'''
Functions for helping with serialization and deserialization of
Bokeh objects.
Certain NunPy array dtypes can be serialized to a binary format for
performance and efficiency. The list of supported dtypes is:
{binary_array_types}
'''
from __future__ import absolute_import
import logging
log = logging.getLogger(__name__)
import base64
import datetime as dt
import math
import sys
from threading import Lock
import uuid
import numpy as np
from ..settings import settings
from .string import format_docstring
from .dependencies import import_optional
pd = import_optional('pandas')
BINARY_ARRAY_TYPES = set([
np.dtype(np.float32),
np.dtype(np.float64),
np.dtype(np.uint8),
np.dtype(np.int8),
np.dtype(np.uint16),
np.dtype(np.int16),
np.dtype(np.uint32),
np.dtype(np.int32),
])
DATETIME_TYPES = set([
dt.datetime,
dt.date,
dt.time,
np.datetime64,
])
if pd:
try:
_pd_timestamp = pd.Timestamp
except AttributeError:
_pd_timestamp = pd.tslib.Timestamp
DATETIME_TYPES.add(_pd_timestamp)
DATETIME_TYPES.add(pd.Timedelta)
DATETIME_TYPES.add(pd.Period)
DATETIME_TYPES.add(type(pd.NaT))
NP_EPOCH = np.datetime64(0, 'ms')
NP_MS_DELTA = np.timedelta64(1, 'ms')
DT_EPOCH = dt.datetime.utcfromtimestamp(0)
__doc__ = format_docstring(__doc__, binary_array_types="\n".join("* ``np." + str(x) + "``" for x in BINARY_ARRAY_TYPES))
_simple_id = 999
_simple_id_lock = Lock()
_dt_tuple = tuple(DATETIME_TYPES)
[docs]def is_datetime_type(obj):
''' Whether an object is any date, time, or datetime type recognized by
Bokeh.
Arg:
obj (object) : the object to test
Returns:
bool : True if ``obj`` is a datetime type
'''
return isinstance(obj, _dt_tuple)
[docs]def is_timedelta_type(obj):
''' Whether an object is any timedelta type recognized by Bokeh.
Arg:
obj (object) : the object to test
Returns:
bool : True if ``obj`` is a timedelta type
'''
return isinstance(obj, (dt.timedelta, np.timedelta64))
[docs]def convert_timedelta_type(obj):
''' Convert any recognized timedelta value to floating point absolute
milliseconds.
Arg:
obj (object) : the object to convert
Returns:
float : milliseconds
'''
if isinstance(obj, dt.timedelta):
return obj.total_seconds() * 1000.
elif isinstance(obj, np.timedelta64):
return (obj / NP_MS_DELTA)
[docs]def convert_datetime_type(obj):
''' Convert any recognized date, time, or datetime value to floating point
milliseconds since epoch.
Arg:
obj (object) : the object to convert
Returns:
float : milliseconds
'''
# Pandas NaT
if pd and obj is pd.NaT:
return np.nan
# Pandas Period
if pd and isinstance(obj, pd.Period):
return obj.to_timestamp().value / 10**6.0
# Pandas Timestamp
if pd and isinstance(obj, _pd_timestamp): return obj.value / 10**6.0
# Pandas Timedelta
elif pd and isinstance(obj, pd.Timedelta): return obj.value / 10**6.0
# Datetime (datetime is a subclass of date)
elif isinstance(obj, dt.datetime):
diff = obj.replace(tzinfo=None) - DT_EPOCH
return diff.total_seconds() * 1000.
# Date
elif isinstance(obj, dt.date):
return (dt.datetime(*obj.timetuple()[:6]) - DT_EPOCH).total_seconds() * 1000
# NumPy datetime64
elif isinstance(obj, np.datetime64):
epoch_delta = obj - NP_EPOCH
return (epoch_delta / NP_MS_DELTA)
# Time
elif isinstance(obj, dt.time):
return (obj.hour * 3600 + obj.minute * 60 + obj.second) * 1000 + obj.microsecond / 1000.
[docs]def convert_datetime_array(array):
''' Convert NumPy datetime arrays to arrays to milliseconds since epoch.
Args:
array : (obj)
A NumPy array of datetime to convert
If the value passed in is not a NumPy array, it will be returned as-is.
Returns:
array
'''
if not isinstance(array, np.ndarray):
return array
try:
dt2001 = np.datetime64('2001')
legacy_datetime64 = (dt2001.astype('int64') ==
dt2001.astype('datetime64[ms]').astype('int64'))
except AttributeError as e:
if e.args == ("'module' object has no attribute 'datetime64'",):
# for compatibility with PyPy that doesn't have datetime64
if 'PyPy' in sys.version:
legacy_datetime64 = False
pass
else:
raise e
else:
raise e
# not quite correct, truncates to ms..
if array.dtype.kind == 'M':
if legacy_datetime64:
if array.dtype == np.dtype('datetime64[ns]'):
array = array.astype('int64') / 10**6.0
else:
array = array.astype('datetime64[us]').astype('int64') / 1000.
elif array.dtype.kind == 'm':
array = array.astype('timedelta64[us]').astype('int64') / 1000.
return array
[docs]def make_id():
''' Return a new unique ID for a Bokeh object.
