188 lines
7.3 KiB
Python
188 lines
7.3 KiB
Python
# --- THIS FILE IS AUTO-GENERATED ---
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# Modifications will be overwitten the next time code generation run.
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from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
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import copy as _copy
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class XBins(_BaseTraceHierarchyType):
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_parent_path_str = "histogram2dcontour"
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_path_str = "histogram2dcontour.xbins"
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_valid_props = {"end", "size", "start"}
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@property
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def end(self):
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"""
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Sets the end value for the x axis bins. The last bin may not
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end exactly at this value, we increment the bin edge by `size`
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from `start` until we reach or exceed `end`. Defaults to the
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maximum data value. Like `start`, for dates use a date string,
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and for category data `end` is based on the category serial
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numbers.
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The 'end' property accepts values of any type
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Returns
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-------
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Any
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"""
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return self["end"]
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@end.setter
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def end(self, val):
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self["end"] = val
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@property
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def size(self):
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"""
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Sets the size of each x axis bin. Default behavior: If `nbinsx`
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is 0 or omitted, we choose a nice round bin size such that the
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number of bins is about the same as the typical number of
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samples in each bin. If `nbinsx` is provided, we choose a nice
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round bin size giving no more than that many bins. For date
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data, use milliseconds or "M<n>" for months, as in
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`axis.dtick`. For category data, the number of categories to
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bin together (always defaults to 1).
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The 'size' property accepts values of any type
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Returns
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-------
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Any
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"""
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return self["size"]
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@size.setter
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def size(self, val):
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self["size"] = val
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@property
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def start(self):
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"""
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Sets the starting value for the x axis bins. Defaults to the
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minimum data value, shifted down if necessary to make nice
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round values and to remove ambiguous bin edges. For example, if
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most of the data is integers we shift the bin edges 0.5 down,
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so a `size` of 5 would have a default `start` of -0.5, so it is
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clear that 0-4 are in the first bin, 5-9 in the second, but
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continuous data gets a start of 0 and bins [0,5), [5,10) etc.
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Dates behave similarly, and `start` should be a date string.
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For category data, `start` is based on the category serial
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numbers, and defaults to -0.5.
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The 'start' property accepts values of any type
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Returns
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-------
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Any
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"""
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return self["start"]
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@start.setter
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def start(self, val):
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self["start"] = val
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@property
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def _prop_descriptions(self):
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return """\
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end
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Sets the end value for the x axis bins. The last bin
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may not end exactly at this value, we increment the bin
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edge by `size` from `start` until we reach or exceed
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`end`. Defaults to the maximum data value. Like
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`start`, for dates use a date string, and for category
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data `end` is based on the category serial numbers.
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size
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Sets the size of each x axis bin. Default behavior: If
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`nbinsx` is 0 or omitted, we choose a nice round bin
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size such that the number of bins is about the same as
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the typical number of samples in each bin. If `nbinsx`
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is provided, we choose a nice round bin size giving no
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more than that many bins. For date data, use
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milliseconds or "M<n>" for months, as in `axis.dtick`.
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For category data, the number of categories to bin
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together (always defaults to 1).
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start
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Sets the starting value for the x axis bins. Defaults
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to the minimum data value, shifted down if necessary to
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make nice round values and to remove ambiguous bin
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edges. For example, if most of the data is integers we
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shift the bin edges 0.5 down, so a `size` of 5 would
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have a default `start` of -0.5, so it is clear that 0-4
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are in the first bin, 5-9 in the second, but continuous
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data gets a start of 0 and bins [0,5), [5,10) etc.
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Dates behave similarly, and `start` should be a date
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string. For category data, `start` is based on the
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category serial numbers, and defaults to -0.5.
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"""
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def __init__(self, arg=None, end=None, size=None, start=None, **kwargs):
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"""
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Construct a new XBins object
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Parameters
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----------
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arg
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dict of properties compatible with this constructor or
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an instance of
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:class:`plotly.graph_objs.histogram2dcontour.XBins`
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end
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Sets the end value for the x axis bins. The last bin
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may not end exactly at this value, we increment the bin
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edge by `size` from `start` until we reach or exceed
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`end`. Defaults to the maximum data value. Like
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`start`, for dates use a date string, and for category
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data `end` is based on the category serial numbers.
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size
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Sets the size of each x axis bin. Default behavior: If
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`nbinsx` is 0 or omitted, we choose a nice round bin
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size such that the number of bins is about the same as
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the typical number of samples in each bin. If `nbinsx`
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is provided, we choose a nice round bin size giving no
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more than that many bins. For date data, use
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milliseconds or "M<n>" for months, as in `axis.dtick`.
|
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For category data, the number of categories to bin
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together (always defaults to 1).
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start
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Sets the starting value for the x axis bins. Defaults
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to the minimum data value, shifted down if necessary to
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make nice round values and to remove ambiguous bin
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edges. For example, if most of the data is integers we
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shift the bin edges 0.5 down, so a `size` of 5 would
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have a default `start` of -0.5, so it is clear that 0-4
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are in the first bin, 5-9 in the second, but continuous
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data gets a start of 0 and bins [0,5), [5,10) etc.
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Dates behave similarly, and `start` should be a date
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string. For category data, `start` is based on the
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category serial numbers, and defaults to -0.5.
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Returns
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-------
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XBins
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"""
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super().__init__("xbins")
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if "_parent" in kwargs:
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self._parent = kwargs["_parent"]
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return
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if arg is None:
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arg = {}
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elif isinstance(arg, self.__class__):
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arg = arg.to_plotly_json()
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elif isinstance(arg, dict):
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arg = _copy.copy(arg)
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else:
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raise ValueError("""\
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The first argument to the plotly.graph_objs.histogram2dcontour.XBins
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constructor must be a dict or
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an instance of :class:`plotly.graph_objs.histogram2dcontour.XBins`""")
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self._skip_invalid = kwargs.pop("skip_invalid", False)
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self._validate = kwargs.pop("_validate", True)
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self._set_property("end", arg, end)
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self._set_property("size", arg, size)
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self._set_property("start", arg, start)
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self._process_kwargs(**dict(arg, **kwargs))
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self._skip_invalid = False
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