `regexs` in the logs. We tail the stdout of the process and look for each regex in `regexs`, starting from last of the previous waitedfor log entries (if any). We fail if the timeout is exceeded or if the underlying process exits before all the `regexs` were found. If timeout is None, no timeout is applied. | def wait_for_logs(self, regexs, timeout=TIMEOUT):
logging.debug("Waiting for {} in the logs".format(regexs))
exs = [re.compile(r) for r in regexs]
start_time = time.time()
pos = self.logsearch_start
while True:
if timeout is not None and time.time() > start_time + t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wait_for_log(self, regex, timeout=TIMEOUT):\n return self.wait_for_logs([regex], timeout)",
"def extract_values(self):\n log_unmatched = []\n stdout_unmatched = []\n stderr_unmatched = []\n result = True\n\n regex_sources = []\n if self.logpath and self.cfg.lo... | [
"0.6554087",
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"0.47347224",
"0.471532",
"0.46489775",
"0.4641251",
"0.4617... | 0.74728626 | 0 |
Look for `regex` in the logs. Convenience wrapper for the common case of only seeking a single entry. | def wait_for_log(self, regex, timeout=TIMEOUT):
return self.wait_for_logs([regex], timeout) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_in_log(self, regex, start=0):\n\n ex = re.compile(regex)\n for l in self.logs[start:]:\n if ex.search(l):\n logging.debug(\"Found '%s' in logs\", regex)\n return l\n\n logging.debug(\"Did not find '%s' in logs\", regex)\n return None",
"... | [
"0.7234513",
"0.64218044",
"0.6214132",
"0.6066417",
"0.60639656",
"0.60621494",
"0.59272736",
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"0.5508064",
"0.5505123",
"0.5483321",
"0.5446021",
"0.543217",
"0.5428485... | 0.55470073 | 14 |
Reorganize chain by creating a fork at height=[height] and remine all mempool transactions into [height + shift], where shift >= 0. Returns hashes of generated blocks. Note that tx's that become invalid at [height] (because coin maturity, locktime etc.) are removed from mempool. The length of the new chain will be orig... | def simple_reorg(self, height, shift=0):
hashes = []
fee_delta = 1000000
orig_len = self.rpc.getblockcount()
old_hash = self.rpc.getblockhash(height)
if height + shift > orig_len:
final_len = height + shift
else:
final_len = 1 + orig_len
# ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mine_reorg(self, length):\n\n # make sure all invalidated blocks are node0's\n self.generatetoaddress(self.nodes[0], length, self.nodes[0].get_deterministic_priv_key().address)\n for x in self.nodes[0].p2ps:\n x.wait_for_block_announcement(int(self.nodes[0].getbestblockhash(), 1... | [
"0.64595574",
"0.58120793",
"0.56435055",
"0.561937",
"0.56143004",
"0.5595991",
"0.54888064",
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"0.5313484",
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"0.50244987",
"0.4982427",
"0.49731052",
"0.49571624",
"0.49556592",
"0.4946499",
"0.4944... | 0.794877 | 0 |
Deploy a revault setup with {n_stakeholders} stakeholders, {n_managers} managers and optionally fund it with {funding} sats. | def deploy(self, n_stakeholders, n_managers, funding=None, csv=None):
(conf_stk, conf_man) = get_participants(n_stakeholders, n_managers)
if csv is None:
# More than 6 months
csv = random.randint(1, 26784)
stk_nodes = []
for i in range(len(conf_stk)):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(token_addr: str, box_addr: str) -> None:\n print(f\"\\nSetting up Contracts....\")\n tokenContract = pytezos.contract(token_addr)\n print(f\"-- Performing Initial Mint to Admin : {pub_key_hash}\")\n tokenContract.initialMint(None).inject(_async=False)\n print(\"-- Funding Fishcake Box Cont... | [
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"0.5209116",
"0.5172304",
"0.5167198",
"0.5145933",
"0.5142364... | 0.76175135 | 0 |
add insert statement to the set. | def add_insert_sql(self, stmt: str) -> 'StatementSet':
self._j_statement_set.addInsertSql(stmt)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert_statement() -> str:\n pass",
"async def _insert_stmt(self):\n raise NotImplementedError",
"def insert(statement: str) -> []:\n raise NotImplementedError",
"def insert(self):\n pass",
"def insert(self):\n self.getDbRecord().insert()\n\n return",
"def in... | [
"0.73348004",
"0.7282123",
"0.7104033",
"0.70376384",
"0.67192733",
"0.66953015",
"0.6664766",
"0.6629884",
"0.65293306",
"0.6495636",
"0.6481297",
"0.643897",
"0.6429661",
"0.6397608",
"0.63722914",
"0.6366629",
"0.63188213",
"0.6221601",
"0.6208461",
"0.6191863",
"0.6189242... | 0.80597764 | 0 |
Optimizes all statements as one entity and adds them as transformations to the underlying StreamExecutionEnvironment. | def attach_as_datastream(self):
self._j_statement_set.attachAsDataStream() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def optimize(self):\n seq = transform.Sequential([transform.SimplifyInference(),\n transform.FuseOps(0),\n transform.ToANormalForm(),\n transform.InferType()])\n return seq(self.mod)",
"def comp... | [
"0.56001043",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186",
"0.55654186"... | 0.0 | -1 |
Adds a statement that the pipeline defined by the given Table object should be written to a table (backed by a DynamicTableSink) that was registered under the specified path or expressed via the given TableDescriptor. | def add_insert(self,
target_path_or_descriptor: Union[str, TableDescriptor],
table,
overwrite: bool = False) -> 'StatementSet':
if isinstance(target_path_or_descriptor, str):
self._j_statement_set.addInsert(target_path_or_descriptor, table._j_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add(self, recordid, tablerow_object, insupdel=0):\n try:\n # Add a statement\n # with recordid\n if insupdel == 0:\n self.statements_insert.append([recordid, tablerow_object])\n elif insupdel == 1:\n self.statements_update.append(... | [
"0.52105063",
"0.5086503",
"0.49902365",
"0.49250248",
"0.47580516",
"0.46843183",
"0.46684784",
"0.46672463",
"0.46520108",
"0.46000272",
"0.45493692",
"0.45402828",
"0.45050877",
"0.45050564",
"0.44975215",
