def _construct_aline_collections(alines, dtix=None):
"""construct arbitrary line collections
Parameters
----------
alines : sequence
sequences of segments, which are sequences of lines,
which are sequences of two or more points ( date[time], price ) or (x,y)
date[time] may be (a) pandas.to_datetime parseable string,
(b) pandas Timestamp, or
(c) python datetime.datetime or datetime.date
alines may also be a dict, containing
the following keys:
'alines' : the same as defined above: sequence of price, or dates, or segments
'colors' : colors for the above alines
'linestyle' : line types for the above alines
'linewidths' : line types for the above alines
dtix: date index for the x-axis, used for converting the dates when
x-values are 'evenly spaced integers' (as when skipping non-trading days)
Returns
-------
ret : list
lines collections
"""
if alines is None:
return None
if isinstance(alines, dict):
aconfig = _process_kwargs(alines, _valid_lines_kwargs())
alines = aconfig['alines']
else:
aconfig = _process_kwargs({}, _valid_lines_kwargs())
#print('aconfig=',aconfig)
#print('alines=',alines)
alines = _alines_validator(alines, returnStandardizedValue=True)
if alines is None:
raise ValueError('Unable to standardize alines value: ' + str(alines))
alines = _convert_segment_dates(alines, dtix)
lw = aconfig['linewidths']
co = aconfig['colors']
ls = aconfig['linestyle']
al = aconfig['alpha']
lcollection = LineCollection(alines,
colors=co,
linewidths=lw,
linestyles=ls,
antialiaseds=(0, ),
alpha=al)
return lcollection
def _valid_plot_kwargs():
'''
Construct and return the "valid kwargs table" for the mplfinance.plot() function.
A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are the
valid key-words for the function. The value for each key is a dict containing
2 specific keys: "Default", and "Validator" with the following values:
"Default" - The default value for the kwarg if none is specified.
"Validator" - A function that takes the caller specified value for the kwarg,
and validates that it is the correct type, and (for kwargs with
a limited set of allowed values) may also validate that the
kwarg value is one of the allowed values.
'''
vkwargs = {
'columns' : { 'Default' : None, # use default names: ('Open', 'High', 'Low', 'Close', 'Volume')
'Validator' : lambda value: isinstance(value, (tuple, list))
and len(value) == 5
and all(isinstance(c, str) for c in value) },
'type' : { 'Default' : 'ohlc',
'Validator' : lambda value: value in _get_valid_plot_types() },
'style' : { 'Default' : None,
'Validator' : _styles._valid_mpf_style },
'volume' : { 'Default' : False,
'Validator' : lambda value: isinstance(value,bool) or isinstance(value,mpl_axes.Axes) },
'mav' : { 'Default' : None,
'Validator' : _mav_validator },
'renko_params' : { 'Default' : dict(),
'Validator' : lambda value: isinstance(value,dict) },
'pnf_params' : { 'Default' : dict(),
'Validator' : lambda value: isinstance(value,dict) },
'study' : { 'Default' : None,
'Validator' : lambda value: _kwarg_not_implemented(value) },
'marketcolors' : { 'Default' : None, # use 'style' for default, instead.
'Validator' : lambda value: isinstance(value,dict) },
'no_xgaps' : { 'Default' : True, # None means follow default logic below:
'Validator' : lambda value: _warn_no_xgaps_deprecated(value) },
'show_nontrading' : { 'Default' : False,
'Validator' : lambda value: isinstance(value,bool) },
'figscale' : { 'Default' : None, # scale base figure size up or down.
