#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2022, Anaconda, Inc., and Bokeh Contributors.
# All rights reserved.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Boilerplate
#-----------------------------------------------------------------------------
from __future__ import annotations
import logging # isort:skip
log = logging.getLogger(__name__)
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
# Standard library imports
from typing import (
TYPE_CHECKING,
Any as TAny,
Dict as TDict,
List as TList,
Sequence,
Tuple,
Union,
overload,
)
# External imports
import numpy as np
# Bokeh imports
from ..core.has_props import abstract
from ..core.properties import (
JSON,
Any,
Bool,
ColumnData,
Dict,
Enum,
Instance,
InstanceDefault,
Int,
Nullable,
Object,
Readonly,
Required,
Seq,
String,
)
from ..model import Model
from ..util.deprecation import deprecated
from ..util.serialization import convert_datetime_array
from ..util.warnings import BokehUserWarning, warn
from .callbacks import CustomJS
from .filters import AllIndices, Filter, IntersectionFilter
from .selections import Selection, SelectionPolicy, UnionRenderers
if TYPE_CHECKING:
import pandas as pd
from typing_extensions import TypeAlias
from ..core.has_props import Setter
#-----------------------------------------------------------------------------
# Globals and constants
#-----------------------------------------------------------------------------
__all__ = (
'AjaxDataSource',
'CDSView',
'ColumnarDataSource',
'ColumnDataSource',
'DataSource',
'GeoJSONDataSource',
'ServerSentDataSource',
'WebDataSource',
)
#-----------------------------------------------------------------------------
# General API
#-----------------------------------------------------------------------------
if TYPE_CHECKING:
import numpy.typing as npt
DataDict: TypeAlias = TDict[str, Union[Sequence[TAny], npt.NDArray[TAny], pd.Series, pd.Index]]
Index: TypeAlias = Union[int, slice, Tuple[Union[int, slice], ...]]
Patches: TypeAlias = TDict[str, TList[Tuple[Index, Any]]]
[docs]@abstract
class DataSource(Model):
''' A base class for data source types.
'''
# explicit __init__ to support Init signatures
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
selected = Readonly(Instance(Selection), default=InstanceDefault(Selection), help="""
An instance of a ``Selection`` that indicates selected indices on this ``DataSource``.
This is a read-only property. You may only change the attributes of this object
to change the selection (e.g., ``selected.indices``).
""")
[docs]@abstract
class ColumnarDataSource(DataSource):
''' A base class for data source types, which can be mapped onto
a columnar format.
'''
# explicit __init__ to support Init signatures
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
selection_policy = Instance(SelectionPolicy, default=InstanceDefault(UnionRenderers), help="""
An instance of a ``SelectionPolicy`` that determines how selections are set.
""")
[docs]class ColumnDataSource(ColumnarDataSource):
''' Maps names of columns to sequences or arrays.
The ``ColumnDataSource`` is a fundamental data structure of Bokeh. Most
plots, data tables, etc. will be driven by a ``ColumnDataSource``.
If the ``ColumnDataSource`` initializer is called with a single argument that
can be any of the following:
* A Python ``dict`` that maps string names to sequences of values, e.g.
lists, arrays, etc.
.. code-block:: python
data = {'x': [1,2,3,4], 'y': np.array([10.0, 20.0, 30.0, 40.0])}
source = ColumnDataSource(data)
.. note::
``ColumnDataSource`` only creates a shallow copy of ``data``. Use e.g.
``ColumnDataSource(copy.deepcopy(data))`` if initializing from another
``ColumnDataSource.data`` object that you want to keep independent.
* A Pandas ``DataFrame`` object
.. code-block:: python
source = ColumnDataSource(df)
In this case the CDS will have columns corresponding to the columns of
the ``DataFrame``. If the ``DataFrame`` columns have multiple levels,
they will be flattened using an underscore (e.g. level_0_col_level_1_col).
The index of the ``DataFrame`` will be flattened to an ``Index`` of tuples
if it's a ``MultiIndex``, and then reset using ``reset_index``. The result
will be a column with the same name if the index was named, or
level_0_name_level_1_name if it was a named ``MultiIndex``. If the
``Index`` did not have a name or the ``MultiIndex`` name could not be
flattened/determined, the ``reset_index`` function will name the index column
``index``, or ``level_0`` if the name ``index`` is not available.