"0.44867492",
"0.44838643",
"0.4481346",
"0.44599575",
"0.4427141",
... | 0.45722073 | 10 |
returns the AST and the execution plan of all statements and Tables. | def explain(self, *extra_details: ExplainDetail) -> str:
TEXT = get_gateway().jvm.org.apache.flink.table.api.ExplainFormat.TEXT
j_extra_details = to_j_explain_detail_arr(extra_details)
return self._j_statement_set.explain(TEXT, j_extra_details) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_statements(self):\n\t\n\t\tif self.tokenizer.get_token() == 'do':\n\t\t\tself.compile_do()\n\t\telif self.tokenizer.get_token() == 'let':\n\t\t\tself.compile_let()\n\t\telif self.tokenizer.get_token() == 'while':\n\t\t\tself.compile_while()\n\t\telif self.tokenizer.get_token() == 'return':\n\t\t\tself.... | [
"0.58426493",
"0.5780248",
"0.5755113",
"0.5742486",
"0.57143164",
"0.5552393",
"0.55485696",
"0.5547247",
"0.55079633",
"0.5506144",
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"0.5367922",
"0.53249174",
"0.531023",
"0.5297551",
"0.52941924",
"0.5287371",
"0.52603763... | 0.0 | -1 |
execute all statements and Tables as a batch. | def execute(self) -> TableResult:
self._t_env._before_execute()
return TableResult(self._j_statement_set.execute()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert_many_execute(self) -> None:\n self.connection.isolation_level = None\n self.cursor.execute('BEGIN TRANSACTION')\n for i in self.__sql_buffer.split(';'):\n self.cursor.execute(i)\n self.__sql_buffer = \"\"\n self.cursor.execute('COMMIT')",
"def run():\r\n ... | [
"0.7232379",
"0.69801813",
"0.69015574",
"0.6610477",
"0.6602783",
"0.65980256",
"0.6545557",
"0.64657193",
"0.6446561",
"0.6377295",
"0.63597274",
"0.63520765",
"0.63237077",
"0.62542707",
"0.6233846",
"0.6204226",
"0.6173758",
"0.61651134",
"0.61408097",
"0.6123944",
"0.612... | 0.612041 | 21 |
Creates a dataset with n_series data series that are each simulated for samples_per_series time steps. The timesteps are delta_t seconds apart. | def create_dataset(n_series=51, samples_per_series=1001, save_to_disk=True):
delta_t = 0.01
x0_in = np.random.random((n_series//2))
x0_out = np.random.random((n_series-n_series//2)) + np.pi - 1
x0 = np.concatenate([x0_in, x0_out])
msd = MassSpringDamper(x=x0, x_dt=tf.zeros_like(x0)) # compute all t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_ensemble_time_series(dsys, n_traj, len_traj):\n\n for i in range(n_traj):\n # Training initial condition.\n x0_train = normal(loc=0.0, scale=1.0, size=(dsys.A.shape[1]))\n\n # Run simulation to generate dataset.\n t, _, x = dlsim(dsys, np.zeros((len_traj, dsys.inputs)), ... | [
"0.65548915",
"0.6483605",
"0.61622024",
"0.60135025",
"0.5952278",
"0.59478754",
"0.5932897",
"0.58320725",
"0.58142924",
"0.5810814",
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"0.579603",
"0.57907796",
"0.57790065",
"0.57643455",
"0.5731143",
"0.5673035",
"0.5666023",
"0.56609744",
"0.56574816",
"0.56... | 0.70332897 | 0 |
Needed because Keras' MSE implementation includes L2 penalty | def my_mse(y_true, y_pred):
return tf.reduce_mean(tf.square(y_true - y_pred), axis=-1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def last_time_step_mse(y_true, y_pred):\n return keras.metrics.mean_squared_error(y_true[:,-1],y_pred[:,-1])",
"def rmse(y_true, y_pred): # -> Any:\n ...",
"def mse_k(self, X, Y):\n\t\treturn np.power(Y - self.predict_soft(X), 2).sum(1).mean(0)",
"def _mse(self):\n error = se... | [
"0.6798121",
"0.6746864",
"0.6709752",
"0.6691435",
"0.6492978",
"0.6477202",
"0.64586943",
"0.63607097",
"0.63523906",
"0.63517386",
"0.6330202",
"0.630452",
"0.6284733",
"0.62675333",
"0.6223726",
"0.6218478",
"0.6213949",
"0.61776364",
"0.61588955",
"0.6142944",
"0.6091366... | 0.61977226 | 17 |
Calculates total energy of a massspringdamper system given a state. | def total_energy(state, k=1, m=1):
return 0.5*k*state[..., 0]*state[..., 0]+0.5*m*state[..., 1]*state[..., 1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def E(self, state):\n \n if state==0: # Invalid state has no energy\n return 0\n return sum([self.calcDistance(state[i+1], state[i]) for i in range(len(state)-1)])",
"def energy(self, state):\n return _modeller.mod_state_optimizer_energy(self._modpt,\n ... | [
"0.72978914",
"0.6800482",
"0.67240715",
"0.6545475",
"0.647643",
"0.63820916",
"0.6224594",
"0.6218873",
"0.6209779",
"0.6199031",
"0.6194541",
"0.61923784",
"0.61691",
"0.6158798",
"0.6013328",
"0.6004666",
"0.5997395",
"0.59853816",
"0.5841618",
"0.5840448",
"0.57865363",
... | 0.7182555 | 1 |
Computes the relative energy drift of x_pred w.r.t. x_true | def relative_energy_drift(x_pred, x_true, t=-1):
energy_pred = total_energy(x_pred[t])
energy_true = total_energy(x_true[t])
return (energy_pred-energy_true) / energy_true | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wrapped_phase_difference(y_true, y_pred):\n\n diff = y_pred - y_true\n diff -= tf.multiply(\n tf.cast(tf.greater_equal(diff, dvpy.tf.pi), \"float32\"), 2.0 * dvpy.tf.pi\n )\n diff += tf.multiply(\n tf.cast(tf.less(diff, -dvpy.tf.pi), \"float32\"), 2.0 * dvpy.tf.pi\n )\n return d... | [
"0.61286956",
"0.6123985",
"0.60035336",
"0.5856947",
"0.5804698",
"0.5800761",
"0.5794037",
"0.5790036",
"0.57635224",
"0.5752601",
"0.56845367",
"0.5673297",
"0.5668639",
"0.56648374",
"0.5648307",
"0.5620171",
"0.5612428",
"0.56122655",
"0.5608644",
"0.5602617",
"0.5596544... | 0.8591493 | 0 |
Computes the relative phase error of x_pred w.r.t. x_true. This is done by finding the locations of the zero crossings in both signals, then corresponding crossings are compared to each other. | def relative_phase_error(x_pred, x_val):
ref_crossings = zero_crossings(x_val[:, 0])
pred_crossings = zero_crossings(x_pred[:, 0])
t_ref = np.mean(np.diff(ref_crossings)) * 2
t_pred = np.mean(np.diff(pred_crossings)) * 2
phase_error = t_ref/t_pred - 1
if len(pred_crossings) < len(ref_crossings) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wrapped_phase_difference(y_true, y_pred):\n\n diff = y_pred - y_true\n diff -= tf.multiply(\n tf.cast(tf.greater_equal(diff, dvpy.tf.pi), \"float32\"), 2.0 * dvpy.tf.pi\n )\n diff += tf.multiply(\n tf.cast(tf.less(diff, -dvpy.tf.pi), \"float32\"), 2.0 * dvpy.tf.pi\n )\n return d... | [
