From bdfb121fafc6a6410a2890ca7cda0ea471c64039 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Wed, 12 Jan 2022 12:21:18 -0500 Subject: [PATCH 01/20] add test_kwarg_help --- tests/test_kwarg_help.py | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 tests/test_kwarg_help.py diff --git a/tests/test_kwarg_help.py b/tests/test_kwarg_help.py new file mode 100644 index 00000000..3eff151f --- /dev/null +++ b/tests/test_kwarg_help.py @@ -0,0 +1,26 @@ +import os +import os.path +import glob +import mplfinance as mpf + +print('mpf.__version__ =',mpf.__version__) # for the record + + +def test_kwarg_help(): + + functions = ['plot', 'make_addplot', 'make_marketcolors', 'make_mpf_style', + 'renko_params', 'pnf_params', 'lines', 'scale_width_adjustment', + 'update_width_config'] + + # just call `kwarg_help()` for each function, + # and make sure there are no exceptions: + + mpf.kwarg_help() + + for func_name in functions: + mpf.kwarg_help(func_name) + + # now call with `sort=True` (again, just making sure no exceptions) + + mpf.kwarg_help('plot',sort=True) + From eb04e2b3c4b1b76e7efdcf098d87268998e79dbd Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Fri, 14 Jan 2022 14:37:11 -0500 Subject: [PATCH 02/20] Update mplfinance_checks.yml --- .github/workflows/mplfinance_checks.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/mplfinance_checks.yml b/.github/workflows/mplfinance_checks.yml index d4661e44..73642c5a 100644 --- a/.github/workflows/mplfinance_checks.yml +++ b/.github/workflows/mplfinance_checks.yml @@ -72,5 +72,9 @@ jobs: run: | git show ${{ github.event.pull_request.base.sha }}:src/mplfinance/_version.py > scripts/tv0.py git show ${{ github.sha }}:src/mplfinance/_version.py > scripts/tv1.py + ls -l tv0.py + cat tv0.py + ls -l tv1.py + cat tv1.py python scripts/version_update_check.py tv0 tv1 From 1cff64ae03909f1d86f5353d8799fc9db4c55dfa Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Fri, 14 Jan 2022 14:39:38 -0500 Subject: [PATCH 03/20] Update mplfinance_checks.yml --- .github/workflows/mplfinance_checks.yml | 4 ---- 1 file changed, 4 deletions(-) diff --git a/.github/workflows/mplfinance_checks.yml b/.github/workflows/mplfinance_checks.yml index 73642c5a..d4661e44 100644 --- a/.github/workflows/mplfinance_checks.yml +++ b/.github/workflows/mplfinance_checks.yml @@ -72,9 +72,5 @@ jobs: run: | git show ${{ github.event.pull_request.base.sha }}:src/mplfinance/_version.py > scripts/tv0.py git show ${{ github.sha }}:src/mplfinance/_version.py > scripts/tv1.py - ls -l tv0.py - cat tv0.py - ls -l tv1.py - cat tv1.py python scripts/version_update_check.py tv0 tv1 From 51137d7b65c4219b4f9241c4981b482a75d34e17 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Tue, 25 Jan 2022 18:36:06 -0500 Subject: [PATCH 04/20] add some kwarg descriptions --- src/mplfinance/_utils.py | 52 +++++++++++++++++++++++++++----------- src/mplfinance/plotting.py | 4 +-- 2 files changed, 39 insertions(+), 17 deletions(-) diff --git a/src/mplfinance/_utils.py b/src/mplfinance/_utils.py index 4c169b10..3a078cc5 100644 --- a/src/mplfinance/_utils.py +++ b/src/mplfinance/_utils.py @@ -387,11 +387,12 @@ def _valid_renko_kwargs(): ''' vkwargs = { 'brick_size' : { 'Default' : 'atr', - 'Description' : '', + 'Description' : 'size of each brick on y-axis (typically price).'+ + ' specify a number, or specify "atr" for average true range.', 'Validator' : lambda value: isinstance(value,(float,int)) or value == 'atr' }, 'atr_length' : { 'Default' : 14, - 'Description' : '', + 'Description' : 'number of periods for atr calculation (if brick size is "atr")', 'Validator' : lambda value: isinstance(value,int) or value == 'total' }, } @@ -416,16 +417,18 @@ def _valid_pnf_kwargs(): ''' vkwargs = { 'box_size' : { 'Default' : 'atr', - 'Description' : '', + 'Description' : 'size of each box on y-axis (typically price).'+ + ' specify a number, or specify "atr" for average true range.', 'Validator' : lambda value: isinstance(value,(float,int)) or value == 'atr' }, 'atr_length' : { 'Default' : 14, - 'Description' : '', + 'Description' : 'number of periods for atr calculation (if box size is "atr")', 'Validator' : lambda value: isinstance(value,int) or value == 'total' }, 'reversal' : { 'Default' : 1, - 'Description' : '', + 'Description' : 'number of boxes, in opposite direction, needed to reverse'+ + ' a trend (i.e. to start a new column).', 'Validator' : lambda value: isinstance(value,int) }, } @@ -451,52 +454,71 @@ def _valid_lines_kwargs(): valid_linestyles = ['-','solid','--','dashed','-.','dashdot',':','dotted',None,' ',''] vkwargs = { 'hlines' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Draw one or more HORIZONTAL LINES across entire plot, by'+ + ' specifying a price, or sequence of prices. May also be a dict'+ + ' with key `hlines` specifying a price or sequence of prices, plus'+ + ' one or more of the following keys: `colors`, `linestyle`,'+ + ' `linewidths`, `alpha`.', 'Validator' : _bypass_kwarg_validation }, 'vlines' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Draw one or more VERTICAL LINES across entire plot, by'+ + ' specifying a date[time], or sequence of date[time]. May also'+ + ' be a dict with key `vlines` specifying a date[time] or sequence'+ + ' of date[time], plus one or more of the following keys:'+ + ' `colors`, `linestyle`, `linewidths`, `alpha`.', 'Validator' : _bypass_kwarg_validation }, 'alines' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Draw one or more ARBITRARY LINES anywhere on the plot, by'+ + ' specifying a sequence of two or more date/price pairs, or by'+ + ' specifying a sequence of sequences of two or more date/price pairs.'+ + ' May also be a dict with key `alines` (as date/price pairs described above),'+ + ' plus one or more of the following keys:'+ + ' `colors`, `linestyle`, `linewidths`, `alpha`.', 'Validator' : _bypass_kwarg_validation }, 'tlines' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Draw one or more TREND LINES by specifying one or more pairs of date[times]'+ + ' between which each trend line should be drawn. May also be a dict with key'+ + ' `tlines` as just described, plus one or more of the following keys:'+ + ' `colors`, `linestyle`, `linewidths`, `alpha`, `tline_use`,`tline_method`.', 'Validator' : _bypass_kwarg_validation }, 'colors' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Color of [hvat]lines (or sequence of colors, if each line to be a different color)', 'Validator' : lambda value: value is None or mcolors.is_color_like(value) or (isinstance(value,(list,tuple)) and all([mcolors.is_color_like(v) for v in value]) ) }, 'linestyle' : { 'Default' : '-', - 'Description' : '', + 'Description' : 'line style of [hvat]lines (or sequence of line styles, if each line to have a different linestyle)', 'Validator' : lambda value: value is None or value in valid_linestyles or all([v in valid_linestyles for v in value]) }, 'linewidths': { 'Default' : None, - 'Description' : '', + 'Description' : 'line width of [hvat]lines (or sequence of line widths, if each line to have a different width)', 'Validator' : lambda value: value is None or isinstance(value,(float,int)) or all([isinstance(v,(float,int)) for v in value]) }, 'alpha' : { 'Default' : 1.0, - 'Description' : '', + 'Description' : 'Opacity of [hvat]lines. float from 0.0 to 1.0 '+ + ' (1.0 means fully opaque; 0.0 means transparent.', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'tline_use' : { 'Default' : 'close', - 'Description' : '', + 'Description' : 'value to use for TREND LINE ("open","high","low","close") or sequence of'+ + ' any combination of "open", "high", "low", "close" to use a average of the'+ + ' specified values to determine the trend line.', 'Validator' : lambda value: isinstance(value,str) or (isinstance(value,(list,tuple)) and all([isinstance(v,str) for v in value]) ) }, 'tline_method': { 'Default' : 'point-to-point', - 'Description' : '', + 'Description' : 'method for TREND LINE determination: "point-to-point" or "least-squares"', 'Validator' : lambda value: value in ['point-to-point','least-squares'] } } diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py index d5558c7a..313b27ae 100644 --- a/src/mplfinance/plotting.py +++ b/src/mplfinance/plotting.py @@ -121,11 +121,11 @@ def _valid_plot_kwargs(): 'Validator' : _mav_validator }, 'renko_params' : { 'Default' : dict(), - 'Description' : '', + 'Description' : 'dict of renko parameters; call `mpf.kwarg_help("renko_params")`', 'Validator' : lambda value: isinstance(value,dict) }, 'pnf_params' : { 'Default' : dict(), - 'Description' : '', + 'Description' : 'dict of point-and-figure parameters; call `mpf.kwarg_help("pnf_params")`', 'Validator' : lambda value: isinstance(value,dict) }, 'study' : { 'Default' : None, From fdacb378fc02f399031a4f2fd8bf37ec6ef8e272 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Wed, 26 Jan 2022 14:10:05 -0500 Subject: [PATCH 05/20] update `make_mpf_style()` kwarg descriptions --- src/mplfinance/_styles.py | 29 +++++++++++++++-------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/src/mplfinance/_styles.py b/src/mplfinance/_styles.py index c57894bf..9de75b11 100644 --- a/src/mplfinance/_styles.py +++ b/src/mplfinance/_styles.py @@ -61,60 +61,61 @@ def _apply_mpfstyle(style): def _valid_make_mpf_style_kwargs(): vkwargs = { 'base_mpf_style': { 'Default' : None, - 'Description' : '', + 'Description' : 'mplfinance style to use as base of new mplfinance style', 'Validator' : lambda value: value in _styles.keys() }, 'base_mpl_style': { 'Default' : None, - 'Description' : '', + 'Description' : 'matplotlib style to use as base of new mplfinance style', 'Validator' : lambda value: isinstance(value,str) }, # and is in plt.style.available 'marketcolors' : { 'Default' : None, - 'Description' : '', + 'Description' : 'market colors object, from `mpf.make_market_colors()`', 'Validator' : lambda value: isinstance(value,dict) }, 'mavcolors' : { 'Default' : None, - 'Description' : '', + 'Description' : 'sequence of colors to use for moving averages', 'Validator' : lambda value: isinstance(value,list) }, # TODO: all([_mpf_is_color_like(v) for v in value.values()]) 'facecolor' : { 'Default' : None, - 'Description' : '', + 'Description' : 'background color for Axes', 'Validator' : lambda value: isinstance(value,str) }, 'edgecolor' : { 'Default' : None, - 'Description' : '', + 'Description' : 'edge color for Axes', 'Validator' : lambda value: isinstance(value,str) }, 'figcolor' : { 'Default' : None, - 'Description' : '', + 'Description' : 'background color for Figure.', 'Validator' : lambda value: isinstance(value,str) }, 'gridcolor' : { 'Default' : None, - 'Description' : '', + 'Description' : 'color for grid lines', 'Validator' : lambda value: isinstance(value,str) }, 'gridstyle' : { 'Default' : None, - 'Description' : '', + 'Description' : "grid line style ('-', '--', '-.', ':', '', offset, on-off-seq)."+ + " (see also: https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)", 'Validator' : lambda value: isinstance(value,str) }, 'gridaxis' : { 'Default' : None, - 'Description' : '', + 'Description' : "grid lines 'vertical', 'horizontal', or 'both'", 'Validator' : lambda value: value in [ 'vertical'[0:len(value)], 'horizontal'[0:len(value)], 'both'[0:len(value)] ] }, 'y_on_right' : { 'Default' : None, - 'Description' : '', + 'Description' : 'True|False primary Axes y-ticks and labels on right.', 'Validator' : lambda value: isinstance(value,bool) }, 'rc' : { 'Default' : None, - 'Description' : '', + 'Description' : 'rcparams overrides (dict) (all other rcparams unchanged)', 'Validator' : lambda value: isinstance(value,dict) }, 'legacy_rc' : { 'Default' : None, # Just in case someone depended upon old behavior - 'Description' : '', + 'Description' : 'rcparams to set (dict) (all other rcparams cleared)', 'Validator' : lambda value: isinstance(value,dict) }, 'style_name' : { 'Default' : None, - 'Description' : '', + 'Description' : 'name for this style; useful when calling `mpf.write_style_file(style,filename)`', 'Validator' : lambda value: isinstance(value,str) }, } From 2622d426d36c3b2e29a6b254979e4f6203233394 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Wed, 26 Jan 2022 18:45:58 -0500 Subject: [PATCH 06/20] add kwarg descriptions for make_marketcolors --- src/mplfinance/_styles.py | 27 ++++++++++++++++----------- 1 file changed, 16 insertions(+), 11 deletions(-) diff --git a/src/mplfinance/_styles.py b/src/mplfinance/_styles.py index 9de75b11..c0002b82 100644 --- a/src/mplfinance/_styles.py +++ b/src/mplfinance/_styles.py @@ -210,55 +210,60 @@ def _valid_mpf_style(value): def _valid_make_marketcolors_kwargs(): vkwargs = { 'up' : { 'Default' : None, - 'Description' : '', + 'Description' : 'color to indicate up', 'Validator' : lambda value: _mpf_is_color_like(value) }, 'down' : { 'Default' : None, - 'Description' : '', + 'Description' : 'color to indicate down', 'Validator' : lambda value: _mpf_is_color_like(value) }, 'hollow' : { 'Default' : None, - 'Description' : '', + 'Description' : "color for hollow candles (for `type=hollow`)", 'Validator' : lambda value: _mpf_is_color_like(value) }, 'alpha' : { 'Default' : None, - 'Description' : '', + 'Description' : 'opacity 0.0 (transparent) to 1.0 (opaque);'+ + ' applies to candles,renko,pnf (but not ohlc bars)', 'Validator' : lambda value: (isinstance(value,float) and 0.0 <= value and 1.0 >= value ) }, 'edge' : { 'Default' : None, - 'Description' : '', + 'Description' : 'color of candle edge; may also be "i" or "inherit"'+ + ' to take color from base_mpf_style', 'Validator' : lambda value: _valid_mpf_color_spec(value) }, 'wick' : { 'Default' : None, - 'Description' : '', + 'Description' : "color of candle wick; may be single color,"+ + " or may be dict with keys 'up' and 'down'", 'Validator' : lambda value: isinstance(value,dict) or isinstance(value,str) or _mpf_is_color_like(value) }, 'ohlc' : { 'Default' : None, - 'Description' : '', + 'Description' : "color of ohlc bars; may be single color,"+ + " or may be dict with keys 'up' and 'down'", 'Validator' : lambda value: isinstance(value,dict) or isinstance(value,str) or _mpf_is_color_like(value) }, 'volume' : { 'Default' : None, - 'Description' : '', + 'Description' : "color of volume bars; may be single color,"+ + " or may be dict with keys 'up' and 'down'", 'Validator' : lambda value: isinstance(value,dict) or isinstance(value,str) or _mpf_is_color_like(value) }, 'vcdopcod' : { 'Default' : False, - 'Description' : '', + 'Description' : 'True/False volume color depends on price change from previous day', 'Validator' : lambda value: isinstance(value,bool) }, 'inherit' : { 'Default' : False, - 'Description' : '', + 'Description' : 'inherit color from base_mpf_style for: edge,volume,ohlc,wick', 'Validator' : lambda value: isinstance(value,bool) }, 'base_mpf_style': { 'Default' : None, - 'Description' : '', + 'Description' : 'mplfinance style market colors as basis for new market colors object', 'Validator' : lambda value: isinstance(value,str) }, } From e85d9ea449ddc7a844c93f5088fb3e6e71b90cb2 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Wed, 26 Jan 2022 19:09:04 -0500 Subject: [PATCH 07/20] kwarg descriptions for 'scale_width_adjustment' and 'update_width_config' --- src/mplfinance/_widths.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/src/mplfinance/_widths.py b/src/mplfinance/_widths.py index a608fbba..3b6813c4 100644 --- a/src/mplfinance/_widths.py +++ b/src/mplfinance/_widths.py @@ -33,31 +33,31 @@ def _get_widths_df(): def _valid_scale_width_kwargs(): vkwargs = { 'ohlc' : { 'Default' : None, - 'Description' : '', + 'Description' : 'length of horizontal open/close tickmarks on ohlc bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'volume' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width of volume bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'candle' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width of candles', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'lines' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width of lines (for line plots and moving averages)', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'volume_linewidth' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width of edges of volume bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'ohlc_linewidth' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width (thickness) of ohlc bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'candle_linewidth' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width of candle edges and wicks', 'Validator' : lambda value: isinstance(value,(float,int)) }, } @@ -70,31 +70,31 @@ def _valid_update_width_kwargs(): vkwargs = { 'ohlc_ticksize' : { 'Default' : None, - 'Description' : '', + 'Description' : 'length of horizontal open/close tickmarks on ohlc bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'ohlc_linewidth' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width (thickness) of ohlc bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'volume_width' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width of volume bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'volume_linewidth' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width of edges of volume bars', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'candle_width' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width of candles', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'candle_linewidth' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width of candle edges and wicks', 'Validator' : lambda value: isinstance(value,(float,int)) }, 'line_width' : { 'Default' : None, - 'Description' : '', + 'Description' : 'width of lines (for line plots and moving averages)', 'Validator' : lambda value: isinstance(value,(float,int)) }, } From 0d6c8dfc4abab81d0e8ad105b2b923f351b3e80d Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Wed, 26 Jan 2022 19:49:01 -0500 Subject: [PATCH 08/20] more kwarg descriptions for `mpf.plot()` --- src/mplfinance/plotting.py | 56 +++++++++++++++++++++++++++----------- 1 file changed, 40 insertions(+), 16 deletions(-) diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py index 313b27ae..bdb4a04b 100644 --- a/src/mplfinance/plotting.py +++ b/src/mplfinance/plotting.py @@ -133,11 +133,13 @@ def _valid_plot_kwargs(): 'Validator' : lambda value: _kwarg_not_implemented(value) }, 'marketcolor_overrides' : { 'Default' : None, - 'Description' : '', + 'Description' : 'sequence of color objects to override market colors.'+ + 'sequence must be same length as ohlc(v) DataFrame. Each'+ + 'color object may be a color, marketcolor object, or None.', 'Validator' : _mco_validator }, 'mco_faceonly' : { 'Default' : False, # If True: Override only the face of the candle - 'Description' : '', + 'Description' : 'True/False marketcolor_overrides only apply to face of candle.', 'Validator' : lambda value: isinstance(value,bool) }, 'no_xgaps' : { 'Default' : True, # None means follow default logic below: @@ -145,7 +147,7 @@ def _valid_plot_kwargs(): 'Validator' : lambda value: _warn_no_xgaps_deprecated(value) }, 'show_nontrading' : { 'Default' : False, - 'Description' : '', + 'Description' : 'True/False show spaces for non-trading days/periods', 'Validator' : lambda value: isinstance(value,bool) }, 'figscale' : { 'Default' : None, # scale base figure size up or down. @@ -184,31 +186,34 @@ def _valid_plot_kwargs(): 'Validator' : lambda value: isinstance(value,(str,dict)) }, 'ylabel' : { 'Default' : 'Price', # y-axis label - 'Description' : '', + 'Description' : 'label for y-axis of main plot', 'Validator' : lambda value: isinstance(value,str) }, 'ylabel_lower' : { 'Default' : None, # y-axis label default logic below - 'Description' : '', + 'Description' : 'label for y-axis of volume', 'Validator' : lambda value: isinstance(value,str) }, 'addplot' : { 'Default' : None, - 'Description' : '', + 'Description' : 'addplot object or sequence of addplot objects (from `mpf.make_addplot()`)', 'Validator' : lambda value: isinstance(value,dict) or (isinstance(value,list) and all([isinstance(d,dict) for d in value])) }, 'savefig' : { 'Default' : None, - 'Description' : '', + 'Description' : 'file name, or BytesIO, or dict with key `fname` plus other keys allowed as '+ + ' kwargs to matplotlib `Figure.savefig()`', 'Validator' : lambda value: isinstance(value,dict) or isinstance(value,str) or isinstance(value, io.BytesIO) or isinstance(value, os.PathLike) }, 'block' : { 'Default' : None, - 'Description' : '', + 'Description' : 'True/False wait for figure to be closed before returning', 'Validator' : lambda value: isinstance(value,bool) }, 'returnfig' : { 'Default' : False, - 'Description' : '', + 'Description' : 'return Figure and list of Axes objects created by mplfinance;'+ + ' user must display plot when ready, usually by calling `mpf.show()`', 'Validator' : lambda value: isinstance(value,bool) }, 'return_calculated_values' : { 'Default' : None, - 'Description' : '', + 'Description' : 'set to a variable containing an empty dict; `mpf.plot()` will fill'+ + ' the dict with various mplfinance calculated values', 'Validator' : lambda value: isinstance(value, dict) and len(value) == 0}, 'set_ylim' : { 'Default' : None, @@ -221,7 +226,7 @@ def _valid_plot_kwargs(): and all([isinstance(v,(int,float)) for v in value])}, 'xlim' : { 'Default' : None, - 'Description' : 'Limits for x-axis as tuple (min, max), i.e. (left,right)', + 'Description' : 'Limits for x-axis as tuple (min,max), i.e. (left,right)', 'Validator' : lambda value: _xlim_validator(value) }, 'set_ylim_panelB' : { 'Default' : None, @@ -230,23 +235,42 @@ def _valid_plot_kwargs(): 'hlines' : { 'Default' : None, 'Description' : '', + 'Description' : 'Draw one or more HORIZONTAL LINES across entire plot, by'+ + ' specifying a price, or sequence of prices. May also be a dict'+ + ' with key `hlines` specifying a price or sequence of prices, plus'+ + ' one or more of the following keys: `colors`, `linestyle`,'+ + ' `linewidths`, `alpha`.', 'Validator' : lambda value: _hlines_validator(value) }, 'vlines' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Draw one or more VERTICAL LINES across entire plot, by'+ + ' specifying a date[time], or sequence of date[time]. May also'+ + ' be a dict with key `vlines` specifying a date[time] or sequence'+ + ' of date[time], plus one or more of the following keys:'+ + ' `colors`, `linestyle`, `linewidths`, `alpha`.', 'Validator' : lambda value: _vlines_validator(value) }, 'alines' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Draw one or more ARBITRARY LINES anywhere on the plot, by'+ + ' specifying a sequence of two or more date/price pairs, or by'+ + ' specifying a sequence of sequences of two or more date/price pairs.'