* A Pandas ``GroupBy`` object
.. code-block:: python
group = df.groupby(('colA', 'ColB'))
In this case the CDS will have columns corresponding to the result of
calling ``group.describe()``. The ``describe`` method generates columns
for statistical measures such as ``mean`` and ``count`` for all the
non-grouped original columns. The CDS columns are formed by joining
original column names with the computed measure. For example, if a
``DataFrame`` has columns ``'year'`` and ``'mpg'``. Then passing
``df.groupby('year')`` to a CDS will result in columns such as
``'mpg_mean'``
If the ``GroupBy.describe`` result has a named index column, then
CDS will also have a column with this name. However, if the index name
(or any subname of a ``MultiIndex``) is ``None``, then the CDS will have
a column generically named ``index`` for the index.
Note this capability to adapt ``GroupBy`` objects may only work with
Pandas ``>=0.20.0``.
.. note::
There is an implicit assumption that all the columns in a given
``ColumnDataSource`` all have the same length at all times. For this
reason, it is usually preferable to update the ``.data`` property
of a data source "all at once".
'''
data: DataDict = ColumnData(String, Seq(Any), help="""
Mapping of column names to sequences of data. The columns can be, e.g,
Python lists or tuples, NumPy arrays, etc.
The .data attribute can also be set from Pandas DataFrames or GroupBy
objects. In these cases, the behaviour is identical to passing the objects
to the ``ColumnDataSource`` initializer.
""").accepts(
Object("pandas.DataFrame"), lambda x: ColumnDataSource._data_from_df(x)
).accepts(
Object("pandas.core.groupby.GroupBy"), lambda x: ColumnDataSource._data_from_groupby(x)
).asserts(lambda _, data: len({len(x) for x in data.values()}) <= 1,
lambda obj, name, data: warn(
"ColumnDataSource's columns must be of the same length. " +
"Current lengths: %s" % ", ".join(sorted(str((k, len(v))) for k, v in data.items())), BokehUserWarning))
@overload
def __init__(self, data: DataDict | pd.DataFrame | pd.core.groupby.GroupBy, **kwargs: TAny) -> None: ...
@overload
def __init__(self, **kwargs: TAny) -> None: ...
[docs] def __init__(self, *args: TAny, **kwargs: TAny) -> None:
''' If called with a single argument that is a dict or
``pandas.DataFrame``, treat that implicitly as the "data" attribute.
'''
if len(args) == 1 and "data" not in kwargs:
kwargs["data"] = args[0]
# TODO (bev) invalid to pass args and "data", check and raise exception
raw_data: DataDict = kwargs.pop("data", {})
import pandas as pd
if not isinstance(raw_data, dict):
if isinstance(raw_data, pd.DataFrame):
raw_data = self._data_from_df(raw_data)
elif isinstance(raw_data, pd.core.groupby.GroupBy):
raw_data = self._data_from_groupby(raw_data)
else:
raise ValueError(f"expected a dict or pandas.DataFrame, got {raw_data}")
super().__init__(**kwargs)
self.data.update(raw_data)
@property
def column_names(self) -> list[str]:
''' A list of the column names in this data source.
'''
return list(self.data)
@staticmethod
def _data_from_df(df: pd.DataFrame) -> DataDict:
''' Create a ``dict`` of columns from a Pandas ``DataFrame``,
suitable for creating a ColumnDataSource.
Args:
df (DataFrame) : data to convert
Returns:
dict[str, np.array]
'''
import pandas as pd
_df = df.copy()
# Flatten columns
if isinstance(df.columns, pd.MultiIndex):
try:
_df.columns = ['_'.join(col) for col in _df.columns.values]
except TypeError:
raise TypeError('Could not flatten MultiIndex columns. '
'use string column names or flatten manually')
# Transform columns CategoricalIndex in list
if isinstance(df.columns, pd.CategoricalIndex):
_df.columns = df.columns.tolist()
# Flatten index
index_name = ColumnDataSource._df_index_name(df)
if index_name == 'index':
_df.index = pd.Index(_df.index.values)
else:
_df.index = pd.Index(_df.index.values, name=index_name)
_df.reset_index(inplace=True)
tmp_data = {c: v.values for c, v in _df.items()}
new_data: DataDict = {}
for k, v in tmp_data.items():
new_data[k] = v
return new_data
@staticmethod
def _data_from_groupby(group: pd.core.groupby.GroupBy) -> DataDict:
''' Create a ``dict`` of columns from a Pandas ``GroupBy``,
suitable for creating a ``ColumnDataSource``.
