def _valid_addplot_kwargs():
valid_linestyles = ('-','solid','--','dashed','-.','dashdot','.','dotted',None,' ','')
valid_types = ('line','scatter','bar', 'ohlc', 'candle')
vkwargs = {
'scatter' : { 'Default' : False,
'Validator' : lambda value: isinstance(value,bool) },
'type' : { 'Default' : 'line',
'Validator' : lambda value: value in valid_types },
'mav' : { 'Default' : None,
'Validator' : _mav_validator },
'panel' : { 'Default' : 0,
'Validator' : lambda value: _valid_panel_id(value) },
'marker' : { 'Default' : 'o',
'Validator' : lambda value: _bypass_kwarg_validation(value) },
'markersize' : { 'Default' : 18,
'Validator' : lambda value: isinstance(value,(int,float)) },
'color' : { 'Default' : None,
'Validator' : lambda value: mcolors.is_color_like(value) or
(isinstance(value,(list,tuple,np.ndarray)) and all([mcolors.is_color_like(v) for v in value])) },
'linestyle' : { 'Default' : None,
'Validator' : lambda value: value in valid_linestyles },
'width' : { 'Default' : None, # width of `bar` or `line`
'Validator' : lambda value: isinstance(value,(int,float)) or
all([isinstance(v,(int,float)) for v in value]) },
'bottom' : { 'Default' : 0, # bottom for `type=bar` plots
'Validator' : lambda value: isinstance(value,(int,float)) or
all([isinstance(v,(int,float)) for v in value]) },
'alpha' : { 'Default' : 1, # alpha of `bar`, `line`, or `scatter`
'Validator' : lambda value: isinstance(value,(int,float)) or
all([isinstance(v,(int,float)) for v in value]) },
'secondary_y' : { 'Default' : 'auto',
'Validator' : lambda value: isinstance(value,bool) or value == 'auto' },
'y_on_right' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,bool) },
'ylabel' : { 'Default' : None,
'Validator' : lambda value: isinstance(value,str) },
'ylim' : {'Default' : None,
'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2
and all([isinstance(v,(int,float)) for v in value])},
}
_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' : 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 _build_panels( figure, config ):
"""
Create and return a DataFrame containing panel information
and Axes objects for each panel, etc.
We allow up to 10 panels, identified by their panel id (panid)
which is an integer 0 through 9.
Parameters
----------
figure : pyplot.Figure
figure on which to create the Axes for the panels
config : dict
config dict from `mplfinance.plot()`
Config
------
The following items are used from `config`:
num_panels : integer (0-9) or None
number of panels to create
addplot : dict or None
value for the `addplot=` kwarg passed into `mplfinance.plot()`
volume_panel : integer (0-9) or None
panel id (0-number_of_panels)
main_panel : integer (0-9) or None
panel id (0-number_of_panels)
panel_ratios : sequence or None
sequence of relative sizes for the panels;
NOTE: If len(panel_ratios) == number of panels (regardless
of whether number of panels was specified or inferred),
then panel ratios are the relative sizes of each panel,
in panel id order, 0 through N (where N = number of panels).
If len(panel_ratios) != number of panels, then len(panel_ratios)
must equal 2, and panel_ratios[0] is the relative size for the 'main'
panel, and panel_ratios[1] is the relative size for all other panels.
If the number of panels == 1, the panel_ratios is ignored.