+ + ' May also be a dict with key `alines` (as date/price pairs described above),'+ + ' plus one or more of the following keys:'+ + ' `colors`, `linestyle`, `linewidths`, `alpha`.', 'Validator' : lambda value: _alines_validator(value) }, 'tlines' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Draw one or more TREND LINES by specifying one or more pairs of date[times]'+ + ' between which each trend line should be drawn. May also be a dict with key'+ + ' `tlines` as just described, plus one or more of the following keys:'+ + ' `colors`, `linestyle`, `linewidths`, `alpha`, `tline_use`,`tline_method`.', 'Validator' : lambda value: _tlines_validator(value) }, 'panel_ratios' : { 'Default' : None, - 'Description' : '', - 'Validator' : lambda value: isinstance(value,(tuple,list)) and len(value) <= 10 and + 'Description' : 'sequence of numbers indicating relative sizes of panels; sequence len'+ + ' must be same as number of panels, or len 2 where first entry is for'+ + ' main panel, and second entry is for all other panels', + 'Validator' : lambda value: isinstance(value,(tuple,list)) and len(value) <= 32 and all([isinstance(v,(int,float)) for v in value]) }, 'main_panel' : { 'Default' : 0, From 74fb1551529e377a47cbc7e577ac247463dc9c3d Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Fri, 28 Jan 2022 12:52:59 -0500 Subject: [PATCH 09/20] more kwarg descriptions --- src/mplfinance/plotting.py | 62 +++++++++++++++++++++----------------- 1 file changed, 34 insertions(+), 28 deletions(-) diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py index bdb4a04b..6d37ed50 100644 --- a/src/mplfinance/plotting.py +++ b/src/mplfinance/plotting.py @@ -129,7 +129,7 @@ def _valid_plot_kwargs(): 'Validator' : lambda value: isinstance(value,dict) }, 'study' : { 'Default' : None, - 'Description' : '', + 'Description' : 'kwarg not implemented', 'Validator' : lambda value: _kwarg_not_implemented(value) }, 'marketcolor_overrides' : { 'Default' : None, @@ -143,7 +143,7 @@ def _valid_plot_kwargs(): 'Validator' : lambda value: isinstance(value,bool) }, 'no_xgaps' : { 'Default' : True, # None means follow default logic below: - 'Description' : '', + 'Description' : 'deprecated', 'Validator' : lambda value: _warn_no_xgaps_deprecated(value) }, 'show_nontrading' : { 'Default' : False, @@ -211,30 +211,29 @@ def _valid_plot_kwargs(): ' user must display plot when ready, usually by calling `mpf.show()`', 'Validator' : lambda value: isinstance(value,bool) }, - 'return_calculated_values' : { 'Default' : None, + 'return_calculated_values' : { 'Default' : None, 'Description' : 'set to a variable containing an empty dict; `mpf.plot()` will fill'+ ' the dict with various mplfinance calculated values', - 'Validator' : lambda value: isinstance(value, dict) and len(value) == 0}, + 'Validator' : lambda value: isinstance(value, dict) and len(value) == 0}, - 'set_ylim' : { 'Default' : None, - 'Description' : '', - 'Validator' : lambda value: _warn_set_ylim_deprecated(value) }, + 'set_ylim' : { 'Default' : None, + 'Description' : 'deprecated', + 'Validator' : lambda value: _warn_set_ylim_deprecated(value) }, - 'ylim' : { 'Default' : None, + 'ylim' : { 'Default' : None, 'Description' : 'Limits for y-axis as tuple (min,max), i.e. (bottom,top)', - 'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 + 'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 and all([isinstance(v,(int,float)) for v in value])}, - 'xlim' : { 'Default' : None, + 'xlim' : { 'Default' : None, 'Description' : 'Limits for x-axis as tuple (min,max), i.e. (left,right)', - 'Validator' : lambda value: _xlim_validator(value) }, + 'Validator' : lambda value: _xlim_validator(value) }, - 'set_ylim_panelB' : { 'Default' : None, - 'Description' : '', - 'Validator' : lambda value: _warn_set_ylim_deprecated(value) }, + 'set_ylim_panelB' : { 'Default' : None, + 'Description' : 'deprecated', + 'Validator' : lambda value: _warn_set_ylim_deprecated(value) }, 'hlines' : { 'Default' : None, - 'Description' : '', 'Description' : 'Draw one or more HORIZONTAL LINES across entire plot, by'+ ' specifying a price, or sequence of prices. May also be a dict'+ ' with key `hlines` specifying a price or sequence of prices, plus'+ @@ -274,19 +273,19 @@ def _valid_plot_kwargs(): all([isinstance(v,(int,float)) for v in value]) }, 'main_panel' : { 'Default' : 0, - 'Description' : '', + 'Description' : 'integer - which panel is the main panel for `.plot()`', 'Validator' : lambda value: _valid_panel_id(value) }, 'volume_panel' : { 'Default' : 1, - 'Description' : '', + 'Description' : 'integer - which panel is the volume panel', 'Validator' : lambda value: _valid_panel_id(value) }, 'num_panels' : { 'Default' : None, - 'Description' : '', - 'Validator' : lambda value: isinstance(value,int) and value in range(1,10+1) }, + 'Description' : 'total number of panels', + 'Validator' : lambda value: isinstance(value,int) and value in range(1,32+1) }, 'datetime_format' : { 'Default' : None, - 'Description' : '', + 'Description' : 'x-axis tick format as valid `strftime()` format string', 'Validator' : lambda value: isinstance(value,str) }, 'xrotation' : { 'Default' : 45, @@ -298,21 +297,32 @@ def _valid_plot_kwargs(): 'Validator' : lambda value: isinstance(value,bool) }, 'closefig' : { 'Default' : 'auto', - 'Description' : '', + 'Description' : 'True|False close the Figure before returning', 'Validator' : lambda value: isinstance(value,bool) }, 'fill_between' : { 'Default' : None, - 'Description' : '', + 'Description' : 'fill between specification as y-value, or sequence of'+ + ' y-values, or dict containing key "y1" plus any additional'+ + ' kwargs for `fill_between()`', '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, - 'Description' : '', + 'Description' : 'True|False implement tight layout (minimal padding around Figure)'+ + ' (see also `scale_padding` kwarg)', 'Validator' : lambda value: isinstance(value,bool) }, + 'scale_padding' : { 'Default' : 1.0, # Issue#193 + 'Description' : 'Increase, > 1.0, or decrease, < 1.0, padding around figure.'+ + ' May also be a dict containing one or more of the following keys:'+ + ' "top", "bottom", "left", "right", to individual scale padding'+ + ' on each side of Figure.', + 'Validator' : lambda value: _scale_padding_validator(value) }, + 'width_adjuster_version' : { 'Default' : 'v1', - 'Description' : '', + 'Description' : 'specify version of object width adjustment algorithm: "v0" or "v1"'+ + ' (See also "widths" tutorial in mplfinance examples folder).', 'Validator' : lambda value: value in ('v0', 'v1') }, 'scale_width_adjustment' : { 'Default' : None, @@ -331,10 +341,6 @@ def _valid_plot_kwargs(): 'Description' : '', 'Validator' : lambda value: isinstance(value,bool) }, - 'scale_padding' : { 'Default' : 1.0, # Issue#193 - 'Description' : '', - 'Validator' : lambda value: _scale_padding_validator(value) }, - 'ax' : { 'Default' : None, 'Description' : 'Matplotlib Axes object on which to plot', 'Validator' : lambda value: isinstance(value,mpl_axes.Axes) }, From 8f3f46807e34d6994eb2c8270e9f5cdd8258a371 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Fri, 28 Jan 2022 13:32:37 -0500 Subject: [PATCH 10/20] more kwarg descriptions --- src/mplfinance/plotting.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py index 6d37ed50..ce0b3547 100644 --- a/src/mplfinance/plotting.py +++ b/src/mplfinance/plotting.py @@ -326,19 +326,22 @@ def _valid_plot_kwargs(): 'Validator' : lambda value: value in ('v0', 'v1') }, 'scale_width_adjustment' : { 'Default' : None, - 'Description' : '', + 'Description' : 'scale width of plot objects wider, > 1.0, or narrower, < 1.0'+ + ' may also be a dict to scale individual widths.'+, + ' (See also "widths" tutorial in mplfinance examples folder).', 'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 }, 'update_width_config' : { 'Default' : None, - 'Description' : '', + 'Description' : 'dict - update individual items in width configuration.'+, + ' (See also "widths" tutorial in mplfinance examples folder).', 'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 }, 'return_width_config' : { 'Default' : None, - 'Description' : '', + 'Description' : 'empty dict variable to be filled with width configuration settings.', 'Validator' : lambda value: isinstance(value,dict) and len(value)==0 }, 'saxbelow' : { 'Default' : True, # Issue#115 Comment#639446764 - 'Description' : '', + 'Description' : 'set the volume Axes below (behind) all other Axes objects', 'Validator' : lambda value: isinstance(value,bool) }, 'ax' : { 'Default' : None, From d6c8071ec5c00d2248d47b555949b7696bd51ca8 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Mon, 31 Jan 2022 13:54:29 -0500 Subject: [PATCH 11/20] yet more kwarg descriptions --- src/mplfinance/plotting.py | 37 +++++++++++++++++++------------------ 1 file changed, 19 insertions(+), 18 deletions(-) diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py index ce0b3547..20eadce2 100644 --- a/src/mplfinance/plotting.py +++ b/src/mplfinance/plotting.py @@ -327,12 +327,12 @@ def _valid_plot_kwargs(): 'scale_width_adjustment' : { 'Default' : None, 'Description' : 'scale width of plot objects wider, > 1.0, or narrower, < 1.0'+ - ' may also be a dict to scale individual widths.'+, + ' may also be a dict to scale individual widths.'+ ' (See also "widths" tutorial in mplfinance examples folder).', 'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 }, 'update_width_config' : { 'Default' : None, - 'Description' : 'dict - update individual items in width configuration.'+, + 'Description' : 'dict - update individual items in width configuration.'+ ' (See also "widths" tutorial in mplfinance examples folder).', 'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 }, @@ -349,24 +349,25 @@ def _valid_plot_kwargs(): 'Validator' : lambda value: isinstance(value,mpl_axes.Axes) }, 'volume_exponent' : { 'Default' : None, - 'Description' : '', + 'Description' : 'integer exponent on the volume axis'+ + ' (or set to "legacy" for old mplfinance style)', 'Validator' : lambda value: isinstance(value,int) or value == 'legacy'}, 'tz_localize' : { 'Default' : True, - 'Description' : '', + 'Description' : 'True|False localize the times in the DatetimeIndex', 'Validator' : lambda value: isinstance(value,bool) }, 'yscale' : { 'Default' : None, - 'Description' : '', + 'Description' : 'y-axis scale: "linear", "log", "symlog", or "logit"', 'Validator' : lambda value: _yscale_validator(value) }, 'volume_yscale' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Volume y-axis scale: "linear", "log", "symlog", or "logit"', 'Validator' : lambda value: _yscale_validator(value) }, 'warn_too_much_data' : { 'Default' : 599, - 'Description' : ('Tolerance for data amount in plot. Default=599 rows.'+ - ' Values greater than \'warn_too_much_data\' will trigger a warning.'), + 'Description' : 'Tolerance for data amount in plot. Default=599 rows.'+ + ' Values greater than \'warn_too_much_data\' will trigger a warning.', 'Validator' : lambda value: isinstance(value,int) }, } @@ -1140,31 +1141,31 @@ def _valid_addplot_kwargs(): vkwargs = { 'scatter' : { 'Default' : False, - 'Description' : '', + 'Description' : "Deprecated. (Use kwarg `type='scatter' instead.", 'Validator' : lambda value: isinstance(value,bool) }, 'type' : { 'Default' : 'line', - 'Description' : '', + 'Description' : 'addplot type: "line","scatter","bar", "ohlc", "candle","step"', 'Validator' : lambda value: value in valid_types }, 'mav' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Moving Average window size(s); (int or tuple of ints)', 'Validator' : _mav_validator }, 'panel' : { 'Default' : 0, - 'Description' : '', + 'Description' : 'Panel (int 0-31) to use for this addplot', 'Validator' : lambda value: _valid_panel_id(value) }, 'marker' : { 'Default' : 'o', - 'Description' : '', + 'Description' : "marker for `type='scatter'` plot", 'Validator' : lambda value: _bypass_kwarg_validation(value) }, 'markersize' : { 'Default' : 18, - 'Description' : 'size of marker for `type=scatter`; default=18', + 'Description' : 'size of marker for `type="scatter"`; default=18', 'Validator' : lambda value: isinstance(value,(int,float)) }, 'color' : { 'Default' : None, - 'Description' : 'color of line, scatter marker, or bar', + 'Description' : 'color (or sequence of colors) of line(s), scatter marker(s), or bar(s).', '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])) }, @@ -1173,15 +1174,15 @@ def _valid_addplot_kwargs(): 'Validator' : lambda value: value in valid_linestyles }, 'linewidths' : { 'Default': None, - 'Description' : '', + 'Description' : 'edge widths of scatter markers', 'Validator' : lambda value: isinstance(value,(int,float)) }, 'edgecolors' : { 'Default': None, - 'Description' : '', + 'Description' : 'edgecolors of scatter markers', 'Validator': lambda value: mcolors.is_color_like(value) or value in valid_edgecolors}, 'width' : { 'Default' : None, # width of `bar` or `line` - 'Description' : '', + 'Description' : 'width of bar or line for `type="bar"` or `type="line"', 'Validator' : lambda value: isinstance(value,(int,float)) or all([isinstance(v,(int,float)) for v in value]) }, From 2ddc71ce1f51e91726e8e40dfa434fbbd1c1548c Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Mon, 31 Jan 2022 15:58:28 -0500 Subject: [PATCH 12/20] last of the unfinished kwarg descriptions --- src/mplfinance/plotting.py | 28 ++++++++++++++++++---------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py index 20eadce2..55f6d614 100644 --- a/src/mplfinance/plotting.py +++ b/src/mplfinance/plotting.py @@ -1196,40 +1196,48 @@ def _valid_addplot_kwargs(): all([isinstance(v,(int,float)) for v in value]) }, 'secondary_y' : { 'Default' : 'auto', - 'Description' : '', + 'Description' : "True|False|'auto' place the additional plot data on a"+ + " secondary y-axis. 'auto' compares the magnitude or the"+ + " addplot data, to data already on the axis, and if it appears"+ + " they are of different magnitudes, then it uses a secondary y-axis."+ + " True or False always override 'auto'.", 'Validator' : lambda value: isinstance(value,bool) or value == 'auto' }, 'y_on_right' : { 'Default' : None, - 'Description' : '', + 'Description' : 'True|False put y-axis tick labels on the right, for this addplot'+ + ' regardless of what the mplfinance style says to to.', 'Validator' : lambda value: isinstance(value,bool) }, - + 'ylabel' : { 'Default' : None, - 'Description' : '', + 'Description' : 'label for y-axis (for this addplot)', 'Validator' : lambda value: isinstance(value,str) }, 'ylim' : {'Default' : None, - 'Description' : '', + 'Description' : 'Limits for addplot y-axis as tuple (min,max), i.e. (bottom,top)', 'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 and all([isinstance(v,(int,float)) for v in value])}, 'title' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Axes Title (subplot title) for this addplot.', 'Validator' : lambda value: isinstance(value,str) }, 'ax' : { 'Default' : None, - 'Description' : '', + 'Description' : 'Matplotlib Axes object on which to plot this addplot', 'Validator' : lambda value: isinstance(value,mpl_axes.Axes) }, 'yscale' : { 'Default' : None, - 'Description' : '', + 'Description' : 'addplot y-axis scale: "linear", "log", "symlog", or "logit"', 'Validator' : lambda value: _yscale_validator(value) }, 'stepwhere' : { 'Default' : 'pre', - 'Description' : '', + 'Description' : "'pre','post', or 'mid': where to place step relative"+ + " to data for `type='step'`", 'Validator' : lambda value : value in valid_stepwheres }, 'marketcolors': { 'Default' : None, # use 'style' for default, instead. - 'Description' : '', + 'Description' : "marketcolors for this addplot (instead of the mplfinance"+ + " style\'s marketcolors). For addplot `type='ohlc'`"+ + " and type='candle'", 'Validator' : lambda value: _is_marketcolor_object(value) }, } From 6afe809b606e12d9be7b8f6da400b7b767780be5 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Mon, 31 Jan 2022 18:51:38 -0500 Subject: [PATCH 13/20] bump version --- src/mplfinance/_version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mplfinance/_version.py b/src/mplfinance/_version.py index 88fb1967..4a6dfc24 100644 --- a/src/mplfinance/_version.py +++ b/src/mplfinance/_version.py @@ -1,5 +1,5 @@ -version_info = (0, 12, 8, 'beta', 8) +version_info = (0, 12, 8, 'beta', 9) _specifier_ = {'alpha': 'a','beta': 'b','candidate': 'rc','final': ''} From 4496ab62db3d190057cd903e2851be58acc98c0d Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Mon, 14 Feb 2022 19:27:45 -0500 Subject: [PATCH 14/20] tweak multicursor, and add ginput example --- examples/scratch_pad/multicursor_macd.py | 2 +- .../scratch_pad/multicursor_macd_ginput.py | 87 +++++++++++++++++++ 2 files changed, 88 insertions(+), 1 deletion(-) create mode 100644 examples/scratch_pad/multicursor_macd_ginput.py diff --git a/examples/scratch_pad/multicursor_macd.py b/examples/scratch_pad/multicursor_macd.py index 0683f3fc..408e3ab7 100644 --- a/examples/scratch_pad/multicursor_macd.py +++ b/examples/scratch_pad/multicursor_macd.py @@ -25,5 +25,5 @@ fig, axlist = mpf.plot(df,type='candle',addplot=apds,figscale=1.1,figratio=(8,5),title='\nMACD', style='blueskies',volume=True,volume_panel=2,panel_ratios=(6,3,2),returnfig=True) -multi = MultiCursor(fig.canvas, axlist, color='r',lw=1.2) +multi = MultiCursor(fig.canvas, axlist, color='r',lw=1.2, horizOn=True, vertOn=True) mpf.show() diff --git a/examples/scratch_pad/multicursor_macd_ginput.py b/examples/scratch_pad/multicursor_macd_ginput.py new file mode 100644 index 00000000..a2910f57 --- /dev/null +++ b/examples/scratch_pad/multicursor_macd_ginput.py @@ -0,0 +1,87 @@ +import pandas as pd +import mplfinance as mpf +from matplotlib.widgets import MultiCursor +from matplotlib.collections import LineCollection + +# read the data: +idf = pd.read_csv('../data/SPY_20110701_20120630_Bollinger.csv',index_col=0,parse_dates=True) +df = idf.loc['2011-07-01':'2011-12-30',:] + + +# macd related calculations: +exp12 = df['Close'].ewm(span=12, adjust=False).mean() +exp26 = df['Close'].ewm(span=26, adjust=False).mean() +macd = exp12 - exp26 +signal = macd.ewm(span=9, adjust=False).mean() +histogram = macd - signal + +# initial plot: +apds = [mpf.make_addplot(exp12,color='lime'), + mpf.make_addplot(exp26,color='c'), + mpf.make_addplot(histogram,type='bar',width=0.7,panel=1, + color='dimgray',alpha=1,secondary_y=False), + mpf.make_addplot(macd,panel=1,color='fuchsia',secondary_y=True), + mpf.make_addplot(signal,panel=1,color='b',secondary_y=True), + ] + +# For some reason, which i have yet to determine, MultiCursor somehow +# causes ymin to be set to zero for the main candlestick Axes, but we +# can correct that problem by passing in specific values: +ymin = min(df['Low']) * 0.98 +ymax = max(df['High']) * 1.02 + +# initial plot with cursor: +fig, axlist = mpf.plot(df,type='candle',addplot=apds,figscale=1.25,figratio=(8,6),title='\nMACD', ylim=(ymin,ymax), + style='blueskies',volume=True,volume_panel=2,panel_ratios=(6,3,2),returnfig=True) +multi = MultiCursor(fig.canvas, axlist[0:2], horizOn=True, vertOn=True, color='pink', lw=1.2) + +# --------------------------------------------------- +# set up an event loop where we wait for two +# mouse clicks, and then draw a line in between them, +# and then wait again for another two mouse clicks. + +# This is a crude way to do it, but its quick and easy. +# Disadvantage is: user has 8 seconds to provide two clicks +# or the first click will be erased. But the 8 seconds +# repeats as long as the user does not close the Figure, +# so user can draw as many trend lines as they want. +# The advantage of doing it this way is we don't have +# to write all the mouse click handling stuff that's +# already written in `Figure.ginput()`. + + +alines = [] + +not_closed = True +def on_close(event): + global not_closed + not_closed = False + +fig.canvas.mpl_connect('close_event', on_close) + +while not_closed: + + vertices = fig.ginput(n=2,timeout=8) + if len(vertices) < 2: + continue + p1 = vertices[0] + p2 = vertices[1] + + d1 = df.index[ round(p1[0]) ] + d2 = df.index[ round(p2[0]) ] + + alines.append( [ (d1,p1[1]), (d2,p2[1]) ] ) + + apds = [mpf.make_addplot(exp12,color='lime',ax=axlist[0]), + mpf.make_addplot(exp26,color='c',ax=axlist[0]), + mpf.make_addplot(histogram,type='bar',width=0.7,panel=1,ax=axlist[2],color='dimgray',alpha=1), + mpf.make_addplot(macd,panel=1,color='fuchsia',ax=axlist[3]), + mpf.make_addplot(signal,panel=1,color='b',ax=axlist[3]) + ] + + mpf.plot(df,ax=axlist[0],type='candle',addplot=apds,ylim=(ymin,ymax), + alines=dict(alines=alines,colors='r'), + style='blueskies',volume=axlist[4],volume_panel=2,panel_ratios=(6,3,2)) + + fig.canvas.draw_idle() + From e3feb07da56a9c6b40cbc52dd7c7d2847d80e1a4 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Fri, 18 Mar 2022 17:26:01 -0400 Subject: [PATCH 15/20] localize datetimes for tlines;proposed fix for bar chart widths --- examples/scratch_pad/plotting.issue508.py | 1272 +++++++++++++++++++++ examples/using_lines.ipynb | 51 +- src/mplfinance/_utils.py | 11 +- 3 files changed, 1300 insertions(+), 34 deletions(-) create mode 100644 examples/scratch_pad/plotting.issue508.py diff --git a/examples/scratch_pad/plotting.issue508.py b/examples/scratch_pad/plotting.issue508.py new file mode 100644 index 00000000..b59fe2d8 --- /dev/null +++ b/examples/scratch_pad/plotting.issue508.py @@ -0,0 +1,1272 @@ +import matplotlib.dates as mdates +import matplotlib.pyplot as plt +import matplotlib.colors as mcolors +import matplotlib.axes as mpl_axes +import matplotlib.figure as mpl_fig +import pandas as pd +import numpy as np +import copy +import io +import os +import math +import warnings +import statistics as stat + +from itertools import cycle +#from pandas.plotting import register_matplotlib_converters +#register_matplotlib_converters() + +from mplfinance._utils import _construct_aline_collections +from mplfinance._utils import _construct_hline_collections +from mplfinance._utils import _construct_vline_collections +from mplfinance._utils import _construct_tline_collections +from mplfinance._utils import _construct_mpf_collections + +from mplfinance._widths import _determine_width_config + +from mplfinance._utils import _updown_colors +from mplfinance._utils import IntegerIndexDateTimeFormatter +from mplfinance._utils import _mscatter +from mplfinance._utils import _check_and_convert_xlim_configuration + +from mplfinance import _styles + +from mplfinance._arg_validators import _check_and_prepare_data, _mav_validator +from mplfinance._arg_validators import _get_valid_plot_types +from mplfinance._arg_validators import _process_kwargs, _validate_vkwargs_dict +from mplfinance._arg_validators import _kwarg_not_implemented, _bypass_kwarg_validation +from mplfinance._arg_validators import _hlines_validator, _vlines_validator +from mplfinance._arg_validators import _alines_validator, _tlines_validator +from mplfinance._arg_validators import _scale_padding_validator, _yscale_validator +from mplfinance._arg_validators import _valid_panel_id, _check_for_external_axes +from mplfinance._arg_validators import _xlim_validator, _mco_validator, _is_marketcolor_object + +from mplfinance._panels import _build_panels +from mplfinance._panels import _set_ticks_on_bottom_panel_only + +from mplfinance._helpers import _determine_format_string +from mplfinance._helpers import _list_of_dict +from mplfinance._helpers import _num_or_seq_of_num +from mplfinance._helpers import _adjust_color_brightness + +VALID_PMOVE_TYPES = ['renko', 'pnf'] + +DEFAULT_FIGRATIO = (8.00,5.75) + +def with_rc_context(func): + ''' + This decoractor creates an rcParams context around a function, so that any changes + the function makes to rcParams will be reversed when the decorated function returns + (therefore those changes have no effect outside of the decorated function). + ''' + def decorator(*args, **kwargs): + with plt.rc_context(): + return func(*args, **kwargs) + return decorator + +def _warn_no_xgaps_deprecated(value): + warnings.warn('\n\n ================================================================= '+ + '\n\n WARNING: `no_xgaps` is /deprecated/:'+ + '\n Default value is now `no_xgaps=True`'+ + '\n However, to set `no_xgaps=False` and silence this warning,'+ + '\n use instead: `show_nontrading=True`.'