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1a18ab2e1cac0d9e7a72751c1f0aa618d9512d5265611b020ce4ab13b329224b | @property
def start(self):
"\n Sets the starting value for the y axis bins. Defaults to the\n minimum data value, shifted down if necessary to make nice\n round values and to remove ambiguous bin edges. For example, if\n most of the data is integers we shift the bin edges 0.5 down,\n so a `size` of 5 would have a default `start` of -0.5, so it is\n clear that 0-4 are in the first bin, 5-9 in the second, but\n continuous data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date string.\n For category data, `start` is based on the category serial\n numbers, and defaults to -0.5. If multiple non-overlaying\n histograms share a subplot, the first explicit `start` is used\n exactly and all others are shifted down (if necessary) to\n differ from that one by an integer number of bins.\n \n The 'start' property accepts values of any type\n\n Returns\n -------\n Any\n "
return self['start'] | Sets the starting value for the y axis bins. Defaults to the
minimum data value, shifted down if necessary to make nice
round values and to remove ambiguous bin edges. For example, if
most of the data is integers we shift the bin edges 0.5 down,
so a `size` of 5 would have a default `start` of -0.5, so it is
clear that 0-4 are in the first bin, 5-9 in the second, but
continuous data gets a start of 0 and bins [0,5), [5,10) etc.
Dates behave similarly, and `start` should be a date string.
For category data, `start` is based on the category serial
numbers, and defaults to -0.5. If multiple non-overlaying
histograms share a subplot, the first explicit `start` is used
exactly and all others are shifted down (if necessary) to
differ from that one by an integer number of bins.
The 'start' property accepts values of any type
Returns
-------
Any | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | start | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def start(self):
"\n Sets the starting value for the y axis bins. Defaults to the\n minimum data value, shifted down if necessary to make nice\n round values and to remove ambiguous bin edges. For example, if\n most of the data is integers we shift the bin edges 0.5 down,\n so a `size` of 5 would have a default `start` of -0.5, so it is\n clear that 0-4 are in the first bin, 5-9 in the second, but\n continuous data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date string.\n For category data, `start` is based on the category serial\n numbers, and defaults to -0.5. If multiple non-overlaying\n histograms share a subplot, the first explicit `start` is used\n exactly and all others are shifted down (if necessary) to\n differ from that one by an integer number of bins.\n \n The 'start' property accepts values of any type\n\n Returns\n -------\n Any\n "
return self['start'] | @property
def start(self):
"\n Sets the starting value for the y axis bins. Defaults to the\n minimum data value, shifted down if necessary to make nice\n round values and to remove ambiguous bin edges. For example, if\n most of the data is integers we shift the bin edges 0.5 down,\n so a `size` of 5 would have a default `start` of -0.5, so it is\n clear that 0-4 are in the first bin, 5-9 in the second, but\n continuous data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date string.\n For category data, `start` is based on the category serial\n numbers, and defaults to -0.5. If multiple non-overlaying\n histograms share a subplot, the first explicit `start` is used\n exactly and all others are shifted down (if necessary) to\n differ from that one by an integer number of bins.\n \n The 'start' property accepts values of any type\n\n Returns\n -------\n Any\n "
return self['start']<|docstring|>Sets the starting value for the y axis bins. Defaults to the
minimum data value, shifted down if necessary to make nice
round values and to remove ambiguous bin edges. For example, if
most of the data is integers we shift the bin edges 0.5 down,
so a `size` of 5 would have a default `start` of -0.5, so it is
clear that 0-4 are in the first bin, 5-9 in the second, but
continuous data gets a start of 0 and bins [0,5), [5,10) etc.
Dates behave similarly, and `start` should be a date string.
For category data, `start` is based on the category serial
numbers, and defaults to -0.5. If multiple non-overlaying
histograms share a subplot, the first explicit `start` is used
exactly and all others are shifted down (if necessary) to
differ from that one by an integer number of bins.
The 'start' property accepts values of any type
Returns
-------
Any<|endoftext|> |
dc640d3693d60c995423443fc80fc18be20050b7c1dd5d167e785c78f9e3c68f | def __init__(self, arg=None, end=None, size=None, start=None, **kwargs):
'\n Construct a new YBins object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.YBins\n end\n Sets the end value for the y axis bins. The last bin\n may not end exactly at this value, we increment the bin\n edge by `size` from `start` until we reach or exceed\n `end`. Defaults to the maximum data value. Like\n `start`, for dates use a date string, and for category\n data `end` is based on the category serial numbers.\n size\n Sets the size of each y axis bin. Default behavior: If\n `nbinsy` is 0 or omitted, we choose a nice round bin\n size such that the number of bins is about the same as\n the typical number of samples in each bin. If `nbinsy`\n is provided, we choose a nice round bin size giving no\n more than that many bins. For date data, use\n milliseconds or "M<n>" for months, as in `axis.dtick`.\n For category data, the number of categories to bin\n together (always defaults to 1). If multiple non-\n overlaying histograms share a subplot, the first\n explicit `size` is used and all others discarded. If no\n `size` is provided,the sample data from all traces is\n combined to determine `size` as described above.\n start\n Sets the starting value for the y axis bins. Defaults\n to the minimum data value, shifted down if necessary to\n make nice round values and to remove ambiguous bin\n edges. For example, if most of the data is integers we\n shift the bin edges 0.5 down, so a `size` of 5 would\n have a default `start` of -0.5, so it is clear that 0-4\n are in the first bin, 5-9 in the second, but continuous\n data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date\n string. For category data, `start` is based on the\n category serial numbers, and defaults to -0.5. If\n multiple non-overlaying histograms share a subplot, the\n first explicit `start` is used exactly and all others\n are shifted down (if necessary) to differ from that one\n by an integer number of bins.\n\n Returns\n -------\n YBins\n '
super(YBins, self).__init__('ybins')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.YBins \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.YBins')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import ybins as v_ybins
self._validators['end'] = v_ybins.EndValidator()
self._validators['size'] = v_ybins.SizeValidator()
self._validators['start'] = v_ybins.StartValidator()
_v = arg.pop('end', None)
self['end'] = (end if (end is not None) else _v)
_v = arg.pop('size', None)
self['size'] = (size if (size is not None) else _v)
_v = arg.pop('start', None)
self['start'] = (start if (start is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | Construct a new YBins object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.YBins
end
Sets the end value for the y axis bins. The last bin
may not end exactly at this value, we increment the bin
edge by `size` from `start` until we reach or exceed
`end`. Defaults to the maximum data value. Like
`start`, for dates use a date string, and for category
data `end` is based on the category serial numbers.
size
Sets the size of each y axis bin. Default behavior: If
`nbinsy` is 0 or omitted, we choose a nice round bin
size such that the number of bins is about the same as
the typical number of samples in each bin. If `nbinsy`
is provided, we choose a nice round bin size giving no
more than that many bins. For date data, use
milliseconds or "M<n>" for months, as in `axis.dtick`.
For category data, the number of categories to bin
together (always defaults to 1). If multiple non-
overlaying histograms share a subplot, the first
explicit `size` is used and all others discarded. If no
`size` is provided,the sample data from all traces is
combined to determine `size` as described above.
start
Sets the starting value for the y axis bins. Defaults
to the minimum data value, shifted down if necessary to
make nice round values and to remove ambiguous bin
edges. For example, if most of the data is integers we
shift the bin edges 0.5 down, so a `size` of 5 would
have a default `start` of -0.5, so it is clear that 0-4
are in the first bin, 5-9 in the second, but continuous
data gets a start of 0 and bins [0,5), [5,10) etc.
Dates behave similarly, and `start` should be a date
string. For category data, `start` is based on the
category serial numbers, and defaults to -0.5. If
multiple non-overlaying histograms share a subplot, the
first explicit `start` is used exactly and all others
are shifted down (if necessary) to differ from that one
by an integer number of bins.
Returns
-------
YBins | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | __init__ | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | def __init__(self, arg=None, end=None, size=None, start=None, **kwargs):
'\n Construct a new YBins object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.YBins\n end\n Sets the end value for the y axis bins. The last bin\n may not end exactly at this value, we increment the bin\n edge by `size` from `start` until we reach or exceed\n `end`. Defaults to the maximum data value. Like\n `start`, for dates use a date string, and for category\n data `end` is based on the category serial numbers.\n size\n Sets the size of each y axis bin. Default behavior: If\n `nbinsy` is 0 or omitted, we choose a nice round bin\n size such that the number of bins is about the same as\n the typical number of samples in each bin. If `nbinsy`\n is provided, we choose a nice round bin size giving no\n more than that many bins. For date data, use\n milliseconds or "M<n>" for months, as in `axis.dtick`.\n For category data, the number of categories to bin\n together (always defaults to 1). If multiple non-\n overlaying histograms share a subplot, the first\n explicit `size` is used and all others discarded. If no\n `size` is provided,the sample data from all traces is\n combined to determine `size` as described above.\n start\n Sets the starting value for the y axis bins. Defaults\n to the minimum data value, shifted down if necessary to\n make nice round values and to remove ambiguous bin\n edges. For example, if most of the data is integers we\n shift the bin edges 0.5 down, so a `size` of 5 would\n have a default `start` of -0.5, so it is clear that 0-4\n are in the first bin, 5-9 in the second, but continuous\n data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date\n string. For category data, `start` is based on the\n category serial numbers, and defaults to -0.5. If\n multiple non-overlaying histograms share a subplot, the\n first explicit `start` is used exactly and all others\n are shifted down (if necessary) to differ from that one\n by an integer number of bins.\n\n Returns\n -------\n YBins\n '
super(YBins, self).__init__('ybins')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.YBins \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.YBins')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import ybins as v_ybins
self._validators['end'] = v_ybins.EndValidator()
self._validators['size'] = v_ybins.SizeValidator()
self._validators['start'] = v_ybins.StartValidator()
_v = arg.pop('end', None)
self['end'] = (end if (end is not None) else _v)
_v = arg.pop('size', None)
self['size'] = (size if (size is not None) else _v)
_v = arg.pop('start', None)
self['start'] = (start if (start is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | def __init__(self, arg=None, end=None, size=None, start=None, **kwargs):
'\n Construct a new YBins object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.YBins\n end\n Sets the end value for the y axis bins. The last bin\n may not end exactly at this value, we increment the bin\n edge by `size` from `start` until we reach or exceed\n `end`. Defaults to the maximum data value. Like\n `start`, for dates use a date string, and for category\n data `end` is based on the category serial numbers.\n size\n Sets the size of each y axis bin. Default behavior: If\n `nbinsy` is 0 or omitted, we choose a nice round bin\n size such that the number of bins is about the same as\n the typical number of samples in each bin. If `nbinsy`\n is provided, we choose a nice round bin size giving no\n more than that many bins. For date data, use\n milliseconds or "M<n>" for months, as in `axis.dtick`.\n For category data, the number of categories to bin\n together (always defaults to 1). If multiple non-\n overlaying histograms share a subplot, the first\n explicit `size` is used and all others discarded. If no\n `size` is provided,the sample data from all traces is\n combined to determine `size` as described above.\n start\n Sets the starting value for the y axis bins. Defaults\n to the minimum data value, shifted down if necessary to\n make nice round values and to remove ambiguous bin\n edges. For example, if most of the data is integers we\n shift the bin edges 0.5 down, so a `size` of 5 would\n have a default `start` of -0.5, so it is clear that 0-4\n are in the first bin, 5-9 in the second, but continuous\n data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date\n string. For category data, `start` is based on the\n category serial numbers, and defaults to -0.5. If\n multiple non-overlaying histograms share a subplot, the\n first explicit `start` is used exactly and all others\n are shifted down (if necessary) to differ from that one\n by an integer number of bins.\n\n Returns\n -------\n YBins\n '
super(YBins, self).__init__('ybins')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.YBins \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.YBins')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import ybins as v_ybins
self._validators['end'] = v_ybins.EndValidator()
self._validators['size'] = v_ybins.SizeValidator()
self._validators['start'] = v_ybins.StartValidator()
_v = arg.pop('end', None)
self['end'] = (end if (end is not None) else _v)
_v = arg.pop('size', None)
self['size'] = (size if (size is not None) else _v)
_v = arg.pop('start', None)
self['start'] = (start if (start is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False<|docstring|>Construct a new YBins object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.YBins
end
Sets the end value for the y axis bins. The last bin
may not end exactly at this value, we increment the bin
edge by `size` from `start` until we reach or exceed
`end`. Defaults to the maximum data value. Like
`start`, for dates use a date string, and for category
data `end` is based on the category serial numbers.
size
Sets the size of each y axis bin. Default behavior: If
`nbinsy` is 0 or omitted, we choose a nice round bin
size such that the number of bins is about the same as
the typical number of samples in each bin. If `nbinsy`
is provided, we choose a nice round bin size giving no
more than that many bins. For date data, use
milliseconds or "M<n>" for months, as in `axis.dtick`.
For category data, the number of categories to bin
together (always defaults to 1). If multiple non-
overlaying histograms share a subplot, the first
explicit `size` is used and all others discarded. If no
`size` is provided,the sample data from all traces is
combined to determine `size` as described above.
start
Sets the starting value for the y axis bins. Defaults
to the minimum data value, shifted down if necessary to
make nice round values and to remove ambiguous bin
edges. For example, if most of the data is integers we
shift the bin edges 0.5 down, so a `size` of 5 would
have a default `start` of -0.5, so it is clear that 0-4
are in the first bin, 5-9 in the second, but continuous
data gets a start of 0 and bins [0,5), [5,10) etc.
Dates behave similarly, and `start` should be a date
string. For category data, `start` is based on the
category serial numbers, and defaults to -0.5. If
multiple non-overlaying histograms share a subplot, the
first explicit `start` is used exactly and all others
are shifted down (if necessary) to differ from that one
by an integer number of bins.
Returns
-------
YBins<|endoftext|> |
bda3600484c44cfba087989feb04f01688bd96ecee44bc98c7e0cbc5ecb8d6e1 | @property
def end(self):
"\n Sets the end value for the x axis bins. The last bin may not\n end exactly at this value, we increment the bin edge by `size`\n from `start` until we reach or exceed `end`. Defaults to the\n maximum data value. Like `start`, for dates use a date string,\n and for category data `end` is based on the category serial\n numbers.\n \n The 'end' property accepts values of any type\n\n Returns\n -------\n Any\n "
return self['end'] | Sets the end value for the x axis bins. The last bin may not
end exactly at this value, we increment the bin edge by `size`
from `start` until we reach or exceed `end`. Defaults to the
maximum data value. Like `start`, for dates use a date string,
and for category data `end` is based on the category serial
numbers.
The 'end' property accepts values of any type
Returns
-------
Any | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | end | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def end(self):
"\n Sets the end value for the x axis bins. The last bin may not\n end exactly at this value, we increment the bin edge by `size`\n from `start` until we reach or exceed `end`. Defaults to the\n maximum data value. Like `start`, for dates use a date string,\n and for category data `end` is based on the category serial\n numbers.\n \n The 'end' property accepts values of any type\n\n Returns\n -------\n Any\n "
return self['end'] | @property
def end(self):
"\n Sets the end value for the x axis bins. The last bin may not\n end exactly at this value, we increment the bin edge by `size`\n from `start` until we reach or exceed `end`. Defaults to the\n maximum data value. Like `start`, for dates use a date string,\n and for category data `end` is based on the category serial\n numbers.\n \n The 'end' property accepts values of any type\n\n Returns\n -------\n Any\n "
return self['end']<|docstring|>Sets the end value for the x axis bins. The last bin may not
end exactly at this value, we increment the bin edge by `size`
from `start` until we reach or exceed `end`. Defaults to the
maximum data value. Like `start`, for dates use a date string,
and for category data `end` is based on the category serial
numbers.
The 'end' property accepts values of any type
Returns
-------
Any<|endoftext|> |
4c35da1549eaeb4de19b4d8da5a7666c3e85768f5ae2a51151eebfa794cfa4f1 | @property
def size(self):
'\n Sets the size of each x axis bin. Default behavior: If `nbinsx`\n is 0 or omitted, we choose a nice round bin size such that the\n number of bins is about the same as the typical number of\n samples in each bin. If `nbinsx` is provided, we choose a nice\n round bin size giving no more than that many bins. For date\n data, use milliseconds or "M<n>" for months, as in\n `axis.dtick`. For category data, the number of categories to\n bin together (always defaults to 1). If multiple non-overlaying\n histograms share a subplot, the first explicit `size` is used\n and all others discarded. If no `size` is provided,the sample\n data from all traces is combined to determine `size` as\n described above.\n \n The \'size\' property accepts values of any type\n\n Returns\n -------\n Any\n '
return self['size'] | Sets the size of each x axis bin. Default behavior: If `nbinsx`
is 0 or omitted, we choose a nice round bin size such that the
number of bins is about the same as the typical number of
samples in each bin. If `nbinsx` is provided, we choose a nice
round bin size giving no more than that many bins. For date
data, use milliseconds or "M<n>" for months, as in
`axis.dtick`. For category data, the number of categories to
bin together (always defaults to 1). If multiple non-overlaying
histograms share a subplot, the first explicit `size` is used
and all others discarded. If no `size` is provided,the sample
data from all traces is combined to determine `size` as
described above.
The 'size' property accepts values of any type
Returns
-------
Any | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | size | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def size(self):
'\n Sets the size of each x axis bin. Default behavior: If `nbinsx`\n is 0 or omitted, we choose a nice round bin size such that the\n number of bins is about the same as the typical number of\n samples in each bin. If `nbinsx` is provided, we choose a nice\n round bin size giving no more than that many bins. For date\n data, use milliseconds or "M<n>" for months, as in\n `axis.dtick`. For category data, the number of categories to\n bin together (always defaults to 1). If multiple non-overlaying\n histograms share a subplot, the first explicit `size` is used\n and all others discarded. If no `size` is provided,the sample\n data from all traces is combined to determine `size` as\n described above.\n \n The \'size\' property accepts values of any type\n\n Returns\n -------\n Any\n '
return self['size'] | @property
def size(self):
'\n Sets the size of each x axis bin. Default behavior: If `nbinsx`\n is 0 or omitted, we choose a nice round bin size such that the\n number of bins is about the same as the typical number of\n samples in each bin. If `nbinsx` is provided, we choose a nice\n round bin size giving no more than that many bins. For date\n data, use milliseconds or "M<n>" for months, as in\n `axis.dtick`. For category data, the number of categories to\n bin together (always defaults to 1). If multiple non-overlaying\n histograms share a subplot, the first explicit `size` is used\n and all others discarded. If no `size` is provided,the sample\n data from all traces is combined to determine `size` as\n described above.\n \n The \'size\' property accepts values of any type\n\n Returns\n -------\n Any\n '
return self['size']<|docstring|>Sets the size of each x axis bin. Default behavior: If `nbinsx`
is 0 or omitted, we choose a nice round bin size such that the
number of bins is about the same as the typical number of
samples in each bin. If `nbinsx` is provided, we choose a nice
round bin size giving no more than that many bins. For date
data, use milliseconds or "M<n>" for months, as in
`axis.dtick`. For category data, the number of categories to
bin together (always defaults to 1). If multiple non-overlaying
histograms share a subplot, the first explicit `size` is used
and all others discarded. If no `size` is provided,the sample
data from all traces is combined to determine `size` as
described above.
The 'size' property accepts values of any type
Returns
-------
Any<|endoftext|> |
84600b54b69156f7e5b4f2bca6eeadf8e1482f72a4f545682a185eccf4aeecef | @property
def start(self):
"\n Sets the starting value for the x axis bins. Defaults to the\n minimum data value, shifted down if necessary to make nice\n round values and to remove ambiguous bin edges. For example, if\n most of the data is integers we shift the bin edges 0.5 down,\n so a `size` of 5 would have a default `start` of -0.5, so it is\n clear that 0-4 are in the first bin, 5-9 in the second, but\n continuous data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date string.\n For category data, `start` is based on the category serial\n numbers, and defaults to -0.5. If multiple non-overlaying\n histograms share a subplot, the first explicit `start` is used\n exactly and all others are shifted down (if necessary) to\n differ from that one by an integer number of bins.\n \n The 'start' property accepts values of any type\n\n Returns\n -------\n Any\n "
return self['start'] | Sets the starting value for the x axis bins. Defaults to the
minimum data value, shifted down if necessary to make nice
round values and to remove ambiguous bin edges. For example, if
most of the data is integers we shift the bin edges 0.5 down,
so a `size` of 5 would have a default `start` of -0.5, so it is
clear that 0-4 are in the first bin, 5-9 in the second, but
continuous data gets a start of 0 and bins [0,5), [5,10) etc.
Dates behave similarly, and `start` should be a date string.
For category data, `start` is based on the category serial
numbers, and defaults to -0.5. If multiple non-overlaying
histograms share a subplot, the first explicit `start` is used
exactly and all others are shifted down (if necessary) to
differ from that one by an integer number of bins.
The 'start' property accepts values of any type
Returns
-------
Any | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | start | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def start(self):
"\n Sets the starting value for the x axis bins. Defaults to the\n minimum data value, shifted down if necessary to make nice\n round values and to remove ambiguous bin edges. For example, if\n most of the data is integers we shift the bin edges 0.5 down,\n so a `size` of 5 would have a default `start` of -0.5, so it is\n clear that 0-4 are in the first bin, 5-9 in the second, but\n continuous data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date string.\n For category data, `start` is based on the category serial\n numbers, and defaults to -0.5. If multiple non-overlaying\n histograms share a subplot, the first explicit `start` is used\n exactly and all others are shifted down (if necessary) to\n differ from that one by an integer number of bins.\n \n The 'start' property accepts values of any type\n\n Returns\n -------\n Any\n "
return self['start'] | @property
def start(self):
"\n Sets the starting value for the x axis bins. Defaults to the\n minimum data value, shifted down if necessary to make nice\n round values and to remove ambiguous bin edges. For example, if\n most of the data is integers we shift the bin edges 0.5 down,\n so a `size` of 5 would have a default `start` of -0.5, so it is\n clear that 0-4 are in the first bin, 5-9 in the second, but\n continuous data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date string.\n For category data, `start` is based on the category serial\n numbers, and defaults to -0.5. If multiple non-overlaying\n histograms share a subplot, the first explicit `start` is used\n exactly and all others are shifted down (if necessary) to\n differ from that one by an integer number of bins.\n \n The 'start' property accepts values of any type\n\n Returns\n -------\n Any\n "
return self['start']<|docstring|>Sets the starting value for the x axis bins. Defaults to the
minimum data value, shifted down if necessary to make nice
round values and to remove ambiguous bin edges. For example, if
most of the data is integers we shift the bin edges 0.5 down,
so a `size` of 5 would have a default `start` of -0.5, so it is
clear that 0-4 are in the first bin, 5-9 in the second, but
continuous data gets a start of 0 and bins [0,5), [5,10) etc.
Dates behave similarly, and `start` should be a date string.
For category data, `start` is based on the category serial
numbers, and defaults to -0.5. If multiple non-overlaying
histograms share a subplot, the first explicit `start` is used
exactly and all others are shifted down (if necessary) to
differ from that one by an integer number of bins.
The 'start' property accepts values of any type
Returns
-------
Any<|endoftext|> |
704054d59be3d68518108cc09648670f346134cd9e217264ddb3ea21194bd230 | def __init__(self, arg=None, end=None, size=None, start=None, **kwargs):
'\n Construct a new XBins object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.XBins\n end\n Sets the end value for the x axis bins. The last bin\n may not end exactly at this value, we increment the bin\n edge by `size` from `start` until we reach or exceed\n `end`. Defaults to the maximum data value. Like\n `start`, for dates use a date string, and for category\n data `end` is based on the category serial numbers.\n size\n Sets the size of each x axis bin. Default behavior: If\n `nbinsx` is 0 or omitted, we choose a nice round bin\n size such that the number of bins is about the same as\n the typical number of samples in each bin. If `nbinsx`\n is provided, we choose a nice round bin size giving no\n more than that many bins. For date data, use\n milliseconds or "M<n>" for months, as in `axis.dtick`.\n For category data, the number of categories to bin\n together (always defaults to 1). If multiple non-\n overlaying histograms share a subplot, the first\n explicit `size` is used and all others discarded. If no\n `size` is provided,the sample data from all traces is\n combined to determine `size` as described above.\n start\n Sets the starting value for the x axis bins. Defaults\n to the minimum data value, shifted down if necessary to\n make nice round values and to remove ambiguous bin\n edges. For example, if most of the data is integers we\n shift the bin edges 0.5 down, so a `size` of 5 would\n have a default `start` of -0.5, so it is clear that 0-4\n are in the first bin, 5-9 in the second, but continuous\n data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date\n string. For category data, `start` is based on the\n category serial numbers, and defaults to -0.5. If\n multiple non-overlaying histograms share a subplot, the\n first explicit `start` is used exactly and all others\n are shifted down (if necessary) to differ from that one\n by an integer number of bins.\n\n Returns\n -------\n XBins\n '
super(XBins, self).__init__('xbins')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.XBins \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.XBins')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import xbins as v_xbins
self._validators['end'] = v_xbins.EndValidator()
self._validators['size'] = v_xbins.SizeValidator()
self._validators['start'] = v_xbins.StartValidator()
_v = arg.pop('end', None)
self['end'] = (end if (end is not None) else _v)
_v = arg.pop('size', None)
self['size'] = (size if (size is not None) else _v)
_v = arg.pop('start', None)
self['start'] = (start if (start is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | Construct a new XBins object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.XBins
end
Sets the end value for the x axis bins. The last bin
may not end exactly at this value, we increment the bin
edge by `size` from `start` until we reach or exceed
`end`. Defaults to the maximum data value. Like
`start`, for dates use a date string, and for category
data `end` is based on the category serial numbers.
size
Sets the size of each x axis bin. Default behavior: If
`nbinsx` is 0 or omitted, we choose a nice round bin
size such that the number of bins is about the same as
the typical number of samples in each bin. If `nbinsx`
is provided, we choose a nice round bin size giving no
more than that many bins. For date data, use
milliseconds or "M<n>" for months, as in `axis.dtick`.
For category data, the number of categories to bin
together (always defaults to 1). If multiple non-
overlaying histograms share a subplot, the first
explicit `size` is used and all others discarded. If no
`size` is provided,the sample data from all traces is
combined to determine `size` as described above.
start
Sets the starting value for the x axis bins. Defaults
to the minimum data value, shifted down if necessary to
make nice round values and to remove ambiguous bin
edges. For example, if most of the data is integers we
shift the bin edges 0.5 down, so a `size` of 5 would
have a default `start` of -0.5, so it is clear that 0-4
are in the first bin, 5-9 in the second, but continuous
data gets a start of 0 and bins [0,5), [5,10) etc.
Dates behave similarly, and `start` should be a date
string. For category data, `start` is based on the
category serial numbers, and defaults to -0.5. If
multiple non-overlaying histograms share a subplot, the
first explicit `start` is used exactly and all others
are shifted down (if necessary) to differ from that one
by an integer number of bins.
Returns
-------
XBins | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | __init__ | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | def __init__(self, arg=None, end=None, size=None, start=None, **kwargs):
'\n Construct a new XBins object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.XBins\n end\n Sets the end value for the x axis bins. The last bin\n may not end exactly at this value, we increment the bin\n edge by `size` from `start` until we reach or exceed\n `end`. Defaults to the maximum data value. Like\n `start`, for dates use a date string, and for category\n data `end` is based on the category serial numbers.\n size\n Sets the size of each x axis bin. Default behavior: If\n `nbinsx` is 0 or omitted, we choose a nice round bin\n size such that the number of bins is about the same as\n the typical number of samples in each bin. If `nbinsx`\n is provided, we choose a nice round bin size giving no\n more than that many bins. For date data, use\n milliseconds or "M<n>" for months, as in `axis.dtick`.\n For category data, the number of categories to bin\n together (always defaults to 1). If multiple non-\n overlaying histograms share a subplot, the first\n explicit `size` is used and all others discarded. If no\n `size` is provided,the sample data from all traces is\n combined to determine `size` as described above.\n start\n Sets the starting value for the x axis bins. Defaults\n to the minimum data value, shifted down if necessary to\n make nice round values and to remove ambiguous bin\n edges. For example, if most of the data is integers we\n shift the bin edges 0.5 down, so a `size` of 5 would\n have a default `start` of -0.5, so it is clear that 0-4\n are in the first bin, 5-9 in the second, but continuous\n data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date\n string. For category data, `start` is based on the\n category serial numbers, and defaults to -0.5. If\n multiple non-overlaying histograms share a subplot, the\n first explicit `start` is used exactly and all others\n are shifted down (if necessary) to differ from that one\n by an integer number of bins.\n\n Returns\n -------\n XBins\n '
super(XBins, self).__init__('xbins')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.XBins \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.XBins')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import xbins as v_xbins
self._validators['end'] = v_xbins.EndValidator()
self._validators['size'] = v_xbins.SizeValidator()
self._validators['start'] = v_xbins.StartValidator()
_v = arg.pop('end', None)
self['end'] = (end if (end is not None) else _v)
_v = arg.pop('size', None)
self['size'] = (size if (size is not None) else _v)
_v = arg.pop('start', None)
self['start'] = (start if (start is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | def __init__(self, arg=None, end=None, size=None, start=None, **kwargs):
'\n Construct a new XBins object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.XBins\n end\n Sets the end value for the x axis bins. The last bin\n may not end exactly at this value, we increment the bin\n edge by `size` from `start` until we reach or exceed\n `end`. Defaults to the maximum data value. Like\n `start`, for dates use a date string, and for category\n data `end` is based on the category serial numbers.\n size\n Sets the size of each x axis bin. Default behavior: If\n `nbinsx` is 0 or omitted, we choose a nice round bin\n size such that the number of bins is about the same as\n the typical number of samples in each bin. If `nbinsx`\n is provided, we choose a nice round bin size giving no\n more than that many bins. For date data, use\n milliseconds or "M<n>" for months, as in `axis.dtick`.\n For category data, the number of categories to bin\n together (always defaults to 1). If multiple non-\n overlaying histograms share a subplot, the first\n explicit `size` is used and all others discarded. If no\n `size` is provided,the sample data from all traces is\n combined to determine `size` as described above.\n start\n Sets the starting value for the x axis bins. Defaults\n to the minimum data value, shifted down if necessary to\n make nice round values and to remove ambiguous bin\n edges. For example, if most of the data is integers we\n shift the bin edges 0.5 down, so a `size` of 5 would\n have a default `start` of -0.5, so it is clear that 0-4\n are in the first bin, 5-9 in the second, but continuous\n data gets a start of 0 and bins [0,5), [5,10) etc.\n Dates behave similarly, and `start` should be a date\n string. For category data, `start` is based on the\n category serial numbers, and defaults to -0.5. If\n multiple non-overlaying histograms share a subplot, the\n first explicit `start` is used exactly and all others\n are shifted down (if necessary) to differ from that one\n by an integer number of bins.\n\n Returns\n -------\n XBins\n '
super(XBins, self).__init__('xbins')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.XBins \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.XBins')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import xbins as v_xbins
self._validators['end'] = v_xbins.EndValidator()
self._validators['size'] = v_xbins.SizeValidator()
self._validators['start'] = v_xbins.StartValidator()
_v = arg.pop('end', None)
self['end'] = (end if (end is not None) else _v)
_v = arg.pop('size', None)
self['size'] = (size if (size is not None) else _v)
_v = arg.pop('start', None)
self['start'] = (start if (start is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False<|docstring|>Construct a new XBins object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.XBins
end
Sets the end value for the x axis bins. The last bin
may not end exactly at this value, we increment the bin
edge by `size` from `start` until we reach or exceed
`end`. Defaults to the maximum data value. Like
`start`, for dates use a date string, and for category
data `end` is based on the category serial numbers.
size
Sets the size of each x axis bin. Default behavior: If
`nbinsx` is 0 or omitted, we choose a nice round bin
size such that the number of bins is about the same as
the typical number of samples in each bin. If `nbinsx`
is provided, we choose a nice round bin size giving no
more than that many bins. For date data, use
milliseconds or "M<n>" for months, as in `axis.dtick`.
For category data, the number of categories to bin
together (always defaults to 1). If multiple non-
overlaying histograms share a subplot, the first
explicit `size` is used and all others discarded. If no
`size` is provided,the sample data from all traces is
combined to determine `size` as described above.
start
Sets the starting value for the x axis bins. Defaults
to the minimum data value, shifted down if necessary to
make nice round values and to remove ambiguous bin
edges. For example, if most of the data is integers we
shift the bin edges 0.5 down, so a `size` of 5 would
have a default `start` of -0.5, so it is clear that 0-4
are in the first bin, 5-9 in the second, but continuous
data gets a start of 0 and bins [0,5), [5,10) etc.
Dates behave similarly, and `start` should be a date
string. For category data, `start` is based on the
category serial numbers, and defaults to -0.5. If
multiple non-overlaying histograms share a subplot, the
first explicit `start` is used exactly and all others
are shifted down (if necessary) to differ from that one
by an integer number of bins.
Returns
-------
XBins<|endoftext|> |
7da21cbbde9b52b319127ef6ac0c65261d8033758381b4b99d74fb53bbdec6a5 | @property
def marker(self):
"\n The 'marker' property is an instance of Marker\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.unselected.Marker\n - A dict of string/value properties that will be passed\n to the Marker constructor\n \n Supported dict properties:\n \n color\n Sets the marker color of unselected points,\n applied only when a selection exists.\n opacity\n Sets the marker opacity of unselected points,\n applied only when a selection exists.\n\n Returns\n -------\n plotly.graph_objs.histogram.unselected.Marker\n "
return self['marker'] | The 'marker' property is an instance of Marker
that may be specified as:
- An instance of plotly.graph_objs.histogram.unselected.Marker
- A dict of string/value properties that will be passed
to the Marker constructor
Supported dict properties:
color
Sets the marker color of unselected points,
applied only when a selection exists.
opacity
Sets the marker opacity of unselected points,
applied only when a selection exists.
Returns
-------
plotly.graph_objs.histogram.unselected.Marker | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | marker | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def marker(self):
"\n The 'marker' property is an instance of Marker\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.unselected.Marker\n - A dict of string/value properties that will be passed\n to the Marker constructor\n \n Supported dict properties:\n \n color\n Sets the marker color of unselected points,\n applied only when a selection exists.\n opacity\n Sets the marker opacity of unselected points,\n applied only when a selection exists.\n\n Returns\n -------\n plotly.graph_objs.histogram.unselected.Marker\n "
return self['marker'] | @property
def marker(self):
"\n The 'marker' property is an instance of Marker\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.unselected.Marker\n - A dict of string/value properties that will be passed\n to the Marker constructor\n \n Supported dict properties:\n \n color\n Sets the marker color of unselected points,\n applied only when a selection exists.\n opacity\n Sets the marker opacity of unselected points,\n applied only when a selection exists.\n\n Returns\n -------\n plotly.graph_objs.histogram.unselected.Marker\n "
return self['marker']<|docstring|>The 'marker' property is an instance of Marker
that may be specified as:
- An instance of plotly.graph_objs.histogram.unselected.Marker
- A dict of string/value properties that will be passed
to the Marker constructor
Supported dict properties:
color
Sets the marker color of unselected points,
applied only when a selection exists.
opacity
Sets the marker opacity of unselected points,
applied only when a selection exists.
Returns
-------
plotly.graph_objs.histogram.unselected.Marker<|endoftext|> |
d175e58247a44978a4d42e39258d2c88c1e783ada7c15d229c3a147acbe9a1dc | @property
def textfont(self):
"\n The 'textfont' property is an instance of Textfont\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.unselected.Textfont\n - A dict of string/value properties that will be passed\n to the Textfont constructor\n \n Supported dict properties:\n \n color\n Sets the text font color of unselected points,\n applied only when a selection exists.\n\n Returns\n -------\n plotly.graph_objs.histogram.unselected.Textfont\n "
return self['textfont'] | The 'textfont' property is an instance of Textfont
that may be specified as:
- An instance of plotly.graph_objs.histogram.unselected.Textfont
- A dict of string/value properties that will be passed
to the Textfont constructor
Supported dict properties:
color
Sets the text font color of unselected points,
applied only when a selection exists.
Returns
-------
plotly.graph_objs.histogram.unselected.Textfont | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | textfont | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def textfont(self):
"\n The 'textfont' property is an instance of Textfont\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.unselected.Textfont\n - A dict of string/value properties that will be passed\n to the Textfont constructor\n \n Supported dict properties:\n \n color\n Sets the text font color of unselected points,\n applied only when a selection exists.\n\n Returns\n -------\n plotly.graph_objs.histogram.unselected.Textfont\n "
return self['textfont'] | @property
def textfont(self):
"\n The 'textfont' property is an instance of Textfont\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.unselected.Textfont\n - A dict of string/value properties that will be passed\n to the Textfont constructor\n \n Supported dict properties:\n \n color\n Sets the text font color of unselected points,\n applied only when a selection exists.\n\n Returns\n -------\n plotly.graph_objs.histogram.unselected.Textfont\n "
return self['textfont']<|docstring|>The 'textfont' property is an instance of Textfont
that may be specified as:
- An instance of plotly.graph_objs.histogram.unselected.Textfont
- A dict of string/value properties that will be passed
to the Textfont constructor
Supported dict properties:
color
Sets the text font color of unselected points,
applied only when a selection exists.
Returns
-------
plotly.graph_objs.histogram.unselected.Textfont<|endoftext|> |
9686f86a8ca5014ea416660d662101bb86b0166df7ff43a20775c60ac978effc | def __init__(self, arg=None, marker=None, textfont=None, **kwargs):
'\n Construct a new Unselected object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Unselected\n marker\n plotly.graph_objs.histogram.unselected.Marker instance\n or dict with compatible properties\n textfont\n plotly.graph_objs.histogram.unselected.Textfont\n instance or dict with compatible properties\n\n Returns\n -------\n Unselected\n '
super(Unselected, self).__init__('unselected')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Unselected \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Unselected')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import unselected as v_unselected
self._validators['marker'] = v_unselected.MarkerValidator()
self._validators['textfont'] = v_unselected.TextfontValidator()
_v = arg.pop('marker', None)
self['marker'] = (marker if (marker is not None) else _v)
_v = arg.pop('textfont', None)
self['textfont'] = (textfont if (textfont is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | Construct a new Unselected object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Unselected
marker
plotly.graph_objs.histogram.unselected.Marker instance
or dict with compatible properties
textfont
plotly.graph_objs.histogram.unselected.Textfont
instance or dict with compatible properties
Returns
-------
Unselected | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | __init__ | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | def __init__(self, arg=None, marker=None, textfont=None, **kwargs):
'\n Construct a new Unselected object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Unselected\n marker\n plotly.graph_objs.histogram.unselected.Marker instance\n or dict with compatible properties\n textfont\n plotly.graph_objs.histogram.unselected.Textfont\n instance or dict with compatible properties\n\n Returns\n -------\n Unselected\n '
super(Unselected, self).__init__('unselected')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Unselected \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Unselected')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import unselected as v_unselected
self._validators['marker'] = v_unselected.MarkerValidator()
self._validators['textfont'] = v_unselected.TextfontValidator()
_v = arg.pop('marker', None)
self['marker'] = (marker if (marker is not None) else _v)
_v = arg.pop('textfont', None)
self['textfont'] = (textfont if (textfont is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | def __init__(self, arg=None, marker=None, textfont=None, **kwargs):
'\n Construct a new Unselected object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Unselected\n marker\n plotly.graph_objs.histogram.unselected.Marker instance\n or dict with compatible properties\n textfont\n plotly.graph_objs.histogram.unselected.Textfont\n instance or dict with compatible properties\n\n Returns\n -------\n Unselected\n '
super(Unselected, self).__init__('unselected')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Unselected \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Unselected')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import unselected as v_unselected
self._validators['marker'] = v_unselected.MarkerValidator()
self._validators['textfont'] = v_unselected.TextfontValidator()
_v = arg.pop('marker', None)
self['marker'] = (marker if (marker is not None) else _v)
_v = arg.pop('textfont', None)
self['textfont'] = (textfont if (textfont is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False<|docstring|>Construct a new Unselected object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Unselected
marker
plotly.graph_objs.histogram.unselected.Marker instance
or dict with compatible properties
textfont
plotly.graph_objs.histogram.unselected.Textfont
instance or dict with compatible properties
Returns
-------
Unselected<|endoftext|> |
0afc1b098cd5e0a39944f88050ce745c7e9eafdf824b465bc8828ca3f96d7768 | @property
def maxpoints(self):
"\n Sets the maximum number of points to keep on the plots from an\n incoming stream. If `maxpoints` is set to 50, only the newest\n 50 points will be displayed on the plot.\n \n The 'maxpoints' property is a number and may be specified as:\n - An int or float in the interval [0, 10000]\n\n Returns\n -------\n int|float\n "
return self['maxpoints'] | Sets the maximum number of points to keep on the plots from an
incoming stream. If `maxpoints` is set to 50, only the newest
50 points will be displayed on the plot.