"0.6281197",
"0.59099185",
"0.58893096",
"0.5852402",
"0.5812844",
"0.57776743",
"0.5741355",
"0.571223",
"0.5664011",
"0.551934",
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"0.5464117",
"0.5453587",
"0.54014504",
"0.53790504",
"0.536854",
"0.5364881",
"0.533917",
... | 0.80148506 | 0 |
Visualize a tf.keras.Model for a single pendulum. | def visualize(model, x_val, PLOT_DIR, TIME_OF_RUN, args, ode_model=True, latent=False, epoch=0, is_mdn=False):
x_val = x_val.reshape(2, -1, 2)
dt = 0.01
t = tf.linspace(0., 10., int(10./dt)+1)
# Compute the predicted trajectories
if ode_model:
x0_extrap = tf.stack([x_val[0, 0]])
x_t_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _visualise_model(\n ax: plt.Axes, model: BaseModel, ts: ArrayLike, model_color: str, growth_period_color: str, maturation_offset: float\n) -> None:\n # Visualise the fit\n ts = np.asarray(ts)\n ys = model.predict(ts)\n ax.plot(ts, ys, c=model_color, label=\"Predicted\")\n\n # Visualise the ti... | [
"0.670003",
"0.59203535",
"0.5844068",
"0.5772857",
"0.5705508",
"0.5521636",
"0.552075",
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"0.53358203",
"0.53293264",
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"0.52825826",
"0.5264045",
"0.52629435",
"0.5232321",
"0.5193793",
"0.5178882",
"0.51681125... | 0.58403814 | 3 |
Find indices of zeros crossings | def zero_crossings(x):
return np.array(np.where(np.diff(np.sign(x)))[0]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zero_cross_elems(x, y):\n assert (x.size == y.size), \"x and y must have equal size!\"\n indices = np.arange(x.size)[zero_cross_boolean_index(y)]\n\n return Zero_Cross_Intervals(x[indices],\n x[indices + 1],\n y[indices],\n ... | [
"0.69026285",
"0.66425437",
"0.66288316",
"0.6570189",
"0.65314287",
"0.6530191",
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"0.622959",
"0.61957747",
"0.6192994",
"0.61845845",
"0.6160345",
"0.61368203",
"0.6043... | 0.7078362 | 0 |
Creates and saves a user with the given credentials. | def create_user(self, username, email: str = None, password: str = None, **kwargs):
return self._create_user(username, email=email, password=password, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create(self, credentials):\n return User.objects.create_user(\n credentials['username'],\n credentials['email'],\n credentials['password']\n )",
"def create_user(self, username=\"foo\", email=\"foo@foo.com\", pwd=\"password\"):\n with app.app_context():\n... | [
"0.8354974",
"0.78025395",
"0.7666511",
"0.7655392",
"0.76072586",
"0.76044446",
"0.7590647",
"0.7588419",
"0.7587881",
"0.7542751",
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"0.7470141",
"0.7467787",
"0.74560905",
"0.7452289",
"0.7449995",
"0.74420017",
"0.74313... | 0.71091014 | 98 |
Creates and saves a superuser with the given credentials. | def create_superuser(self, username, email: str = None, password: str = None, **kwargs):
kwargs.setdefault('is_staff', True)
kwargs.setdefault('is_superuser', True)
return self._create_user(username, email=email, password=password, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_superuser(self, username, firstname, lastname, email, password):\n user = self.create_user(\n username=username,\n firstname=firstname,\n lastname=lastname,\n email=email,\n password=password,\n )\n user.is_admin = True\n ... | [
"0.7665751",
"0.76599526",
"0.7633325",
"0.7613368",
"0.7548139",
"0.75175565",
"0.75017494",
"0.7498751",
"0.7497807",
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"0.7431043",
"0.7430459",
"0.7427848",
"0.7412361",
"0.74012953... | 0.74214536 | 19 |
Get all valid knight's moves from location x0, y0 on the 9x9 sudoku grid. | def getAllValidMoves(x0, y0):
deltas = [
(-2, -1),
(-2, +1),
(+2, -1),
(+2, +1),
(-1, -2),
(-1, +2),
(+1, -2),
(+1, +2),
]
validPositions = []
for (x, y) in deltas:
xCandidate = x0 + x
yCandidate = y0 + y
if 0 < xCa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_legal_moves(self):\n moves = []\n if self.player_locations[self.whose_turn] is None:\n return self.get_blank_locations()\n matrix = [(1,0), (-1,0), (0,1), (0,-1), (1,1), (1,-1), (-1, 1), (-1,-1)]\n\n for dx, dy in matrix:\n x,y = self.player_locations[self.... | [
"0.7800498",
"0.7416829",
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"0.7355546",
"0.72740203",
"0.7235792",
"0.71068656",
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"0.6994112",
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"0.6734217",
"0.67146814",
"0.67090935",
"0.6682379",
"0.66746056",
"0.6647... | 0.73486286 | 4 |
Check that no cells share the same value as a specific cell when they're a knight's move away. | def exactly_one_knights_move(variables):
cnf = []
for valid_move in variables[:-1]:
# print(valid_move)
cnf.append([-valid_move, -variables[-1]])
return cnf | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_lost (grid):\r\n adjacent = False\r\n zero_value = False\r\n for i in range(4): \r\n for j in range(4):\r\n if grid[i][j] == 0:\r\n zero_value = True\r\n break\r\n for i in range(3):\r\n for j in range(3):\r\n if grid[i][... | [
"0.6331355",
"0.6326156",
"0.63063097",
"0.6245713",
"0.6176819",
"0.6123772",
"0.60940635",
"0.609174",
"0.60422224",
"0.6031074",
"0.59380674",
"0.5923852",
"0.5835901",
"0.58009046",
"0.5739902",
"0.57345146",
"0.572951",
"0.5717221",
"0.56704354",
"0.566835",
"0.56444776"... | 0.0 | -1 |
Check that all cells in 'variales' have unique values. | def exactly_one(variables):
cnf = [variables]
n = len(variables)
for i in range(n):
for j in range(i + 1, n):
v1 = variables[i]
v2 = variables[j]
cnf.append([-v1, -v2])
return cnf | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def assert_unique_cols_unique(self, df):\n assert not df.duplicated(self.unique_cols).any()",
"def test_check_unique_var(self):\n\n for component_name in [\"var\", \"raw.var\"]:\n with self.subTest(component_name=component_name):\n\n # Resetting validator\n ... | [
"0.63620746",
"0.62129706",
"0.6201802",
"0.6193316",
"0.61617726",
"0.6146202",
