Writing Containers to a tabular format

The TableWriter/TableReader sub-classes allow you to write a ctapipe.core.Container class and its meta-data to an output table. They treat the Fields in the Container as columns in the output, and automatically generate a schema. Here we will go through an example of writing out data and reading it back with Pandas, PyTables, and a ctapipe.io.TableReader:

In this example, we will use the HDF5TableWriter, which writes to HDF5 datasets using PyTables. Currently this is the only implemented TableWriter.

Caveats to think about: * vector columns in Containers can be written, but some lilbraries like Pandas can not read those (so you must use pytables or astropy to read outputs that have vector columns) * units are stored in the table metadata, but some libraries like Pandas ignore them and all other metadata

Create some example Containers

from ctapipe.io import HDF5TableWriter
from ctapipe.core import Container, Field
from astropy import units as u
import numpy as np

class VariousTypesContainer(Container):

    a_int = Field(int, 'some int value')
    a_float = Field(float, 'some float value with a unit', unit=u.m)
    a_bool = Field(bool, 'some bool value')
    a_np_int = Field(np.int64, 'a numpy int')
    a_np_float = Field(np.float64, 'a numpy float')
    a_np_bool = Field(np.bool_, 'np.bool')

let’s also make a dummy stream (generator) that will create a series of these containers

def create_stream(n_event):

    data = VariousTypesContainer()
    for i in range(n_event):

        data.a_int = int(i)
        data.a_float = float(i) * u.cm # note unit conversion will happen
        data.a_bool = (i % 2) == 0
        data.a_np_int = np.int64(i)
        data.a_np_float = np.float64(i)
        data.a_np_bool = np.bool((i % 2) == 0)

        yield data

for data in create_stream(2):

    for key, val in data.items():

        print('{}: {}, type : {}'.format(key, val, type(val)))

a_int: 0, type : <class 'int'>
a_float: 0.0 cm, type : <class 'astropy.units.quantity.Quantity'>
a_bool: True, type : <class 'bool'>
a_np_int: 0, type : <class 'numpy.int64'>
a_np_float: 0.0, type : <class 'numpy.float64'>
a_np_bool: True, type : <class 'bool'>
a_int: 1, type : <class 'int'>
a_float: 1.0 cm, type : <class 'astropy.units.quantity.Quantity'>
a_bool: False, type : <class 'bool'>
a_np_int: 1, type : <class 'numpy.int64'>
a_np_float: 1.0, type : <class 'numpy.float64'>
a_np_bool: False, type : <class 'bool'>

/tmp/ipykernel_992/3018134255.py:11: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  data.a_np_bool = np.bool((i % 2) == 0)

Writing the Data (and good practices)

How not to do it:

h5_table = HDF5TableWriter('container.h5', group_name='data')

for data in create_stream(10):

    h5_table.write('table', data)

h5_table.close()

/tmp/ipykernel_992/3018134255.py:11: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  data.a_np_bool = np.bool((i % 2) == 0)

In that case the file is not garenteed to close properly for instance if one does a mistake in the for loop. Let’s just add a stupid mistake and see what happens.

try:
    h5_table = HDF5TableWriter('container.h5', group_name='data')

    for data in create_stream(10):

        h5_table.write('table', data)
        0/0  # cause an error

    h5_table.close()
except Exception as err:
    print("FAILED!", err)

FAILED! division by zero

/tmp/ipykernel_992/3018134255.py:11: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  data.a_np_bool = np.bool((i % 2) == 0)

Now the file did not close properly. So let’s try to correct the mistake and execute the code again.

try:
    h5_table = HDF5TableWriter('container.h5', group_name='data')

    for data in create_stream(10):

        h5_table.write('table', data)
        0/0  # cause an error
    h5_table.close()
except Exception as err:
    print("FAILED!", err)

FAILED! The file 'container.h5' is already opened.  Please close it before reopening in write mode.

Ah it seems that the file did not close! Now I am stuck. Maybe I should restart the kernel? ahh no I don’t want to loose everything. Can I just close it ?

h5_table.close()

It worked!