+ + '\n\n ================================================================ ', + category=DeprecationWarning) + return isinstance(value,bool) + +def _warn_set_ylim_deprecated(value): + warnings.warn('\n\n ================================================================= '+ + '\n\n WARNING: `set_ylim=(ymin,ymax)` kwarg '+ + '\n has been replaced with: '+ + '\n `ylim=(ymin,ymax)`.'+ + '\n\n ================================================================ ', + category=DeprecationWarning) + return isinstance(value,bool) + + +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') + 'Description' : ('Column names to be used when plotting the data.'+ + ' 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', + 'Description' : 'Plot type: '+str(_get_valid_plot_types()), + 'Validator' : lambda value: value in _get_valid_plot_types() }, + + 'style' : { 'Default' : None, + 'Description' : 'plot style; see `mpf.available_styles()`', + 'Validator' : _styles._valid_mpf_style }, + + 'volume' : { 'Default' : False, + 'Description' : 'Plot volume: True, False, or set to Axes object on which to plot.', + 'Validator' : lambda value: isinstance(value,bool) or isinstance(value,mpl_axes.Axes) }, + + 'mav' : { 'Default' : None, + 'Description' : 'Moving Average window size(s); (int or tuple of ints)', + 'Validator' : _mav_validator }, + + 'renko_params' : { 'Default' : dict(), + 'Description' : 'dict of renko parameters; call `mpf.kwarg_help("renko_params")`', + 'Validator' : lambda value: isinstance(value,dict) }, + + 'pnf_params' : { 'Default' : dict(), + 'Description' : 'dict of point-and-figure parameters; call `mpf.kwarg_help("pnf_params")`', + 'Validator' : lambda value: isinstance(value,dict) }, + + 'study' : { 'Default' : None, + 'Description' : 'kwarg not implemented', + 'Validator' : lambda value: _kwarg_not_implemented(value) }, + + 'marketcolor_overrides' : { 'Default' : None, + 'Description' : 'sequence of color objects to override market colors.'+ + 'sequence must be same length as ohlc(v) DataFrame. Each'+ + 'color object may be a color, marketcolor object, or None.', + 'Validator' : _mco_validator }, + + 'mco_faceonly' : { 'Default' : False, # If True: Override only the face of the candle + 'Description' : 'True/False marketcolor_overrides only apply to face of candle.', + 'Validator' : lambda value: isinstance(value,bool) }, + + 'no_xgaps' : { 'Default' : True, # None means follow default logic below: + 'Description' : 'deprecated', + 'Validator' : lambda value: _warn_no_xgaps_deprecated(value) }, + + 'show_nontrading' : { 'Default' : False, + 'Description' : 'True/False show spaces for non-trading days/periods', + 'Validator' : lambda value: isinstance(value,bool) }, + + 'figscale' : { 'Default' : None, # scale base figure size up or down. + 'Description' : 'Scale figure size up (if > 1) or down (if < 1)', + 'Validator' : lambda value: isinstance(value,float) or isinstance(value,int) }, + + 'figratio' : { 'Default' : None, # aspect ratio; scaled to 8.0 height + 'Description' : 'Aspect ratio of the figure. Default: (8.00,5.75)', + '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 + 'Description' : ('Figure size: overrides both figscale and figratio,'+ + ' else defaults to figratio*figscale'), + 'Validator' : lambda value: isinstance(value,(tuple,list)) + and len(value) == 2 + and isinstance(value[0],(float,int)) + and isinstance(value[1],(float,int)) }, + + 'fontscale' : { 'Default' : None, # scale all fonts up or down + 'Description' : 'Scale font sizes up (if > 1) or down (if < 1)', + 'Validator' : lambda value: isinstance(value,float) or isinstance(value,int) }, + + 'linecolor' : { 'Default' : None, # line color in line plot + 'Description' : 'Line color for `type=line`', + 'Validator' : lambda value: mcolors.is_color_like(value) }, + + 'title' : { 'Default' : None, # Figure Title + 'Description' : 'Figure Title (see also `axtitle`)', + 'Validator' : lambda value: isinstance(value,(str,dict)) }, + + 'axtitle' : { 'Default' : None, # Axes Title (subplot title) + 'Description' : 'Axes Title (subplot title)', + 'Validator' : lambda value: isinstance(value,(str,dict)) }, + + 'ylabel' : { 'Default' : 'Price', # y-axis label + 'Description' : 'label for y-axis of main plot', + 'Validator' : lambda value: isinstance(value,str) }, + + 'ylabel_lower' : { 'Default' : None, # y-axis label default logic below + 'Description' : 'label for y-axis of volume', + 'Validator' : lambda value: isinstance(value,str) }, + + 'addplot' : { 'Default' : None, + 'Description' : 'addplot object or sequence of addplot objects (from `mpf.make_addplot()`)', + 'Validator' : lambda value: isinstance(value,dict) or (isinstance(value,list) and all([isinstance(d,dict) for d in value])) }, + + 'savefig' : { 'Default' : None, + 'Description' : 'file name, or BytesIO, or dict with key `fname` plus other keys allowed as '+ + ' kwargs to matplotlib `Figure.savefig()`', + 'Validator' : lambda value: isinstance(value,dict) or isinstance(value,str) or isinstance(value, io.BytesIO) or isinstance(value, os.PathLike) }, + + 'block' : { 'Default' : None, + 'Description' : 'True/False wait for figure to be closed before returning', + 'Validator' : lambda value: isinstance(value,bool) }, + + 'returnfig' : { 'Default' : False, + 'Description' : 'return Figure and list of Axes objects created by mplfinance;'+ + ' user must display plot when ready, usually by calling `mpf.show()`', + 'Validator' : lambda value: isinstance(value,bool) }, + + 'return_calculated_values' : { 'Default' : None, + 'Description' : 'set to a variable containing an empty dict; `mpf.plot()` will fill'+ + ' the dict with various mplfinance calculated values', + 'Validator' : lambda value: isinstance(value, dict) and len(value) == 0}, + + 'set_ylim' : { 'Default' : None, + 'Description' : 'deprecated', + 'Validator' : lambda value: _warn_set_ylim_deprecated(value) }, + + 'ylim' : { 'Default' : None, + 'Description' : 'Limits for y-axis as tuple (min,max), i.e. (bottom,top)', + 'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 + and all([isinstance(v,(int,float)) for v in value])}, + + 'xlim' : { 'Default' : None, + 'Description' : 'Limits for x-axis as tuple (min,max), i.e. (left,right)', + 'Validator' : lambda value: _xlim_validator(value) }, + + 'set_ylim_panelB' : { 'Default' : None, + 'Description' : 'deprecated', + 'Validator' : lambda value: _warn_set_ylim_deprecated(value) }, + + 'hlines' : { 'Default' : None, + 'Description' : 'Draw one or more HORIZONTAL LINES across entire plot, by'+ + ' specifying a price, or sequence of prices. May also be a dict'+ + ' with key `hlines` specifying a price or sequence of prices, plus'+ + ' one or more of the following keys: `colors`, `linestyle`,'+ + ' `linewidths`, `alpha`.', + 'Validator' : lambda value: _hlines_validator(value) }, + + 'vlines' : { 'Default' : None, + 'Description' : 'Draw one or more VERTICAL LINES across entire plot, by'+ + ' specifying a date[time], or sequence of date[time]. May also'+ + ' be a dict with key `vlines` specifying a date[time] or sequence'+ + ' of date[time], plus one or more of the following keys:'+ + ' `colors`, `linestyle`, `linewidths`, `alpha`.', + 'Validator' : lambda value: _vlines_validator(value) }, + + 'alines' : { 'Default' : None, + 'Description' : 'Draw one or more ARBITRARY LINES anywhere on the plot, by'+ + ' specifying a sequence of two or more date/price pairs, or by'+ + ' specifying a sequence of sequences of two or more date/price pairs.'+ + ' May also be a dict with key `alines` (as date/price pairs described above),'+ + ' plus one or more of the following keys:'+ + ' `colors`, `linestyle`, `linewidths`, `alpha`.', + 'Validator' : lambda value: _alines_validator(value) }, + + 'tlines' : { 'Default' : None, + 'Description' : 'Draw one or more TREND LINES by specifying one or more pairs of date[times]'+ + ' between which each trend line should be drawn. May also be a dict with key'+ + ' `tlines` as just described, plus one or more of the following keys:'+ + ' `colors`, `linestyle`, `linewidths`, `alpha`, `tline_use`,`tline_method`.', + 'Validator' : lambda value: _tlines_validator(value) }, + + 'panel_ratios' : { 'Default' : None, + 'Description' : 'sequence of numbers indicating relative sizes of panels; sequence len'+ + ' must be same as number of panels, or len 2 where first entry is for'+ + ' main panel, and second entry is for all other panels', + 'Validator' : lambda value: isinstance(value,(tuple,list)) and len(value) <= 32 and + all([isinstance(v,(int,float)) for v in value]) }, + + 'main_panel' : { 'Default' : 0, + 'Description' : 'integer - which panel is the main panel for `.plot()`', + 'Validator' : lambda value: _valid_panel_id(value) }, + + 'volume_panel' : { 'Default' : 1, + 'Description' : 'integer - which panel is the volume panel', + 'Validator' : lambda value: _valid_panel_id(value) }, + + 'num_panels' : { 'Default' : None, + 'Description' : 'total number of panels', + 'Validator' : lambda value: isinstance(value,int) and value in range(1,32+1) }, + + 'datetime_format' : { 'Default' : None, + 'Description' : 'x-axis tick format as valid `strftime()` format string', + 'Validator' : lambda value: isinstance(value,str) }, + + 'xrotation' : { 'Default' : 45, + 'Description' : 'Angle (degrees) for x-axis tick labels; 90=vertical', + 'Validator' : lambda value: isinstance(value,(int,float)) }, + + 'axisoff' : { 'Default' : False, + 'Description' : '`axisoff=True` means do NOT display any axis.', + 'Validator' : lambda value: isinstance(value,bool) }, + + 'closefig' : { 'Default' : 'auto', + 'Description' : 'True|False close the Figure before returning', + 'Validator' : lambda value: isinstance(value,bool) }, + + 'fill_between' : { 'Default' : None, + 'Description' : 'fill between specification as y-value, or sequence of'+ + ' y-values, or dict containing key "y1" plus any additional'+ + ' kwargs for `fill_between()`', + '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, + 'Description' : 'True|False implement tight layout (minimal padding around Figure)'+ + ' (see also `scale_padding` kwarg)', + 'Validator' : lambda value: isinstance(value,bool) }, + + 'scale_padding' : { 'Default' : 1.0, # Issue#193 + 'Description' : 'Increase, > 1.0, or decrease, < 1.0, padding around figure.'+ + ' May also be a dict containing one or more of the following keys:'+ + ' "top", "bottom", "left", "right", to individual scale padding'+ + ' on each side of Figure.', + 'Validator' : lambda value: _scale_padding_validator(value) }, + + 'width_adjuster_version' : { 'Default' : 'v1', + 'Description' : 'specify version of object width adjustment algorithm: "v0" or "v1"'+ + ' (See also "widths" tutorial in mplfinance examples folder).', + 'Validator' : lambda value: value in ('v0', 'v1') }, + + 'scale_width_adjustment' : { 'Default' : None, + 'Description' : 'scale width of plot objects wider, > 1.0, or narrower, < 1.0'+ + ' may also be a dict to scale individual widths.'+ + ' (See also "widths" tutorial in mplfinance examples folder).', + 'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 }, + + 'update_width_config' : { 'Default' : None, + 'Description' : 'dict - update individual items in width configuration.'+ + ' (See also "widths" tutorial in mplfinance examples folder).', + 'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 }, + + 'return_width_config' : { 'Default' : None, + 'Description' : 'empty dict variable to be filled with width configuration settings.', + 'Validator' : lambda value: isinstance(value,dict) and len(value)==0 }, + + 'saxbelow' : { 'Default' : True, # Issue#115 Comment#639446764 + 'Description' : 'set the volume Axes below (behind) all other Axes objects', + 'Validator' : lambda value: isinstance(value,bool) }, + + 'ax' : { 'Default' : None, + 'Description' : 'Matplotlib Axes object on which to plot', + 'Validator' : lambda value: isinstance(value,mpl_axes.Axes) }, + + 'volume_exponent' : { 'Default' : None, + 'Description' : 'integer exponent on the volume axis'+ + ' (or set to "legacy" for old mplfinance style)', + 'Validator' : lambda value: isinstance(value,int) or value == 'legacy'}, + + 'tz_localize' : { 'Default' : True, + 'Description' : 'True|False localize the times in the DatetimeIndex', + 'Validator' : lambda value: isinstance(value,bool) }, + + 'yscale' : { 'Default' : None, + 'Description' : 'y-axis scale: "linear", "log", "symlog", or "logit"', + 'Validator' : lambda value: _yscale_validator(value) }, + + 'volume_yscale' : { 'Default' : None, + 'Description' : 'Volume y-axis scale: "linear", "log", "symlog", or "logit"', + 'Validator' : lambda value: _yscale_validator(value) }, + + 'warn_too_much_data' : { 'Default' : 599, + 'Description' : 'Tolerance for data amount in plot. Default=599 rows.'+ + ' Values greater than \'warn_too_much_data\' will trigger a warning.', + 'Validator' : lambda value: isinstance(value,int) }, + } + + _validate_vkwargs_dict(vkwargs) + + return vkwargs + +###@with_rc_context +def plot( data, **kwargs ): + """ + Given a Pandas DataFrame containing columns Open,High,Low,Close and optionally Volume + with a DatetimeIndex, plot the data. + Available plots include ohlc bars, candlestick, and line plots. + Also provide visually analysis in the form of common technical studies, such as: + moving averages, renko, etc. + Also provide ability to plot trading signals, and/or addtional user-defined data. + """ + + config = _process_kwargs(kwargs, _valid_plot_kwargs()) + + # translate alias types: + config['type'] = _get_valid_plot_types(config['type']) + + dates,opens,highs,lows,closes,volumes = _check_and_prepare_data(data, config) + + config['xlim'] = _check_and_convert_xlim_configuration(data, config) + + if config['type'] in VALID_PMOVE_TYPES and config['addplot'] is not None: + err = "`addplot` is not supported for `type='" + config['type'] +"'`" + raise ValueError(err) + + if config['marketcolor_overrides'] is not None: + if len(config['marketcolor_overrides']) != len(dates): + raise ValueError('`marketcolor_overrides` must be same length as dataframe.') + + external_axes_mode = _check_for_external_axes(config) + + if external_axes_mode: + if config['figscale'] is not None: + warnings.warn('\n\n ================================================================= '+ + '\n\n WARNING: `figscale` has NO effect in External Axes Mode.'+ + '\n\n ================================================================ ', + category=UserWarning) + if config['figratio'] is not None: + warnings.warn('\n\n ================================================================= '+ + '\n\n WARNING: `figratio` has NO effect in External Axes Mode.'+ + '\n\n ================================================================ ', + category=UserWarning) + if config['figsize'] is not None: + warnings.warn('\n\n ================================================================= '+ + '\n\n WARNING: `figsize` has NO effect in External Axes Mode.'+ + '\n\n ================================================================ ', + category=UserWarning) + else: + if config['figscale'] is None: config['figscale'] = 1.0 + if config['figratio'] is None: config['figratio'] = DEFAULT_FIGRATIO + + style = config['style'] + + if external_axes_mode and hasattr(config['ax'],'mpfstyle') and style is None: + style = config['ax'].mpfstyle + elif style is None: + style = 'default' + + if isinstance(style,str): + style = _styles._get_mpfstyle(style) + + config['style'] = style + + if isinstance(style,dict): + if not external_axes_mode: _styles._apply_mpfstyle(style) + else: + raise TypeError('style should be a `dict`; why is it not?') + + # ---------------------------------------------------------------------- + # TODO: Add some warnings, or raise an exception, if external_axes_mode + # and user is trying to figscale, figratio, or figsize. + # ---------------------------------------------------------------------- + + if not external_axes_mode: + fig = plt.figure() + _adjust_figsize(fig,config) + else: + fig = None + + _adjust_fontsize(config) + + if config['volume'] and volumes is None: + raise ValueError('Request for volume, but NO volume data.') + + if external_axes_mode: + panels = None + if config['volume']: + volumeAxes = config['volume'] + volumeAxes.set_axisbelow(config['saxbelow']) + else: + panels = _build_panels(fig, config) + volumeAxes = panels.at[config['volume_panel'],'axes'][0] if config['volume'] is True else None + + fmtstring = _determine_format_string(dates, config['datetime_format']) + + ptype = config['type'] + + if config['show_nontrading']: + formatter = mdates.DateFormatter(fmtstring) + xdates = dates + else: + formatter = IntegerIndexDateTimeFormatter(dates, fmtstring) + xdates = np.arange(len(dates)) + + if external_axes_mode: + axA1 = config['ax'] + axA1.set_axisbelow(config['saxbelow']) + else: + axA1 = panels.at[config['main_panel'],'axes'][0] + + # Will have to handle widths config separately for PMOVE types ?? + config['_width_config'] = _determine_width_config(xdates, config) + + + rwc = config['return_width_config'] + if isinstance(rwc,dict) and len(rwc)==0: + config['return_width_config'].update(config['_width_config']) + + + collections = None + if ptype == 'line': + lw = config['_width_config']['line_width'] + axA1.plot(xdates, closes, color=config['linecolor'], linewidth=lw) + else: + collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style) + + if ptype in VALID_PMOVE_TYPES: + collections, calculated_values = collections + volumes = calculated_values['volumes'] + pmove_dates = calculated_values['dates'] + pmove_values = calculated_values['values'] + if all([isinstance(v,(list,tuple)) for v in pmove_values]): + pmove_avgvals = [sum(v)/len(v) for v in pmove_values] + else: + pmove_avgvals = pmove_values + pmove_size = calculated_values['size'] + pmove_counts = calculated_values['counts'] if 'counts' in calculated_values else None + formatter = IntegerIndexDateTimeFormatter(pmove_dates, fmtstring) + xdates = np.arange(len(pmove_dates)) + + if collections is not None: + for collection in collections: + axA1.add_collection(collection) + + if ptype in VALID_PMOVE_TYPES: + mavprices = _plot_mav(axA1,config,xdates,pmove_avgvals) + else: + mavprices = _plot_mav(axA1,config,xdates,closes) + + avg_dist_between_points = (xdates[-1] - xdates[0]) / float(len(xdates)) + if not config['tight_layout']: + minx = xdates[0] - avg_dist_between_points + maxx = xdates[-1] + avg_dist_between_points + else: + minx = xdates[0] - (0.45 * avg_dist_between_points) + maxx = xdates[-1] + (0.45 * avg_dist_between_points) + + if len(xdates) == 1: # kludge special case + minx = minx - 0.75 + maxx = maxx + 0.75 + if ptype not in VALID_PMOVE_TYPES: + _lows = lows + _highs = highs + else: + _lows = pmove_avgvals + _highs = [value+pmove_size for value in pmove_avgvals] + + miny = np.nanmin(_lows) + maxy = np.nanmax(_highs) + + if config['ylim'] is not None: + axA1.set_ylim(config['ylim'][0], config['ylim'][1]) + elif config['tight_layout']: + ydelta = 0.01 * (maxy-miny) + if miny > 0.0: + # don't let it go negative: + setminy = max(0.9*miny,miny-ydelta) + else: + setminy = miny-ydelta + axA1.set_ylim(setminy,maxy+ydelta) + + if config['xlim'] is not None: + axA1.set_xlim(config['xlim'][0], config['xlim'][1]) + elif config['tight_layout']: + axA1.set_xlim(minx,maxx) + + if (config['ylim'] is None and + config['xlim'] is None and + not config['tight_layout']): + corners = (minx, miny), (maxx, maxy) + axA1.update_datalim(corners) + + if config['return_calculated_values'] is not None: + retdict = config['return_calculated_values'] + if ptype == 'renko': + retdict['renko_bricks' ] = pmove_values + retdict['renko_dates' ] = mdates.num2date(pmove_dates) + retdict['renko_size' ] = pmove_size + retdict['renko_volumes'] = volumes if config['volume'] else None + elif ptype == 'pnf': + retdict['pnf_dates' ] = mdates.num2date(pmove_dates) + retdict['pnf_counts' ] = pmove_counts + retdict['pnf_values' ] = pmove_values + retdict['pnf_avgvals' ] = pmove_avgvals + retdict['pnf_size' ] = pmove_size + retdict['pnf_volumes' ] = volumes if config['volume'] else None + if config['mav'] is not None: + mav = config['mav'] + if len(mav) != len(mavprices): + warnings.warn('len(mav)='+str(len(mav))+' BUT len(mavprices)='+str(len(mavprices))) + else: + for jj in range(0,len(mav)): + retdict['mav' + str(mav[jj])] = mavprices[jj] + retdict['minx'] = minx + retdict['maxx'] = maxx + retdict['miny'] = miny + retdict['maxy'] = maxy + + # Note: these are NOT mutually exclusive, so the order of this + # if/elif is important: VALID_PMOVE_TYPES must be first. + if ptype in VALID_PMOVE_TYPES: + dtix = pd.DatetimeIndex([dt for dt in mdates.num2date(pmove_dates)]) + elif not config['show_nontrading']: + dtix = data.index + else: + dtix = None + + line_collections = [] + line_collections.append(_construct_aline_collections(config['alines'], dtix)) + line_collections.append(_construct_hline_collections(config['hlines'], minx, maxx)) + line_collections.append(_construct_vline_collections(config['vlines'], dtix, miny, maxy)) + tlines = config['tlines'] + if isinstance(tlines,(list,tuple)) and all([isinstance(item,dict) for item in tlines]): + pass + else: + tlines = [tlines,] + for tline_item in tlines: + line_collections.append(_construct_tline_collections(tline_item, dtix, dates, opens, highs, lows, closes)) + + for collection in line_collections: + if collection is not None: + axA1.add_collection(collection) + + datalen = len(xdates) + if config['volume']: + vup,vdown = style['marketcolors']['volume'].values() + #-- print('vup,vdown=',vup,vdown) + vcolors = _updown_colors(vup, vdown, opens, closes, use_prev_close=style['marketcolors']['vcdopcod']) + #-- print('len(vcolors),len(opens),len(closes)=',len(vcolors),len(opens),len(closes)) + #-- print('vcolors=',vcolors) + + w = config['_width_config']['volume_width'] + lw = config['_width_config']['volume_linewidth'] + + adjc = _adjust_color_brightness(vcolors,0.90) + volumeAxes.bar(xdates,volumes,width=w,linewidth=lw,color=vcolors,ec=adjc) + vymin = 0.3 * np.nanmin(volumes) + vymax = 1.1 * np.nanmax(volumes) + volumeAxes.set_ylim(vymin,vymax) + + xrotation = config['xrotation'] + if not external_axes_mode: + _set_ticks_on_bottom_panel_only(panels,formatter,rotation=xrotation) + else: + axA1.tick_params(axis='x',rotation=xrotation) + axA1.xaxis.set_major_formatter(formatter) + + ysd = config['yscale'] + if isinstance(ysd,dict): + yscale = ysd['yscale'] + del ysd['yscale'] + axA1.set_yscale(yscale,**ysd) + elif isinstance(ysd,str): + axA1.set_yscale(ysd) + + + addplot = config['addplot'] + if addplot is not None and ptype not in VALID_PMOVE_TYPES: + # NOTE: If in external_axes_mode, then all code relating + # to panels and secondary_y becomes irrrelevant. + # If the user wants something on a secondary_y then user should + # determine that externally, and pass in the appropriate axes. + + if not external_axes_mode: + # Calculate the Order of Magnitude Range ('mag') + # If addplot['secondary_y'] == 'auto', then: If the addplot['data'] + # is out of the Order of Magnitude Range, then use secondary_y. + + lo = math.log(max(math.fabs(np.nanmin(lows)),1e-7),10) - 0.5 + hi = math.log(max(math.fabs(np.nanmax(highs)),1e-7),10) + 0.5 + + panels['mag'] = [None]*len(panels) # create 'mag'nitude column + + panels.at[config['main_panel'],'mag'] = {'lo':lo,'hi':hi} # update main panel magnitude range + + if config['volume']: + lo = math.log(max(math.fabs(np.nanmin(volumes)),1e-7),10) - 0.5 + hi = math.log(max(math.fabs(np.nanmax(volumes)),1e-7),10) + 0.5 + panels.at[config['volume_panel'],'mag'] = {'lo':lo,'hi':hi} + + 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))) + + for apdict in addplot: + + panid = apdict['panel'] + if not external_axes_mode: + if panid == 'main' : panid = 0 # for backwards compatibility + elif panid == 'lower': panid = 1 # for backwards compatibility + if apdict['y_on_right'] is not None: + panels.at[panid,'y_on_right'] = apdict['y_on_right'] + + aptype = apdict['type'] + if aptype == 'ohlc' or aptype == 'candle': + ax = _addplot_collections(panid,panels,apdict,xdates,config) + _addplot_apply_supplements(ax,apdict) + else: + apdata = apdict['data'] + if isinstance(apdata,list) and not isinstance(apdata[0],(float,int)): + raise TypeError('apdata is list but NOT of float or int') + if isinstance(apdata,pd.DataFrame): + havedf = True + else: + havedf = False # must be a single series or array + apdata = [apdata,] # make it iterable + for column in apdata: + ydata = apdata.loc[:,column] if havedf else column + ax = _addplot_columns(panid,panels,ydata,apdict,xdates,config) + _addplot_apply_supplements(ax,apdict) + + # fill_between is NOT supported for external_axes_mode + # (caller can easily call ax.fill_between() themselves). + if config['fill_between'] is not None and not external_axes_mode: + fb = config['fill_between'] + panid = config['main_panel'] + if isinstance(fb,dict): + if 'x' in fb: + raise ValueError('fill_between dict may not contain `x`') + if 'panel' in fb: + panid = fb['panel'] + del fb['panel'] + else: + fb = dict(y1=fb) + fb['x'] = xdates + ax = panels.at[panid,'axes'][0] + ax.fill_between(**fb) + + # put the primary axis on one side, + # and the twinx() on the "other" side: + if not external_axes_mode: + for panid,row in panels.iterrows(): + ax = row['axes'] + y_on_right = style['y_on_right'] if row['y_on_right'] is None else row['y_on_right'] + _set_ylabels_side(ax[0],ax[1],y_on_right) + else: + y_on_right = style['y_on_right'] + _set_ylabels_side(axA1,None,y_on_right) + + # TODO: ================================================================ + # TODO: Investigate: + # TODO: =========== + # TODO: It appears to me that there may be some or significant overlap + # TODO: between what the following functions actually do: + # TODO: At the very least, all four of them appear to communicate + # TODO: to matplotlib that the xaxis should be treated as dates: + # TODO: -> 'ax.autoscale_view()' + # TODO: -> 'ax.xaxis_dates()' + # TODO: -> 'plt.autofmt_xdates()' + # TODO: -> 'fig.autofmt_xdate()' + # TODO: ================================================================ + + + #if config['autofmt_xdate']: + #print('CALLING fig.autofmt_xdate()') + #fig.autofmt_xdate() + + axA1.autoscale_view() # Is this really necessary?? + # It appears to me, based on experience coding types 'ohlc' and 'candle' + # for `addplot`, that this IS necessary when the only thing done to the + # the axes is .add_collection(). (However, if ax.plot() .scatter() or + # .bar() was called, then possibly this is not necessary; not entirely + # sure, but it definitely was necessary to get 'ohlc' and 'candle' + # working in `addplot`). + + axA1.set_ylabel(config['ylabel']) + + if config['volume']: + if external_axes_mode: + volumeAxes.tick_params(axis='x',rotation=xrotation) + volumeAxes.xaxis.set_major_formatter(formatter) + + vscale = 'linear' + ysd = config['volume_yscale'] + if isinstance(ysd,dict): + yscale = ysd['yscale'] + del ysd['yscale'] + volumeAxes.set_yscale(yscale,**ysd) + vscale = yscale + elif isinstance(ysd,str): + volumeAxes.set_yscale(ysd) + vscale = ysd + offset = '' + if vscale == 'linear': + vxp = config['volume_exponent'] + if vxp == 'legacy': + volumeAxes.figure.canvas.draw() # This is needed to calculate offset + offset = volumeAxes.yaxis.get_major_formatter().get_offset() + if len(offset) > 0: + offset = (' x '+offset) + elif isinstance(vxp,int) and vxp > 0: + volumeAxes.ticklabel_format(useOffset=False,scilimits=(vxp,vxp),axis='y') + offset = ' $10^{'+str(vxp)+'}$' + elif isinstance(vxp,int) and vxp == 0: + volumeAxes.ticklabel_format(useOffset=False,style='plain',axis='y') + offset = '' + else: + offset = '' + scilims = plt.rcParams['axes.formatter.limits'] + if scilims[0] < scilims[1]: + for power in (5,4,3,2,1): + xp = scilims[1]*power + if vymax >= 10.**xp: + volumeAxes.ticklabel_format(useOffset=False,scilimits=(xp,xp),axis='y') + offset = ' $10^{'+str(xp)+'}$' + break + elif scilims[0] == scilims[1] and scilims[1] != 0: + volumeAxes.ticklabel_format(useOffset=False,scilimits=scilims,axis='y') + offset = ' $10^'+str(scilims[1])+'$' + volumeAxes.yaxis.offsetText.set_visible(False) + + if config['ylabel_lower'] is None: + vol_label = 'Volume'+offset + else: + if len(offset) > 0: + offset = '\n'+offset + vol_label = config['ylabel_lower'] + offset + volumeAxes.set_ylabel(vol_label) + + if config['title'] is not None: + if config['tight_layout']: + # IMPORTANT: `y=0.89` is based on the top of the top panel + # being at 0.18+0.7 = 0.88. See _panels.py + # If the value changes there, then it needs to change here. + title_kwargs = dict(va='bottom', y=0.89) + else: + title_kwargs = dict(va='center') + if isinstance(config['title'],dict): + title_dict = config['title'] + if 'title' not in title_dict: + raise ValueError('Must have "title" entry in title dict') + else: + title = title_dict['title'] + del title_dict['title'] + title_kwargs.update(title_dict) # allows override default values set by mplfinance above + else: + title = config['title'] # config['title'] is a string + fig.suptitle(title,**title_kwargs) + + + if config['axtitle'] is not None: + axA1.set_title(config['axtitle']) + + if not external_axes_mode: + for panid,row in panels.iterrows(): + if not row['used2nd']: + row['axes'][1].set_visible(False) + + if external_axes_mode: + return None + + # Should we create a new kwarg to return a flattened axes list + # versus a list of tuples of primary and secondary axes? + # For now, for backwards compatibility, we flatten axes list: + axlist = [ax for axes in panels['axes'] for ax in axes] + + if config['axisoff']: + for ax in axlist: + ax.set_axis_off() + + if config['savefig'] is not None: + save = config['savefig'] + if isinstance(save,dict): + if config['tight_layout'] and 'bbox_inches' not in save: + plt.savefig(**save,bbox_inches='tight') + else: + plt.savefig(**save) + else: + if config['tight_layout']: + plt.savefig(save,bbox_inches='tight') + else: + plt.savefig(save) + if config['closefig']: # True or 'auto' + plt.close(fig) + elif not config['returnfig']: + plt.show(block=config['block']) # https://stackoverflow.com/a/13361748/1639359 + if config['closefig'] == True or (config['block'] and config['closefig']): + plt.close(fig) + + if config['returnfig']: + if config['closefig'] == True: plt.close(fig) + return (fig, axlist) + + # rcp = copy.deepcopy(plt.rcParams) + # rcpdf = rcParams_to_df(rcp) + # print('type(rcpdf)=',type(rcpdf)) + # print('rcpdfhead(3)=',rcpdf.head(3)) + # return # rcpdf + +def _adjust_figsize(fig,config): + if fig is None: + return + if config['figsize'] is None: + w,h = config['figratio'] + r = float(w)/float(h) + if r < 0.20 or r > 5.0: + raise ValueError('"figratio" (aspect ratio) must be between 0.20 and 5.0 (but is '+str(r)+')') + default_scale = DEFAULT_FIGRATIO[1]/h + h *= default_scale + w *= default_scale + base = (w,h) + figscale = config['figscale'] + fsize = [d*figscale for d in base] + else: + fsize = config['figsize'] + fig.set_size_inches(fsize) + +def _adjust_fontsize(config): + if config['fontscale'] is None: + return + if not isinstance(plt.rcParams['font.size'],(float,int)): + warnings.warn('\n\n ================================================================= '+ + '\n\n WARNING: Unable to scale fonts: plt.rcParams["font.size"] is NOT a float!'+ + '\n\n ================================================================ ', + category=UserWarning) + return + plt.rcParams['font.size'] *= config['fontscale'] + # -------------------------------------------- + # From: matplotlib.font_manager.font_scalings: + # font_scalings = { + # 'xx-small': 0.579, + # 'x-small': 0.694, + # 'small': 0.833, + # 'medium': 1.0, + # 'large': 1.200, + # 'x-large': 1.440, + # 'xx-large': 1.728, + # 'larger': 1.2, + # 'smaller': 0.833, + # None: 1.0, + # } + # -------------------------------------------- + fontstuff = ['axes.labelsize','axes.titlesize', 'figure.titlesize','legend.fontsize', + 'legend.title_fontsize','xtick.labelsize','ytick.labelsize'] + for item in fontstuff: + if isinstance(plt.rcParams[item],(float,int)): + plt.rcParams[item] *= config['fontscale'] + +def _addplot_collections(panid,panels,apdict,xdates,config): + + apdata = apdict['data'] + aptype = apdict['type'] + external_axes_mode = apdict['ax'] is not None + + #--------------------------------------------------------------# + # Note: _auto_secondary_y() sets the 'magnitude' column in the + # `panels` dataframe, which is needed for automatically + # determining if secondary_y is needed. Therefore we call + # _auto_secondary_y() for *all* addplots, even those that + # are set to True or False (not 'auto') for secondary_y + # because their magnitudes may be needed if *any* apdicts + # contain secondary_y='auto'. + # In theory we could first loop through all apdicts to see + # if any have secondary_y='auto', but since that is the + # default value, we will just assume we have at least one. + + valid_apc_types = ['ohlc','candle'] + if aptype not in valid_apc_types: + raise TypeError('Invalid aptype='+str(aptype)+'. Must be one of '+str(valid_apc_types)) + if not isinstance(apdata,pd.DataFrame): + raise TypeError('addplot type "'+aptype+'" MUST be accompanied by addplot data of type `pd.DataFrame`') + d,o,h,l,c,v = _check_and_prepare_data(apdata,config) + + mc = apdict['marketcolors'] + if _is_marketcolor_object(mc): + apstyle = config['style'].copy() + apstyle['marketcolors'] = mc + else: + apstyle = config['style'] + + collections = _construct_mpf_collections(aptype,d,xdates,o,h,l,c,v,config,apstyle) + + if not external_axes_mode: + lo = math.log(max(math.fabs(np.nanmin(l)),1e-7),10) - 0.5 + hi = math.log(max(math.fabs(np.nanmax(h)),1e-7),10) + 0.5 + secondary_y = _auto_secondary_y( panels, panid, lo, hi ) + if 'auto' != apdict['secondary_y']: + secondary_y = apdict['secondary_y'] + if secondary_y: + ax = panels.at[panid,'axes'][1] + panels.at[panid,'used2nd'] = True + else: + ax = panels.at[panid,'axes'][0] + else: + ax = apdict['ax'] + + for coll in collections: + ax.add_collection(coll) + if apdict['mav'] is not None: + apmavprices = _plot_mav(ax,config,xdates,c,apdict['mav']) + ax.autoscale_view() + return ax + +def _addplot_columns(panid,panels,ydata,apdict,xdates,config): + external_axes_mode = apdict['ax'] is not None + if not external_axes_mode: + secondary_y = False + if apdict['secondary_y'] == 'auto': + yd = [y for y in ydata if not math.isnan(y)] + ymhi = math.log(max(math.fabs(np.nanmax(yd)),1e-7),10) + ymlo = math.log(max(math.fabs(np.nanmin(yd)),1e-7),10) + secondary_y = _auto_secondary_y( panels, panid, ymlo, ymhi ) + else: + secondary_y = apdict['secondary_y'] + #print("apdict['secondary_y'] says secondary_y is",secondary_y) + + if secondary_y: + ax = panels.at[panid,'axes'][1] + panels.at[panid,'used2nd'] = True + else: + ax = panels.at[panid,'axes'][0] + else: + ax = apdict['ax'] + + aptype = apdict['type'] + if aptype == 'scatter': + size = apdict['markersize'] + mark = apdict['marker'] + color = apdict['color'] + alpha = apdict['alpha'] + edgecolors = apdict['edgecolors'] + linewidths = apdict['linewidths'] + + if isinstance(mark,(list,tuple,np.ndarray)): + _mscatter(xdates, ydata, ax=ax, m=mark, s=size, color=color, alpha=alpha, edgecolors=edgecolors, linewidths=linewidths) + else: + ax.scatter(xdates, ydata, s=size, marker=mark, color=color, alpha=alpha, edgecolors=edgecolors, linewidths=linewidths) + elif aptype == 'bar': + w = config['_width_config']['volume_width'] + lw = config['_width_config']['volume_linewidth'] + ## volumeAxes.bar(xdates,volumes,width=w,linewidth=lw,color=vcol + width = w if apdict['width'] is None else apdict['width'] + linew = lw if apdict['width'] is None else 0.25*apdict['width'] + print('width=',width," apdict['width']=",apdict['width']) + bottom = apdict['bottom'] + color = apdict['color'] + alpha = apdict['alpha'] + print('bar: xdates[0:10]=',xdates[0:10],' ydata[0:10]=',ydata[0:10],' len(ydata)=',len(ydata),' ax=',ax) + ax.bar(xdates,ydata,width=width,linewidth=linew,bottom=bottom,color=color,alpha=alpha) + elif aptype == 'line': + ls = apdict['linestyle'] + color = apdict['color'] + width = apdict['width'] if apdict['width'] is not None else 1.6*config['_width_config']['line_width'] + alpha = apdict['alpha'] + ax.plot(xdates,ydata,linestyle=ls,color=color,linewidth=width,alpha=alpha) + elif aptype == 'step': + stepwhere = apdict['stepwhere'] + ls = apdict['linestyle'] + color = apdict['color'] + width = apdict['width'] if apdict['width'] is not None else 1.6*config['_width_config']['line_width'] + alpha = apdict['alpha'] + print('step: xdates[0:10]=',xdates[0:10],' ydata[0:10]=',ydata[0:10],' len(ydata)=',len(ydata),' ax=',ax) + ax.step(xdates,ydata,where = stepwhere,linestyle=ls,color=color,linewidth=width,alpha=alpha) + else: + raise ValueError('addplot type "'+str(aptype)+'" NOT yet supported.') + + if apdict['mav'] is not None: + apmavprices = _plot_mav(ax,config,xdates,ydata,apdict['mav']) + + return ax + +def _addplot_apply_supplements(ax,apdict): + if (apdict['ylabel'] is not None): + ax.set_ylabel(apdict['ylabel']) + if apdict['ylim'] is not None: + ax.set_ylim(apdict['ylim'][0],apdict['ylim'][1]) + if apdict['title'] is not None: + ax.set_title(apdict['title']) + ysd = apdict['yscale'] + if isinstance(ysd,dict): + yscale = ysd['yscale'] + del ysd['yscale'] + ax.set_yscale(yscale,**ysd) + elif isinstance(ysd,str): + ax.set_yscale(ysd) + +def _set_ylabels_side(ax_pri,ax_sec,primary_on_right): + # put the primary axis on one side, + # and the twinx() on the "other" side: + if primary_on_right == True: + ax_pri.yaxis.set_label_position('right') + ax_pri.yaxis.tick_right() + if ax_sec is not None: + ax_sec.yaxis.set_label_position('left') + ax_sec.yaxis.tick_left() + else: # treat non-True as False, whether False, None, or anything else. + ax_pri.yaxis.set_label_position('left') + ax_pri.yaxis.tick_left() + if ax_sec is not None: + ax_sec.yaxis.set_label_position('right') + ax_sec.yaxis.tick_right() + +def _plot_mav(ax,config,xdates,prices,apmav=None,apwidth=None): + style = config['style'] + if apmav is not None: + mavgs = apmav + else: + mavgs = config['mav'] + mavp_list = [] + if mavgs is not None: + shift = None + if isinstance(mavgs,dict): + shift = mavgs['shift'] + mavgs = mavgs['period'] + if isinstance(mavgs,int): + mavgs = mavgs, # convert to tuple + if len(mavgs) > 7: + mavgs = mavgs[0:7] # take at most 7 + + if style['mavcolors'] is not None: + mavc = cycle(style['mavcolors']) + else: + mavc = None + + for idx,mav in enumerate(mavgs): + mean = pd.Series(prices).rolling(mav).mean() + if shift is not None: + mean = mean.shift(periods=shift[idx]) + mavprices = mean.values + lw = config['_width_config']['line_width'] + if mavc: + ax.plot(xdates, mavprices, linewidth=lw, color=next(mavc)) + else: + ax.plot(xdates, mavprices, linewidth=lw) + mavp_list.append(mavprices) + return mavp_list + +def _auto_secondary_y( panels, panid, ylo, yhi ): + # If mag(nitude) for this panel is not yet set, then set it + # here, as this is the first ydata to be plotted on this panel: + # i.e. consider this to be the 'primary' axis for this panel. + secondary_y = False + p = panid,'mag' + if panels.at[p] is None: + panels.at[p] = {'lo':ylo,'hi':yhi} + elif ylo < panels.at[p]['lo'] or yhi > panels.at[p]['hi']: + secondary_y = True + #if secondary_y: + # print('auto says USE secondary_y ... for panel',panid) + #else: + # print('auto says do NOT use secondary_y ... for panel',panid) + return secondary_y + +def _valid_addplot_kwargs(): + + valid_linestyles = ('-','solid','--','dashed','-.','dashdot','.','dotted',None,' ','') + valid_types = ('line','scatter','bar', 'ohlc', 'candle','step') + valid_stepwheres = ('pre','post','mid') + valid_edgecolors = ('face', 'none', None) + + vkwargs = { + 'scatter' : { 'Default' : False, + 'Description' : "Deprecated. (Use kwarg `type='scatter' instead.", + 'Validator' : lambda value: isinstance(value,bool) }, + + 'type' : { 'Default' : 'line', + 'Description' : 'addplot type: "line","scatter","bar", "ohlc", "candle","step"', + 'Validator' : lambda value: value in valid_types }, + + 'mav' : { 'Default' : None, + 'Description' : 'Moving Average window size(s); (int or tuple of ints)', + 'Validator' : _mav_validator }, + + 'panel' : { 'Default' : 0, + 'Description' : 'Panel (int 0-31) to use for this addplot', + 'Validator' : lambda value: _valid_panel_id(value) }, + + 'marker' : { 'Default' : 'o', + 'Description' : "marker for `type='scatter'` plot", + 'Validator' : lambda value: _bypass_kwarg_validation(value) }, + + 'markersize' : { 'Default' : 18, + 'Description' : 'size of marker for `type="scatter"`; default=18', + 'Validator' : lambda value: isinstance(value,(int,float)) }, + + 'color' : { 'Default' : None, + 'Description' : 'color (or sequence of colors) of line(s), scatter marker(s), or bar(s).', + '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, + 'Description' : 'line style for `type=line` ('+str(valid_linestyles)+')', + 'Validator' : lambda value: value in valid_linestyles }, + + 'linewidths' : { 'Default': None, + 'Description' : 'edge widths of scatter markers', + 'Validator' : lambda value: isinstance(value,(int,float)) }, + + 'edgecolors' : { 'Default': None, + 'Description' : 'edgecolors of scatter markers', + 'Validator': lambda value: mcolors.is_color_like(value) or value in valid_edgecolors}, + + 'width' : { 'Default' : None, # width of `bar` or `line` + 'Description' : 'width of bar or line for `type="bar"` or `type="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 + 'Description' : 'bottom value for `type=bar` bars. Default=0', + '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` + 'Description' : 'opacity for 0.0 (transparent) to 1.0 (opaque)', + 'Validator' : lambda value: isinstance(value,(int,float)) or + all([isinstance(v,(int,float)) for v in value]) }, + + 'secondary_y' : { 'Default' : 'auto', + 'Description' : "True|False|'auto' place the additional plot data on a"+ + " secondary y-axis. 'auto' compares the magnitude or the"+ + " addplot data, to data already on the axis, and if it appears"+ + " they are of different magnitudes, then it uses a secondary y-axis."