"0.61371833",
"0.6137029",
"0.5997458",
"0.5884578",
"0.58836",
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"0.5720199",
"0.5682489",
"0.56469285",
"0.5644524",
"0.56409854",
"0.5598229",
"0.55876446",
"0.55558944... | 0.0 | -1 |
Transform the cell X(i, j, k) into an integer for conversion to CNF. | def transform(i, j, k):
return i * N * N + j * N + k + 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getnum__(self, i, j):\n return self.pos_to_num[(i, j)]",
"def as_integer(self):\n if self.symbol == '+':\n temp = [self.attribute_col, 1]\n elif self.symbol == '-':\n temp = [self.attribute_col, -1]\n else:\n temp = [self.attribute_col, 0]\n ... | [
"0.6103872",
"0.58493155",
"0.5766707",
"0.5734533",
"0.56284475",
"0.55735785",
"0.55456716",
"0.5451561",
"0.54266685",
"0.5409159",
"0.5409159",
"0.5409159",
"0.5409159",
"0.53852427",
"0.5341426",
"0.5300994",
"0.5292215",
"0.5251497",
"0.5190708",
"0.5190708",
"0.5190708... | 0.54423225 | 8 |
Convert the integer back into i, j, k values. | def inverse_transform(v):
v, k = divmod(v - 1, N)
v, j = divmod(v, N)
v, i = divmod(v, N)
return i, j, k | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getnum__(self, i, j):\n return self.pos_to_num[(i, j)]",
"def to_ijv(self):\n row_indices = []\n column_indices = []\n nonzero_elements = []\n k = 0\n for key, value in self.data.items():\n if value == 0:\n continue\n row, col =... | [
"0.5823448",
"0.5436596",
"0.5428236",
"0.5380349",
"0.52834296",
"0.52059615",
"0.5202263",
"0.51605237",
"0.51520085",
"0.514841",
"0.51293755",
"0.510118",
"0.5076202",
"0.5036652",
"0.5031226",
"0.5031054",
"0.50288296",
"0.5008367",
"0.5007212",
"0.49944344",
"0.49824622... | 0.495023 | 24 |
Given a sting CSV line, return a list of strings. | def csv_readline(line):
for row in csv.reader([line]):
return row | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_csv_line(line):\r\n return line.strip().split(',')",
"def read(self, line):\n data = []\n if six.PY3 and type(line) == six.binary_type:\n line = line.decode('utf-8')\n\n csv_reader = csv.reader(six.StringIO(line),\n delimiter=self.delimiter,\n ... | [
"0.8216797",
"0.73310727",
"0.72",
"0.71712154",
"0.7040743",
"0.6812084",
"0.6807555",
"0.67872983",
"0.6727834",
"0.6690727",
"0.6648978",
"0.6611844",
"0.65976346",
"0.6556839",
"0.65206516",
"0.6474821",
"0.63950497",
"0.6366489",
"0.6366489",
"0.63588786",
"0.6346434",
... | 0.6590794 | 14 |
Ordering is set by a comma delimited ?ordering=... query parameter. The `ordering` query parameter can be overridden by setting the `ordering_param` value on the OrderingFilter or by specifying an `ORDERING_PARAM` value in the API settings. | def get_ordering(self, request, queryset, view):
ordering = []
params = get_datatables_ordering(request.query_params)
if params:
fields = [param.strip() for param in params.split(',')]
ordering = self.remove_invalid_fields(queryset, fields, view, request)
if o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _sort_by_query_string_param(self, songs):\n orderable_fields_dict = {\n 'name': Lower('name'),\n 'artist': Lower('artist__name'),\n 'avgRating': 'average_rating',\n 'year': 'year'\n }\n\n order_by = self.request.query_params.get('orderBy', None)\... | [
"0.6548646",
"0.6339288",
"0.63350976",
"0.6280332",
"0.6231859",
"0.60011864",
"0.59715325",
"0.5950593",
"0.5941029",
"0.58408827",
"0.58376485",
"0.5808046",
"0.5758433",
"0.57560813",
"0.5746289",
"0.57187814",
"0.56474346",
"0.56353605",
"0.55608726",
"0.554913",
"0.5512... | 0.5934701 | 9 |
Set authentication for the Webhook. Can be either a app or user token. | def authenticate(self, auth_token: str) -> None:
self.__authenticate = True
self.__auth_token = auth_token
if not self.callback_url.startswith('https'):
raise Exception('HTTPS is required for authenticated webhook.\n'
+ 'Either use non authenticated webhoo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def authentication_hook(self):\n pass",
"def authentication(self, authentication):\n self._authentication = authentication",
"def set_requests_auth(self):\n self.__auth = OAuth2(token=self.bearer_token)",
"def enable_auth(self):\n\n self._api_manager.enable_auth()",
"def for_aut... | [
"0.6512903",
"0.6280482",
"0.6232747",
"0.6179822",
"0.6173788",
"0.6144986",
"0.60863155",
"0.60844374",
"0.60590637",
"0.60274845",
"0.5969733",
"0.5961362",
"0.5910693",
"0.5896948",
"0.5857367",
"0.5832172",
"0.5830799",
"0.5819944",
"0.5808844",
"0.5794098",
"0.57879853"... | 0.63118255 | 1 |
Stops the Webhook Please make sure to unsubscribe from all subscriptions! | def stop(self):
if self.__hook_runner is not None:
self.__hook_loop.call_soon_threadsafe(self.__hook_loop.stop)
self.__hook_runner = None
self.__hook_thread.join() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop(self):\n self.unhook()",
"async def on_shutdown():\n # Remove webhook.\n await bot.delete_webhook()",
"def stop(self):\n self.running = False\n with self.lock:\n self.websockets.clear()\n self.poller.release()",
"def stop(self):\n log.info(... | [
"0.73477376",
"0.730306",
"0.70386827",
"0.69969374",
"0.69407964",
"0.6834157",
"0.6826018",
"0.6722071",
"0.67102283",
"0.66708004",
"0.66708004",
"0.6646933",
"0.66421884",
"0.6629264",
"0.66051805",
"0.65731984",
"0.65622634",
"0.652264",
"0.6496202",
"0.6496202",
"0.6496... | 0.6445198 | 27 |
Subscribe to Twitch Topic | def _subscribe(self, callback_path: str, topic_url: str, mode: str = "subscribe"):
data = {'hub.callback': self.callback_url + callback_path,
'hub.mode': mode,
'hub.topic': topic_url,
'hub.lease_seconds': self.subscribe_least_seconds}
if self.secret is not... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subscribe(self, topic):\n\t\tself.topic=topic\n\t\tself.client.subscribe(self.topic)",
"def subscribe( self, topic ):\n logging.info( \"Subscribing to topic %s\" %topic )\n try:\n self.client.subscribe( topic )\n except Exception as error:\n print( error )",
"def ... | [
"0.72031987",
"0.7145145",
"0.6991258",
"0.6840349",
"0.6744704",
"0.67444885",
"0.6698804",
"0.6673859",
"0.6664869",
"0.664405",
"0.6601021",
"0.65822107",
"0.65822107",
