def open_sheet(self, defpath=None): self.blockSignals(True) path = ([defpath] if defpath else QFileDialog.getOpenFileName( self, 'Open CSV', os.getenv('Home'), 'CSV(*.csv)')) reset_bang(globals, 'filepath', first(path)) self.check_change = False if (first(path) != ''): with open(first(path), 'r', newline='') as csv_file: self.setRowCount(0) my_file = csv.reader(csv_file, dialect='excel') for row_data in my_file: row = self.rowCount() self.insertRow(row) (self.setColumnCount(len(row_data)) if (len(row_data) > COLS) else None) for [column, stuff] in enumerate(row_data): item = QTableWidgetItem(stuff) self.setItem(row, column, item) _hy_anon_var_9 = self.setRowCount(ROWS) _hy_anon_var_10 = _hy_anon_var_9 else: _hy_anon_var_10 = None self.init_cells() (self.set_header_style(True) if globals['header'] else None) debug(self.used_row_count()) self.check_change = True reset_bang(globals, 'filechanged', False) self.set_title() self.update_preview() self.undo_stack.clear() return self.blockSignals(False)
def E(x): if is_coll(x): if A(first(x)): return D(first(x), list(rest(x))) elif not A(x): y = list(map(E, x)) return E(y) if A(first(y)) else y return x
def save_sheet_html(self): path = QFileDialog.getSaveFileName(self, 'Save HTML', os.getenv('Home'), 'HTML(*.html)') if (first(path) != ''): with open(first(path), 'w') as file: file.write(htmlExport.qtable_to_html(self, globals['header'])) _hy_anon_var_13 = file.close() _hy_anon_var_14 = _hy_anon_var_13 else: _hy_anon_var_14 = None return _hy_anon_var_14
def fetch_result_dbpedia(query): results = [] conn = connect(hyx_XasteriskXdb_pathXasteriskX) cur = conn.cursor() cur.execute('select data from dbpedia where query = ? limit 1', [query]) d = cur.fetchall() if len(d) > 0: results = json.loads(first(first(d))) _hy_anon_var_5 = None else: _hy_anon_var_5 = None conn.close() return results
def copy_selection(self, clipboard_mode=CLIPBOARD_MODE_CLIPBOARD): 'Int(0,2) -> Void\n Copies the current selection to the clipboard. Depending on the clipboard-mode to define which clipboard system is used\n QClipboard::Clipboard\t0\tindicates that data should be stored and retrieved from the global clipboard.\n QClipboard::Selection\t1\tindicates that data should be stored and retrieved from the global mouse selection. Support for Selection is provided only on systems with a global mouse selection (e.g. X11).\n QClipboard::FindBuffer\t2\tindicates that data should be stored and retrieved from the Find buffer. This mode is used for holding search strings on macOS.\n http://doc.qt.io/qt-5/qclipboard.html#Mode-enum' (log('WARNING: Copy only works on first selection') if (len(self.selectedRanges()) > 1) else None) r = first(self.selectedRanges()) copy_content = '' try: for row in range(r.topRow(), inc(r.bottomRow())): for col in range(r.leftColumn(), inc(r.rightColumn())): item = self.item(row, col) if (item != None): copy_content = (copy_content + item.text()) if (not (col == r.rightColumn())): copy_content = (copy_content + '\t') _hy_anon_var_3 = None else: _hy_anon_var_3 = None _hy_anon_var_4 = _hy_anon_var_3 else: _hy_anon_var_4 = None if (not (row == r.bottomRow())): copy_content = (copy_content + '\n') _hy_anon_var_5 = None else: _hy_anon_var_5 = None _hy_anon_var_6 = globals['clipboard'].setText( copy_content, clipboard_mode) except AttributeError as e: _hy_anon_var_6 = log('WARING: No selection available') return _hy_anon_var_6
def paste(self, clipboard_mode=CLIPBOARD_MODE_CLIPBOARD): 'Void (Enum(0-2)) -> Void\n Inserts the clipboard, at the upper left corner of the current selection' (log('WARNING: Paste only works on first selection') if (len(self.selectedRanges()) > 1) else None) r = first(self.selectedRanges()) paste_list = self.parse_for_paste( first(globals['clipboard'].text('plain', clipboard_mode))) if (r == None): start_row = self.currentRow() start_col = self.currentColumn() _hy_anon_var_2 = None else: start_col = r.leftColumn() start_row = r.topRow() _hy_anon_var_2 = None command = Command_Paste(self, start_row, start_col, paste_list, 'Paste') return self.undo_stack.push(command)