+ + " True or False always override 'auto'.", + 'Validator' : lambda value: isinstance(value,bool) or value == 'auto' }, + + 'y_on_right' : { 'Default' : None, + 'Description' : 'True|False put y-axis tick labels on the right, for this addplot'+ + ' regardless of what the mplfinance style says to to.', + 'Validator' : lambda value: isinstance(value,bool) }, + + 'ylabel' : { 'Default' : None, + 'Description' : 'label for y-axis (for this addplot)', + 'Validator' : lambda value: isinstance(value,str) }, + + 'ylim' : {'Default' : None, + 'Description' : 'Limits for addplot y-axis as tuple (min,max), i.e. (bottom,top)', + 'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 + and all([isinstance(v,(int,float)) for v in value])}, + + 'title' : { 'Default' : None, + 'Description' : 'Axes Title (subplot title) for this addplot.', + 'Validator' : lambda value: isinstance(value,str) }, + + 'ax' : { 'Default' : None, + 'Description' : 'Matplotlib Axes object on which to plot this addplot', + 'Validator' : lambda value: isinstance(value,mpl_axes.Axes) }, + + 'yscale' : { 'Default' : None, + 'Description' : 'addplot y-axis scale: "linear", "log", "symlog", or "logit"', + 'Validator' : lambda value: _yscale_validator(value) }, + + 'stepwhere' : { 'Default' : 'pre', + 'Description' : "'pre','post', or 'mid': where to place step relative"+ + " to data for `type='step'`", + 'Validator' : lambda value : value in valid_stepwheres }, + + 'marketcolors': { 'Default' : None, # use 'style' for default, instead. + 'Description' : "marketcolors for this addplot (instead of the mplfinance"+ + " style\'s marketcolors). For addplot `type='ohlc'`"+ + " and type='candle'", + 'Validator' : lambda value: _is_marketcolor_object(value) }, + } + + _validate_vkwargs_dict(vkwargs) + + return vkwargs + + +def make_addplot(data, **kwargs): + ''' + Take data (pd.Series, pd.DataFrame, np.ndarray of floats, list of floats), and + kwargs (see valid_addplot_kwargs_table) and construct a correctly structured dict + to be passed into plot() using kwarg `addplot`. + NOTE WELL: len(data) here must match the len(data) passed into plot() + ''' + if not isinstance(data, (pd.Series, pd.DataFrame, np.ndarray, list)): + raise TypeError('Wrong type for data, in make_addplot()') + + config = _process_kwargs(kwargs, _valid_addplot_kwargs()) + + # kwarg `type` replaces kwarg `scatter` + if config['scatter'] == True and config['type'] == 'line': + config['type'] = 'scatter' + + return dict( data=data, **config) diff --git a/examples/using_lines.ipynb b/examples/using_lines.ipynb index 30ff6336..0da70611 100644 --- a/examples/using_lines.ipynb +++ b/examples/using_lines.ipynb @@ -242,7 +242,7 @@ { "data": { "text/plain": [ - "'0.12.5a0'" + "'0.12.8b9'" ] }, "execution_count": 4, @@ -277,7 +277,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -308,7 +308,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -357,7 +357,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -594,12 +594,13 @@ } ], "source": [ + "wtmd=dict(warn_too_much_data=len(idf)+1)\n", "vls=['2019-11-05 09:30',\n", " '2019-11-06 09:30',\n", " '2019-11-07 09:30',\n", " '2019-11-08 09:30']\n", - "mpf.plot(idf,vlines=dict(vlines=vls,colors=('r','g','b','c'))) # different color for each line\n", - "mpf.plot(idf,vlines=dict(vlines=vls,colors='c')) # one color for all lines" + "mpf.plot(idf,vlines=dict(vlines=vls,colors=('r','g','b','c')),**wtmd) # different color for each line\n", + "mpf.plot(idf,vlines=dict(vlines=vls,colors='c'),**wtmd) # one color for all lines" ] }, { @@ -811,7 +812,7 @@ "outputs": [ { "data": { - "image/png": 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LAABgAfT0AYCPKMkQLVe3AHwfoQ8AfERJhmjzX93Cbrfr6KmU0mgSABNheBcAAMAC6OkDAB/BEC3Kmru9wJwVbi6EPgDwEfmHaPMr6sP2assCpYmzws2F0AcAPq6oHj+O2YNVOBwOZ/g8lJBs6S88hD4A8AL01sEbcciBuRD6AMALlKS37kD8aQIiPIL9zlwIfQDg4+x2Ox++AAh9AOAN6K0DUFKEPgDwAvTWXRsTSgPXx+TMAAAAFkDoAwCUGrvdrmM/psgwDJd6Jh0OR4EfAGWH4V0AgMcweS9QfujpAwAAsAB6+gAAHsPkvUD5IfQBADyGM5KB8sPwLgAAgAUQ+gAAACyA0AcAAGABHNMHAD6IK1QAuBI9fQAAABZA6AMAALAAhncBAKbAkDRQtgh9AABLI2zCKhjeBQAAsABCHwAAgAUQ+gAAACyA0AcAAGABhD4AMBGHw1HgB8Dl10WjOpGy2Wy8LkqAs3cBwESaNazu6SYA8FH09AEAAFgAPX0AYCKHEpKdvzscDrVqEuXB1gDwJYQ+ADARu93u6SYA8FEM7wIAAFgAoQ8AAKAIDodDjetGqnHdSJ84a5jQB5QCX3tjAAD4Ho7pA65wtdDGsVYAUDqKep/lPbbsEfqAK1xtnrSjp1LKuSUA4JuKOiud99iyx/Au8D95Q7QAAPgievqAIhyIP81QAwCUEW98j/WFIWlCH1AEu93udS9mAPAW3vge6wtD0gzvAgAAWAA9fQAAoMzZ7Xav6xnLzxuHpK9E6AMAALgObxySvhLDuwAAABZA6AMAALAAQh8AlCEu0QfALAh9AAAAFlCqoS8rK6s0Hw4AAAClpEShLzc3V6tXr9bgwYN18803q02bNs77Jk2apOTk5BI3EJeHhxrViZTNZmN4CAAAFEuxp2y5dOmSRo4cqb1790qSDMOQzWaTJKWnp2vDhg365ptvtHr1alWrVs3tx1+zZo3i4uJ08uRJRUREqGPHjnriiSdUpUoVSdKRI0e0cOFC7du3T5J0yy23aOrUqWrUqJHzMZKSkrRo0SLt3btX586dU1RUlIYMGaKBAwc62woAAGAFxe7pe+ONN/T1119Lklq3bl1g7pqMjAxVqFBBP//8s15//XW3H3v58uWaOXOmevfurXXr1mnWrFnauXOnxo8fL8MwlJKSoiFDhkiSVq1apbi4OPn7+2vo0KH67bffnG0YNmyYTpw4ocWLF2vjxo0aNmyYnnvuOS1fvry4mw0AAOCVih36Nm7cKJvNpiVLlmjVqlUKCQlx3letWjW98847MgxD27dvd+txDcPQsmXL1KdPHw0fPlzR0dG64447NHbsWO3bt0/x8fFasWKFLl68qEWLFqlJkya68cYbtXDhQqWmpmrlypWSpL179+rEiRN66qmnFBsbq3r16mnAgAHq2LGj1q1bV9zNBgAAHuRwOAr9XG8ZXFbs4d2ff/5ZFSpU0D333FPk/a1bt1ZQUJDbx/XZbDatX79e/v7+BW6vUaOGpMtDx7t27VJsbKzCw8Od94eHh6t169basWOHRo8e7bzdz69grg0MDHSrPQAAoPzlD2v5f28ZE1Vo2Ssv79asYfWya5gXK3boCwkJ0a+//qrk5GRVr164uN9//70uXrxYIJi5KiIiotBtW7duVXBwsGJiYpSYmKhu3boVWiY6OlpbtmyRJLVr104NGjTQK6+8opdeekmVK1fW7t279cUXX2jq1KlXfW6b7fKPmeRvT3Z24W8s3n5ZmNKWVy93/4/5l3d3PyjJup5U3FpZEfuV69ivXEetrs6d4OZq/XztNehue4od+tq0aaPPP/9co0aN0rBhw5SdnS1J2rFjhw4fPqx33nlHNptNLVu2LO5TOH322WdavXq1JkyYoLCwMKWnpxcYTs4TGhqq1NRUSZd79P7+979rzJgx6tChgwICApSTk6MJEyZowIABV32uBrUjinxsT8r/DefGGwp/wzEMozyb4zUa1C785eFa8te5YVSEW2G6JOuagbu1sjL2K9exX7mOWrkuPT290Gvhyr/zTyHncDicn+u+9hpMT3evPcUOfY899ph27Nih+Ph4TZs2zXl73tCqYRjy8/PTyJEji/sUkqRPPvlEU6ZMUc+ePQsM217PpUuXNG7cOEnS0qVLVbVqVe3Zs0evvPKKKlWqdNXgl5h0QcHB5hr/v97xCAmnL5RTS7yDzXb5DTQx6YLcycP565xw+kKx3xjcXdeTilsrK2K/ch37leuo1dUdTvz98DCHw+Ec1v3xTLoCAsrndWT212BGRrpbyxc79N16661asGCBZs2apfT0wk8aHBysmTNnqm3btsV9CsXFxWnevHkaOHCgpk+f7pxmJa+370qpqanO4eQ1a9bowIED+vzzz1WrVi1JUosWLZScnKznn39eDz30kAICCm++Ych0L7z87fnPD6cL7exma69ZuPu/zL+sK+te7XgTM+5D1+ONbfaUst6vSmtdM/DGNnsKtSos/2edp15HZn8NutueYoc+SerVq5c6d+6srVu36ocfflB6erpCQ0PVpEkT3X333QoLCyv2Y69cuVJz587VpEmTCvUWNmzYUCdOnCi0zvHjx53z9B07dkxhYWHOwJenQYMGysjI0JkzZwrd5w3sdrtb33BQdjhQGO4qqtfebD0HAHxXiUKfdPms2b59+5ZGW5z27Nmj2bNna+rUqRo2bFih+zt37qxXX31VKSkpioyMlCSdPXtW+/fv1+TJkyVJtWvXVmpqqn755Rfnmb/S5TBot9uLNWE0AJREqybXP+sQAMpKiS7DlpmZqcWLF+u1114rcPujjz6q5557zjlRsjsMw9CcOXMUGxurHj166MyZMwV+0tPTNWDAAEVERGjy5MmKj49XfHy8Jk+erOrVq6tfv36SpD59+qhSpUqaOHGivv32W508eVJr1qzRmjVr1Ldv3yKHdgF3HEpIdv4ciD/t6ebAB9ntdh09laKjp1LoEQRQYsVOPhcvXtTAgQN1+PBhPfjggwXuS0lJ0YoVK/Tll1/qvffec+ts2KSkJB07dkyS1KlTp0L3jxs3TuPHj3ce79e/f3/ZbDa1b99e77zzjoKDgyVdniD673//u15++WWNHDlSFy9eVK1atTR8+HCNGTOmuJsNOPEhDHcdiD/NfgPAY4od+pYtW6ZDhw5JkoKCggrcV6NGDR0+fFjHjh3T0qVLNXHiRJcfNyoqSvHx8dddLjo6WkuXLr3mMs2bN9fbb7/t8nMDQFmy2+2EPgAeU+zh3Q8//FA2m00zZ87U008/XeC+pUuX6plnnpFhGPrXv/5V4kYCAACUlNUv4Vbsnr6ffvpJFSpU0KBBg4q8f+DAgVqwYIGSkpKK3TgAAIDS4srJVL48M0Oxe/pCQkKUmZl51VCXmJiorKws5zF2AAAA8Jxi9/S1atVKO3fu1KOPPqqRI0eqefPmCg0N1a+//qoDBw5o2bJlstlsatGiRWm2FwAAoFhcOZnqUELBK4EU1TvorYod+h599FHt2rVLJ0+e1MyZMwvdbxiGbDabhg4dWqIGAgAAlAZXTqby5ZOtij2826FDB82YMUMBAQEyDKPQT0BAgJ588kl17ty5NNsLAACAYijRDMWDBg3S3XffrY8++kjff/+9UlNTFRISoiZNmqhXr16qV69eabUTAAAAJVDiy1LUrFlTo0aNKo22AAAAoIy4HPqSkpLk7+/vvI6tO1Ox1K5d2/2WAQAAoNS4HPq6dOmiypUra/fu3c6/bTbbddez2Ww6ePBg8VsIAACAEnNreNcwjGv+DQAAYCZ2u73QBMxW5XLo69Onj0JDQwv87UpPH4BrczgczhngDyUk+/R0AQAAz3E59C1YsOCafwMAAMC8ij1P3xNPPKE//elPDPECAAB4gWJP2bJr1y5lZ2czxAsAAOAFit3T1717d128eFFbt24tzfYAAACgDBS7p69Xr15KSUnRE088obvuukutW7dWRESE/PwK58g+ffqUqJEAAAAomWKHvsGDB0u6PG3Lpk2btGnTpiKXs9lshD7gCg6Ho8jfy7sNeWcNH05MVkAAZw0DgC8rdujLfwIHJ3MA7skLWwAAlJdih7758+eXZjsAAABQhooV+vbu3avExERduHBBderUUffu3VW3bt3Sbhvgsw4lJDt/dzgcatUkyoOtAQBYgduh75lnntH7779f4LYlS5ZoxowZ6t+/f6k1DPBlXHUDAFDe3JqyZePGjVq9erUMwyjwk52drTlz5ujAgQNl1U4AAACUgFuhb82aNZKkmjVr6tlnn9XSpUs1ceJEhYeHKzc3V//4xz/KpJEAAAAoGbeGd7///nvZbDYtWbJErVq1kiR17txZMTEx+uMf/0hPHwAAgEm51dOXmpqqihUrOgNfnnbt2jnvBwAAgPm4Ffpyc3NVsWLFQrfn3Zabm1s6rQIAAECpKva1dwEAAOA93J6yJTs7W3v37i3yKhxXu+/WW28tfgsBAABQYm6HvrS0NOd1d/Oz2WxF3mez2XTw4MHitxAAAAAl5nbo4zq7AAAA3set0PfAAw+UVTtwBYfDUeTvAAAAxeFW6Js/f35ZtQNXaNawuqebAAAAfAhn7wIAAFOz2+069mOKDMPg2uUl4PYxfSgfhxKSnb87HA61ahLlwdYAAABvR+gzKb7JwBUOh8N5KMChhGT2GwDAVRH6AMjhcOjKE/MJkADgWwh9Po6eILiiZUzhwweOnkq56vLsVwDgfQh9sDSmxgEAWAWhD5bG1DiX/eeH0woIoLcOAHwZoQ+A7HY7oQ8AfByhD5bG1DgAAKsg9MHSOAEBAGAVhD7gf+x2+zXPWAUAwJsR+gATKeoMYnojAQClgdAHmEhRxxTS+wgAKA2EPh/E3HMAAOBKhD4fxNxznlWSIdoD8acZzgUAlAlCH1DKSjJEa7fbTRv66EEGAO9G6PNBzD1X/vLO/G1cN9LTTSkz9CADgHcj9Pkgs/YUWUH+wA0AgJkQ+oBS5MuBmx5kAPBufp5uAK7Pbrfr2I8pMgzDp0NFaXA4HGpUJ1I2m43jzkpZ3vGGZj7uEABwdYQ+AAAACyD0AQAAWAChDwAAwAIIfQDKlcPhUOO6kWpcN5LjLgGgHBH6AAAALIApWwAvw5UxAADFQeizkJJcExbmwZUxzI9gDsCMCH0WUpJrwgJwHcEcgBkR+gAvw5UxAADFYdrQt2bNGsXFxenkyZOKiIhQx44d9cQTT6hKlSqSpCNHjmjhwoXat2+fJOmWW27R1KlT1ahRowKP88knn+iNN95QYmKiqlWrpr59++rxxx+Xn5/1zmE5EH+a4VwfwP/Q/Iq6BjP/NwCeZsrks3z5cs2cOVO9e/fWunXrNGvWLO3cuVPjx4+XYRhKSUnRkCFDJEmrVq1SXFyc/P39NXToUP3222/Ox9m0aZMmT56sfv366ZNPPtH//d//aenSpXrrrbc8tWkedeVltPgQAsoGrzUAZmS6nj7DMLRs2TL16dNHw4cPlyRFR0dr7NixmjlzpuLj47VlyxZdvHhRixYtUnh4uCRp4cKFuuOOO7Ry5UqNHj1akvTCCy9o4MCBeuSRRyRJderUUWRkpHMdANbhcDicx9odSkgmiAGwHNOFPpvNpvXr18vf37/A7TVq1JAkpaena9euXYqNjS0Q3sLDw9W6dWvt2LFDo0eP1qFDh3Ty5En17NmzwON07ty57DcCAADAZEwX+iQpIiKi0G1bt25VcHCwYmJilJiYqG7duhVaJjo6Wlu2bJEkHTp0yHn76NGj9d1336lSpUoaNGiQhgwZIpvNVuRz22yXf8wmr03uti3/8mbdttLkjdtbkjZbbd2SKOnzFvc1aEXUynXUynWeqJXZP1PcbY8pQ9+VPvvsM61evVoTJkxQWFiY0tPTFRISUmi50NBQpaamSpLOnj0rSXrmmWc0YsQI/elPf9LmzZs1f/58ZWdn67HHHivyuRrUjijysc2iQe3Cgfha8s8R1jAqwueHtPJvb4Pa3rG9Jfkflda67tbKU20uidJ6Xndfg1ZGrVxHrVxXnrUy+2doerp77TF96Pvkk080ZcoU9ezZ03msniuys7MlSY888ojuv/9+SVLz5s119OhRvfXWWxo2bFihIdJanzoAACAASURBVGRJSky6oOBg802marNd3tETky7IMFxfL/8Om3D6Qrl+wDZtcPn4qcOJ5Xf8VP7tTUy6oIAAc71Ai1KS/1FpreturTzV5pIo6fMW9zVoRdTKddTKdZ6olafer1yVkZHu1vKmDn1xcXGaN2+eBg4cqOnTpzuHZPN6+66UmprqPM4vLCxMknTjjTcWWOaWW27Rpk2b9NNPP6lOnTqFHsMwZOoXnrvty79seW6b1Z63JErSZqutWxKl9bzesl+ZAbVyHbVynTe+b5QVd9tj2tC3cuVKzZ07V5MmTdLIkSML3NewYUOdOHGi0DrHjx93ztNXv359SdKvv/5aYBnjfxUKDQ0tg1bDTBwOR6EXhNm+pQEAUF5MOU/fnj17NHv2bE2dOrVQ4JMun4H77bffKiXl90uInT17Vvv371eXLl0kXe7RCw4O1ubNmwusu3fvXtWoUaPIk0XgW1rGRKlZw+oFfgAAsCrThT7DMDRnzhzFxsaqR48eOnPmTIGf9PR0DRgwQBEREZo8ebLi4+MVHx+vyZMnq3r16urXr58kKSgoSGPGjNG7776rFStW6MSJE3r77be1detWPf744x7eSgAAgPJluuHdpKQkHTt2TJLUqVOnQvePGzdO48ePdx7v179/f9lsNrVv317vvPOOgoODncuOGjVKFSpU0N/+9jfNnz9ftWrV0pw5c/Tggw+W2/Z4M2+fzPY/P5z2ihM5YH75XwuHE5PZrwB4JdOFvqioKMXHx193uejoaC1duvS6yw0dOlRDhw4tjabBy9jtdj6cAQD4H9MN7wIAAKD0ma6nD8g/lHYg/nShYWVvG2YGAMAMCH0wtVZNogrddvRUShFLAgCAa2F4FwAAwALo6YOpFTW8CwAA3Efog6nZ7XZCHwAApYDhXQAAAAugpw+lxuFwFPm7rz4vAADehNCHUuOpa9tyTV0AQFmw2+0+NWMEoQ8AikAPMgBfQ+hDqTmUkOz83eFwFDnHni89L7xXUSHuyhOG6EEG4GsIfSg1njrLlrN74S4m/QZgRYQ+ACgCPcgAfA2hD4DluDLpNz3IAHwNoQ+A5TDpNwArYnJmAAAAC6CnD/AwX5sHCgBgTvT0wafY7XYd+zFFhmEwfAcAQD6EPgAAAAsg9AEAAFgAx/T5OI4XA0qXw+GQYRS8jUMJAHgDQh8AuKFlDFfzAOCdGN4FAACwAHr6AMAN//nhtAICGM4F4H0IfQDgBrvdTugD4JUY3gUAALAAQh8AAIAFEPoAAAAsgGP6UIDD4bju73mYmwwAAO9B6EMBzRpWL/L2Vk2KPzcZgREAAM8j9KHMlSQwonRdr/cWRbPb7Tr2Y4oaRkUo4fSFQlfkAABvQOhDAYcSkp2/OxwOZ2A7EH+a3jkfcLWeXE8p615gQi4A/I7QhwKu9oFrt9uL/WFMYMTVlHUvsNlCLgB4EqEPZa4kgRGl62o9uQAA30foAywkf/gurePUSjJEW9a9wIRcAPgdoQ9AiZRkiLase4GvDLmcQATAypicGQAAwALo6QN8hKfmQ+REHQDwDoQ+wEd4aj5ETtQBAO/A8C4AAIAF0NMH+AiGWQEA10LoA3wEw6wAgGtheBcAAMACCH0AAAAWQOgDAACwAEIfAACABRD6AAAALIDQB8BrOBwONa4bqcZ1I4u8AgkA4OoIfQAAABbAPH0Aylz+Xjl66ADAMwh9KBN2u71crvsK79CsYXVPNwEALI/hXQAAAAugpw9XRW8dSsuhhGTn7w6HQ62aRHmwNQBgTYQ+wIt5SzDnmsAA4HkM7wIAAFgAoQ8AAMACCH0AAAAWQOgDAACwAE7kAOCViprkmRNGAODqCH0A3GaGs4aLmvbF020CADNjeBcAAMAC6OkD4JUOxJ9mOBcA3GDanr41a9aod+/eio2N1V133aUZM2bo3LlzzvuPHDmiESNGKDY2VrGxsRo5cqSOHTt21cc7ceKEWrdurcGDB5dH8wGUMbvdXugHAHB1pgx9y5cv18yZM9W7d2+tW7dOs2bN0s6dOzV+/HgZhqGUlBQNGTJEkrRq1SrFxcXJ399fQ4cO1W+//Vbo8QzD0IwZM5SdnV3emwIXORyOAj9AHvYNACgdphveNQxDy5YtU58+fTR8+HBJUnR0tMaOHauZM2cqPj5eW7Zs0cWLF7Vo0SKFh4dLkhYuXKg77rhDK1eu1OjRows85qpVq5SYmKi7775bKSkc6G1GzRpW93QTYFLsGwBQOkzX02ez2bR+/Xo9/fTTBW6vUaOGJCk9PV27du1SbGysM/BJUnh4uFq3bq0dO3YUWO/nn3/WCy+8oOnTpys4OLjsNwAAAMCETBf6JCkiIkJhYWEFbtu6dauCg4MVExOjxMRE1a1bt9B60dHRSkhIKHDbs88+q7Zt26p79+5l2maUzKGEZOfPgfjTnm4OTIR9AwBKh+mGd4vy2WefafXq1ZowYYLCwsKUnp6ukJCQQsuFhoYqNTXV+fdHH32kvXv3auPGjS4/l812+cds8tpkxraVhsDA3w/Cz7+Nxfl/+HqtSpMnauXu/7c0942SYL9yHbVyHbVyHbUqzN1amD70ffLJJ5oyZYp69uxZ6Fi9azl//rzmzp2ryZMnO4eGXdGgdkSRgdIsGtSO8HQTylz+g/UbRkUU+6xMK9SqtJRnrUry/y2tfaMk2K9cR61cR61cR61+l57u3nugqUNfXFyc5s2bp4EDB2r69Omy/S/S5vX2XSk1NdV5nN+cOXPUtGlT9e/f363nTEy6oOBg850haLNd3tETky7IMDzdmrKV/4M94fQFtz/YrVSrkvJErUry/y3pvlES7Feuo1auo1auo1aFZWQUzkLXYtrQt3LlSs2dO1eTJk3SyJEjC9zXsGFDnThxotA6x48fV6NGjSRJGzdulJ+fn1q0aOG8Pzc3V4ZhqHnz5po3b5769OlT6DEMQ6bemczevtKQf/tKsr1WqFVpKc9aleT/W1r7RkmwX7mOWrmOWrmOWv3O3TqYMvTt2bNHs2fP1tSpUzVs2LBC93fu3FmvvvqqUlJSFBkZKUk6e/as9u/fr8mTJ0uSPv7440LrLV68WL/88ovmz5+vmjVrluk2AAAAmInpQp9hGJozZ45iY2PVo0cPnTlzpsD9wcHBGjBggP7xj39o8uTJevLJJyVJ8+fPV/Xq1dWvXz9JUkxMTKHHrlSpklJTU4u8D+Zht9t19BTzKQIAUJpMF/qSkpKcl1Pr1KlTofvHjRun8ePHO4/369+/v2w2m9q3b6933nmHufgAAACKYLrQFxUVpfj4+OsuFx0draVLl7r12AsWLChuswAAALyaKSdnBgAAQOki9AEAAFgAoQ8AAMACCH0AAAAWQOgDAACwAEIfAACABRD6AAAALMB08/QB8G1ccQUAPIOePgAAAAsg9AEAAFgAoQ8AAMACCH0AAAAWQOgDAACwAEIfAACABRD6AAAALIDQBwAAYAGEPgAAAAsg9AEAAFgAoQ8AAMACCH0AAAAWQOgDAACwAEIfAACABRD6AAAALIDQBwAAYAGEPgAAAAsg9AEAAFgAoQ8AAMACCH0AAAAWQOgDAACwAEIfAACABRD6AAAALIDQBwAAYAGEPgAAAAsg9AEAAFgAoQ8AAMACCH0AAAAWQOgDAACwAEIfAACABQR4ugEA4Cq73a6jp1I83QwA8Er09AEAAFgAoQ8AAMACCH0AAAAWQOgDAACwAEIfAACABRD6AAAALIDQBwAAYAGEPgAAAAsg9AEAAFgAoQ8AAMACCH0AAAAWQOgDAACwAEIfAACABRD6AAAALIDQBwAAYAGEPgAAAAsI8HQDzMAwDOfvGRkZHmzJ1dlsUnq6XRkZ6crXXBSBWrmOWrmOWrmOWrmOWrmOWhWWP7MYLhSF0KeCRWsXG+PBlgAAALgvIyNDoaGh11yG4V0AAAALsBmu9Af6uNzcXJ09e1aSFBwcLJvN5uEWAQAAXJthGM7RyqpVq8rP79p9eYQ+AAAAC2B4FwAAwAIIfQAAABZA6AMAwMvk5uZ6ugnwQoQ+i+DQTcCzeA2ipNLS0jRz5kxJuu4B+1aXPxTz2vsde42PSk9P1/nz55WRkSHDMGSz2fhmiBL79ddflZSUpFOnTnm6KaZ37tw5HTt2TAcPHlROTg6vQZRIWlqaHn74Yb3//vuKi4uTRJi5moyMDE2YMEHbt2+XJNlsNmr1P0zO7IO+++47vfrqq0pMTFRYWJjuuOMOjRs3Tna73RkAcdl///tfrV69WqdPn1bdunXVpUsXdejQQQEBvDSutG/fPi1cuFAnT55U+/btNXXqVNWoUcPTzTKlb775Rs8995x++eUXZWdnq0OHDlq4cKECAwM93TTTOXnypEJDQ1W5cmVPN8W00tLSdN9996l169Zq3ry5jhw5Ikm8l1/Fhg0btGnTJiUlJSknJ0ddunRxBj+r18z/z3/+85893QiUngMHDmjkyJG69dZbdfvttyspKUm7du1Sdna2brnlFsvv8Pl98803GjJkiKKiohQeHq4vv/xSO3fu1JEjR9SpUyf5+/t7uommcfDgQQ0bNkw9e/ZUv3791LZtW9WvX79AjXhDvezAgQN69NFHdd9996l///6qUKGCtm3bpooVK6pNmzaebp6pJCQk6N5779XBgwd1xx13KCgoyNNNMp20tDTdf//9at26tV555RVJ0vvvv6/u3bsrKCiI11wRUlNT9emnnyomJka7du1S1apV1aBBA4Kf6OnzKWlpaVq8eLH69++viRMnSpL69eun0aNHa8uWLXr88cc93ELz+O2337Rw4UINHjxYkydPlnS5fq+88orWrVun8+fPa/HixZbvmckbEvn000/Vo0cPTZgwwXnfqVOndOHCBYWFhalu3bry9/dXTk6OpcNyenq6XnvtNQ0ZMkRPPPGEJOkPf/iDvv76ayUlJRVYNjc31/LHZSUnJys8PFz79+/XxIkT9dJLL9Hjl09aWpq6du2qtm3b6uWXX5Z0eQLes2fPKjk5WVWrVrV8iClKs2bNVLNmTTVu3Fjx8fF65ZVXZLPZdOedd1q+VtZ+x/FBSUlJql+/viQpOztbwcHBGjp0qA4ePKiEhATPNs5EHA6HkpOTdcMNN0iSsrKyFBoaqokTJ+q2227TZ599pilTpigrK8vDLfUsm80mm82mH3/8UefOnXPe/sorr2jEiBEaMGCABg4cqFGjRunixYvy9/e39HFrhmEoKSlJDRo0kCRdunRJNptNbdu2VXR0tN59912tXLlS6enp8vPzs/xxRnv37lV0dLRmz56tQ4cOacqUKTp//rynm2UKubm5+stf/qJ27do5A58k3XTTTWrRooXi4uKcx4rid3lfpoKDg9WyZUtNmjRJlStX1pIlS/TFF19IunyoSnp6uodb6hmEPh+SnZ2t48eP65dffpEk53FpERERfMBcITs7W2fOnHHWKjAwUFlZWapQoYI6dOigFi1a6MyZM3rttdcsXTfDMJSbm6vKlSs7L/Xz3nvv6YMPPtCIESO0dOlS3X///fruu+80ePBgZWVlWbr3yuFw6OjRo0pOTpYkVaxYUYcPH9batWu1fv16vf766/rLX/6iXr166eTJk5Y/wDwzM1ONGjVSt27d9OSTT+rAgQOaPHkywU+Xz84dNGiQXnrpJedteftKbGysDh8+rJycHElM35Kfn5+fwsLCFBsbq82bN6tp06YaPHiwqlWrpiVLlujhhx/WokWLlJ2dbcnXHsf0+RC73a5Lly7ppptuUnR0tPP2H3/8URs3btRjjz2m0NDQQsMBubm5lvu2GBgYqKNHj+rjjz9W/fr11ahRI+ew5BdffKGqVauqdu3a+uqrr3TXXXcpODjYwy32jLyevipVquill15SZGSkKlasqPbt26tv376qV6+ebrvtNkVEROhf//qX0tPT1aFDB08322OCgoIUGhqqTp06qWrVqrp48aKGDh2qrl27aubMmRo9erSaNWum7du3a/fu3erTp4+lh8OjoqIUERGhG264QdHR0YqKitKaNWv03Xff6fbbb1dQUJClhy8jIiIkSTk5OfLz83PWoX79+nrzzTd16dIltW/f3rL1kQp/fuXtL0eOHNGuXbs0cOBA1a9fXw0bNtR7772nkydP6pFHHlG7du0seYwfoc+LnTp1SvHx8fr2229VtWpVBQcHq1OnToqOji6wI588eVIffvihBg8erJCQEGdPzNy5c9WuXTtLnKl6Za1CQ0NVs2ZNffXVV/r000+dx+5t27ZNCxYs0DPPPKPevXvrxRdfVN26ddWsWTMPb0H5ydt3srOz5efnV6Cnb/ny5fryyy/VqVMnNW3aVNnZ2QoMDFTLli21c+dOJScnq3fv3pZ5Ez1+/Lj27Nmj7du3KzQ0VCEhIbrllltUtWpV5eTkKDAwUF26dFHPnj0VFBSkgIAARUdH68yZM9q1a5fuu+8+hYeHe3ozykVCQoK2bNnifL0FBwerZs2aatiwoaTLX8Tq1atXIPh16tTJ+YXr9ddfV5s2bXw6JKenp+v555/XP//5T3388cfKyclR1apVFRIS4gx+hmGoQoUKunDhgnbu3Kkbb7zRkmfRZ2ZmKicnR3a73VmbPDabTeHh4Vq3bp26du2q0NBQvfzyyzpy5IiaNGmio0ePKjw8XI0aNbLMe1Ue3/+091Fff/21pkyZoqCgIP38888KCgpSnz591L9/f9WrV69A6MvKylJgYKACAwOdL4w//vGP2rt3r5566ilPbka5uLJWFStWVN++fTVq1Cg9//zzWrx4sZ577jmFhIQoKytLc+bMUfPmzSVd/kadmZnp4S0oP/v379fSpUu1aNEiBQcHKzs7WwEBAfLz81OfPn2UlJSkbdu2KTExUZLk7+/v3NduvPFG51QSVrBv3z7nfnXixAm98cYbGjNmjAYNGqSgoCBnYI6KipJ0edjJ4XDIbrerTp06qlOnjkJDQz28FeVj7969Gj9+vKpUqaITJ05oxYoV+r//+z8NHTpU0u9TjwQHB6tr164yDEPz5s3TU089peeff17Tpk3T0aNHNXr0aE9uRplKS0tTnz59VKtWLTVp0kTfffedFixYoEaNGmnOnDmqX7++80SpwMBA3XvvvXr//fe1ceNGxcTEqEKFCp7ehHKTmZmprl27KigoSB988IGCgoKctcnblypVqqRLly4pKSlJb775pnbu3KlVq1bp3LlzevHFFxUXF6cOHTooJCTEw1tTzgx4nSNHjhi33Xab8de//tU4ceKEkZKSYkybNs3o2LGj8fDDDxvx8fGGYRhGdna2YRiGsWHDBiM2Nta5/ogRI4xu3boZWVlZBZbzRVerVfv27Y3+/fsbp06dci733XffOf82DMPIzMw0+vXrZ2zYsMFTzS83ubm5RlZWljFgwACjSZMmxqOPPmqkp6cbhmE49xPDMIy9e/caw4cPN5o0aWK89tprRmpqqpGdnW1kZmYaDz/8sDFz5kxPbUK5Onr0qNGxY0dj+fLlxk8//WT8+uuvxh//+EfjtttuM44cOWIYxuWa5klJSXH+npmZaQwfPtx48sknCyzjq+Lj440OHToYy5YtM86cOWNkZmYao0ePNrp27XrV7U9NTTU+/PBDo23btkbTpk2Ne++917kf5uTklGfzy0VOTo7x5JNPGiNHjixw+1//+leja9euRseOHY2jR48ahmEYDofDWbelS5caN954o7FixYpyb7MnHT9+3GjXrp3RsmVL44EHHjAyMjIMw/j9syxvHxk/frzRunVro0uXLsZ//vMf5/pff/21kZSUVP4NNwGGd73Qpk2bdO7cOT3zzDOqVKmSgoODdffdd8vPz09fffWVtm7dqltvvVVVq1aVJJ09e1abN29Wt27dNGnSJJ06dUrr16+X3W539uT4qqvVyt/fX19++aU2bdqkm266STExMapRo4YyMzO1d+9eHTp0SC+88IJSU1P19NNP+/zJCXlXi3j77bfVokULxcfH64svvtC9996rihUrKisrS/7+/qpdu7ZiYmJkGIb+3//7f9q2bZvWr1+vDz/8UGlpaXr99dcL9P75qs2bNys5OVnTpk1TWFiYgoKC1KFDB61atUoVK1ZU27ZtJcl55vPChQv173//W99++62WLl2qc+fO6a233rJErdatWyc/Pz9NnTrVOeIQEBCg/fv366GHHnIul7cPGv8bvqxTp47WrVunBg0aaM2aNc73K18d3l2xYoViY2N12223OV9vN910k6pWrar9+/dr1apV6tKli6pUqeI8Q7VRo0b64Ycf9NFHH6l///6WmWJq9+7d2rVrl8aPH6/PP/9cmzZtUq9evRQYGFhg2qijR4/qhx9+0BtvvKEWLVo4X2u1a9dWWFiYh7fCM3z7k8xHnThxQj/++KPzzTNvWpFhw4ZpxIgRyszM1Jw5c5zzgtWpU0cOh0MPPfSQpQKfdO1ajRo1SpcuXdLcuXN1+vRpSdLp06f15JNPavHixZIuT4KaN/+cr8ur0+zZs/Xoo4/qyJEjGj16tDIyMpxnN0uX58CaNm2aVq5c6QzMd955pz744APnfuXLIUaSjh07phMnTjiP0zMMQ0FBQYqIiFBKSoqk34csDcNQcHCwNmzYoK+//lp16tTRBx98oICAAEvU6vDhwzpx4oT8/Pyc7zeXLl1SWFiYJk6cqAcffNB5pRc/Pz/ncWtjxoyR3W5XXFycs1a+/H517tw557RagYGBys7OliR1795df/rTnxQaGqqnn35aZ8+edYaaiIgIPfnkk/rggw8sc6iAdPnYx+joaA0YMEATJkzQ6dOnNWjQIOe0UQ6HQ5I0fvx4rVu3znm4jq+/1lxBT58XysnJ0caNG1WjRg01adJE/v7+zoPuW7RooczMTG3ZskWhoaFq3bq17Ha79u/fr8jISL333nuWCXySe7W6+eabVa1aNd1999166KGHNHDgQOfyVqhVQECAtm3bpnvvvVft2rVTbm6utm3bpt27dxfq8bPb7apVq5Y6d+6sLl266Oabb5afn59ycnIsUauTJ0/qm2++0T333KPQ0FDZbDbZ7XZt3LhRFStW1D333OM8qzA8PFxdunTRgAED9OCDD6pr167y8/OzzH515MgRJSUl6d5771WFChV07NgxDR8+XE2bNlXjxo0VGBiozZs36/Dhw2rfvr2Cg4P15Zdfavv27Xr//fedAchXa5XX+5SWluZ8r4qJiXHuI35+fmrcuLFycnL0+eefq1atWmrevLnzvsjISEsFPkmqW7euDMNQbGysGjdurCpVquhf//qXNm/erF69eqlChQrKzMxUQECA85g9K85SURRCnxcKCAjQ1q1blZCQoJiYGFWvXr3AG8RNN92kAwcOaOfOnRoyZIgqVKigRo0aacSIEZb4xpyfO7V65JFH5Ofnp8qVKyssLMw53OSrw0lXqlChgm6//XZVr15dAQEBatWqVZHBL8/58+cLTWXj68PgeSIjI9W9e3fnpZ3yPkz++c9/qmbNmrrzzjsLnFH4yy+/qHLlys59yTAMy+xXNWvWVNeuXVWtWjVlZ2fr3//+t5o0aaIZM2aoY8eOuueee5Senq5169apU6dOqlOnjqpVq6aBAwda4v0qb98JDQ3Vtm3bFB8f75y+Jv97VZs2bbR161YdP35cvXr1ssxr7Uq5ubmqWLGiWrZsKenyVGWNGzdW5cqVncEv72x56fJ1eCMiIiwXjK+G0OdlDMNQeHi4GjZsqDfffFPnz593ftPx8/Nz9sTUrFlT7777rvNDPC/sWCnEuFurzp07q2rVqgW+DVrtm2HetTxzc3Nlt9sLBb/7779f/v7++tOf/qQff/zReeyaleTtV1WqVClwm8Ph0N/+9jc1b95c7du3d34ojxkzRgcOHNDdd9/tXN4q+5VhGIqIiFB4eLgz6NavX1/t27d3HjaR1+v+1ltvKTY2Vi1atFBAQIBzDjWrvF9VrVpV9erV0/Lly/Xjjz+qbt26ql27doH3qpMnTyopKUkPPPCAp5vrMXmvnbwvW3nvVfmD35YtW/Twww/rzTff1EsvvaQHH3zQMlMjXY/vfn3yUXlvhO3bt9cLL7ygp556Srm5uRo9erRat27tPJA3JydHtWvXVmRkZIH1rfTt0N1ahYeHW+bD+Grytj/vC0KFChU0fPhwGYahFStWaNSoUfLz81NiYqJefPFFD7fWM4raR/Juy3+igWEYGj16tBITE7Vx48ZybaNZFPUFKv/JBnm1+umnnxQdHa26detedX1fZxiG7rjjDi1evFgTJkzQokWLNGTIEHXv3t1Zs19++UU1atQoNFmzleWfu7Bnz56SpCVLlqh169bO960r9ysro6fP5Io6DiHv75iYGDVo0EBvv/22fvjhBzkcDjVt2lRnzpzR4sWLVaFCBQ0aNMgybwzUynWuHN+S/1v0zTffrJycHK1Zs0aRkZFat26d89hQX/8i4eqxQP7+/vroo4/UuHFj3XrrrXrsscd06tQpbdiwgVpd4dSpU/rpp59UtWpVJScna8GCBbLb7Ro7dqzP1+h6GjVqpBYtWmjTpk36/PPPdfToUf38889au3atNmzYoAULFhQakbC6vPeqwMBAtWjRQjt37tT58+f13nvvWWpifVfYDMOCF5/zEnkH+GZlZSklJUU1atQocnqHvXv36sUXX9SxY8eUm5urmjVrqmLFilq1apXsdrvz9H5fRq1c52qtrvTYY4/p/Pnzev/99y1xrJXkfq0GDx6syMhI+fn56dChQ5Y6U97VWqWnp2vBggX69NNPValSJVWqVEmStHr1aufVFXx1SPd67y95H8c2m02HDx/Wli1b9PHHHyskJESVK1fW5MmT1bRp0/JqrkcV97148eLF+tvf/qZVq1ZZplbuIPSZXE5Ojh5++GG1adNGM2bMKHR/3gvj3LlzOn/+vA4eZjLxhgAADJdJREFUPKgaNWro1ltvtdSZpxK1csf1anWlKVOm6KuvvtKWLVssE2LyuFKrvHDz+uuva8mSJWrevLnlzpSXXN+vvv/+e3355Zf67bffVLduXT3wwAM+/xrMH2a///575+Ugr3dFiNzcXOXk5DgPt7CC4tbq+++/18SJE/XCCy+oVatW5dFUr+Obry4f4u/vr5tvvlnbt29XYmKiGjRoUOD+vG9CVapUUZUqVXTDDTc477PK9Bl5qJXrrler/FJTU9WmTRvNnz/fMj18+blSq7zerDvvvFOHDh3Syy+/TK2uUivDMNSiRQu1aNGiwO2+/BrMf0LKtGnTtGfPHmVmZqpPnz4aMGCA6tWrV+R6eV9UfX30Ib/i1kq6PJXLqlWrCh3Ljt9xTJ8X8PPz04YNG9SqVSs1atTI5eNmrPRGkYdauc7VWlWoUEGtWrWy1Dx8V3KlVrm5uapevbruu+8+S83Dd6Xr1Sr/pNX5b/fV12D+Ycqnn35aBw4ccF5D+J///KfOnTunG264ocigYrXj9kpSK+nye1XeVC0oGqHPRPLP6yX9/qZYr149HT58WB9//LH69u1rmUvtXAu1cl1p1spXP5jzlKRWV35AU6tr71dWCTR525mZman9+/dr0qRJ6tSpk7p16yaHw6EPPvhAZ8+edU45YmXUquwR+kzEz89PGRkZevbZZ1W9enXZ7Xbnt5YaNWroiy++UM2aNdWwYUPLzy5OrVxHrVxHrVxHrVw3b948TZ48Wbm5ufrDH/6giIgISVK7du106dIlrV279rq9WFZBrcoWoc8k8r4lf/jhh1qxYoXWrl2r/fv3y263q379+qpdu7Z27Nih+Ph43XfffZZ+A6VWrqNWrqNWrqNW13ZlL+ilS5d05MgR/fDDD+rUqZPq1avnXKZdu3bKysrSRx99pISEBLVs2dIZdKyAWpUvQp+HXTnJZrNmzTRy5EgFBQXpzJkzevPNN/XVV18pMzNTvXr10t///nfdcMMNlpxsklq5jlq5jlq5jlq5Ji/ErF27VpUqVVKbNm1Us2ZN7du3T7t27VKnTp1UuXLlAmHm/Pnz2r17twYNGmSpS4ZRq/LFlC0elHeg98WLF/XRRx/p4sWLqlGjhrp37y7p8nENhw4d0urVq7V7925Jl78FDRkyRI8//rgl5pTLQ61cR61cR61cR63cs2PHDo0aNUoPPfSQxo4dq5o1a2r79u2aNWuWgoOD9eqrr6p+/foFpic5f/68JY9Vo1blh54+D8k7CzItLU39+/fXkSNHtGPHDu3YsUPnz59Xhw4dFBAQoJo1a+r2229Xv379dOnSJWVkZGj79u3q3r27Za4lSK1cR61cR61cR63cFx0draCgIP31r39VWlqamjdvrhtvvFH169fXli1btHnzZnXs2PH/t3dvIVG1exzHf2scjwOOI1aWDR5Qx0NJpWbZiUwLpFIMKgSv6sKki0LKQLCEwpCkwErLEC8MhASjl8wwI0TRFLMIUjQxMxkHc9JSR1JnvRdt1879srfPJnVk5ve5GkxjzZdh+M+aZz0LOp1OOYvlqFeestUKkmlFWa1W5bHFYpEPHz4snz17VpZlWR4aGpITExNlg8EgX79+Xfm9nz9/Ko+7urrkI0eOyE1NTSt30DbCVuLYShxbiWOrxVmtVnl2dnbBz2ZmZpTHZWVlssFgkHNzc2Wj0SjLsiy/evVKTkpKknfv3i0PDAys6PHaElvZnuOca7ex8fFx5Qo2q9UKAGhoaIDVakVxcTEAoKioCCqVCgcPHkRlZSVu3boFAMrtwQAgLCwMFosFbW1ttnkiK4CtxLGVOLYSx1aLm52dBfBrm5H5rxxv3ryJ/v5+qNVqzM3NAQBOnz6NCxcuoLq6Gnfu3IHRaMS+fftw8eJF6HQ6h7jIha1WD8fbOdQG3r9/j2vXruH48eNISUlRXvR9fX1wcnLC1NQUCgoK0NPTg6qqKkxMTKCrqwulpaUYHBwEAKSnp2PLli1wcnKCr68vfH19bfmUlg1biWMrcWwljq0WNzExgbS0NKSmpiIrKwsA0NLSgoqKCrx48QKlpaXQ6/XKGrRTp07h+/fvuH//Ptzd3ZGRkYHExETEx8fDw8PDxs9mebHV6sI1fctsenoaeXl5aG9vx8zMDAAgJCQEKpUKLi4uSE5OxvDwMCorK5GXl4fg4GBotVplS4Te3l6Mjo7i/PnzUKvVqKurQ3l5OS5dumR3exSxlTi2EsdW4thqcRMTE0hJScHo6Ciam5vh6uqK6Oho6PV6aLVadHR0oLa2Fnv27IFOp8Ps7CxUKhV8fHxQX1+PlpYWyLKMXbt22f29dNlq9eGZvmXm5uaGlJQUNDc3o7u7GxaLBZIkITk5GVu3bgUAPHr0CENDQwsWOg8ODiI+Ph6FhYXQarXKbZ0iIiJQX19vl1sgsJU4thLHVuLY6n+zWq24fPkyYmJikJWVhdLSUhQVFWFubg6ZmZlIT08HADx48ACZmZkoKSmBv78/gF9t09LSEBQUhMjISOUMqr1iq9WJZ/qW0fynX71eD5PJhICAAJhMJnR0dMDT0xPBwcGQJAkDAwOora1FbGwsAgICoFKpUFNTg9jYWERGRkKSJOXqOa1Wa5dXwbGVOLYSx1bi2GpxkiQhMjISx44dg5eXFwIDAzE1NYWKigqo1WrExMRg8+bNcHZ2Rnt7O54+fYr4+Hi4u7vj4cOHePfuHbKzs+Hj42Prp7Ls2Gp14tC3jOYXnTo7O6OzsxM9PT0oLCzE69ev0djYCK1Wi6CgIAQGBqK/vx93797FmzdvUF5ejvHxceTk5Cj7Wtn7/lZsJY6txLGVOLYS4+npqTz29vZGUFAQJicn/zHMuLq6orOzEyUlJWhoaEBLSwtu3LiBdevW2fDoVxZbrT4c+pbY/JqE+Svf5vcU2rZtG4qLi+Hh4YHc3Fw8f/4cra2t0Gq1CA8PR1xcHDQaDcxmMwwGA27fvq1c1WSvb6BsJY6txLGVOLb6czqdbsEw4+LigujoaGzatAkGgwEbN27E2rVrkZ+fj5CQEFsfrk2x1Spgm51i7NOHDx/kq1evyl1dXQt+Pr9vVXFxsZyWliZPTk7KZrNZPnnypHz06FH56dOnyt5FFotF+bvf9y+yN2wljq3EsZU4tlpafX19ck5OjmwwGOSampoF/zY3N2ejo1qd2Mp2eKZviRiNRmRkZKC1tRU1NTUwm80wm80ICwtTFqFqNBqUlZXB29sbcXFxSEhIQENDA9ra2qBWqxEeHg5nZ2cAv9bX2OviVbYSx1bi2EocWy09nU6HwMBA9Pb2Ynh4GPv374ckScqZT+4x929sZTsc+pbIzMwMjEYjnJyc4OHhgdnZWdTV1aG+vh6SJGHDhg3Q6/WYmprC48ePsWPHDvj5+eHAgQOoqqqCLMtISkpS/j97ftGzlTi2EsdW4thqac1/Pe7t7Y3u7m709PTgxIkTyiDs6H1+x1a2xaFvibi7uyMqKgqfP3/G1NQU1q9fj3PnzqGzsxN//fUXqqurodFooFarMTg4CD8/P4SGhsLNzQ2pqak4dOiQw6yFYStxbCWOrcSx1dIaGRmBRqMBALx8+RJOTk5ITEyEWs1d0f4TW9kWh74l5OHhgfDwcHz69AmdnZ3w8vLClStXEBUVha9fv6Kqqgrfvn3D27dvYTabkZqaCkmS4OLiApVK5VCLoNlKHFuJYytxbLU02trakJ6ejubmZjx79gyNjY0oKCiwu7uQLAW2sj0OfUtMo9EgIiICw8PDqKurw9jYmPLJODAwEM7OzhgYGMDHjx/h7+8Pg8Gg/K2jvYGylTi2EsdW4tjqz1mtVkxPT8NisUCv1yM/Px+hoaG2PqxVia1sT5JlWbb1QdijkZER3Lt3Dy0tLUhISEB2djaAXy/6L1++4MmTJzhz5ozDL34G2Or/wVbi2EocW/25ubm5BRcj0H/HVrbDM33LRKPRIDw8HCaTCU1NTTCZTNi5cyckSYJWq8X27dv5Fcm/sJU4thLHVuLY6s+pVCpehCCIrWyHQ98y+v2NtLm5GX19fdi7d++C3+Eb6C9sJY6txLGVOLYisn+8XGaZrVmzBpmZmfjx4wfGx8eV+1vSP7GVOLYSx1bi2IrIvnFN3woZGxuDp6cnVCoV30gXwVbi2EocW4ljKyL7xKFvhVmtVn5FIoitxLGVOLYSx1ZE9oVDHxEREZED4Ec4IiIiIgfAoY+IiIjIAXDoIyIiInIAHPqIiIiIHACHPiIiIiIHwKGPiIiIyAFw6CMiIiJyABz6iIiIiBwAhz4iIiIiB/A3KNnubNp4sdoAAAAASUVORK5CYII=\n", + "image/png": 