"0.65822107",
"0.6478522",
"0.6478522",
"0.64706546",
"0.64471954",
"0.6439257",
"0.6435605",
"0.6413491... | 0.63418204 | 25 |
Subscribe to user follow topic. Set only from_id if you want to know if User with that id follows someone.\n Set only to_id if you want to know if someone follows User with that id.\n Set both if you only want to know if from_id follows to_id.\n | def subscribe_user_follow(self,
from_id: Union[str, None],
to_id: Union[str, None],
callback_func: Union[Callable[[UUID, dict], None], None]) -> Tuple[bool, UUID]:
param_dict = {"first": 1,
"from_id":... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def follow_me(self, action = 'follow_topic'):\n data = {\n 'params' : json.dumps({\n 'topic_id' : self.soup.find('div', id = 'zh-topic-desc')['data-resourceid']\n }),\n '_xsrf' : self.session.getCookie()['_xsrf'],\n 'method' : action\n ... | [
"0.6426532",
"0.6417592",
"0.6319717",
"0.6229763",
"0.6068537",
"0.592255",
"0.5917339",
"0.59045386",
"0.59028536",
"0.58812976",
"0.58408505",
"0.58146167",
"0.5809982",
"0.5805806",
"0.5790301",
"0.5789063",
"0.57648915",
"0.57457554",
"0.5718259",
"0.56877375",
"0.566789... | 0.7383402 | 0 |
Unsubscribe from user follow topic | def unsubscribe_user_follow(self, uuid: UUID) -> bool:
return self._generic_unsubscribe('/users/follows', uuid) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unfollow_me(self):\n return self.follow_me('unfollow_topic')",
"def _onUnsubscribe(self, client:mqtt.Client, userdata:Any, mid:int) -> None:\n\t\t# TODO doc, error check when not connected, not subscribed\n\t\tfor t in self.subscribedTopics.values():\n\t\t\tif t.mid == mid:\n\t\t\t\tdel self.subscribe... | [
"0.78618807",
"0.7404355",
"0.7356452",
"0.7301059",
"0.7293563",
"0.72068113",
"0.7114952",
"0.6996647",
"0.69802845",
"0.69039005",
"0.6884112",
"0.68658125",
"0.6806643",
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"0.6806643",
"0.6806643",
"0.6806643",
"0.67019063",
"0.6628263",
"0.6621598",
"0.6608499... | 0.7143689 | 6 |
Subscribe to stream changed topic\n | def subscribe_stream_changed(self,
user_id: str,
callback_func: Union[Callable[[UUID, dict], None], None]) -> Tuple[bool, UUID]:
param_dict = {"user_id": user_id}
url = build_url(TWITCH_API_BASE_URL + "streams", param_dict)
uuid =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _on_connect(self, client, userdata, flags, rc):\n self.subscribe(self.topic)",
"def subscribe(self, topic):\n\t\tself.topic=topic\n\t\tself.client.subscribe(self.topic)",
"def subscribe(self, topic):\n self.topic = topic\n self.client.on_connect = self.on_connect\n self.client.o... | [
"0.73587894",
"0.73050404",
"0.7253479",
"0.7140035",
"0.71158516",
"0.70430756",
"0.70430756",
"0.70430756",
"0.7021174",
"0.70088357",
"0.69907594",
"0.6969615",
"0.6907298",
"0.68529415",
"0.68044573",
"0.6784355",
"0.67176783",
"0.6712941",
"0.66814595",
"0.6646636",
"0.6... | 0.5841582 | 91 |
Unsubscribe from stream changed topic | def unsubscribe_stream_changed(self, uuid: UUID) -> bool:
return self._generic_unsubscribe('/streams', uuid) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def unsubscribe(self, topic: str, subscription_id: int = None) -> None:\n ...",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(self, topic):\n request = protos.Req... | [
"0.774863",
"0.7619209",
"0.7619209",
"0.7619209",
"0.7619209",
"0.7619209",
"0.7328704",
"0.7293971",
"0.71787375",
"0.71762586",
"0.71287817",
"0.7125174",
"0.7024157",
"0.7020863",
"0.7006512",
"0.695951",
"0.6945188",
"0.6900266",
"0.68850166",
"0.68574595",
"0.68465644",... | 0.70876384 | 12 |
Subscribe to subscription event topic\n | def subscribe_user_changed(self,
user_id: str,
callback_func: Union[Callable[[UUID, dict], None], None]) -> Tuple[bool, UUID]:
param_dict = {"id": user_id}
url = build_url(TWITCH_API_BASE_URL + "users", param_dict)
uuid = get_uuid()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subscribe( self, topic ):\n logging.info( \"Subscribing to topic %s\" %topic )\n try:\n self.client.subscribe( topic )\n except Exception as error:\n print( error )",
"def on_subscribe( client, userdata, mid, granted_qos ):\n logging.info( \"Topic successfully su... | [
"0.78011537",
"0.76737946",
"0.7650545",
"0.7637412",
"0.76196575",
"0.7488806",
"0.74484235",
"0.7360226",
"0.724094",
"0.7222296",
"0.7222296",
"0.7222296",
"0.72069705",
"0.7187491",
"0.71457654",
"0.71090275",
"0.7108999",
"0.7093177",
"0.6963686",
"0.6943664",
"0.6907351... | 0.0 | -1 |
Unsubscribe from subscription event topic | def unsubscribe_user_changed(self, uuid: UUID) -> bool:
return self._generic_unsubscribe("/users/changed", uuid) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def unsubscribe(self, topic: str, subscription_id: int = None) -> None:\n ...",
"def unsubscribeFromEvent(eventName,subscriber):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def un... | [
"0.8265341",
"0.79855394",
"0.78613484",
"0.78613484",
"0.78613484",
"0.78613484",
"0.78613484",
"0.76457715",
"0.7578656",
"0.75465965",
"0.7486301",
"0.74483675",
"0.7354071",
"0.733398",
"0.731288",
"0.73032236",
"0.72957045",
"0.7293375",
"0.7292126",
"0.7288204",
"0.7229... | 0.0 | -1 |
Subscribe to Extension transaction topic\n | def subscribe_extension_transaction_created(self,
extension_id: str,
callback_func: Union[Callable[[UUID, dict], None], None]) \
-> Tuple[bool, UUID]:
if not self.__authenticate:
# this requir... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subscribe_topic(self):\n req = {\n \"op\": \"subscribe\",\n \"args\": [\n \"instrument\",\n \"trade\",\n \"orderBook10\",\n \"execution\",\n \"order\",\n \"position\",\n \"margi... | [
"0.7804112",
"0.6831071",
"0.6830902",
"0.6788986",
"0.67115897",
"0.6696487",
"0.6696487",
"0.6696487",
"0.66258013",
"0.6586794",
"0.64005786",
"0.6380222",
"0.637175",
"0.633139",
"0.6320845",
"0.62829703",
"0.6257005",
"0.6228517",
"0.6197933",
"0.6196007",
"0.6182721",
... | 0.5676822 | 76 |
Unsubscribe from Extension transaction created Topic | def unsubscribe_extension_transactions_created(self, uuid: UUID) -> bool:
return self._generic_unsubscribe('/extensions/transactions', uuid) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def unsubscribe(self, topic: str, subscription_id: int = None) -> None:\n ...",
"def unsubscribe(endpoint: str, topic: str, timeout: int = 5):\n global logger\n logger.info(f\"Unsubscribing from topic '{topic}' ...\")\n action = {\"action\": \"unsubscribe\", \"topic\": topic}\n reply = s... | [
"0.7785302",
"0.70840764",
"0.7039646",
"0.6972927",
"0.68595046",
"0.6841361",
"0.6763793",
"0.6763793",
"0.6763793",
"0.6763793",
"0.6763793",
"0.66854125",
"0.6625891",
"0.65613663",
"0.6455564",
"0.64483654",
"0.64307076",
"0.6417031",
"0.64117914",
"0.64080447",
"0.63859... | 0.6056249 | 47 |
Subscribe to Moderator Change Events topic\n | def subscribe_moderator_change_events(self,
broadcaster_id: str,
user_id: Union[str, None],
callback_func: Union[Callable[[UUID, dict], None]]) -> Tuple[bool, UUID]:
params = {
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def listen(client, main):\n\n @client.event\n async def on_message_edit(old, message):\n main.message_handler(message, True)",
"def process_IN_MODIFY(self, event):",
"def _event_subscribe(self) -> None:\n self.gateway.groups.subscribe(\n self.group_data_callback,\n eve... | [
"0.630276",
"0.613077",
"0.60775363",
"0.6059872",
"0.6059872",
"0.6059872",
"0.6039669",
"0.6039669",
"0.59692985",
"0.5942631",
"0.58832145",
"0.5874928",
"0.5851946",
"0.5843113",
"0.58037174",
"0.58028126",
"0.5791291",
"0.5728118",
"0.5724599",
"0.57148063",
"0.5711235",... | 0.58341825 | 14 |
Unsubscribe from Moderator Change Events Topic | def unsubscribe_moderator_change_events(self, uuid: UUID) -> bool:
return self._generic_unsubscribe('/moderation/moderators/events', uuid) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(self):\n pass # pragma: no cover",
"def unsubscribeFromEvent(eventName,subscriber):",
"def unsubscribe(observer):",
"def unsu... | [
"0.76056266",
"0.76056266",
"0.76056266",
"0.76056266",
"0.76056266",
"0.74647355",
"0.7060999",
"0.70378864",
"0.70378864",
"0.69733167",
"0.6935927",
"0.67767143",
"0.6701206",
"0.6625047",
"0.6624158",
"0.65858805",
"0.6540649",
"0.64986974",
"0.648971",
"0.64648473",
"0.6... | 0.6845972 | 11 |
Subscribe to Channel Ban Change Events\n | def subscribe_channel_ban_change_events(self,
broadcaster_id: str,
user_id: Union[str, None],
callback_func: Union[Callable[[UUID, dict], None]]) -> Tuple[bool, UUID]:
params = {
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_channel_change(self, new_channel):\n pass",
"def subscribe_balances(self, update_handler):\n pass",
"def subscribe(receiver):",
"def subscribe(receiver):",
"def subscribe(receiver):",
"def subscribe(receiver, updateInterval=10):",
"def listen_channel_ban(self, broadcaster_user_id: ... | [
"0.6570176",
"0.6233181",
"0.6006282",
"0.6006282",
"0.6006282",
"0.5961182",
"0.5947485",
"0.5890927",
"0.57617646",
"0.5761531",
"0.5756199",
"0.5721502",
"0.57208514",
"0.5667496",
"0.5582555",
"0.5528739",
"0.5528062",
"0.5526559",
"0.55081624",
"0.54988754",
"0.54754287"... | 0.6141255 | 2 |
Unsubscribe from Channel Ban Change Events Topic | def unsubscribe_channel_ban_change_events(self, uuid: UUID) -> bool:
return self._generic_unsubscribe('/moderation/banned/events', uuid) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(self, channel, update_handler=None):\n pass",
"def cb_stop(self, update, context):\n\n print(f\"Unsubscribing chat_id '{upd... | [
"0.7299835",
"0.7299835",
"0.7299835",
"0.7299835",
"0.7299835",
"0.7290285",
"0.69569016",
"0.69497174",
"0.6807146",
"0.6805143",
"0.67663234",
"0.67325085",
"0.6711645",
"0.667798",
"0.6638506",
"0.64776814",
"0.6413881",
"0.63525087",
"0.6308385",
"0.63021916",
"0.6288255... | 0.7349533 | 0 |
Subscribe to Subscription Events Topic\n | def subscribe_subscription_events(self,
broadcaster_id: str,
callback_func: Union[Callable[[UUID, dict], None]],
user_id: Union[str, None] = None,
gifter_id: Union[str,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subscribe_topic(self):\n req = {\n \"op\": \"subscribe\",\n \"args\": [\n \"instrument\",\n \"trade\",\n \"orderBook10\",\n \"execution\",\n \"order\",\n \"position\",\n \"margi... | [
"0.74868757",
"0.7477948",
"0.73847705",
"0.72339535",
"0.71468717",
"0.7072319",
"0.70474637",
"0.7040364",
"0.7040364",
"0.7040364",
"0.69917065",
"0.6967835",
"0.69677424",
"0.6963225",
"0.69115204",
"0.6878046",
"0.6867905",
"0.685218",
"0.6698295",
"0.66679764",
"0.66251... | 0.0 | -1 |
Unsubscribe from Subscription Events Topic | def unsubscribe_subscription_events(self, uuid: UUID) -> bool:
return self._generic_unsubscribe('/subscriptions/events', uuid) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def unsubscribe(self, topic: str, subscription_id: int = None) -> None:\n ...",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribeFromEvent(eventName,subscriber):",
"def un... | [
"0.81594926",
"0.79041",
"0.79041",
"0.79041",
"0.79041",
"0.79041",
"0.78738904",
"0.7636601",
"0.76090956",
"0.75597286",
"0.75595737",
"0.73996174",
"0.73495823",
"0.73371434",
"0.72978514",
"0.72589266",
"0.72108454",
"0.7209852",
"0.7209852",
"0.7180335",
"0.7138884",
... | 0.70789266 | 25 |
Subscribe to Hype Train Events\n | def subscribe_hype_train_events(self,
broadcaster_id: str,
callback_func: Union[Callable[[UUID, dict], None]]) -> Tuple[bool, UUID]:
params = {
'broadcaster_id': broadcaster_id,
'first': 1
}
url = bui... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def listen(limb, hd):\n def callback(data):\n playback(limb, hd, str(data.data))\n\n rospy.Subscriber(\"handshake/play\", std_msgs.msg.String, callback)\n rospy.loginfo('listening...')\n rospy.spin()",
"def on_connect(client, userdata, flags, rcdata):\n client.subscribe(\"diy/system/fire\",... | [
"0.6272829",
"0.62018526",
"0.61964303",
"0.6183167",
"0.6157085",
"0.6144037",
"0.61242986",
"0.61156505",
"0.60952926",
"0.60716414",
"0.60716414",
"0.60473037",
"0.59852266",
"0.5935041",
"0.5913895",
"0.59126216",
"0.58995676",
"0.58355355",
"0.57930493",
"0.57867795",
"0... | 0.5715374 | 22 |
Unsubscribe from Hype Train Events Topic | def unsubscribe_hype_train_events(self, uuid: UUID) -> bool:
return self._generic_unsubscribe('/hypetrain/events', uuid) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(receiver):",
"def unsubscribe(self):\n pass # pragma: no cover",
"def unsubscribeFromEvent(eventName,subscriber):",
"def unsubscribe(self, subject):\n ... | [
"0.73012006",
"0.73012006",
"0.73012006",
"0.73012006",
"0.73012006",
"0.72183186",
"0.7197908",
"0.6796366",
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"0.662526",
"0.6577521",
"0.6576188",
"0.65284336",
"0.65141696",
"0.65022695",
"0.64... | 0.7315941 | 0 |
Its possible to have a CPP library target that has no source (more of a dependency bundle). | def EmptyTarget(self):
return not self.objects | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_native_binary_target_no_warning(testdir: Testdir) -> None:\n with temp_env_update({'PRISMA_PY_DEBUG': '0'}):\n result = testdir.generate(options='binaryTargets = [\"native\"]')\n\n stdout = result.stdout.decode('utf-8')\n assert 'Warning' not in stdout\n assert 'binaryTargets option' no... | [
"0.5936038",
"0.5624769",
"0.55687964",
"0.54797125",
"0.5355298",
"0.5304638",
"0.5248008",
"0.5209485",
"0.5129513",
"0.5115217",
"0.5066067",
"0.5064064",
"0.5053785",
"0.5047533",
"0.50184554",
"0.4957185",
"0.4948288",
"0.49479493",
"0.49328604",
"0.49280024",
"0.4923139... | 0.0 | -1 |
Add paddings to make image shape divisible by 32 | def get_validation_augmentation():
test_transform = [
albu.Resize(192, 192)
]
return albu.Compose(test_transform) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def padding(img, n):\n img = np.pad(img, [(n, n), (n, n)], mode='constant', constant_values=0)\n\n return img",
"def padding(image, padded_size):\n image_row, image_col = image.shape #asigna alto y ancho de la imagen \n\n padded_image = np.zeros((image_row + padded_size*2, image_col + padded_size*2))... | [
"0.6846387",
"0.6843057",
"0.68196994",
"0.6801745",
"0.6762657",
"0.6717646",
"0.6697821",
"0.6673578",
"0.6591452",
"0.65731627",
"0.65608454",
"0.65184844",
"0.65169907",
"0.6513039",
"0.64664114",
"0.6415563",
"0.6397664",
"0.6343387",
"0.6340076",
"0.6332432",
"0.633052"... | 0.0 | -1 |
Creates a stitchedge by connecting a parent and child loop | def connect_loops(self, parent_loop_id: int, child_loop_id: int,
pull_direction: Pull_Direction = Pull_Direction.BtF,
stack_position: Optional[int] = None, depth: int = 0, parent_offset: int = 0):
assert parent_loop_id in self, f"parent loop {parent_loop_id} is not in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_synapses(self):\n pass # Ignore if child does not implement.",
"def __init__(self, parent, layer):\n pass",
"def __init__(self, parent=None):\n super(fsScene, self).__init__(parent)\n # Location of mouse events and whether the mouse button is down\n self.mouseDown = ... | [
"0.57947505",
"0.546964",
"0.53975177",
"0.53660804",
"0.53570944",
"0.5344863",
"0.5323155",
"0.53197694",
"0.52232933",
"0.5204966",
"0.51902634",
"0.51798797",
"0.5172197",
"0.5172197",
"0.51611084",
"0.51545936",
"0.5146673",
"0.51419324",
"0.5136733",
"0.5130358",
"0.511... | 0.52773476 | 8 |
Course information will be used to generate instruction for knitting machines and visualizations that structure knitted objects like grids. Evaluation of a course structure should be done in O(nm) time where n is the number of loops in the graph and m is the largest number of parent loops pulled through a single loop (... | def get_courses(self) -> Tuple[Dict[int, float], Dict[float, List[int]]]:
loop_ids_to_course = {}
course_to_loop_ids = {0: []}
current_course_id = 0
# Iterate through the loops
for current_loop_id in range(self.last_loop_id + 1):
# Check if there are any parent loop... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_course(self):\n courses = []\n aprovacao_d = {}\n # semestral\n for rate_it in self.__semestral_rate:\n # pega uma lista no qual o primeiro elemento é a taxa, o segundo\n # e o terceiro são quantidades\n rate_data = self.analysis[\"semestral_ra... | [
"0.63598067",
"0.62408775",
"0.60766065",
"0.58932865",
"0.5790156",
"0.5698993",
"0.5653367",
"0.56354576",
"0.56241685",
"0.5595133",
"0.55622107",
"0.5559223",
"0.54188114",
"0.53574604",
"0.53306824",
"0.5325274",
"0.5309439",
"0.5280769",
"0.5270331",
"0.5261296",
"0.524... | 0.60680026 | 3 |
Instanciation of Splitter class. We specify the number of runs to leave out for the test set. | def __init__(self, out_per_fold):
self.out_per_fold = out_per_fold
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_split(self) -> NoReturn:\n raise NotImplementedError",
"def _setup_splits(self):\n #ntot = self.reredux_conf['nperfile']\n ntot = self.reredux_conf['Ngals']\n npersplit = self.runconf['nper']\n\n self.beglist, self.endlist = get_splits(ntot, npersplit)",
"def _spli... | [
"0.71266425",
"0.6380145",
"0.6368681",
"0.63563967",
"0.63418573",
"0.6236902",
"0.6120404",
"0.60342914",
"0.59201735",
"0.582116",
"0.581839",
"0.5780446",
"0.57682496",
"0.576467",
"0.57456785",
"0.5707095",
"0.5682275",
"0.5664317",
"0.5649972",
"0.56174487",
"0.5604603"... | 0.0 | -1 |
Split lists in differents folds for cross validation. | def split(self, X_train, Y_train, run_train=None, run_test=None):
result = []
logo = LeavePOut(self.out_per_fold)
for train, test in logo.split(X_train):
y_train = [Y_train[i] for i in train]
x_train = [X_train[i] for i in train]
y_test = [Y_train[i] for i in ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_folds(self, splits):\n\n train = [splits.copy() for i in range(self.n_splits)]\n holdout = [train[i].pop(i) for i in range(self.n_splits)]\n train_flat = [list(chain(*row)) for row in train]\n\n return list(zip(train_flat, holdout))",
"def split_cv(length, num_folds):\n sp... | [
"0.72968256",
"0.719841",
"0.69764197",
"0.6945098",
"0.6917422",
"0.6894226",
"0.687894",
"0.6873099",
"0.68583196",
"0.68282145",
"0.6812268",
"0.6787838",
"0.6777427",
"0.6756678",
"0.67225647",
"0.6717021",
"0.6692794",
"0.6687437",
"0.6682434",
"0.6654291",
"0.66536283",... | 0.58894753 | 77 |
Return items to be featured on homepage | def on_homepage(self):