'Validator' : lambda value: isinstance(value,float) or isinstance(value,int) },
'figratio' : { 'Default' : None, # aspect ratio; scaled to 8.0 height
'Validator' : lambda value: isinstance(value,(tuple,list))
and len(value) == 2
and isinstance(value[0],(float,int))
and isinstance(value[1],(float,int)) },
'figsize' : { 'Default' : None, # figure size; overrides figratio and figscale
'Validator' : lambda value: isinstance(value,(tuple,list))
and len(value) == 2
and isinstance(value[0],(float,int))
and isinstance(value[1],(float,int)) },
'linecolor' : { 'Default' : None, # line color in line plot
'Validator' : lambda value: mcolors.is_color_like(value) },
'title' : { 'Default' : None, # Figure Title
'Validator' : lambda value: isinstance(value,(str,dict)) },
'axtitle' : { 'Default' : None, # Axes Title (subplot title)
'Validator' : lambda value: isinstance(value,str) },
'ylabel' : { 'Default' : 'Price', # y-axis label
'Validator' : lambda value: isinstance(value,str) },
'ylabel_lower' : { 'Default' : None, # y-axis label default logic below
'Validator' : lambda value: isinstance(value,str) },
'addplot' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,dict) or (isinstance(value,list) and all([isinstance(d,dict) for d in value])) },
'savefig' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,dict) or isinstance(value,str) or isinstance(value, io.BytesIO) },
'block' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,bool) },
'returnfig' : { 'Default' : False,
'Validator' : lambda value: isinstance(value,bool) },
'return_calculated_values' : {'Default' : None,
'Validator' : lambda value: isinstance(value, dict) and len(value) == 0},
'set_ylim' : {'Default' : None,
'Validator' : lambda value: _warn_set_ylim_deprecated(value) },
'ylim' : {'Default' : None,
'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2
and all([isinstance(v,(int,float)) for v in value])},
'xlim' : {'Default' : None,
'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2
and all([isinstance(v,(int,float)) for v in value])},
'set_ylim_panelB' : {'Default' : None,
'Validator' : lambda value: _warn_set_ylim_deprecated(value) },
'hlines' : { 'Default' : None,
'Validator' : lambda value: _hlines_validator(value) },
'vlines' : { 'Default' : None,
'Validator' : lambda value: _vlines_validator(value) },
'alines' : { 'Default' : None,
'Validator' : lambda value: _alines_validator(value) },
'tlines' : { 'Default' : None,
'Validator' : lambda value: _tlines_validator(value) },
'panel_ratios' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,(tuple,list)) and len(value) <= 10 and
all([isinstance(v,(int,float)) for v in value]) },
'main_panel' : { 'Default' : 0,
'Validator' : lambda value: _valid_panel_id(value) },
'volume_panel' : { 'Default' : 1,
'Validator' : lambda value: _valid_panel_id(value) },
'num_panels' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,int) and value in range(1,10+1) },
'datetime_format' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,str) },
'xrotation' : { 'Default' : 45,
'Validator' : lambda value: isinstance(value,(int,float)) },
'axisoff' : { 'Default' : False,
'Validator' : lambda value: isinstance(value,bool) },
'closefig' : { 'Default' : 'auto',
'Validator' : lambda value: isinstance(value,bool) },
'fill_between' : { 'Default' : None,
'Validator' : lambda value: _num_or_seq_of_num(value) or
(isinstance(value,dict) and 'y1' in value and
_num_or_seq_of_num(value['y1'])) },
'tight_layout' : { 'Default' : False,
'Validator' : lambda value: isinstance(value,bool) },
'width_adjuster_version' : { 'Default' : 'v1',
'Validator' : lambda value: value in ('v0', 'v1') },
'scale_width_adjustment' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 },
'update_width_config' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 },
'return_width_config' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,dict) and len(value)==0 },
'saxbelow' : { 'Default' : True, # Issue#115 Comment#639446764
'Validator' : lambda value: isinstance(value,bool) },
'scale_padding' : { 'Default' : 1.0, # Issue#193
'Validator' : lambda value: _scale_padding_validator(value) },
'ax' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,mpl_axes.Axes) },
'volume_exponent' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,int) or value == 'legacy'},
'tz_localize' : { 'Default' : True,
'Validator' : lambda value: isinstance(value,bool) },
}
_validate_vkwargs_dict(vkwargs)
return vkwargs
def _valid_plot_kwargs():
'''
Construct and return the "valid kwargs table" for the mplfinance.plot() function.
A valid kwargs table is a `dict` of `dict`s. The keys of the outer dict are the
valid key-words for the function. The value for each key is a dict containing
2 specific keys: "Default", and "Validator" with the following values:
"Default" - The default value for the kwarg if none is specified.