The 'maxpoints' property is a number and may be specified as:
- An int or float in the interval [0, 10000]
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | maxpoints | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def maxpoints(self):
"\n Sets the maximum number of points to keep on the plots from an\n incoming stream. If `maxpoints` is set to 50, only the newest\n 50 points will be displayed on the plot.\n \n The 'maxpoints' property is a number and may be specified as:\n - An int or float in the interval [0, 10000]\n\n Returns\n -------\n int|float\n "
return self['maxpoints'] | @property
def maxpoints(self):
"\n Sets the maximum number of points to keep on the plots from an\n incoming stream. If `maxpoints` is set to 50, only the newest\n 50 points will be displayed on the plot.\n \n The 'maxpoints' property is a number and may be specified as:\n - An int or float in the interval [0, 10000]\n\n Returns\n -------\n int|float\n "
return self['maxpoints']<|docstring|>Sets the maximum number of points to keep on the plots from an
incoming stream. If `maxpoints` is set to 50, only the newest
50 points will be displayed on the plot.
The 'maxpoints' property is a number and may be specified as:
- An int or float in the interval [0, 10000]
Returns
-------
int|float<|endoftext|> |
9ee89ca0a116ef27726a38f905fa13bf1a602e8cc7ff8112e2b9958a1fbfb79e | @property
def token(self):
"\n The stream id number links a data trace on a plot with a\n stream. See https://plot.ly/settings for more details.\n \n The 'token' property is a string and must be specified as:\n - A non-empty string\n\n Returns\n -------\n str\n "
return self['token'] | The stream id number links a data trace on a plot with a
stream. See https://plot.ly/settings for more details.
The 'token' property is a string and must be specified as:
- A non-empty string
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | token | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def token(self):
"\n The stream id number links a data trace on a plot with a\n stream. See https://plot.ly/settings for more details.\n \n The 'token' property is a string and must be specified as:\n - A non-empty string\n\n Returns\n -------\n str\n "
return self['token'] | @property
def token(self):
"\n The stream id number links a data trace on a plot with a\n stream. See https://plot.ly/settings for more details.\n \n The 'token' property is a string and must be specified as:\n - A non-empty string\n\n Returns\n -------\n str\n "
return self['token']<|docstring|>The stream id number links a data trace on a plot with a
stream. See https://plot.ly/settings for more details.
The 'token' property is a string and must be specified as:
- A non-empty string
Returns
-------
str<|endoftext|> |
ac627320e19151e5f3757cee66207f0a0bfd456cc8fbf2b3382d6eec85f0d27f | def __init__(self, arg=None, maxpoints=None, token=None, **kwargs):
'\n Construct a new Stream object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Stream\n maxpoints\n Sets the maximum number of points to keep on the plots\n from an incoming stream. If `maxpoints` is set to 50,\n only the newest 50 points will be displayed on the\n plot.\n token\n The stream id number links a data trace on a plot with\n a stream. See https://plot.ly/settings for more\n details.\n\n Returns\n -------\n Stream\n '
super(Stream, self).__init__('stream')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Stream \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Stream')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import stream as v_stream
self._validators['maxpoints'] = v_stream.MaxpointsValidator()
self._validators['token'] = v_stream.TokenValidator()
_v = arg.pop('maxpoints', None)
self['maxpoints'] = (maxpoints if (maxpoints is not None) else _v)
_v = arg.pop('token', None)
self['token'] = (token if (token is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | Construct a new Stream object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Stream
maxpoints
Sets the maximum number of points to keep on the plots
from an incoming stream. If `maxpoints` is set to 50,
only the newest 50 points will be displayed on the
plot.
token
The stream id number links a data trace on a plot with
a stream. See https://plot.ly/settings for more
details.
Returns
-------
Stream | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | __init__ | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | def __init__(self, arg=None, maxpoints=None, token=None, **kwargs):
'\n Construct a new Stream object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Stream\n maxpoints\n Sets the maximum number of points to keep on the plots\n from an incoming stream. If `maxpoints` is set to 50,\n only the newest 50 points will be displayed on the\n plot.\n token\n The stream id number links a data trace on a plot with\n a stream. See https://plot.ly/settings for more\n details.\n\n Returns\n -------\n Stream\n '
super(Stream, self).__init__('stream')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Stream \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Stream')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import stream as v_stream
self._validators['maxpoints'] = v_stream.MaxpointsValidator()
self._validators['token'] = v_stream.TokenValidator()
_v = arg.pop('maxpoints', None)
self['maxpoints'] = (maxpoints if (maxpoints is not None) else _v)
_v = arg.pop('token', None)
self['token'] = (token if (token is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | def __init__(self, arg=None, maxpoints=None, token=None, **kwargs):
'\n Construct a new Stream object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Stream\n maxpoints\n Sets the maximum number of points to keep on the plots\n from an incoming stream. If `maxpoints` is set to 50,\n only the newest 50 points will be displayed on the\n plot.\n token\n The stream id number links a data trace on a plot with\n a stream. See https://plot.ly/settings for more\n details.\n\n Returns\n -------\n Stream\n '
super(Stream, self).__init__('stream')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Stream \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Stream')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import stream as v_stream
self._validators['maxpoints'] = v_stream.MaxpointsValidator()
self._validators['token'] = v_stream.TokenValidator()
_v = arg.pop('maxpoints', None)
self['maxpoints'] = (maxpoints if (maxpoints is not None) else _v)
_v = arg.pop('token', None)
self['token'] = (token if (token is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False<|docstring|>Construct a new Stream object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Stream
maxpoints
Sets the maximum number of points to keep on the plots
from an incoming stream. If `maxpoints` is set to 50,
only the newest 50 points will be displayed on the
plot.
token
The stream id number links a data trace on a plot with
a stream. See https://plot.ly/settings for more
details.
Returns
-------
Stream<|endoftext|> |
502f3e9b7ea77e0be5ce6cf320f843a5a13486404ed53d93c7679f53fa07a1c2 | @property
def marker(self):
"\n The 'marker' property is an instance of Marker\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.selected.Marker\n - A dict of string/value properties that will be passed\n to the Marker constructor\n \n Supported dict properties:\n \n color\n Sets the marker color of selected points.\n opacity\n Sets the marker opacity of selected points.\n\n Returns\n -------\n plotly.graph_objs.histogram.selected.Marker\n "
return self['marker'] | The 'marker' property is an instance of Marker
that may be specified as:
- An instance of plotly.graph_objs.histogram.selected.Marker
- A dict of string/value properties that will be passed
to the Marker constructor
Supported dict properties:
color
Sets the marker color of selected points.
opacity
Sets the marker opacity of selected points.
Returns
-------
plotly.graph_objs.histogram.selected.Marker | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | marker | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def marker(self):
"\n The 'marker' property is an instance of Marker\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.selected.Marker\n - A dict of string/value properties that will be passed\n to the Marker constructor\n \n Supported dict properties:\n \n color\n Sets the marker color of selected points.\n opacity\n Sets the marker opacity of selected points.\n\n Returns\n -------\n plotly.graph_objs.histogram.selected.Marker\n "
return self['marker'] | @property
def marker(self):
"\n The 'marker' property is an instance of Marker\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.selected.Marker\n - A dict of string/value properties that will be passed\n to the Marker constructor\n \n Supported dict properties:\n \n color\n Sets the marker color of selected points.\n opacity\n Sets the marker opacity of selected points.\n\n Returns\n -------\n plotly.graph_objs.histogram.selected.Marker\n "
return self['marker']<|docstring|>The 'marker' property is an instance of Marker
that may be specified as:
- An instance of plotly.graph_objs.histogram.selected.Marker
- A dict of string/value properties that will be passed
to the Marker constructor
Supported dict properties:
color
Sets the marker color of selected points.
opacity
Sets the marker opacity of selected points.
Returns
-------
plotly.graph_objs.histogram.selected.Marker<|endoftext|> |
0ac492bc1ad06aded430ea5f6b526d9bce957ede771ed25130b97234e9df879e | @property
def textfont(self):
"\n The 'textfont' property is an instance of Textfont\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.selected.Textfont\n - A dict of string/value properties that will be passed\n to the Textfont constructor\n \n Supported dict properties:\n \n color\n Sets the text font color of selected points.\n\n Returns\n -------\n plotly.graph_objs.histogram.selected.Textfont\n "
return self['textfont'] | The 'textfont' property is an instance of Textfont
that may be specified as:
- An instance of plotly.graph_objs.histogram.selected.Textfont
- A dict of string/value properties that will be passed
to the Textfont constructor
Supported dict properties:
color
Sets the text font color of selected points.
Returns
-------
plotly.graph_objs.histogram.selected.Textfont | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | textfont | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def textfont(self):
"\n The 'textfont' property is an instance of Textfont\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.selected.Textfont\n - A dict of string/value properties that will be passed\n to the Textfont constructor\n \n Supported dict properties:\n \n color\n Sets the text font color of selected points.\n\n Returns\n -------\n plotly.graph_objs.histogram.selected.Textfont\n "
return self['textfont'] | @property
def textfont(self):
"\n The 'textfont' property is an instance of Textfont\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.selected.Textfont\n - A dict of string/value properties that will be passed\n to the Textfont constructor\n \n Supported dict properties:\n \n color\n Sets the text font color of selected points.\n\n Returns\n -------\n plotly.graph_objs.histogram.selected.Textfont\n "
return self['textfont']<|docstring|>The 'textfont' property is an instance of Textfont
that may be specified as:
- An instance of plotly.graph_objs.histogram.selected.Textfont
- A dict of string/value properties that will be passed
to the Textfont constructor
Supported dict properties:
color
Sets the text font color of selected points.
Returns
-------
plotly.graph_objs.histogram.selected.Textfont<|endoftext|> |
27b4646310d26031a88954236181f5b2c300c9f64fad8e22309aa975c33c5a87 | def __init__(self, arg=None, marker=None, textfont=None, **kwargs):
'\n Construct a new Selected object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Selected\n marker\n plotly.graph_objs.histogram.selected.Marker instance or\n dict with compatible properties\n textfont\n plotly.graph_objs.histogram.selected.Textfont instance\n or dict with compatible properties\n\n Returns\n -------\n Selected\n '
super(Selected, self).__init__('selected')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Selected \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Selected')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import selected as v_selected
self._validators['marker'] = v_selected.MarkerValidator()
self._validators['textfont'] = v_selected.TextfontValidator()
_v = arg.pop('marker', None)
self['marker'] = (marker if (marker is not None) else _v)
_v = arg.pop('textfont', None)
self['textfont'] = (textfont if (textfont is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | Construct a new Selected object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Selected
marker
plotly.graph_objs.histogram.selected.Marker instance or
dict with compatible properties
textfont
plotly.graph_objs.histogram.selected.Textfont instance
or dict with compatible properties
Returns
-------
Selected | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | __init__ | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | def __init__(self, arg=None, marker=None, textfont=None, **kwargs):
'\n Construct a new Selected object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Selected\n marker\n plotly.graph_objs.histogram.selected.Marker instance or\n dict with compatible properties\n textfont\n plotly.graph_objs.histogram.selected.Textfont instance\n or dict with compatible properties\n\n Returns\n -------\n Selected\n '
super(Selected, self).__init__('selected')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Selected \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Selected')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import selected as v_selected
self._validators['marker'] = v_selected.MarkerValidator()
self._validators['textfont'] = v_selected.TextfontValidator()
_v = arg.pop('marker', None)
self['marker'] = (marker if (marker is not None) else _v)
_v = arg.pop('textfont', None)
self['textfont'] = (textfont if (textfont is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | def __init__(self, arg=None, marker=None, textfont=None, **kwargs):
'\n Construct a new Selected object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Selected\n marker\n plotly.graph_objs.histogram.selected.Marker instance or\n dict with compatible properties\n textfont\n plotly.graph_objs.histogram.selected.Textfont instance\n or dict with compatible properties\n\n Returns\n -------\n Selected\n '
super(Selected, self).__init__('selected')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Selected \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Selected')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import selected as v_selected
self._validators['marker'] = v_selected.MarkerValidator()
self._validators['textfont'] = v_selected.TextfontValidator()
_v = arg.pop('marker', None)
self['marker'] = (marker if (marker is not None) else _v)
_v = arg.pop('textfont', None)
self['textfont'] = (textfont if (textfont is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False<|docstring|>Construct a new Selected object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Selected
marker
plotly.graph_objs.histogram.selected.Marker instance or
dict with compatible properties
textfont
plotly.graph_objs.histogram.selected.Textfont instance
or dict with compatible properties
Returns
-------
Selected<|endoftext|> |
528b7d5efcf6f6c140e45c7be29ce44aea19b225de58026ced82c1bfb2a20b68 | @property
def autocolorscale(self):
"\n Determines whether the colorscale is a default palette\n (`autocolorscale: true`) or the palette determined by\n `marker.colorscale`. Has an effect only if in `marker.color`is\n set to a numerical array. In case `colorscale` is unspecified\n or `autocolorscale` is true, the default palette will be\n chosen according to whether numbers in the `color` array are\n all positive, all negative or mixed.\n \n The 'autocolorscale' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['autocolorscale'] | Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`marker.colorscale`. Has an effect only if in `marker.color`is
set to a numerical array. In case `colorscale` is unspecified
or `autocolorscale` is true, the default palette will be
chosen according to whether numbers in the `color` array are
all positive, all negative or mixed.
The 'autocolorscale' property must be specified as a bool
(either True, or False)
Returns
-------
bool | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | autocolorscale | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def autocolorscale(self):
"\n Determines whether the colorscale is a default palette\n (`autocolorscale: true`) or the palette determined by\n `marker.colorscale`. Has an effect only if in `marker.color`is\n set to a numerical array. In case `colorscale` is unspecified\n or `autocolorscale` is true, the default palette will be\n chosen according to whether numbers in the `color` array are\n all positive, all negative or mixed.\n \n The 'autocolorscale' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['autocolorscale'] | @property
def autocolorscale(self):
"\n Determines whether the colorscale is a default palette\n (`autocolorscale: true`) or the palette determined by\n `marker.colorscale`. Has an effect only if in `marker.color`is\n set to a numerical array. In case `colorscale` is unspecified\n or `autocolorscale` is true, the default palette will be\n chosen according to whether numbers in the `color` array are\n all positive, all negative or mixed.\n \n The 'autocolorscale' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['autocolorscale']<|docstring|>Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`marker.colorscale`. Has an effect only if in `marker.color`is
set to a numerical array. In case `colorscale` is unspecified
or `autocolorscale` is true, the default palette will be
chosen according to whether numbers in the `color` array are
all positive, all negative or mixed.
The 'autocolorscale' property must be specified as a bool
(either True, or False)
Returns
-------
bool<|endoftext|> |
4d060a21ceaeeaf82f8425d2cf7552b6158323b60b9aedccf536a2740bcdb10d | @property
def cauto(self):
"\n Determines whether or not the color domain is computed with\n respect to the input data (here in `marker.color`) or the\n bounds set in `marker.cmin` and `marker.cmax` Has an effect\n only if in `marker.color`is set to a numerical array. Defaults\n to `false` when `marker.cmin` and `marker.cmax` are set by the\n user.\n \n The 'cauto' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['cauto'] | Determines whether or not the color domain is computed with
respect to the input data (here in `marker.color`) or the
bounds set in `marker.cmin` and `marker.cmax` Has an effect
only if in `marker.color`is set to a numerical array. Defaults
to `false` when `marker.cmin` and `marker.cmax` are set by the
user.
The 'cauto' property must be specified as a bool
(either True, or False)
Returns
-------
bool | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | cauto | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def cauto(self):
"\n Determines whether or not the color domain is computed with\n respect to the input data (here in `marker.color`) or the\n bounds set in `marker.cmin` and `marker.cmax` Has an effect\n only if in `marker.color`is set to a numerical array. Defaults\n to `false` when `marker.cmin` and `marker.cmax` are set by the\n user.\n \n The 'cauto' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['cauto'] | @property
def cauto(self):
"\n Determines whether or not the color domain is computed with\n respect to the input data (here in `marker.color`) or the\n bounds set in `marker.cmin` and `marker.cmax` Has an effect\n only if in `marker.color`is set to a numerical array. Defaults\n to `false` when `marker.cmin` and `marker.cmax` are set by the\n user.\n \n The 'cauto' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['cauto']<|docstring|>Determines whether or not the color domain is computed with
respect to the input data (here in `marker.color`) or the
bounds set in `marker.cmin` and `marker.cmax` Has an effect
only if in `marker.color`is set to a numerical array. Defaults
to `false` when `marker.cmin` and `marker.cmax` are set by the
user.
The 'cauto' property must be specified as a bool
(either True, or False)
Returns
-------
bool<|endoftext|> |
144cd7ac41bd38b674bda0821800bdbd8475ad33edbbe0dec208e07d6a0f1920 | @property
def cmax(self):
"\n Sets the upper bound of the color domain. Has an effect only if\n in `marker.color`is set to a numerical array. Value should have\n the same units as in `marker.color` and if set, `marker.cmin`\n must be set as well.\n \n The 'cmax' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n "
return self['cmax'] | Sets the upper bound of the color domain. Has an effect only if
in `marker.color`is set to a numerical array. Value should have
the same units as in `marker.color` and if set, `marker.cmin`
must be set as well.
The 'cmax' property is a number and may be specified as:
- An int or float
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | cmax | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def cmax(self):
"\n Sets the upper bound of the color domain. Has an effect only if\n in `marker.color`is set to a numerical array. Value should have\n the same units as in `marker.color` and if set, `marker.cmin`\n must be set as well.\n \n The 'cmax' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n "
return self['cmax'] | @property
def cmax(self):
"\n Sets the upper bound of the color domain. Has an effect only if\n in `marker.color`is set to a numerical array. Value should have\n the same units as in `marker.color` and if set, `marker.cmin`\n must be set as well.\n \n The 'cmax' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n "
return self['cmax']<|docstring|>Sets the upper bound of the color domain. Has an effect only if
in `marker.color`is set to a numerical array. Value should have
the same units as in `marker.color` and if set, `marker.cmin`
must be set as well.
The 'cmax' property is a number and may be specified as:
- An int or float
Returns
-------
int|float<|endoftext|> |
50165e54f3c993800956554925f5a7df81bd22e9d95b72fef063a31890299ff1 | @property
def cmid(self):
"\n Sets the mid-point of the color domain by scaling `marker.cmin`\n and/or `marker.cmax` to be equidistant to this point. Has an\n effect only if in `marker.color`is set to a numerical array.\n Value should have the same units as in `marker.color`. Has no\n effect when `marker.cauto` is `false`.\n \n The 'cmid' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n "
return self['cmid'] | Sets the mid-point of the color domain by scaling `marker.cmin`
and/or `marker.cmax` to be equidistant to this point. Has an
effect only if in `marker.color`is set to a numerical array.
Value should have the same units as in `marker.color`. Has no
effect when `marker.cauto` is `false`.
The 'cmid' property is a number and may be specified as:
- An int or float
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | cmid | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def cmid(self):
"\n Sets the mid-point of the color domain by scaling `marker.cmin`\n and/or `marker.cmax` to be equidistant to this point. Has an\n effect only if in `marker.color`is set to a numerical array.\n Value should have the same units as in `marker.color`. Has no\n effect when `marker.cauto` is `false`.\n \n The 'cmid' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n "
return self['cmid'] | @property
def cmid(self):
"\n Sets the mid-point of the color domain by scaling `marker.cmin`\n and/or `marker.cmax` to be equidistant to this point. Has an\n effect only if in `marker.color`is set to a numerical array.\n Value should have the same units as in `marker.color`. Has no\n effect when `marker.cauto` is `false`.\n \n The 'cmid' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n "
return self['cmid']<|docstring|>Sets the mid-point of the color domain by scaling `marker.cmin`
and/or `marker.cmax` to be equidistant to this point. Has an
effect only if in `marker.color`is set to a numerical array.
Value should have the same units as in `marker.color`. Has no
effect when `marker.cauto` is `false`.
The 'cmid' property is a number and may be specified as:
- An int or float
Returns
-------
int|float<|endoftext|> |
be932ffaf7dca155a73f9b5a96937c4aeb478b6e1fc49764479f5e32783184c4 | @property
def cmin(self):
"\n Sets the lower bound of the color domain. Has an effect only if\n in `marker.color`is set to a numerical array. Value should have\n the same units as in `marker.color` and if set, `marker.cmax`\n must be set as well.\n \n The 'cmin' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n "
return self['cmin'] | Sets the lower bound of the color domain. Has an effect only if
in `marker.color`is set to a numerical array. Value should have
the same units as in `marker.color` and if set, `marker.cmax`
must be set as well.
The 'cmin' property is a number and may be specified as:
- An int or float
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | cmin | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def cmin(self):
"\n Sets the lower bound of the color domain. Has an effect only if\n in `marker.color`is set to a numerical array. Value should have\n the same units as in `marker.color` and if set, `marker.cmax`\n must be set as well.\n \n The 'cmin' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n "
return self['cmin'] | @property
def cmin(self):
"\n Sets the lower bound of the color domain. Has an effect only if\n in `marker.color`is set to a numerical array. Value should have\n the same units as in `marker.color` and if set, `marker.cmax`\n must be set as well.\n \n The 'cmin' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n "
return self['cmin']<|docstring|>Sets the lower bound of the color domain. Has an effect only if
in `marker.color`is set to a numerical array. Value should have
the same units as in `marker.color` and if set, `marker.cmax`
must be set as well.
The 'cmin' property is a number and may be specified as:
- An int or float
Returns
-------
int|float<|endoftext|> |
ba46b7143b0fecdf90cb3c902627448cb15a542d629161fd68e3736cd1ccdd53 | @property
def color(self):
"\n Sets themarkercolor. It accepts either a specific color or an\n array of numbers that are mapped to the colorscale relative to\n the max and min values of the array or relative to\n `marker.cmin` and `marker.cmax` if set.\n \n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n - A number that will be interpreted as a color\n according to histogram.marker.colorscale\n - A list or array of any of the above\n\n Returns\n -------\n str|numpy.ndarray\n "
return self['color'] | Sets themarkercolor. It accepts either a specific color or an
array of numbers that are mapped to the colorscale relative to
the max and min values of the array or relative to
`marker.cmin` and `marker.cmax` if set.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, saddlebrown, salmon, sandybrown,
seagreen, seashell, sienna, silver, skyblue,
slateblue, slategray, slategrey, snow, springgreen,
steelblue, tan, teal, thistle, tomato, turquoise,
violet, wheat, white, whitesmoke, yellow,
yellowgreen
- A number that will be interpreted as a color
according to histogram.marker.colorscale
- A list or array of any of the above
Returns
-------
str|numpy.ndarray | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | color | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def color(self):
"\n Sets themarkercolor. It accepts either a specific color or an\n array of numbers that are mapped to the colorscale relative to\n the max and min values of the array or relative to\n `marker.cmin` and `marker.cmax` if set.\n \n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n - A number that will be interpreted as a color\n according to histogram.marker.colorscale\n - A list or array of any of the above\n\n Returns\n -------\n str|numpy.ndarray\n "
return self['color'] | @property
def color(self):
"\n Sets themarkercolor. It accepts either a specific color or an\n array of numbers that are mapped to the colorscale relative to\n the max and min values of the array or relative to\n `marker.cmin` and `marker.cmax` if set.\n \n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n - A number that will be interpreted as a color\n according to histogram.marker.colorscale\n - A list or array of any of the above\n\n Returns\n -------\n str|numpy.ndarray\n "
return self['color']<|docstring|>Sets themarkercolor. It accepts either a specific color or an
array of numbers that are mapped to the colorscale relative to
the max and min values of the array or relative to
`marker.cmin` and `marker.cmax` if set.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, saddlebrown, salmon, sandybrown,
seagreen, seashell, sienna, silver, skyblue,
slateblue, slategray, slategrey, snow, springgreen,
steelblue, tan, teal, thistle, tomato, turquoise,
violet, wheat, white, whitesmoke, yellow,
yellowgreen
- A number that will be interpreted as a color
according to histogram.marker.colorscale
- A list or array of any of the above
Returns
-------
str|numpy.ndarray<|endoftext|> |
cba16693f4c606abb0475401e33a13ed5566570fd1008770571de3b94965f81c | @property
def colorbar(self):
'\n The \'colorbar\' property is an instance of ColorBar\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.marker.ColorBar\n - A dict of string/value properties that will be passed\n to the ColorBar constructor\n \n Supported dict properties:\n \n bgcolor\n Sets the color of padded area.\n bordercolor\n Sets the axis line color.\n borderwidth\n Sets the width (in px) or the border enclosing\n this color bar.\n dtick\n Sets the step in-between ticks on this axis.\n Use with `tick0`. Must be a positive number, or\n special strings available to "log" and "date"\n axes. If the axis `type` is "log", then ticks\n are set every 10^(n*dtick) where n is the tick\n number. For example, to set a tick mark at 1,\n 10, 100, 1000, ... set dtick to 1. To set tick\n marks at 1, 100, 10000, ... set dtick to 2. To\n set tick marks at 1, 5, 25, 125, 625, 3125, ...\n set dtick to log_10(5), or 0.69897000433. "log"\n has several special values; "L<f>", where `f`\n is a positive number, gives ticks linearly\n spaced in value (but not position). For example\n `tick0` = 0.1, `dtick` = "L0.5" will put ticks\n at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10\n plus small digits between, use "D1" (all\n digits) or "D2" (only 2 and 5). `tick0` is\n ignored for "D1" and "D2". If the axis `type`\n is "date", then you must convert the time to\n milliseconds. For example, to set the interval\n between ticks to one day, set `dtick` to\n 86400000.0. "date" also has special values\n "M<n>" gives ticks spaced by a number of\n months. `n` must be a positive integer. To set\n ticks on the 15th of every third month, set\n `tick0` to "2000-01-15" and `dtick` to "M3". To\n set ticks every 4 years, set `dtick` to "M48"\n exponentformat\n Determines a formatting rule for the tick\n exponents. For example, consider the number\n 1,000,000,000. If "none", it appears as\n 1,000,000,000. If "e", 1e+9. If "E", 1E+9. If\n "power", 1x10^9 (with 9 in a super script). If\n "SI", 1G. If "B", 1B.\n len\n Sets the length of the color bar This measure\n excludes the padding of both ends. That is, the\n color bar length is this length minus the\n padding on both ends.\n lenmode\n Determines whether this color bar\'s length\n (i.e. the measure in the color variation\n direction) is set in units of plot "fraction"\n or in *pixels. Use `len` to set the value.\n nticks\n Specifies the maximum number of ticks for the\n particular axis. The actual number of ticks\n will be chosen automatically to be less than or\n equal to `nticks`. Has an effect only if\n `tickmode` is set to "auto".\n outlinecolor\n Sets the axis line color.\n outlinewidth\n Sets the width (in px) of the axis line.\n separatethousands\n If "true", even 4-digit integers are separated\n showexponent\n If "all", all exponents are shown besides their\n significands. If "first", only the exponent of\n the first tick is shown. If "last", only the\n exponent of the last tick is shown. If "none",\n no exponents appear.\n showticklabels\n Determines whether or not the tick labels are\n drawn.\n showtickprefix\n If "all", all tick labels are displayed with a\n prefix. If "first", only the first tick is\n displayed with a prefix. If "last", only the\n last tick is displayed with a suffix. If\n "none", tick prefixes are hidden.\n showticksuffix\n Same as `showtickprefix` but for tick suffixes.\n thickness\n Sets the thickness of the color bar This\n measure excludes the size of the padding, ticks\n and labels.\n thicknessmode\n Determines whether this color bar\'s thickness\n (i.e. the measure in the constant color\n direction) is set in units of plot "fraction"\n or in "pixels". Use `thickness` to set the\n value.\n tick0\n Sets the placement of the first tick on this\n axis. Use with `dtick`. If the axis `type` is\n "log", then you must take the log of your\n starting tick (e.g. to set the starting tick to\n 100, set the `tick0` to 2) except when\n `dtick`=*L<f>* (see `dtick` for more info). If\n the axis `type` is "date", it should be a date\n string, like date data. If the axis `type` is\n "category", it should be a number, using the\n scale where each category is assigned a serial\n number from zero in the order it appears.\n tickangle\n Sets the angle of the tick labels with respect\n to the horizontal. For example, a `tickangle`\n of -90 draws the tick labels vertically.\n tickcolor\n Sets the tick color.\n tickfont\n Sets the color bar\'s tick label font\n tickformat\n Sets the tick label formatting rule using d3\n formatting mini-languages which are very\n similar to those in Python. For numbers, see: h\n ttps://github.com/d3/d3-format/blob/master/READ\n ME.md#locale_format And for dates see:\n https://github.com/d3/d3-time-\n format/blob/master/README.md#locale_format We\n add one item to d3\'s date formatter: "%{n}f"\n for fractional seconds with n digits. For\n example, *2016-10-13 09:15:23.456* with\n tickformat "%H~%M~%S.%2f" would display\n "09~15~23.46"\n tickformatstops\n plotly.graph_objs.histogram.marker.colorbar.Tic\n kformatstop instance or dict with compatible\n properties\n tickformatstopdefaults\n When used in a template (as layout.template.dat\n a.histogram.marker.colorbar.tickformatstopdefau\n lts), sets the default property values to use\n for elements of\n histogram.marker.colorbar.tickformatstops\n ticklen\n Sets the tick length (in px).\n tickmode\n Sets the tick mode for this axis. If "auto",\n the number of ticks is set via `nticks`. If\n "linear", the placement of the ticks is\n determined by a starting position `tick0` and a\n tick step `dtick` ("linear" is the default\n value if `tick0` and `dtick` are provided). If\n "array", the placement of the ticks is set via\n `tickvals` and the tick text is `ticktext`.\n ("array" is the default value if `tickvals` is\n provided).\n tickprefix\n Sets a tick label prefix.\n ticks\n Determines whether ticks are drawn or not. If\n "", this axis\' ticks are not drawn. If\n "outside" ("inside"), this axis\' are drawn\n outside (inside) the axis lines.\n ticksuffix\n Sets a tick label suffix.\n ticktext\n Sets the text displayed at the ticks position\n via `tickvals`. Only has an effect if\n `tickmode` is set to "array". Used with\n `tickvals`.\n ticktextsrc\n Sets the source reference on plot.ly for\n ticktext .\n tickvals\n Sets the values at which ticks on this axis\n appear. Only has an effect if `tickmode` is set\n to "array". Used with `ticktext`.\n tickvalssrc\n Sets the source reference on plot.ly for\n tickvals .\n tickwidth\n Sets the tick width (in px).\n title\n plotly.graph_objs.histogram.marker.colorbar.Tit\n le instance or dict with compatible properties\n titlefont\n Deprecated: Please use\n histogram.marker.colorbar.title.font instead.\n Sets this color bar\'s title font. Note that the\n title\'s font used to be set by the now\n deprecated `titlefont` attribute.\n titleside\n Deprecated: Please use\n histogram.marker.colorbar.title.side instead.\n Determines the location of color bar\'s title\n with respect to the color bar. Note that the\n title\'s location used to be set by the now\n deprecated `titleside` attribute.\n x\n Sets the x position of the color bar (in plot\n fraction).\n xanchor\n Sets this color bar\'s horizontal position\n anchor. This anchor binds the `x` position to\n the "left", "center" or "right" of the color\n bar.\n xpad\n Sets the amount of padding (in px) along the x\n direction.\n y\n Sets the y position of the color bar (in plot\n fraction).\n yanchor\n Sets this color bar\'s vertical position anchor\n This anchor binds the `y` position to the\n "top", "middle" or "bottom" of the color bar.\n ypad\n Sets the amount of padding (in px) along the y\n direction.\n\n Returns\n -------\n plotly.graph_objs.histogram.marker.ColorBar\n '
return self['colorbar'] | The 'colorbar' property is an instance of ColorBar
that may be specified as:
- An instance of plotly.graph_objs.histogram.marker.ColorBar
- A dict of string/value properties that will be passed
to the ColorBar constructor
Supported dict properties:
bgcolor
Sets the color of padded area.
bordercolor
Sets the axis line color.
borderwidth
Sets the width (in px) or the border enclosing
this color bar.
dtick
Sets the step in-between ticks on this axis.
Use with `tick0`. Must be a positive number, or
special strings available to "log" and "date"
axes. If the axis `type` is "log", then ticks
are set every 10^(n*dtick) where n is the tick
number. For example, to set a tick mark at 1,
10, 100, 1000, ... set dtick to 1. To set tick
marks at 1, 100, 10000, ... set dtick to 2. To
set tick marks at 1, 5, 25, 125, 625, 3125, ...
set dtick to log_10(5), or 0.69897000433. "log"
has several special values; "L<f>", where `f`
is a positive number, gives ticks linearly
spaced in value (but not position). For example
`tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10
plus small digits between, use "D1" (all
digits) or "D2" (only 2 and 5). `tick0` is
ignored for "D1" and "D2". If the axis `type`
is "date", then you must convert the time to
milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to
86400000.0. "date" also has special values
"M<n>" gives ticks spaced by a number of
months. `n` must be a positive integer. To set
ticks on the 15th of every third month, set
`tick0` to "2000-01-15" and `dtick` to "M3". To
set ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick
exponents. For example, consider the number
1,000,000,000. If "none", it appears as
1,000,000,000. If "e", 1e+9. If "E", 1E+9. If
"power", 1x10^9 (with 9 in a super script). If
"SI", 1G. If "B", 1B.
len
Sets the length of the color bar This measure
excludes the padding of both ends. That is, the
color bar length is this length minus the
padding on both ends.
lenmode
Determines whether this color bar's length
(i.e. the measure in the color variation
direction) is set in units of plot "fraction"
or in *pixels. Use `len` to set the value.
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks
will be chosen automatically to be less than or
equal to `nticks`. Has an effect only if
`tickmode` is set to "auto".
outlinecolor
Sets the axis line color.
outlinewidth
Sets the width (in px) of the axis line.
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of
the first tick is shown. If "last", only the
exponent of the last tick is shown. If "none",
no exponents appear.
showticklabels
Determines whether or not the tick labels are
drawn.
showtickprefix
If "all", all tick labels are displayed with a
prefix. If "first", only the first tick is
displayed with a prefix. If "last", only the
last tick is displayed with a suffix. If
"none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thickness
Sets the thickness of the color bar This
measure excludes the size of the padding, ticks
and labels.
thicknessmode
Determines whether this color bar's thickness
(i.e. the measure in the constant color
direction) is set in units of plot "fraction"
or in "pixels". Use `thickness` to set the
value.
tick0
Sets the placement of the first tick on this
axis. Use with `dtick`. If the axis `type` is
"log", then you must take the log of your
starting tick (e.g. to set the starting tick to
100, set the `tick0` to 2) except when
`dtick`=*L<f>* (see `dtick` for more info). If
the axis `type` is "date", it should be a date
string, like date data. If the axis `type` is
"category", it should be a number, using the
scale where each category is assigned a serial
number from zero in the order it appears.
tickangle
Sets the angle of the tick labels with respect
to the horizontal. For example, a `tickangle`
of -90 draws the tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the color bar's tick label font
tickformat
Sets the tick label formatting rule using d3
formatting mini-languages which are very
similar to those in Python. For numbers, see: h
ttps://github.com/d3/d3-format/blob/master/READ
ME.md#locale_format And for dates see:
https://github.com/d3/d3-time-
format/blob/master/README.md#locale_format We
add one item to d3's date formatter: "%{n}f"
for fractional seconds with n digits. For
example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display
"09~15~23.46"
tickformatstops
plotly.graph_objs.histogram.marker.colorbar.Tic
kformatstop instance or dict with compatible
properties
tickformatstopdefaults
When used in a template (as layout.template.dat
a.histogram.marker.colorbar.tickformatstopdefau
lts), sets the default property values to use
for elements of
histogram.marker.colorbar.tickformatstops
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto",
the number of ticks is set via `nticks`. If
"linear", the placement of the ticks is
determined by a starting position `tick0` and a
tick step `dtick` ("linear" is the default
value if `tick0` and `dtick` are provided). If
"array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`.
("array" is the default value if `tickvals` is
provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If
"", this axis' ticks are not drawn. If
"outside" ("inside"), this axis' are drawn
outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position
via `tickvals`. Only has an effect if
`tickmode` is set to "array". Used with
`tickvals`.
ticktextsrc
Sets the source reference on plot.ly for
ticktext .
tickvals
Sets the values at which ticks on this axis
appear. Only has an effect if `tickmode` is set
to "array". Used with `ticktext`.
tickvalssrc
Sets the source reference on plot.ly for
tickvals .
tickwidth
Sets the tick width (in px).
title
plotly.graph_objs.histogram.marker.colorbar.Tit
le instance or dict with compatible properties
titlefont
Deprecated: Please use
histogram.marker.colorbar.title.font instead.
Sets this color bar's title font. Note that the
title's font used to be set by the now
deprecated `titlefont` attribute.
titleside
Deprecated: Please use
histogram.marker.colorbar.title.side instead.
Determines the location of color bar's title
with respect to the color bar. Note that the
title's location used to be set by the now
deprecated `titleside` attribute.
x
Sets the x position of the color bar (in plot
fraction).
xanchor
Sets this color bar's horizontal position
anchor. This anchor binds the `x` position to
the "left", "center" or "right" of the color
bar.
xpad
Sets the amount of padding (in px) along the x
direction.
y
Sets the y position of the color bar (in plot
fraction).
yanchor
Sets this color bar's vertical position anchor
This anchor binds the `y` position to the
"top", "middle" or "bottom" of the color bar.
ypad
Sets the amount of padding (in px) along the y
direction.