Normally this function will return simple monotonically increasing integer
IDs (as strings) for identifying Bokeh objects within a Document. However,
if it is desirable to have globally unique for every object, this behavior
can be overridden by setting the environment variable ``BOKEH_SIMPLE_IDS=no``.
Returns:
str
'''
global _simple_id
if settings.simple_ids(True):
with _simple_id_lock:
_simple_id += 1
return str(_simple_id)
else:
return make_globally_unique_id()
[docs]def make_globally_unique_id():
''' Return a globally unique UUID.
Some situtations, e.g. id'ing dynamically created Divs in HTML documents,
always require globally unique IDs.
Returns:
str
'''
return str(uuid.uuid4())
[docs]def array_encoding_disabled(array):
''' Determine whether an array may be binary encoded.
The NumPy array dtypes that can be encoded are:
{binary_array_types}
Args:
array (np.ndarray) : the array to check
Returns:
bool
'''
# disable binary encoding for non-supported dtypes
return array.dtype not in BINARY_ARRAY_TYPES
array_encoding_disabled.__doc__ = format_docstring(array_encoding_disabled.__doc__,
binary_array_types="\n ".join("* ``np." + str(x) + "``"
for x in BINARY_ARRAY_TYPES))
[docs]def serialize_array(array, force_list=False, buffers=None):
''' Transforms a NumPy array into serialized form.
Args:
array (np.ndarray) : the NumPy array to transform
force_list (bool, optional) : whether to only output to standard lists
This function can encode some dtypes using a binary encoding, but
setting this argument to True will override that and cause only
standard Python lists to be emitted. (default: False)
buffers (set, optional) :
If binary buffers are desired, the buffers parameter may be
provided, and any columns that may be sent as binary buffers
will be added to the set. If None, then only base64 encoding
will be used (default: None)
If force_list is True, then this value will be ignored, and
no buffers will be generated.
**This is an "out" parameter**. The values it contains will be
modified in-place.
Returns:
list or dict
'''
if isinstance(array, np.ma.MaskedArray):
array = array.filled(np.nan) # Set masked values to nan
if (array_encoding_disabled(array) or force_list):
return transform_array_to_list(array)
if not array.flags['C_CONTIGUOUS']:
array = np.ascontiguousarray(array)
if buffers is None:
return encode_base64_dict(array)
else:
return encode_binary_dict(array, buffers)
[docs]def traverse_data(obj, use_numpy=True, buffers=None):
''' Recursively traverse an object until a flat list is found.
If NumPy is available, the flat list is converted to a numpy array
and passed to transform_array() to handle ``nan``, ``inf``, and
``-inf``.
Otherwise, iterate through all items, converting non-JSON items
Args:
obj (list) : a list of values or lists
use_numpy (bool, optional) toggle NumPy as a dependency for testing
This argument is only useful for testing (default: True)
'''
if use_numpy and all(isinstance(el, np.ndarray) for el in obj):
return [transform_array(el, buffers=buffers) for el in obj]
obj_copy = []
for item in obj:
# Check the base/common case first for performance reasons
# Also use type(x) is float because it's faster than isinstance
if type(item) is float:
if math.isnan(item):
item = 'NaN'
elif math.isinf(item):
if item > 0:
item = 'Infinity'
else:
item = '-Infinity'
obj_copy.append(item)
elif isinstance(item, (list, tuple)): # check less common type second
obj_copy.append(traverse_data(item))
else:
obj_copy.append(item)
return obj_copy
[docs]def encode_binary_dict(array, buffers):
''' Send a numpy array as an unencoded binary buffer
The encoded format is a dict with the following structure:
.. code:: python
{
'__buffer__' : << an ID to locate the buffer >>,
'shape' : << array shape >>,
'dtype' : << dtype name >>,
'order' : << byte order at origin (little or big)>>
}
Args:
array (np.ndarray) : an array to encode
buffers (set) :
Set to add buffers to
**This is an "out" parameter**. The values it contains will be
modified in-place.
Returns:
dict
'''
buffer_id = make_id()
buf = (dict(id=buffer_id), array.tobytes())
buffers.append(buf)
return {
'__buffer__' : buffer_id,
'shape' : array.shape,
'dtype' : array.dtype.name,
'order' : sys.byteorder
}
[docs]def encode_base64_dict(array):
''' Encode a NumPy array using base64:
The encoded format is a dict with the following structure:
.. code:: python
{
'__ndarray__' : << base64 encoded array data >>,
'shape' : << array shape >>,
'dtype' : << dtype name >>,
}
Args:
array (np.ndarray) : an array to encode
Returns:
dict
'''
return {
'__ndarray__' : base64.b64encode(array.data).decode('utf-8'),
'shape' : array.shape,
'dtype' : array.dtype.name
}
[docs]def decode_base64_dict(data):
''' Decode a base64 encoded array into a NumPy array.
Args:
data (dict) : encoded array data to decode
Data should have the format encoded by :func:`encode_base64_dict`.
Returns:
np.ndarray
'''
b64 = base64.b64decode(data['__ndarray__'])
array = np.copy(np.frombuffer(b64, dtype=data['dtype']))
if len(data['shape']) > 1:
array = array.reshape(data['shape'])
return array
