Source code for bokeh.util.serialization

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:


from __future__ import absolute_import

import logging
log = logging.getLogger(__name__)

import base64
import datetime as dt
import math

from six import iterkeys

import numpy as np

from .string import format_docstring
from .dependencies import import_optional

pd = import_optional('pandas')



if pd:
        _pd_timestamp = pd.Timestamp
    except AttributeError:
        _pd_timestamp = pd.tslib.Timestamp

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 = 1000

_dt_tuple = tuple(DATETIME_TYPES)

[docs]def is_datetime_type(obj): ''' Whether an object is any date, datetime, or time delta 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 convert_datetime_type(obj): ''' Convert any recognized date, datetime or time delta value to floating point milliseconds Date and Datetime values are converted to milliseconds since epoch. TimeDeleta values are converted to absolute milliseconds. Arg: obj (object) : the object to convert Returns: float : milliseconds ''' # 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): return (obj - DT_EPOCH).total_seconds() * 1000. + obj.microsecond / 1000. # Timedelta (timedelta is class in the datetime library) elif isinstance(obj, dt.timedelta): return obj.total_seconds() * 1000. # Date elif isinstance(obj, 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) # Numpy timedelta64 elif isinstance(obj, np.timedelta64): return (obj / NP_MS_DELTA) # Time elif isinstance(obj, dt.time): return (obj.hour * 3600 + obj.minute * 60 + obj.second) * 1000 + obj.microsecond / 1000.
[docs]def make_id(): ''' Return a new unique ID for a Bokeh object. Normally this function will return UUIDs to use for identifying Bokeh objects. This is especally important for Bokeh objects stored on a Bokeh server. However, it is convenient to have more human-readable IDs during development, so this behavior can be overridden by setting the environment variable ``BOKEH_SIMPLE_IDS=yes``. ''' global _simple_id import uuid from ..settings import settings if settings.simple_ids(False): _simple_id += 1 new_id = _simple_id else: new_id = uuid.uuid4() return str(new_id)
[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 transform_array(array, force_list=False): ''' Transform a NumPy arrays into serialized format Converts un-serializable dtypes and returns JSON serializable format Args: array (np.ndarray) : a NumPy array to be transformed 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) Returns: JSON ''' # Check for astype failures (putative Numpy < 1.7) 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'",): import sys # 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 serialize_array(array, force_list)
[docs]def transform_array_to_list(array): ''' Transforms a NumPy array into a list of values Args: array (np.nadarray) : the NumPy array series to transform Returns: list or dict ''' if (array.dtype.kind in ('u', 'i', 'f') and (~np.isfinite(array)).any()): transformed = array.astype('object') transformed[np.isnan(array)] = 'NaN' transformed[np.isposinf(array)] = 'Infinity' transformed[np.isneginf(array)] = '-Infinity' return transformed.tolist() elif (array.dtype.kind == 'O' and pd and pd.isnull(array).any()): transformed = array.astype('object') transformed[pd.isnull(array)] = 'NaN' return transformed.tolist() return array.tolist()
[docs]def transform_series(series, force_list=False): ''' Transforms a Pandas series into serialized form Args: series (pd.Series) : the Pandas series 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) Returns: list or dict ''' vals = series.values return transform_array(vals, force_list)
[docs]def serialize_array(array, force_list=False): ''' 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) Returns: list or dict ''' if isinstance(array, 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) return encode_base64_dict(array)
[docs]def traverse_data(obj, use_numpy=True): ''' 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) 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 transform_column_source_data(data): ''' Transform ColumnSourceData data to a serialized format Args: data (dict) : the mapping of names to data columns to transform Returns: JSON compatible dict ''' data_copy = {} for key in iterkeys(data): if pd and isinstance(data[key], (pd.Series, pd.Index)): data_copy[key] = transform_series(data[key]) elif isinstance(data[key], np.ndarray): data_copy[key] = transform_array(data[key]) else: data_copy[key] = traverse_data(data[key]) return data_copy
[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('utf-8'), 'shape' : array.shape, 'dtype' : }
[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.fromstring(b64, dtype=data['dtype']) if len(data['shape']) > 1: array = array.reshape(data['shape']) return array