Returns
-------
plotly.graph_objs.histogram.marker.ColorBar | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | colorbar | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def colorbar(self):
'\n The \'colorbar\' property is an instance of ColorBar\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.marker.ColorBar\n - A dict of string/value properties that will be passed\n to the ColorBar constructor\n \n Supported dict properties:\n \n bgcolor\n Sets the color of padded area.\n bordercolor\n Sets the axis line color.\n borderwidth\n Sets the width (in px) or the border enclosing\n this color bar.\n dtick\n Sets the step in-between ticks on this axis.\n Use with `tick0`. Must be a positive number, or\n special strings available to "log" and "date"\n axes. If the axis `type` is "log", then ticks\n are set every 10^(n*dtick) where n is the tick\n number. For example, to set a tick mark at 1,\n 10, 100, 1000, ... set dtick to 1. To set tick\n marks at 1, 100, 10000, ... set dtick to 2. To\n set tick marks at 1, 5, 25, 125, 625, 3125, ...\n set dtick to log_10(5), or 0.69897000433. "log"\n has several special values; "L<f>", where `f`\n is a positive number, gives ticks linearly\n spaced in value (but not position). For example\n `tick0` = 0.1, `dtick` = "L0.5" will put ticks\n at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10\n plus small digits between, use "D1" (all\n digits) or "D2" (only 2 and 5). `tick0` is\n ignored for "D1" and "D2". If the axis `type`\n is "date", then you must convert the time to\n milliseconds. For example, to set the interval\n between ticks to one day, set `dtick` to\n 86400000.0. "date" also has special values\n "M<n>" gives ticks spaced by a number of\n months. `n` must be a positive integer. To set\n ticks on the 15th of every third month, set\n `tick0` to "2000-01-15" and `dtick` to "M3". To\n set ticks every 4 years, set `dtick` to "M48"\n exponentformat\n Determines a formatting rule for the tick\n exponents. For example, consider the number\n 1,000,000,000. If "none", it appears as\n 1,000,000,000. If "e", 1e+9. If "E", 1E+9. If\n "power", 1x10^9 (with 9 in a super script). If\n "SI", 1G. If "B", 1B.\n len\n Sets the length of the color bar This measure\n excludes the padding of both ends. That is, the\n color bar length is this length minus the\n padding on both ends.\n lenmode\n Determines whether this color bar\'s length\n (i.e. the measure in the color variation\n direction) is set in units of plot "fraction"\n or in *pixels. Use `len` to set the value.\n nticks\n Specifies the maximum number of ticks for the\n particular axis. The actual number of ticks\n will be chosen automatically to be less than or\n equal to `nticks`. Has an effect only if\n `tickmode` is set to "auto".\n outlinecolor\n Sets the axis line color.\n outlinewidth\n Sets the width (in px) of the axis line.\n separatethousands\n If "true", even 4-digit integers are separated\n showexponent\n If "all", all exponents are shown besides their\n significands. If "first", only the exponent of\n the first tick is shown. If "last", only the\n exponent of the last tick is shown. If "none",\n no exponents appear.\n showticklabels\n Determines whether or not the tick labels are\n drawn.\n showtickprefix\n If "all", all tick labels are displayed with a\n prefix. If "first", only the first tick is\n displayed with a prefix. If "last", only the\n last tick is displayed with a suffix. If\n "none", tick prefixes are hidden.\n showticksuffix\n Same as `showtickprefix` but for tick suffixes.\n thickness\n Sets the thickness of the color bar This\n measure excludes the size of the padding, ticks\n and labels.\n thicknessmode\n Determines whether this color bar\'s thickness\n (i.e. the measure in the constant color\n direction) is set in units of plot "fraction"\n or in "pixels". Use `thickness` to set the\n value.\n tick0\n Sets the placement of the first tick on this\n axis. Use with `dtick`. If the axis `type` is\n "log", then you must take the log of your\n starting tick (e.g. to set the starting tick to\n 100, set the `tick0` to 2) except when\n `dtick`=*L<f>* (see `dtick` for more info). If\n the axis `type` is "date", it should be a date\n string, like date data. If the axis `type` is\n "category", it should be a number, using the\n scale where each category is assigned a serial\n number from zero in the order it appears.\n tickangle\n Sets the angle of the tick labels with respect\n to the horizontal. For example, a `tickangle`\n of -90 draws the tick labels vertically.\n tickcolor\n Sets the tick color.\n tickfont\n Sets the color bar\'s tick label font\n tickformat\n Sets the tick label formatting rule using d3\n formatting mini-languages which are very\n similar to those in Python. For numbers, see: h\n ttps://github.com/d3/d3-format/blob/master/READ\n ME.md#locale_format And for dates see:\n https://github.com/d3/d3-time-\n format/blob/master/README.md#locale_format We\n add one item to d3\'s date formatter: "%{n}f"\n for fractional seconds with n digits. For\n example, *2016-10-13 09:15:23.456* with\n tickformat "%H~%M~%S.%2f" would display\n "09~15~23.46"\n tickformatstops\n plotly.graph_objs.histogram.marker.colorbar.Tic\n kformatstop instance or dict with compatible\n properties\n tickformatstopdefaults\n When used in a template (as layout.template.dat\n a.histogram.marker.colorbar.tickformatstopdefau\n lts), sets the default property values to use\n for elements of\n histogram.marker.colorbar.tickformatstops\n ticklen\n Sets the tick length (in px).\n tickmode\n Sets the tick mode for this axis. If "auto",\n the number of ticks is set via `nticks`. If\n "linear", the placement of the ticks is\n determined by a starting position `tick0` and a\n tick step `dtick` ("linear" is the default\n value if `tick0` and `dtick` are provided). If\n "array", the placement of the ticks is set via\n `tickvals` and the tick text is `ticktext`.\n ("array" is the default value if `tickvals` is\n provided).\n tickprefix\n Sets a tick label prefix.\n ticks\n Determines whether ticks are drawn or not. If\n , this axis\' ticks are not drawn. If\n "outside" ("inside"), this axis\' are drawn\n outside (inside) the axis lines.\n ticksuffix\n Sets a tick label suffix.\n ticktext\n Sets the text displayed at the ticks position\n via `tickvals`. Only has an effect if\n `tickmode` is set to "array". Used with\n `tickvals`.\n ticktextsrc\n Sets the source reference on plot.ly for\n ticktext .\n tickvals\n Sets the values at which ticks on this axis\n appear. Only has an effect if `tickmode` is set\n to "array". Used with `ticktext`.\n tickvalssrc\n Sets the source reference on plot.ly for\n tickvals .\n tickwidth\n Sets the tick width (in px).\n title\n plotly.graph_objs.histogram.marker.colorbar.Tit\n le instance or dict with compatible properties\n titlefont\n Deprecated: Please use\n histogram.marker.colorbar.title.font instead.\n Sets this color bar\'s title font. Note that the\n title\'s font used to be set by the now\n deprecated `titlefont` attribute.\n titleside\n Deprecated: Please use\n histogram.marker.colorbar.title.side instead.\n Determines the location of color bar\'s title\n with respect to the color bar. Note that the\n title\'s location used to be set by the now\n deprecated `titleside` attribute.\n x\n Sets the x position of the color bar (in plot\n fraction).\n xanchor\n Sets this color bar\'s horizontal position\n anchor. This anchor binds the `x` position to\n the "left", "center" or "right" of the color\n bar.\n xpad\n Sets the amount of padding (in px) along the x\n direction.\n y\n Sets the y position of the color bar (in plot\n fraction).\n yanchor\n Sets this color bar\'s vertical position anchor\n This anchor binds the `y` position to the\n "top", "middle" or "bottom" of the color bar.\n ypad\n Sets the amount of padding (in px) along the y\n direction.\n\n Returns\n -------\n plotly.graph_objs.histogram.marker.ColorBar\n '
return self['colorbar'] | @property
def colorbar(self):
'\n The \'colorbar\' property is an instance of ColorBar\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.marker.ColorBar\n - A dict of string/value properties that will be passed\n to the ColorBar constructor\n \n Supported dict properties:\n \n bgcolor\n Sets the color of padded area.\n bordercolor\n Sets the axis line color.\n borderwidth\n Sets the width (in px) or the border enclosing\n this color bar.\n dtick\n Sets the step in-between ticks on this axis.\n Use with `tick0`. Must be a positive number, or\n special strings available to "log" and "date"\n axes. If the axis `type` is "log", then ticks\n are set every 10^(n*dtick) where n is the tick\n number. For example, to set a tick mark at 1,\n 10, 100, 1000, ... set dtick to 1. To set tick\n marks at 1, 100, 10000, ... set dtick to 2. To\n set tick marks at 1, 5, 25, 125, 625, 3125, ...\n set dtick to log_10(5), or 0.69897000433. "log"\n has several special values; "L<f>", where `f`\n is a positive number, gives ticks linearly\n spaced in value (but not position). For example\n `tick0` = 0.1, `dtick` = "L0.5" will put ticks\n at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10\n plus small digits between, use "D1" (all\n digits) or "D2" (only 2 and 5). `tick0` is\n ignored for "D1" and "D2". If the axis `type`\n is "date", then you must convert the time to\n milliseconds. For example, to set the interval\n between ticks to one day, set `dtick` to\n 86400000.0. "date" also has special values\n "M<n>" gives ticks spaced by a number of\n months. `n` must be a positive integer. To set\n ticks on the 15th of every third month, set\n `tick0` to "2000-01-15" and `dtick` to "M3". To\n set ticks every 4 years, set `dtick` to "M48"\n exponentformat\n Determines a formatting rule for the tick\n exponents. For example, consider the number\n 1,000,000,000. If "none", it appears as\n 1,000,000,000. If "e", 1e+9. If "E", 1E+9. If\n "power", 1x10^9 (with 9 in a super script). If\n "SI", 1G. If "B", 1B.\n len\n Sets the length of the color bar This measure\n excludes the padding of both ends. That is, the\n color bar length is this length minus the\n padding on both ends.\n lenmode\n Determines whether this color bar\'s length\n (i.e. the measure in the color variation\n direction) is set in units of plot "fraction"\n or in *pixels. Use `len` to set the value.\n nticks\n Specifies the maximum number of ticks for the\n particular axis. The actual number of ticks\n will be chosen automatically to be less than or\n equal to `nticks`. Has an effect only if\n `tickmode` is set to "auto".\n outlinecolor\n Sets the axis line color.\n outlinewidth\n Sets the width (in px) of the axis line.\n separatethousands\n If "true", even 4-digit integers are separated\n showexponent\n If "all", all exponents are shown besides their\n significands. If "first", only the exponent of\n the first tick is shown. If "last", only the\n exponent of the last tick is shown. If "none",\n no exponents appear.\n showticklabels\n Determines whether or not the tick labels are\n drawn.\n showtickprefix\n If "all", all tick labels are displayed with a\n prefix. If "first", only the first tick is\n displayed with a prefix. If "last", only the\n last tick is displayed with a suffix. If\n "none", tick prefixes are hidden.\n showticksuffix\n Same as `showtickprefix` but for tick suffixes.\n thickness\n Sets the thickness of the color bar This\n measure excludes the size of the padding, ticks\n and labels.\n thicknessmode\n Determines whether this color bar\'s thickness\n (i.e. the measure in the constant color\n direction) is set in units of plot "fraction"\n or in "pixels". Use `thickness` to set the\n value.\n tick0\n Sets the placement of the first tick on this\n axis. Use with `dtick`. If the axis `type` is\n "log", then you must take the log of your\n starting tick (e.g. to set the starting tick to\n 100, set the `tick0` to 2) except when\n `dtick`=*L<f>* (see `dtick` for more info). If\n the axis `type` is "date", it should be a date\n string, like date data. If the axis `type` is\n "category", it should be a number, using the\n scale where each category is assigned a serial\n number from zero in the order it appears.\n tickangle\n Sets the angle of the tick labels with respect\n to the horizontal. For example, a `tickangle`\n of -90 draws the tick labels vertically.\n tickcolor\n Sets the tick color.\n tickfont\n Sets the color bar\'s tick label font\n tickformat\n Sets the tick label formatting rule using d3\n formatting mini-languages which are very\n similar to those in Python. For numbers, see: h\n ttps://github.com/d3/d3-format/blob/master/READ\n ME.md#locale_format And for dates see:\n https://github.com/d3/d3-time-\n format/blob/master/README.md#locale_format We\n add one item to d3\'s date formatter: "%{n}f"\n for fractional seconds with n digits. For\n example, *2016-10-13 09:15:23.456* with\n tickformat "%H~%M~%S.%2f" would display\n "09~15~23.46"\n tickformatstops\n plotly.graph_objs.histogram.marker.colorbar.Tic\n kformatstop instance or dict with compatible\n properties\n tickformatstopdefaults\n When used in a template (as layout.template.dat\n a.histogram.marker.colorbar.tickformatstopdefau\n lts), sets the default property values to use\n for elements of\n histogram.marker.colorbar.tickformatstops\n ticklen\n Sets the tick length (in px).\n tickmode\n Sets the tick mode for this axis. If "auto",\n the number of ticks is set via `nticks`. If\n "linear", the placement of the ticks is\n determined by a starting position `tick0` and a\n tick step `dtick` ("linear" is the default\n value if `tick0` and `dtick` are provided). If\n "array", the placement of the ticks is set via\n `tickvals` and the tick text is `ticktext`.\n ("array" is the default value if `tickvals` is\n provided).\n tickprefix\n Sets a tick label prefix.\n ticks\n Determines whether ticks are drawn or not. If\n , this axis\' ticks are not drawn. If\n "outside" ("inside"), this axis\' are drawn\n outside (inside) the axis lines.\n ticksuffix\n Sets a tick label suffix.\n ticktext\n Sets the text displayed at the ticks position\n via `tickvals`. Only has an effect if\n `tickmode` is set to "array". Used with\n `tickvals`.\n ticktextsrc\n Sets the source reference on plot.ly for\n ticktext .\n tickvals\n Sets the values at which ticks on this axis\n appear. Only has an effect if `tickmode` is set\n to "array". Used with `ticktext`.\n tickvalssrc\n Sets the source reference on plot.ly for\n tickvals .\n tickwidth\n Sets the tick width (in px).\n title\n plotly.graph_objs.histogram.marker.colorbar.Tit\n le instance or dict with compatible properties\n titlefont\n Deprecated: Please use\n histogram.marker.colorbar.title.font instead.\n Sets this color bar\'s title font. Note that the\n title\'s font used to be set by the now\n deprecated `titlefont` attribute.\n titleside\n Deprecated: Please use\n histogram.marker.colorbar.title.side instead.\n Determines the location of color bar\'s title\n with respect to the color bar. Note that the\n title\'s location used to be set by the now\n deprecated `titleside` attribute.\n x\n Sets the x position of the color bar (in plot\n fraction).\n xanchor\n Sets this color bar\'s horizontal position\n anchor. This anchor binds the `x` position to\n the "left", "center" or "right" of the color\n bar.\n xpad\n Sets the amount of padding (in px) along the x\n direction.\n y\n Sets the y position of the color bar (in plot\n fraction).\n yanchor\n Sets this color bar\'s vertical position anchor\n This anchor binds the `y` position to the\n "top", "middle" or "bottom" of the color bar.\n ypad\n Sets the amount of padding (in px) along the y\n direction.\n\n Returns\n -------\n plotly.graph_objs.histogram.marker.ColorBar\n '
return self['colorbar']<|docstring|>The 'colorbar' property is an instance of ColorBar
that may be specified as:
- An instance of plotly.graph_objs.histogram.marker.ColorBar
- A dict of string/value properties that will be passed
to the ColorBar constructor
Supported dict properties:
bgcolor
Sets the color of padded area.
bordercolor
Sets the axis line color.
borderwidth
Sets the width (in px) or the border enclosing
this color bar.
dtick
Sets the step in-between ticks on this axis.
Use with `tick0`. Must be a positive number, or
special strings available to "log" and "date"
axes. If the axis `type` is "log", then ticks
are set every 10^(n*dtick) where n is the tick
number. For example, to set a tick mark at 1,
10, 100, 1000, ... set dtick to 1. To set tick
marks at 1, 100, 10000, ... set dtick to 2. To
set tick marks at 1, 5, 25, 125, 625, 3125, ...
set dtick to log_10(5), or 0.69897000433. "log"
has several special values; "L<f>", where `f`
is a positive number, gives ticks linearly
spaced in value (but not position). For example
`tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10
plus small digits between, use "D1" (all
digits) or "D2" (only 2 and 5). `tick0` is
ignored for "D1" and "D2". If the axis `type`
is "date", then you must convert the time to
milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to
86400000.0. "date" also has special values
"M<n>" gives ticks spaced by a number of
months. `n` must be a positive integer. To set
ticks on the 15th of every third month, set
`tick0` to "2000-01-15" and `dtick` to "M3". To
set ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick
exponents. For example, consider the number
1,000,000,000. If "none", it appears as
1,000,000,000. If "e", 1e+9. If "E", 1E+9. If
"power", 1x10^9 (with 9 in a super script). If
"SI", 1G. If "B", 1B.
len
Sets the length of the color bar This measure
excludes the padding of both ends. That is, the
color bar length is this length minus the
padding on both ends.
lenmode
Determines whether this color bar's length
(i.e. the measure in the color variation
direction) is set in units of plot "fraction"
or in *pixels. Use `len` to set the value.
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks
will be chosen automatically to be less than or
equal to `nticks`. Has an effect only if
`tickmode` is set to "auto".
outlinecolor
Sets the axis line color.
outlinewidth
Sets the width (in px) of the axis line.
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of
the first tick is shown. If "last", only the
exponent of the last tick is shown. If "none",
no exponents appear.
showticklabels
Determines whether or not the tick labels are
drawn.
showtickprefix
If "all", all tick labels are displayed with a
prefix. If "first", only the first tick is
displayed with a prefix. If "last", only the
last tick is displayed with a suffix. If
"none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thickness
Sets the thickness of the color bar This
measure excludes the size of the padding, ticks
and labels.
thicknessmode
Determines whether this color bar's thickness
(i.e. the measure in the constant color
direction) is set in units of plot "fraction"
or in "pixels". Use `thickness` to set the
value.
tick0
Sets the placement of the first tick on this
axis. Use with `dtick`. If the axis `type` is
"log", then you must take the log of your
starting tick (e.g. to set the starting tick to
100, set the `tick0` to 2) except when
`dtick`=*L<f>* (see `dtick` for more info). If
the axis `type` is "date", it should be a date
string, like date data. If the axis `type` is
"category", it should be a number, using the
scale where each category is assigned a serial
number from zero in the order it appears.
tickangle
Sets the angle of the tick labels with respect
to the horizontal. For example, a `tickangle`
of -90 draws the tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the color bar's tick label font
tickformat
Sets the tick label formatting rule using d3
formatting mini-languages which are very
similar to those in Python. For numbers, see: h
ttps://github.com/d3/d3-format/blob/master/READ
ME.md#locale_format And for dates see:
https://github.com/d3/d3-time-
format/blob/master/README.md#locale_format We
add one item to d3's date formatter: "%{n}f"
for fractional seconds with n digits. For
example, *2016-10-13 09:15:23.456* with
tickformat "%H~%M~%S.%2f" would display
"09~15~23.46"
tickformatstops
plotly.graph_objs.histogram.marker.colorbar.Tic
kformatstop instance or dict with compatible
properties
tickformatstopdefaults
When used in a template (as layout.template.dat
a.histogram.marker.colorbar.tickformatstopdefau
lts), sets the default property values to use
for elements of
histogram.marker.colorbar.tickformatstops
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto",
the number of ticks is set via `nticks`. If
"linear", the placement of the ticks is
determined by a starting position `tick0` and a
tick step `dtick` ("linear" is the default
value if `tick0` and `dtick` are provided). If
"array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`.
("array" is the default value if `tickvals` is
provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If
"", this axis' ticks are not drawn. If
"outside" ("inside"), this axis' are drawn
outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position
via `tickvals`. Only has an effect if
`tickmode` is set to "array". Used with
`tickvals`.
ticktextsrc
Sets the source reference on plot.ly for
ticktext .
tickvals
Sets the values at which ticks on this axis
appear. Only has an effect if `tickmode` is set
to "array". Used with `ticktext`.
tickvalssrc
Sets the source reference on plot.ly for
tickvals .
tickwidth
Sets the tick width (in px).
title
plotly.graph_objs.histogram.marker.colorbar.Tit
le instance or dict with compatible properties
titlefont
Deprecated: Please use
histogram.marker.colorbar.title.font instead.
Sets this color bar's title font. Note that the
title's font used to be set by the now
deprecated `titlefont` attribute.
titleside
Deprecated: Please use
histogram.marker.colorbar.title.side instead.
Determines the location of color bar's title
with respect to the color bar. Note that the
title's location used to be set by the now
deprecated `titleside` attribute.
x
Sets the x position of the color bar (in plot
fraction).
xanchor
Sets this color bar's horizontal position
anchor. This anchor binds the `x` position to
the "left", "center" or "right" of the color
bar.
xpad
Sets the amount of padding (in px) along the x
direction.
y
Sets the y position of the color bar (in plot
fraction).
yanchor
Sets this color bar's vertical position anchor
This anchor binds the `y` position to the
"top", "middle" or "bottom" of the color bar.
ypad
Sets the amount of padding (in px) along the y
direction.
Returns
-------
plotly.graph_objs.histogram.marker.ColorBar<|endoftext|> |
e706024b0433813439c15a7ab0051bf3a99670ab62a3e4ad3ec42635c8ca904e | @property
def colorscale(self):
"\n Sets the colorscale. Has an effect only if in `marker.color`is\n set to a numerical array. The colorscale must be an array\n containing arrays mapping a normalized value to an rgb, rgba,\n hex, hsl, hsv, or named color string. At minimum, a mapping for\n the lowest (0) and highest (1) values are required. For\n example, `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`. To\n control the bounds of the colorscale in color space,\n use`marker.cmin` and `marker.cmax`. Alternatively, `colorscale`\n may be a palette name string of the following list: Greys,YlGnB\n u,Greens,YlOrRd,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland\n ,Jet,Hot,Blackbody,Earth,Electric,Viridis,Cividis.\n \n The 'colorscale' property is a colorscale and may be\n specified as:\n - A list of 2-element lists where the first element is the\n normalized color level value (starting at 0 and ending at 1), \n and the second item is a valid color string.\n (e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])\n - One of the following named colorscales:\n ['Greys', 'YlGnBu', 'Greens', 'YlOrRd', 'Bluered', 'RdBu',\n 'Reds', 'Blues', 'Picnic', 'Rainbow', 'Portland', 'Jet',\n 'Hot', 'Blackbody', 'Earth', 'Electric', 'Viridis', 'Cividis']\n\n Returns\n -------\n str\n "
return self['colorscale'] | Sets the colorscale. Has an effect only if in `marker.color`is
set to a numerical array. The colorscale must be an array
containing arrays mapping a normalized value to an rgb, rgba,
hex, hsl, hsv, or named color string. At minimum, a mapping for
the lowest (0) and highest (1) values are required. For
example, `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`. To
control the bounds of the colorscale in color space,
use`marker.cmin` and `marker.cmax`. Alternatively, `colorscale`
may be a palette name string of the following list: Greys,YlGnB
u,Greens,YlOrRd,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland
,Jet,Hot,Blackbody,Earth,Electric,Viridis,Cividis.
The 'colorscale' property is a colorscale and may be
specified as:
- A list of 2-element lists where the first element is the
normalized color level value (starting at 0 and ending at 1),
and the second item is a valid color string.
(e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])
- One of the following named colorscales:
['Greys', 'YlGnBu', 'Greens', 'YlOrRd', 'Bluered', 'RdBu',
'Reds', 'Blues', 'Picnic', 'Rainbow', 'Portland', 'Jet',
'Hot', 'Blackbody', 'Earth', 'Electric', 'Viridis', 'Cividis']
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | colorscale | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def colorscale(self):
"\n Sets the colorscale. Has an effect only if in `marker.color`is\n set to a numerical array. The colorscale must be an array\n containing arrays mapping a normalized value to an rgb, rgba,\n hex, hsl, hsv, or named color string. At minimum, a mapping for\n the lowest (0) and highest (1) values are required. For\n example, `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`. To\n control the bounds of the colorscale in color space,\n use`marker.cmin` and `marker.cmax`. Alternatively, `colorscale`\n may be a palette name string of the following list: Greys,YlGnB\n u,Greens,YlOrRd,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland\n ,Jet,Hot,Blackbody,Earth,Electric,Viridis,Cividis.\n \n The 'colorscale' property is a colorscale and may be\n specified as:\n - A list of 2-element lists where the first element is the\n normalized color level value (starting at 0 and ending at 1), \n and the second item is a valid color string.\n (e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])\n - One of the following named colorscales:\n ['Greys', 'YlGnBu', 'Greens', 'YlOrRd', 'Bluered', 'RdBu',\n 'Reds', 'Blues', 'Picnic', 'Rainbow', 'Portland', 'Jet',\n 'Hot', 'Blackbody', 'Earth', 'Electric', 'Viridis', 'Cividis']\n\n Returns\n -------\n str\n "
return self['colorscale'] | @property
def colorscale(self):
"\n Sets the colorscale. Has an effect only if in `marker.color`is\n set to a numerical array. The colorscale must be an array\n containing arrays mapping a normalized value to an rgb, rgba,\n hex, hsl, hsv, or named color string. At minimum, a mapping for\n the lowest (0) and highest (1) values are required. For\n example, `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`. To\n control the bounds of the colorscale in color space,\n use`marker.cmin` and `marker.cmax`. Alternatively, `colorscale`\n may be a palette name string of the following list: Greys,YlGnB\n u,Greens,YlOrRd,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland\n ,Jet,Hot,Blackbody,Earth,Electric,Viridis,Cividis.\n \n The 'colorscale' property is a colorscale and may be\n specified as:\n - A list of 2-element lists where the first element is the\n normalized color level value (starting at 0 and ending at 1), \n and the second item is a valid color string.\n (e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])\n - One of the following named colorscales:\n ['Greys', 'YlGnBu', 'Greens', 'YlOrRd', 'Bluered', 'RdBu',\n 'Reds', 'Blues', 'Picnic', 'Rainbow', 'Portland', 'Jet',\n 'Hot', 'Blackbody', 'Earth', 'Electric', 'Viridis', 'Cividis']\n\n Returns\n -------\n str\n "
return self['colorscale']<|docstring|>Sets the colorscale. Has an effect only if in `marker.color`is
set to a numerical array. The colorscale must be an array
containing arrays mapping a normalized value to an rgb, rgba,
hex, hsl, hsv, or named color string. At minimum, a mapping for
the lowest (0) and highest (1) values are required. For
example, `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`. To
control the bounds of the colorscale in color space,
use`marker.cmin` and `marker.cmax`. Alternatively, `colorscale`
may be a palette name string of the following list: Greys,YlGnB
u,Greens,YlOrRd,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland
,Jet,Hot,Blackbody,Earth,Electric,Viridis,Cividis.
The 'colorscale' property is a colorscale and may be
specified as:
- A list of 2-element lists where the first element is the
normalized color level value (starting at 0 and ending at 1),
and the second item is a valid color string.
(e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])
- One of the following named colorscales:
['Greys', 'YlGnBu', 'Greens', 'YlOrRd', 'Bluered', 'RdBu',
'Reds', 'Blues', 'Picnic', 'Rainbow', 'Portland', 'Jet',
'Hot', 'Blackbody', 'Earth', 'Electric', 'Viridis', 'Cividis']
Returns
-------
str<|endoftext|> |
634fbde503198521f74e0c543a35359ffb01371186bc082e968970fb1d8dc444 | @property
def colorsrc(self):
"\n Sets the source reference on plot.ly for color .\n \n The 'colorsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['colorsrc'] | Sets the source reference on plot.ly for color .
The 'colorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | colorsrc | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def colorsrc(self):
"\n Sets the source reference on plot.ly for color .\n \n The 'colorsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['colorsrc'] | @property
def colorsrc(self):
"\n Sets the source reference on plot.ly for color .\n \n The 'colorsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['colorsrc']<|docstring|>Sets the source reference on plot.ly for color .
The 'colorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str<|endoftext|> |
669a98332243d70ef6d982f00adcbcb2de9b467eb8bd71fbebe02f3be4c2873f | @property
def line(self):
"\n The 'line' property is an instance of Line\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.marker.Line\n - A dict of string/value properties that will be passed\n to the Line constructor\n \n Supported dict properties:\n \n autocolorscale\n Determines whether the colorscale is a default\n palette (`autocolorscale: true`) or the palette\n determined by `marker.line.colorscale`. Has an\n effect only if in `marker.line.color`is set to\n a numerical array. In case `colorscale` is\n unspecified or `autocolorscale` is true, the\n default palette will be chosen according to\n whether numbers in the `color` array are all\n positive, all negative or mixed.\n cauto\n Determines whether or not the color domain is\n computed with respect to the input data (here\n in `marker.line.color`) or the bounds set in\n `marker.line.cmin` and `marker.line.cmax` Has\n an effect only if in `marker.line.color`is set\n to a numerical array. Defaults to `false` when\n `marker.line.cmin` and `marker.line.cmax` are\n set by the user.\n cmax\n Sets the upper bound of the color domain. Has\n an effect only if in `marker.line.color`is set\n to a numerical array. Value should have the\n same units as in `marker.line.color` and if\n set, `marker.line.cmin` must be set as well.\n cmid\n Sets the mid-point of the color domain by\n scaling `marker.line.cmin` and/or\n `marker.line.cmax` to be equidistant to this\n point. Has an effect only if in\n `marker.line.color`is set to a numerical array.\n Value should have the same units as in\n `marker.line.color`. Has no effect when\n `marker.line.cauto` is `false`.\n cmin\n Sets the lower bound of the color domain. Has\n an effect only if in `marker.line.color`is set\n to a numerical array. Value should have the\n same units as in `marker.line.color` and if\n set, `marker.line.cmax` must be set as well.\n color\n Sets themarker.linecolor. It accepts either a\n specific color or an array of numbers that are\n mapped to the colorscale relative to the max\n and min values of the array or relative to\n `marker.line.cmin` and `marker.line.cmax` if\n set.\n colorscale\n Sets the colorscale. Has an effect only if in\n `marker.line.color`is set to a numerical array.\n The colorscale must be an array containing\n arrays mapping a normalized value to an rgb,\n rgba, hex, hsl, hsv, or named color string. At\n minimum, a mapping for the lowest (0) and\n highest (1) values are required. For example,\n `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`. To\n control the bounds of the colorscale in color\n space, use`marker.line.cmin` and\n `marker.line.cmax`. Alternatively, `colorscale`\n may be a palette name string of the following\n list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,R\n eds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Black\n body,Earth,Electric,Viridis,Cividis.\n colorsrc\n Sets the source reference on plot.ly for color\n .\n reversescale\n Reverses the color mapping if true. Has an\n effect only if in `marker.line.color`is set to\n a numerical array. If true, `marker.line.cmin`\n will correspond to the last color in the array\n and `marker.line.cmax` will correspond to the\n first color.\n width\n Sets the width (in px) of the lines bounding\n the marker points.\n widthsrc\n Sets the source reference on plot.ly for width\n .\n\n Returns\n -------\n plotly.graph_objs.histogram.marker.Line\n "
return self['line'] | The 'line' property is an instance of Line
that may be specified as:
- An instance of plotly.graph_objs.histogram.marker.Line
- A dict of string/value properties that will be passed
to the Line constructor
Supported dict properties:
autocolorscale
Determines whether the colorscale is a default
palette (`autocolorscale: true`) or the palette
determined by `marker.line.colorscale`. Has an
effect only if in `marker.line.color`is set to
a numerical array. In case `colorscale` is
unspecified or `autocolorscale` is true, the
default palette will be chosen according to
whether numbers in the `color` array are all
positive, all negative or mixed.
cauto
Determines whether or not the color domain is
computed with respect to the input data (here
in `marker.line.color`) or the bounds set in
`marker.line.cmin` and `marker.line.cmax` Has
an effect only if in `marker.line.color`is set
to a numerical array. Defaults to `false` when
`marker.line.cmin` and `marker.line.cmax` are
set by the user.
cmax
Sets the upper bound of the color domain. Has
an effect only if in `marker.line.color`is set
to a numerical array. Value should have the
same units as in `marker.line.color` and if
set, `marker.line.cmin` must be set as well.
cmid
Sets the mid-point of the color domain by
scaling `marker.line.cmin` and/or
`marker.line.cmax` to be equidistant to this
point. Has an effect only if in
`marker.line.color`is set to a numerical array.
Value should have the same units as in
`marker.line.color`. Has no effect when
`marker.line.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has
an effect only if in `marker.line.color`is set
to a numerical array. Value should have the
same units as in `marker.line.color` and if
set, `marker.line.cmax` must be set as well.
color
Sets themarker.linecolor. It accepts either a
specific color or an array of numbers that are
mapped to the colorscale relative to the max
and min values of the array or relative to
`marker.line.cmin` and `marker.line.cmax` if
set.
colorscale
Sets the colorscale. Has an effect only if in
`marker.line.color`is set to a numerical array.
The colorscale must be an array containing
arrays mapping a normalized value to an rgb,
rgba, hex, hsl, hsv, or named color string. At
minimum, a mapping for the lowest (0) and
highest (1) values are required. For example,
`[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`. To
control the bounds of the colorscale in color
space, use`marker.line.cmin` and
`marker.line.cmax`. Alternatively, `colorscale`
may be a palette name string of the following
list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,R
eds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Black
body,Earth,Electric,Viridis,Cividis.
colorsrc
Sets the source reference on plot.ly for color
.
reversescale
Reverses the color mapping if true. Has an
effect only if in `marker.line.color`is set to
a numerical array. If true, `marker.line.cmin`
will correspond to the last color in the array
and `marker.line.cmax` will correspond to the
first color.
width
Sets the width (in px) of the lines bounding
the marker points.
widthsrc
Sets the source reference on plot.ly for width
.
Returns
-------
plotly.graph_objs.histogram.marker.Line | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | line | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def line(self):
"\n The 'line' property is an instance of Line\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.marker.Line\n - A dict of string/value properties that will be passed\n to the Line constructor\n \n Supported dict properties:\n \n autocolorscale\n Determines whether the colorscale is a default\n palette (`autocolorscale: true`) or the palette\n determined by `marker.line.colorscale`. Has an\n effect only if in `marker.line.color`is set to\n a numerical array. In case `colorscale` is\n unspecified or `autocolorscale` is true, the\n default palette will be chosen according to\n whether numbers in the `color` array are all\n positive, all negative or mixed.\n cauto\n Determines whether or not the color domain is\n computed with respect to the input data (here\n in `marker.line.color`) or the bounds set in\n `marker.line.cmin` and `marker.line.cmax` Has\n an effect only if in `marker.line.color`is set\n to a numerical array. Defaults to `false` when\n `marker.line.cmin` and `marker.line.cmax` are\n set by the user.\n cmax\n Sets the upper bound of the color domain. Has\n an effect only if in `marker.line.color`is set\n to a numerical array. Value should have the\n same units as in `marker.line.color` and if\n set, `marker.line.cmin` must be set as well.\n cmid\n Sets the mid-point of the color domain by\n scaling `marker.line.cmin` and/or\n `marker.line.cmax` to be equidistant to this\n point. Has an effect only if in\n `marker.line.color`is set to a numerical array.\n Value should have the same units as in\n `marker.line.color`. Has no effect when\n `marker.line.cauto` is `false`.\n cmin\n Sets the lower bound of the color domain. Has\n an effect only if in `marker.line.color`is set\n to a numerical array. Value should have the\n same units as in `marker.line.color` and if\n set, `marker.line.cmax` must be set as well.\n color\n Sets themarker.linecolor. It accepts either a\n specific color or an array of numbers that are\n mapped to the colorscale relative to the max\n and min values of the array or relative to\n `marker.line.cmin` and `marker.line.cmax` if\n set.\n colorscale\n Sets the colorscale. Has an effect only if in\n `marker.line.color`is set to a numerical array.\n The colorscale must be an array containing\n arrays mapping a normalized value to an rgb,\n rgba, hex, hsl, hsv, or named color string. At\n minimum, a mapping for the lowest (0) and\n highest (1) values are required. For example,\n `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`. To\n control the bounds of the colorscale in color\n space, use`marker.line.cmin` and\n `marker.line.cmax`. Alternatively, `colorscale`\n may be a palette name string of the following\n list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,R\n eds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Black\n body,Earth,Electric,Viridis,Cividis.\n colorsrc\n Sets the source reference on plot.ly for color\n .\n reversescale\n Reverses the color mapping if true. Has an\n effect only if in `marker.line.color`is set to\n a numerical array. If true, `marker.line.cmin`\n will correspond to the last color in the array\n and `marker.line.cmax` will correspond to the\n first color.\n width\n Sets the width (in px) of the lines bounding\n the marker points.\n widthsrc\n Sets the source reference on plot.ly for width\n .\n\n Returns\n -------\n plotly.graph_objs.histogram.marker.Line\n "
return self['line'] | @property
def line(self):
"\n The 'line' property is an instance of Line\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.marker.Line\n - A dict of string/value properties that will be passed\n to the Line constructor\n \n Supported dict properties:\n \n autocolorscale\n Determines whether the colorscale is a default\n palette (`autocolorscale: true`) or the palette\n determined by `marker.line.colorscale`. Has an\n effect only if in `marker.line.color`is set to\n a numerical array. In case `colorscale` is\n unspecified or `autocolorscale` is true, the\n default palette will be chosen according to\n whether numbers in the `color` array are all\n positive, all negative or mixed.\n cauto\n Determines whether or not the color domain is\n computed with respect to the input data (here\n in `marker.line.color`) or the bounds set in\n `marker.line.cmin` and `marker.line.cmax` Has\n an effect only if in `marker.line.color`is set\n to a numerical array. Defaults to `false` when\n `marker.line.cmin` and `marker.line.cmax` are\n set by the user.\n cmax\n Sets the upper bound of the color domain. Has\n an effect only if in `marker.line.color`is set\n to a numerical array. Value should have the\n same units as in `marker.line.color` and if\n set, `marker.line.cmin` must be set as well.\n cmid\n Sets the mid-point of the color domain by\n scaling `marker.line.cmin` and/or\n `marker.line.cmax` to be equidistant to this\n point. Has an effect only if in\n `marker.line.color`is set to a numerical array.\n Value should have the same units as in\n `marker.line.color`. Has no effect when\n `marker.line.cauto` is `false`.\n cmin\n Sets the lower bound of the color domain. Has\n an effect only if in `marker.line.color`is set\n to a numerical array. Value should have the\n same units as in `marker.line.color` and if\n set, `marker.line.cmax` must be set as well.\n color\n Sets themarker.linecolor. It accepts either a\n specific color or an array of numbers that are\n mapped to the colorscale relative to the max\n and min values of the array or relative to\n `marker.line.cmin` and `marker.line.cmax` if\n set.\n colorscale\n Sets the colorscale. Has an effect only if in\n `marker.line.color`is set to a numerical array.\n The colorscale must be an array containing\n arrays mapping a normalized value to an rgb,\n rgba, hex, hsl, hsv, or named color string. At\n minimum, a mapping for the lowest (0) and\n highest (1) values are required. For example,\n `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`. To\n control the bounds of the colorscale in color\n space, use`marker.line.cmin` and\n `marker.line.cmax`. Alternatively, `colorscale`\n may be a palette name string of the following\n list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,R\n eds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Black\n body,Earth,Electric,Viridis,Cividis.\n colorsrc\n Sets the source reference on plot.ly for color\n .\n reversescale\n Reverses the color mapping if true. Has an\n effect only if in `marker.line.color`is set to\n a numerical array. If true, `marker.line.cmin`\n will correspond to the last color in the array\n and `marker.line.cmax` will correspond to the\n first color.\n width\n Sets the width (in px) of the lines bounding\n the marker points.\n widthsrc\n Sets the source reference on plot.ly for width\n .\n\n Returns\n -------\n plotly.graph_objs.histogram.marker.Line\n "
return self['line']<|docstring|>The 'line' property is an instance of Line
that may be specified as:
- An instance of plotly.graph_objs.histogram.marker.Line
- A dict of string/value properties that will be passed
to the Line constructor
Supported dict properties:
autocolorscale
Determines whether the colorscale is a default
palette (`autocolorscale: true`) or the palette
determined by `marker.line.colorscale`. Has an
effect only if in `marker.line.color`is set to
a numerical array. In case `colorscale` is
unspecified or `autocolorscale` is true, the
default palette will be chosen according to
whether numbers in the `color` array are all
positive, all negative or mixed.
cauto
Determines whether or not the color domain is
computed with respect to the input data (here
in `marker.line.color`) or the bounds set in
`marker.line.cmin` and `marker.line.cmax` Has
an effect only if in `marker.line.color`is set
to a numerical array. Defaults to `false` when
`marker.line.cmin` and `marker.line.cmax` are
set by the user.
cmax
Sets the upper bound of the color domain. Has
an effect only if in `marker.line.color`is set
to a numerical array. Value should have the
same units as in `marker.line.color` and if
set, `marker.line.cmin` must be set as well.
cmid
Sets the mid-point of the color domain by
scaling `marker.line.cmin` and/or
`marker.line.cmax` to be equidistant to this
point. Has an effect only if in
`marker.line.color`is set to a numerical array.
Value should have the same units as in
`marker.line.color`. Has no effect when
`marker.line.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has
an effect only if in `marker.line.color`is set
to a numerical array. Value should have the
same units as in `marker.line.color` and if
set, `marker.line.cmax` must be set as well.
color
Sets themarker.linecolor. It accepts either a
specific color or an array of numbers that are
mapped to the colorscale relative to the max
and min values of the array or relative to
`marker.line.cmin` and `marker.line.cmax` if
set.
colorscale
Sets the colorscale. Has an effect only if in
`marker.line.color`is set to a numerical array.
The colorscale must be an array containing
arrays mapping a normalized value to an rgb,
rgba, hex, hsl, hsv, or named color string. At
minimum, a mapping for the lowest (0) and
highest (1) values are required. For example,
`[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`. To
control the bounds of the colorscale in color
space, use`marker.line.cmin` and
`marker.line.cmax`. Alternatively, `colorscale`
may be a palette name string of the following
list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,R
eds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Black
body,Earth,Electric,Viridis,Cividis.
colorsrc
Sets the source reference on plot.ly for color
.
reversescale
Reverses the color mapping if true. Has an
effect only if in `marker.line.color`is set to
a numerical array. If true, `marker.line.cmin`
will correspond to the last color in the array
and `marker.line.cmax` will correspond to the
first color.
width
Sets the width (in px) of the lines bounding
the marker points.
widthsrc
Sets the source reference on plot.ly for width
.
Returns
-------
plotly.graph_objs.histogram.marker.Line<|endoftext|> |
be61beef094d6c59fb320a4175889023d7f44da5362f3fa62905963d0390a8ec | @property
def opacity(self):
"\n Sets the opacity of the bars.\n \n The 'opacity' property is a number and may be specified as:\n - An int or float in the interval [0, 1]\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n int|float|numpy.ndarray\n "
return self['opacity'] | Sets the opacity of the bars.
The 'opacity' property is a number and may be specified as:
- An int or float in the interval [0, 1]
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
int|float|numpy.ndarray | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | opacity | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def opacity(self):
"\n Sets the opacity of the bars.\n \n The 'opacity' property is a number and may be specified as:\n - An int or float in the interval [0, 1]\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n int|float|numpy.ndarray\n "
return self['opacity'] | @property
def opacity(self):
"\n Sets the opacity of the bars.\n \n The 'opacity' property is a number and may be specified as:\n - An int or float in the interval [0, 1]\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n int|float|numpy.ndarray\n "
return self['opacity']<|docstring|>Sets the opacity of the bars.
The 'opacity' property is a number and may be specified as:
- An int or float in the interval [0, 1]
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
int|float|numpy.ndarray<|endoftext|> |
0f025110bda981e2841ad5e7b38b4a9b33c878f05879cb69d42c996622232193 | @property
def opacitysrc(self):
"\n Sets the source reference on plot.ly for opacity .\n \n The 'opacitysrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['opacitysrc'] | Sets the source reference on plot.ly for opacity .