# While it's easy enough to just query the default manager
# to do this, providing this convenience method abstracts
# away the way homepage items are designated in case
# we change the way that designation is done.
return self.filter(on_homepage=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def featured(self):\n \n featured_content = []\n \n def rnd_content_items(return_items=1, **kwargs):\n if 'limit' not in kwargs:\n kwargs['limit'] = 3\n # searching due_date limits results to only assignments; if we want\n # all art... | [
"0.7098284",
"0.6752768",
"0.65510976",
"0.6305554",
"0.6284082",
"0.6271135",
"0.62244487",
"0.6103396",
"0.6074179",
"0.60473037",
"0.5998711",
"0.58087254",
"0.5795663",
"0.57800406",
"0.57797366",
"0.5759702",
"0.57143676",
"0.57087076",
"0.57039094",
"0.5650906",
"0.5648... | 0.63614005 | 3 |
Says when a member joined. | async def joined(self, ctx, member: discord.Member):
await ctx.send('{0.name} joined in {0.joined_at}'.format(member)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def on_member_join(self, member: Member):\n\n if not self._is_tracked(member.guild, EventPriority.join):\n return\n\n em = self.em_base(\n member,\n f\"User {member.mention} ({member.name}) joined\",\n EventColors.join.value\n )\n\n em.a... | [
"0.78859025",
"0.7684899",
"0.76168865",
"0.76168865",
"0.76168865",
"0.76117444",
"0.7605289",
"0.7578849",
"0.7570187",
"0.7469505",
"0.7328319",
"0.72358775",
"0.7097512",
"0.6860103",
"0.6832718",
"0.66840136",
"0.664821",
"0.65940976",
"0.65013736",
"0.64450365",
"0.6405... | 0.7771115 | 1 |
Given a triangulation object and a field `q` defined at the vertices of the triangulation, return integral |grad q|^2 dx | def square_gradient(tri, q):
finder = tri.get_trifinder()
interp = LinearTriInterpolator(tri, q, trifinder = finder)
integral = 0.0
num_triangles, _ = np.shape(tri.triangles)
for n in range(num_triangles):
ele = tri.triangles[n, :]
x = tri.x[ele]
y = tri.y[ele]
a ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gradh(self,q):\n gx = self.d_i(q) / self.e1u\n gy = self.d_j(q) / self.e2v\n #\n return gx,gy",
"def integrate_dphi_phi(self, i, j):\n def func(x):\n return self.shape_function_deriv(i, x) * self.shape_function(j, x)\n i, err = quadrature(func, -1, 1)\n ... | [
"0.61055577",
"0.588669",
"0.5814507",
"0.57111114",
"0.5700496",
"0.5698189",
"0.56779283",
"0.5656769",
"0.5652398",
"0.5644207",
"0.5642131",
"0.56034553",
"0.5586597",
"0.55844885",
"0.5546834",
"0.5504967",
"0.5503718",
"0.54716414",
"0.54594284",
"0.5456545",
"0.5454004... | 0.6430995 | 0 |
Get the errors from the Lcurve runs | def get_l_curve_results(directory, overwrite = False):
costs = []
tikhs = []
regs = []
glacier = "helheim"
extension = ".tar.gz"
# If the output hasn't been cached, retrieve it from the giant archives
cached_output = os.path.join(directory, "helheim_l_curve.txt")
if not os.path.exists... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, errors: List[float]) -> List[float]:",
"def error(self) -> Sequence[float]:\n errors = []\n for line, sign in zip(self.marker_lines, (-1, 1)):\n if self._orientation == Orientation.UP_DOWN:\n picket_pos = self._fit(line.center.y)\n mlc_pos... | [
"0.6367377",
"0.63289833",
"0.63179725",
"0.62470627",
"0.6190272",
"0.6103401",
"0.6064777",
"0.60606426",
"0.6049611",
"0.6049317",
"0.60475177",
"0.60305744",
"0.5965578",
"0.59578276",
"0.5941514",
"0.5910615",
"0.5905703",
"0.5876154",
"0.5869304",
"0.5868508",
"0.583170... | 0.0 | -1 |
This script analyzes the results of the main function and produces the Lcurve plot. | def analyze(argv):
costs, tikhs, regs = get_l_curve_results(argv[0])
fig = plt.figure()
ax = fig.add_subplot(111)
plt.scatter(tikhs, costs)
for k in range(len(regs)):
exponent = math.log(regs[k], 10)
ax.annotate(r'$10^{{{0:.2f}}}$'.format(exponent),
xy = (tikhs[k... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot():\n xvals = np.arange(-50, 250, step=0.1)\n\n fig = plt.figure()\n plt.suptitle(\"Gaussian with smooth transition to power law\")\n\n A0vals = [10, 11]\n avals = [5*10**-3, 10**-3, 5*10**-4]\n ttvals = [10., 50., 100.]\n cvals = [-0.1, -0.9, -5./3., -4.]\n offset = [-30, 0.0, 30]\... | [
"0.70325994",
"0.6906087",
"0.6865391",
"0.6744682",
"0.6360003",
"0.6259489",
"0.62437195",
"0.6224446",
"0.6207582",
"0.61919683",
"0.619153",
"0.61545587",
"0.6145968",
"0.6132787",
"0.61294043",
"0.60725796",
"0.6056807",
"0.60417926",
"0.6017504",
"0.60079277",
"0.600358... | 0.6874636 | 2 |
This script runs several inversions for Helheim glacier using different values of the regularization parameter in order to generate the familiar Lcurve plot. | def main(argv):
rmin = float(argv[0])
rmax = float(argv[1])
nr = int(argv[2])
rs = np.logspace(rmin, rmax, nr)
for n in range(nr):
r = rs[n]
log_file_name = "helheim_lambda-" + str(r) + ".txt"
run.main(["-g", "helheim",
"-r", str(r),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def L_curveTV(f,lam_init = 2.0, q = 0.9):\n lam = lam_init\n max_iter = 50\n residual_list = np.zeros(max_iter)\n size_list = np.zeros(max_iter)\n error = np.zeros(max_iter)\n alt_error = np.zeros(max_iter)\n \n for i in range(max_iter): #range(max_iter):\n u = ChambollePock_denoise(... | [
"0.62898856",
"0.62495583",
"0.62437105",
"0.6178935",
"0.60789984",
"0.59641284",
"0.5952815",
"0.59437585",
"0.5928498",
"0.58640605",
"0.58632755",
"0.58602977",
"0.58514357",
"0.5839851",
"0.58129305",
"0.5812024",
"0.5801944",
"0.57913727",
"0.57665944",
"0.57651573",
"0... | 0.0 | -1 |
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