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\n", 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" ] @@ -840,7 +841,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -887,7 +888,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -953,7 +954,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1105,7 +1106,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1268,7 +1269,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/src/mplfinance/_utils.py b/src/mplfinance/_utils.py index 3a078cc5..447a52ea 100644 --- a/src/mplfinance/_utils.py +++ b/src/mplfinance/_utils.py @@ -557,7 +557,6 @@ def _construct_ohlc_collections(dates, opens, highs, lows, closes, marketcolors= if marketcolors is None: mktcolors = _get_mpfstyle('classic')['marketcolors']['ohlc'] - #print('default mktcolors=',mktcolors) else: mktcolors = marketcolors['ohlc'] @@ -635,7 +634,6 @@ def _construct_candlestick_collections(dates, opens, highs, lows, closes, market if marketcolors is None: marketcolors = _get_mpfstyle('classic')['marketcolors'] - #print('default market colors:',marketcolors) datalen = len(dates) @@ -719,7 +717,6 @@ def _construct_hollow_candlestick_collections(dates, opens, highs, lows, closes, if marketcolors is None: marketcolors = _get_mpfstyle('classic')['marketcolors'] - #print('default market colors:',marketcolors) datalen = len(dates) @@ -828,7 +825,6 @@ def _construct_renko_collections(dates, highs, lows, volumes, config_renko_param renko_params = _process_kwargs(config_renko_params, _valid_renko_kwargs()) if marketcolors is None: marketcolors = _get_mpfstyle('classic')['marketcolors'] - #print('default market colors:',marketcolors) brick_size = renko_params['brick_size'] atr_length = renko_params['atr_length'] @@ -1005,7 +1001,6 @@ def _construct_pointnfig_collections(dates, highs, lows, volumes, config_pointnf pointnfig_params = _process_kwargs(config_pointnfig_params, _valid_pnf_kwargs()) if marketcolors is None: marketcolors = _get_mpfstyle('classic')['marketcolors'] - #print('default market colors:',marketcolors) box_size = pointnfig_params['box_size'] atr_length = pointnfig_params['atr_length'] @@ -1218,9 +1213,6 @@ def _construct_aline_collections(alines, dtix=None): 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)) @@ -1403,6 +1395,7 @@ def _construct_tline_collections(tlines, dtix, dates, opens, highs, lows, closes # reconstruct the data frame: df = pd.DataFrame({'open':opens,'high':highs,'low':lows,'close':closes}, index=pd.DatetimeIndex(mdates.num2date(dates)) ) + df.index = df.index.tz_localize(None) # possible `tvalue`s : close,open,high,low,oc_avg,hl_avg,ohlc_avg,hilo # 'hilo' means high on the up trend, low on the down trend. @@ -1436,7 +1429,7 @@ def _tline_lsq(dfslice,tline_use): x1, x2 = xs[0], xs[-1] y1 = m*x1 + b y2 = m*x2 + b - x1, x2 = mdates.num2date(x1), mdates.num2date(x2) + x1, x2 = mdates.num2date(x1).replace(tzinfo=None), mdates.num2date(x2).replace(tzinfo=None) return ((x1,y1),(x2,y2)) if isinstance(tline_use,str): From 09abcda9e1e90e41fbc35103da8a218bc75a3ca3 Mon Sep 17 00:00:00 2001 From: Arief Anbiya <35247692+anbarief@users.noreply.github.com> Date: Wed, 2 Mar 2022 08:41:46 +0700 Subject: [PATCH 16/20] Removing TODO comment Remove the comment `` # ---------------------------------------------------------------------- # TODO: Add some warnings, or raise an exception, if external_axes_mode # and user is trying to figscale, figratio, or figsize. # ----------------------------------------------------------------------`` Since this TODO has been done in line 420. --- src/mplfinance/plotting.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/src/mplfinance/plotting.py b/src/mplfinance/plotting.py index 55f6d614..2d816fe2 100644 --- a/src/mplfinance/plotting.py +++ b/src/mplfinance/plotting.py @@ -442,11 +442,6 @@ def plot( data, **kwargs ): else: raise TypeError('style should be a `dict`; why is it not?') - # ---------------------------------------------------------------------- - # TODO: Add some warnings, or raise an exception, if external_axes_mode - # and user is trying to figscale, figratio, or figsize. - # ---------------------------------------------------------------------- - if not external_axes_mode: fig = plt.figure() _adjust_figsize(fig,config) From 00966d38a48d7515eb98a2ea09faa4d26179fe28 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Thu, 3 Mar 2022 17:51:50 -0500 Subject: [PATCH 17/20] Create CODE_OF_CONDUCT.md --- CODE_OF_CONDUCT.md | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 CODE_OF_CONDUCT.md diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..fb073385 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,4 @@ + +#### All typical collaboration codes of conduct apply: + +### Treat people fairly, with respect, and overall [be a mensch](https://www.google.com/search?q=mensch). From 790e1d32c6393481f22647fc5d5d5604737090a1 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Thu, 3 Mar 2022 19:42:35 -0500 Subject: [PATCH 18/20] Update CONTRIBUTING.md --- CONTRIBUTING.md | 33 ++++++++++++++++++++++++++++++--- 1 file changed, 30 insertions(+), 3 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 427b17a4..ed5d4b47 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,9 +1,36 @@ -- Contributing can as simple as **asking questions**, participating in discussions, suggesting enhancements, etc. **All of these are valuable!** There are many ways to contribute. +## Contributing Basics -- All of the usual/typical open source contribution guidelines apply (see for example, **[Matplotlib Contributing](https://matplotlib.org/3.1.1/devel/contributing.html)** and **[Open Source Guide to Contributing](https://opensource.guide/how-to-contribute/)**). Therefore, here, on this page, we will mention just a few items that we may be particular about in **mplfinance**. +- Contributing can be as simple as **asking questions**, participating in discussions, suggesting enhancements, etc. **All of these are valuable!** There are many ways to contribute. We also very much appreciate when you share the creative things you've done *using* mplfinance (both code and plot images). And, of course, writing code for mplfinance is also a great way to contribute. Thank you. +- All of the usual/typical open source contribution guidelines apply (see for example, **[Matplotlib Contributing](https://matplotlib.org/stable/devel/contributing.html)** and **[Open Source Guide to Contributing](https://opensource.guide/how-to-contribute/)**). Therefore, here, on this page, we will mention just a few items that we may be particular about in **mplfinance**. -- Coding: +--- + +## Fork Clone Workflow +- The standard workflow for contributing on GitHub is called **Fork/Clone**. For those who may not be familiar, here is a brief summary and some reference links. + - *We assume you are familiar with **git** basics: `git clone`, `git commit`, etc*. +- Note: a "Fork" is just a `git clone` *that is created on, and that lives on, GitHub*. You create a fork using the **Fork** button on GitHub: This allows GitHub to track the relationship between the original github repository, and your Fork. +- The basic workflow is: + 1. Create a **Fork** of the mplfinance repository. (See references below for details.) The fork will exist under *your* github account. + 2. **Clone** *your* Fork to your local machine (`git clone`). + 3. Work on your cloned copy of the repository, `git commit` the changes, and then **`git push`** them *to your GitHub fork*. + 4. When you are satisfied with the code in your fork then, **on the GitHub page for your fork, *open a Pull Request (PR)***. A Pull Request effectively asks for the changes in your fork be pulled into the main mplfinance repository. The PR provides, on github, a place to see the changes, and to post comments and discussion about them. + 5. After code review, if you are asked by a maintainer to make additional changes, you do *not* have to re-enter another Pull Request (as long as the original PR is still open). Rather, make the changes in your local clone, and simply `git push` them to your fork again. The changes will automatically flow into the open Pull Request. + 6. When done, the maintainer of the repository will merge the changes from your fork into the mplfinance repository. The PR will automatically be closed. (Your fork, however, will continue to exist, and can be used again for additional Pull Requests in the future; See GitHub documentation for how to keep your Fork up to date). + +- Some References: +- GitHub documentation: + - **https://docs.github.com/en/get-started/quickstart/contributing-to-projects** +- and some user gists: + - https://gist.github.com/Chaser324/ce0505fbed06b947d962 + - https://gist.github.com/rjdmoore/ed014fba0ee2c7e75060ccd01b726cb8 + +--- + +## Coding Standards +- I am not super strict about adhearing to every aspect of PEP 8, *nor am I lenient*. I tend to walk the middle of the road: If something is a good and common, then it should be adheared to. +- Here are a few items that I (perhaps uniquely) tend to care about in particular: - If you write code, please don't use tabs; rather use spaces. - If you add a significant feature --that is, a feature for which explaining its usage takes more than just a few sentences-- please also create a "tutorial notebook" for that feature. **[For examples of tutorial notebooks, please see the jupyter notebooks in the examples folder.](https://github.com/matplotlib/mplfinance/tree/master/examples)** - If you add a significant feature, please also create a regression test file **[in the tests folder](https://github.com/matplotlib/mplfinance/tree/master/tests)**, similar to the other regression tests that are there. *Often, the simplest way to do this is to take a few of the examples from the feature's "tutorial notebook"* (see previous point). + - If you work on a pre-existing code file, please try to more-or-less emulate the style that already exists in that file. From b5674647d90af829f4b311bed715383f2601c8a8 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Wed, 30 Mar 2022 18:08:12 -0400 Subject: [PATCH 19/20] mav cross point example --- examples/scratch_pad/mav_cross_issue518.ipynb | 570 ++++++++++++++++++ 1 file changed, 570 insertions(+) create mode 100644 examples/scratch_pad/mav_cross_issue518.ipynb diff --git a/examples/scratch_pad/mav_cross_issue518.ipynb b/examples/scratch_pad/mav_cross_issue518.ipynb new file mode 100644 index 00000000..8fb16638 --- /dev/null +++ b/examples/scratch_pad/mav_cross_issue518.ipynb @@ -0,0 +1,570 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# This allows multiple outputs from a single jupyter notebook cell:\n", + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = \"all\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.3.1'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "pd.__version__ " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.12.8b9'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import mplfinance as mpf\n", + "mpf.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Highlighting Moving Average Crossovers\n", + "\n", + "### Issue #518 https://github.com/matplotlib/mplfinance/issues/518" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('../data/SPY_20110701_20120630_Bollinger.csv',\n", + " index_col=0,parse_dates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.iloc[0:int(len(df)/2.5)]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mav8mav16delta
Date
2011-07-21132.042498NaNNaN
2011-07-22132.439999NaNNaN
2011-07-25132.688749133.011249-0.322499
2011-07-26132.988751132.9743740.014377
2011-07-27132.852501132.7737490.078752
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" + ], + "text/plain": [ + " mav8 mav16 delta\n", + "Date \n", + "2011-07-21 132.042498 NaN NaN\n", + "2011-07-22 132.439999 NaN NaN\n", + "2011-07-25 132.688749 133.011249 -0.322499\n", + "2011-07-26 132.988751 132.974374 0.014377\n", + "2011-07-27 132.852501 132.773749 0.078752" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "
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mav8mav16delta
Date
2011-11-15125.662501125.4762520.186250
2011-11-16125.487501125.540627-0.053125
2011-11-17124.968751125.403751-0.435000
2011-11-18124.231252124.988126-0.756874
2011-11-21123.793752124.429376-0.635624
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" + ], + "text/plain": [ + " mav8 mav16 delta\n", + "Date \n", + "2011-11-15 125.662501 125.476252 0.186250\n", + "2011-11-16 125.487501 125.540627 -0.053125\n", + "2011-11-17 124.968751 125.403751 -0.435000\n", + "2011-11-18 124.231252 124.988126 -0.756874\n", + "2011-11-21 123.793752 124.429376 -0.635624" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdf.iloc[13:].head()\n", + "mdf.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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NAQGOZGNKSgQAR8HsysUZiA/tW51JlEGPZ5eOxfyRMQAAAeChz47gREVT1w8kRfGJlHvTpk1Yu3Yt7rjjjrOSGCeNBoqudne2Tclt7A21xuWpgTgOeTXNeHhDNkxWO/JrivDzMfFIDA/svxfE4Dy/vh5zjktx7YjYkLPiUXpc/3duOhLCAzA9NRJZbYlLXwX66/CXC0YhNliPdfsdU7rf312E/3ehekoKfOX8OnnaTsUnK1999RXuueceXHzxxbj11ls7vV96UgSCg5X/LSI9KULuJvQLtcblqf46DsZWK2754ABM1vYZEKfrTZg9JqFfXu9Mg/H8+mrMlfva11eZkxl3Vn2G0uMaBmBmZny/PPcjV4Rh86lqVDa2Yuupatj8dBgZ3/EXYF+l9PPrZDT6e3R/RScr77zzDh577DFcc801eOCBB7qcBZRbUgeDwTKArfOMRuN4E+WW1EGI7u/vK9Qal6f68zgIIfD4NyeRc0a39foDJZidGuHdFzvDYDy/vh7z7rwa6XKIFjhdXAfA9+PylisnJuKlbXkAgCe/PIb7F6mjtMDXzm9zs2f1p4pNVlavXo2//vWvuOuuu3DzzTd3e38h4BMnyFfa6Sm1xuWp/jgOnx8ux7fZjnVHDf46+Ok0aDBZsa+oHq0We4+LD/tiMJ5fX4xZCIGcSkdSG23wR2SQ/qwYfDEub1oyNgGr9xajrtmCTScqcf30FCSFK3MLgt7wlfPraRsVWWD7448/4uGHH8a9997bo0SFSK1OVRnxr+9PSz/fvXAEZqU7pmq2Wu04UMw9VKhdRWNr+6aFsVxPpCNBeh1+MzsdgGN69Jo9xTK3iHpCccmKEAKPPPIIJk+ejIsuugiVlZVu/zh1mQaTjw+USItcLZ2QiHkjYjB9aKR0+86C2s4eSoPQyar2z8fhMcqv4ZPL9bPSEKx3LI/x9fEKVDa1ytwi6o7ikpWSkhKcOnUKe/bswZw5c87698Ybb8jdRKIBc7CkAYBjyfDl5wwFAExJiYBzq5Of8pisULscH125dqCFB/nj0vGJAACrXWDtPvauKJ3ialaSk5ORnZ0tdzOIZFfZ1IqSehMAICM+FIH+jm+CIQF+GJcYhoMlDSiuN6G4rgXJEeoZc6fec9arAL63xspAu3JyEj46UIJWqx1fHC7H1VlDEB2sl7tZ1AnF9awQkcPB4gbp8sTkMLfb3IaC8tm7Qg6n2oaBgvU6JIT17xo8vi4iyB9Lxjqm/pttdqzeUyRzi6grTFaIFOpASXvx7ISkcLfbXJOVn5isEID6FgsqmswAgGExwdD6yupgMvrllGQEts2m++JwGcobWbuiVExWiBTK2bOi02owNtF94aph0QbEtHVZHyhugKltLxUavHJcimtHsLi2RyINeiyd4KhdsdgF3ttdKHOLqDNMVogUqKbZjMK6FgDAqNhgBPm7b+yp0Wik3hWzjVOYyb1ehcW1PfeLrGRpZtCGYxUoqW+RuUXUESYrRAp0qKS9XmVCcniH93GvW6nr7yaRwp1y6VkZyTVWeiws0B9XTEwC4Nix+j872buiRExWiBTItadkYlJYh/fJSgmHrm0O8w+51bDY7B3ejwaH01XNAAA/rQapkZwd5okrJiUhNMAxOXbjiUqcqGiC9YzfJ7sQMFlsjtqgxlYU1jYjp7IJR0obsLewDttza7Alp4o9M/1EcVOXiai9XkWrAcZ1kqwE6/0wLTUCO/JqUdlkxtfHKrBk3MBsbEjKIoRAWaNjmnt8aAD8dfwe6omQAD8sy0rGaz/mwy6A3609AADQ67TQ+2lgtgqYe/hlwF+nwRvXZCGpn3dEH2z4jiZSmPoWC3JrHN+SR8QEI1jf+XeK66alSJff21PE3pVBqqnVhhaL49zHhwXI3BrfdNmEREQEue8EbLbZ0dRq63GiAgAWm8AOl80kyTvYs0KkMIdK2+tVxid1XK/ilBkfihlDI/FTfi0qGlux4Vg5Lh6X2N9NJIWpcFkuPi6EyUpvBPnr8Nclo/HJwVI0mKxoNtvQbLai1WpHgJ+27Z/O5bLW7fpWqw0fHSgFABwpa8TlE2UOSGWYrBB5mRACdS0WBOv9erUj8kHXepXkjoeAXP16eoq01sp7u4twweh46DkMMKiUtw0BAY5hIOqdzPhQ3Pez0O7v2AGbXWD9kXKYrHYcLWv0csuIyQqRF9mFwLPf5eCroxUAgOhgPRJCA5AQFojEMMf/CWEBSAwLRGxIgFQg6+pA20wgDYDxndSruMqMD8XMtEipduWro+XSvic0OFQ0mqXLcUxWZKHTapARF4IDJQ2oaGxFZVMrYtnL5TVMVoi86LXt+VKiAgDVRjOqjWYc6eCbllbj6LJ3TWBiQvQ41bYZXXq0AWGB/mc9riPXT0/FjrZNDd/fXYSfj47vVa8O+SbXlVfZsyKfMYlh0peNo2WNmDeC58JbmKwQeclbP+RizV7H7q1aDTAqLgTlja2obbZ0eH+7AMoaW1HW2Ap0sOnrmUvsd2VUXAhmpUdhe24NqoxmfHm0HJdNYO/KYFHhMgzEmhX5jEloH0JyJCsxMrZGXZisEHnB1pwqrNrQvlv4H+YNkwpdTRYbyhpbUd5gQmlDK8oaTChraEVpownlDa1obLV2+JzT0yI7vL4zv56egu25jlkI7+8pwoVj2LsyWJS7DAPFsmdFNmcmK+Q9TFaI+uhYWSP++r8TEMLx87VTh7jNyAn01yEtyoC0KEOHj29qtbYnMA0mlDe2Ii3KgOmpER61Y2RsCGYPi8IPp2tQbTTjiyNluLxtZU5SN+dsoCiDP4urZRQR5I/k8EAU15twsqIJZpud58NLmKwQ9dHbOwtgsTkylQsy43DjjFSPHh8S4IcRsSEY4YUl0q+fnoofTjt6V1bvKcJFY+MR4Kfr5lHkyyw2O2qMjp4VFtfKb0xCKIrrTbDYBU5WNGFsYvdF8tQ9pnxEfWC22nGwraAuISwQd503HBrN2TN8BsrwmGDMHRYNAKhptuCLw+WytYUGRmWTGW2deqxXUQDXHdI5FOQ9TFaI+uBIWQNarY7VLeeMjIGfArp8r5vevqrt6r1FMFlsMraG+lsFZwIpypiE9p4UJiveI/8nK5EP21fUvoDb7BHRMrak3fCYYJw73NGW2mYLPj9cJnOLqD+5TlvmMJD80qIMMPg7hl6PlDVCOIvZqE+YrBD1wd7C9mRl1nDlTFP89fQUOAej1uwtRgt7V1TLbal9Jiuy02k1yIx31J9VG82oaDJ38wjqCSYrRL3U1GpFdoWjm3doVBDiw5Szy2p6dDDmtfX01LVY8Pkh9q6oFYeBlGeMW91KQxf3pJ5iskLUSwdL6mFv6+HNGhIha1s6ct30VKl35YN9xWgxs3dFjdyGgVhgqwisW/E+JitEveQ6BJSV0vPVZgdKWpQB80c6hqbqWiz49FCpzC2i/uDsWQn00yIskKtRKMGYeM4I8jYmK0S9tLeoDoBjaf2JycpLVgDgumnttStr9xWj2dzxarnkm4QQUs1KXGiArNPmqV1ooB9SI4MAACcrjWi1slezr5isEPVCtdGM/JoWAEBGXChCApT5jXZolAHnjXL0rjSYrPj0IGtX1KTeZJWmzrNeRVmcS+/b7AK51c0yt8b3MVkh6gVnrwqgzCEgV9dNS4G27Qv32n3FMLJ3RTUqWK+iWMNigqXLp6qMMrZEHZisEPXCPpd6lclDlJ2spEQacN6oWABAY6sVnx5k7YpauM0ECmOyoiTDotv3AmPPSt8xWSHykBBC6lkJ8NNibILy9/741dT23pV1+0rQ1MlOz+RbOBNIuYZFt/esnK5mz0pfMVkh8lBRnQmVbQs9jUsMg95P+b9GKZFBWOjSu7LxRKXMLSJv4Oq1yhUe5I9ogz8A4HRVM1ey7SPlf8oSKYwv1au4WjIuQbrMbml1cF29lgW2yuOsW2lstaLayJVs+4LJCpGHcirbu3QnJvlOspISESRdLqprkbEl5C3OmhUNgJhgvbyNobOkuw0F8QtCXzBZIfJQscsf+pTIoC7uqSzhQf4IbZtiXcxkRRWcw0DRwXpF7PhN7obFtBfZsm6lb/juJvJQcb0JABAe6KfY9VU6kxzh2L+oosnMhap8XKvVhroWCwDWqyiVW5FtFXtW+oLJCpEHTBYbqtrGnpMjfKdXxSk5vL3NJW1JF/mmisb2GgjWqyhTSmQQdG3T8HLZs9InTFaIPOD6Bz45XDm7LPfUkIj2NhfVMVnxZa7FtexZUSa9TovUti81BbUtsNjsMrfIdzFZIfKAW7Ligz0rQ1hkqxpcvdY3OOtWrHaBwlr+zvUWkxUiDxTXt3/Y+GLPimubXWMh3+O6xgqHgZTLdUYQlwzoPSYrRB4odhk6SfLFZMWtZ4XDQL6sggvC+QTXZfc5I6j3mKwQeaDYrWbF94aBQgL8EBHkWFWT05d9W1lD+3uRPSvK5bqhIdda6T0mK0QecA6dhAX6ITTQt6YtOzmLbGuaLWjmDsw+y5en0A8mMcF6hAToAACnuftyrzFZIeqhVqtN2hPIF+tVnFx7hIo5fdknuU6hT/LBHr7BRKPRSOutVBnNaDBZZG6Rb2KyQtRDpfXtNQK+OBPIiTOCfF9pg+usNN9NnAeLYSyy7TMmK0Q9VOTjM4GcXP+4FbPI1icV+/h6P4ON27L7HArqFQ509lCr1YbPDpUhOMAP5w6P5hjxIFTi4zOBnNiz4vtc1/vx5ffiYMHpy33Hv7g9IITAk9+exJacagDAP7ecxpxhUbhgdBwmD4mQllMmdXNdl2SIDw8DJbmttcKeFV9U4uOz0gab9Ki+T1+22QVOVjbhcGkDDpU04ERFE6KC9bh0fCIWjIyB3k/dAyVMVnpg04kqKVEBALPNjk0nq7DpZBWiDf6YNzIGC0bGYnR8CDQaJi5qVaySb7NB/jrEBOtRZTSzZ8VHuSbOvvxeHCyC9DokhQeipN6E3OpmmK32bpOLFosNx8oapeTkaHkjTBb35formsw4Xn4Sr/2YjysmJuI2lx4ctWGy0o3KplY8v/WU9PO5w6Oxv7geDSbHlM/qZgs+PlCKjw+UIiE0AAtGxmDBqFgMizYwcVEZZ7ISGuCHsEB/mVvTN8kRgW0zE6xoNFl9dhr2YOUckgzW6xDGc+cTxieGoaTeBJPVju25NZg/Msbt9haLDXsK6nCoLTk5WdkEu+j8+QL8tGi1OpKXaqMZr2zPx6acavxj6TgE+uv6MxRZKPZdvm7dOrz55psoKChAZGQklixZgj/+8Y/w9x+4PxJ2IfD0xhw0tdoAAAtHxeD+RRmw2Oz4Ka8WXx+vwK78Wlja3lFlja1YvbcYq/cWIzUyyJG4jIxFSiS7aX2d2WpHZduKoWqYfTEkPAgHihsAOL6lZwaGytwi6imzzS5tYpgcHsgvRT7iZ5mx+Pp4BQDgf8cr3JIVi82O3689gIIu9g6KDdFjfGIYxiWFYXxiGNKiDTha1oi1+4qx/XQNBICciiZ8eKAEv5qa0t/hDDhFJiuffvopVq5ciXvvvRcLFy5EdnY2Vq5ciebmZqxatWrA2vHZoTLsKawD4FjYZ8W5wwEA/jot5gyPxpzh0WhqtWLb6Wp8d6IKe4vqpEy4oLYFb+8sxNs7CzEiNhjnjYrB9cFcZdJXlTSY4PySo4bZF641N4V1LciMZ7LiK8obTNLnTJIP104NNhOTwxEXokdFkxm7CmpR02xGlEEPAPg2u/KsRCUtyoBxiaEY35acxIed/bkzLjEM4xLDcKKiCf+37gDsAvhgTzEuHpuA8CDf7v09kyKTlRdeeAEXXXQRbrjhBgBASkoKqqqqsGrVKvz+979HfHx8v7ehvLEVr2zPk36+Z+HIDrvKQwL8sHh0PBaPjkdtsxnfn6rGppNVOFTSIN0np9KInEoj3vixAP/vwkyckxbV7+0n7ypxqxHw/T8QnL7su0o4bdknaTUanJ8Rh/f3FMEugE0nKnHlpGTY7AJr9hZJ97v7vBGYlR7lUbIxKi4EF46JxxdHytFsseG93UX4/dz0/ghDNoorH87Ly0NhYSHmzZvndv25554Lu92O77//fkDacby8URoPvHR8AqamRnT7mEiDHpeMT8Rzl4/Hmhum4rez05ARFyLdbrUL/O9YRX81mfqR66Z/ahgGcl3UTkl7BAkhUNdiwdGyRmzPrXbb/4Yc1FLoPRgtyoyVLn9zvBIAsO10tfT5MjE5DD8fE9+rXpHrpqcgoK1o97NDpShX2e+O4npWcnNzAQCpqalu1ycmJsLf3x+nT58ekHackxaFxaPjEOSvw/Jzhnr8+NiQAFw1ORlXTU5GcV0Lbl6zH61WO3fd9FFqmyqaFB4IDQAB+acv24Ujif/scBkKa1vQbLG53Z4QGoBJQ8IxKTkck4aEIzZkcA+nqu29OJikRBowOj4Ex8qbkFNlxKkqI97f096rcs2U3teaxIYE4MbZ6fj3llOw2AXe2lmIP58/0hvNVgTFJStNTU0AgOBg9ylYGo0GwcHB0u1n0mgc/7wlwF+LP3npRA+JDMLQqCCcqDCiuM6EVqtNNdXazmOu9hq/Erc1VgLPitfXjkOAnxbxYQEoa2htm74sOizU7O+4jpU14p9bT+N4ece/14CjcH3DsQpsaOuVTA4PdCQvQ8IxOTkcUcF6r7ZJ6efSNbns6L3YGaXHNVDkPg6LMuNwrO39/uymHORUOr7AjooLxtTU8F63S6MBfjdvON7dkYemVhu+OV6BZVlJbgvSKYmncSouWemt9KSIsxIcJZmYEokTFUYIAFY/HYYlR8jdJK9KT4qQuwn9qqzRsWlcWKAfJg2P6XQGhi8dh5HxoShraIXRbEN4hAExXfRYeDsuk8WGlZ8exjqXb5UAkBplwNBox7+wQH/sLajF3oI6mK3t60sU15tQXG/C+iPlAIDhscE4Z3g0Fo1JwNyRnZ8bTyn1XFa2bWBo0OswZWSsx/EqNa6BJtdxuCHCgBe35cJiE8iuaE/S7/xZBoYPiezz89923kg88dVxCACr95Xiteun9vk5+4PR6NlQl+KSlbCwMAA4qwdFCAGj0SjdfqbckjoYDMrdzTLW0H6ovz9WjhCVfLvRaBy/9LkldRBdrAngy8w2O0ra6joSwwKRW1J/1n188ThEuRSMbz9WjnFJZ/9u9VdcL23Lxbp9JdLPQ6OCsOLcYchKiXC735XjE2C22nGkrAH7i+qxv6gBx8obYXVZgOJUpRGnKo14d0cBHr94DGak9e0DX8nn0mYXKKhxLNeeEBrQ4XuxM0qOayAp4TjMTIvC96faFxpNiQjCyMhAnC6u6/VzOuOalxaBV4P1qDaasfFYOXZml3f5RUQuzc2elUQoLlkZNmwYACA/Px+TJ0+Wri8qKoLFYsGIESM6fJwQUPQvoOuum6erjIpua28o/fj3RUmd+7TlruL0peOQ4lJke6rKiLGJHX8RALwbl80u8G22o7jQX6vBzbPTcOm4BPjptB2+hr9Oi0nJEZiUHAHMcCyedaS0EfuL67G/qB7ZFY3SVN4tOVWYPrTv304BZZ7L8sZW2NqCTQ4P6lX7lBiXHOQ8Dj/LiHVLVq7OSoYGGq+0J8BPhwvHxOOdXYUQALbmVGPpxKS+P7GXeRqr4mYDpaSkYNiwYfjuu+/crt+4cSP8/Pwwd+5cmVrWN+nRrntDcCMrX+I2+0IFM4GcRrnMVHPtju5vh0oaUNvs6AWdkRaJKyYmwU/X84+iIH8dpqZGYPk5Q/HCVRPwyfIZ0iyI3QV1ECr+S+w6zVxN78XBZvrQSEQZHMMgcaEBWJgR280jPDN/RPuCc5tdtorxZYpLVgDg9ttvx9dff40333wTxcXF+Pbbb/Gvf/0Lv/71rxEdHS1383ol0qCXuuIcPSvq/UBVm10FtdLlFBUtwjUiNhjOPTizuyhwdWpqteLH3Bo0tm010VtbTlVJl+eNiOninj0TEuCHCW1DWFVGM/JrlDMV29tcC725xorv8tdp8chFo3HZ+EQ8cfEY+HuQrPdEWrQBqW0rpx8ubUBl24rHvkyRycrixYvx1FNP4cMPP8QFF1yARx99FNdffz3uueceuZvWJ5kJjlVC601W6ZslKZvRbMU3bUtkB/ppMVNFC/oF+Omk4cn82ma0mG1d3n/VV8fxl/XHcP27e7DpRGWvEm6bXUjd3/46DWb2sb7EyXUdpN2FtZ3f0cdxjRX1yIwPxYp5wzDUZUdmb3LtXdl6yvd7VxSZrADAJZdcgvXr1+Pw4cPYvHkzbrvtNmi1im1ujziTFYBDQb7i2+xKtLTtdLowIxYhAYor8+qTjHjHUJBdACcrO+9dKahpxt4iRzFnvcmKv/7vBB788rjH39jchoCGRsKg987xnJrSnvTsLqjzynMqEddYoZ5y7bXcmlPVxT19g2//9fcxGW7JCheHUzohBD47VCb9fOn4RBlb0z9c9wTqaq2Tb9oKYl1tz63BTe/vw/ojZT3uZfH2EJDT0KggxLStt3KwpMFtqrOaFLcNA/nrNIgJ8e76MqQuadEGpLX12hwubfT5oSAmKwNotMtsi1z2rCjewZIG5LVNEx2XGIrhMcpdx6e3XLeDOF7R2OF97HYhJStaDXDn/OGIbCsONJpt+Nt3p3D3p0fc6ik60l9DQIBj0UjnUFCr1Y7DpQ1dP8AH2YVAab3jD05iWCC0g311N+rWvBHtNZ5bfbzQlsnKABoRFyIVNLJnRfk+O1QqXVZjrwrg2Nk1sG0mTWdFtj/l1qCi0fFHclpqJJaMS8Ab10zGosw46T77i+uxfPV+rNtXLE2tPdPh0vYhoOleHAJymuKyRssuFQ4FVRvNMNscPUYsrqWemOc2K8i3h4KYrAygQH8dhrTNJsmvae70Q53kV2004/vTNQCAiCB/zBnum7PQuqPTajAi1tG7UtbYirqWswu/P9nXvsrs+W1TLMMC/fHn80fiiYvHID7UMcut1WrHv3/Iwx8+OojcDpLxLS4flvOGe28IyGlKSgScfQ17Cuu8/vxyc5u2zHoV6oGhUQaktw0FHS1rlL50+CImKwPMud6KxSba9mQhJVp/pExKJi8cEw+9l6cWKklmvMt6K+XuQ0GtVhu+aqvbMfjrMHuY+2yoaUMj8fovJ2PphEQpUThe3oTffnAA/9lZAEtbT4DNLqQZCf46Dc5J994QkFN4kD9GxjqG6k5VGVHTtiy9WhS7TlvmGivUQ/NGus4K8t3eFfV+AivUMJe6B9atKI/FZsdPeTX4/LDjD7RWAywZlyBzq/qXa7JyZpHt9tM1aGx1rKty7ohoBPidvQFnkF6H284dhueuGC+tQ2O1C7y9sxBXvbELV76xE0tf+6lfh4CcpqS2J0F7iur65TXkUlDbnqwkhTFZoZ5xrVvZdILJCvXQMLeVbFm3ogQ2u8Cewjo8uykHV72xC/d/cQw1bX9YZ6ZFScMcapUR5zIj6IyVbP93vH0W0PndrLI5LjEMr1w9CddMGSLVZjW2OtYUMrqs4TLfi7OAzjTNZb2VPSqrWzlQ3L4P0EiXwmiirqRGGjCirccxu6IJp6p88++OuhaN8AGuewSxZ6X/FNe14Kuj5UiJNGDBqJizhnHsQuBwaQO+O1mFrTnVHdZqhAf64dfTUwaqybJJDAtAWKAfGkxWZJc3QggBjUaDmmaztHpvXIgeE5PDu30uvZ8WN50zFPNGROPl7XnIrW6GXqeFv04Df50W4xPDvDpl+UxjEkIR6K+FyWLH7sI62IVQxayZBpMFOZWOPzLDY4IREeTZjrU0uF00Jh7/2HIagGOI+w/zhsvcIs8xWRlg8WEBCPLXosVix2kfzXCVrMViw3u7i/DhvmJY2mpOXt+RjysnJuGicfEorG3BphNV2JJThaoOahoC/bWYlRaFBaNiMDU1UtW1Kk4ajQYZcSHYVVCHepMVZY2tSAwLxKYTldIGgednxHn0R39EbAievnRcP7W4c45ND8OxI68Wtc0W5FY3q2LK+cHiBmkzzUk9SBqJXJ03KhYv/5AHk9WOb7MrccusNAT6nz2kq2RMVgaYVqNBenQwjpY1oqyxFUazFcH9NH4/2GzJqcJL23JR2eSehFQbzXh5ex5e/TEPHU3A0uu0mJEWiQUjYzBjaKTP/RJ7Q2Z8qDTdN7u8CTVGM978qUC6/WeZ3t1orT85kxUAOF7eqIpkZa9L/c3kIUxWyDMhAX6YPzIGG45VwGi2YXNOFRaPjpe7WR7hX0kZpEcbcLTMMesir7oZY10Wi6Pe2Xa6Gg9vyJZ+9tNqcNmERJQ2mPBD2xRk10TFT+tYRGzByBjMSo/qt4JPX+FaZPu/4xU4UtYAU9s2A0smJGJolMEr29cPhJGx7bH46vj8mfa1bXWg1QATkvl5QZ67aGwCNhxz7HO2/kh5j5OVyqZWHCxuwJCIQGS4rHg90Ab3J7RMXOtWTlcbmax4wZdHyqXL04dG4v/mpktr2hTUNuODvcU4UNyApPBALBgZgznDohEayLe/k+tKtj/lt28EmDUkHM9cNRElnaxuq0SuPSlqSFaqjWZpJlBGXCh7YqlXRseHID3agNzqZhwta0RutRHp0R33OhbWNmPb6RpsO10tzRDU67R457osxITIM+GA73oZuM8IYpFtX5mtdmmmRLTBH48tGQ2NS31FaqQB9ywcKVfzfEKkQY/40ACUuywaNSYhFI9cNNrnhsVCA/2kWE5VGX2+yHZ/UfssIA4BUW9pNBpcNCYeL3yfC8DRu3LbucMAOPZBO1HRJCUortPknQL8tPCXsYaPyYoM0jkjyKsOlTbA1LZx3dTUSLdEhXouMz5ESlaGxwTj8YvHIEjvW4mK0/CYYJQ3tqLFYkdpvQnJEb674qtrvUpWCpMV6r3zM+LwyvZ8mG12fJNdgZlpkdiRV4sfTlejoqnjRRSHRRswZ1g0Fo+OQ7iMs9CYrMggNNAPMcF6VBnNyK02SlNFqXd2F7QPW0wbGiFfQ3zc0glJ2F1Qh6FRBjx8YSZCAnz342FETDC25zpqlXKqjD6drDh7Vvx1GoxJkK9mgHxfaKAf5o2IxjfZlWhqteHPnx096z4aAGMTwzBnWBRmD4tGkkL2ofLdTyMfNywmGFVGM5pabagymhEr0zigGuzKrwPg+CXLGhIhZ1N82vikMHyyfAa0Gvh88jw81r1upT/XdulPpQ0mlLX1do1NCOtwBWEiTywZlyDtou7kr9VgckoE5gyLwjnpUYgy6GVqXeeYrMgkPdqAnW2FjKerjExWeqmyqRW5NY6htMz4EFm7KdVAp/XtJMVphEuRrXMxNV+012VDRtarkDeMTQjFJeMT8FNeLUYnhGJ2ehRmpEUqvnBb2a1TsfQzimxnpEV1cW/qzG6XJdWnpnp/czzyTfGhAQjW62A023x6RhCLa8nbNBoNbp83HJgnd0s8o/7lORWKy+57h1u9isu+MDS4aTQaqXelymjucDsFpRNCYF/bLLcgf63b9HKiwYbJikxSIoOkzd5yuaFhrzg2IHR8mIcE6JAp44JFpDwjYn17vZX82hZpp+oJSeHwGwRbPxB1hu9+meh1WqRGOoaCCmpbYLXZZW6R78muaEJjqxWAo7BWLfUW5B3DY1xWsvXBupUvDpdJlydxCIgGOSYrMnLWrVjtAoV1Zy/CQ13ble86ZZn1KuTOtWclx8d6Vgprm/FZW7IS4KfFeSN9czYTkbcwWZGRa5Et61Y8t4v1KtSF1Mgg+LX1tvnaMNC/f8iDrW0zq6uzkmVb4pxIKZisyCjdbY8gJiueqG+xILvCsWdFWpSBU7/pLP46LdKinEOtzWi12mRuUc/sLqiTdo2OCdbjqsnJMreISH5MVmQ03K1nxbe++cltf3G9tIvyVPaqUCecmxrahWOHc6Wz2QX+vS1X+nn5OUMR5GN7MxH1ByYrMoprWwsCYM+Kp5y9KgAwMZnFh9Sx4T5Wt/Ll0XJpkcOMuBAszIiVuUVEysBkRUYajUbqpq5obEVT28wW6t7JyvZkZVRsx9ucE7muZKv0upWmVive+qlA+vn3c9N9erdoIm9isiIz1yLbvBr2rvSEEEJaQj0iyB/Rwcrbx4KUYbgPJSvv7S6SFq+bPyIa4xLDZG4RkXIwWZFZuttKtsr+MFWKiiYzGkyOXqiRscE+v+ke9Z+QAD8khDqKr09VGWEXQuYWdayk3oRPDpQAcOyufPOsNHkbRKQwTFZkNizGfY8g6p7rENDIWC5BTl1z1q20WOw4VNIgc2s69sr2PFjaKsavnJSMhLBAmVtEpCxMVmSWHuUyfbmKyUpPuO6iO4L1KtSNc4dHS5dX7ymSsSUdO1Bcj+9PVQMAIg3+uGYKpyoTnYnJisxCA/0QG+KoucitNkIotJtaSdizQp5YMDIWiWGOoaBdBXXILm+UuUXt7ELgJZepyr+ZORQGvZ+MLSJSJiYrCuCsWzGabahsMsvcGuVz9qwE63XSHyGizui0GizLGiL9/L6Celf+d7wCJ9vezyNignFBZpzMLSJSJiYrCuC27D5nBHWpptmMKqMjoRvB4lrqoQtGxyHa4A8A2Ha6RhEz71rMNrz+Y77082/npHEzTqJOMFlRgNSIIOlyab1JxpYon2u9CoeAqKf0Oq3bsvVKqF1Zs7cINc2Oqcqzh0Vh8pAIeRtEpGBMVhQgMby98r+EyUqXWFxLvbVkbALCAh31IJtOVMr6u1be2Iq1+xxTlf20GtzKqcpEXWKyogCu0xRLG5isdIXFtdRbQXodLp+YBMCxV9AHe+XrXXltex7MNjsA4LIJiUh26V0lorMxWVGAmGC9tJV9GZOVLjn3dwnw0yKFH/DkocvGJ8LQtjHg/45Xwmy1D3gbjpY1YtPJKgBAWKAfrpuWMuBtIPI1TFYUQKfVIL5tlc3SBhOnL3eiqdUqdd0Piw5mMSJ5LDTQD3Pb1l0x2+w4NsDTmIUQePH709LPN8xIRUgApyoTdYfJikIktg0FtVjsqDdxQ8OOuBXXxrFehXpn0pD2Xbr3F9UP6GtvOlGFY+WOocyhUUFYMjZhQF+fyFcxWVEI1yJbzgjqWE4V61Wo7yYmtycrB4oHLlk5XWXEv1x6VX43O529g0Q9xGRFIVwXN2ORbcdOus4EimHPCvVOfGiA1JN5tLxxQOpWcquNuPvTw1Kv6cy0SEwbGtnvr0ukFkxWFIIzgrrnHAby02qQ5rKQHpGnJiaHAQAsNoEjZf27uWFedTPu/vSIlKhkxofgvp+N6tfXJFIbJisKkchkpUsmiw0FtY5VR9OiDNDr+Nal3pvkNhTUf8lKfk0z7vr0MOpaHIu/ZcSF4MlLxrKolshD/MRXiCSXmpWyhlYZW6JM2RVNsLdNkuJicNRXA1G3YrHZsWrDcbdE5alLmagQ9QaTFYUICfBDaNuHGAtsz7a5bV0KwH02B1FvxIUGSF8QjpU1otVq8/prfHSgBPk1LQCAYdEG9qgQ9QGTFQVJaCuyrWhqhdU28ItVKZXVZsfmHEeyEuCnxez0KJlbRGog1a3YBY6WeXe9lYrGVryzsxAAoNUA9ywcidBAJipEvaW4ZKW5uRnPPvssLrjgAkycOBGLFy/Gv//9b1gsFrmb1u+cdSt2AVQ0mWVujXLsKaxDQ1tx4jnpUTDo+aFPfedat7Lfy0NBL27LhaltltHF4xIwKo5T7Yn6QnGf+n/84x9x4MABrFq1CpmZmfjxxx/x8MMPo6WlBXfeeafczetXbmutNJjc6lgGs40n2oeAFo6KkbElpCYT+6nIdld+Lb4/VQ0AiAjyx29mDvXacxMNVorqWTl16hS+++47/OlPf8KiRYuQmpqKZcuWYfHixXj//fflbl6/c1trhXUrAIAWiw0/5Do++EMD/DAtlWtTkHfEhgQg2aVuxWTpe92K2WrHP7e2L/x26+w01qkQeYGifovS09Oxbds2hIe7F1DGx8ejpaUFdrsdWq2i8iuv4vTls/2YWwOTxdGdfu7waPhzyjJ50cTkcBTXm2Btq1vJSono0/N9dKAExW1fNMYnheFnGbFeaCURKeqTX6vVIjY2Fnq9XrrOarVi69atmDBhgqoTFcA9WeHuyw6bTlRKl88bxQ9+8q5JXpzCbLMLfHqwFICjqPYP5w6DRsPl9Im8YcB6VlpbW1FUVNTp7f7+/khNTT3r+meffRanT5/Gf/7zny6fX6Nx/FMqZ9u6amN8WAA0AASA0oZWRcfj1JO4equ+xYKdBXUAgJhgPSYkhyn2mPTncZCTWuNymjgkTLq8OacKv5wyBIYAHQDPY/4pvwZVRkdh/My0KAxX2HpAaj+XPaXW4+BrcXnazgFLVk6cOIErr7yy09uTk5OxadMm6WchBJ588km89dZbWLVqFaZOndrl86cnRSA4WFkfDh1JT4ro8vakiCAU17WgvKkVw5K7vq+SdBdXb7z/UwFsbSvBLc1KxsgU5der9MdxUAK1xjUMwMQh4ThQVI+iOhOe35aLl66dAsDzmB/+3wnp8s3zhiv291et59JTaj0OvhKX0ejv0f0HLFkZP348srOze3Rfi8WCe++9F19//TWeeuopXHLJJd0+JrekDgaDcqc3azSON1FuSR2E6Px+scH+KK5rQV2zBQdPVym+OK+ncfXGBzvzpctTksJwurjOuy/gRf15HOSk1rhc3TFvGG5bdxBGsw1fHynHQ58cxCNXTPQo5rIGE7ZkO4Ys40IDMCTYX3Hv18FwLntCrcfB1+JqbjZ2fycXivtLKITAn//8Z2zevBmvvvoqzjnnnB4+Dj5xgrprZ0JYINA2jbK03oQRsb6xPoO3j39FYysOth2HlIggjIgJVsX59VVqjQsAUiMNeOCCDPzli6OwC+CdXUWYOjwG42N7/p5bf6QczrteNCYeWo1GscdLzefSE2o9Dr4Sl6dtVFzF6r/+9S9s3LjRo0RFTdxnBA3ePYI2n6ySPvzPGxXDQkXqVzOGRuLW2WnSz3/68CCOl/dsVVurzY6vjpYDcBTWLh4d1x9NJBrUFJWslJaW4t///jd+9atfITU1FZWVlW7/zGb1r+rK6csOG09yFhANrCsmJuHnYxyJRqvVjpXrj6GyqfsvDD/m1aKm2TEEPTs9GjEhAd08gog8pahhoB07dsBiseC1117Da6+9dtbt//nPfzBjxgwZWjZwzlzFdjAqqG1GTqVjPDMjLgRDIoJkbhENBhqNBrfPG46iOhMOlTSg2mjBQ18ex98vH4cAP12nj/vicJl0ecm4+IFoKtGgo6hkZenSpVi6dKnczZBVAlexxSaX5fXP4/L6NID8dVqsujATKz48hOK6FmRXNOHpjTl4YNGoDociS+pbsLuwDoCjV7Svi8oRUccUNQxEQGSQPwL9HKelbBDWrAghpIXgNADmj2CyQgMrIsgfr98wFUH+jt/D705W4f09Ha8R9d7u9usvGusorCUi72OyojAajUaqWylrMMHuC2XdXpRd0SQtVz5pSDjH/0kWmQlhuH/RKDhTjzd2FGBb2+aETgeL67HhWAUAIFivw8/HcAiIqL8wWVEg51CQxS5Q1aT+omJXXF6flGL2sGjcdE77jsmPf3MCOZVNAACLzY6/bz4l3XbTOUMREeTZIldE1HNMVhTItch2MO0RZLMLfHfSUa/ir9Vg7rBomVtEg93VWck4v20zQlPbDKGaZjPW7itGQW0LACAzPgRLxibI2Uwi1WOyokAJg3StlQPF9dIU0OlpkQgNVFT9Nw1CGo0Gdy0YgdHxjsUZK5rMuP/zo3hnVyEAx7oqd84fDp2WtSpE/YnJigIN1t2XN7oMAS3kEBAphN5Pi4cvHI3YEMdu8CcrjbDYHLVkV0xM8plVpol8GZMVBUp0nb48SJIVs9WO79sKGIP8tZiZpvxNC2nwiArW4+ELRyPAr/0jMy5Ej+unn71TPBF5H5MVBRqMw0A782thNNsAAHOGRXe5CBeRHEbFheDP54+EVuMY/rlj/nAE6fk+JRoILApQoCB/HSKC/FHXYhk0PSscAiJfMG9EDNKjDbALIC3KIHdziAYNJisKlRgWiLoWC6qNZpitduj91NsJZjRb8WNeDQDHglxcBZSULDWSSQrRQFPvX0Af51q3Utao7t6VbadqpILF+SNiOLOCiIjcMFlRKPcNDdVdt+K+EByX1yciIndMVhTKrchWxRsa1jSbsbeoDgCQEBqAMQmh8jaIiIgUh8mKQrkNA6m4yHbLySrY27Y/Om9UbIc72xIR0eDGZEWhEt2mL6s3Wdl4okq6zFlARETUESYrChUbEgBnnWmZSmtWSupNOFbeCAAYFm1AWjRnWRAR0dmYrCiUTqtBfKijd6W0wQQhhMwt8r7vTnKHZSIi6h6TFQVz1q0YzTY0tlplbo33bW1bXh8AzhvJWUBERNQxJisK5r6hobqGgupbLDhVaQQAjIgJRrxLrERERK6YrChYgoqLbA8U18M5sDV5SLisbSEiImVjsqJgieHq3X15X3G9dHkyl9cnIqIuMFlRsEQVLwy3r9CRrOi0GoxP5EJwRETUOSYrCqbWmpXKplYU1rUAADLjQmDQcz9NIiLqHJMVBQsL9EOQv+MUqWkYaL/bEBDrVYiIqGtMVhRMo9FIvSvlja2w2dWx1opzCAgAJidHyNcQIiLyCUxWFM45I8hqF6gymmVuTd8JIaTiWr1Oy40LiYioW0xWFM69bsX3h4JKG0yoaHTU34xLCoXej29BIiLqGv9SKJzr7stqmBG0l0NARETkISYrCpcY7jJ9udH3kxW39VW4GBwREfUAkxWFc1vFtt63py8LIbC/yJGsBOt1GBUXInOLiIjIFzBZUbiE0PZhIF+vWcmtbkZdiwUAMCEpDDqtRuYWERGRL2CyonCB/jpEGfwB+P5aK+5DQBHyNYSIiHwKkxUfkNRWt1LTbEFlk+8OBR0ra5QuT2K9ChER9RCTFR8wxWWjv00nKuVrSB+5rhOT7FI4TERE1BUmKz5g4ahY6fLGE1UytqRvqtuSlWC9DoH+OplbQ0REvoLJig9IjgjC6HjHzJlTVUbkVTfL3CLPCSFQ05asRAfrZW4NERH5EiYrPsK1d+VbHxwKMpptMFntAJisEBGRZ5is+Ij5I2PgnOm78UQl7MK3NjWsaW6vV2GyQkREnmCy4iMiDXqp0LaisRWHSxvkbZCHql2Ka6MMTFaIiKjnmKz4kIUZLoW22b41FOSarMSwZ4WIiDzAZMWHzEmPRmDbLsVbcqphsdllblHP1Rgt0uWoYH8ZW0JERL6GyYoPCdLrMHtYFACgsdWKXQW1Mreo51zXWGHNChEReYLJio9xW3PFh4aC3ApsWbNCREQeYLLiY6akRCA80A8AsD23FkazVeYW9YxbgS17VoiIyANMVnyMn06L+SNjAABmmx3fn6qWuUU947p6bRBXryUiIg8wWfFB7svv+8ZQUHXbMBCnLRMRkaeYrPigMQmhSAxzbAS4v6jebYhFiZrNVpgsXL2WiIh6h8mKD9JoNFiY4RgKsgvgu5PK7l2pdpsJxGnLRETkGUUnK3V1dZg9ezbOO+88uZuiOOf70Kygapc1VtizQkREnlJ0svLYY4+hrq5O7mYoUkqkAaNigwEAJyqNKKhV7k7MXGqfiIj6QrHJytatW/H111/jkksukbspiuUry+9XcxNDIiLqA0UmK01NTXjooYewYsUKJCUlyd0cxVowMtZlJ+YqCIXuxFzN1WuJiKgP/ORuQEeeffZZREZG4sYbb8SLL77Yo8doNI5/SuVsmzfbGBOix+Qh4dhTWI/SBhOOlTdibGKY916gB3oSl+vqtTEhekWfp97qj/OrBGqNqytqjVmtcXlKrcfB1+LytJ0Dlqy0traiqKio09v9/f2RmpqK3bt3Y926dVi7di10up4vHpaeFIHg4GBvNLVfpSdFePX5rp4xFHsKDwIAdhU34OKpqV59/p7qKq5ma/uGi1NGxCI4QJE5sld4+/wqhVrj6opaY1ZrXJ5S63HwlbiMRs9mhg7YX40TJ07gyiuv7PT25ORkfPXVV3jggQdwww03YMyYMR49f25JHQwGS/d3lIlG43gT5ZbUwZujNZnRQdDrtDDb7PjvvmL8anIS/HQDN7rXk7iKahzFv0H+WpRXNQ1Y2wZSf51fuak1rq6oNWa1xuUptR4HX4urudno0f0HLFkZP348srOzu7zPM888Az8/P6xYscLj5xcCPnGCvN1Og78fZqVHYnNONepNVuwqqMPMtCjvvUAPdRVXTdvU5ehgvU+co77wlfehp9QaV1fUGrNa4/KUWo+Dr8TlaRsV1R//5ZdforS0FJMnT5aus9vtEEJgzJgx+P3vf4/bbrtNxhYq08KMWGzOcewRtDG7UpZkpTMtZhuaLTYA3G2ZiIh6R1HJyuuvvw6LxX0o5/3338fGjRvx+uuvIzo6WqaWKdu01EiEBvihsdWK7bk1aDHbEKRXxmaBnLZMRER9pahkJT09/azroqOj4e/vj1GjRsnQIt/gr9Ni3ohofHGkHCarHdtyq/GzjDi5mwXgjAXhmKwQEVEvKHKdFfKcUheI4xorRETUV4pPVlasWIFNmzbJ3QzFG5cYhrjQAADAnsI6t7VN5OSWrLBmhYiIekHxyQr1jFajwcKR7Tsxbz5ZJXOLHGpYs0JERH3EZEVFznepU9l4QhlDQe7DQJ4tAkRERAQwWVGVtGgDhsc4VvE9Xt6EoroWmVsEVBvbZ3exZ4WIiHqDyYrKLBwVI11WwlCQs2cl0F8Lg15Rk8+IiMhHMFlRmTnD2teiya6Qf2l7Z80Ki2uJiKi3mKyoTGJ4IAL9HKc1v21PHrm0WGwwmttWr+UQEBER9RKTFZXRajRIjQwCAJQ2mGB22fF4oNVwQTgiIvICJisqNDTKAMAxhblQxiJbt6X2OQxERES9xGRFhZzJCiDvUBCnLRMRkTcwWVEh12QlT8ZkpYbTlomIyAuYrKhQWlSQdFnOnpWyRpN0OSY4QLZ2EBGRb2OyokLxoYEIaJsRJGfPSkFNe71MSmRQF/ckIiLqHJMVFdJpNVJyUFJvgtkmz4ygglpHohSs1yHKwJoVIiLqHSYrKpXmOiOoduBnBLWYbahochTYpkYaoNFoBrwNRESkDkxWVGpopLwzglynTA+N4hAQERH1HpMVlUqLdimyrR34ZMX1NVNZr0JERH3AZEWl3HpWqgd+GMi1uDbVpS1ERESeYrKiUglhgdDr2vYIkqFnpYA9K0RE5CVMVlRKp23fI6iormXAZwQVtBX1+us0SAgLHNDXJiIidWGyomLOwla7AIoHcI8gq82O4nrHgnBDIoKg03ImEBER9R6TFRWTa9n9kgYTbHbhaAOHgIiIqI+YrKiY+4aGA9ezwuJaIiLyJiYrKpYm0+7LrgW9XGafiIj6ismKiiWGBcJf56gXGchhoIJa1wXh2LNCRER9w2RFxXRaDVIiHD0bxfUmWAZoRpBzeX8NgCERnAlERER9w2RF5ZxDQTa7QHGdqd9fTwghrbGSEBaIAD9dv78mERGpG5MVlXMrsh2AxeEqm8xosTh6cLgYHBEReQOTFZUbOsBFtly5loiIvI3Jiso5a1YAoGgAhoHyXYprU1lcS0REXsBkReWSwgPhXD+2aABWsS1w6b3hgnBEROQNTFZUTu+nRUJYAABHsiKE6NfXK6zlgnBERORdTFYGgSFtQ0FGsw21LZZ+fS3nMFCkwR+hgX79+lpERDQ4MFkZBIa41q3U9t9QUIPJgrq2ZCg1gkNARETkHUxWBgHXZKWwH+tWClhcS0RE/YDJyiDguj9PfxbZuiUrLK4lIiIvYbIyCAwZoOnLrjOBWFxLRETewmRlEIgN0SPAz3GqC/uxZoU9K0RE1B+YrAwCWo0GyeGODQVLGkyw2ftn+rIzWQny1yI2RN8vr0FERIMPk5VBwjkUZLMLlDV4fyio1WqTnjc10gCNRtPNI4iIiHqGycog4Vpk2x8zgopqTXD216RwCIiIiLyIycog4V5k6/1khRsYEhFRf2GyMki4bmjYH0W2rhsYDuVMICIi8iImK4NEckSgdLk/pi+7JkAcBiIiIm9isjJIhAX6I7xtr57+GAbKb1tjRadtn3lERETkDUxWBhFnj0eV0YwWs81rz2uzC6loNyksEH46vq2IiMh7+FdlEHErsq33Xu9KcW0LLDbHXKDUKA4BERGRdzFZGUSG9FORbU5lo3SZy+wTEZG3KTJZ2bFjB5YtW4YJEyZgzpw5ePTRR2E2m+Vuls9L6afpyzkVTdJlTlsmIiJvU1yycuDAASxfvhyzZs3C+vXr8cgjj+Dzzz/HI488InfTfF5/rbVyqsIoXWayQkRE3uYndwPO9Le//Q3nnnsubr/9dgBASkoKXnjhBVitVplb5vuSIgKhASDg3enLOZXsWSEiov6jqGSlrq4OO3fuxLPPPut2/bRp02RqkbrodVokhAWgtKEVRXUtEEL0eQ8fIYQ0DBQTrIdBr6i3FBERqYCihoGys7Nht9sRGhqKP/7xj5g9ezYWLFiA5557DhaLRe7mqYJzKMhotqG2ue/HtK7FgvoWx/NwJhAREfWHAfsa3NraiqKiok5v9/f3R3V1NQDg0UcfxY033oibb74ZO3fuxNNPP42GhgY8+OCDnT5eo3H8Uypn2+RuY0pkEHYV1AEACuqaER2i79PzFZyxzL7c8clFKefX29QaV1fUGrNa4/KUWo+Dr8XlaTsHLFk5ceIErrzyyk5vT05OlupULrzwQlx99dUAgNGjR6O0tBTvvPMObrvtNkRFRXX4+PSkCAQHB3u/4V6WnhQh6+ufMzIWHx8oBQAUNppxRXLf2vNjUb10OSs9CsP6+Hy+Tu7z21/UGldX1BqzWuPylFqPg6/EZTT6e3T/AUtWxo8fj+zs7C7vs2nTJgDAuHHj3K6fOnUq3nzzTZw8eRIzZszo8LG5JXUwGJQ7VKTRON5EuSV1EEK+dqSGtvekfHOkDJeMjuvT8+3NrZYuG7TA6eK6Pj2fr1LK+fU2tcbVFbXGrNa4PKXW4+BrcTU3G7u/kwtFVUOmpaUBAOrr692uF21HPiQkpNPHCgGfOEFytzMmOACpkUEoqG3BsbJGNJqsCAno/dsgv6Z9GCg1wuAT56A/yX1++4ta4+qKWmNWa1yeUutx8JW4PG2jogpshw0bhpSUFHzzzTdu1+/evRsBAQFSMkN9MyUlAgBgF8CB4vqu79wNZ81KsF6HSINn3XpEREQ9oahkBQDuuOMObNq0Cc8//zwKCwuxbt06rF69Gtdff71P1KT4gqy2ZAUA9hTW9fp5apvNqGhsBQAMjTL0eRo0ERFRRxQ1DAQAS5YsgRACL7/8Ml555RVER0fjtttuw/Lly+VummpMSg6DTquBzS6wu21mkCcqm1rx4f4SrD9SJl2XwsXgiIionyguWQGAiy++GBdffLHczVAtg94Po+NDcbi0AcX1JpQ3mBAfFtjt4/JrmrF2XzG+za6E1d4+4Oin1WDhqNj+bDIREQ1iikxWqP9NTY3A4dIGAMDuwjpcNDah0/seKW3Amr3F2J5b43a9XqfF4jFxuGtxJmwmi08UdRERke9hsjJITUmJwFs/FQAA9naQrAghsDO/Fmv2FuNgSYPbbSEBOlwyLhFLJyYiOliPodHBg3bKMhER9T8mK4NURlwIgvU6GM027Cmsh80upDqW705WYs3eYuRWN7s9JjpYjysnJWHJ2HjuAURERAOGf3EGKZ1Wg8lDwrHtdA0aW63IqWxCXGgAHtmQjQNn9KSkRARhWVYyFmbEQq9T3AQyIiJSOSYrg9iUlAhsO+2oQ/noQAkOljSgssks3T46PhRXT0nGrPQoaDktmYiIZMJkZRCb4rLeysYTVdLlaIM//vyzUcgaEs61U4iISHZMVgaxpPBAJIQGoKxtYTcAGJcYigcXZyI6uG+7MRMREXkLCxAGMY1Gg9nD2nexvmR8Ap65bBwTFSIiUhT2rAxyv56eivAgfwyLDsY56VHdP4CIiGiAMVkZ5EIC/HDt1BS5m0FERNQpDgMRERGRojFZISIiIkVjskJERESKxmSFiIiIFI3JChERESkakxUiIiJSNCYrREREpGhMVoiIiEjRmKwQERGRojFZISIiIkXz6eX2hRDS5ebmZhlb0j2NBjAa/dHcbIRLs32eWuPylFqPg1rj6opaY1ZrXJ5S63Hwtbhc/2aLHjTYp5MV12BnTh4lY0uIiIioN5qbmxESEtLlfTgMRERERIqmET3pf1Eou92OqqoqAIDBYIBGo5G5RURERNQdIYQ0OhITEwOttuu+E59OVoiIiEj9OAxEREREisZkhYiIiBSNyQoREREpGpMVL1Fj6c8PP/yATZs2yd0M2fHcqgvPp3rx3KoXk5U+KC8vx65duwBAVTOR8vPzcfXVV2PFihUwm81yN0cWPLfqwvOpXjy3gwOTFQ85M/fdu3dj3rx5eP7555Gbm+t2m6+y2+34y1/+ggsuuADp6enYuHEjFi9eLHezBgzPrbrwfKoXz+3gw2TFQ87M/fvvv0dQUBCOHDmCLVu2uN3mi7Zs2YIxY8bg+PHj+Oyzz/D4448jMjISNptN7qYNGJ5bdeH5VC+e28GHyUovVFRUYMOGDXjttdeQkZGBDz74AAcPHgTgu1m9TqcDANx0000YNcqxdYHZbJauB3w3Nk/w3KoLz6d68dwOLkxWumE0GtHU1OR2XV5eHoYPH46srCzcddddyM3NxYYNG9DS0gKNRuMTbybXNgohMGfOHMyZMwevvPIKcnJyAAB6vR41NTX48ssvsXfvXhQXF8vV3H7Bc6uuc8vzqa7z6YrnVr3ntscEderJJ58UixYtEsuWLRP333+/OHDggBBCiMbGRpGXlyfd7/bbbxfTp08XmzdvlqupHnnxxRfF6tWrRWtrqxBCCIvFIoQQorCwUGRkZIjXX39dtLa2ipdfflnMmDFDLFmyRIwfP17Mnz9ffP/998JqtcrZfK/guVXXueX5VNf5dMVzq95z6wkmKx0oLy8X1113nbj88svF559/Lt544w3xs5/9TMybN08cOnRIup/zzVVVVSXGjBkj/vjHP4rKykohhBA2m02Wtndnw4YNIiMjQyxevFicOHFCut4ZyyOPPCJmzZolHn/8cfHb3/5WbNq0SeTl5YnvvvtOXHnllWLJkiVuHxC+hudWXeeW51Nd59MVz616z21vMFnpwObNm8WCBQvE6dOnpet2794tLr/8crF48WJRUVEhXe98cz3//PNi9OjR4qOPPhrw9vaUyWQS1157rfjNb34jpkyZIh555BHR3NwshBBSlm6320VWVpaYNWuW2LRpk9vjt2/fLjIyMqTr7Xb7wAbgBTy36jq3PJ/qOp+ueG7Ve257gzUrHfj+++9hMBiQnJwMu90OAJg8eTJuv/12VFRU4I033oDVagUAaafIFStWIDo6Gh9++CHy8vIAAAUFBSgtLZUlho4UFRXBaDTi7rvvxk033YSPPvoIhw8fBuAo7LJYLNBoNHjyySdx++23Y8GCBW6PDwsLQ2BgIPLz8wH4ZtU9z626zi3Pp7rOpyueW/We295gsuLC+QuRnp6O/Px8WCwWaLVa2Gw2aLVaTJs2Db/85S+xZs0anDx5EoDjl8T5C3Pfffdh7969+OKLL/DQQw9h0aJFOHTokGzxnEmj0WDq1KnIyMjAhRdeiJiYGLz11luoq6sDAPj5+QEAzj//fPziF7+QHifaisBOnjyJuLg4LFy4cMDb3lc8t+o6tzyf6jqfrnhu1Xtu+0Tejh1l2rJli5g2bZp48cUXhRDu457Z2dli3rx5YuXKlUIIR7edazfczJkzRUZGhrj66qvFkSNHBrbhXXC20Ww2CyEc3aZr1qwRmZmZYv369WeN7ZaVlYl//etf4vvvvxfHjh0Tb7/9tjjnnHPEE088IVpbW32265HnVl3nludTXefTFc+tes9tbwy6ZMU5ttkR54mvqKgQ119/vViyZIkoKytze5zRaBSPP/64mDp1qqirq5Meu3nzZnHJJZeI2bNny1KN3lVcnamurhbXXnutWLp0qSgqKnK7raioSPzmN78RWVlZYtGiRWLRokVi/fr13mrugPPlc9sbaji3XX0Q+/L57M0fGDWcz57y5XPbG4Pp3PbFoElWTCaTePLJJ8X9998vnn/+eWn625mZrPMX5b333hOzZ88WTzzxxFm3ffTRR+Lcc88Vx48fF0II0dTUJBYtWiSeffbZgQjFTU/j6szmzZvFmDFjxMsvvyxMJpPbbdXV1SIvL0/s2rXL6+32JpPJJF5++WXx7rvvil27dp1VrObki+e2J3F1xlfPrclkEv/4xz/EM888I7788kvpD5Uafld7EldnfPV8umptbRU//PBDt1+ufO3c9jSuzqjh3Pa3QZGsrF+/XkyfPl386le/Evfdd5+YPXu2mDt3rmhoaHC7n9Vqld5szc3NYsWKFWLWrFlix44dQoj2rH7jxo0iMzNTFBYWSo91zpUfSJ7Edea3OefPRqNR3HPPPWLOnDmioKBAtLa2ikceeURKepTus88+E1OmTBFXXHGFuOSSS8S0adPEH//4x7Pu52vn1pO41HRu169fL6ZMmSJ++ctfiptvvlnMmDFDXHrppaKqqsrtfr52Pj2JS03n01VJSYlYuHChmDdvnti+fbsQouNeJl87t57EpdZzOxBUn6wUFhaKZcuWiffff1+6bteuXSIrK0s8//zzQgjHNxvXb6ubN28WBw4cECdPnhRXXHGFmDdvnsjLy5N+EVatWiXuuuuuXmfR3tCbuLZt2ya2bt0qhGj/JbHb7SInJ0fMmjVL/OIXvxCzZs0Ss2fPlr6tKFlxcbFYunSpeP3114UQjm+u7777rpg+fbpYuXKl9I3V9Tz5wrntTVxqOLeVlZVi2bJlUtw2m01s375dLFiwQNxwww0iJydHCNE+3i+Eb5zP3sSlhvN5JueXq3HjxomHH35Y+lLl+gfc1z6HhfA8LjWe24Gg2mTF+SZYu3atmDp1qtsiQtXV1WLJkiXimWeecXtMfn6+uPXWW8XkyZPF2rVrhRBCHDp0SFx66aVi5syZYvny5eKKK64Qs2fPljLogdaXuLKyssQHH3xw1nPu2rVLTJo0ScyfP198/PHH/RuAFziPwYcffigmTJgg9u/fL93W3Nws3n33XTF69Gi3grW8vDyfObe9iUsN53b79u1iwoQJZ3V3b9u2TWRkZIgnnnhCNDY2CiGEyM3NVfz5dOpNXGo4n65sNpv41a9+JdasWSOeeOIJMWHCBPH11193eF9f+Bx26k1caju3A8VP7tlI3lZbW4vQ0FBp+pfZbIbZbEZzc7N0H71ej6CgIEyaNAnV1dWIjo5GSUkJli5dinnz5uGzzz7DkCFDAADjxo3D66+/ju+//x45OTmIiIjA8uXLfTKu//73v1Jczue466678M033+CGG27AvffeO+BxeeLMY9DY2AitVovg4GDpPkFBQZg3bx7eeustrF69GlOmTIEQApdffrnPnNvexOXr5xYA6uvrodPpEBgY6Hb97NmzsWzZMqxbtw4zZszA2LFjccUVVyj2fJ6pN3Gp4Xy6KisrQ1hYGLKysnDJJZdgzZo1+PjjjzF+/HgkJiZCCAGNRoPS0lJFfw6fqTdxqe3cDhh5cyXvef/998WiRYvENddcI2666SYp47ZYLGLmzJniggsuEK+//rp4++23xfTp08XcuXPFnDlzxNy5c8UHH3wgzGazKC0tPet55Z4a1l9xOb366qtnVaArTWfH4NixY2Ls2LHilVdecbt/SUmJmD9/vsjIyJC+wbiugumk1HPb17iclHpuP/30U/H3v/9dmsXh7CWqqqoSkyZNEm+++aZ0vfMcNTQ0iKysLHHvvfeKpqamDuOS+3z2V1xOSj2frs48Bq7nxHVI48033xQZGRnivffek65z3regoOCs51XaufVWXE6+cG7l5tPJit1uFyaTSaxcuVIsWLBAvPPOO+KTTz4Rl156qZg1a5ZYvXq1EEKIvXv3iscee0zccsstYtKkSeI///mPqK6uFocOHRIPPvigmDRpkti2bZvM0bRTa1ye6OoYnHPOOVI36b333iuysrLE999/L41df/755+Kpp54SjzzyiFi8eLGcYZxFrXH11KlTp8SYMWPE6NGjz3pv1tfXixUrVoh58+a5zZBxXv7HP/4hpk+fLs2iURK1xuWJzo5BZ4nGokWLxNKlS8WxY8eEEMrdx0etcfkan05WhHAUmi5cuFD873//c7vuzjvvFOPHjxcHDx6Urr/nnnvEihUr3B5fXV0tzj33XPHYY48JIXo+LbS/qTUuT3R2DO644w4xceJEcfjwYakYddKkSWL58uVi2bJlYtKkSeLLL78UW7ZsEeeff770oaEUao2rJ1atWiWWLl0qLrjgAnHdddedNRtm/fr1YuzYseKFF14QQrh/0B86dEiMGzdOfPXVV0II+b9tu1JrXJ7o7hg4OZPvrVu3ioyMDPHcc8/JMounp9Qal6/x+eX29+7di7q6OmRmZkrXDRkyBLfccgtSUlLw3HPPoampCU1NTdi3bx+mTZvm9niDwYCEhAQcOXIEgGNvBiVQa1ye6OwY3HrrrUhMTMTf/vY3xMXF4d///jduu+02+Pn5YcSIEfjiiy/w85//HMHBwbBarYiNjZUxirOpNa7uNDU1ITs7G8uXL8d9992HXbt24bvvvoPNZpPuM336dFx44YV4+eWXkZubK+35AgDBwcEICwuTllVXyp4oao3LEz05Bk5+fn4QQmDu3LmYM2cOPvzwQ+zZswcAUFdXh1OnTg108zul1rh8kc8mK6Jtn4ShQ4eiqakJ5eXlAACLxQIAGDlyJG6++WZs374dGzduREhICMLCwvDNN9/AZDJJz1NZWYnGxkZcfvnlAx9EB9Qalyd6cgxuvfVWbN++HevXr0dcXBxuuukmvPTSS3j00UeRnJwMACgsLERERITbHwY5qTWungoKCkJaWhpmzJiBKVOmYM6cOXjttddQVFQk3ScmJgbLli1DUlISHnzwQbfbGhsbERAQgBEjRsjR/E6pNS5P9OQYuHLu//Pggw+iqqoK69evxwsvvICZM2di/fr10u1yU2tcPknObh1vyMnJEYsXLxa33XabdJ3rcs2//vWvxeWXXy6EcEwJzczMFPfee69Yv369WL9+vfjZz34mrrvuOlFSUiJL+zuj1rg80d0xuP7668XSpUuFEI5u9aNHj4o1a9aIrVu3is2bN4sLLrhAvPTSS7K0vStqjasrzmEPZ7e43W4XP/30k5gwYYL4+9//7rZqp9VqFTt27BATJkwQv/jFL8Qrr7wiPv/8c7Fo0SJx9913C6PRKEsMHVFrXJ7w5Bh0ZPny5SIjI+OsoVG5qTUuX+XzyYrNZhMPPPCAmDt3rttMGedta9euFRkZGdKaFa+++qpYsGCBWLRokZg/f75iP/TVGpcnenIMMjMzxe7du4UQjhUtZ82aJebNmydmzZolXn75Zdna3hW1xuUpk8kkVq1aJaZNm+a2Uqfzj8SOHTvEvffeK6666iqxePFin3lPqzUuT3R2DFzl5OSI5cuXi7Fjx4p33313gFvYO2qNyxcoNlnZvn27qK6uFkJ0XnDm/OX/8ccfxaJFi8Stt94q3eb88D948KCYPXu22+ZPTU1NIjs7u9vMuD+oNS5P9OcxqKurEwcPHhQtLS391fxOqTWu7vQk7s7k5OSIOXPmiLvuusttQzpXNTU1shQqqjUuT/TnMbjpppvEihUrRFNTk1fa6gm1xqVmikpWbDabaGlpEbfeeqsYPXq0tHphZ/d1XWb56aefFtOnTxdvvPGGdLsQQpSXl4uMjAyxadMmt+sHklrj8oRaj4Fa4+qOp3GfORvN+bPVahWvv/66GDNmjDQt9I033pCm5w80tcblif4+Bs7eBufGnANFrXENFoqq0NNqtSgqKsKpU6eg0+mwYcMGFBQUAGgvTgQAm80GrVYLPz8/nDhxAl999RV+97vfYcaMGXjmmWfw448/Siu7fvPNN8jKysKECROk12BcA0+tx0CtcXXH07h1Oh1OnTqFdevWAWifnabT6bB06VJMmDABDz/8MC644AI899xzCAsLG/igoN64PNHfxyAyMhKAo3iVcVFPKe5TcPXq1Zg5cyaefvpp/PDDD/j2228BuE/n0+l0MJvNWLVqFZYtW4Y9e/YgODgYd911F8477zysWLECy5cvxy233IKnn34aS5YsQXR0tFwhAVBvXJ5Q6zFQa1zd8TTuK6+8EtnZ2WdN+2xubkZTUxNKS0uxZMkSHDhwABdeeOGAxuJKrXF5Qq3HQK1xDQaK2huopaUFJpMJ06ZNw+LFi5GZmYl169Zh6tSpmDBhgrTPQmtrK6699loAwFtvvYWJEycCcEwJff755/HVV1/h9OnTMJvNeOKJJxAVFSVnWKqNyxNqPQZqjas7fY3b6cUXX8Tzzz+PhQsX4p133kFERIQM0bRTa1yeUOsxUGtcg8bAjzwJsXv3brFq1aoO90rIzs6Wxuj37dsn7UrqHAd03lZQUHBWYZTcY/tqjcsTaj0Gao2rO/0Vt9Mnn3widuzY0U+t75xa4/KEWo+BWuMa7AY8WWltbRUXXnihtNmT2WwWQpxdke0sRLzzzjvF9OnTxebNmwe6qR5Ra1yeUOsxUGtc3VFr3GqNyxNqPQZqjYtkKLD94osv0NLSgqlTp+Lll19Gfn4+gLOXmHb+/Je//AVGoxGffvopKisrAbgXQymFWuPyhFqPgVrj6o5a41ZrXJ5Q6zFQa1w0gAW2drsdQgjs2rULF198MR5++GEYjUasXbsWLS0tZ91fp9PBZrMhKioKv/3tb7FhwwZs2bJFuv3o0aPSXhpyUmtcnlDrMVBrXN1Ra9xqjcsTaj0Gao2L2vVbsnLs2DFs2LBB2khPq9VCo9EgJCQE559/PoYNG4arr74a69atw6FDhzp8Dmf2e9tttyEhIQGff/453nrrLcyYMQPPPfectKfKQFJrXJ5Q6zFQa1zdUWvcao3LE2o9BmqNi7rg7XGlxsZGsWLFCjF58mRx6aWXijFjxoh77rlHWprYdXXV/Px8cf7554vf/va3oqampsvnfe2110RGRobIysoSr7/+ureb3S21xuUJtR4DtcbVHbXGrda4PKHWY6DWuKh7Xk9WHn74YXH11VeL4uJiUVFRITZt2iRmzZolrrnmGlFeXi6EaC9ustvtYs2aNSIzM1N8/vnnUiW2azFUfX29uP/++0VmZqZ47LHHpIKpgabWuDyh1mOg1ri6o9a41RqXJ9R6DNQaF3XPa8mK3W4XBQUFYtasWWLNmjXSdUIIsWbNGjF37lxx++23u10vhBDV1dXiV7/6lbjssss6nGr2t7/9TSxdulTk5OR4q6keUWtcnlDrMVBrXN1Ra9xqjcsTaj0Gao2Leq5PyUphYaH46aefRHFxsRBCiNraWjFu3DjxzTffCCHat9ZuaWkRTz31lMjIyJB2mXXNYLds2SLGjh0rdb9t3LhRPProo2fdb6CoNS5PqPUYqDWu7qg1brXG5Qm1HgO1xkW906sC25aWFtx777249tpr8cADD+DSSy/FSy+9hMrKSqSnp2PTpk0AAL1eD5vNhsDAQFx00UXIzMzEM888AwDw9/eXpojNmDEDV155JV5++WVcddVVWLFiBQICAgAAfn4Dt8iuWuPyhFqPgVrj6o5a41ZrXJ5Q6zFQa1zUNx4nKw0NDfjzn/+MkpISvPLKK3j00Udx8cUX45///CeqqqqQkpKC/fv34+DBg26PGzNmDBYtWoTi4mJs3rwZQPt8dpvNhrq6OtTX12PUqFH46aefcPfddwM4e358f1FrXJ5Q6zFQa1zdUWvcao3LE2o9BmqNi7zA066YXbt2iZkzZ4p9+/ZJ11VWVoqf//zn4qGHHhLHjh0TGRkZ4m9/+5vUTecseDpw4ICYM2eOeOWVV6THbtq0SWRkZIilS5eKY8eOedocr1FrXJ5Q6zFQa1zdUWvcao3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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the delta just for fun:\n", + "mdf.iloc[17:].plot(y=['delta'])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mav8mav16delta
Date
2011-07-21132.042498NaNNaN
2011-07-22132.439999NaNNaN
2011-07-25132.688749133.011249-0.322499
\n", + "
" + ], + "text/plain": [ + " mav8 mav16 delta\n", + "Date \n", + "2011-07-21 132.042498 NaN NaN\n", + "2011-07-22 132.439999 NaN NaN\n", + "2011-07-25 132.688749 133.011249 -0.322499" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "
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mav8mav16delta
Date
2011-11-17124.968751125.403751-0.435000
2011-11-18124.231252124.988126-0.756874
2011-11-21123.793752124.429376-0.635624