The 'opacitysrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | opacitysrc | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def opacitysrc(self):
"\n Sets the source reference on plot.ly for opacity .\n \n The 'opacitysrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['opacitysrc'] | @property
def opacitysrc(self):
"\n Sets the source reference on plot.ly for opacity .\n \n The 'opacitysrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['opacitysrc']<|docstring|>Sets the source reference on plot.ly for opacity .
The 'opacitysrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str<|endoftext|> |
099b512cf382b93d512ce58b963a176b31cd8d2a5b54825174c8302fa07bad46 | @property
def reversescale(self):
"\n Reverses the color mapping if true. Has an effect only if in\n `marker.color`is set to a numerical array. If true,\n `marker.cmin` will correspond to the last color in the array\n and `marker.cmax` will correspond to the first color.\n \n The 'reversescale' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['reversescale'] | Reverses the color mapping if true. Has an effect only if in
`marker.color`is set to a numerical array. If true,
`marker.cmin` will correspond to the last color in the array
and `marker.cmax` will correspond to the first color.
The 'reversescale' property must be specified as a bool
(either True, or False)
Returns
-------
bool | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | reversescale | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def reversescale(self):
"\n Reverses the color mapping if true. Has an effect only if in\n `marker.color`is set to a numerical array. If true,\n `marker.cmin` will correspond to the last color in the array\n and `marker.cmax` will correspond to the first color.\n \n The 'reversescale' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['reversescale'] | @property
def reversescale(self):
"\n Reverses the color mapping if true. Has an effect only if in\n `marker.color`is set to a numerical array. If true,\n `marker.cmin` will correspond to the last color in the array\n and `marker.cmax` will correspond to the first color.\n \n The 'reversescale' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['reversescale']<|docstring|>Reverses the color mapping if true. Has an effect only if in
`marker.color`is set to a numerical array. If true,
`marker.cmin` will correspond to the last color in the array
and `marker.cmax` will correspond to the first color.
The 'reversescale' property must be specified as a bool
(either True, or False)
Returns
-------
bool<|endoftext|> |
e6c302582eba377617c2fd503022a57bb60ec0b5135af5ee97a6c7397512414a | @property
def showscale(self):
"\n Determines whether or not a colorbar is displayed for this\n trace. Has an effect only if in `marker.color`is set to a\n numerical array.\n \n The 'showscale' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['showscale'] | Determines whether or not a colorbar is displayed for this
trace. Has an effect only if in `marker.color`is set to a
numerical array.
The 'showscale' property must be specified as a bool
(either True, or False)
Returns
-------
bool | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | showscale | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def showscale(self):
"\n Determines whether or not a colorbar is displayed for this\n trace. Has an effect only if in `marker.color`is set to a\n numerical array.\n \n The 'showscale' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['showscale'] | @property
def showscale(self):
"\n Determines whether or not a colorbar is displayed for this\n trace. Has an effect only if in `marker.color`is set to a\n numerical array.\n \n The 'showscale' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['showscale']<|docstring|>Determines whether or not a colorbar is displayed for this
trace. Has an effect only if in `marker.color`is set to a
numerical array.
The 'showscale' property must be specified as a bool
(either True, or False)
Returns
-------
bool<|endoftext|> |
6ded6612592195ec37c67eff1437384c2ad6ce3a8f156eab4f955489db5308c0 | def __init__(self, arg=None, autocolorscale=None, cauto=None, cmax=None, cmid=None, cmin=None, color=None, colorbar=None, colorscale=None, colorsrc=None, line=None, opacity=None, opacitysrc=None, reversescale=None, showscale=None, **kwargs):
"\n Construct a new Marker object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Marker\n autocolorscale\n Determines whether the colorscale is a default palette\n (`autocolorscale: true`) or the palette determined by\n `marker.colorscale`. Has an effect only if in\n `marker.color`is set to a numerical array. In case\n `colorscale` is unspecified or `autocolorscale` is\n true, the default palette will be chosen according to\n whether numbers in the `color` array are all positive,\n all negative or mixed.\n cauto\n Determines whether or not the color domain is computed\n with respect to the input data (here in `marker.color`)\n or the bounds set in `marker.cmin` and `marker.cmax`\n Has an effect only if in `marker.color`is set to a\n numerical array. Defaults to `false` when `marker.cmin`\n and `marker.cmax` are set by the user.\n cmax\n Sets the upper bound of the color domain. Has an effect\n only if in `marker.color`is set to a numerical array.\n Value should have the same units as in `marker.color`\n and if set, `marker.cmin` must be set as well.\n cmid\n Sets the mid-point of the color domain by scaling\n `marker.cmin` and/or `marker.cmax` to be equidistant to\n this point. Has an effect only if in `marker.color`is\n set to a numerical array. Value should have the same\n units as in `marker.color`. Has no effect when\n `marker.cauto` is `false`.\n cmin\n Sets the lower bound of the color domain. Has an effect\n only if in `marker.color`is set to a numerical array.\n Value should have the same units as in `marker.color`\n and if set, `marker.cmax` must be set as well.\n color\n Sets themarkercolor. It accepts either a specific color\n or an array of numbers that are mapped to the\n colorscale relative to the max and min values of the\n array or relative to `marker.cmin` and `marker.cmax` if\n set.\n colorbar\n plotly.graph_objs.histogram.marker.ColorBar instance or\n dict with compatible properties\n colorscale\n Sets the colorscale. Has an effect only if in\n `marker.color`is set to a numerical array. The\n colorscale must be an array containing arrays mapping a\n normalized value to an rgb, rgba, hex, hsl, hsv, or\n named color string. At minimum, a mapping for the\n lowest (0) and highest (1) values are required. For\n example, `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`.\n To control the bounds of the colorscale in color space,\n use`marker.cmin` and `marker.cmax`. Alternatively,\n `colorscale` may be a palette name string of the\n following list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu\n ,Reds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Blackbody,E\n arth,Electric,Viridis,Cividis.\n colorsrc\n Sets the source reference on plot.ly for color .\n line\n plotly.graph_objs.histogram.marker.Line instance or\n dict with compatible properties\n opacity\n Sets the opacity of the bars.\n opacitysrc\n Sets the source reference on plot.ly for opacity .\n reversescale\n Reverses the color mapping if true. Has an effect only\n if in `marker.color`is set to a numerical array. If\n true, `marker.cmin` will correspond to the last color\n in the array and `marker.cmax` will correspond to the\n first color.\n showscale\n Determines whether or not a colorbar is displayed for\n this trace. Has an effect only if in `marker.color`is\n set to a numerical array.\n\n Returns\n -------\n Marker\n "
super(Marker, self).__init__('marker')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Marker \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Marker')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import marker as v_marker
self._validators['autocolorscale'] = v_marker.AutocolorscaleValidator()
self._validators['cauto'] = v_marker.CautoValidator()
self._validators['cmax'] = v_marker.CmaxValidator()
self._validators['cmid'] = v_marker.CmidValidator()
self._validators['cmin'] = v_marker.CminValidator()
self._validators['color'] = v_marker.ColorValidator()
self._validators['colorbar'] = v_marker.ColorBarValidator()
self._validators['colorscale'] = v_marker.ColorscaleValidator()
self._validators['colorsrc'] = v_marker.ColorsrcValidator()
self._validators['line'] = v_marker.LineValidator()
self._validators['opacity'] = v_marker.OpacityValidator()
self._validators['opacitysrc'] = v_marker.OpacitysrcValidator()
self._validators['reversescale'] = v_marker.ReversescaleValidator()
self._validators['showscale'] = v_marker.ShowscaleValidator()
_v = arg.pop('autocolorscale', None)
self['autocolorscale'] = (autocolorscale if (autocolorscale is not None) else _v)
_v = arg.pop('cauto', None)
self['cauto'] = (cauto if (cauto is not None) else _v)
_v = arg.pop('cmax', None)
self['cmax'] = (cmax if (cmax is not None) else _v)
_v = arg.pop('cmid', None)
self['cmid'] = (cmid if (cmid is not None) else _v)
_v = arg.pop('cmin', None)
self['cmin'] = (cmin if (cmin is not None) else _v)
_v = arg.pop('color', None)
self['color'] = (color if (color is not None) else _v)
_v = arg.pop('colorbar', None)
self['colorbar'] = (colorbar if (colorbar is not None) else _v)
_v = arg.pop('colorscale', None)
self['colorscale'] = (colorscale if (colorscale is not None) else _v)
_v = arg.pop('colorsrc', None)
self['colorsrc'] = (colorsrc if (colorsrc is not None) else _v)
_v = arg.pop('line', None)
self['line'] = (line if (line is not None) else _v)
_v = arg.pop('opacity', None)
self['opacity'] = (opacity if (opacity is not None) else _v)
_v = arg.pop('opacitysrc', None)
self['opacitysrc'] = (opacitysrc if (opacitysrc is not None) else _v)
_v = arg.pop('reversescale', None)
self['reversescale'] = (reversescale if (reversescale is not None) else _v)
_v = arg.pop('showscale', None)
self['showscale'] = (showscale if (showscale is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | Construct a new Marker object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Marker
autocolorscale
Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`marker.colorscale`. Has an effect only if in
`marker.color`is set to a numerical array. In case
`colorscale` is unspecified or `autocolorscale` is
true, the default palette will be chosen according to
whether numbers in the `color` array are all positive,
all negative or mixed.
cauto
Determines whether or not the color domain is computed
with respect to the input data (here in `marker.color`)
or the bounds set in `marker.cmin` and `marker.cmax`
Has an effect only if in `marker.color`is set to a
numerical array. Defaults to `false` when `marker.cmin`
and `marker.cmax` are set by the user.
cmax
Sets the upper bound of the color domain. Has an effect
only if in `marker.color`is set to a numerical array.
Value should have the same units as in `marker.color`
and if set, `marker.cmin` must be set as well.
cmid
Sets the mid-point of the color domain by scaling
`marker.cmin` and/or `marker.cmax` to be equidistant to
this point. Has an effect only if in `marker.color`is
set to a numerical array. Value should have the same
units as in `marker.color`. Has no effect when
`marker.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has an effect
only if in `marker.color`is set to a numerical array.
Value should have the same units as in `marker.color`
and if set, `marker.cmax` must be set as well.
color
Sets themarkercolor. It accepts either a specific color
or an array of numbers that are mapped to the
colorscale relative to the max and min values of the
array or relative to `marker.cmin` and `marker.cmax` if
set.
colorbar
plotly.graph_objs.histogram.marker.ColorBar instance or
dict with compatible properties
colorscale
Sets the colorscale. Has an effect only if in
`marker.color`is set to a numerical array. The
colorscale must be an array containing arrays mapping a
normalized value to an rgb, rgba, hex, hsl, hsv, or
named color string. At minimum, a mapping for the
lowest (0) and highest (1) values are required. For
example, `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`.
To control the bounds of the colorscale in color space,
use`marker.cmin` and `marker.cmax`. Alternatively,
`colorscale` may be a palette name string of the
following list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu
,Reds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Blackbody,E
arth,Electric,Viridis,Cividis.
colorsrc
Sets the source reference on plot.ly for color .
line
plotly.graph_objs.histogram.marker.Line instance or
dict with compatible properties
opacity
Sets the opacity of the bars.
opacitysrc
Sets the source reference on plot.ly for opacity .
reversescale
Reverses the color mapping if true. Has an effect only
if in `marker.color`is set to a numerical array. If
true, `marker.cmin` will correspond to the last color
in the array and `marker.cmax` will correspond to the
first color.
showscale
Determines whether or not a colorbar is displayed for
this trace. Has an effect only if in `marker.color`is
set to a numerical array.
Returns
-------
Marker | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | __init__ | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | def __init__(self, arg=None, autocolorscale=None, cauto=None, cmax=None, cmid=None, cmin=None, color=None, colorbar=None, colorscale=None, colorsrc=None, line=None, opacity=None, opacitysrc=None, reversescale=None, showscale=None, **kwargs):
"\n Construct a new Marker object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Marker\n autocolorscale\n Determines whether the colorscale is a default palette\n (`autocolorscale: true`) or the palette determined by\n `marker.colorscale`. Has an effect only if in\n `marker.color`is set to a numerical array. In case\n `colorscale` is unspecified or `autocolorscale` is\n true, the default palette will be chosen according to\n whether numbers in the `color` array are all positive,\n all negative or mixed.\n cauto\n Determines whether or not the color domain is computed\n with respect to the input data (here in `marker.color`)\n or the bounds set in `marker.cmin` and `marker.cmax`\n Has an effect only if in `marker.color`is set to a\n numerical array. Defaults to `false` when `marker.cmin`\n and `marker.cmax` are set by the user.\n cmax\n Sets the upper bound of the color domain. Has an effect\n only if in `marker.color`is set to a numerical array.\n Value should have the same units as in `marker.color`\n and if set, `marker.cmin` must be set as well.\n cmid\n Sets the mid-point of the color domain by scaling\n `marker.cmin` and/or `marker.cmax` to be equidistant to\n this point. Has an effect only if in `marker.color`is\n set to a numerical array. Value should have the same\n units as in `marker.color`. Has no effect when\n `marker.cauto` is `false`.\n cmin\n Sets the lower bound of the color domain. Has an effect\n only if in `marker.color`is set to a numerical array.\n Value should have the same units as in `marker.color`\n and if set, `marker.cmax` must be set as well.\n color\n Sets themarkercolor. It accepts either a specific color\n or an array of numbers that are mapped to the\n colorscale relative to the max and min values of the\n array or relative to `marker.cmin` and `marker.cmax` if\n set.\n colorbar\n plotly.graph_objs.histogram.marker.ColorBar instance or\n dict with compatible properties\n colorscale\n Sets the colorscale. Has an effect only if in\n `marker.color`is set to a numerical array. The\n colorscale must be an array containing arrays mapping a\n normalized value to an rgb, rgba, hex, hsl, hsv, or\n named color string. At minimum, a mapping for the\n lowest (0) and highest (1) values are required. For\n example, `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`.\n To control the bounds of the colorscale in color space,\n use`marker.cmin` and `marker.cmax`. Alternatively,\n `colorscale` may be a palette name string of the\n following list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu\n ,Reds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Blackbody,E\n arth,Electric,Viridis,Cividis.\n colorsrc\n Sets the source reference on plot.ly for color .\n line\n plotly.graph_objs.histogram.marker.Line instance or\n dict with compatible properties\n opacity\n Sets the opacity of the bars.\n opacitysrc\n Sets the source reference on plot.ly for opacity .\n reversescale\n Reverses the color mapping if true. Has an effect only\n if in `marker.color`is set to a numerical array. If\n true, `marker.cmin` will correspond to the last color\n in the array and `marker.cmax` will correspond to the\n first color.\n showscale\n Determines whether or not a colorbar is displayed for\n this trace. Has an effect only if in `marker.color`is\n set to a numerical array.\n\n Returns\n -------\n Marker\n "
super(Marker, self).__init__('marker')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Marker \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Marker')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import marker as v_marker
self._validators['autocolorscale'] = v_marker.AutocolorscaleValidator()
self._validators['cauto'] = v_marker.CautoValidator()
self._validators['cmax'] = v_marker.CmaxValidator()
self._validators['cmid'] = v_marker.CmidValidator()
self._validators['cmin'] = v_marker.CminValidator()
self._validators['color'] = v_marker.ColorValidator()
self._validators['colorbar'] = v_marker.ColorBarValidator()
self._validators['colorscale'] = v_marker.ColorscaleValidator()
self._validators['colorsrc'] = v_marker.ColorsrcValidator()
self._validators['line'] = v_marker.LineValidator()
self._validators['opacity'] = v_marker.OpacityValidator()
self._validators['opacitysrc'] = v_marker.OpacitysrcValidator()
self._validators['reversescale'] = v_marker.ReversescaleValidator()
self._validators['showscale'] = v_marker.ShowscaleValidator()
_v = arg.pop('autocolorscale', None)
self['autocolorscale'] = (autocolorscale if (autocolorscale is not None) else _v)
_v = arg.pop('cauto', None)
self['cauto'] = (cauto if (cauto is not None) else _v)
_v = arg.pop('cmax', None)
self['cmax'] = (cmax if (cmax is not None) else _v)
_v = arg.pop('cmid', None)
self['cmid'] = (cmid if (cmid is not None) else _v)
_v = arg.pop('cmin', None)
self['cmin'] = (cmin if (cmin is not None) else _v)
_v = arg.pop('color', None)
self['color'] = (color if (color is not None) else _v)
_v = arg.pop('colorbar', None)
self['colorbar'] = (colorbar if (colorbar is not None) else _v)
_v = arg.pop('colorscale', None)
self['colorscale'] = (colorscale if (colorscale is not None) else _v)
_v = arg.pop('colorsrc', None)
self['colorsrc'] = (colorsrc if (colorsrc is not None) else _v)
_v = arg.pop('line', None)
self['line'] = (line if (line is not None) else _v)
_v = arg.pop('opacity', None)
self['opacity'] = (opacity if (opacity is not None) else _v)
_v = arg.pop('opacitysrc', None)
self['opacitysrc'] = (opacitysrc if (opacitysrc is not None) else _v)
_v = arg.pop('reversescale', None)
self['reversescale'] = (reversescale if (reversescale is not None) else _v)
_v = arg.pop('showscale', None)
self['showscale'] = (showscale if (showscale is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | def __init__(self, arg=None, autocolorscale=None, cauto=None, cmax=None, cmid=None, cmin=None, color=None, colorbar=None, colorscale=None, colorsrc=None, line=None, opacity=None, opacitysrc=None, reversescale=None, showscale=None, **kwargs):
"\n Construct a new Marker object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Marker\n autocolorscale\n Determines whether the colorscale is a default palette\n (`autocolorscale: true`) or the palette determined by\n `marker.colorscale`. Has an effect only if in\n `marker.color`is set to a numerical array. In case\n `colorscale` is unspecified or `autocolorscale` is\n true, the default palette will be chosen according to\n whether numbers in the `color` array are all positive,\n all negative or mixed.\n cauto\n Determines whether or not the color domain is computed\n with respect to the input data (here in `marker.color`)\n or the bounds set in `marker.cmin` and `marker.cmax`\n Has an effect only if in `marker.color`is set to a\n numerical array. Defaults to `false` when `marker.cmin`\n and `marker.cmax` are set by the user.\n cmax\n Sets the upper bound of the color domain. Has an effect\n only if in `marker.color`is set to a numerical array.\n Value should have the same units as in `marker.color`\n and if set, `marker.cmin` must be set as well.\n cmid\n Sets the mid-point of the color domain by scaling\n `marker.cmin` and/or `marker.cmax` to be equidistant to\n this point. Has an effect only if in `marker.color`is\n set to a numerical array. Value should have the same\n units as in `marker.color`. Has no effect when\n `marker.cauto` is `false`.\n cmin\n Sets the lower bound of the color domain. Has an effect\n only if in `marker.color`is set to a numerical array.\n Value should have the same units as in `marker.color`\n and if set, `marker.cmax` must be set as well.\n color\n Sets themarkercolor. It accepts either a specific color\n or an array of numbers that are mapped to the\n colorscale relative to the max and min values of the\n array or relative to `marker.cmin` and `marker.cmax` if\n set.\n colorbar\n plotly.graph_objs.histogram.marker.ColorBar instance or\n dict with compatible properties\n colorscale\n Sets the colorscale. Has an effect only if in\n `marker.color`is set to a numerical array. The\n colorscale must be an array containing arrays mapping a\n normalized value to an rgb, rgba, hex, hsl, hsv, or\n named color string. At minimum, a mapping for the\n lowest (0) and highest (1) values are required. For\n example, `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`.\n To control the bounds of the colorscale in color space,\n use`marker.cmin` and `marker.cmax`. Alternatively,\n `colorscale` may be a palette name string of the\n following list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu\n ,Reds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Blackbody,E\n arth,Electric,Viridis,Cividis.\n colorsrc\n Sets the source reference on plot.ly for color .\n line\n plotly.graph_objs.histogram.marker.Line instance or\n dict with compatible properties\n opacity\n Sets the opacity of the bars.\n opacitysrc\n Sets the source reference on plot.ly for opacity .\n reversescale\n Reverses the color mapping if true. Has an effect only\n if in `marker.color`is set to a numerical array. If\n true, `marker.cmin` will correspond to the last color\n in the array and `marker.cmax` will correspond to the\n first color.\n showscale\n Determines whether or not a colorbar is displayed for\n this trace. Has an effect only if in `marker.color`is\n set to a numerical array.\n\n Returns\n -------\n Marker\n "
super(Marker, self).__init__('marker')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Marker \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Marker')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import marker as v_marker
self._validators['autocolorscale'] = v_marker.AutocolorscaleValidator()
self._validators['cauto'] = v_marker.CautoValidator()
self._validators['cmax'] = v_marker.CmaxValidator()
self._validators['cmid'] = v_marker.CmidValidator()
self._validators['cmin'] = v_marker.CminValidator()
self._validators['color'] = v_marker.ColorValidator()
self._validators['colorbar'] = v_marker.ColorBarValidator()
self._validators['colorscale'] = v_marker.ColorscaleValidator()
self._validators['colorsrc'] = v_marker.ColorsrcValidator()
self._validators['line'] = v_marker.LineValidator()
self._validators['opacity'] = v_marker.OpacityValidator()
self._validators['opacitysrc'] = v_marker.OpacitysrcValidator()
self._validators['reversescale'] = v_marker.ReversescaleValidator()
self._validators['showscale'] = v_marker.ShowscaleValidator()
_v = arg.pop('autocolorscale', None)
self['autocolorscale'] = (autocolorscale if (autocolorscale is not None) else _v)
_v = arg.pop('cauto', None)
self['cauto'] = (cauto if (cauto is not None) else _v)
_v = arg.pop('cmax', None)
self['cmax'] = (cmax if (cmax is not None) else _v)
_v = arg.pop('cmid', None)
self['cmid'] = (cmid if (cmid is not None) else _v)
_v = arg.pop('cmin', None)
self['cmin'] = (cmin if (cmin is not None) else _v)
_v = arg.pop('color', None)
self['color'] = (color if (color is not None) else _v)
_v = arg.pop('colorbar', None)
self['colorbar'] = (colorbar if (colorbar is not None) else _v)
_v = arg.pop('colorscale', None)
self['colorscale'] = (colorscale if (colorscale is not None) else _v)
_v = arg.pop('colorsrc', None)
self['colorsrc'] = (colorsrc if (colorsrc is not None) else _v)
_v = arg.pop('line', None)
self['line'] = (line if (line is not None) else _v)
_v = arg.pop('opacity', None)
self['opacity'] = (opacity if (opacity is not None) else _v)
_v = arg.pop('opacitysrc', None)
self['opacitysrc'] = (opacitysrc if (opacitysrc is not None) else _v)
_v = arg.pop('reversescale', None)
self['reversescale'] = (reversescale if (reversescale is not None) else _v)
_v = arg.pop('showscale', None)
self['showscale'] = (showscale if (showscale is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False<|docstring|>Construct a new Marker object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Marker
autocolorscale
Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`marker.colorscale`. Has an effect only if in
`marker.color`is set to a numerical array. In case
`colorscale` is unspecified or `autocolorscale` is
true, the default palette will be chosen according to
whether numbers in the `color` array are all positive,
all negative or mixed.
cauto
Determines whether or not the color domain is computed
with respect to the input data (here in `marker.color`)
or the bounds set in `marker.cmin` and `marker.cmax`
Has an effect only if in `marker.color`is set to a
numerical array. Defaults to `false` when `marker.cmin`
and `marker.cmax` are set by the user.
cmax
Sets the upper bound of the color domain. Has an effect
only if in `marker.color`is set to a numerical array.
Value should have the same units as in `marker.color`
and if set, `marker.cmin` must be set as well.
cmid
Sets the mid-point of the color domain by scaling
`marker.cmin` and/or `marker.cmax` to be equidistant to
this point. Has an effect only if in `marker.color`is
set to a numerical array. Value should have the same
units as in `marker.color`. Has no effect when
`marker.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has an effect
only if in `marker.color`is set to a numerical array.
Value should have the same units as in `marker.color`
and if set, `marker.cmax` must be set as well.
color
Sets themarkercolor. It accepts either a specific color
or an array of numbers that are mapped to the
colorscale relative to the max and min values of the
array or relative to `marker.cmin` and `marker.cmax` if
set.
colorbar
plotly.graph_objs.histogram.marker.ColorBar instance or
dict with compatible properties
colorscale
Sets the colorscale. Has an effect only if in
`marker.color`is set to a numerical array. The
colorscale must be an array containing arrays mapping a
normalized value to an rgb, rgba, hex, hsl, hsv, or
named color string. At minimum, a mapping for the
lowest (0) and highest (1) values are required. For
example, `[[0, 'rgb(0,0,255)', [1, 'rgb(255,0,0)']]`.
To control the bounds of the colorscale in color space,
use`marker.cmin` and `marker.cmax`. Alternatively,
`colorscale` may be a palette name string of the
following list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu
,Reds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Blackbody,E
arth,Electric,Viridis,Cividis.
colorsrc
Sets the source reference on plot.ly for color .
line
plotly.graph_objs.histogram.marker.Line instance or
dict with compatible properties
opacity
Sets the opacity of the bars.
opacitysrc
Sets the source reference on plot.ly for opacity .
reversescale
Reverses the color mapping if true. Has an effect only
if in `marker.color`is set to a numerical array. If
true, `marker.cmin` will correspond to the last color
in the array and `marker.cmax` will correspond to the
first color.
showscale
Determines whether or not a colorbar is displayed for
this trace. Has an effect only if in `marker.color`is
set to a numerical array.
Returns
-------
Marker<|endoftext|> |
5301c30ab78e6f1ab9e86d272eddd3b60e05cb7e8fe1f029c50097b55be21da0 | @property
def align(self):
"\n Sets the horizontal alignment of the text content within hover\n label box. Has an effect only if the hover label text spans\n more two or more lines\n \n The 'align' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n ['left', 'right', 'auto']\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n Any|numpy.ndarray\n "
return self['align'] | Sets the horizontal alignment of the text content within hover
label box. Has an effect only if the hover label text spans
more two or more lines
The 'align' property is an enumeration that may be specified as:
- One of the following enumeration values:
['left', 'right', 'auto']
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
Any|numpy.ndarray | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | align | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def align(self):
"\n Sets the horizontal alignment of the text content within hover\n label box. Has an effect only if the hover label text spans\n more two or more lines\n \n The 'align' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n ['left', 'right', 'auto']\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n Any|numpy.ndarray\n "
return self['align'] | @property
def align(self):
"\n Sets the horizontal alignment of the text content within hover\n label box. Has an effect only if the hover label text spans\n more two or more lines\n \n The 'align' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n ['left', 'right', 'auto']\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n Any|numpy.ndarray\n "
return self['align']<|docstring|>Sets the horizontal alignment of the text content within hover
label box. Has an effect only if the hover label text spans
more two or more lines
The 'align' property is an enumeration that may be specified as:
- One of the following enumeration values:
['left', 'right', 'auto']
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
Any|numpy.ndarray<|endoftext|> |
8bb4f058c296dead8179caeb61e31cf03ea2256a2f91f1f861e313e0c3ac601d | @property
def alignsrc(self):
"\n Sets the source reference on plot.ly for align .\n \n The 'alignsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['alignsrc'] | Sets the source reference on plot.ly for align .
The 'alignsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | alignsrc | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def alignsrc(self):
"\n Sets the source reference on plot.ly for align .\n \n The 'alignsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['alignsrc'] | @property
def alignsrc(self):
"\n Sets the source reference on plot.ly for align .\n \n The 'alignsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['alignsrc']<|docstring|>Sets the source reference on plot.ly for align .
The 'alignsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str<|endoftext|> |
8eed9d5868858181967d59795b94e8b1991f40d2e49e51e585885e581309d25d | @property
def bgcolor(self):
"\n Sets the background color of the hover labels for this trace\n \n The 'bgcolor' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n - A list or array of any of the above\n\n Returns\n -------\n str|numpy.ndarray\n "
return self['bgcolor'] | Sets the background color of the hover labels for this trace
The 'bgcolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, saddlebrown, salmon, sandybrown,
seagreen, seashell, sienna, silver, skyblue,
slateblue, slategray, slategrey, snow, springgreen,
steelblue, tan, teal, thistle, tomato, turquoise,
violet, wheat, white, whitesmoke, yellow,
yellowgreen
- A list or array of any of the above
Returns
-------
str|numpy.ndarray | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | bgcolor | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def bgcolor(self):
"\n Sets the background color of the hover labels for this trace\n \n The 'bgcolor' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n - A list or array of any of the above\n\n Returns\n -------\n str|numpy.ndarray\n "
return self['bgcolor'] | @property
def bgcolor(self):
"\n Sets the background color of the hover labels for this trace\n \n The 'bgcolor' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n - A list or array of any of the above\n\n Returns\n -------\n str|numpy.ndarray\n "
return self['bgcolor']<|docstring|>Sets the background color of the hover labels for this trace
The 'bgcolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, saddlebrown, salmon, sandybrown,
seagreen, seashell, sienna, silver, skyblue,
slateblue, slategray, slategrey, snow, springgreen,
steelblue, tan, teal, thistle, tomato, turquoise,
violet, wheat, white, whitesmoke, yellow,
yellowgreen
- A list or array of any of the above
Returns
-------
str|numpy.ndarray<|endoftext|> |
734645bbcd2b110057995038d6a194ed2dc34c7bb8130f6693e04452f07c63db | @property
def bgcolorsrc(self):
"\n Sets the source reference on plot.ly for bgcolor .\n \n The 'bgcolorsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['bgcolorsrc'] | Sets the source reference on plot.ly for bgcolor .
The 'bgcolorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | bgcolorsrc | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def bgcolorsrc(self):
"\n Sets the source reference on plot.ly for bgcolor .\n \n The 'bgcolorsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['bgcolorsrc'] | @property
def bgcolorsrc(self):
"\n Sets the source reference on plot.ly for bgcolor .\n \n The 'bgcolorsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['bgcolorsrc']<|docstring|>Sets the source reference on plot.ly for bgcolor .
The 'bgcolorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str<|endoftext|> |
3b97d65abd351dea22ea2dfb5811566428a74fb01ef95a7154a0b517335380ab | @property
def bordercolor(self):
"\n Sets the border color of the hover labels for this trace.\n \n The 'bordercolor' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n - A list or array of any of the above\n\n Returns\n -------\n str|numpy.ndarray\n "
return self['bordercolor'] | Sets the border color of the hover labels for this trace.
The 'bordercolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, saddlebrown, salmon, sandybrown,
seagreen, seashell, sienna, silver, skyblue,
slateblue, slategray, slategrey, snow, springgreen,
steelblue, tan, teal, thistle, tomato, turquoise,
violet, wheat, white, whitesmoke, yellow,
yellowgreen
- A list or array of any of the above
Returns
-------
str|numpy.ndarray | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | bordercolor | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def bordercolor(self):
"\n Sets the border color of the hover labels for this trace.\n \n The 'bordercolor' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n - A list or array of any of the above\n\n Returns\n -------\n str|numpy.ndarray\n "
return self['bordercolor'] | @property
def bordercolor(self):
"\n Sets the border color of the hover labels for this trace.\n \n The 'bordercolor' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n - A list or array of any of the above\n\n Returns\n -------\n str|numpy.ndarray\n "
return self['bordercolor']<|docstring|>Sets the border color of the hover labels for this trace.
The 'bordercolor' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, saddlebrown, salmon, sandybrown,
seagreen, seashell, sienna, silver, skyblue,
slateblue, slategray, slategrey, snow, springgreen,
steelblue, tan, teal, thistle, tomato, turquoise,
violet, wheat, white, whitesmoke, yellow,
yellowgreen
- A list or array of any of the above
Returns
-------
str|numpy.ndarray<|endoftext|> |
9a11abb94a4c3010e01ce26afaa2d33cb993e0e6a7c8c797929bda4cc941e357 | @property
def bordercolorsrc(self):
"\n Sets the source reference on plot.ly for bordercolor .\n \n The 'bordercolorsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['bordercolorsrc'] | Sets the source reference on plot.ly for bordercolor .
The 'bordercolorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | bordercolorsrc | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def bordercolorsrc(self):
"\n Sets the source reference on plot.ly for bordercolor .\n \n The 'bordercolorsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['bordercolorsrc'] | @property
def bordercolorsrc(self):
"\n Sets the source reference on plot.ly for bordercolor .\n \n The 'bordercolorsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['bordercolorsrc']<|docstring|>Sets the source reference on plot.ly for bordercolor .
The 'bordercolorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str<|endoftext|> |
bae2cddcac603a6e3a784d4bbb19c1c19493a9c339a0ca38da9f1507164370b7 | @property
def font(self):
'\n Sets the font used in hover labels.\n \n The \'font\' property is an instance of Font\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.hoverlabel.Font\n - A dict of string/value properties that will be passed\n to the Font constructor\n \n Supported dict properties:\n \n color\n \n colorsrc\n Sets the source reference on plot.ly for color\n .\n family\n HTML font family - the typeface that will be\n applied by the web browser. The web browser\n will only be able to apply a font if it is\n available on the system which it operates.\n Provide multiple font families, separated by\n commas, to indicate the preference in which to\n apply fonts if they aren\'t available on the\n system. The plotly service (at https://plot.ly\n or on-premise) generates images on a server,\n where only a select number of fonts are\n installed and supported. These include "Arial",\n "Balto", "Courier New", "Droid Sans",, "Droid\n Serif", "Droid Sans Mono", "Gravitas One", "Old\n Standard TT", "Open Sans", "Overpass", "PT Sans\n Narrow", "Raleway", "Times New Roman".\n familysrc\n Sets the source reference on plot.ly for\n family .\n size\n \n sizesrc\n Sets the source reference on plot.ly for size\n .\n\n Returns\n -------\n plotly.graph_objs.histogram.hoverlabel.Font\n '
return self['font'] | Sets the font used in hover labels.
The 'font' property is an instance of Font
that may be specified as:
- An instance of plotly.graph_objs.histogram.hoverlabel.Font
- A dict of string/value properties that will be passed
to the Font constructor
Supported dict properties:
color
colorsrc
Sets the source reference on plot.ly for color
.
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The plotly service (at https://plot.ly
or on-premise) generates images on a server,
where only a select number of fonts are
installed and supported. These include "Arial",
"Balto", "Courier New", "Droid Sans",, "Droid
Serif", "Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
familysrc
Sets the source reference on plot.ly for
family .
size
sizesrc
Sets the source reference on plot.ly for size
.
Returns
-------
plotly.graph_objs.histogram.hoverlabel.Font | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | font | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def font(self):
'\n Sets the font used in hover labels.\n \n The \'font\' property is an instance of Font\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.hoverlabel.Font\n - A dict of string/value properties that will be passed\n to the Font constructor\n \n Supported dict properties:\n \n color\n \n colorsrc\n Sets the source reference on plot.ly for color\n .\n family\n HTML font family - the typeface that will be\n applied by the web browser. The web browser\n will only be able to apply a font if it is\n available on the system which it operates.\n Provide multiple font families, separated by\n commas, to indicate the preference in which to\n apply fonts if they aren\'t available on the\n system. The plotly service (at https://plot.ly\n or on-premise) generates images on a server,\n where only a select number of fonts are\n installed and supported. These include "Arial",\n "Balto", "Courier New", "Droid Sans",, "Droid\n Serif", "Droid Sans Mono", "Gravitas One", "Old\n Standard TT", "Open Sans", "Overpass", "PT Sans\n Narrow", "Raleway", "Times New Roman".\n familysrc\n Sets the source reference on plot.ly for\n family .\n size\n \n sizesrc\n Sets the source reference on plot.ly for size\n .\n\n Returns\n -------\n plotly.graph_objs.histogram.hoverlabel.Font\n '
return self['font'] | @property
def font(self):
'\n Sets the font used in hover labels.\n \n The \'font\' property is an instance of Font\n that may be specified as:\n - An instance of plotly.graph_objs.histogram.hoverlabel.Font\n - A dict of string/value properties that will be passed\n to the Font constructor\n \n Supported dict properties:\n \n color\n \n colorsrc\n Sets the source reference on plot.ly for color\n .\n family\n HTML font family - the typeface that will be\n applied by the web browser. The web browser\n will only be able to apply a font if it is\n available on the system which it operates.\n Provide multiple font families, separated by\n commas, to indicate the preference in which to\n apply fonts if they aren\'t available on the\n system. The plotly service (at https://plot.ly\n or on-premise) generates images on a server,\n where only a select number of fonts are\n installed and supported. These include "Arial",\n "Balto", "Courier New", "Droid Sans",, "Droid\n Serif", "Droid Sans Mono", "Gravitas One", "Old\n Standard TT", "Open Sans", "Overpass", "PT Sans\n Narrow", "Raleway", "Times New Roman".\n familysrc\n Sets the source reference on plot.ly for\n family .\n size\n \n sizesrc\n Sets the source reference on plot.ly for size\n .\n\n Returns\n -------\n plotly.graph_objs.histogram.hoverlabel.Font\n '
return self['font']<|docstring|>Sets the font used in hover labels.
The 'font' property is an instance of Font
that may be specified as:
- An instance of plotly.graph_objs.histogram.hoverlabel.Font
- A dict of string/value properties that will be passed
to the Font constructor
Supported dict properties:
color
colorsrc
Sets the source reference on plot.ly for color
.
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The plotly service (at https://plot.ly
or on-premise) generates images on a server,
where only a select number of fonts are
installed and supported. These include "Arial",
"Balto", "Courier New", "Droid Sans",, "Droid
Serif", "Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
familysrc
Sets the source reference on plot.ly for
family .
size
sizesrc
Sets the source reference on plot.ly for size
.
Returns
-------
plotly.graph_objs.histogram.hoverlabel.Font<|endoftext|> |
2f7bde1e26f505002de05329478ebeb33b5e87126b92506d5a350ae09df0303b | @property
def namelength(self):
"\n Sets the default length (in number of characters) of the trace\n name in the hover labels for all traces. -1 shows the whole\n name regardless of length. 0-3 shows the first 0-3 characters,\n and an integer >3 will show the whole name if it is less than\n that many characters, but if it is longer, will truncate to\n `namelength - 3` characters and add an ellipsis.\n \n The 'namelength' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [-1, 9223372036854775807]\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n int|numpy.ndarray\n "
return self['namelength'] | Sets the default length (in number of characters) of the trace
name in the hover labels for all traces. -1 shows the whole
name regardless of length. 0-3 shows the first 0-3 characters,
and an integer >3 will show the whole name if it is less than
that many characters, but if it is longer, will truncate to
`namelength - 3` characters and add an ellipsis.
The 'namelength' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [-1, 9223372036854775807]
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
int|numpy.ndarray | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | namelength | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def namelength(self):
"\n Sets the default length (in number of characters) of the trace\n name in the hover labels for all traces. -1 shows the whole\n name regardless of length. 0-3 shows the first 0-3 characters,\n and an integer >3 will show the whole name if it is less than\n that many characters, but if it is longer, will truncate to\n `namelength - 3` characters and add an ellipsis.\n \n The 'namelength' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [-1, 9223372036854775807]\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n int|numpy.ndarray\n "
return self['namelength'] | @property
def namelength(self):
"\n Sets the default length (in number of characters) of the trace\n name in the hover labels for all traces. -1 shows the whole\n name regardless of length. 0-3 shows the first 0-3 characters,\n and an integer >3 will show the whole name if it is less than\n that many characters, but if it is longer, will truncate to\n `namelength - 3` characters and add an ellipsis.\n \n The 'namelength' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [-1, 9223372036854775807]\n - A tuple, list, or one-dimensional numpy array of the above\n\n Returns\n -------\n int|numpy.ndarray\n "
return self['namelength']<|docstring|>Sets the default length (in number of characters) of the trace
name in the hover labels for all traces. -1 shows the whole
name regardless of length. 0-3 shows the first 0-3 characters,
and an integer >3 will show the whole name if it is less than
that many characters, but if it is longer, will truncate to
`namelength - 3` characters and add an ellipsis.
The 'namelength' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [-1, 9223372036854775807]
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
int|numpy.ndarray<|endoftext|> |
03355f85603df3f35b986c27a5a58e1f38b29593abb1771dbc701124cbf2eea7 | @property
def namelengthsrc(self):
"\n Sets the source reference on plot.ly for namelength .\n \n The 'namelengthsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['namelengthsrc'] | Sets the source reference on plot.ly for namelength .
The 'namelengthsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | namelengthsrc | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def namelengthsrc(self):
"\n Sets the source reference on plot.ly for namelength .\n \n The 'namelengthsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['namelengthsrc'] | @property
def namelengthsrc(self):
"\n Sets the source reference on plot.ly for namelength .\n \n The 'namelengthsrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['namelengthsrc']<|docstring|>Sets the source reference on plot.ly for namelength .