def save_sheet_csv(self, defpath=None): path = ([defpath] if defpath else QFileDialog.getSaveFileName( self, 'Save CSV', os.getenv('Home'), 'CSV(*.csv)')) if (first(path) != ''): with open(first(path), 'w', newline='') as csv_file: writer = csv.writer(csv_file, dialect='excel') for row in range(inc(self.used_row_count())): row_data = [] for col in range(inc(self.used_column_count())): item = self.item(row, col) (row_data.append(item.text()) if (item != None) else row_data.append('')) writer.writerow(row_data) _hy_anon_var_11 = None _hy_anon_var_12 = _hy_anon_var_11 else: _hy_anon_var_12 = None reset_bang(globals, 'filepath', first(path)) reset_bang(globals, 'filechanged', False) return self.set_title()
def kgn(): while True: query = get_query() if query == 'quit' or query == 'q': break _hy_anon_var_4 = None else: _hy_anon_var_4 = None elist = entities_in_text(query) people_found_on_dbpedia = [] places_found_on_dbpedia = [] organizations_found_on_dbpedia = [] global short_comment_to_uri short_comment_to_uri = {} for key in elist: type_uri = entity_type_to_type_uri[key] for name in elist[key]: dbp = dbpedia_get_entities_by_name(name, type_uri) for d in dbp: short_comment = shorten_comment(second(second(d)), second(first(d))) people_found_on_dbpedia.extend( [name + ' || ' + short_comment]) if key == 'PERSON' else None places_found_on_dbpedia.extend( [name + ' || ' + short_comment]) if key == 'GPE' else None organizations_found_on_dbpedia.extend( [name + ' || ' + short_comment]) if key == 'ORG' else None user_selected_entities = select_entities( people_found_on_dbpedia, places_found_on_dbpedia, organizations_found_on_dbpedia) uri_list = [] for entity in user_selected_entities['entities']: short_comment = entity[4 + entity.index(' || '):None:None] uri_list.extend([short_comment_to_uri[short_comment]]) relation_data = ( hyx_entity_results_XgreaterHthan_signXrelationship_links(uri_list)) print('\nDiscovered relationship links:') pprint(relation_data)
def on_epoch_end(epoch, not_used=None): print() print('----- Generating text after Epoch:', epoch) start_index = random.randint(0, len(text) - maxlen - 1) for diversity in [0.2, 0.5, 1.0, 1.2]: print('----- diversity:', diversity) generated = '' sentence = text[start_index:start_index + maxlen:None] generated = generated + sentence print('----- Generating with seed:', sentence) sys.stdout.write(generated) for i in range(400): x_pred = np.zeros([1, maxlen, len(chars)]) for [t, char] in [j for j in enumerate(sentence)]: x_pred[0][t][char_indices[char]] = 1 preds = first(model.predict(x_pred, verbose=0)) print('** preds=', preds) next_index = sample(preds, diversity) next_char = indices_char[next_index] sentence = sentence[1:None:None] + next_char sys.stdout.write(next_char) sys.stdout.flush() print()
from hy.core.language import first, flatten, last, rest vektor = [1, 2, 3, 4] vektor[1] vektor[-1] vektor[-2] first(vektor) last(vektor) rest(vektor) list(rest(vektor)) list(rest(vektor)) vektor[1:5] vektor[1:] vektor[-5:-2] vektor[-3:-2] vektor2 = list(range(20)) vektor2[2:-1:2] vektor2[-1:0:-1] vektor2[-1:0:-2] vektor[2] = 42 vektor[10] = -1 vektor.append(5) vektor.append(6) matice = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] matice2 = [[1], [2, 3], [4, 5, 6], [7, 8, 9, 10]] flatten(matice) flatten(matice2)
from keras.optimizers import RMSprop from keras.utils.data_utils import get_file import numpy as np import random import sys import io path = get_file('nietzsche.txt', origin= 'https://s3.amazonaws.com/text-datasets/nietzsche.txt') _hy_anon_var_1 = None with io.open(path, encoding='utf-8') as f: text = f.read() _hy_anon_var_1 = None print('corpus length:', len(text)) chars = sorted(list(set(text))) print('total chars (unique characters in input text):', len(chars)) char_indices = dict([(last(i), first(i)) for i in enumerate(chars)]) indices_char = dict([i for i in enumerate(chars)]) maxlen = 40 step = 3 sentences = list() next_chars = list() print('Create sentences and next_chars data...') for i in range(0, len(text) - maxlen, step): sentences.append(text[i:i + maxlen:None]) next_chars.append(text[i + maxlen]) print('Vectorization...') x = np.zeros([len(sentences), maxlen, len(chars)], dtype=np.bool) y = np.zeros([len(sentences), len(chars)], dtype=np.bool) for [i, sentence] in [j for j in enumerate(sentences)]: for [t, char] in [j for j in enumerate(sentence)]: x[i][t][char_indices[char]] = 1
def __repr__(self): return "(\xce\xbb%s.%s)" % (first(self), C(second(self)))