Better to use context management!

try:
    with HDF5TableWriter('container.h5', group_name='data') as h5_table:

        for data in create_stream(10):

            h5_table.write('table', data)
            0/0
except Exception as err:
    print("FAILED:", err)
print('Done')

FAILED: division by zero
Done

/tmp/ipykernel_992/3018134255.py:11: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  data.a_np_bool = np.bool((i % 2) == 0)

!ls container.h5

container.h5

Appending new Containers

To append some new containers we need to set the writing in append mode by using: ‘mode=a’. But let’s now first look at what happens if we don’t.

for i in range(2):

    with HDF5TableWriter('container.h5', mode='w', group_name='data_{}'.format(i)) as h5_table:

        for data in create_stream(10):

            h5_table.write('table', data)

        print(h5_table.h5file)

container.h5 (File) ''
Last modif.: '2022-02-17T12:43:11+00:00'
Object Tree:
/ (RootGroup) ''
/data_0 (Group) ''
/data_0/table (Table(0,)fletcher32, shuffle, blosc:zstd(5)) 'Storage of VariousTypesContainer'

container.h5 (File) ''
Last modif.: '2022-02-17T12:43:11+00:00'
Object Tree:
/ (RootGroup) ''
/data_1 (Group) ''
/data_1/table (Table(0,)fletcher32, shuffle, blosc:zstd(5)) 'Storage of VariousTypesContainer'

/tmp/ipykernel_992/3018134255.py:11: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  data.a_np_bool = np.bool((i % 2) == 0)
/tmp/ipykernel_992/3018134255.py:11: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  data.a_np_bool = np.bool((i % 2) == 0)

!rm -f container.h5

Ok so the writer destroyed the content of the file each time it opens the file. Now let’s try to append some data group to it! (using mode=’a’)

for i in range(2):

    with HDF5TableWriter('container.h5', mode='a', group_name='data_{}'.format(i)) as h5_table:

        for data in create_stream(10):

            h5_table.write('table', data)

        print(h5_table.h5file)

container.h5 (File) ''
Last modif.: '2022-02-17T12:43:11+00:00'
Object Tree:
/ (RootGroup) ''
/data_0 (Group) ''
/data_0/table (Table(0,)fletcher32, shuffle, blosc:zstd(5)) 'Storage of VariousTypesContainer'

container.h5 (File) ''
Last modif.: '2022-02-17T12:43:11+00:00'
Object Tree:
/ (RootGroup) ''
/data_0 (Group) ''
/data_0/table (Table(10,)fletcher32, shuffle, blosc:zstd(5)) 'Storage of VariousTypesContainer'
/data_1 (Group) ''
/data_1/table (Table(0,)fletcher32, shuffle, blosc:zstd(5)) 'Storage of VariousTypesContainer'

/tmp/ipykernel_992/3018134255.py:11: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  data.a_np_bool = np.bool((i % 2) == 0)
/tmp/ipykernel_992/3018134255.py:11: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  data.a_np_bool = np.bool((i % 2) == 0)

So we can append some data groups. As long as the data group_name does not already exists. Let’s try to overwrite the data group : data_1

try:
    with HDF5TableWriter('container.h5', mode='a', group_name='data_1') as h5_table:
        for data in create_stream(10):
            h5_table.write('table', data)
except Exception as err:
    print("Failed as expected:", err)

/tmp/ipykernel_992/3018134255.py:11: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  data.a_np_bool = np.bool((i % 2) == 0)

Good ! I cannot overwrite my data.

print(bool(h5_table.h5file.isopen))

False

Reading the Data

Reading the whole table at once:

For this, you have several choices. Since we used the HDF5TableWriter in this example, we have at least these options avilable:

• Pandas

• PyTables

• Astropy Table

For other TableWriter implementations, others may be possible (depending on format)

Reading with Pandas:

Pandas is a convenient way to read the output. HOWEVER BE WARNED that so far Pandas does not support reading the table meta-data or units for colums, so that information is lost!

import pandas as pd

data = pd.read_hdf('container.h5', key='/data_0/table')
data.head()

	a_int	a_float	a_bool	a_np_int	a_np_float	a_np_bool
0	0	0.00	True	0	0.0	True
1	1	0.01	False	1	1.0	False
2	2	0.02	True	2	2.0	True
3	3	0.03	False	3	3.0	False
4	4	0.04	True	4	4.0	True

Reading with PyTables

import tables
h5  = tables.open_file('container.h5')
table = h5.root['data_0']['table']
table