The data generated is the result of running ``describe``
on the group.
Args:
group (GroupBy) : data to convert
Returns:
dict[str, np.array]
'''
return ColumnDataSource._data_from_df(group.describe())
@staticmethod
def _df_index_name(df: pd.DataFrame) -> str:
''' Return the Bokeh-appropriate column name for a ``DataFrame`` index
If there is no named index, then `"index" is returned.
If there is a single named index, then ``df.index.name`` is returned.
If there is a multi-index, and the index names are all strings, then
the names are joined with '_' and the result is returned, e.g. for a
multi-index ``['ind1', 'ind2']`` the result will be "ind1_ind2".
Otherwise if any index name is not a string, the fallback name "index"
is returned.
Args:
df (DataFrame) : the ``DataFrame`` to find an index name for
Returns:
str
'''
if df.index.name:
return df.index.name
elif df.index.names:
try:
return "_".join(df.index.names)
except TypeError:
return "index"
else:
return "index"
[docs] @classmethod
def from_df(cls, data: pd.DataFrame) -> DataDict:
''' Create a ``dict`` of columns from a Pandas ``DataFrame``,
suitable for creating a ``ColumnDataSource``.
Args:
data (DataFrame) : data to convert
Returns:
dict[str, np.array]
'''
return cls._data_from_df(data)
[docs] @classmethod
def from_groupby(cls, data: pd.core.groupby.GroupBy) -> DataDict:
''' Create a ``dict`` of columns from a Pandas ``GroupBy``,
suitable for creating a ``ColumnDataSource``.
The data generated is the result of running ``describe``
on the group.
Args:
data (Groupby) : data to convert
Returns:
dict[str, np.array]
'''
return cls._data_from_df(data.describe())
[docs] def to_df(self) -> pd.DataFrame:
''' Convert this data source to pandas ``DataFrame``.
Returns:
DataFrame
'''
import pandas as pd
return pd.DataFrame(self.data)
[docs] def add(self, data: Sequence[Any], name: str | None = None) -> str:
''' Appends a new column of data to the data source.
Args:
data (seq) : new data to add
name (str, optional) : column name to use.
If not supplied, generate a name of the form "Series ####"
Returns:
str: the column name used
'''
if name is None:
n = len(self.data)
while f"Series {n}" in self.data:
n += 1
name = f"Series {n}"
self.data[name] = data
return name
[docs] def remove(self, name: str) -> None:
''' Remove a column of data.
Args:
name (str) : name of the column to remove
Returns:
None
.. note::
If the column name does not exist, a warning is issued.
'''
try:
del self.data[name]
except (ValueError, KeyError):
warn(f"Unable to find column '{name}' in data source")
[docs] def stream(self, new_data: DataDict, rollover: int | None = None) -> None:
''' Efficiently update data source columns with new append-only data.
In cases where it is necessary to update data columns in, this method
can efficiently send only the new data, instead of requiring the
entire data set to be re-sent.
Args:
new_data (dict[str, seq]) : a mapping of column names to sequences of
new data to append to each column.
All columns of the data source must be present in ``new_data``,
with identical-length append data.
rollover (int, optional) : A maximum column size, above which data
from the start of the column begins to be discarded. If None,
then columns will continue to grow unbounded (default: None)
Returns:
None
Raises:
ValueError
Example:
.. code-block:: python
source = ColumnDataSource(data=dict(foo=[], bar=[]))
# has new, identical-length updates for all columns in source
new_data = {
'foo' : [10, 20],
'bar' : [100, 200],
}
source.stream(new_data)
'''
# calls internal implementation
self._stream(new_data, rollover)
def _stream(self, new_data: DataDict | pd.Series | pd.DataFrame,
rollover: int | None = None, setter: Setter | None = None) -> None:
''' Internal implementation to efficiently update data source columns
with new append-only data. The internal implementation adds the setter
attribute. [https://github.com/bokeh/bokeh/issues/6577]
In cases where it is necessary to update data columns in, this method
can efficiently send only the new data, instead of requiring the
entire data set to be re-sent.
Args:
new_data (dict[str, seq] or DataFrame or Series) : a mapping of
column names to sequences of new data to append to each column,
a pandas DataFrame, or a pandas Series in case of a single row -
in this case the Series index is used as column names
All columns of the data source must be present in ``new_data``,
with identical-length append data.
rollover (int, optional) : A maximum column size, above which data
from the start of the column begins to be discarded. If None,
then columns will continue to grow unbounded (default: None)
setter (ClientSession or ServerSession or None, optional) :
This is used to prevent "boomerang" updates to Bokeh apps.