The 'namelengthsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str<|endoftext|> |
42736314e9bada41bd7eaa7cceaf16c876ff343211d8756c3f778f57010629ad | def __init__(self, arg=None, align=None, alignsrc=None, bgcolor=None, bgcolorsrc=None, bordercolor=None, bordercolorsrc=None, font=None, namelength=None, namelengthsrc=None, **kwargs):
'\n Construct a new Hoverlabel object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Hoverlabel\n align\n Sets the horizontal alignment of the text content\n within hover label box. Has an effect only if the hover\n label text spans more two or more lines\n alignsrc\n Sets the source reference on plot.ly for align .\n bgcolor\n Sets the background color of the hover labels for this\n trace\n bgcolorsrc\n Sets the source reference on plot.ly for bgcolor .\n bordercolor\n Sets the border color of the hover labels for this\n trace.\n bordercolorsrc\n Sets the source reference on plot.ly for bordercolor .\n font\n Sets the font used in hover labels.\n namelength\n Sets the default length (in number of characters) of\n the trace name in the hover labels for all traces. -1\n shows the whole name regardless of length. 0-3 shows\n the first 0-3 characters, and an integer >3 will show\n the whole name if it is less than that many characters,\n but if it is longer, will truncate to `namelength - 3`\n characters and add an ellipsis.\n namelengthsrc\n Sets the source reference on plot.ly for namelength .\n\n Returns\n -------\n Hoverlabel\n '
super(Hoverlabel, self).__init__('hoverlabel')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Hoverlabel \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Hoverlabel')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import hoverlabel as v_hoverlabel
self._validators['align'] = v_hoverlabel.AlignValidator()
self._validators['alignsrc'] = v_hoverlabel.AlignsrcValidator()
self._validators['bgcolor'] = v_hoverlabel.BgcolorValidator()
self._validators['bgcolorsrc'] = v_hoverlabel.BgcolorsrcValidator()
self._validators['bordercolor'] = v_hoverlabel.BordercolorValidator()
self._validators['bordercolorsrc'] = v_hoverlabel.BordercolorsrcValidator()
self._validators['font'] = v_hoverlabel.FontValidator()
self._validators['namelength'] = v_hoverlabel.NamelengthValidator()
self._validators['namelengthsrc'] = v_hoverlabel.NamelengthsrcValidator()
_v = arg.pop('align', None)
self['align'] = (align if (align is not None) else _v)
_v = arg.pop('alignsrc', None)
self['alignsrc'] = (alignsrc if (alignsrc is not None) else _v)
_v = arg.pop('bgcolor', None)
self['bgcolor'] = (bgcolor if (bgcolor is not None) else _v)
_v = arg.pop('bgcolorsrc', None)
self['bgcolorsrc'] = (bgcolorsrc if (bgcolorsrc is not None) else _v)
_v = arg.pop('bordercolor', None)
self['bordercolor'] = (bordercolor if (bordercolor is not None) else _v)
_v = arg.pop('bordercolorsrc', None)
self['bordercolorsrc'] = (bordercolorsrc if (bordercolorsrc is not None) else _v)
_v = arg.pop('font', None)
self['font'] = (font if (font is not None) else _v)
_v = arg.pop('namelength', None)
self['namelength'] = (namelength if (namelength is not None) else _v)
_v = arg.pop('namelengthsrc', None)
self['namelengthsrc'] = (namelengthsrc if (namelengthsrc is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | Construct a new Hoverlabel object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Hoverlabel
align
Sets the horizontal alignment of the text content
within hover label box. Has an effect only if the hover
label text spans more two or more lines
alignsrc
Sets the source reference on plot.ly for align .
bgcolor
Sets the background color of the hover labels for this
trace
bgcolorsrc
Sets the source reference on plot.ly for bgcolor .
bordercolor
Sets the border color of the hover labels for this
trace.
bordercolorsrc
Sets the source reference on plot.ly for bordercolor .
font
Sets the font used in hover labels.
namelength
Sets the default length (in number of characters) of
the trace name in the hover labels for all traces. -1
shows the whole name regardless of length. 0-3 shows
the first 0-3 characters, and an integer >3 will show
the whole name if it is less than that many characters,
but if it is longer, will truncate to `namelength - 3`
characters and add an ellipsis.
namelengthsrc
Sets the source reference on plot.ly for namelength .
Returns
-------
Hoverlabel | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | __init__ | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | def __init__(self, arg=None, align=None, alignsrc=None, bgcolor=None, bgcolorsrc=None, bordercolor=None, bordercolorsrc=None, font=None, namelength=None, namelengthsrc=None, **kwargs):
'\n Construct a new Hoverlabel object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Hoverlabel\n align\n Sets the horizontal alignment of the text content\n within hover label box. Has an effect only if the hover\n label text spans more two or more lines\n alignsrc\n Sets the source reference on plot.ly for align .\n bgcolor\n Sets the background color of the hover labels for this\n trace\n bgcolorsrc\n Sets the source reference on plot.ly for bgcolor .\n bordercolor\n Sets the border color of the hover labels for this\n trace.\n bordercolorsrc\n Sets the source reference on plot.ly for bordercolor .\n font\n Sets the font used in hover labels.\n namelength\n Sets the default length (in number of characters) of\n the trace name in the hover labels for all traces. -1\n shows the whole name regardless of length. 0-3 shows\n the first 0-3 characters, and an integer >3 will show\n the whole name if it is less than that many characters,\n but if it is longer, will truncate to `namelength - 3`\n characters and add an ellipsis.\n namelengthsrc\n Sets the source reference on plot.ly for namelength .\n\n Returns\n -------\n Hoverlabel\n '
super(Hoverlabel, self).__init__('hoverlabel')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Hoverlabel \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Hoverlabel')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import hoverlabel as v_hoverlabel
self._validators['align'] = v_hoverlabel.AlignValidator()
self._validators['alignsrc'] = v_hoverlabel.AlignsrcValidator()
self._validators['bgcolor'] = v_hoverlabel.BgcolorValidator()
self._validators['bgcolorsrc'] = v_hoverlabel.BgcolorsrcValidator()
self._validators['bordercolor'] = v_hoverlabel.BordercolorValidator()
self._validators['bordercolorsrc'] = v_hoverlabel.BordercolorsrcValidator()
self._validators['font'] = v_hoverlabel.FontValidator()
self._validators['namelength'] = v_hoverlabel.NamelengthValidator()
self._validators['namelengthsrc'] = v_hoverlabel.NamelengthsrcValidator()
_v = arg.pop('align', None)
self['align'] = (align if (align is not None) else _v)
_v = arg.pop('alignsrc', None)
self['alignsrc'] = (alignsrc if (alignsrc is not None) else _v)
_v = arg.pop('bgcolor', None)
self['bgcolor'] = (bgcolor if (bgcolor is not None) else _v)
_v = arg.pop('bgcolorsrc', None)
self['bgcolorsrc'] = (bgcolorsrc if (bgcolorsrc is not None) else _v)
_v = arg.pop('bordercolor', None)
self['bordercolor'] = (bordercolor if (bordercolor is not None) else _v)
_v = arg.pop('bordercolorsrc', None)
self['bordercolorsrc'] = (bordercolorsrc if (bordercolorsrc is not None) else _v)
_v = arg.pop('font', None)
self['font'] = (font if (font is not None) else _v)
_v = arg.pop('namelength', None)
self['namelength'] = (namelength if (namelength is not None) else _v)
_v = arg.pop('namelengthsrc', None)
self['namelengthsrc'] = (namelengthsrc if (namelengthsrc is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | def __init__(self, arg=None, align=None, alignsrc=None, bgcolor=None, bgcolorsrc=None, bordercolor=None, bordercolorsrc=None, font=None, namelength=None, namelengthsrc=None, **kwargs):
'\n Construct a new Hoverlabel object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Hoverlabel\n align\n Sets the horizontal alignment of the text content\n within hover label box. Has an effect only if the hover\n label text spans more two or more lines\n alignsrc\n Sets the source reference on plot.ly for align .\n bgcolor\n Sets the background color of the hover labels for this\n trace\n bgcolorsrc\n Sets the source reference on plot.ly for bgcolor .\n bordercolor\n Sets the border color of the hover labels for this\n trace.\n bordercolorsrc\n Sets the source reference on plot.ly for bordercolor .\n font\n Sets the font used in hover labels.\n namelength\n Sets the default length (in number of characters) of\n the trace name in the hover labels for all traces. -1\n shows the whole name regardless of length. 0-3 shows\n the first 0-3 characters, and an integer >3 will show\n the whole name if it is less than that many characters,\n but if it is longer, will truncate to `namelength - 3`\n characters and add an ellipsis.\n namelengthsrc\n Sets the source reference on plot.ly for namelength .\n\n Returns\n -------\n Hoverlabel\n '
super(Hoverlabel, self).__init__('hoverlabel')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Hoverlabel \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Hoverlabel')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import hoverlabel as v_hoverlabel
self._validators['align'] = v_hoverlabel.AlignValidator()
self._validators['alignsrc'] = v_hoverlabel.AlignsrcValidator()
self._validators['bgcolor'] = v_hoverlabel.BgcolorValidator()
self._validators['bgcolorsrc'] = v_hoverlabel.BgcolorsrcValidator()
self._validators['bordercolor'] = v_hoverlabel.BordercolorValidator()
self._validators['bordercolorsrc'] = v_hoverlabel.BordercolorsrcValidator()
self._validators['font'] = v_hoverlabel.FontValidator()
self._validators['namelength'] = v_hoverlabel.NamelengthValidator()
self._validators['namelengthsrc'] = v_hoverlabel.NamelengthsrcValidator()
_v = arg.pop('align', None)
self['align'] = (align if (align is not None) else _v)
_v = arg.pop('alignsrc', None)
self['alignsrc'] = (alignsrc if (alignsrc is not None) else _v)
_v = arg.pop('bgcolor', None)
self['bgcolor'] = (bgcolor if (bgcolor is not None) else _v)
_v = arg.pop('bgcolorsrc', None)
self['bgcolorsrc'] = (bgcolorsrc if (bgcolorsrc is not None) else _v)
_v = arg.pop('bordercolor', None)
self['bordercolor'] = (bordercolor if (bordercolor is not None) else _v)
_v = arg.pop('bordercolorsrc', None)
self['bordercolorsrc'] = (bordercolorsrc if (bordercolorsrc is not None) else _v)
_v = arg.pop('font', None)
self['font'] = (font if (font is not None) else _v)
_v = arg.pop('namelength', None)
self['namelength'] = (namelength if (namelength is not None) else _v)
_v = arg.pop('namelengthsrc', None)
self['namelengthsrc'] = (namelengthsrc if (namelengthsrc is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False<|docstring|>Construct a new Hoverlabel object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Hoverlabel
align
Sets the horizontal alignment of the text content
within hover label box. Has an effect only if the hover
label text spans more two or more lines
alignsrc
Sets the source reference on plot.ly for align .
bgcolor
Sets the background color of the hover labels for this
trace
bgcolorsrc
Sets the source reference on plot.ly for bgcolor .
bordercolor
Sets the border color of the hover labels for this
trace.
bordercolorsrc
Sets the source reference on plot.ly for bordercolor .
font
Sets the font used in hover labels.
namelength
Sets the default length (in number of characters) of
the trace name in the hover labels for all traces. -1
shows the whole name regardless of length. 0-3 shows
the first 0-3 characters, and an integer >3 will show
the whole name if it is less than that many characters,
but if it is longer, will truncate to `namelength - 3`
characters and add an ellipsis.
namelengthsrc
Sets the source reference on plot.ly for namelength .
Returns
-------
Hoverlabel<|endoftext|> |
a8b3418511a8a5a6c97e31707ad0f82dbb9209ac9cc66899b6f69a4d5cb3cb5c | @property
def array(self):
"\n Sets the data corresponding the length of each error bar.\n Values are plotted relative to the underlying data.\n \n The 'array' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['array'] | Sets the data corresponding the length of each error bar.
Values are plotted relative to the underlying data.
The 'array' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | array | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def array(self):
"\n Sets the data corresponding the length of each error bar.\n Values are plotted relative to the underlying data.\n \n The 'array' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['array'] | @property
def array(self):
"\n Sets the data corresponding the length of each error bar.\n Values are plotted relative to the underlying data.\n \n The 'array' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['array']<|docstring|>Sets the data corresponding the length of each error bar.
Values are plotted relative to the underlying data.
The 'array' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray<|endoftext|> |
5227ac08b4dc63e1219b2315a6290375ca8d6e8eb5f13225f773ab860b1a1f89 | @property
def arrayminus(self):
"\n Sets the data corresponding the length of each error bar in the\n bottom (left) direction for vertical (horizontal) bars Values\n are plotted relative to the underlying data.\n \n The 'arrayminus' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['arrayminus'] | Sets the data corresponding the length of each error bar in the
bottom (left) direction for vertical (horizontal) bars Values
are plotted relative to the underlying data.
The 'arrayminus' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | arrayminus | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def arrayminus(self):
"\n Sets the data corresponding the length of each error bar in the\n bottom (left) direction for vertical (horizontal) bars Values\n are plotted relative to the underlying data.\n \n The 'arrayminus' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['arrayminus'] | @property
def arrayminus(self):
"\n Sets the data corresponding the length of each error bar in the\n bottom (left) direction for vertical (horizontal) bars Values\n are plotted relative to the underlying data.\n \n The 'arrayminus' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['arrayminus']<|docstring|>Sets the data corresponding the length of each error bar in the
bottom (left) direction for vertical (horizontal) bars Values
are plotted relative to the underlying data.
The 'arrayminus' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray<|endoftext|> |
d119ce6f009ac84f22bd443609c6f99703dd9bb616d0d437445c0fe7f4f74738 | @property
def arrayminussrc(self):
"\n Sets the source reference on plot.ly for arrayminus .\n \n The 'arrayminussrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arrayminussrc'] | Sets the source reference on plot.ly for arrayminus .
The 'arrayminussrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | arrayminussrc | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def arrayminussrc(self):
"\n Sets the source reference on plot.ly for arrayminus .\n \n The 'arrayminussrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arrayminussrc'] | @property
def arrayminussrc(self):
"\n Sets the source reference on plot.ly for arrayminus .\n \n The 'arrayminussrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arrayminussrc']<|docstring|>Sets the source reference on plot.ly for arrayminus .
The 'arrayminussrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str<|endoftext|> |
97b42d78e3bd868f4a134676d8c89a01781b1b4229b4e10055cdd13954798610 | @property
def arraysrc(self):
"\n Sets the source reference on plot.ly for array .\n \n The 'arraysrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arraysrc'] | Sets the source reference on plot.ly for array .
The 'arraysrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | arraysrc | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def arraysrc(self):
"\n Sets the source reference on plot.ly for array .\n \n The 'arraysrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arraysrc'] | @property
def arraysrc(self):
"\n Sets the source reference on plot.ly for array .\n \n The 'arraysrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arraysrc']<|docstring|>Sets the source reference on plot.ly for array .
The 'arraysrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str<|endoftext|> |
ca2c1342635b7080b021a94eef655c9569138dcfb1c3b3d16be6cb7a62563c59 | @property
def color(self):
"\n Sets the stoke color of the error bars.\n \n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n\n Returns\n -------\n str\n "
return self['color'] | Sets the stoke color of the error bars.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, saddlebrown, salmon, sandybrown,
seagreen, seashell, sienna, silver, skyblue,
slateblue, slategray, slategrey, snow, springgreen,
steelblue, tan, teal, thistle, tomato, turquoise,
violet, wheat, white, whitesmoke, yellow,
yellowgreen
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | color | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def color(self):
"\n Sets the stoke color of the error bars.\n \n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n\n Returns\n -------\n str\n "
return self['color'] | @property
def color(self):
"\n Sets the stoke color of the error bars.\n \n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n\n Returns\n -------\n str\n "
return self['color']<|docstring|>Sets the stoke color of the error bars.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, saddlebrown, salmon, sandybrown,
seagreen, seashell, sienna, silver, skyblue,
slateblue, slategray, slategrey, snow, springgreen,
steelblue, tan, teal, thistle, tomato, turquoise,
violet, wheat, white, whitesmoke, yellow,
yellowgreen
Returns
-------
str<|endoftext|> |
81a8ccbc11c7a6aff9db370d8a6e4a445526c9bd4b3e6a03e8249b1780c3645d | @property
def symmetric(self):
"\n Determines whether or not the error bars have the same length\n in both direction (top/bottom for vertical bars, left/right for\n horizontal bars.\n \n The 'symmetric' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['symmetric'] | Determines whether or not the error bars have the same length
in both direction (top/bottom for vertical bars, left/right for
horizontal bars.
The 'symmetric' property must be specified as a bool
(either True, or False)
Returns
-------
bool | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | symmetric | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def symmetric(self):
"\n Determines whether or not the error bars have the same length\n in both direction (top/bottom for vertical bars, left/right for\n horizontal bars.\n \n The 'symmetric' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['symmetric'] | @property
def symmetric(self):
"\n Determines whether or not the error bars have the same length\n in both direction (top/bottom for vertical bars, left/right for\n horizontal bars.\n \n The 'symmetric' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['symmetric']<|docstring|>Determines whether or not the error bars have the same length
in both direction (top/bottom for vertical bars, left/right for
horizontal bars.
The 'symmetric' property must be specified as a bool
(either True, or False)
Returns
-------
bool<|endoftext|> |
4d66f714783437288099d4aeabba8dc0326f464f0c6a125001a37d71c0db30c3 | @property
def thickness(self):
"\n Sets the thickness (in px) of the error bars.\n \n The 'thickness' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['thickness'] | Sets the thickness (in px) of the error bars.
The 'thickness' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | thickness | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def thickness(self):
"\n Sets the thickness (in px) of the error bars.\n \n The 'thickness' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['thickness'] | @property
def thickness(self):
"\n Sets the thickness (in px) of the error bars.\n \n The 'thickness' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['thickness']<|docstring|>Sets the thickness (in px) of the error bars.
The 'thickness' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float<|endoftext|> |
6aa02d65fb6a81195cae8507f0a14f6605e9f9c19d2d77c0a96557a9a7fa80cf | @property
def traceref(self):
"\n The 'traceref' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['traceref'] | The 'traceref' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | traceref | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def traceref(self):
"\n The 'traceref' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['traceref'] | @property
def traceref(self):
"\n The 'traceref' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['traceref']<|docstring|>The 'traceref' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int<|endoftext|> |
069e81462e2241c45d5f255cbb783c55a8334748e56f6809914a0e65fdf4dec6 | @property
def tracerefminus(self):
"\n The 'tracerefminus' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['tracerefminus'] | The 'tracerefminus' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | tracerefminus | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def tracerefminus(self):
"\n The 'tracerefminus' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['tracerefminus'] | @property
def tracerefminus(self):
"\n The 'tracerefminus' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['tracerefminus']<|docstring|>The 'tracerefminus' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int<|endoftext|> |
d40f7715ff4fffb822bdb1628d39760aaec99e7656fd5aa77de9a73a4cb0c9bc | @property
def type(self):
'\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value. Set this\n constant in `value`. If "percent", the bar lengths correspond\n to a percentage of underlying data. Set this percentage in\n `value`. If "sqrt", the bar lengths correspond to the sqaure of\n the underlying data. If "array", the bar lengths are set with\n data set `array`.\n \n The \'type\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'percent\', \'constant\', \'sqrt\', \'data\']\n\n Returns\n -------\n Any\n '
return self['type'] | Determines the rule used to generate the error bars. If
*constant`, the bar lengths are of a constant value. Set this
constant in `value`. If "percent", the bar lengths correspond
to a percentage of underlying data. Set this percentage in
`value`. If "sqrt", the bar lengths correspond to the sqaure of
the underlying data. If "array", the bar lengths are set with
data set `array`.
The 'type' property is an enumeration that may be specified as:
- One of the following enumeration values:
['percent', 'constant', 'sqrt', 'data']
Returns
-------
Any | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | type | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def type(self):
'\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value. Set this\n constant in `value`. If "percent", the bar lengths correspond\n to a percentage of underlying data. Set this percentage in\n `value`. If "sqrt", the bar lengths correspond to the sqaure of\n the underlying data. If "array", the bar lengths are set with\n data set `array`.\n \n The \'type\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'percent\', \'constant\', \'sqrt\', \'data\']\n\n Returns\n -------\n Any\n '
return self['type'] | @property
def type(self):
'\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value. Set this\n constant in `value`. If "percent", the bar lengths correspond\n to a percentage of underlying data. Set this percentage in\n `value`. If "sqrt", the bar lengths correspond to the sqaure of\n the underlying data. If "array", the bar lengths are set with\n data set `array`.\n \n The \'type\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'percent\', \'constant\', \'sqrt\', \'data\']\n\n Returns\n -------\n Any\n '
return self['type']<|docstring|>Determines the rule used to generate the error bars. If
*constant`, the bar lengths are of a constant value. Set this
constant in `value`. If "percent", the bar lengths correspond
to a percentage of underlying data. Set this percentage in
`value`. If "sqrt", the bar lengths correspond to the sqaure of
the underlying data. If "array", the bar lengths are set with
data set `array`.
The 'type' property is an enumeration that may be specified as:
- One of the following enumeration values:
['percent', 'constant', 'sqrt', 'data']
Returns
-------
Any<|endoftext|> |
6b76fa155f1fe7f1046cca507ba5530d5eab00f170bbe2cd7db74404a18e07c9 | @property
def value(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars.\n \n The \'value\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['value'] | Sets the value of either the percentage (if `type` is set to
"percent") or the constant (if `type` is set to "constant")
corresponding to the lengths of the error bars.
The 'value' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | value | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def value(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars.\n \n The \'value\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['value'] | @property
def value(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars.\n \n The \'value\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['value']<|docstring|>Sets the value of either the percentage (if `type` is set to
"percent") or the constant (if `type` is set to "constant")
corresponding to the lengths of the error bars.
The 'value' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float<|endoftext|> |
8575d77a31dd7c9cc4ff4c52485ded3072a6cf84852c50c2e94d9c5e686081b8 | @property
def valueminus(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars in the bottom\n (left) direction for vertical (horizontal) bars\n \n The \'valueminus\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['valueminus'] | Sets the value of either the percentage (if `type` is set to
"percent") or the constant (if `type` is set to "constant")
corresponding to the lengths of the error bars in the bottom
(left) direction for vertical (horizontal) bars
The 'valueminus' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | valueminus | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def valueminus(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars in the bottom\n (left) direction for vertical (horizontal) bars\n \n The \'valueminus\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['valueminus'] | @property
def valueminus(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars in the bottom\n (left) direction for vertical (horizontal) bars\n \n The \'valueminus\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['valueminus']<|docstring|>Sets the value of either the percentage (if `type` is set to
"percent") or the constant (if `type` is set to "constant")
corresponding to the lengths of the error bars in the bottom
(left) direction for vertical (horizontal) bars
The 'valueminus' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float<|endoftext|> |
c4628805814a732d7fa4a534a26fdddfd2a09c20c985ab6cd27b4befc676e6b7 | @property
def visible(self):
"\n Determines whether or not this set of error bars is visible.\n \n The 'visible' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['visible'] | Determines whether or not this set of error bars is visible.
The 'visible' property must be specified as a bool
(either True, or False)
Returns
-------
bool | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | visible | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def visible(self):
"\n Determines whether or not this set of error bars is visible.\n \n The 'visible' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['visible'] | @property
def visible(self):
"\n Determines whether or not this set of error bars is visible.\n \n The 'visible' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['visible']<|docstring|>Determines whether or not this set of error bars is visible.
The 'visible' property must be specified as a bool
(either True, or False)
Returns
-------
bool<|endoftext|> |
60e04a4534186665cbee0778238e695e1250b413efbcea1fdcff37d4e66d2bed | @property
def width(self):
"\n Sets the width (in px) of the cross-bar at both ends of the\n error bars.\n \n The 'width' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['width'] | Sets the width (in px) of the cross-bar at both ends of the
error bars.
The 'width' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | width | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def width(self):
"\n Sets the width (in px) of the cross-bar at both ends of the\n error bars.\n \n The 'width' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['width'] | @property
def width(self):
"\n Sets the width (in px) of the cross-bar at both ends of the\n error bars.\n \n The 'width' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['width']<|docstring|>Sets the width (in px) of the cross-bar at both ends of the
error bars.
The 'width' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float<|endoftext|> |
05f8d8657c3d6ca187913da83c66ee6bf26a1dfe84b4073eacaacb0aa5df9339 | def __init__(self, arg=None, array=None, arrayminus=None, arrayminussrc=None, arraysrc=None, color=None, symmetric=None, thickness=None, traceref=None, tracerefminus=None, type=None, value=None, valueminus=None, visible=None, width=None, **kwargs):
'\n Construct a new ErrorY object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.ErrorY\n array\n Sets the data corresponding the length of each error\n bar. Values are plotted relative to the underlying\n data.\n arrayminus\n Sets the data corresponding the length of each error\n bar in the bottom (left) direction for vertical\n (horizontal) bars Values are plotted relative to the\n underlying data.\n arrayminussrc\n Sets the source reference on plot.ly for arrayminus .\n arraysrc\n Sets the source reference on plot.ly for array .\n color\n Sets the stoke color of the error bars.\n symmetric\n Determines whether or not the error bars have the same\n length in both direction (top/bottom for vertical bars,\n left/right for horizontal bars.\n thickness\n Sets the thickness (in px) of the error bars.\n traceref\n\n tracerefminus\n\n type\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value.\n Set this constant in `value`. If "percent", the bar\n lengths correspond to a percentage of underlying data.\n Set this percentage in `value`. If "sqrt", the bar\n lengths correspond to the sqaure of the underlying\n data. If "array", the bar lengths are set with data set\n `array`.\n value\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars.\n valueminus\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars in the bottom (left) direction for vertical\n (horizontal) bars\n visible\n Determines whether or not this set of error bars is\n visible.\n width\n Sets the width (in px) of the cross-bar at both ends of\n the error bars.\n\n Returns\n -------\n ErrorY\n '
super(ErrorY, self).__init__('error_y')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.ErrorY \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.ErrorY')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import error_y as v_error_y
self._validators['array'] = v_error_y.ArrayValidator()
self._validators['arrayminus'] = v_error_y.ArrayminusValidator()
self._validators['arrayminussrc'] = v_error_y.ArrayminussrcValidator()
self._validators['arraysrc'] = v_error_y.ArraysrcValidator()
self._validators['color'] = v_error_y.ColorValidator()
self._validators['symmetric'] = v_error_y.SymmetricValidator()
self._validators['thickness'] = v_error_y.ThicknessValidator()
self._validators['traceref'] = v_error_y.TracerefValidator()
self._validators['tracerefminus'] = v_error_y.TracerefminusValidator()
self._validators['type'] = v_error_y.TypeValidator()
self._validators['value'] = v_error_y.ValueValidator()
self._validators['valueminus'] = v_error_y.ValueminusValidator()
self._validators['visible'] = v_error_y.VisibleValidator()
self._validators['width'] = v_error_y.WidthValidator()
_v = arg.pop('array', None)
self['array'] = (array if (array is not None) else _v)
_v = arg.pop('arrayminus', None)
self['arrayminus'] = (arrayminus if (arrayminus is not None) else _v)
_v = arg.pop('arrayminussrc', None)
self['arrayminussrc'] = (arrayminussrc if (arrayminussrc is not None) else _v)
_v = arg.pop('arraysrc', None)
self['arraysrc'] = (arraysrc if (arraysrc is not None) else _v)
_v = arg.pop('color', None)
self['color'] = (color if (color is not None) else _v)
_v = arg.pop('symmetric', None)
self['symmetric'] = (symmetric if (symmetric is not None) else _v)
_v = arg.pop('thickness', None)
self['thickness'] = (thickness if (thickness is not None) else _v)
_v = arg.pop('traceref', None)
self['traceref'] = (traceref if (traceref is not None) else _v)
_v = arg.pop('tracerefminus', None)
self['tracerefminus'] = (tracerefminus if (tracerefminus is not None) else _v)
_v = arg.pop('type', None)
self['type'] = (type if (type is not None) else _v)
_v = arg.pop('value', None)
self['value'] = (value if (value is not None) else _v)
_v = arg.pop('valueminus', None)
self['valueminus'] = (valueminus if (valueminus is not None) else _v)
_v = arg.pop('visible', None)
self['visible'] = (visible if (visible is not None) else _v)
_v = arg.pop('width', None)
self['width'] = (width if (width is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | Construct a new ErrorY object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.ErrorY
array
Sets the data corresponding the length of each error
bar. Values are plotted relative to the underlying
data.
arrayminus
Sets the data corresponding the length of each error
bar in the bottom (left) direction for vertical
(horizontal) bars Values are plotted relative to the
underlying data.
arrayminussrc
Sets the source reference on plot.ly for arrayminus .
arraysrc
Sets the source reference on plot.ly for array .
color
Sets the stoke color of the error bars.
symmetric
Determines whether or not the error bars have the same
length in both direction (top/bottom for vertical bars,
left/right for horizontal bars.
thickness
Sets the thickness (in px) of the error bars.
traceref
tracerefminus
type
Determines the rule used to generate the error bars. If
*constant`, the bar lengths are of a constant value.
Set this constant in `value`. If "percent", the bar
lengths correspond to a percentage of underlying data.
Set this percentage in `value`. If "sqrt", the bar
lengths correspond to the sqaure of the underlying
data. If "array", the bar lengths are set with data set
`array`.
value
Sets the value of either the percentage (if `type` is
set to "percent") or the constant (if `type` is set to
"constant") corresponding to the lengths of the error
bars.
valueminus
Sets the value of either the percentage (if `type` is
set to "percent") or the constant (if `type` is set to
"constant") corresponding to the lengths of the error
bars in the bottom (left) direction for vertical
(horizontal) bars
visible
Determines whether or not this set of error bars is
visible.
width
Sets the width (in px) of the cross-bar at both ends of
the error bars.
Returns
-------
ErrorY | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | __init__ | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | def __init__(self, arg=None, array=None, arrayminus=None, arrayminussrc=None, arraysrc=None, color=None, symmetric=None, thickness=None, traceref=None, tracerefminus=None, type=None, value=None, valueminus=None, visible=None, width=None, **kwargs):
'\n Construct a new ErrorY object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.ErrorY\n array\n Sets the data corresponding the length of each error\n bar. Values are plotted relative to the underlying\n data.\n arrayminus\n Sets the data corresponding the length of each error\n bar in the bottom (left) direction for vertical\n (horizontal) bars Values are plotted relative to the\n underlying data.\n arrayminussrc\n Sets the source reference on plot.ly for arrayminus .\n arraysrc\n Sets the source reference on plot.ly for array .\n color\n Sets the stoke color of the error bars.\n symmetric\n Determines whether or not the error bars have the same\n length in both direction (top/bottom for vertical bars,\n left/right for horizontal bars.\n thickness\n Sets the thickness (in px) of the error bars.\n traceref\n\n tracerefminus\n\n type\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value.\n Set this constant in `value`. If "percent", the bar\n lengths correspond to a percentage of underlying data.\n Set this percentage in `value`. If "sqrt", the bar\n lengths correspond to the sqaure of the underlying\n data. If "array", the bar lengths are set with data set\n `array`.\n value\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars.\n valueminus\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars in the bottom (left) direction for vertical\n (horizontal) bars\n visible\n Determines whether or not this set of error bars is\n visible.\n width\n Sets the width (in px) of the cross-bar at both ends of\n the error bars.\n\n Returns\n -------\n ErrorY\n '
super(ErrorY, self).__init__('error_y')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.ErrorY \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.ErrorY')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import error_y as v_error_y
self._validators['array'] = v_error_y.ArrayValidator()
self._validators['arrayminus'] = v_error_y.ArrayminusValidator()
self._validators['arrayminussrc'] = v_error_y.ArrayminussrcValidator()
self._validators['arraysrc'] = v_error_y.ArraysrcValidator()
self._validators['color'] = v_error_y.ColorValidator()
self._validators['symmetric'] = v_error_y.SymmetricValidator()
self._validators['thickness'] = v_error_y.ThicknessValidator()
self._validators['traceref'] = v_error_y.TracerefValidator()
self._validators['tracerefminus'] = v_error_y.TracerefminusValidator()
self._validators['type'] = v_error_y.TypeValidator()
self._validators['value'] = v_error_y.ValueValidator()
self._validators['valueminus'] = v_error_y.ValueminusValidator()
self._validators['visible'] = v_error_y.VisibleValidator()
self._validators['width'] = v_error_y.WidthValidator()
_v = arg.pop('array', None)
self['array'] = (array if (array is not None) else _v)
_v = arg.pop('arrayminus', None)
self['arrayminus'] = (arrayminus if (arrayminus is not None) else _v)
_v = arg.pop('arrayminussrc', None)
self['arrayminussrc'] = (arrayminussrc if (arrayminussrc is not None) else _v)
_v = arg.pop('arraysrc', None)
self['arraysrc'] = (arraysrc if (arraysrc is not None) else _v)
_v = arg.pop('color', None)
self['color'] = (color if (color is not None) else _v)
_v = arg.pop('symmetric', None)
self['symmetric'] = (symmetric if (symmetric is not None) else _v)
_v = arg.pop('thickness', None)
self['thickness'] = (thickness if (thickness is not None) else _v)
_v = arg.pop('traceref', None)
self['traceref'] = (traceref if (traceref is not None) else _v)
_v = arg.pop('tracerefminus', None)
self['tracerefminus'] = (tracerefminus if (tracerefminus is not None) else _v)
_v = arg.pop('type', None)
self['type'] = (type if (type is not None) else _v)
_v = arg.pop('value', None)
self['value'] = (value if (value is not None) else _v)
_v = arg.pop('valueminus', None)
self['valueminus'] = (valueminus if (valueminus is not None) else _v)
_v = arg.pop('visible', None)
self['visible'] = (visible if (visible is not None) else _v)
_v = arg.pop('width', None)
self['width'] = (width if (width is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | def __init__(self, arg=None, array=None, arrayminus=None, arrayminussrc=None, arraysrc=None, color=None, symmetric=None, thickness=None, traceref=None, tracerefminus=None, type=None, value=None, valueminus=None, visible=None, width=None, **kwargs):
'\n Construct a new ErrorY object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.ErrorY\n array\n Sets the data corresponding the length of each error\n bar. Values are plotted relative to the underlying\n data.\n arrayminus\n Sets the data corresponding the length of each error\n bar in the bottom (left) direction for vertical\n (horizontal) bars Values are plotted relative to the\n underlying data.\n arrayminussrc\n Sets the source reference on plot.ly for arrayminus .\n arraysrc\n Sets the source reference on plot.ly for array .\n color\n Sets the stoke color of the error bars.\n symmetric\n Determines whether or not the error bars have the same\n length in both direction (top/bottom for vertical bars,\n left/right for horizontal bars.\n thickness\n Sets the thickness (in px) of the error bars.\n traceref\n\n tracerefminus\n\n type\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value.\n Set this constant in `value`. If "percent", the bar\n lengths correspond to a percentage of underlying data.\n Set this percentage in `value`. If "sqrt", the bar\n lengths correspond to the sqaure of the underlying\n data. If "array", the bar lengths are set with data set\n `array`.\n value\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars.\n valueminus\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars in the bottom (left) direction for vertical\n (horizontal) bars\n visible\n Determines whether or not this set of error bars is\n visible.\n width\n Sets the width (in px) of the cross-bar at both ends of\n the error bars.\n\n Returns\n -------\n ErrorY\n '
super(ErrorY, self).__init__('error_y')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.ErrorY \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.ErrorY')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import error_y as v_error_y
self._validators['array'] = v_error_y.ArrayValidator()
self._validators['arrayminus'] = v_error_y.ArrayminusValidator()
self._validators['arrayminussrc'] = v_error_y.ArrayminussrcValidator()
self._validators['arraysrc'] = v_error_y.ArraysrcValidator()
self._validators['color'] = v_error_y.ColorValidator()
self._validators['symmetric'] = v_error_y.SymmetricValidator()
self._validators['thickness'] = v_error_y.ThicknessValidator()
self._validators['traceref'] = v_error_y.TracerefValidator()
self._validators['tracerefminus'] = v_error_y.TracerefminusValidator()
self._validators['type'] = v_error_y.TypeValidator()
self._validators['value'] = v_error_y.ValueValidator()
self._validators['valueminus'] = v_error_y.ValueminusValidator()
self._validators['visible'] = v_error_y.VisibleValidator()
self._validators['width'] = v_error_y.WidthValidator()
_v = arg.pop('array', None)
self['array'] = (array if (array is not None) else _v)
_v = arg.pop('arrayminus', None)
self['arrayminus'] = (arrayminus if (arrayminus is not None) else _v)
_v = arg.pop('arrayminussrc', None)
self['arrayminussrc'] = (arrayminussrc if (arrayminussrc is not None) else _v)
_v = arg.pop('arraysrc', None)
self['arraysrc'] = (arraysrc if (arraysrc is not None) else _v)
_v = arg.pop('color', None)
self['color'] = (color if (color is not None) else _v)
_v = arg.pop('symmetric', None)
self['symmetric'] = (symmetric if (symmetric is not None) else _v)
_v = arg.pop('thickness', None)
self['thickness'] = (thickness if (thickness is not None) else _v)
_v = arg.pop('traceref', None)
self['traceref'] = (traceref if (traceref is not None) else _v)
_v = arg.pop('tracerefminus', None)
self['tracerefminus'] = (tracerefminus if (tracerefminus is not None) else _v)
_v = arg.pop('type', None)
self['type'] = (type if (type is not None) else _v)
_v = arg.pop('value', None)
self['value'] = (value if (value is not None) else _v)
_v = arg.pop('valueminus', None)
self['valueminus'] = (valueminus if (valueminus is not None) else _v)
_v = arg.pop('visible', None)
self['visible'] = (visible if (visible is not None) else _v)
_v = arg.pop('width', None)
self['width'] = (width if (width is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False<|docstring|>Construct a new ErrorY object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.ErrorY
array
Sets the data corresponding the length of each error
bar. Values are plotted relative to the underlying
data.
arrayminus
Sets the data corresponding the length of each error
bar in the bottom (left) direction for vertical
(horizontal) bars Values are plotted relative to the
underlying data.
arrayminussrc
Sets the source reference on plot.ly for arrayminus .
arraysrc
Sets the source reference on plot.ly for array .
color
Sets the stoke color of the error bars.
symmetric
Determines whether or not the error bars have the same
length in both direction (top/bottom for vertical bars,
left/right for horizontal bars.
thickness
Sets the thickness (in px) of the error bars.
traceref
tracerefminus
type
Determines the rule used to generate the error bars. If
*constant`, the bar lengths are of a constant value.
Set this constant in `value`. If "percent", the bar
lengths correspond to a percentage of underlying data.
Set this percentage in `value`. If "sqrt", the bar
lengths correspond to the sqaure of the underlying
data. If "array", the bar lengths are set with data set
`array`.
value
Sets the value of either the percentage (if `type` is
set to "percent") or the constant (if `type` is set to
"constant") corresponding to the lengths of the error
bars.
valueminus
Sets the value of either the percentage (if `type` is
set to "percent") or the constant (if `type` is set to
"constant") corresponding to the lengths of the error
bars in the bottom (left) direction for vertical
(horizontal) bars
visible
Determines whether or not this set of error bars is
visible.
width
Sets the width (in px) of the cross-bar at both ends of
the error bars.
Returns
-------
ErrorY<|endoftext|> |
a8b3418511a8a5a6c97e31707ad0f82dbb9209ac9cc66899b6f69a4d5cb3cb5c | @property
def array(self):
"\n Sets the data corresponding the length of each error bar.\n Values are plotted relative to the underlying data.\n \n The 'array' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['array'] | Sets the data corresponding the length of each error bar.
Values are plotted relative to the underlying data.
The 'array' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | array | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def array(self):
"\n Sets the data corresponding the length of each error bar.\n Values are plotted relative to the underlying data.\n \n The 'array' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['array'] | @property
def array(self):
"\n Sets the data corresponding the length of each error bar.\n Values are plotted relative to the underlying data.\n \n The 'array' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['array']<|docstring|>Sets the data corresponding the length of each error bar.
Values are plotted relative to the underlying data.
The 'array' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray<|endoftext|> |
5227ac08b4dc63e1219b2315a6290375ca8d6e8eb5f13225f773ab860b1a1f89 | @property
def arrayminus(self):
"\n Sets the data corresponding the length of each error bar in the\n bottom (left) direction for vertical (horizontal) bars Values\n are plotted relative to the underlying data.\n \n The 'arrayminus' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['arrayminus'] | Sets the data corresponding the length of each error bar in the
bottom (left) direction for vertical (horizontal) bars Values
are plotted relative to the underlying data.
