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)))