"Validator" - A function that takes the caller specified value for the kwarg,
and validates that it is the correct type, and (for kwargs with
a limited set of allowed values) may also validate that the
kwarg value is one of the allowed values.
'''
vkwargs = {
'columns': {
'Default': ('Open', 'High', 'Low', 'Close', 'Volume'),
'Validator':
lambda value: isinstance(value, (tuple, list)) and len(value) == 5
and all(isinstance(c, str) for c in value)
},
'type': {
'Default':
'ohlc',
'Validator':
lambda value: value in [
'candle', 'candlestick', 'ohlc', 'bars', 'ohlc_bars', 'line',
'renko', 'pnf'
]
},
'style': {
'Default':
'default',
'Validator':
lambda value: value in _styles.available_styles() or isinstance(
value, dict)
},
'volume': {
'Default': False,
'Validator': lambda value: isinstance(value, bool)
},
'mav': {
'Default': None,
'Validator': _mav_validator
},
'renko_params': {
'Default': dict(),
'Validator': lambda value: isinstance(value, dict)
},
'pnf_params': {
'Default': dict(),
'Validator': lambda value: isinstance(value, dict)
},
'study': {
'Default': None,
'Validator': lambda value: _kwarg_not_implemented(value)
},
'marketcolors': {
'Default': None, # use 'style' for default, instead.
'Validator': lambda value: isinstance(value, dict)
},
'no_xgaps': {
'Default': True, # None means follow default logic below:
'Validator': lambda value: _warn_no_xgaps_deprecated(value)
},
'show_nontrading': {
'Default': False,
'Validator': lambda value: isinstance(value, bool)
},
'figscale': {
'Default':
1.0, # scale base figure size up or down.
'Validator':
lambda value: isinstance(value, float) or isinstance(value, int)
},
'figratio': {
'Default':
(8.00,
5.75), # aspect ratio; will equal fig size when figscale=1.0
'Validator':
lambda value: isinstance(value,
(tuple, list)) and len(value) == 2 and
isinstance(value[0],
(float, int)) and isinstance(value[1], (float, int))
},
'linecolor': {
'Default': 'k', # line color in line plot
'Validator': lambda value: mcolors.is_color_like(value)
},
'title': {
'Default': None, # Plot Title
'Validator': lambda value: isinstance(value, str)
},
'ylabel': {
'Default': 'Price', # y-axis label
'Validator': lambda value: isinstance(value, str)
},
'ylabel_lower': {
'Default': None, # y-axis label default logic below
'Validator': lambda value: isinstance(value, str)
},
#'xlabel' : { 'Default' : None, # x-axis label, default is None because obvious it's time or date
# 'Validator' : lambda value: isinstance(value,str) },
'addplot': {
'Default':
None,
'Validator':
lambda value: isinstance(value, dict) or (isinstance(
value, list) and all([isinstance(d, dict) for d in value]))
},
'savefig': {
'Default':
None,
'Validator':
lambda value: isinstance(value, dict) or isinstance(value, str) or
isinstance(value, io.BytesIO)
},
'block': {
'Default': True,
'Validator': lambda value: isinstance(value, bool)
},
'returnfig': {
'Default': False,
'Validator': lambda value: isinstance(value, bool)
},
'return_calculated_values': {
'Default': None,
'Validator':
lambda value: isinstance(value, dict) and len(value) == 0
},
'set_ylim': {
'Default':
None,
'Validator':
lambda value: isinstance(value, (list, tuple)) and len(value) == 2
and all([isinstance(v, (int, float)) for v in value])
},
'set_ylim_panelB': {
'Default':
None,
'Validator':
lambda value: isinstance(value, (list, tuple)) and len(value) == 2
and all([isinstance(v, (int, float)) for v in value])
},
'hlines': {
'Default': None,
'Validator': lambda value: _hlines_validator(value)
},
'vlines': {
'Default': None,
'Validator': lambda value: _vlines_validator(value)
},
'alines': {
'Default': None,
'Validator': lambda value: _alines_validator(value)
},
'tlines': {
'Default': None,
'Validator': lambda value: _tlines_validator(value)
},
}
_validate_vkwargs_dict(vkwargs)
return vkwargs