The 'arrayminus' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | arrayminus | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def arrayminus(self):
"\n Sets the data corresponding the length of each error bar in the\n bottom (left) direction for vertical (horizontal) bars Values\n are plotted relative to the underlying data.\n \n The 'arrayminus' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['arrayminus'] | @property
def arrayminus(self):
"\n Sets the data corresponding the length of each error bar in the\n bottom (left) direction for vertical (horizontal) bars Values\n are plotted relative to the underlying data.\n \n The 'arrayminus' property is an array that may be specified as a tuple,\n list, numpy array, or pandas Series\n\n Returns\n -------\n numpy.ndarray\n "
return self['arrayminus']<|docstring|>Sets the data corresponding the length of each error bar in the
bottom (left) direction for vertical (horizontal) bars Values
are plotted relative to the underlying data.
The 'arrayminus' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray<|endoftext|> |
d119ce6f009ac84f22bd443609c6f99703dd9bb616d0d437445c0fe7f4f74738 | @property
def arrayminussrc(self):
"\n Sets the source reference on plot.ly for arrayminus .\n \n The 'arrayminussrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arrayminussrc'] | Sets the source reference on plot.ly for arrayminus .
The 'arrayminussrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | arrayminussrc | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def arrayminussrc(self):
"\n Sets the source reference on plot.ly for arrayminus .\n \n The 'arrayminussrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arrayminussrc'] | @property
def arrayminussrc(self):
"\n Sets the source reference on plot.ly for arrayminus .\n \n The 'arrayminussrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arrayminussrc']<|docstring|>Sets the source reference on plot.ly for arrayminus .
The 'arrayminussrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str<|endoftext|> |
97b42d78e3bd868f4a134676d8c89a01781b1b4229b4e10055cdd13954798610 | @property
def arraysrc(self):
"\n Sets the source reference on plot.ly for array .\n \n The 'arraysrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arraysrc'] | Sets the source reference on plot.ly for array .
The 'arraysrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | arraysrc | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def arraysrc(self):
"\n Sets the source reference on plot.ly for array .\n \n The 'arraysrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arraysrc'] | @property
def arraysrc(self):
"\n Sets the source reference on plot.ly for array .\n \n The 'arraysrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n "
return self['arraysrc']<|docstring|>Sets the source reference on plot.ly for array .
The 'arraysrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str<|endoftext|> |
ca2c1342635b7080b021a94eef655c9569138dcfb1c3b3d16be6cb7a62563c59 | @property
def color(self):
"\n Sets the stoke color of the error bars.\n \n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n\n Returns\n -------\n str\n "
return self['color'] | Sets the stoke color of the error bars.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, saddlebrown, salmon, sandybrown,
seagreen, seashell, sienna, silver, skyblue,
slateblue, slategray, slategrey, snow, springgreen,
steelblue, tan, teal, thistle, tomato, turquoise,
violet, wheat, white, whitesmoke, yellow,
yellowgreen
Returns
-------
str | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | color | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def color(self):
"\n Sets the stoke color of the error bars.\n \n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n\n Returns\n -------\n str\n "
return self['color'] | @property
def color(self):
"\n Sets the stoke color of the error bars.\n \n The 'color' property is a color and may be specified as:\n - A hex string (e.g. '#ff0000')\n - An rgb/rgba string (e.g. 'rgb(255,0,0)')\n - An hsl/hsla string (e.g. 'hsl(0,100%,50%)')\n - An hsv/hsva string (e.g. 'hsv(0,100%,100%)')\n - A named CSS color:\n aliceblue, antiquewhite, aqua, aquamarine, azure,\n beige, bisque, black, blanchedalmond, blue,\n blueviolet, brown, burlywood, cadetblue,\n chartreuse, chocolate, coral, cornflowerblue,\n cornsilk, crimson, cyan, darkblue, darkcyan,\n darkgoldenrod, darkgray, darkgrey, darkgreen,\n darkkhaki, darkmagenta, darkolivegreen, darkorange,\n darkorchid, darkred, darksalmon, darkseagreen,\n darkslateblue, darkslategray, darkslategrey,\n darkturquoise, darkviolet, deeppink, deepskyblue,\n dimgray, dimgrey, dodgerblue, firebrick,\n floralwhite, forestgreen, fuchsia, gainsboro,\n ghostwhite, gold, goldenrod, gray, grey, green,\n greenyellow, honeydew, hotpink, indianred, indigo,\n ivory, khaki, lavender, lavenderblush, lawngreen,\n lemonchiffon, lightblue, lightcoral, lightcyan,\n lightgoldenrodyellow, lightgray, lightgrey,\n lightgreen, lightpink, lightsalmon, lightseagreen,\n lightskyblue, lightslategray, lightslategrey,\n lightsteelblue, lightyellow, lime, limegreen,\n linen, magenta, maroon, mediumaquamarine,\n mediumblue, mediumorchid, mediumpurple,\n mediumseagreen, mediumslateblue, mediumspringgreen,\n mediumturquoise, mediumvioletred, midnightblue,\n mintcream, mistyrose, moccasin, navajowhite, navy,\n oldlace, olive, olivedrab, orange, orangered,\n orchid, palegoldenrod, palegreen, paleturquoise,\n palevioletred, papayawhip, peachpuff, peru, pink,\n plum, powderblue, purple, red, rosybrown,\n royalblue, saddlebrown, salmon, sandybrown,\n seagreen, seashell, sienna, silver, skyblue,\n slateblue, slategray, slategrey, snow, springgreen,\n steelblue, tan, teal, thistle, tomato, turquoise,\n violet, wheat, white, whitesmoke, yellow,\n yellowgreen\n\n Returns\n -------\n str\n "
return self['color']<|docstring|>Sets the stoke color of the error bars.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, saddlebrown, salmon, sandybrown,
seagreen, seashell, sienna, silver, skyblue,
slateblue, slategray, slategrey, snow, springgreen,
steelblue, tan, teal, thistle, tomato, turquoise,
violet, wheat, white, whitesmoke, yellow,
yellowgreen
Returns
-------
str<|endoftext|> |
201c5498a6c962c9a5709599d741c11f2f2b87ff7e62abc0dfbbb3c1aad0acfc | @property
def copy_ystyle(self):
"\n The 'copy_ystyle' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['copy_ystyle'] | The 'copy_ystyle' property must be specified as a bool
(either True, or False)
Returns
-------
bool | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | copy_ystyle | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def copy_ystyle(self):
"\n The 'copy_ystyle' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['copy_ystyle'] | @property
def copy_ystyle(self):
"\n The 'copy_ystyle' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['copy_ystyle']<|docstring|>The 'copy_ystyle' property must be specified as a bool
(either True, or False)
Returns
-------
bool<|endoftext|> |
81a8ccbc11c7a6aff9db370d8a6e4a445526c9bd4b3e6a03e8249b1780c3645d | @property
def symmetric(self):
"\n Determines whether or not the error bars have the same length\n in both direction (top/bottom for vertical bars, left/right for\n horizontal bars.\n \n The 'symmetric' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['symmetric'] | Determines whether or not the error bars have the same length
in both direction (top/bottom for vertical bars, left/right for
horizontal bars.
The 'symmetric' property must be specified as a bool
(either True, or False)
Returns
-------
bool | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | symmetric | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def symmetric(self):
"\n Determines whether or not the error bars have the same length\n in both direction (top/bottom for vertical bars, left/right for\n horizontal bars.\n \n The 'symmetric' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['symmetric'] | @property
def symmetric(self):
"\n Determines whether or not the error bars have the same length\n in both direction (top/bottom for vertical bars, left/right for\n horizontal bars.\n \n The 'symmetric' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['symmetric']<|docstring|>Determines whether or not the error bars have the same length
in both direction (top/bottom for vertical bars, left/right for
horizontal bars.
The 'symmetric' property must be specified as a bool
(either True, or False)
Returns
-------
bool<|endoftext|> |
4d66f714783437288099d4aeabba8dc0326f464f0c6a125001a37d71c0db30c3 | @property
def thickness(self):
"\n Sets the thickness (in px) of the error bars.\n \n The 'thickness' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['thickness'] | Sets the thickness (in px) of the error bars.
The 'thickness' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | thickness | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def thickness(self):
"\n Sets the thickness (in px) of the error bars.\n \n The 'thickness' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['thickness'] | @property
def thickness(self):
"\n Sets the thickness (in px) of the error bars.\n \n The 'thickness' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['thickness']<|docstring|>Sets the thickness (in px) of the error bars.
The 'thickness' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float<|endoftext|> |
6aa02d65fb6a81195cae8507f0a14f6605e9f9c19d2d77c0a96557a9a7fa80cf | @property
def traceref(self):
"\n The 'traceref' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['traceref'] | The 'traceref' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | traceref | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def traceref(self):
"\n The 'traceref' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['traceref'] | @property
def traceref(self):
"\n The 'traceref' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['traceref']<|docstring|>The 'traceref' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int<|endoftext|> |
069e81462e2241c45d5f255cbb783c55a8334748e56f6809914a0e65fdf4dec6 | @property
def tracerefminus(self):
"\n The 'tracerefminus' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['tracerefminus'] | The 'tracerefminus' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | tracerefminus | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def tracerefminus(self):
"\n The 'tracerefminus' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['tracerefminus'] | @property
def tracerefminus(self):
"\n The 'tracerefminus' property is a integer and may be specified as:\n - An int (or float that will be cast to an int)\n in the interval [0, 9223372036854775807]\n\n Returns\n -------\n int\n "
return self['tracerefminus']<|docstring|>The 'tracerefminus' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int<|endoftext|> |
d40f7715ff4fffb822bdb1628d39760aaec99e7656fd5aa77de9a73a4cb0c9bc | @property
def type(self):
'\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value. Set this\n constant in `value`. If "percent", the bar lengths correspond\n to a percentage of underlying data. Set this percentage in\n `value`. If "sqrt", the bar lengths correspond to the sqaure of\n the underlying data. If "array", the bar lengths are set with\n data set `array`.\n \n The \'type\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'percent\', \'constant\', \'sqrt\', \'data\']\n\n Returns\n -------\n Any\n '
return self['type'] | Determines the rule used to generate the error bars. If
*constant`, the bar lengths are of a constant value. Set this
constant in `value`. If "percent", the bar lengths correspond
to a percentage of underlying data. Set this percentage in
`value`. If "sqrt", the bar lengths correspond to the sqaure of
the underlying data. If "array", the bar lengths are set with
data set `array`.
The 'type' property is an enumeration that may be specified as:
- One of the following enumeration values:
['percent', 'constant', 'sqrt', 'data']
Returns
-------
Any | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | type | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def type(self):
'\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value. Set this\n constant in `value`. If "percent", the bar lengths correspond\n to a percentage of underlying data. Set this percentage in\n `value`. If "sqrt", the bar lengths correspond to the sqaure of\n the underlying data. If "array", the bar lengths are set with\n data set `array`.\n \n The \'type\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'percent\', \'constant\', \'sqrt\', \'data\']\n\n Returns\n -------\n Any\n '
return self['type'] | @property
def type(self):
'\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value. Set this\n constant in `value`. If "percent", the bar lengths correspond\n to a percentage of underlying data. Set this percentage in\n `value`. If "sqrt", the bar lengths correspond to the sqaure of\n the underlying data. If "array", the bar lengths are set with\n data set `array`.\n \n The \'type\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'percent\', \'constant\', \'sqrt\', \'data\']\n\n Returns\n -------\n Any\n '
return self['type']<|docstring|>Determines the rule used to generate the error bars. If
*constant`, the bar lengths are of a constant value. Set this
constant in `value`. If "percent", the bar lengths correspond
to a percentage of underlying data. Set this percentage in
`value`. If "sqrt", the bar lengths correspond to the sqaure of
the underlying data. If "array", the bar lengths are set with
data set `array`.
The 'type' property is an enumeration that may be specified as:
- One of the following enumeration values:
['percent', 'constant', 'sqrt', 'data']
Returns
-------
Any<|endoftext|> |
6b76fa155f1fe7f1046cca507ba5530d5eab00f170bbe2cd7db74404a18e07c9 | @property
def value(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars.\n \n The \'value\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['value'] | Sets the value of either the percentage (if `type` is set to
"percent") or the constant (if `type` is set to "constant")
corresponding to the lengths of the error bars.
The 'value' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | value | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def value(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars.\n \n The \'value\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['value'] | @property
def value(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars.\n \n The \'value\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['value']<|docstring|>Sets the value of either the percentage (if `type` is set to
"percent") or the constant (if `type` is set to "constant")
corresponding to the lengths of the error bars.
The 'value' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float<|endoftext|> |
8575d77a31dd7c9cc4ff4c52485ded3072a6cf84852c50c2e94d9c5e686081b8 | @property
def valueminus(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars in the bottom\n (left) direction for vertical (horizontal) bars\n \n The \'valueminus\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['valueminus'] | Sets the value of either the percentage (if `type` is set to
"percent") or the constant (if `type` is set to "constant")
corresponding to the lengths of the error bars in the bottom
(left) direction for vertical (horizontal) bars
The 'valueminus' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | valueminus | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def valueminus(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars in the bottom\n (left) direction for vertical (horizontal) bars\n \n The \'valueminus\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['valueminus'] | @property
def valueminus(self):
'\n Sets the value of either the percentage (if `type` is set to\n "percent") or the constant (if `type` is set to "constant")\n corresponding to the lengths of the error bars in the bottom\n (left) direction for vertical (horizontal) bars\n \n The \'valueminus\' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n '
return self['valueminus']<|docstring|>Sets the value of either the percentage (if `type` is set to
"percent") or the constant (if `type` is set to "constant")
corresponding to the lengths of the error bars in the bottom
(left) direction for vertical (horizontal) bars
The 'valueminus' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float<|endoftext|> |
c4628805814a732d7fa4a534a26fdddfd2a09c20c985ab6cd27b4befc676e6b7 | @property
def visible(self):
"\n Determines whether or not this set of error bars is visible.\n \n The 'visible' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['visible'] | Determines whether or not this set of error bars is visible.
The 'visible' property must be specified as a bool
(either True, or False)
Returns
-------
bool | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | visible | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def visible(self):
"\n Determines whether or not this set of error bars is visible.\n \n The 'visible' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['visible'] | @property
def visible(self):
"\n Determines whether or not this set of error bars is visible.\n \n The 'visible' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n "
return self['visible']<|docstring|>Determines whether or not this set of error bars is visible.
The 'visible' property must be specified as a bool
(either True, or False)
Returns
-------
bool<|endoftext|> |
60e04a4534186665cbee0778238e695e1250b413efbcea1fdcff37d4e66d2bed | @property
def width(self):
"\n Sets the width (in px) of the cross-bar at both ends of the\n error bars.\n \n The 'width' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['width'] | Sets the width (in px) of the cross-bar at both ends of the
error bars.
The 'width' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | width | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def width(self):
"\n Sets the width (in px) of the cross-bar at both ends of the\n error bars.\n \n The 'width' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['width'] | @property
def width(self):
"\n Sets the width (in px) of the cross-bar at both ends of the\n error bars.\n \n The 'width' property is a number and may be specified as:\n - An int or float in the interval [0, inf]\n\n Returns\n -------\n int|float\n "
return self['width']<|docstring|>Sets the width (in px) of the cross-bar at both ends of the
error bars.
The 'width' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float<|endoftext|> |
2fca14366a52cdec1c128949d30d26f46de11b3a0e7e1216a1cde38a0d817c21 | def __init__(self, arg=None, array=None, arrayminus=None, arrayminussrc=None, arraysrc=None, color=None, copy_ystyle=None, symmetric=None, thickness=None, traceref=None, tracerefminus=None, type=None, value=None, valueminus=None, visible=None, width=None, **kwargs):
'\n Construct a new ErrorX object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.ErrorX\n array\n Sets the data corresponding the length of each error\n bar. Values are plotted relative to the underlying\n data.\n arrayminus\n Sets the data corresponding the length of each error\n bar in the bottom (left) direction for vertical\n (horizontal) bars Values are plotted relative to the\n underlying data.\n arrayminussrc\n Sets the source reference on plot.ly for arrayminus .\n arraysrc\n Sets the source reference on plot.ly for array .\n color\n Sets the stoke color of the error bars.\n copy_ystyle\n\n symmetric\n Determines whether or not the error bars have the same\n length in both direction (top/bottom for vertical bars,\n left/right for horizontal bars.\n thickness\n Sets the thickness (in px) of the error bars.\n traceref\n\n tracerefminus\n\n type\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value.\n Set this constant in `value`. If "percent", the bar\n lengths correspond to a percentage of underlying data.\n Set this percentage in `value`. If "sqrt", the bar\n lengths correspond to the sqaure of the underlying\n data. If "array", the bar lengths are set with data set\n `array`.\n value\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars.\n valueminus\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars in the bottom (left) direction for vertical\n (horizontal) bars\n visible\n Determines whether or not this set of error bars is\n visible.\n width\n Sets the width (in px) of the cross-bar at both ends of\n the error bars.\n\n Returns\n -------\n ErrorX\n '
super(ErrorX, self).__init__('error_x')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.ErrorX \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.ErrorX')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import error_x as v_error_x
self._validators['array'] = v_error_x.ArrayValidator()
self._validators['arrayminus'] = v_error_x.ArrayminusValidator()
self._validators['arrayminussrc'] = v_error_x.ArrayminussrcValidator()
self._validators['arraysrc'] = v_error_x.ArraysrcValidator()
self._validators['color'] = v_error_x.ColorValidator()
self._validators['copy_ystyle'] = v_error_x.CopyYstyleValidator()
self._validators['symmetric'] = v_error_x.SymmetricValidator()
self._validators['thickness'] = v_error_x.ThicknessValidator()
self._validators['traceref'] = v_error_x.TracerefValidator()
self._validators['tracerefminus'] = v_error_x.TracerefminusValidator()
self._validators['type'] = v_error_x.TypeValidator()
self._validators['value'] = v_error_x.ValueValidator()
self._validators['valueminus'] = v_error_x.ValueminusValidator()
self._validators['visible'] = v_error_x.VisibleValidator()
self._validators['width'] = v_error_x.WidthValidator()
_v = arg.pop('array', None)
self['array'] = (array if (array is not None) else _v)
_v = arg.pop('arrayminus', None)
self['arrayminus'] = (arrayminus if (arrayminus is not None) else _v)
_v = arg.pop('arrayminussrc', None)
self['arrayminussrc'] = (arrayminussrc if (arrayminussrc is not None) else _v)
_v = arg.pop('arraysrc', None)
self['arraysrc'] = (arraysrc if (arraysrc is not None) else _v)
_v = arg.pop('color', None)
self['color'] = (color if (color is not None) else _v)
_v = arg.pop('copy_ystyle', None)
self['copy_ystyle'] = (copy_ystyle if (copy_ystyle is not None) else _v)
_v = arg.pop('symmetric', None)
self['symmetric'] = (symmetric if (symmetric is not None) else _v)
_v = arg.pop('thickness', None)
self['thickness'] = (thickness if (thickness is not None) else _v)
_v = arg.pop('traceref', None)
self['traceref'] = (traceref if (traceref is not None) else _v)
_v = arg.pop('tracerefminus', None)
self['tracerefminus'] = (tracerefminus if (tracerefminus is not None) else _v)
_v = arg.pop('type', None)
self['type'] = (type if (type is not None) else _v)
_v = arg.pop('value', None)
self['value'] = (value if (value is not None) else _v)
_v = arg.pop('valueminus', None)
self['valueminus'] = (valueminus if (valueminus is not None) else _v)
_v = arg.pop('visible', None)
self['visible'] = (visible if (visible is not None) else _v)
_v = arg.pop('width', None)
self['width'] = (width if (width is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | Construct a new ErrorX object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.ErrorX
array
Sets the data corresponding the length of each error
bar. Values are plotted relative to the underlying
data.
arrayminus
Sets the data corresponding the length of each error
bar in the bottom (left) direction for vertical
(horizontal) bars Values are plotted relative to the
underlying data.
arrayminussrc
Sets the source reference on plot.ly for arrayminus .
arraysrc
Sets the source reference on plot.ly for array .
color
Sets the stoke color of the error bars.
copy_ystyle
symmetric
Determines whether or not the error bars have the same
length in both direction (top/bottom for vertical bars,
left/right for horizontal bars.
thickness
Sets the thickness (in px) of the error bars.
traceref
tracerefminus
type
Determines the rule used to generate the error bars. If
*constant`, the bar lengths are of a constant value.
Set this constant in `value`. If "percent", the bar
lengths correspond to a percentage of underlying data.
Set this percentage in `value`. If "sqrt", the bar
lengths correspond to the sqaure of the underlying
data. If "array", the bar lengths are set with data set
`array`.
value
Sets the value of either the percentage (if `type` is
set to "percent") or the constant (if `type` is set to
"constant") corresponding to the lengths of the error
bars.
valueminus
Sets the value of either the percentage (if `type` is
set to "percent") or the constant (if `type` is set to
"constant") corresponding to the lengths of the error
bars in the bottom (left) direction for vertical
(horizontal) bars
visible
Determines whether or not this set of error bars is
visible.
width
Sets the width (in px) of the cross-bar at both ends of
the error bars.
Returns
-------
ErrorX | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | __init__ | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | def __init__(self, arg=None, array=None, arrayminus=None, arrayminussrc=None, arraysrc=None, color=None, copy_ystyle=None, symmetric=None, thickness=None, traceref=None, tracerefminus=None, type=None, value=None, valueminus=None, visible=None, width=None, **kwargs):
'\n Construct a new ErrorX object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.ErrorX\n array\n Sets the data corresponding the length of each error\n bar. Values are plotted relative to the underlying\n data.\n arrayminus\n Sets the data corresponding the length of each error\n bar in the bottom (left) direction for vertical\n (horizontal) bars Values are plotted relative to the\n underlying data.\n arrayminussrc\n Sets the source reference on plot.ly for arrayminus .\n arraysrc\n Sets the source reference on plot.ly for array .\n color\n Sets the stoke color of the error bars.\n copy_ystyle\n\n symmetric\n Determines whether or not the error bars have the same\n length in both direction (top/bottom for vertical bars,\n left/right for horizontal bars.\n thickness\n Sets the thickness (in px) of the error bars.\n traceref\n\n tracerefminus\n\n type\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value.\n Set this constant in `value`. If "percent", the bar\n lengths correspond to a percentage of underlying data.\n Set this percentage in `value`. If "sqrt", the bar\n lengths correspond to the sqaure of the underlying\n data. If "array", the bar lengths are set with data set\n `array`.\n value\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars.\n valueminus\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars in the bottom (left) direction for vertical\n (horizontal) bars\n visible\n Determines whether or not this set of error bars is\n visible.\n width\n Sets the width (in px) of the cross-bar at both ends of\n the error bars.\n\n Returns\n -------\n ErrorX\n '
super(ErrorX, self).__init__('error_x')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.ErrorX \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.ErrorX')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import error_x as v_error_x
self._validators['array'] = v_error_x.ArrayValidator()
self._validators['arrayminus'] = v_error_x.ArrayminusValidator()
self._validators['arrayminussrc'] = v_error_x.ArrayminussrcValidator()
self._validators['arraysrc'] = v_error_x.ArraysrcValidator()
self._validators['color'] = v_error_x.ColorValidator()
self._validators['copy_ystyle'] = v_error_x.CopyYstyleValidator()
self._validators['symmetric'] = v_error_x.SymmetricValidator()
self._validators['thickness'] = v_error_x.ThicknessValidator()
self._validators['traceref'] = v_error_x.TracerefValidator()
self._validators['tracerefminus'] = v_error_x.TracerefminusValidator()
self._validators['type'] = v_error_x.TypeValidator()
self._validators['value'] = v_error_x.ValueValidator()
self._validators['valueminus'] = v_error_x.ValueminusValidator()
self._validators['visible'] = v_error_x.VisibleValidator()
self._validators['width'] = v_error_x.WidthValidator()
_v = arg.pop('array', None)
self['array'] = (array if (array is not None) else _v)
_v = arg.pop('arrayminus', None)
self['arrayminus'] = (arrayminus if (arrayminus is not None) else _v)
_v = arg.pop('arrayminussrc', None)
self['arrayminussrc'] = (arrayminussrc if (arrayminussrc is not None) else _v)
_v = arg.pop('arraysrc', None)
self['arraysrc'] = (arraysrc if (arraysrc is not None) else _v)
_v = arg.pop('color', None)
self['color'] = (color if (color is not None) else _v)
_v = arg.pop('copy_ystyle', None)
self['copy_ystyle'] = (copy_ystyle if (copy_ystyle is not None) else _v)
_v = arg.pop('symmetric', None)
self['symmetric'] = (symmetric if (symmetric is not None) else _v)
_v = arg.pop('thickness', None)
self['thickness'] = (thickness if (thickness is not None) else _v)
_v = arg.pop('traceref', None)
self['traceref'] = (traceref if (traceref is not None) else _v)
_v = arg.pop('tracerefminus', None)
self['tracerefminus'] = (tracerefminus if (tracerefminus is not None) else _v)
_v = arg.pop('type', None)
self['type'] = (type if (type is not None) else _v)
_v = arg.pop('value', None)
self['value'] = (value if (value is not None) else _v)
_v = arg.pop('valueminus', None)
self['valueminus'] = (valueminus if (valueminus is not None) else _v)
_v = arg.pop('visible', None)
self['visible'] = (visible if (visible is not None) else _v)
_v = arg.pop('width', None)
self['width'] = (width if (width is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | def __init__(self, arg=None, array=None, arrayminus=None, arrayminussrc=None, arraysrc=None, color=None, copy_ystyle=None, symmetric=None, thickness=None, traceref=None, tracerefminus=None, type=None, value=None, valueminus=None, visible=None, width=None, **kwargs):
'\n Construct a new ErrorX object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.ErrorX\n array\n Sets the data corresponding the length of each error\n bar. Values are plotted relative to the underlying\n data.\n arrayminus\n Sets the data corresponding the length of each error\n bar in the bottom (left) direction for vertical\n (horizontal) bars Values are plotted relative to the\n underlying data.\n arrayminussrc\n Sets the source reference on plot.ly for arrayminus .\n arraysrc\n Sets the source reference on plot.ly for array .\n color\n Sets the stoke color of the error bars.\n copy_ystyle\n\n symmetric\n Determines whether or not the error bars have the same\n length in both direction (top/bottom for vertical bars,\n left/right for horizontal bars.\n thickness\n Sets the thickness (in px) of the error bars.\n traceref\n\n tracerefminus\n\n type\n Determines the rule used to generate the error bars. If\n *constant`, the bar lengths are of a constant value.\n Set this constant in `value`. If "percent", the bar\n lengths correspond to a percentage of underlying data.\n Set this percentage in `value`. If "sqrt", the bar\n lengths correspond to the sqaure of the underlying\n data. If "array", the bar lengths are set with data set\n `array`.\n value\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars.\n valueminus\n Sets the value of either the percentage (if `type` is\n set to "percent") or the constant (if `type` is set to\n "constant") corresponding to the lengths of the error\n bars in the bottom (left) direction for vertical\n (horizontal) bars\n visible\n Determines whether or not this set of error bars is\n visible.\n width\n Sets the width (in px) of the cross-bar at both ends of\n the error bars.\n\n Returns\n -------\n ErrorX\n '
super(ErrorX, self).__init__('error_x')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.ErrorX \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.ErrorX')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import error_x as v_error_x
self._validators['array'] = v_error_x.ArrayValidator()
self._validators['arrayminus'] = v_error_x.ArrayminusValidator()
self._validators['arrayminussrc'] = v_error_x.ArrayminussrcValidator()
self._validators['arraysrc'] = v_error_x.ArraysrcValidator()
self._validators['color'] = v_error_x.ColorValidator()
self._validators['copy_ystyle'] = v_error_x.CopyYstyleValidator()
self._validators['symmetric'] = v_error_x.SymmetricValidator()
self._validators['thickness'] = v_error_x.ThicknessValidator()
self._validators['traceref'] = v_error_x.TracerefValidator()
self._validators['tracerefminus'] = v_error_x.TracerefminusValidator()
self._validators['type'] = v_error_x.TypeValidator()
self._validators['value'] = v_error_x.ValueValidator()
self._validators['valueminus'] = v_error_x.ValueminusValidator()
self._validators['visible'] = v_error_x.VisibleValidator()
self._validators['width'] = v_error_x.WidthValidator()
_v = arg.pop('array', None)
self['array'] = (array if (array is not None) else _v)
_v = arg.pop('arrayminus', None)
self['arrayminus'] = (arrayminus if (arrayminus is not None) else _v)
_v = arg.pop('arrayminussrc', None)
self['arrayminussrc'] = (arrayminussrc if (arrayminussrc is not None) else _v)
_v = arg.pop('arraysrc', None)
self['arraysrc'] = (arraysrc if (arraysrc is not None) else _v)
_v = arg.pop('color', None)
self['color'] = (color if (color is not None) else _v)
_v = arg.pop('copy_ystyle', None)
self['copy_ystyle'] = (copy_ystyle if (copy_ystyle is not None) else _v)
_v = arg.pop('symmetric', None)
self['symmetric'] = (symmetric if (symmetric is not None) else _v)
_v = arg.pop('thickness', None)
self['thickness'] = (thickness if (thickness is not None) else _v)
_v = arg.pop('traceref', None)
self['traceref'] = (traceref if (traceref is not None) else _v)
_v = arg.pop('tracerefminus', None)
self['tracerefminus'] = (tracerefminus if (tracerefminus is not None) else _v)
_v = arg.pop('type', None)
self['type'] = (type if (type is not None) else _v)
_v = arg.pop('value', None)
self['value'] = (value if (value is not None) else _v)
_v = arg.pop('valueminus', None)
self['valueminus'] = (valueminus if (valueminus is not None) else _v)
_v = arg.pop('visible', None)
self['visible'] = (visible if (visible is not None) else _v)
_v = arg.pop('width', None)
self['width'] = (width if (width is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False<|docstring|>Construct a new ErrorX object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.ErrorX
array
Sets the data corresponding the length of each error
bar. Values are plotted relative to the underlying
data.
arrayminus
Sets the data corresponding the length of each error
bar in the bottom (left) direction for vertical
(horizontal) bars Values are plotted relative to the
underlying data.
arrayminussrc
Sets the source reference on plot.ly for arrayminus .
arraysrc
Sets the source reference on plot.ly for array .
color
Sets the stoke color of the error bars.
copy_ystyle
symmetric
Determines whether or not the error bars have the same
length in both direction (top/bottom for vertical bars,
left/right for horizontal bars.
thickness
Sets the thickness (in px) of the error bars.
traceref
tracerefminus
type
Determines the rule used to generate the error bars. If
*constant`, the bar lengths are of a constant value.
Set this constant in `value`. If "percent", the bar
lengths correspond to a percentage of underlying data.
Set this percentage in `value`. If "sqrt", the bar
lengths correspond to the sqaure of the underlying
data. If "array", the bar lengths are set with data set
`array`.
value
Sets the value of either the percentage (if `type` is
set to "percent") or the constant (if `type` is set to
"constant") corresponding to the lengths of the error
bars.
valueminus
Sets the value of either the percentage (if `type` is
set to "percent") or the constant (if `type` is set to
"constant") corresponding to the lengths of the error
bars in the bottom (left) direction for vertical
(horizontal) bars
visible
Determines whether or not this set of error bars is
visible.
width
Sets the width (in px) of the cross-bar at both ends of
the error bars.
Returns
-------
ErrorX<|endoftext|> |
0d2e6a04754228c96246d11011ae63198b1403a37bcb648b13e6c560d8a71f9e | @property
def currentbin(self):
'\n Only applies if cumulative is enabled. Sets whether the current\n bin is included, excluded, or has half of its value included in\n the current cumulative value. "include" is the default for\n compatibility with various other tools, however it introduces a\n half-bin bias to the results. "exclude" makes the opposite\n half-bin bias, and "half" removes it.\n \n The \'currentbin\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'include\', \'exclude\', \'half\']\n\n Returns\n -------\n Any\n '
return self['currentbin'] | Only applies if cumulative is enabled. Sets whether the current
bin is included, excluded, or has half of its value included in
the current cumulative value. "include" is the default for
compatibility with various other tools, however it introduces a
half-bin bias to the results. "exclude" makes the opposite
half-bin bias, and "half" removes it.
The 'currentbin' property is an enumeration that may be specified as:
- One of the following enumeration values:
['include', 'exclude', 'half']
Returns
-------
Any | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | currentbin | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def currentbin(self):
'\n Only applies if cumulative is enabled. Sets whether the current\n bin is included, excluded, or has half of its value included in\n the current cumulative value. "include" is the default for\n compatibility with various other tools, however it introduces a\n half-bin bias to the results. "exclude" makes the opposite\n half-bin bias, and "half" removes it.\n \n The \'currentbin\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'include\', \'exclude\', \'half\']\n\n Returns\n -------\n Any\n '
return self['currentbin'] | @property
def currentbin(self):
'\n Only applies if cumulative is enabled. Sets whether the current\n bin is included, excluded, or has half of its value included in\n the current cumulative value. "include" is the default for\n compatibility with various other tools, however it introduces a\n half-bin bias to the results. "exclude" makes the opposite\n half-bin bias, and "half" removes it.\n \n The \'currentbin\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'include\', \'exclude\', \'half\']\n\n Returns\n -------\n Any\n '
return self['currentbin']<|docstring|>Only applies if cumulative is enabled. Sets whether the current
bin is included, excluded, or has half of its value included in
the current cumulative value. "include" is the default for
compatibility with various other tools, however it introduces a
half-bin bias to the results. "exclude" makes the opposite
half-bin bias, and "half" removes it.
The 'currentbin' property is an enumeration that may be specified as:
- One of the following enumeration values:
['include', 'exclude', 'half']
Returns
-------
Any<|endoftext|> |
7fa397aa1553db4b130e44d215c857080ff06fced6c1932d9bae451b9c12571b | @property
def direction(self):
'\n Only applies if cumulative is enabled. If "increasing"\n (default) we sum all prior bins, so the result increases from\n left to right. If "decreasing" we sum later bins so the result\n decreases from left to right.\n \n The \'direction\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'increasing\', \'decreasing\']\n\n Returns\n -------\n Any\n '
return self['direction'] | Only applies if cumulative is enabled. If "increasing"
(default) we sum all prior bins, so the result increases from
left to right. If "decreasing" we sum later bins so the result
decreases from left to right.
The 'direction' property is an enumeration that may be specified as:
- One of the following enumeration values:
['increasing', 'decreasing']
Returns
-------
Any | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | direction | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def direction(self):
'\n Only applies if cumulative is enabled. If "increasing"\n (default) we sum all prior bins, so the result increases from\n left to right. If "decreasing" we sum later bins so the result\n decreases from left to right.\n \n The \'direction\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'increasing\', \'decreasing\']\n\n Returns\n -------\n Any\n '
return self['direction'] | @property
def direction(self):
'\n Only applies if cumulative is enabled. If "increasing"\n (default) we sum all prior bins, so the result increases from\n left to right. If "decreasing" we sum later bins so the result\n decreases from left to right.\n \n The \'direction\' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [\'increasing\', \'decreasing\']\n\n Returns\n -------\n Any\n '
return self['direction']<|docstring|>Only applies if cumulative is enabled. If "increasing"
(default) we sum all prior bins, so the result increases from
left to right. If "decreasing" we sum later bins so the result
decreases from left to right.
The 'direction' property is an enumeration that may be specified as:
- One of the following enumeration values:
['increasing', 'decreasing']
Returns
-------
Any<|endoftext|> |
525113d0e0cb7db860d9bcf9e1681f7eafd1cc6c07ec85bdef517a4eed798106 | @property
def enabled(self):
'\n If true, display the cumulative distribution by summing the\n binned values. Use the `direction` and `centralbin` attributes\n to tune the accumulation method. Note: in this mode, the\n "density" `histnorm` settings behave the same as their\n equivalents without "density": "" and "density" both rise to\n the number of data points, and "probability" and *probability\n density* both rise to the number of sample points.\n \n The \'enabled\' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n '
return self['enabled'] | If true, display the cumulative distribution by summing the
binned values. Use the `direction` and `centralbin` attributes
to tune the accumulation method. Note: in this mode, the
"density" `histnorm` settings behave the same as their
equivalents without "density": "" and "density" both rise to
the number of data points, and "probability" and *probability
density* both rise to the number of sample points.
The 'enabled' property must be specified as a bool
(either True, or False)
Returns
-------
bool | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | enabled | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | @property
def enabled(self):
'\n If true, display the cumulative distribution by summing the\n binned values. Use the `direction` and `centralbin` attributes\n to tune the accumulation method. Note: in this mode, the\n "density" `histnorm` settings behave the same as their\n equivalents without "density": and "density" both rise to\n the number of data points, and "probability" and *probability\n density* both rise to the number of sample points.\n \n The \'enabled\' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n '
return self['enabled'] | @property
def enabled(self):
'\n If true, display the cumulative distribution by summing the\n binned values. Use the `direction` and `centralbin` attributes\n to tune the accumulation method. Note: in this mode, the\n "density" `histnorm` settings behave the same as their\n equivalents without "density": and "density" both rise to\n the number of data points, and "probability" and *probability\n density* both rise to the number of sample points.\n \n The \'enabled\' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n '
return self['enabled']<|docstring|>If true, display the cumulative distribution by summing the
binned values. Use the `direction` and `centralbin` attributes
to tune the accumulation method. Note: in this mode, the
"density" `histnorm` settings behave the same as their
equivalents without "density": "" and "density" both rise to
the number of data points, and "probability" and *probability
density* both rise to the number of sample points.
The 'enabled' property must be specified as a bool
(either True, or False)
Returns
-------
bool<|endoftext|> |
856a2633822178f59001e8a35ef3c4f2181fc24b0b189dbbbc92c14d004b32e6 | def __init__(self, arg=None, currentbin=None, direction=None, enabled=None, **kwargs):
'\n Construct a new Cumulative object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Cumulative\n currentbin\n Only applies if cumulative is enabled. Sets whether the\n current bin is included, excluded, or has half of its\n value included in the current cumulative value.\n "include" is the default for compatibility with various\n other tools, however it introduces a half-bin bias to\n the results. "exclude" makes the opposite half-bin\n bias, and "half" removes it.\n direction\n Only applies if cumulative is enabled. If "increasing"\n (default) we sum all prior bins, so the result\n increases from left to right. If "decreasing" we sum\n later bins so the result decreases from left to right.\n enabled\n If true, display the cumulative distribution by summing\n the binned values. Use the `direction` and `centralbin`\n attributes to tune the accumulation method. Note: in\n this mode, the "density" `histnorm` settings behave the\n same as their equivalents without "density": "" and\n "density" both rise to the number of data points, and\n "probability" and *probability density* both rise to\n the number of sample points.\n\n Returns\n -------\n Cumulative\n '
super(Cumulative, self).__init__('cumulative')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Cumulative \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Cumulative')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import cumulative as v_cumulative
self._validators['currentbin'] = v_cumulative.CurrentbinValidator()
self._validators['direction'] = v_cumulative.DirectionValidator()
self._validators['enabled'] = v_cumulative.EnabledValidator()
_v = arg.pop('currentbin', None)
self['currentbin'] = (currentbin if (currentbin is not None) else _v)
_v = arg.pop('direction', None)
self['direction'] = (direction if (direction is not None) else _v)
_v = arg.pop('enabled', None)
self['enabled'] = (enabled if (enabled is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | Construct a new Cumulative object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Cumulative
currentbin
Only applies if cumulative is enabled. Sets whether the
current bin is included, excluded, or has half of its
value included in the current cumulative value.
"include" is the default for compatibility with various
other tools, however it introduces a half-bin bias to
the results. "exclude" makes the opposite half-bin
bias, and "half" removes it.
direction
Only applies if cumulative is enabled. If "increasing"
(default) we sum all prior bins, so the result
increases from left to right. If "decreasing" we sum
later bins so the result decreases from left to right.
enabled
If true, display the cumulative distribution by summing
the binned values. Use the `direction` and `centralbin`
attributes to tune the accumulation method. Note: in
this mode, the "density" `histnorm` settings behave the
same as their equivalents without "density": "" and
"density" both rise to the number of data points, and
"probability" and *probability density* both rise to
the number of sample points.