(default: None)
In the context of a Bokeh server application, incoming updates
to properties will be annotated with the session that is
doing the updating. This value is propagated through any
subsequent change notifications that the update triggers.
The session can compare the event setter to itself, and
suppress any updates that originate from itself.
Returns:
None
Raises:
ValueError
Example:
.. code-block:: python
source = ColumnDataSource(data=dict(foo=[], bar=[]))
# has new, identical-length updates for all columns in source
new_data = {
'foo' : [10, 20],
'bar' : [100, 200],
}
source.stream(new_data)
'''
import pandas as pd
needs_length_check = True
if isinstance(new_data, (pd.Series, pd.DataFrame)):
if isinstance(new_data, pd.Series):
new_data = new_data.to_frame().T
needs_length_check = False # DataFrame lengths equal by definition
_df = new_data
newkeys = set(_df.columns)
index_name = ColumnDataSource._df_index_name(_df)
newkeys.add(index_name)
new_data = dict(_df.items())
new_data[index_name] = _df.index.values
else:
newkeys = set(new_data.keys())
oldkeys = set(self.data.keys())
if newkeys != oldkeys:
missing = oldkeys - newkeys
extra = newkeys - oldkeys
if missing and extra:
raise ValueError(
"Must stream updates to all existing columns (missing: %s, extra: %s)" % (", ".join(sorted(missing)), ", ".join(sorted(extra)))
)
elif missing:
raise ValueError("Must stream updates to all existing columns (missing: %s)" % ", ".join(sorted(missing)))
else:
raise ValueError("Must stream updates to all existing columns (extra: %s)" % ", ".join(sorted(extra)))
if needs_length_check:
lengths: set[int] = set()
arr_types = (np.ndarray, pd.Series)
for _, x in new_data.items():
if isinstance(x, arr_types):
if len(x.shape) != 1:
raise ValueError("stream(...) only supports 1d sequences, got ndarray with size %r" % (x.shape,))
lengths.add(x.shape[0])
else:
lengths.add(len(x))
if len(lengths) > 1:
raise ValueError("All streaming column updates must be the same length")
# slightly awkward that we have to call convert_datetime_array here ourselves
# but the downstream code expects things to already be ms-since-epoch
for key, values in new_data.items():
if pd and isinstance(values, (pd.Series, pd.Index)):
values = values.values
old_values = self.data[key]
# Apply the transformation if the new data contains datetimes
# but the current data has already been transformed
if (isinstance(values, np.ndarray) and values.dtype.kind.lower() == 'm' and
isinstance(old_values, np.ndarray) and old_values.dtype.kind.lower() != 'm'):
new_data[key] = convert_datetime_array(values)
else:
new_data[key] = values
self.data._stream(self.document, self, new_data, rollover, setter)
[docs] def patch(self, patches: Patches, setter: Setter | None = None) -> None:
''' Efficiently update data source columns at specific locations
If it is only necessary to update a small subset of data in a
``ColumnDataSource``, this method can be used to efficiently update only
the subset, instead of requiring the entire data set to be sent.
This method should be passed a dictionary that maps column names to
lists of tuples that describe a patch change to apply. To replace
individual items in columns entirely, the tuples should be of the
form:
.. code-block:: python
(index, new_value) # replace a single column value
# or
(slice, new_values) # replace several column values
Values at an index or slice will be replaced with the corresponding
new values.
In the case of columns whose values are other arrays or lists, (e.g.
image or patches glyphs), it is also possible to patch "subregions".
In this case the first item of the tuple should be a whose first
element is the index of the array item in the CDS patch, and whose
subsequent elements are integer indices or slices into the array item:
.. code-block:: python
# replace the entire 10th column of the 2nd array:
+----------------- index of item in column data source
|
| +--------- row subindex into array item
| |
| | +- column subindex into array item
V V V
([2, slice(None), 10], new_values)
Imagining a list of 2d NumPy arrays, the patch above is roughly
equivalent to:
.. code-block:: python
data = [arr1, arr2, ...] # list of 2d arrays
data[2][:, 10] = new_data
There are some limitations to the kinds of slices and data that can
be accepted.