/data_0/table (Table(10,)fletcher32, shuffle, blosc:zstd(5)) 'Storage of VariousTypesContainer'
  description := {
  "a_int": Int64Col(shape=(), dflt=0, pos=0),
  "a_float": Float64Col(shape=(), dflt=0.0, pos=1),
  "a_bool": BoolCol(shape=(), dflt=False, pos=2),
  "a_np_int": Int64Col(shape=(), dflt=0, pos=3),
  "a_np_float": Float64Col(shape=(), dflt=0.0, pos=4),
  "a_np_bool": BoolCol(shape=(), dflt=False, pos=5)}
  byteorder := 'little'
  chunkshape := (1927,)

note that here we can still access the metadata

table.attrs

/data_0/table._v_attrs (AttributeSet), 25 attributes:
   [CLASS := 'TABLE',
    CTAPIPE_VERSION := '0.12.1.dev1+g71bebb8f',
    FIELD_0_FILL := 0,
    FIELD_0_NAME := 'a_int',
    FIELD_1_FILL := 0.0,
    FIELD_1_NAME := 'a_float',
    FIELD_2_FILL := False,
    FIELD_2_NAME := 'a_bool',
    FIELD_3_FILL := 0,
    FIELD_3_NAME := 'a_np_int',
    FIELD_4_FILL := 0.0,
    FIELD_4_NAME := 'a_np_float',
    FIELD_5_FILL := False,
    FIELD_5_NAME := 'a_np_bool',
    NROWS := 10,
    TITLE := 'Storage of VariousTypesContainer',
    VERSION := '2.7',
    a_bool_DESC := 'some bool value',
    a_float_DESC := 'some float value with a unit',
    a_float_TRANSFORM := 'quantity',
    a_float_UNIT := 'm',
    a_int_DESC := 'some int value',
    a_np_bool_DESC := 'np.bool',
    a_np_float_DESC := 'a numpy float',
    a_np_int_DESC := 'a numpy int']

Reading one-row-at-a-time:

Rather than using the full-table methods, if you want to read it row-by-row (e.g. to maintain compatibility with an existing event loop), you can use a TableReader instance.

The advantage here is that units and other metadata are retained and re-applied

from ctapipe.io import HDF5TableReader

def read(mode):

    print('reading mode {}'.format(mode))

    with HDF5TableReader('container.h5', mode=mode) as h5_table:

        for group_name in ['data_0/', 'data_1/']:

            group_name = '/{}table'.format(group_name)
            print(group_name)

            for data in h5_table.read(group_name, VariousTypesContainer()):

                print(data.as_dict())

read('r')

reading mode r
/data_0/table
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}
/data_1/table
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}

read('r+')

reading mode r+
/data_0/table
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}
/data_1/table
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}

read('a')

reading mode a
/data_0/table
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}
/data_1/table
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}

read('w')

reading mode w
/data_0/table
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}
/data_1/table
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}
{'a_int': 0, 'a_float': <Quantity 0. m>, 'a_bool': True, 'a_np_int': 0, 'a_np_float': 0.0, 'a_np_bool': True}
{'a_int': 1, 'a_float': <Quantity 0.01 m>, 'a_bool': False, 'a_np_int': 1, 'a_np_float': 1.0, 'a_np_bool': False}
{'a_int': 2, 'a_float': <Quantity 0.02 m>, 'a_bool': True, 'a_np_int': 2, 'a_np_float': 2.0, 'a_np_bool': True}
{'a_int': 3, 'a_float': <Quantity 0.03 m>, 'a_bool': False, 'a_np_int': 3, 'a_np_float': 3.0, 'a_np_bool': False}
{'a_int': 4, 'a_float': <Quantity 0.04 m>, 'a_bool': True, 'a_np_int': 4, 'a_np_float': 4.0, 'a_np_bool': True}
{'a_int': 5, 'a_float': <Quantity 0.05 m>, 'a_bool': False, 'a_np_int': 5, 'a_np_float': 5.0, 'a_np_bool': False}
{'a_int': 6, 'a_float': <Quantity 0.06 m>, 'a_bool': True, 'a_np_int': 6, 'a_np_float': 6.0, 'a_np_bool': True}
{'a_int': 7, 'a_float': <Quantity 0.07 m>, 'a_bool': False, 'a_np_int': 7, 'a_np_float': 7.0, 'a_np_bool': False}
{'a_int': 8, 'a_float': <Quantity 0.08 m>, 'a_bool': True, 'a_np_int': 8, 'a_np_float': 8.0, 'a_np_bool': True}
{'a_int': 9, 'a_float': <Quantity 0.09 m>, 'a_bool': False, 'a_np_int': 9, 'a_np_float': 9.0, 'a_np_bool': False}