Returns
-------
Cumulative | WatchDogs_Visualisation/oldApps/tweet-map/venv2/lib/python3.7/site-packages/plotly/graph_objs/histogram/__init__.py | __init__ | tnreddy09/WatchDogs_StockMarketAnalysis | 6 | python | def __init__(self, arg=None, currentbin=None, direction=None, enabled=None, **kwargs):
'\n Construct a new Cumulative object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Cumulative\n currentbin\n Only applies if cumulative is enabled. Sets whether the\n current bin is included, excluded, or has half of its\n value included in the current cumulative value.\n "include" is the default for compatibility with various\n other tools, however it introduces a half-bin bias to\n the results. "exclude" makes the opposite half-bin\n bias, and "half" removes it.\n direction\n Only applies if cumulative is enabled. If "increasing"\n (default) we sum all prior bins, so the result\n increases from left to right. If "decreasing" we sum\n later bins so the result decreases from left to right.\n enabled\n If true, display the cumulative distribution by summing\n the binned values. Use the `direction` and `centralbin`\n attributes to tune the accumulation method. Note: in\n this mode, the "density" `histnorm` settings behave the\n same as their equivalents without "density": and\n "density" both rise to the number of data points, and\n "probability" and *probability density* both rise to\n the number of sample points.\n\n Returns\n -------\n Cumulative\n '
super(Cumulative, self).__init__('cumulative')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Cumulative \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Cumulative')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import cumulative as v_cumulative
self._validators['currentbin'] = v_cumulative.CurrentbinValidator()
self._validators['direction'] = v_cumulative.DirectionValidator()
self._validators['enabled'] = v_cumulative.EnabledValidator()
_v = arg.pop('currentbin', None)
self['currentbin'] = (currentbin if (currentbin is not None) else _v)
_v = arg.pop('direction', None)
self['direction'] = (direction if (direction is not None) else _v)
_v = arg.pop('enabled', None)
self['enabled'] = (enabled if (enabled is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False | def __init__(self, arg=None, currentbin=None, direction=None, enabled=None, **kwargs):
'\n Construct a new Cumulative object\n \n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of plotly.graph_objs.histogram.Cumulative\n currentbin\n Only applies if cumulative is enabled. Sets whether the\n current bin is included, excluded, or has half of its\n value included in the current cumulative value.\n "include" is the default for compatibility with various\n other tools, however it introduces a half-bin bias to\n the results. "exclude" makes the opposite half-bin\n bias, and "half" removes it.\n direction\n Only applies if cumulative is enabled. If "increasing"\n (default) we sum all prior bins, so the result\n increases from left to right. If "decreasing" we sum\n later bins so the result decreases from left to right.\n enabled\n If true, display the cumulative distribution by summing\n the binned values. Use the `direction` and `centralbin`\n attributes to tune the accumulation method. Note: in\n this mode, the "density" `histnorm` settings behave the\n same as their equivalents without "density": and\n "density" both rise to the number of data points, and\n "probability" and *probability density* both rise to\n the number of sample points.\n\n Returns\n -------\n Cumulative\n '
super(Cumulative, self).__init__('cumulative')
if (arg is None):
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError('The first argument to the plotly.graph_objs.histogram.Cumulative \nconstructor must be a dict or \nan instance of plotly.graph_objs.histogram.Cumulative')
self._skip_invalid = kwargs.pop('skip_invalid', False)
from plotly.validators.histogram import cumulative as v_cumulative
self._validators['currentbin'] = v_cumulative.CurrentbinValidator()
self._validators['direction'] = v_cumulative.DirectionValidator()
self._validators['enabled'] = v_cumulative.EnabledValidator()
_v = arg.pop('currentbin', None)
self['currentbin'] = (currentbin if (currentbin is not None) else _v)
_v = arg.pop('direction', None)
self['direction'] = (direction if (direction is not None) else _v)
_v = arg.pop('enabled', None)
self['enabled'] = (enabled if (enabled is not None) else _v)
self._process_kwargs(**dict(arg, **kwargs))
self._skip_invalid = False<|docstring|>Construct a new Cumulative object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly.graph_objs.histogram.Cumulative
currentbin
Only applies if cumulative is enabled. Sets whether the
current bin is included, excluded, or has half of its
value included in the current cumulative value.
"include" is the default for compatibility with various
other tools, however it introduces a half-bin bias to
the results. "exclude" makes the opposite half-bin
bias, and "half" removes it.
direction
Only applies if cumulative is enabled. If "increasing"
(default) we sum all prior bins, so the result
increases from left to right. If "decreasing" we sum
later bins so the result decreases from left to right.
enabled
If true, display the cumulative distribution by summing
the binned values. Use the `direction` and `centralbin`
attributes to tune the accumulation method. Note: in
this mode, the "density" `histnorm` settings behave the
same as their equivalents without "density": "" and
"density" both rise to the number of data points, and
"probability" and *probability density* both rise to
the number of sample points.
Returns
-------
Cumulative<|endoftext|> |
e6784566e3f121b57105776808a13c49851ac778e154b34b4089d344b83dd4ac | def play_game(min_bound=0, max_bound=11, num_trials=5, seed=False):
'Program randomly chooses a positive integer. Then prompts the user to guess the chosen number. In each wrong attempt the program will give a hint that the number is greater or smaller than the one guessed. Optionally, the user is also allowed to find out more information about the number, by querying the variable attributes.\n\nParameters:\n min_bound (int>=0): minimum bound of range the program can choose a number from. Default is 0. \n\n max_bound (int>=1): maximum bound of range the program can choose a number from. Default is 10.\n\n num_trials (int>=1): number of chances user has to guess the correct number. Default is 5.\n\nReturns:\n val (int): 1 if the user huessed correctly, 0 if not.\n '
if (max_bound <= min_bound):
raise ValueError
if (num_trials <= 0):
raise ValueError
if seed:
random.seed(seed)
chosen = random.randint(min_bound, max_bound)
trial = num_trials
while (trial >= 0):
if (trial == 0):
print('Game over.')
else:
print('You have {} goes left.'.format(trial))
guess = int(input('Please guess an integer between {} and {}: '.format(min_bound, (max_bound - 1))))
[txt, val] = utils.eval_guess(chosen, guess)
print(txt)
trial = (trial - 1)
if (val == 1):
break
return val | Program randomly chooses a positive integer. Then prompts the user to guess the chosen number. In each wrong attempt the program will give a hint that the number is greater or smaller than the one guessed. Optionally, the user is also allowed to find out more information about the number, by querying the variable attributes.
Parameters:
min_bound (int>=0): minimum bound of range the program can choose a number from. Default is 0.
max_bound (int>=1): maximum bound of range the program can choose a number from. Default is 10.
num_trials (int>=1): number of chances user has to guess the correct number. Default is 5.
Returns:
val (int): 1 if the user huessed correctly, 0 if not. | guess_number_game/guess_number_game/guess_number.py | play_game | karinsasaki/Python_ML_projects | 0 | python | def play_game(min_bound=0, max_bound=11, num_trials=5, seed=False):
'Program randomly chooses a positive integer. Then prompts the user to guess the chosen number. In each wrong attempt the program will give a hint that the number is greater or smaller than the one guessed. Optionally, the user is also allowed to find out more information about the number, by querying the variable attributes.\n\nParameters:\n min_bound (int>=0): minimum bound of range the program can choose a number from. Default is 0. \n\n max_bound (int>=1): maximum bound of range the program can choose a number from. Default is 10.\n\n num_trials (int>=1): number of chances user has to guess the correct number. Default is 5.\n\nReturns:\n val (int): 1 if the user huessed correctly, 0 if not.\n '
if (max_bound <= min_bound):
raise ValueError
if (num_trials <= 0):
raise ValueError
if seed:
random.seed(seed)
chosen = random.randint(min_bound, max_bound)
trial = num_trials
while (trial >= 0):
if (trial == 0):
print('Game over.')
else:
print('You have {} goes left.'.format(trial))
guess = int(input('Please guess an integer between {} and {}: '.format(min_bound, (max_bound - 1))))
[txt, val] = utils.eval_guess(chosen, guess)
print(txt)
trial = (trial - 1)
if (val == 1):
break
return val | def play_game(min_bound=0, max_bound=11, num_trials=5, seed=False):
'Program randomly chooses a positive integer. Then prompts the user to guess the chosen number. In each wrong attempt the program will give a hint that the number is greater or smaller than the one guessed. Optionally, the user is also allowed to find out more information about the number, by querying the variable attributes.\n\nParameters:\n min_bound (int>=0): minimum bound of range the program can choose a number from. Default is 0. \n\n max_bound (int>=1): maximum bound of range the program can choose a number from. Default is 10.\n\n num_trials (int>=1): number of chances user has to guess the correct number. Default is 5.\n\nReturns:\n val (int): 1 if the user huessed correctly, 0 if not.\n '
if (max_bound <= min_bound):
raise ValueError
if (num_trials <= 0):
raise ValueError
if seed:
random.seed(seed)
chosen = random.randint(min_bound, max_bound)
trial = num_trials
while (trial >= 0):
if (trial == 0):
print('Game over.')
else:
print('You have {} goes left.'.format(trial))
guess = int(input('Please guess an integer between {} and {}: '.format(min_bound, (max_bound - 1))))
[txt, val] = utils.eval_guess(chosen, guess)
print(txt)
trial = (trial - 1)
if (val == 1):
break
return val<|docstring|>Program randomly chooses a positive integer. Then prompts the user to guess the chosen number. In each wrong attempt the program will give a hint that the number is greater or smaller than the one guessed. Optionally, the user is also allowed to find out more information about the number, by querying the variable attributes.
Parameters:
min_bound (int>=0): minimum bound of range the program can choose a number from. Default is 0.
max_bound (int>=1): maximum bound of range the program can choose a number from. Default is 10.
num_trials (int>=1): number of chances user has to guess the correct number. Default is 5.
Returns:
val (int): 1 if the user huessed correctly, 0 if not.<|endoftext|> |
16b4ad3d8a5aeb052f353bf211656f066616d81bc9ddab308ade60c84b49535a | def compare_record(record_one, record_two):
'This is meant to be a strict comparison for exact agreement...'
assert isinstance(record_one, SeqRecord)
assert isinstance(record_two, SeqRecord)
assert (record_one.seq is not None)
assert (record_two.seq is not None)
if (record_one.id != record_two.id):
return False
if (record_one.name != record_two.name):
return False
if (record_one.description != record_two.description):
return False
if (len(record_one) != len(record_two)):
return False
if (isinstance(record_one.seq, UnknownSeq) and isinstance(record_two.seq, UnknownSeq)):
if (record_one.seq._character != record_two.seq._character):
return False
elif (str(record_one.seq) != str(record_two.seq)):
return False
for key in set(record_one.letter_annotations).intersection(record_two.letter_annotations):
if (record_one.letter_annotations[key] != record_two.letter_annotations[key]):
return False
return True | This is meant to be a strict comparison for exact agreement... | Tests/test_SeqIO.py | compare_record | zachcp/biopython | 5 | python | def compare_record(record_one, record_two):
assert isinstance(record_one, SeqRecord)
assert isinstance(record_two, SeqRecord)
assert (record_one.seq is not None)
assert (record_two.seq is not None)
if (record_one.id != record_two.id):
return False
if (record_one.name != record_two.name):
return False
if (record_one.description != record_two.description):
return False
if (len(record_one) != len(record_two)):
return False
if (isinstance(record_one.seq, UnknownSeq) and isinstance(record_two.seq, UnknownSeq)):
if (record_one.seq._character != record_two.seq._character):
return False
elif (str(record_one.seq) != str(record_two.seq)):
return False
for key in set(record_one.letter_annotations).intersection(record_two.letter_annotations):
if (record_one.letter_annotations[key] != record_two.letter_annotations[key]):
return False
return True | def compare_record(record_one, record_two):
assert isinstance(record_one, SeqRecord)
assert isinstance(record_two, SeqRecord)
assert (record_one.seq is not None)
assert (record_two.seq is not None)
if (record_one.id != record_two.id):
return False
if (record_one.name != record_two.name):
return False
if (record_one.description != record_two.description):
return False
if (len(record_one) != len(record_two)):
return False
if (isinstance(record_one.seq, UnknownSeq) and isinstance(record_two.seq, UnknownSeq)):
if (record_one.seq._character != record_two.seq._character):
return False
elif (str(record_one.seq) != str(record_two.seq)):
return False
for key in set(record_one.letter_annotations).intersection(record_two.letter_annotations):
if (record_one.letter_annotations[key] != record_two.letter_annotations[key]):
return False
return True<|docstring|>This is meant to be a strict comparison for exact agreement...<|endoftext|> |
07a8dafbce45de0cffaacabf2ea224740ad437009c45b1f8a02ec894c43664df | def record_summary(record, indent=' '):
'Returns a concise summary of a SeqRecord object as a string'
if (record.id == record.name):
answer = ("%sID and Name='%s',\n%sSeq='" % (indent, record.id, indent))
else:
answer = ("%sID = '%s', Name='%s',\n%sSeq='" % (indent, record.id, record.name, indent))
if (record.seq is None):
answer += 'None'
else:
if (len(record.seq) > 50):
answer += ((str(record.seq[:40]) + '...') + str(record.seq[(- 7):]))
else:
answer += str(record.seq)
answer += ("', length=%i" % len(record.seq))
return answer | Returns a concise summary of a SeqRecord object as a string | Tests/test_SeqIO.py | record_summary | zachcp/biopython | 5 | python | def record_summary(record, indent=' '):
if (record.id == record.name):
answer = ("%sID and Name='%s',\n%sSeq='" % (indent, record.id, indent))
else:
answer = ("%sID = '%s', Name='%s',\n%sSeq='" % (indent, record.id, record.name, indent))
if (record.seq is None):
answer += 'None'
else:
if (len(record.seq) > 50):
answer += ((str(record.seq[:40]) + '...') + str(record.seq[(- 7):]))
else:
answer += str(record.seq)
answer += ("', length=%i" % len(record.seq))
return answer | def record_summary(record, indent=' '):
if (record.id == record.name):
answer = ("%sID and Name='%s',\n%sSeq='" % (indent, record.id, indent))
else:
answer = ("%sID = '%s', Name='%s',\n%sSeq='" % (indent, record.id, record.name, indent))
if (record.seq is None):
answer += 'None'
else:
if (len(record.seq) > 50):
answer += ((str(record.seq[:40]) + '...') + str(record.seq[(- 7):]))
else:
answer += str(record.seq)
answer += ("', length=%i" % len(record.seq))
return answer<|docstring|>Returns a concise summary of a SeqRecord object as a string<|endoftext|> |
ecc7ae63c8e357730587f8d1a3f9d718acfcf7138feaf40e51066dfdaf701892 | def alignment_summary(alignment, index=' '):
'Returns a concise summary of an Alignment object as a string'
answer = []
alignment_len = alignment.get_alignment_length()
rec_count = len(alignment)
for i in range(min(5, alignment_len)):
answer.append(((index + col_summary(alignment.get_column(i))) + (' alignment column %i' % i)))
if (alignment_len > 5):
i = (alignment_len - 1)
answer.append(((index + col_summary(('|' * rec_count))) + ' ...'))
answer.append(((index + col_summary(alignment.get_column(i))) + (' alignment column %i' % i)))
return '\n'.join(answer) | Returns a concise summary of an Alignment object as a string | Tests/test_SeqIO.py | alignment_summary | zachcp/biopython | 5 | python | def alignment_summary(alignment, index=' '):
answer = []
alignment_len = alignment.get_alignment_length()
rec_count = len(alignment)
for i in range(min(5, alignment_len)):
answer.append(((index + col_summary(alignment.get_column(i))) + (' alignment column %i' % i)))
if (alignment_len > 5):
i = (alignment_len - 1)
answer.append(((index + col_summary(('|' * rec_count))) + ' ...'))
answer.append(((index + col_summary(alignment.get_column(i))) + (' alignment column %i' % i)))
return '\n'.join(answer) | def alignment_summary(alignment, index=' '):
answer = []
alignment_len = alignment.get_alignment_length()
rec_count = len(alignment)
for i in range(min(5, alignment_len)):
answer.append(((index + col_summary(alignment.get_column(i))) + (' alignment column %i' % i)))
if (alignment_len > 5):
i = (alignment_len - 1)
answer.append(((index + col_summary(('|' * rec_count))) + ' ...'))
answer.append(((index + col_summary(alignment.get_column(i))) + (' alignment column %i' % i)))
return '\n'.join(answer)<|docstring|>Returns a concise summary of an Alignment object as a string<|endoftext|> |
50b068c5a4df081a92528b442a7e88b516d08599ec6235ac07598e18588453dd | def is_valid(url):
'\n Checks whether `url` is a valid URL.\n '
parsed = urlparse(url)
return (bool(parsed.netloc) and bool(parsed.scheme)) | Checks whether `url` is a valid URL. | api/website_info/utils.py | is_valid | gpiechnik2/senter | 2 | python | def is_valid(url):
'\n \n '
parsed = urlparse(url)
return (bool(parsed.netloc) and bool(parsed.scheme)) | def is_valid(url):
'\n \n '
parsed = urlparse(url)
return (bool(parsed.netloc) and bool(parsed.scheme))<|docstring|>Checks whether `url` is a valid URL.<|endoftext|> |
fd867bd3bfd7e34121f0b0d75f19aef1a6f8cde2d2ac980502432140b6790760 | def thinning(fillmap, max_iter=100):
'Fill area of line with surrounding fill color.\n\n # Arguments\n fillmap: an image.\n max_iter: max iteration number.\n\n # Returns\n an image.\n '
line_id = 0
(h, w) = fillmap.shape[:2]
result = fillmap.copy()
for iterNum in range(max_iter):
line_points = np.where((result == line_id))
if (not (len(line_points[0]) > 0)):
break
line_mask = np.full((h, w), 255, np.uint8)
line_mask[line_points] = 0
line_border_mask = (cv2.morphologyEx(line_mask, cv2.MORPH_DILATE, cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3)), anchor=((- 1), (- 1)), iterations=1) - line_mask)
line_border_points = np.where((line_border_mask == 255))
result_tmp = result.copy()
for (i, _) in enumerate(line_border_points[0]):
(x, y) = (line_border_points[1][i], line_border_points[0][i])
if (((x - 1) > 0) and (result[y][(x - 1)] != line_id)):
result_tmp[y][x] = result[y][(x - 1)]
continue
if (((x - 1) > 0) and ((y - 1) > 0) and (result[(y - 1)][(x - 1)] != line_id)):
result_tmp[y][x] = result[(y - 1)][(x - 1)]
continue
if (((y - 1) > 0) and (result[(y - 1)][x] != line_id)):
result_tmp[y][x] = result[(y - 1)][x]
continue
if (((y - 1) > 0) and ((x + 1) < w) and (result[(y - 1)][(x + 1)] != line_id)):
result_tmp[y][x] = result[(y - 1)][(x + 1)]
continue
if (((x + 1) < w) and (result[y][(x + 1)] != line_id)):
result_tmp[y][x] = result[y][(x + 1)]
continue
if (((x + 1) < w) and ((y + 1) < h) and (result[(y + 1)][(x + 1)] != line_id)):
result_tmp[y][x] = result[(y + 1)][(x + 1)]
continue
if (((y + 1) < h) and (result[(y + 1)][x] != line_id)):
result_tmp[y][x] = result[(y + 1)][x]
continue
if (((y + 1) < h) and ((x - 1) > 0) and (result[(y + 1)][(x - 1)] != line_id)):
result_tmp[y][x] = result[(y + 1)][(x - 1)]
continue
result = result_tmp.copy()
return result | Fill area of line with surrounding fill color.
# Arguments
fillmap: an image.
max_iter: max iteration number.
# Returns
an image. | models/sgm_model/linefiller/thinning.py | thinning | JanValJanus/AnimeInterp | 245 | python | def thinning(fillmap, max_iter=100):
'Fill area of line with surrounding fill color.\n\n # Arguments\n fillmap: an image.\n max_iter: max iteration number.\n\n # Returns\n an image.\n '
line_id = 0
(h, w) = fillmap.shape[:2]
result = fillmap.copy()
for iterNum in range(max_iter):
line_points = np.where((result == line_id))
if (not (len(line_points[0]) > 0)):
break
line_mask = np.full((h, w), 255, np.uint8)
line_mask[line_points] = 0
line_border_mask = (cv2.morphologyEx(line_mask, cv2.MORPH_DILATE, cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3)), anchor=((- 1), (- 1)), iterations=1) - line_mask)
line_border_points = np.where((line_border_mask == 255))
result_tmp = result.copy()
for (i, _) in enumerate(line_border_points[0]):
(x, y) = (line_border_points[1][i], line_border_points[0][i])
if (((x - 1) > 0) and (result[y][(x - 1)] != line_id)):
result_tmp[y][x] = result[y][(x - 1)]
continue
if (((x - 1) > 0) and ((y - 1) > 0) and (result[(y - 1)][(x - 1)] != line_id)):
result_tmp[y][x] = result[(y - 1)][(x - 1)]
continue
if (((y - 1) > 0) and (result[(y - 1)][x] != line_id)):
result_tmp[y][x] = result[(y - 1)][x]
continue
if (((y - 1) > 0) and ((x + 1) < w) and (result[(y - 1)][(x + 1)] != line_id)):
result_tmp[y][x] = result[(y - 1)][(x + 1)]
continue
if (((x + 1) < w) and (result[y][(x + 1)] != line_id)):
result_tmp[y][x] = result[y][(x + 1)]
continue
if (((x + 1) < w) and ((y + 1) < h) and (result[(y + 1)][(x + 1)] != line_id)):
result_tmp[y][x] = result[(y + 1)][(x + 1)]
continue
if (((y + 1) < h) and (result[(y + 1)][x] != line_id)):
result_tmp[y][x] = result[(y + 1)][x]
continue
if (((y + 1) < h) and ((x - 1) > 0) and (result[(y + 1)][(x - 1)] != line_id)):
result_tmp[y][x] = result[(y + 1)][(x - 1)]
continue
result = result_tmp.copy()
return result | def thinning(fillmap, max_iter=100):
'Fill area of line with surrounding fill color.\n\n # Arguments\n fillmap: an image.\n max_iter: max iteration number.\n\n # Returns\n an image.\n '
line_id = 0
(h, w) = fillmap.shape[:2]
result = fillmap.copy()
for iterNum in range(max_iter):
line_points = np.where((result == line_id))
if (not (len(line_points[0]) > 0)):
break
line_mask = np.full((h, w), 255, np.uint8)
line_mask[line_points] = 0
line_border_mask = (cv2.morphologyEx(line_mask, cv2.MORPH_DILATE, cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3)), anchor=((- 1), (- 1)), iterations=1) - line_mask)
line_border_points = np.where((line_border_mask == 255))
result_tmp = result.copy()
for (i, _) in enumerate(line_border_points[0]):
(x, y) = (line_border_points[1][i], line_border_points[0][i])
if (((x - 1) > 0) and (result[y][(x - 1)] != line_id)):
result_tmp[y][x] = result[y][(x - 1)]
continue
if (((x - 1) > 0) and ((y - 1) > 0) and (result[(y - 1)][(x - 1)] != line_id)):
result_tmp[y][x] = result[(y - 1)][(x - 1)]
continue
if (((y - 1) > 0) and (result[(y - 1)][x] != line_id)):
result_tmp[y][x] = result[(y - 1)][x]
continue
if (((y - 1) > 0) and ((x + 1) < w) and (result[(y - 1)][(x + 1)] != line_id)):
result_tmp[y][x] = result[(y - 1)][(x + 1)]
continue
if (((x + 1) < w) and (result[y][(x + 1)] != line_id)):
result_tmp[y][x] = result[y][(x + 1)]
continue
if (((x + 1) < w) and ((y + 1) < h) and (result[(y + 1)][(x + 1)] != line_id)):
result_tmp[y][x] = result[(y + 1)][(x + 1)]
continue
if (((y + 1) < h) and (result[(y + 1)][x] != line_id)):
result_tmp[y][x] = result[(y + 1)][x]
continue
if (((y + 1) < h) and ((x - 1) > 0) and (result[(y + 1)][(x - 1)] != line_id)):
result_tmp[y][x] = result[(y + 1)][(x - 1)]
continue
result = result_tmp.copy()
return result<|docstring|>Fill area of line with surrounding fill color.
# Arguments
fillmap: an image.
max_iter: max iteration number.
# Returns
an image.<|endoftext|> |
31759b3d35846480e76543e39030ed13fd2da2645e022c7e83a40600b6715ace | def osm_notification_handler():
'Connects on OSM Kafka Bus and subscribes to NS-related topics.'
consumer = KafkaConsumer(bootstrap_servers=KAFKA_SERVER, client_id=KAFKA_CLIENT_ID, enable_auto_commit=True, value_deserializer=(lambda v: yaml.safe_load(v.decode('utf-8', 'ignore'))), api_version=KAFKA_API_VERSION, group_id=KAFKA_GROUP_ID)
consumer.subscribe(KAFKA_TOPICS)
logger.info('Initialized Kafka Consumer & subscribed to OSM topics')
for msg in consumer:
operation = msg.key.decode('ascii')
message = msg.value
if (operation == INSTANTIATE):
logger.info('Instantiation of NS with UUID {} started'.format(message['nsInstanceId']))
ns_pre_instantiation_handler(message['operationParams'])
elif (operation == TERMINATE):
logger.info('Termination of NS with UUID {} started'.format(message['nsInstanceId']))
ns = Instance.objects.filter(uuid=message['nsInstanceId'])
if ns.exists():
ns.update(state='terminate')
elif (operation == SCALE):
scale_vnf_type = message['operationParams']['scaleVnfData']['scaleVnfType']
if (scale_vnf_type == SCALE_OUT):
logger.info('Scaling-out VNF of NS with UUID {} started'.format(message['nsInstanceId']))
elif (scale_vnf_type == SCALE_IN):
logger.info('Scaling-in VNF of NS with UUID {} started'.format(message['nsInstanceId']))
elif (operation == INSTANTIATED):
ns = Instance.objects.filter(uuid=message['nsr_id'])
if ns.exists():
if (message['operationState'] == 'COMPLETED'):
ns.update(state='active')
ns_instantiation_handler(ns[0])
logger.info('Instantiation of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
logger.info('Instantiation of NS with UUID {} failed'.format(ns[0].uuid))
ns.delete()
elif (operation == TERMINATED):
ns = Instance.objects.filter(uuid=message['nsr_id'])
if ns.exists():
if (message['operationState'] == 'COMPLETED'):
ns.update(state='deleted')
ns_termination_handler(ns[0])
logger.info('Termination of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
ns.update(state='active')
logger.info('Termination of NS with UUID {} failed'.format(ns[0].uuid))
elif (operation == SCALED):
ns = Instance.objects.filter(uuid=message['nsr_id'])
if ns.exists():
if (scale_vnf_type == SCALE_OUT):
if (message['operationState'] == 'COMPLETED'):
vnf_scaling_out_handler(ns[0])
logger.info('Scaling-out VNF of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
logger.info('Scaling-out VNF of NS with UUID {} failed'.format(ns[0].uuid))
elif (scale_vnf_type == SCALE_IN):
if (message['operationState'] == 'COMPLETED'):
vnf_scaling_in_handler(ns[0])
logger.info('Scaling-in VNF of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
logger.info('Scaling-in VNF of NS with UUID {} failed'.format(ns[0].uuid)) | Connects on OSM Kafka Bus and subscribes to NS-related topics. | api/management/commands/osm_notifications.py | osm_notification_handler | 5g-media/accounting-agent | 0 | python | def osm_notification_handler():
consumer = KafkaConsumer(bootstrap_servers=KAFKA_SERVER, client_id=KAFKA_CLIENT_ID, enable_auto_commit=True, value_deserializer=(lambda v: yaml.safe_load(v.decode('utf-8', 'ignore'))), api_version=KAFKA_API_VERSION, group_id=KAFKA_GROUP_ID)
consumer.subscribe(KAFKA_TOPICS)
logger.info('Initialized Kafka Consumer & subscribed to OSM topics')
for msg in consumer:
operation = msg.key.decode('ascii')
message = msg.value
if (operation == INSTANTIATE):
logger.info('Instantiation of NS with UUID {} started'.format(message['nsInstanceId']))
ns_pre_instantiation_handler(message['operationParams'])
elif (operation == TERMINATE):
logger.info('Termination of NS with UUID {} started'.format(message['nsInstanceId']))
ns = Instance.objects.filter(uuid=message['nsInstanceId'])
if ns.exists():
ns.update(state='terminate')
elif (operation == SCALE):
scale_vnf_type = message['operationParams']['scaleVnfData']['scaleVnfType']
if (scale_vnf_type == SCALE_OUT):
logger.info('Scaling-out VNF of NS with UUID {} started'.format(message['nsInstanceId']))
elif (scale_vnf_type == SCALE_IN):
logger.info('Scaling-in VNF of NS with UUID {} started'.format(message['nsInstanceId']))
elif (operation == INSTANTIATED):
ns = Instance.objects.filter(uuid=message['nsr_id'])
if ns.exists():
if (message['operationState'] == 'COMPLETED'):
ns.update(state='active')
ns_instantiation_handler(ns[0])
logger.info('Instantiation of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
logger.info('Instantiation of NS with UUID {} failed'.format(ns[0].uuid))
ns.delete()
elif (operation == TERMINATED):
ns = Instance.objects.filter(uuid=message['nsr_id'])
if ns.exists():
if (message['operationState'] == 'COMPLETED'):
ns.update(state='deleted')
ns_termination_handler(ns[0])
logger.info('Termination of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
ns.update(state='active')
logger.info('Termination of NS with UUID {} failed'.format(ns[0].uuid))
elif (operation == SCALED):
ns = Instance.objects.filter(uuid=message['nsr_id'])
if ns.exists():
if (scale_vnf_type == SCALE_OUT):
if (message['operationState'] == 'COMPLETED'):
vnf_scaling_out_handler(ns[0])
logger.info('Scaling-out VNF of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
logger.info('Scaling-out VNF of NS with UUID {} failed'.format(ns[0].uuid))
elif (scale_vnf_type == SCALE_IN):
if (message['operationState'] == 'COMPLETED'):
vnf_scaling_in_handler(ns[0])
logger.info('Scaling-in VNF of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
logger.info('Scaling-in VNF of NS with UUID {} failed'.format(ns[0].uuid)) | def osm_notification_handler():
consumer = KafkaConsumer(bootstrap_servers=KAFKA_SERVER, client_id=KAFKA_CLIENT_ID, enable_auto_commit=True, value_deserializer=(lambda v: yaml.safe_load(v.decode('utf-8', 'ignore'))), api_version=KAFKA_API_VERSION, group_id=KAFKA_GROUP_ID)
consumer.subscribe(KAFKA_TOPICS)
logger.info('Initialized Kafka Consumer & subscribed to OSM topics')
for msg in consumer:
operation = msg.key.decode('ascii')
message = msg.value
if (operation == INSTANTIATE):
logger.info('Instantiation of NS with UUID {} started'.format(message['nsInstanceId']))
ns_pre_instantiation_handler(message['operationParams'])
elif (operation == TERMINATE):
logger.info('Termination of NS with UUID {} started'.format(message['nsInstanceId']))
ns = Instance.objects.filter(uuid=message['nsInstanceId'])
if ns.exists():
ns.update(state='terminate')
elif (operation == SCALE):
scale_vnf_type = message['operationParams']['scaleVnfData']['scaleVnfType']
if (scale_vnf_type == SCALE_OUT):
logger.info('Scaling-out VNF of NS with UUID {} started'.format(message['nsInstanceId']))
elif (scale_vnf_type == SCALE_IN):
logger.info('Scaling-in VNF of NS with UUID {} started'.format(message['nsInstanceId']))
elif (operation == INSTANTIATED):
ns = Instance.objects.filter(uuid=message['nsr_id'])
if ns.exists():
if (message['operationState'] == 'COMPLETED'):
ns.update(state='active')
ns_instantiation_handler(ns[0])
logger.info('Instantiation of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
logger.info('Instantiation of NS with UUID {} failed'.format(ns[0].uuid))
ns.delete()
elif (operation == TERMINATED):
ns = Instance.objects.filter(uuid=message['nsr_id'])
if ns.exists():
if (message['operationState'] == 'COMPLETED'):
ns.update(state='deleted')
ns_termination_handler(ns[0])
logger.info('Termination of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
ns.update(state='active')
logger.info('Termination of NS with UUID {} failed'.format(ns[0].uuid))
elif (operation == SCALED):
ns = Instance.objects.filter(uuid=message['nsr_id'])
if ns.exists():
if (scale_vnf_type == SCALE_OUT):
if (message['operationState'] == 'COMPLETED'):
vnf_scaling_out_handler(ns[0])
logger.info('Scaling-out VNF of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
logger.info('Scaling-out VNF of NS with UUID {} failed'.format(ns[0].uuid))
elif (scale_vnf_type == SCALE_IN):
if (message['operationState'] == 'COMPLETED'):
vnf_scaling_in_handler(ns[0])
logger.info('Scaling-in VNF of NS with UUID {} completed'.format(ns[0].uuid))
elif (message['operationState'] == 'FAILED'):
logger.info('Scaling-in VNF of NS with UUID {} failed'.format(ns[0].uuid))<|docstring|>Connects on OSM Kafka Bus and subscribes to NS-related topics.<|endoftext|> |
066848554c47f4ed8441365e21100e9e8633da5bb7f9f7de43ae638d376ee799 | def ApplyStorageDrsRecommendation_Task(self, key):
"Applies a recommendation from the recommendation list. Each recommendation can\n be applied only once. In the case of CreateVm and CloneVm a VirtualMachine is\n returned. Other workflows don't have a return value.Applies a recommendation\n from the recommendation list. Each recommendation can be applied only once. In\n the case of CreateVm and CloneVm a VirtualMachine is returned. Other workflows\n don't have a return value.Applies a recommendation from the recommendation\n list. Each recommendation can be applied only once. In the case of CreateVm and\n CloneVm a VirtualMachine is returned. Other workflows don't have a return\n value.\n \n :param key: The key fields of the Recommendations that are applied.\n \n "
return self.delegate('ApplyStorageDrsRecommendation_Task')(key) | Applies a recommendation from the recommendation list. Each recommendation can
be applied only once. In the case of CreateVm and CloneVm a VirtualMachine is
returned. Other workflows don't have a return value.Applies a recommendation
from the recommendation list. Each recommendation can be applied only once. In
the case of CreateVm and CloneVm a VirtualMachine is returned. Other workflows
don't have a return value.Applies a recommendation from the recommendation
list. Each recommendation can be applied only once. In the case of CreateVm and
CloneVm a VirtualMachine is returned. Other workflows don't have a return
value.
:param key: The key fields of the Recommendations that are applied. | pyvisdk/mo/storage_resource_manager.py | ApplyStorageDrsRecommendation_Task | Infinidat/pyvisdk | 0 | python | def ApplyStorageDrsRecommendation_Task(self, key):
"Applies a recommendation from the recommendation list. Each recommendation can\n be applied only once. In the case of CreateVm and CloneVm a VirtualMachine is\n returned. Other workflows don't have a return value.Applies a recommendation\n from the recommendation list. Each recommendation can be applied only once. In\n the case of CreateVm and CloneVm a VirtualMachine is returned. Other workflows\n don't have a return value.Applies a recommendation from the recommendation\n list. Each recommendation can be applied only once. In the case of CreateVm and\n CloneVm a VirtualMachine is returned. Other workflows don't have a return\n value.\n \n :param key: The key fields of the Recommendations that are applied.\n \n "
return self.delegate('ApplyStorageDrsRecommendation_Task')(key) | def ApplyStorageDrsRecommendation_Task(self, key):
"Applies a recommendation from the recommendation list. Each recommendation can\n be applied only once. In the case of CreateVm and CloneVm a VirtualMachine is\n returned. Other workflows don't have a return value.Applies a recommendation\n from the recommendation list. Each recommendation can be applied only once. In\n the case of CreateVm and CloneVm a VirtualMachine is returned. Other workflows\n don't have a return value.Applies a recommendation from the recommendation\n list. Each recommendation can be applied only once. In the case of CreateVm and\n CloneVm a VirtualMachine is returned. Other workflows don't have a return\n value.\n \n :param key: The key fields of the Recommendations that are applied.\n \n "
return self.delegate('ApplyStorageDrsRecommendation_Task')(key)<|docstring|>Applies a recommendation from the recommendation list. Each recommendation can
be applied only once. In the case of CreateVm and CloneVm a VirtualMachine is
returned. Other workflows don't have a return value.Applies a recommendation
from the recommendation list. Each recommendation can be applied only once. In
the case of CreateVm and CloneVm a VirtualMachine is returned. Other workflows
don't have a return value.Applies a recommendation from the recommendation
list. Each recommendation can be applied only once. In the case of CreateVm and
CloneVm a VirtualMachine is returned. Other workflows don't have a return
value.
:param key: The key fields of the Recommendations that are applied.<|endoftext|> |
3893b0448087aef5613d5c1a09ef006758f99b575a7ef3cfe64a9e81964f9c55 | def ApplyStorageDrsRecommendationToPod_Task(self, pod, key):
'Applies a recommendation from the recommendation list. Each recommendation can\n be applied only once.Applies a recommendation from the recommendation list.\n Each recommendation can be applied only once.\n \n :param pod: The storage pod.\n \n :param key: The key field of the Recommendation.\n \n '
return self.delegate('ApplyStorageDrsRecommendationToPod_Task')(pod, key) | Applies a recommendation from the recommendation list. Each recommendation can
be applied only once.Applies a recommendation from the recommendation list.
Each recommendation can be applied only once.
:param pod: The storage pod.
:param key: The key field of the Recommendation. | pyvisdk/mo/storage_resource_manager.py | ApplyStorageDrsRecommendationToPod_Task | Infinidat/pyvisdk | 0 | python | def ApplyStorageDrsRecommendationToPod_Task(self, pod, key):
'Applies a recommendation from the recommendation list. Each recommendation can\n be applied only once.Applies a recommendation from the recommendation list.\n Each recommendation can be applied only once.\n \n :param pod: The storage pod.\n \n :param key: The key field of the Recommendation.\n \n '
return self.delegate('ApplyStorageDrsRecommendationToPod_Task')(pod, key) | def ApplyStorageDrsRecommendationToPod_Task(self, pod, key):
'Applies a recommendation from the recommendation list. Each recommendation can\n be applied only once.Applies a recommendation from the recommendation list.\n Each recommendation can be applied only once.\n \n :param pod: The storage pod.\n \n :param key: The key field of the Recommendation.\n \n '
return self.delegate('ApplyStorageDrsRecommendationToPod_Task')(pod, key)<|docstring|>Applies a recommendation from the recommendation list. Each recommendation can
be applied only once.Applies a recommendation from the recommendation list.
Each recommendation can be applied only once.
:param pod: The storage pod.
:param key: The key field of the Recommendation.<|endoftext|> |
587eec63d7f7f4cfa9d4cb882ccbc35508bb297c38074409a57e109a246f039a | def CancelStorageDrsRecommendation(self, key):
'Cancels a recommendation. Currently only initial placement recommendations can\n be cancelled. Migration recommendations cannot.\n \n :param key: The key field of the Recommendation.\n \n '
return self.delegate('CancelStorageDrsRecommendation')(key) | Cancels a recommendation. Currently only initial placement recommendations can
be cancelled. Migration recommendations cannot.
:param key: The key field of the Recommendation. | pyvisdk/mo/storage_resource_manager.py | CancelStorageDrsRecommendation | Infinidat/pyvisdk | 0 | python | def CancelStorageDrsRecommendation(self, key):
'Cancels a recommendation. Currently only initial placement recommendations can\n be cancelled. Migration recommendations cannot.\n \n :param key: The key field of the Recommendation.\n \n '
return self.delegate('CancelStorageDrsRecommendation')(key) | def CancelStorageDrsRecommendation(self, key):
'Cancels a recommendation. Currently only initial placement recommendations can\n be cancelled. Migration recommendations cannot.\n \n :param key: The key field of the Recommendation.\n \n '
return self.delegate('CancelStorageDrsRecommendation')(key)<|docstring|>Cancels a recommendation. Currently only initial placement recommendations can
be cancelled. Migration recommendations cannot.
:param key: The key field of the Recommendation.<|endoftext|> |
ef64a0efefbd7283e1dc258b5959d99521bec70302c4951df2b90c3ed3d83c50 | def ConfigureDatastoreIORM_Task(self, datastore, spec):
'Changes configuration of storage I/O resource management for a given datastore.\n The changes are applied to all the backing storage devices for the datastore.\n Currently we only support storage I/O resource management on VMFS volumes. In\n order to enable storage I/O resource management on a datstore, we require that\n all the hosts that are attached to the datastore support this feature.Changes\n configuration of storage I/O resource management for a given datastore. The\n changes are applied to all the backing storage devices for the datastore.\n Currently we only support storage I/O resource management on VMFS volumes. In\n order to enable storage I/O resource management on a datstore, we require that\n all the hosts that are attached to the datastore support this feature.\n \n :param datastore: The datastore to be configured.\n \n :param spec: The configuration spec.\n \n '
return self.delegate('ConfigureDatastoreIORM_Task')(datastore, spec) | Changes configuration of storage I/O resource management for a given datastore.
The changes are applied to all the backing storage devices for the datastore.
Currently we only support storage I/O resource management on VMFS volumes. In
order to enable storage I/O resource management on a datstore, we require that
all the hosts that are attached to the datastore support this feature.Changes
configuration of storage I/O resource management for a given datastore. The
changes are applied to all the backing storage devices for the datastore.