* Negative ``start``, ``stop``, or ``step`` values for slices will
result in a ``ValueError``.
* In a slice, ``start > stop`` will result in a ``ValueError``
* When patching 1d or 2d subitems, the subitems must be NumPy arrays.
* New values must be supplied as a **flattened one-dimensional array**
of the appropriate size.
Args:
patches (dict[str, list[tuple]]) : lists of patches for each column
Returns:
None
Raises:
ValueError
Example:
The following example shows how to patch entire column elements. In this case,
.. code-block:: python
source = ColumnDataSource(data=dict(foo=[10, 20, 30], bar=[100, 200, 300]))
patches = {
'foo' : [ (slice(2), [11, 12]) ],
'bar' : [ (0, 101), (2, 301) ],
}
source.patch(patches)
After this operation, the value of the ``source.data`` will be:
.. code-block:: python
dict(foo=[11, 12, 30], bar=[101, 200, 301])
For a more comprehensive example, see :bokeh-tree:`examples/server/app/patch_app.py`.
'''
extra = set(patches.keys()) - set(self.data.keys())
if extra:
raise ValueError("Can only patch existing columns (extra: %s)" % ", ".join(sorted(extra)))
for name, patch in patches.items():
col_len = len(self.data[name])
for ind, _ in patch:
# integer index, patch single value of 1d column
if isinstance(ind, int):
if ind > col_len or ind < 0:
raise ValueError("Out-of bounds index (%d) in patch for column: %s" % (ind, name))
# slice index, patch multiple values of 1d column
elif isinstance(ind, slice):
_check_slice(ind)
if ind.stop is not None and ind.stop > col_len:
raise ValueError("Out-of bounds slice index stop (%d) in patch for column: %s" % (ind.stop, name))
# multi-index, patch sub-regions of "n-d" column
elif isinstance(ind, (list, tuple)):
if len(ind) == 0:
raise ValueError("Empty (length zero) patch multi-index")
if len(ind) == 1:
raise ValueError("Patch multi-index must contain more than one subindex")
ind_0 = ind[0]
if not isinstance(ind_0, int):
raise ValueError("Initial patch sub-index may only be integer, got: %s" % ind_0)
if ind_0 > col_len or ind_0 < 0:
raise ValueError("Out-of bounds initial sub-index (%d) in patch for column: %s" % (ind, name))
if not isinstance(self.data[name][ind_0], np.ndarray):
raise ValueError("Can only sub-patch into columns with NumPy array items")
if len(self.data[name][ind_0].shape) != (len(ind)-1):
raise ValueError("Shape mismatch between patch slice and sliced data")
elif isinstance(ind_0, slice):
_check_slice(ind_0)
if ind_0.stop is not None and ind_0.stop > col_len:
raise ValueError("Out-of bounds initial slice sub-index stop (%d) in patch for column: %s" % (ind.stop, name))
# Note: bounds of sub-indices after the first are not checked!
for subind in ind[1:]:
if not isinstance(subind, (int, slice)):
raise ValueError("Invalid patch sub-index: %s" % subind)
if isinstance(subind, slice):
_check_slice(subind)
else:
raise ValueError("Invalid patch index: %s" % ind)
self.data._patch(self.document, self, patches, setter)
[docs]class CDSView(Model):
''' A view into a ``ColumnDataSource`` that represents a row-wise subset.
'''
def __init__(self, *args: TAny, **kwargs: TAny) -> None:
if "source" in kwargs:
del kwargs["source"]
deprecated("CDSView.source is no longer needed, and is now ignored. In a future release, passing source will result an error.")
super().__init__(*args, **kwargs)
filter = Instance(Filter, default=InstanceDefault(AllIndices), help="""
Defines the subset of indices to use from the data source this view applies to.
By default all indices are used (``AllIndices`` filter). This can be changed by
using specialized filters like ``IndexFilter``, ``BooleanFilter``, etc. Filters
can be composed using set operations to create non-trivial data masks. This can
be accomplished by directly using models like ``InversionFilter``, ``UnionFilter``,
etc., or by using set operators on filters, e.g.:
.. code-block:: python
# filters everything but indexes 10 and 11
cds_view.filter &= ~IndexFilter(indices=[10, 11])
""")
@property
def filters(self) -> list[Filter]:
deprecated("CDSView.filters was deprecated in bokeh 3.0. Use CDSView.filter instead.")