Returns
----------
panels : pandas.DataFrame
dataframe indexed by panel id (panid) and having the following columns:
axes : tuple of matplotlib.Axes (primary and secondary) for each column.
used secondary : bool indicating whether or not the seconday Axes is in use.
relative size : height of panel as proportion of sum of all relative sizes
"""
num_panels = config['num_panels']
addplot = config['addplot']
volume = config['volume']
volume_panel = config['volume_panel']
num_panels = config['num_panels']
main_panel = config['main_panel']
panel_ratios = config['panel_ratios']
if not _valid_panel_id(main_panel):
raise ValueError('main_panel id must be integer 0 to 9, but is '+str(main_panel))
if num_panels is None: # then infer the number of panels:
pset = {0} # start with a set including only panel zero
if addplot is not None:
if isinstance(addplot,dict):
addplot = [addplot,] # make list of dict to be consistent
elif not _list_of_dict(addplot):
raise TypeError('addplot must be `dict`, or `list of dict`, NOT '+str(type(addplot)))
backwards_panel_compatibility = {'main':0,'lower':1,'A':0,'B':1,'C':2}
for apdict in addplot:
panel = apdict['panel']
if panel in backwards_panel_compatibility:
panel = backwards_panel_compatibility[panel]
if not _valid_panel_id(panel):
raise ValueError('addplot panel must be integer 0 to 9, but is "'+str(panel)+'"')
pset.add(panel)
if volume is True:
if not _valid_panel_id(volume_panel):
raise ValueError('volume_panel must be integer 0 to 9, but is "'+str(volume_panel)+'"')
pset.add(volume_panel)
pset.add(main_panel)
pset = sorted(pset)
missing = [m for m in range(len(pset)) if m not in pset]
if len(missing) != 0:
raise ValueError('inferred panel list is missing panels: '+str(missing))
else:
if not isinstance(num_panels,int) or num_panels < 1 or num_panels > 10:
raise ValueError('num_panels must be integer 1 to 10, but is "'+str(volume_panel)+'"')
pset = range(0,num_panels)
_nones = [None]*len(pset)
panels = pd.DataFrame(dict(axes=_nones,
relsize=_nones,
lift=_nones,
height=_nones,
used2nd=[False]*len(pset),
title=_nones,
ylabel=_nones),
index=pset)
panels.index.name = 'panid'
# Now determine the height for each panel:
# ( figure, num_panels='infer', addplot=None, volume_panel=None, main_panel=0, panel_ratios=None ):
if panel_ratios is not None:
if not isinstance(panel_ratios,(list,tuple)):
raise TypeError('panel_ratios must be a list or tuple')
if len(panel_ratios) != len(panels) and not (len(panel_ratios)==2 and len(panels) > 2):
err = 'len(panel_ratios) must be 2, or must be same as number of panels'
err += '\nlen(panel_ratios)='+str(len(panel_ratios))+' num panels='+str(len(panels))
raise ValueError(err)
if len(panel_ratios) == 2 and len(panels) > 2:
pratios = [panel_ratios[1]]*len(panels)
pratios[main_panel] = panel_ratios[0]
else:
pratios = panel_ratios
else:
pratios = [2]*len(panels)
pratios[main_panel] = 5
panels['relsize'] = pratios
#print('len(panels)=',len(panels))
#print('len(pratios)=',len(pratios))
#print('pratios=')
#print(pratios)
#print('panels=')
#print(panels)
psum = sum(pratios)
for panid,size in enumerate(pratios):
panels.at[panid,'height'] = 0.7 * size / psum
# Now create the Axes:
for panid,row in panels.iterrows():
height = row.height
lift = panels['height'].loc[panid+1:].sum()
panels.at[panid,'lift'] = lift
if panid == 0:
# rect = [left, bottom, width, height]
ax0 = figure.add_axes( [0.15, 0.18+lift, 0.70, height] )
else:
ax0 = figure.add_axes( [0.15, 0.18+lift, 0.70, height], sharex=panels.at[0,'axes'][0] )
ax1 = ax0.twinx()
ax1.grid(False)
if config['saxbelow']: # issue#115 issuecomment-639446764
ax0.set_axisbelow(True) # so grid does not show through plot data on any panel.
elif panid == volume_panel:
ax0.set_axisbelow(True) # so grid does not show through volume bars.
panels.at[panid,'axes'] = (ax0,ax1)
return panels