Currently we only support storage I/O resource management on VMFS volumes. In
order to enable storage I/O resource management on a datstore, we require that
all the hosts that are attached to the datastore support this feature.
:param datastore: The datastore to be configured.
:param spec: The configuration spec. | pyvisdk/mo/storage_resource_manager.py | ConfigureDatastoreIORM_Task | Infinidat/pyvisdk | 0 | python | def ConfigureDatastoreIORM_Task(self, datastore, spec):
'Changes configuration of storage I/O resource management for a given datastore.\n The changes are applied to all the backing storage devices for the datastore.\n Currently we only support storage I/O resource management on VMFS volumes. In\n order to enable storage I/O resource management on a datstore, we require that\n all the hosts that are attached to the datastore support this feature.Changes\n configuration of storage I/O resource management for a given datastore. The\n changes are applied to all the backing storage devices for the datastore.\n Currently we only support storage I/O resource management on VMFS volumes. In\n order to enable storage I/O resource management on a datstore, we require that\n all the hosts that are attached to the datastore support this feature.\n \n :param datastore: The datastore to be configured.\n \n :param spec: The configuration spec.\n \n '
return self.delegate('ConfigureDatastoreIORM_Task')(datastore, spec) | def ConfigureDatastoreIORM_Task(self, datastore, spec):
'Changes configuration of storage I/O resource management for a given datastore.\n The changes are applied to all the backing storage devices for the datastore.\n Currently we only support storage I/O resource management on VMFS volumes. In\n order to enable storage I/O resource management on a datstore, we require that\n all the hosts that are attached to the datastore support this feature.Changes\n configuration of storage I/O resource management for a given datastore. The\n changes are applied to all the backing storage devices for the datastore.\n Currently we only support storage I/O resource management on VMFS volumes. In\n order to enable storage I/O resource management on a datstore, we require that\n all the hosts that are attached to the datastore support this feature.\n \n :param datastore: The datastore to be configured.\n \n :param spec: The configuration spec.\n \n '
return self.delegate('ConfigureDatastoreIORM_Task')(datastore, spec)<|docstring|>Changes configuration of storage I/O resource management for a given datastore.
The changes are applied to all the backing storage devices for the datastore.
Currently we only support storage I/O resource management on VMFS volumes. In
order to enable storage I/O resource management on a datstore, we require that
all the hosts that are attached to the datastore support this feature.Changes
configuration of storage I/O resource management for a given datastore. The
changes are applied to all the backing storage devices for the datastore.
Currently we only support storage I/O resource management on VMFS volumes. In
order to enable storage I/O resource management on a datstore, we require that
all the hosts that are attached to the datastore support this feature.
:param datastore: The datastore to be configured.
:param spec: The configuration spec.<|endoftext|> |
1589f27c7a262932b613bd33a2d5ee345eac2a8319129ea3b13786630676c0d1 | def ConfigureStorageDrsForPod_Task(self, pod, spec, modify):
'Change the storage DRS configuration for a pod StoragePod.\n \n :param pod: The storage pod.\n \n :param spec: A set of storage Drs configuration changes to apply to the storage pod. The specification can be a complete set of changes or a partial set of changes, applied incrementally.\n \n :param modify: Flag to specify whether the specification ("spec") should be applied incrementally. If "modify" is false and the operation succeeds, then the configuration of the storage pod matches the specification exactly; in this case any unset portions of the specification will result in unset or default portions of the configuration.\n \n '
return self.delegate('ConfigureStorageDrsForPod_Task')(pod, spec, modify) | Change the storage DRS configuration for a pod StoragePod.
:param pod: The storage pod.
:param spec: A set of storage Drs configuration changes to apply to the storage pod. The specification can be a complete set of changes or a partial set of changes, applied incrementally.
:param modify: Flag to specify whether the specification ("spec") should be applied incrementally. If "modify" is false and the operation succeeds, then the configuration of the storage pod matches the specification exactly; in this case any unset portions of the specification will result in unset or default portions of the configuration. | pyvisdk/mo/storage_resource_manager.py | ConfigureStorageDrsForPod_Task | Infinidat/pyvisdk | 0 | python | def ConfigureStorageDrsForPod_Task(self, pod, spec, modify):
'Change the storage DRS configuration for a pod StoragePod.\n \n :param pod: The storage pod.\n \n :param spec: A set of storage Drs configuration changes to apply to the storage pod. The specification can be a complete set of changes or a partial set of changes, applied incrementally.\n \n :param modify: Flag to specify whether the specification ("spec") should be applied incrementally. If "modify" is false and the operation succeeds, then the configuration of the storage pod matches the specification exactly; in this case any unset portions of the specification will result in unset or default portions of the configuration.\n \n '
return self.delegate('ConfigureStorageDrsForPod_Task')(pod, spec, modify) | def ConfigureStorageDrsForPod_Task(self, pod, spec, modify):
'Change the storage DRS configuration for a pod StoragePod.\n \n :param pod: The storage pod.\n \n :param spec: A set of storage Drs configuration changes to apply to the storage pod. The specification can be a complete set of changes or a partial set of changes, applied incrementally.\n \n :param modify: Flag to specify whether the specification ("spec") should be applied incrementally. If "modify" is false and the operation succeeds, then the configuration of the storage pod matches the specification exactly; in this case any unset portions of the specification will result in unset or default portions of the configuration.\n \n '
return self.delegate('ConfigureStorageDrsForPod_Task')(pod, spec, modify)<|docstring|>Change the storage DRS configuration for a pod StoragePod.
:param pod: The storage pod.
:param spec: A set of storage Drs configuration changes to apply to the storage pod. The specification can be a complete set of changes or a partial set of changes, applied incrementally.
:param modify: Flag to specify whether the specification ("spec") should be applied incrementally. If "modify" is false and the operation succeeds, then the configuration of the storage pod matches the specification exactly; in this case any unset portions of the specification will result in unset or default portions of the configuration.<|endoftext|> |
042dc9fe101d0ab9717b9968a1b8a71f0928a2798b501319d22172957306cce9 | def QueryIORMConfigOption(self, host):
'Query configuration options for storage I/O resource management.\n \n :param host: [in] - The host VC will forward the query to. This parameter is ignored by host if this method is called on a host directly.\n \n '
return self.delegate('QueryIORMConfigOption')(host) | Query configuration options for storage I/O resource management.
:param host: [in] - The host VC will forward the query to. This parameter is ignored by host if this method is called on a host directly. | pyvisdk/mo/storage_resource_manager.py | QueryIORMConfigOption | Infinidat/pyvisdk | 0 | python | def QueryIORMConfigOption(self, host):
'Query configuration options for storage I/O resource management.\n \n :param host: [in] - The host VC will forward the query to. This parameter is ignored by host if this method is called on a host directly.\n \n '
return self.delegate('QueryIORMConfigOption')(host) | def QueryIORMConfigOption(self, host):
'Query configuration options for storage I/O resource management.\n \n :param host: [in] - The host VC will forward the query to. This parameter is ignored by host if this method is called on a host directly.\n \n '
return self.delegate('QueryIORMConfigOption')(host)<|docstring|>Query configuration options for storage I/O resource management.
:param host: [in] - The host VC will forward the query to. This parameter is ignored by host if this method is called on a host directly.<|endoftext|> |
89256994dd49c3b6042d34d81fe266b644cbc402f7df726be79c4984027e770e | def RecommendDatastores(self, storageSpec):
"This method returns a StoragePlacementResult object. This API is intended to\n replace the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.This\n method returns a StoragePlacementResult object. This API is intended to replace\n the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.This\n method returns a StoragePlacementResult object. This API is intended to replace\n the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.This\n method returns a StoragePlacementResult object. This API is intended to replace\n the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.\n \n :param storageSpec: \n \n "
return self.delegate('RecommendDatastores')(storageSpec) | This method returns a StoragePlacementResult object. This API is intended to
replace the following existing APIs for SDRS-enabled pods: CreateVm:
StoragePlacementSpec::type == create = CreateVM_Task AddDisk:
StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:
StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:
StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec
parameter in StoragePlacementSpec is required for all workflows. It specifies
which SDRS-enabled pod the user has selected for the VM and/or for each disk.
For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user
selected SDRS pod for the VM, i.e., its system files. For all workflows,
PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given
disk. Note that a DiskLocator must be specified for each disk that the user
requests to create, migrate or clone into an SDRS pod, even if it's the same
pod as the VM or the user has manually selected a datastore within the pod. If
the user has manually selected a datastore, the datastore must be specified in
the workflow specific fields as described below. For CreateVm and AddDisk, the
manually selected datastore must be specified in ConfigSpec.files or
ConfigSpec.deviceChange.device.backing.datastore, the fields should will be
unset if the user wants SDRS to recommend the datastore. For RelocateVm, the
manually selected datastore must be specified in RelocateSpec.datastore or
RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS
recommendations. For CloneVm, the manually selected datastore must be specified
in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the
fields should be unset iff the user wants SDRS recommendations. The remaining
expected input parameters in StoragePlacementSpec will be the same as those for
the existing API as determined by StoragePlacementSpec::type. If a parameter is
optional in the existing API, it will also be optional in the new API.This
method returns a StoragePlacementResult object. This API is intended to replace
the following existing APIs for SDRS-enabled pods: CreateVm:
StoragePlacementSpec::type == create = CreateVM_Task AddDisk:
StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:
StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:
StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec
parameter in StoragePlacementSpec is required for all workflows. It specifies
which SDRS-enabled pod the user has selected for the VM and/or for each disk.
For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user
selected SDRS pod for the VM, i.e., its system files. For all workflows,
PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given
disk. Note that a DiskLocator must be specified for each disk that the user
requests to create, migrate or clone into an SDRS pod, even if it's the same
pod as the VM or the user has manually selected a datastore within the pod. If
the user has manually selected a datastore, the datastore must be specified in
the workflow specific fields as described below. For CreateVm and AddDisk, the
manually selected datastore must be specified in ConfigSpec.files or
ConfigSpec.deviceChange.device.backing.datastore, the fields should will be
unset if the user wants SDRS to recommend the datastore. For RelocateVm, the
manually selected datastore must be specified in RelocateSpec.datastore or
RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS
recommendations. For CloneVm, the manually selected datastore must be specified
in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the
fields should be unset iff the user wants SDRS recommendations. The remaining
expected input parameters in StoragePlacementSpec will be the same as those for
the existing API as determined by StoragePlacementSpec::type. If a parameter is
optional in the existing API, it will also be optional in the new API.This
method returns a StoragePlacementResult object. This API is intended to replace
the following existing APIs for SDRS-enabled pods: CreateVm:
StoragePlacementSpec::type == create = CreateVM_Task AddDisk:
StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:
StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:
StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec
parameter in StoragePlacementSpec is required for all workflows. It specifies
which SDRS-enabled pod the user has selected for the VM and/or for each disk.
For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user
selected SDRS pod for the VM, i.e., its system files. For all workflows,
PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given
disk. Note that a DiskLocator must be specified for each disk that the user
requests to create, migrate or clone into an SDRS pod, even if it's the same
pod as the VM or the user has manually selected a datastore within the pod. If
the user has manually selected a datastore, the datastore must be specified in
the workflow specific fields as described below. For CreateVm and AddDisk, the
manually selected datastore must be specified in ConfigSpec.files or
ConfigSpec.deviceChange.device.backing.datastore, the fields should will be
unset if the user wants SDRS to recommend the datastore. For RelocateVm, the
manually selected datastore must be specified in RelocateSpec.datastore or
RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS
recommendations. For CloneVm, the manually selected datastore must be specified
in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the
fields should be unset iff the user wants SDRS recommendations. The remaining
expected input parameters in StoragePlacementSpec will be the same as those for
the existing API as determined by StoragePlacementSpec::type. If a parameter is
optional in the existing API, it will also be optional in the new API.This
method returns a StoragePlacementResult object. This API is intended to replace
the following existing APIs for SDRS-enabled pods: CreateVm:
StoragePlacementSpec::type == create = CreateVM_Task AddDisk:
StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:
StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:
StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec
parameter in StoragePlacementSpec is required for all workflows. It specifies
which SDRS-enabled pod the user has selected for the VM and/or for each disk.
For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user
selected SDRS pod for the VM, i.e., its system files. For all workflows,
PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given
disk. Note that a DiskLocator must be specified for each disk that the user
requests to create, migrate or clone into an SDRS pod, even if it's the same
pod as the VM or the user has manually selected a datastore within the pod. If
the user has manually selected a datastore, the datastore must be specified in
the workflow specific fields as described below. For CreateVm and AddDisk, the
manually selected datastore must be specified in ConfigSpec.files or
ConfigSpec.deviceChange.device.backing.datastore, the fields should will be
unset if the user wants SDRS to recommend the datastore. For RelocateVm, the
manually selected datastore must be specified in RelocateSpec.datastore or
RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS
recommendations. For CloneVm, the manually selected datastore must be specified
in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the
fields should be unset iff the user wants SDRS recommendations. The remaining
expected input parameters in StoragePlacementSpec will be the same as those for
the existing API as determined by StoragePlacementSpec::type. If a parameter is
optional in the existing API, it will also be optional in the new API.
:param storageSpec: | pyvisdk/mo/storage_resource_manager.py | RecommendDatastores | Infinidat/pyvisdk | 0 | python | def RecommendDatastores(self, storageSpec):
"This method returns a StoragePlacementResult object. This API is intended to\n replace the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.This\n method returns a StoragePlacementResult object. This API is intended to replace\n the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.This\n method returns a StoragePlacementResult object. This API is intended to replace\n the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.This\n method returns a StoragePlacementResult object. This API is intended to replace\n the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.\n \n :param storageSpec: \n \n "
return self.delegate('RecommendDatastores')(storageSpec) | def RecommendDatastores(self, storageSpec):
"This method returns a StoragePlacementResult object. This API is intended to\n replace the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.This\n method returns a StoragePlacementResult object. This API is intended to replace\n the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.This\n method returns a StoragePlacementResult object. This API is intended to replace\n the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.This\n method returns a StoragePlacementResult object. This API is intended to replace\n the following existing APIs for SDRS-enabled pods: CreateVm:\n StoragePlacementSpec::type == create = CreateVM_Task AddDisk:\n StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:\n StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:\n StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec\n parameter in StoragePlacementSpec is required for all workflows. It specifies\n which SDRS-enabled pod the user has selected for the VM and/or for each disk.\n For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user\n selected SDRS pod for the VM, i.e., its system files. For all workflows,\n PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given\n disk. Note that a DiskLocator must be specified for each disk that the user\n requests to create, migrate or clone into an SDRS pod, even if it's the same\n pod as the VM or the user has manually selected a datastore within the pod. If\n the user has manually selected a datastore, the datastore must be specified in\n the workflow specific fields as described below. For CreateVm and AddDisk, the\n manually selected datastore must be specified in ConfigSpec.files or\n ConfigSpec.deviceChange.device.backing.datastore, the fields should will be\n unset if the user wants SDRS to recommend the datastore. For RelocateVm, the\n manually selected datastore must be specified in RelocateSpec.datastore or\n RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS\n recommendations. For CloneVm, the manually selected datastore must be specified\n in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the\n fields should be unset iff the user wants SDRS recommendations. The remaining\n expected input parameters in StoragePlacementSpec will be the same as those for\n the existing API as determined by StoragePlacementSpec::type. If a parameter is\n optional in the existing API, it will also be optional in the new API.\n \n :param storageSpec: \n \n "
return self.delegate('RecommendDatastores')(storageSpec)<|docstring|>This method returns a StoragePlacementResult object. This API is intended to
replace the following existing APIs for SDRS-enabled pods: CreateVm:
StoragePlacementSpec::type == create = CreateVM_Task AddDisk:
StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:
StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:
StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec
parameter in StoragePlacementSpec is required for all workflows. It specifies
which SDRS-enabled pod the user has selected for the VM and/or for each disk.
For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user
selected SDRS pod for the VM, i.e., its system files. For all workflows,
PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given
disk. Note that a DiskLocator must be specified for each disk that the user
requests to create, migrate or clone into an SDRS pod, even if it's the same
pod as the VM or the user has manually selected a datastore within the pod. If
the user has manually selected a datastore, the datastore must be specified in
the workflow specific fields as described below. For CreateVm and AddDisk, the
manually selected datastore must be specified in ConfigSpec.files or
ConfigSpec.deviceChange.device.backing.datastore, the fields should will be
unset if the user wants SDRS to recommend the datastore. For RelocateVm, the
manually selected datastore must be specified in RelocateSpec.datastore or
RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS
recommendations. For CloneVm, the manually selected datastore must be specified
in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the
fields should be unset iff the user wants SDRS recommendations. The remaining
expected input parameters in StoragePlacementSpec will be the same as those for
the existing API as determined by StoragePlacementSpec::type. If a parameter is
optional in the existing API, it will also be optional in the new API.This
method returns a StoragePlacementResult object. This API is intended to replace
the following existing APIs for SDRS-enabled pods: CreateVm:
StoragePlacementSpec::type == create = CreateVM_Task AddDisk:
StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:
StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:
StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec
parameter in StoragePlacementSpec is required for all workflows. It specifies
which SDRS-enabled pod the user has selected for the VM and/or for each disk.
For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user
selected SDRS pod for the VM, i.e., its system files. For all workflows,
PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given
disk. Note that a DiskLocator must be specified for each disk that the user
requests to create, migrate or clone into an SDRS pod, even if it's the same
pod as the VM or the user has manually selected a datastore within the pod. If
the user has manually selected a datastore, the datastore must be specified in
the workflow specific fields as described below. For CreateVm and AddDisk, the
manually selected datastore must be specified in ConfigSpec.files or
ConfigSpec.deviceChange.device.backing.datastore, the fields should will be
unset if the user wants SDRS to recommend the datastore. For RelocateVm, the
manually selected datastore must be specified in RelocateSpec.datastore or
RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS
recommendations. For CloneVm, the manually selected datastore must be specified
in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the
fields should be unset iff the user wants SDRS recommendations. The remaining
expected input parameters in StoragePlacementSpec will be the same as those for
the existing API as determined by StoragePlacementSpec::type. If a parameter is
optional in the existing API, it will also be optional in the new API.This
method returns a StoragePlacementResult object. This API is intended to replace
the following existing APIs for SDRS-enabled pods: CreateVm:
StoragePlacementSpec::type == create = CreateVM_Task AddDisk:
StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:
StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:
StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec
parameter in StoragePlacementSpec is required for all workflows. It specifies
which SDRS-enabled pod the user has selected for the VM and/or for each disk.
For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user
selected SDRS pod for the VM, i.e., its system files. For all workflows,
PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given
disk. Note that a DiskLocator must be specified for each disk that the user
requests to create, migrate or clone into an SDRS pod, even if it's the same
pod as the VM or the user has manually selected a datastore within the pod. If
the user has manually selected a datastore, the datastore must be specified in
the workflow specific fields as described below. For CreateVm and AddDisk, the
manually selected datastore must be specified in ConfigSpec.files or
ConfigSpec.deviceChange.device.backing.datastore, the fields should will be
unset if the user wants SDRS to recommend the datastore. For RelocateVm, the
manually selected datastore must be specified in RelocateSpec.datastore or
RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS
recommendations. For CloneVm, the manually selected datastore must be specified
in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the
fields should be unset iff the user wants SDRS recommendations. The remaining
expected input parameters in StoragePlacementSpec will be the same as those for
the existing API as determined by StoragePlacementSpec::type. If a parameter is
optional in the existing API, it will also be optional in the new API.This
method returns a StoragePlacementResult object. This API is intended to replace
the following existing APIs for SDRS-enabled pods: CreateVm:
StoragePlacementSpec::type == create = CreateVM_Task AddDisk:
StoragePlacementSpec::type == reconfigure = ReconfigVM_Task RelocateVm:
StoragePlacementSpec::type == relocate = RelocateVM_Task CloneVm:
StoragePlacementSpec::type == clone = CloneVM_Task The PodSelectionSpec
parameter in StoragePlacementSpec is required for all workflows. It specifies
which SDRS-enabled pod the user has selected for the VM and/or for each disk.
For CreateVm, RelocateVm and CloneVm, PodSelectionSpec.storagePod is the user
selected SDRS pod for the VM, i.e., its system files. For all workflows,
PodSelectionSpec.disk.storagePod is the user selected SDRS pod for the given
disk. Note that a DiskLocator must be specified for each disk that the user
requests to create, migrate or clone into an SDRS pod, even if it's the same
pod as the VM or the user has manually selected a datastore within the pod. If
the user has manually selected a datastore, the datastore must be specified in
the workflow specific fields as described below. For CreateVm and AddDisk, the
manually selected datastore must be specified in ConfigSpec.files or
ConfigSpec.deviceChange.device.backing.datastore, the fields should will be
unset if the user wants SDRS to recommend the datastore. For RelocateVm, the
manually selected datastore must be specified in RelocateSpec.datastore or
RelocateSpec.disk.datastore; the fields should be unset iff the user wants SDRS
recommendations. For CloneVm, the manually selected datastore must be specified
in CloneSpec.location.datastore or CloneSpec.location.disk[].datastore; the
fields should be unset iff the user wants SDRS recommendations. The remaining
expected input parameters in StoragePlacementSpec will be the same as those for
the existing API as determined by StoragePlacementSpec::type. If a parameter is
optional in the existing API, it will also be optional in the new API.
:param storageSpec:<|endoftext|> |
70c29380fd310a7e52b60865a145a2768a1360304b659cdb2e1bf2ddcff5efff | def RefreshStorageDrsRecommendation(self, pod):
'Make Storage DRS invoke again on the specified pod StoragePod and return a new\n list of recommendations. Concurrent "refresh" requests may be combined together\n and trigger only one Storage DRS invocation.\n \n :param pod: The storage pod. The recommendations generated is stored at PodStorageDrsEntry#recommendation.\n \n '
return self.delegate('RefreshStorageDrsRecommendation')(pod) | Make Storage DRS invoke again on the specified pod StoragePod and return a new
list of recommendations. Concurrent "refresh" requests may be combined together
and trigger only one Storage DRS invocation.
:param pod: The storage pod. The recommendations generated is stored at PodStorageDrsEntry#recommendation. | pyvisdk/mo/storage_resource_manager.py | RefreshStorageDrsRecommendation | Infinidat/pyvisdk | 0 | python | def RefreshStorageDrsRecommendation(self, pod):
'Make Storage DRS invoke again on the specified pod StoragePod and return a new\n list of recommendations. Concurrent "refresh" requests may be combined together\n and trigger only one Storage DRS invocation.\n \n :param pod: The storage pod. The recommendations generated is stored at PodStorageDrsEntry#recommendation.\n \n '
return self.delegate('RefreshStorageDrsRecommendation')(pod) | def RefreshStorageDrsRecommendation(self, pod):
'Make Storage DRS invoke again on the specified pod StoragePod and return a new\n list of recommendations. Concurrent "refresh" requests may be combined together\n and trigger only one Storage DRS invocation.\n \n :param pod: The storage pod. The recommendations generated is stored at PodStorageDrsEntry#recommendation.\n \n '
return self.delegate('RefreshStorageDrsRecommendation')(pod)<|docstring|>Make Storage DRS invoke again on the specified pod StoragePod and return a new
list of recommendations. Concurrent "refresh" requests may be combined together
and trigger only one Storage DRS invocation.
:param pod: The storage pod. The recommendations generated is stored at PodStorageDrsEntry#recommendation.<|endoftext|> |
61bb95f0f814ef02df56fdcbccd7796904b600f51e213d5ce96c3a009502383d | def debug(msg):
'If in debug mode, send a debug message to stdout.'
if DEBUG_ENABLED:
print('Debug: {}'.format(msg)) | If in debug mode, send a debug message to stdout. | shopify_alexa.py | debug | johntelforduk/shopify-alexa-skill | 0 | python | def debug(msg):
if DEBUG_ENABLED:
print('Debug: {}'.format(msg)) | def debug(msg):
if DEBUG_ENABLED:
print('Debug: {}'.format(msg))<|docstring|>If in debug mode, send a debug message to stdout.<|endoftext|> |
9260269b7d7d6bc0061d541ab0f3d7d0cd15bd92a95e1ac51f2e35425aeb6cb4 | def build_speech_response(title: str, ssml_output: str, plain_output: str) -> dict:
'Build a speech JSON representation of the title, output text, and end of session.'
return {'outputSpeech': {'type': 'SSML', 'ssml': ssml_output}, 'card': {'type': 'Simple', 'title': title, 'content': plain_output}, 'shouldEndSession': True} | Build a speech JSON representation of the title, output text, and end of session. | shopify_alexa.py | build_speech_response | johntelforduk/shopify-alexa-skill | 0 | python | def build_speech_response(title: str, ssml_output: str, plain_output: str) -> dict:
return {'outputSpeech': {'type': 'SSML', 'ssml': ssml_output}, 'card': {'type': 'Simple', 'title': title, 'content': plain_output}, 'shouldEndSession': True} | def build_speech_response(title: str, ssml_output: str, plain_output: str) -> dict:
return {'outputSpeech': {'type': 'SSML', 'ssml': ssml_output}, 'card': {'type': 'Simple', 'title': title, 'content': plain_output}, 'shouldEndSession': True}<|docstring|>Build a speech JSON representation of the title, output text, and end of session.<|endoftext|> |
3e62722f77e8eeadd2818aff19fc4d39cce85c6e437b8fde0d5828d76d43f113 | def build_response(session_attributes, speech_response):
'Build the full response JSON from the speech response.'
return {'version': '1.0', 'sessionAttributes': session_attributes, 'response': speech_response} | Build the full response JSON from the speech response. | shopify_alexa.py | build_response | johntelforduk/shopify-alexa-skill | 0 | python | def build_response(session_attributes, speech_response):
return {'version': '1.0', 'sessionAttributes': session_attributes, 'response': speech_response} | def build_response(session_attributes, speech_response):
return {'version': '1.0', 'sessionAttributes': session_attributes, 'response': speech_response}<|docstring|>Build the full response JSON from the speech response.<|endoftext|> |
6125985538a0d4e994c3d63690842ff034fbfc623af543888d0a7e2839138a1a | def lambda_handler(event, context):
'Function called by Lambda. Output JSON returned to Alexa.'
assert (event is not '')
assert (context is not '')
print('event =', event)
print('context =', context)
this_skill = Skill()
this_skill.shop.get_store_info()
today = this_skill.date_as_str(delta_days=0)
two_days_ago = this_skill.date_as_str(delta_days=(- 2))
this_skill.shop.get_orders(from_date=two_days_ago, to_date=today)
message = 'Hi, this is the Shopify Alexa skill. You can ask me things like, "How many orders have I had today?"'
if (event['request']['type'] == 'IntentRequest'):
intent = event['request']['intent']['name']
print('intent =', intent)
if (intent == 'OrdersTodayIntent'):
message = this_skill.number_orders_today()
elif (intent == 'OrdersYesterdayIntent'):
message = this_skill.number_orders_yesterday()
elif (intent == 'GrossSalesTodayIntent'):
message = this_skill.gross_sales_today()
elif (intent == 'GrossSalesYesterdayIntent'):
message = this_skill.gross_sales_yesterday()
elif (intent == 'MostRecentOrderIntent'):
message = this_skill.most_recent_order()
card_title = 'Shopify Skill'
speech_output = (('<speak>' + message) + '</speak>')
card_output = message
return build_response(session_attributes={}, speech_response=build_speech_response(title=card_title, ssml_output=speech_output, plain_output=card_output)) | Function called by Lambda. Output JSON returned to Alexa. | shopify_alexa.py | lambda_handler | johntelforduk/shopify-alexa-skill | 0 | python | def lambda_handler(event, context):
assert (event is not )
assert (context is not )
print('event =', event)
print('context =', context)
this_skill = Skill()
this_skill.shop.get_store_info()
today = this_skill.date_as_str(delta_days=0)
two_days_ago = this_skill.date_as_str(delta_days=(- 2))
this_skill.shop.get_orders(from_date=two_days_ago, to_date=today)
message = 'Hi, this is the Shopify Alexa skill. You can ask me things like, "How many orders have I had today?"'
if (event['request']['type'] == 'IntentRequest'):
intent = event['request']['intent']['name']
print('intent =', intent)
if (intent == 'OrdersTodayIntent'):
message = this_skill.number_orders_today()
elif (intent == 'OrdersYesterdayIntent'):
message = this_skill.number_orders_yesterday()
elif (intent == 'GrossSalesTodayIntent'):
message = this_skill.gross_sales_today()
elif (intent == 'GrossSalesYesterdayIntent'):
message = this_skill.gross_sales_yesterday()
elif (intent == 'MostRecentOrderIntent'):
message = this_skill.most_recent_order()
card_title = 'Shopify Skill'
speech_output = (('<speak>' + message) + '</speak>')
card_output = message
return build_response(session_attributes={}, speech_response=build_speech_response(title=card_title, ssml_output=speech_output, plain_output=card_output)) | def lambda_handler(event, context):
assert (event is not )
assert (context is not )
print('event =', event)
print('context =', context)
this_skill = Skill()
this_skill.shop.get_store_info()
today = this_skill.date_as_str(delta_days=0)
two_days_ago = this_skill.date_as_str(delta_days=(- 2))
this_skill.shop.get_orders(from_date=two_days_ago, to_date=today)
message = 'Hi, this is the Shopify Alexa skill. You can ask me things like, "How many orders have I had today?"'
if (event['request']['type'] == 'IntentRequest'):
intent = event['request']['intent']['name']
print('intent =', intent)
if (intent == 'OrdersTodayIntent'):
message = this_skill.number_orders_today()
elif (intent == 'OrdersYesterdayIntent'):
message = this_skill.number_orders_yesterday()
elif (intent == 'GrossSalesTodayIntent'):
message = this_skill.gross_sales_today()
elif (intent == 'GrossSalesYesterdayIntent'):
message = this_skill.gross_sales_yesterday()
elif (intent == 'MostRecentOrderIntent'):
message = this_skill.most_recent_order()
card_title = 'Shopify Skill'
speech_output = (('<speak>' + message) + '</speak>')
card_output = message
return build_response(session_attributes={}, speech_response=build_speech_response(title=card_title, ssml_output=speech_output, plain_output=card_output))<|docstring|>Function called by Lambda. Output JSON returned to Alexa.<|endoftext|> |
22a5374a2f2be2e4dcefb94623238a8465270d35801d04f8e295b70f3e5a900a | def __init__(self):
'Get ready tp make calls to Shopify API.'
load_dotenv(verbose=True)
shop_name = getenv('SHOP_NAME')
api_version = getenv('API_VERSION')
api_key = getenv('API_KEY')
password = getenv('PASSWORD')
self.shop_url = ('https://%s:[email protected]/admin/api/%s/' % (api_key, password, shop_name, api_version))
self.orders = []
self.money_format = ''
self.timezone = ''
self.timezone_offset = '' | Get ready tp make calls to Shopify API. | shopify_alexa.py | __init__ | johntelforduk/shopify-alexa-skill | 0 | python | def __init__(self):
load_dotenv(verbose=True)
shop_name = getenv('SHOP_NAME')
api_version = getenv('API_VERSION')
api_key = getenv('API_KEY')
password = getenv('PASSWORD')
self.shop_url = ('https://%s:[email protected]/admin/api/%s/' % (api_key, password, shop_name, api_version))
self.orders = []
self.money_format =
self.timezone =
self.timezone_offset = | def __init__(self):
load_dotenv(verbose=True)
shop_name = getenv('SHOP_NAME')
api_version = getenv('API_VERSION')
api_key = getenv('API_KEY')
password = getenv('PASSWORD')
self.shop_url = ('https://%s:[email protected]/admin/api/%s/' % (api_key, password, shop_name, api_version))
self.orders = []
self.money_format =
self.timezone =
self.timezone_offset = <|docstring|>Get ready tp make calls to Shopify API.<|endoftext|> |
af3619a75ce66327142f96afb31ac27b5c1a67e0261183176c78687db1b144ba | def get_store_info(self):
'Obtain some reference data about the store.'
url = (self.shop_url + 'shop.json')
request = requests.get(url)
if (request.status_code != 200):
debug('Shopify.get_store_info : request.status_code = '.format(request.status_code))
else:
request_dict = json.loads(s=request.text)
request_shop = request_dict['shop']
self.money_format = request_shop['money_format']
self.timezone = request_shop['timezone']
self.timezone_offset = self.timezone[4:10]
debug('Shopify.get_store_info : self.money_format = {}'.format(self.money_format))
debug('Shopify.get_store_info : self.timezone = {}'.format(self.timezone))
debug('Shopify.get_store_info : self.timezone_offset = {}'.format(self.timezone_offset)) | Obtain some reference data about the store. | shopify_alexa.py | get_store_info | johntelforduk/shopify-alexa-skill | 0 | python | def get_store_info(self):
url = (self.shop_url + 'shop.json')
request = requests.get(url)
if (request.status_code != 200):
debug('Shopify.get_store_info : request.status_code = '.format(request.status_code))
else:
request_dict = json.loads(s=request.text)
request_shop = request_dict['shop']
self.money_format = request_shop['money_format']
self.timezone = request_shop['timezone']
self.timezone_offset = self.timezone[4:10]
debug('Shopify.get_store_info : self.money_format = {}'.format(self.money_format))
debug('Shopify.get_store_info : self.timezone = {}'.format(self.timezone))
debug('Shopify.get_store_info : self.timezone_offset = {}'.format(self.timezone_offset)) | def get_store_info(self):
url = (self.shop_url + 'shop.json')
request = requests.get(url)
if (request.status_code != 200):
debug('Shopify.get_store_info : request.status_code = '.format(request.status_code))
else:
request_dict = json.loads(s=request.text)
request_shop = request_dict['shop']
self.money_format = request_shop['money_format']
self.timezone = request_shop['timezone']
self.timezone_offset = self.timezone[4:10]
debug('Shopify.get_store_info : self.money_format = {}'.format(self.money_format))
debug('Shopify.get_store_info : self.timezone = {}'.format(self.timezone))
debug('Shopify.get_store_info : self.timezone_offset = {}'.format(self.timezone_offset))<|docstring|>Obtain some reference data about the store.<|endoftext|> |
ccce2c599b8fa4c708f7073ebb397cd7793d126e7a6b6562690db9d74f293367 | @staticmethod
def is_date(test: str) -> bool:
"Return true iff the parm string is a valid date in format yyyy-mm-dd. For example '2020-04-29'."
if (len(test) != 10):
debug('Shopify.is_date : test = {}'.format(test))
return False
year = test[0:4]
month = test[5:7]
day = test[8:10]
if (year.isdecimal() and month.isdecimal() and day.isdecimal()):
return True
debug('Shopify.is_date : year = {} month = {} day = {}'.format(year, month, day))
return False | Return true iff the parm string is a valid date in format yyyy-mm-dd. For example '2020-04-29'. | shopify_alexa.py | is_date | johntelforduk/shopify-alexa-skill | 0 | python | @staticmethod
def is_date(test: str) -> bool:
if (len(test) != 10):
debug('Shopify.is_date : test = {}'.format(test))
return False
year = test[0:4]
month = test[5:7]
day = test[8:10]
if (year.isdecimal() and month.isdecimal() and day.isdecimal()):
return True
debug('Shopify.is_date : year = {} month = {} day = {}'.format(year, month, day))
return False | @staticmethod
def is_date(test: str) -> bool:
if (len(test) != 10):
debug('Shopify.is_date : test = {}'.format(test))
return False
year = test[0:4]
month = test[5:7]
day = test[8:10]
if (year.isdecimal() and month.isdecimal() and day.isdecimal()):
return True
debug('Shopify.is_date : year = {} month = {} day = {}'.format(year, month, day))
return False<|docstring|>Return true iff the parm string is a valid date in format yyyy-mm-dd. For example '2020-04-29'.<|endoftext|> |
943d16355778228189cad63f3624c3bb07d33cff1680cc46824164588dd76185 | @staticmethod
def date_from_datetime(date_time: str) -> str:
'For parm datetime string in format yyyy-mm-ddThh:mm:ss+zz:zz, return the date part only.'
date_only = date_time[0:10]
return date_only | For parm datetime string in format yyyy-mm-ddThh:mm:ss+zz:zz, return the date part only. | shopify_alexa.py | date_from_datetime | johntelforduk/shopify-alexa-skill | 0 | python | @staticmethod
def date_from_datetime(date_time: str) -> str:
date_only = date_time[0:10]
return date_only | @staticmethod
def date_from_datetime(date_time: str) -> str:
date_only = date_time[0:10]
return date_only<|docstring|>For parm datetime string in format yyyy-mm-ddThh:mm:ss+zz:zz, return the date part only.<|endoftext|> |
b393617810e014efc8082cca8064464bbeae817c5651a04c861eeee746a0fc7f | def get_orders(self, from_date: str, to_date: str):
'Create a list of orders between parm From and To dates.'
assert self.is_date(test=from_date)
assert self.is_date(test=to_date)
url = ((((((((((((self.shop_url + 'orders.json') + '?created_at_min=') + from_date) + 'T00:00:00') + self.timezone_offset) + '&created_at_max=') + to_date) + 'T23:59:59') + self.timezone_offset) + '&status=any') + '&limit=250') + '&fields=id,created_at,total_price,status,financial_status')
request = requests.get(url)
if (request.status_code != 200):
debug('Shopify.get_orders : request.status_code = '.format(request.status_code))
self.orders = None
else:
request_dict = json.loads(s=request.text)
if DEBUG_ENABLED:
print('Shopify.get_orders : request_dict =', request_dict)
request_orders = request_dict['orders']
if DEBUG_ENABLED:
print('Shopify.get_orders : orders =', request_orders)
self.orders = request_orders | Create a list of orders between parm From and To dates. | shopify_alexa.py | get_orders | johntelforduk/shopify-alexa-skill | 0 | python | def get_orders(self, from_date: str, to_date: str):
assert self.is_date(test=from_date)
assert self.is_date(test=to_date)
url = ((((((((((((self.shop_url + 'orders.json') + '?created_at_min=') + from_date) + 'T00:00:00') + self.timezone_offset) + '&created_at_max=') + to_date) + 'T23:59:59') + self.timezone_offset) + '&status=any') + '&limit=250') + '&fields=id,created_at,total_price,status,financial_status')
request = requests.get(url)
if (request.status_code != 200):
debug('Shopify.get_orders : request.status_code = '.format(request.status_code))
self.orders = None
else:
request_dict = json.loads(s=request.text)
if DEBUG_ENABLED:
print('Shopify.get_orders : request_dict =', request_dict)
request_orders = request_dict['orders']
if DEBUG_ENABLED:
print('Shopify.get_orders : orders =', request_orders)
self.orders = request_orders | def get_orders(self, from_date: str, to_date: str):
assert self.is_date(test=from_date)
assert self.is_date(test=to_date)
url = ((((((((((((self.shop_url + 'orders.json') + '?created_at_min=') + from_date) + 'T00:00:00') + self.timezone_offset) + '&created_at_max=') + to_date) + 'T23:59:59') + self.timezone_offset) + '&status=any') + '&limit=250') + '&fields=id,created_at,total_price,status,financial_status')
request = requests.get(url)
if (request.status_code != 200):
debug('Shopify.get_orders : request.status_code = '.format(request.status_code))
self.orders = None
else:
request_dict = json.loads(s=request.text)
if DEBUG_ENABLED:
print('Shopify.get_orders : request_dict =', request_dict)
request_orders = request_dict['orders']
if DEBUG_ENABLED:
print('Shopify.get_orders : orders =', request_orders)
self.orders = request_orders<|docstring|>Create a list of orders between parm From and To dates.<|endoftext|> |
1971993a294ce1f3b9d0222ecb4e3d02ee49a307aaa6a1a369422ec2ebd06bca | def orders_on_date(self, target_date: str) -> list:
'For parm date (in yyyy-mm-dd format), return a list containing the orders on that day.'
assert self.is_date(target_date)
output = []
for each_order in self.orders:
order_date = self.date_from_datetime(each_order['created_at'])
if (order_date == target_date):
output.append(each_order)
return output | For parm date (in yyyy-mm-dd format), return a list containing the orders on that day. | shopify_alexa.py | orders_on_date | johntelforduk/shopify-alexa-skill | 0 | python | def orders_on_date(self, target_date: str) -> list:
assert self.is_date(target_date)
output = []
for each_order in self.orders:
order_date = self.date_from_datetime(each_order['created_at'])
if (order_date == target_date):
output.append(each_order)
return output | def orders_on_date(self, target_date: str) -> list:
assert self.is_date(target_date)
output = []
for each_order in self.orders:
order_date = self.date_from_datetime(each_order['created_at'])
if (order_date == target_date):
output.append(each_order)
return output<|docstring|>For parm date (in yyyy-mm-dd format), return a list containing the orders on that day.<|endoftext|> |
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