本文整理汇总了Python中tables.Float64Col方法的典型用法代码示例。如果您正苦于以下问题：Python tables.Float64Col方法的具体用法？Python tables.Float64Col怎么用？Python tables.Float64Col使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tables的用法示例。
在下文中一共展示了tables.Float64Col方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_clean
# 需要导入模块: import tables [as 别名]
# 或者: from tables import Float64Col [as 别名]
def test_clean(self):
"""Test successful cleaning up of data.
"""
table_def = {
'time': tables.Float64Col(pos=0),
'temperature': tables.Float64Col(pos=1),
'pressure': tables.Float64Col(pos=2),
'mass_fractions': tables.Float64Col(pos=3, shape=(2))
}
with TemporaryDirectory() as temp_dir:
sim = Simulation(0, None, 'gri30.cti', path=temp_dir)
sim.save_file = os.path.join(sim.path, str(sim.idx) + '.h5')
with tables.open_file(sim.save_file, mode='w', title='0') as h5file:
table = h5file.create_table(where=h5file.root, name='simulation',
description=table_def
)
# Row instance to save timestep information to
timestep = table.row
timestep['time'] = 1.0
timestep['temperature'] = 1.0
timestep['pressure'] = 1.0
timestep['mass_fractions'] = np.ones(2)
timestep.append()
table.flush()
sim.clean()
assert not os.path.isfile(sim.save_file) 示例2: test_process_results
# 需要导入模块: import tables [as 别名]
# 或者: from tables import Float64Col [as 别名]
def test_process_results(self):
"""Test processing of ignition results using artificial data.
"""
table_def = {
'time': tables.Float64Col(pos=0),
'temperature': tables.Float64Col(pos=1),
'pressure': tables.Float64Col(pos=2),
'mass_fractions': tables.Float64Col(pos=3, shape=(2))
}
with TemporaryDirectory() as temp_dir:
sim = Simulation(0, None, 'gri30.cti', path=temp_dir)
sim.save_file = os.path.join(sim.path, str(sim.idx) + '.h5')
time_initial = np.arange(0, 10, 0.02)
temp_initial = 200 * np.ones(len(time_initial))
# ignition delay (temp = 600) will be at 10.5 s
time_ramp = np.arange(10, 11.001, 0.005)
temp_ramp = 200 + 800 * (time_ramp - 10)
time_flat = np.arange(11.005, 15, 0.01)
temp_flat = 1000 * np.ones(len(time_flat))
times = np.concatenate((time_initial, time_ramp, time_flat))
temps = np.concatenate((temp_initial, temp_ramp, temp_flat))
# add a very small number to account for floating-point roundoff error
idx = len(temp_initial) + int((len(time_ramp) - 1) / 2)
temps[idx] += 1e-9
with tables.open_file(sim.save_file, mode='w', title='0') as h5file:
table = h5file.create_table(where=h5file.root, name='simulation',
description=table_def
)
# Row instance to save timestep information to
timestep = table.row
for time, temp in zip(times, temps):
timestep['time'] = time
timestep['temperature'] = temp
timestep['pressure'] = 1.0
timestep['mass_fractions'] = np.ones(2)
timestep.append()
table.flush()
ignition_delay, sampled_data = sim.process_results()
assert np.allclose(ignition_delay, 10.5)
initial_temp = 200.
delta = 40.
for idx in range(20):
assert np.allclose(sampled_data[idx], [initial_temp + delta, 1, 1, 1])
delta += 40. 示例3: test_process_results_skip_data
# 需要导入模块: import tables [as 别名]
# 或者: from tables import Float64Col [as 别名]
def test_process_results_skip_data(self):
"""Test processing of ignition results, skipping data sampling, using artificial data.
"""
table_def = {
'time': tables.Float64Col(pos=0),
'temperature': tables.Float64Col(pos=1),
'pressure': tables.Float64Col(pos=2),
'mass_fractions': tables.Float64Col(pos=3, shape=(2))
}
with TemporaryDirectory() as temp_dir:
sim = Simulation(0, None, 'gri30.cti', path=temp_dir)
sim.save_file = os.path.join(sim.path, str(sim.idx) + '.h5')
with tables.open_file(sim.save_file, mode='w', title='0') as h5file:
table = h5file.create_table(where=h5file.root, name='simulation',
description=table_def
)
# Row instance to save timestep information to
timestep = table.row
time_initial = np.arange(0, 10, 0.02)
temp_initial = 200 * np.ones(len(time_initial))
# ignition delay (temp = 600) will be at 10.5 s
time_ramp = np.arange(10, 11.02, 0.02)
temp_ramp = 200 + 800 * (time_ramp - 10)
time_flat = np.arange(11.02, 15, 0.02)
temp_flat = 1000 * np.ones(len(time_flat))
times = np.concatenate((time_initial, time_ramp, time_flat))
temps = np.concatenate((temp_initial, temp_ramp, temp_flat))
# add a very small number to account for floating-point roundoff error
idx = len(temp_initial) + 25
temps[idx] += 1e-8
for time, temp in zip(times, temps):
timestep['time'] = time
timestep['temperature'] = temp
timestep['pressure'] = 1.0
timestep['mass_fractions'] = np.ones(2)
timestep.append()
table.flush()
sim.process_results(skip_data=True)
assert np.allclose(sim.ignition_delay, 10.5)
assert not hasattr(sim, 'sampled_data')