filter = self.filter
if isinstance(filter, IntersectionFilter):
return filter.operands
elif isinstance(filter, AllIndices):
return []
else:
return [filter]
@filters.setter
def filters(self, filters: list[Filter]) -> None:
deprecated("CDSView.filters was deprecated in bokeh 3.0. Use CDSView.filter instead.")
if len(filters) == 0:
self.filter = AllIndices()
elif len(filters) == 1:
self.filter = filters[0]
else:
self.filter = IntersectionFilter(operands=filters)
[docs]class GeoJSONDataSource(ColumnarDataSource):
'''
'''
# explicit __init__ to support Init signatures
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
geojson = Required(JSON, help="""
GeoJSON that contains features for plotting. Currently
``GeoJSONDataSource`` can only process a ``FeatureCollection`` or
``GeometryCollection``.
""")
[docs]@abstract
class WebDataSource(ColumnDataSource):
''' Base class for web column data sources that can update from data
URLs.
.. note::
This base class is typically not useful to instantiate on its own.
'''
# explicit __init__ to support Init signatures
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
adapter = Nullable(Instance(CustomJS), help="""
A JavaScript callback to adapt raw JSON responses to Bokeh ``ColumnDataSource``
format.
If provided, this callback is executes immediately after the JSON data is
received, but before appending or replacing data in the data source. The
``CustomJS`` callback will receive the ``AjaxDataSource`` as ``cb_obj`` and
will receive the raw JSON response as ``cb_data.response``. The callback
code should return a ``data`` object suitable for a Bokeh ``ColumnDataSource``
(i.e. a mapping of string column names to arrays of data).
""")
max_size = Nullable(Int, help="""
Maximum size of the data columns. If a new fetch would result in columns
larger than ``max_size``, then earlier data is dropped to make room.
""")
mode = Enum("replace", "append", help="""
Whether to append new data to existing data (up to ``max_size``), or to
replace existing data entirely.
""")
data_url = Required(String, help="""
A URL to to fetch data from.
""")
[docs]class ServerSentDataSource(WebDataSource):
''' A data source that can populate columns by receiving server sent
events endpoints.
'''
# explicit __init__ to support Init signatures
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
[docs]class AjaxDataSource(WebDataSource):
''' A data source that can populate columns by making Ajax calls to REST
endpoints.
The ``AjaxDataSource`` can be especially useful if you want to make a
standalone document (i.e. not backed by the Bokeh server) that can still
dynamically update using an existing REST API.
The response from the REST API should match the ``.data`` property of a
standard ``ColumnDataSource``, i.e. a JSON dict that maps names to arrays
of values:
.. code-block:: python
{
'x' : [1, 2, 3, ...],
'y' : [9, 3, 2, ...]
}
Alternatively, if the REST API returns a different format, a ``CustomJS``
callback can be provided to convert the REST response into Bokeh format,
via the ``adapter`` property of this data source.
Initial data can be set by specifying the ``data`` property directly.
This is necessary when used in conjunction with a ``FactorRange``, even
if the columns in `data`` are empty.
A full example can be seen at :bokeh-tree:`examples/basic/data/ajax_source.py`
'''
# explicit __init__ to support Init signatures
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
polling_interval = Nullable(Int, help="""
A polling interval (in milliseconds) for updating data source.
""")
method = Enum('POST', 'GET', help="""
Specify the HTTP method to use for the Ajax request (GET or POST)
""")
if_modified = Bool(False, help="""
Whether to include an ``If-Modified-Since`` header in Ajax requests
to the server. If this header is supported by the server, then only
new data since the last request will be returned.
""")
content_type = String(default='application/json', help="""
Set the "contentType" parameter for the Ajax request.
""")
http_headers = Dict(String, String, help="""
Specify HTTP headers to set for the Ajax request.
Example:
.. code-block:: python
ajax_source.headers = { 'x-my-custom-header': 'some value' }
""")
#-----------------------------------------------------------------------------
# Dev API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Private API
#-----------------------------------------------------------------------------
def _check_slice(s: slice) -> None:
if (s.start is not None and s.stop is not None and s.start > s.stop):
raise ValueError("Patch slices must have start < end, got %s" % s)
if (s.start is not None and s.start < 0) or \
(s.stop is not None and s.stop < 0) or \
(s.step is not None and s.step < 0):
raise ValueError("Patch slices must have non-negative (start, stop, step) values, got %s" % s)
#-----------------------------------------------------------------------------
# Code
#-----------------------------------------------------------------------------