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" + ], + "text/plain": [ + " mav8 mav16 delta\n", + "Date \n", + "2011-11-17 124.968751 125.403751 -0.435000\n", + "2011-11-18 124.231252 124.988126 -0.756874\n", + "2011-11-21 123.793752 124.429376 -0.635624" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdf.head(16).iloc[-3:]\n", + "mdf.tail(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "- The loop in the next notebook cell iterates through the rows of the moving average dataframe, and find the point immediately following where the two moving averages have crossed. \n", + "\n", + "\n", + "- It then compares the absolute value of the moving average at that point, with the absolute value of the moving average at the previous point, to see which is smaller: thus choosing the row where the two moving averages are closest together.\n", + "\n", + " - This is _not_ the exact crossover point, but it is the row closest to the exact crossover point.\n", + " - We then update the list of scatter points at this row with _the average of the two moving averages at that row._" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "prev = mdf['delta'].iloc[0]\n", + "premav = (mdf['mav8'].iloc[0] + mdf['mav16'].iloc[0]) / 2.0\n", + "scatter = [float('nan')]*len(mdf)\n", + "jj = 0\n", + "for ix, row in mdf.iterrows():\n", + " value = row['delta']\n", + " if ((value < 0 and prev > 0) or \n", + " (value > 0 and prev < 0)):\n", + " #print(ix,value,prev)\n", + " #print(row['mav8'],row['mav16'],premav)\n", + " if abs(prev) < abs(value):\n", + " #print('premav=',premav)\n", + " scatter[jj-1] = premav\n", + " else:\n", + " scatter[jj] = (row['mav8'] + row['mav16']) / 2.0\n", + " prev = value\n", + " premav = (row['mav8'] + row['mav16']) / 2.0\n", + " jj += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ap = mpf.make_addplot(scatter,type='scatter',markersize=175,alpha=0.45,color='r')\n", + "mpf.plot(df,type='candle',volume=True,mav=(8,16),figscale=1.5,addplot=ap)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 45fe8c009f6bf154efce46fc88ad519c07049103 Mon Sep 17 00:00:00 2001 From: Daniel Goldfarb Date: Tue, 3 May 2022 18:58:09 -0400 Subject: [PATCH 20/20] cleanup: plotting.issue508.py no longer needed --- examples/scratch_pad/plotting.issue508.py | 1272 --------------------- 1 file changed, 1272 deletions(-) delete mode 100644 examples/scratch_pad/plotting.issue508.py diff --git a/examples/scratch_pad/plotting.issue508.py b/examples/scratch_pad/plotting.issue508.py deleted file mode 100644 index b59fe2d8..00000000 --- a/examples/scratch_pad/plotting.issue508.py +++ /dev/null @@ -1,1272 +0,0 @@ -import matplotlib.dates as mdates -import matplotlib.pyplot as plt -import matplotlib.colors as mcolors -import matplotlib.axes as mpl_axes -import matplotlib.figure as mpl_fig -import pandas as pd -import numpy as np -import copy -import io -import os -import math -import warnings -import statistics as stat - -from itertools import cycle -#from pandas.plotting import register_matplotlib_converters -#register_matplotlib_converters() - -from mplfinance._utils import _construct_aline_collections -from mplfinance._utils import _construct_hline_collections -from mplfinance._utils import _construct_vline_collections -from mplfinance._utils import _construct_tline_collections -from mplfinance._utils import _construct_mpf_collections - -from mplfinance._widths import _determine_width_config - -from mplfinance._utils import _updown_colors -from mplfinance._utils import IntegerIndexDateTimeFormatter -from mplfinance._utils import _mscatter -from mplfinance._utils import _check_and_convert_xlim_configuration - -from mplfinance import _styles - -from mplfinance._arg_validators import _check_and_prepare_data, _mav_validator -from mplfinance._arg_validators import _get_valid_plot_types -from mplfinance._arg_validators import _process_kwargs, _validate_vkwargs_dict -from mplfinance._arg_validators import _kwarg_not_implemented, _bypass_kwarg_validation -from mplfinance._arg_validators import _hlines_validator, _vlines_validator -from mplfinance._arg_validators import _alines_validator, _tlines_validator -from mplfinance._arg_validators import _scale_padding_validator, _yscale_validator -from mplfinance._arg_validators import _valid_panel_id, _check_for_external_axes -from mplfinance._arg_validators import _xlim_validator, _mco_validator, _is_marketcolor_object - -from mplfinance._panels import _build_panels -from mplfinance._panels import _set_ticks_on_bottom_panel_only - -from mplfinance._helpers import _determine_format_string -from mplfinance._helpers import _list_of_dict -from mplfinance._helpers import _num_or_seq_of_num -from mplfinance._helpers import _adjust_color_brightness - -VALID_PMOVE_TYPES = ['renko', 'pnf'] - -DEFAULT_FIGRATIO = (8.00,5.75) - -def with_rc_context(func): - ''' - This decoractor creates an rcParams context around a function, so that any changes - the function makes to rcParams will be reversed when the decorated function returns - (therefore those changes have no effect outside of the decorated function). - ''' - def decorator(*args, **kwargs): - with plt.rc_context(): - return func(*args, **kwargs) - return decorator - -def _warn_no_xgaps_deprecated(value): - warnings.warn('\n\n ================================================================= '+ - '\n\n WARNING: `no_xgaps` is /deprecated/:'+ - '\n Default value is now `no_xgaps=True`'+ - '\n However, to set `no_xgaps=False` and silence this warning,'+ - '\n use instead: `show_nontrading=True`.'+ - '\n\n ================================================================ ', - category=DeprecationWarning) - return isinstance(value,bool) - -def _warn_set_ylim_deprecated(value): - warnings.warn('\n\n ================================================================= '+ - '\n\n WARNING: `set_ylim=(ymin,ymax)` kwarg '+ - '\n has been replaced with: '+ - '\n `ylim=(ymin,ymax)`.'+ - '\n\n ================================================================ ', - category=DeprecationWarning) - return isinstance(value,bool) - - -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') - 'Description' : ('Column names to be used when plotting the data.'+ - ' 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', - 'Description' : 'Plot type: '+str(_get_valid_plot_types()), - 'Validator' : lambda value: value in _get_valid_plot_types() }, - - 'style' : { 'Default' : None, - 'Description' : 'plot style; see `mpf.available_styles()`', - 'Validator' : _styles._valid_mpf_style }, - - 'volume' : { 'Default' : False, - 'Description' : 'Plot volume: True, False, or set to Axes object on which to plot.', - 'Validator' : lambda value: isinstance(value,bool) or isinstance(value,mpl_axes.Axes) }, - - 'mav' : { 'Default' : None, - 'Description' : 'Moving Average window size(s); (int or tuple of ints)', - 'Validator' : _mav_validator }, - - 'renko_params' : { 'Default' : dict(), - 'Description' : 'dict of renko parameters; call `mpf.kwarg_help("renko_params")`', - 'Validator' : lambda value: isinstance(value,dict) }, - - 'pnf_params' : { 'Default' : dict(), - 'Description' : 'dict of point-and-figure parameters; call `mpf.kwarg_help("pnf_params")`', - 'Validator' : lambda value: isinstance(value,dict) }, - - 'study' : { 'Default' : None, - 'Description' : 'kwarg not implemented', - 'Validator' : lambda value: _kwarg_not_implemented(value) }, - - 'marketcolor_overrides' : { 'Default' : None, - 'Description' : 'sequence of color objects to override market colors.'+ - 'sequence must be same length as ohlc(v) DataFrame. Each'+ - 'color object may be a color, marketcolor object, or None.', - 'Validator' : _mco_validator }, - - 'mco_faceonly' : { 'Default' : False, # If True: Override only the face of the candle - 'Description' : 'True/False marketcolor_overrides only apply to face of candle.', - 'Validator' : lambda value: isinstance(value,bool) }, - - 'no_xgaps' : { 'Default' : True, # None means follow default logic below: - 'Description' : 'deprecated', - 'Validator' : lambda value: _warn_no_xgaps_deprecated(value) }, - - 'show_nontrading' : { 'Default' : False, - 'Description' : 'True/False show spaces for non-trading days/periods', - 'Validator' : lambda value: isinstance(value,bool) }, - - 'figscale' : { 'Default' : None, # scale base figure size up or down. - 'Description' : 'Scale figure size up (if > 1) or down (if < 1)', - 'Validator' : lambda value: isinstance(value,float) or isinstance(value,int) }, - - 'figratio' : { 'Default' : None, # aspect ratio; scaled to 8.0 height - 'Description' : 'Aspect ratio of the figure. Default: (8.00,5.75)', - '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 - 'Description' : ('Figure size: overrides both figscale and figratio,'+ - ' else defaults to figratio*figscale'), - 'Validator' : lambda value: isinstance(value,(tuple,list)) - and len(value) == 2 - and isinstance(value[0],(float,int)) - and isinstance(value[1],(float,int)) }, - - 'fontscale' : { 'Default' : None, # scale all fonts up or down - 'Description' : 'Scale font sizes up (if > 1) or down (if < 1)', - 'Validator' : lambda value: isinstance(value,float) or isinstance(value,int) }, - - 'linecolor' : { 'Default' : None, # line color in line plot - 'Description' : 'Line color for `type=line`', - 'Validator' : lambda value: mcolors.is_color_like(value) }, - - 'title' : { 'Default' : None, # Figure Title - 'Description' : 'Figure Title (see also `axtitle`)', - 'Validator' : lambda value: isinstance(value,(str,dict)) }, - - 'axtitle' : { 'Default' : None, # Axes Title (subplot title) - 'Description' : 'Axes Title (subplot title)', - 'Validator' : lambda value: isinstance(value,(str,dict)) }, - - 'ylabel' : { 'Default' : 'Price', # y-axis label - 'Description' : 'label for y-axis of main plot', - 'Validator' : lambda value: isinstance(value,str) }, - - 'ylabel_lower' : { 'Default' : None, # y-axis label default logic below - 'Description' : 'label for y-axis of volume', - 'Validator' : lambda value: isinstance(value,str) }, - - 'addplot' : { 'Default' : None, - 'Description' : 'addplot object or sequence of addplot objects (from `mpf.make_addplot()`)', - 'Validator' : lambda value: isinstance(value,dict) or (isinstance(value,list) and all([isinstance(d,dict) for d in value])) }, - - 'savefig' : { 'Default' : None, - 'Description' : 'file name, or BytesIO, or dict with key `fname` plus other keys allowed as '+ - ' kwargs to matplotlib `Figure.savefig()`', - 'Validator' : lambda value: isinstance(value,dict) or isinstance(value,str) or isinstance(value, io.BytesIO) or isinstance(value, os.PathLike) }, - - 'block' : { 'Default' : None, - 'Description' : 'True/False wait for figure to be closed before returning', - 'Validator' : lambda value: isinstance(value,bool) }, - - 'returnfig' : { 'Default' : False, - 'Description' : 'return Figure and list of Axes objects created by mplfinance;'+ - ' user must display plot when ready, usually by calling `mpf.show()`', - 'Validator' : lambda value: isinstance(value,bool) }, - - 'return_calculated_values' : { 'Default' : None, - 'Description' : 'set to a variable containing an empty dict; `mpf.plot()` will fill'+ - ' the dict with various mplfinance calculated values', - 'Validator' : lambda value: isinstance(value, dict) and len(value) == 0}, - - 'set_ylim' : { 'Default' : None, - 'Description' : 'deprecated', - 'Validator' : lambda value: _warn_set_ylim_deprecated(value) }, - - 'ylim' : { 'Default' : None, - 'Description' : 'Limits for y-axis as tuple (min,max), i.e. (bottom,top)', - 'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 - and all([isinstance(v,(int,float)) for v in value])}, - - 'xlim' : { 'Default' : None, - 'Description' : 'Limits for x-axis as tuple (min,max), i.e. (left,right)', - 'Validator' : lambda value: _xlim_validator(value) }, - - 'set_ylim_panelB' : { 'Default' : None, - 'Description' : 'deprecated', - 'Validator' : lambda value: _warn_set_ylim_deprecated(value) }, - - 'hlines' : { 'Default' : None, - 'Description' : 'Draw one or more HORIZONTAL LINES across entire plot, by'+ - ' specifying a price, or sequence of prices. May also be a dict'+ - ' with key `hlines` specifying a price or sequence of prices, plus'+ - ' one or more of the following keys: `colors`, `linestyle`,'+ - ' `linewidths`, `alpha`.', - 'Validator' : lambda value: _hlines_validator(value) }, - - 'vlines' : { 'Default' : None, - 'Description' : 'Draw one or more VERTICAL LINES across entire plot, by'+ - ' specifying a date[time], or sequence of date[time]. May also'+ - ' be a dict with key `vlines` specifying a date[time] or sequence'+ - ' of date[time], plus one or more of the following keys:'+ - ' `colors`, `linestyle`, `linewidths`, `alpha`.', - 'Validator' : lambda value: _vlines_validator(value) }, - - 'alines' : { 'Default' : None, - 'Description' : 'Draw one or more ARBITRARY LINES anywhere on the plot, by'+ - ' specifying a sequence of two or more date/price pairs, or by'+ - ' specifying a sequence of sequences of two or more date/price pairs.'+ - ' May also be a dict with key `alines` (as date/price pairs described above),'+ - ' plus one or more of the following keys:'+ - ' `colors`, `linestyle`, `linewidths`, `alpha`.', - 'Validator' : lambda value: _alines_validator(value) }, - - 'tlines' : { 'Default' : None, - 'Description' : 'Draw one or more TREND LINES by specifying one or more pairs of date[times]'+ - ' between which each trend line should be drawn. May also be a dict with key'+ - ' `tlines` as just described, plus one or more of the following keys:'+ - ' `colors`, `linestyle`, `linewidths`, `alpha`, `tline_use`,`tline_method`.', - 'Validator' : lambda value: _tlines_validator(value) }, - - 'panel_ratios' : { 'Default' : None, - 'Description' : 'sequence of numbers indicating relative sizes of panels; sequence len'+ - ' must be same as number of panels, or len 2 where first entry is for'+ - ' main panel, and second entry is for all other panels', - 'Validator' : lambda value: isinstance(value,(tuple,list)) and len(value) <= 32 and - all([isinstance(v,(int,float)) for v in value]) }, - - 'main_panel' : { 'Default' : 0, - 'Description' : 'integer - which panel is the main panel for `.plot()`', - 'Validator' : lambda value: _valid_panel_id(value) }, - - 'volume_panel' : { 'Default' : 1, - 'Description' : 'integer - which panel is the volume panel', - 'Validator' : lambda value: _valid_panel_id(value) }, - - 'num_panels' : { 'Default' : None, - 'Description' : 'total number of panels', - 'Validator' : lambda value: isinstance(value,int) and value in range(1,32+1) }, - - 'datetime_format' : { 'Default' : None, - 'Description' : 'x-axis tick format as valid `strftime()` format string', - 'Validator' : lambda value: isinstance(value,str) }, - - 'xrotation' : { 'Default' : 45, - 'Description' : 'Angle (degrees) for x-axis tick labels; 90=vertical', - 'Validator' : lambda value: isinstance(value,(int,float)) }, - - 'axisoff' : { 'Default' : False, - 'Description' : '`axisoff=True` means do NOT display any axis.', - 'Validator' : lambda value: isinstance(value,bool) }, - - 'closefig' : { 'Default' : 'auto', - 'Description' : 'True|False close the Figure before returning', - 'Validator' : lambda value: isinstance(value,bool) }, - - 'fill_between' : { 'Default' : None, - 'Description' : 'fill between specification as y-value, or sequence of'+ - ' y-values, or dict containing key "y1" plus any additional'+ - ' kwargs for `fill_between()`', - '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, - 'Description' : 'True|False implement tight layout (minimal padding around Figure)'+ - ' (see also `scale_padding` kwarg)', - 'Validator' : lambda value: isinstance(value,bool) }, - - 'scale_padding' : { 'Default' : 1.0, # Issue#193 - 'Description' : 'Increase, > 1.0, or decrease, < 1.0, padding around figure.'+ - ' May also be a dict containing one or more of the following keys:'+ - ' "top", "bottom", "left", "right", to individual scale padding'+ - ' on each side of Figure.', - 'Validator' : lambda value: _scale_padding_validator(value) }, - - 'width_adjuster_version' : { 'Default' : 'v1', - 'Description' : 'specify version of object width adjustment algorithm: "v0" or "v1"'+ - ' (See also "widths" tutorial in mplfinance examples folder).', - 'Validator' : lambda value: value in ('v0', 'v1') }, - - 'scale_width_adjustment' : { 'Default' : None, - 'Description' : 'scale width of plot objects wider, > 1.0, or narrower, < 1.0'+ - ' may also be a dict to scale individual widths.'+ - ' (See also "widths" tutorial in mplfinance examples folder).', - 'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 }, - - 'update_width_config' : { 'Default' : None, - 'Description' : 'dict - update individual items in width configuration.'+ - ' (See also "widths" tutorial in mplfinance examples folder).', - 'Validator' : lambda value: isinstance(value,dict) and len(value) > 0 }, - - 'return_width_config' : { 'Default' : None, - 'Description' : 'empty dict variable to be filled with width configuration settings.', - 'Validator' : lambda value: isinstance(value,dict) and len(value)==0 }, - - 'saxbelow' : { 'Default' : True, # Issue#115 Comment#639446764 - 'Description' : 'set the volume Axes below (behind) all other Axes objects', - 'Validator' : lambda value: isinstance(value,bool) }, - - 'ax' : { 'Default' : None, - 'Description' : 'Matplotlib Axes object on which to plot', - 'Validator' : lambda value: isinstance(value,mpl_axes.Axes) }, - - 'volume_exponent' : { 'Default' : None, - 'Description' : 'integer exponent on the volume axis'+ - ' (or set to "legacy" for old mplfinance style)', - 'Validator' : lambda value: isinstance(value,int) or value == 'legacy'}, - - 'tz_localize' : { 'Default' : True, - 'Description' : 'True|False localize the times in the DatetimeIndex', - 'Validator' : lambda value: isinstance(value,bool) }, - - 'yscale' : { 'Default' : None, - 'Description' : 'y-axis scale: "linear", "log", "symlog", or "logit"', - 'Validator' : lambda value: _yscale_validator(value) }, - - 'volume_yscale' : { 'Default' : None, - 'Description' : 'Volume y-axis scale: "linear", "log", "symlog", or "logit"', - 'Validator' : lambda value: _yscale_validator(value) }, - - 'warn_too_much_data' : { 'Default' : 599, - 'Description' : 'Tolerance for data amount in plot. Default=599 rows.'+ - ' Values greater than \'warn_too_much_data\' will trigger a warning.', - 'Validator' : lambda value: isinstance(value,int) }, - } - - _validate_vkwargs_dict(vkwargs) - - return vkwargs - -###@with_rc_context -def plot( data, **kwargs ): - """ - Given a Pandas DataFrame containing columns Open,High,Low,Close and optionally Volume - with a DatetimeIndex, plot the data. - Available plots include ohlc bars, candlestick, and line plots. - Also provide visually analysis in the form of common technical studies, such as: - moving averages, renko, etc. - Also provide ability to plot trading signals, and/or addtional user-defined data. - """ - - config = _process_kwargs(kwargs, _valid_plot_kwargs()) - - # translate alias types: - config['type'] = _get_valid_plot_types(config['type']) - - dates,opens,highs,lows,closes,volumes = _check_and_prepare_data(data, config) - - config['xlim'] = _check_and_convert_xlim_configuration(data, config) - - if config['type'] in VALID_PMOVE_TYPES and config['addplot'] is not None: - err = "`addplot` is not supported for `type='" + config['type'] +"'`" - raise ValueError(err) - - if config['marketcolor_overrides'] is not None: - if len(config['marketcolor_overrides']) != len(dates): - raise ValueError('`marketcolor_overrides` must be same length as dataframe.') - - external_axes_mode = _check_for_external_axes(config) - - if external_axes_mode: - if config['figscale'] is not None: - warnings.warn('\n\n ================================================================= '+ - '\n\n WARNING: `figscale` has NO effect in External Axes Mode.'+ - '\n\n ================================================================ ', - category=UserWarning) - if config['figratio'] is not None: - warnings.warn('\n\n ================================================================= '+ - '\n\n WARNING: `figratio` has NO effect in External Axes Mode.'+ - '\n\n ================================================================ ', - category=UserWarning) - if config['figsize'] is not None: - warnings.warn('\n\n ================================================================= '+ - '\n\n WARNING: `figsize` has NO effect in External Axes Mode.'+ - '\n\n ================================================================ ', - category=UserWarning) - else: - if config['figscale'] is None: config['figscale'] = 1.0 - if config['figratio'] is None: config['figratio'] = DEFAULT_FIGRATIO - - style = config['style'] - - if external_axes_mode and hasattr(config['ax'],'mpfstyle') and style is None: - style = config['ax'].mpfstyle - elif style is None: - style = 'default' - - if isinstance(style,str): - style = _styles._get_mpfstyle(style) - - config['style'] = style - - if isinstance(style,dict): - if not external_axes_mode: _styles._apply_mpfstyle(style) - else: - raise TypeError('style should be a `dict`; why is it not?') - - # ---------------------------------------------------------------------- - # TODO: Add some warnings, or raise an exception, if external_axes_mode - # and user is trying to figscale, figratio, or figsize. - # ---------------------------------------------------------------------- - - if not external_axes_mode: - fig = plt.figure() - _adjust_figsize(fig,config) - else: - fig = None - - _adjust_fontsize(config) - - if config['volume'] and volumes is None: - raise ValueError('Request for volume, but NO volume data.') - - if external_axes_mode: - panels = None - if config['volume']: - volumeAxes = config['volume'] - volumeAxes.set_axisbelow(config['saxbelow']) - else: - panels = _build_panels(fig, config) - volumeAxes = panels.at[config['volume_panel'],'axes'][0] if config['volume'] is True else None - - fmtstring = _determine_format_string(dates, config['datetime_format']) - - ptype = config['type'] - - if config['show_nontrading']: - formatter = mdates.DateFormatter(fmtstring) - xdates = dates - else: - formatter = IntegerIndexDateTimeFormatter(dates, fmtstring) - xdates = np.arange(len(dates)) - - if external_axes_mode: - axA1 = config['ax'] - axA1.set_axisbelow(config['saxbelow']) - else: - axA1 = panels.at[config['main_panel'],'axes'][0] - - # Will have to handle widths config separately for PMOVE types ?? - config['_width_config'] = _determine_width_config(xdates, config) - - - rwc = config['return_width_config'] - if isinstance(rwc,dict) and len(rwc)==0: - config['return_width_config'].update(config['_width_config']) - - - collections = None - if ptype == 'line': - lw = config['_width_config']['line_width'] - axA1.plot(xdates, closes, color=config['linecolor'], linewidth=lw) - else: - collections =_construct_mpf_collections(ptype,dates,xdates,opens,highs,lows,closes,volumes,config,style) - - if ptype in VALID_PMOVE_TYPES: - collections, calculated_values = collections - volumes = calculated_values['volumes'] - pmove_dates = calculated_values['dates'] - pmove_values = calculated_values['values'] - if all([isinstance(v,(list,tuple)) for v in pmove_values]): - pmove_avgvals = [sum(v)/len(v) for v in pmove_values] - else: - pmove_avgvals = pmove_values - pmove_size = calculated_values['size'] - pmove_counts = calculated_values['counts'] if 'counts' in calculated_values else None - formatter = IntegerIndexDateTimeFormatter(pmove_dates, fmtstring) - xdates = np.arange(len(pmove_dates)) - - if collections is not None: - for collection in collections: - axA1.add_collection(collection) - - if ptype in VALID_PMOVE_TYPES: - mavprices = _plot_mav(axA1,config,xdates,pmove_avgvals) - else: - mavprices = _plot_mav(axA1,config,xdates,closes) - - avg_dist_between_points = (xdates[-1] - xdates[0]) / float(len(xdates)) - if not config['tight_layout']: - minx = xdates[0] - avg_dist_between_points - maxx = xdates[-1] + avg_dist_between_points - else: - minx = xdates[0] - (0.45 * avg_dist_between_points) - maxx = xdates[-1] + (0.45 * avg_dist_between_points) - - if len(xdates) == 1: # kludge special case - minx = minx - 0.75 - maxx = maxx + 0.75 - if ptype not in VALID_PMOVE_TYPES: - _lows = lows - _highs = highs - else: - _lows = pmove_avgvals - _highs = [value+pmove_size for value in pmove_avgvals] - - miny = np.nanmin(_lows) - maxy = np.nanmax(_highs) - - if config['ylim'] is not None: - axA1.set_ylim(config['ylim'][0], config['ylim'][1]) - elif config['tight_layout']: - ydelta = 0.01 * (maxy-miny) - if miny > 0.0: - # don't let it go negative: - setminy = max(0.9*miny,miny-ydelta) - else: - setminy = miny-ydelta - axA1.set_ylim(setminy,maxy+ydelta) - - if config['xlim'] is not None: - axA1.set_xlim(config['xlim'][0], config['xlim'][1]) - elif config['tight_layout']: - axA1.set_xlim(minx,maxx) - - if (config['ylim'] is None and - config['xlim'] is None and - not config['tight_layout']): - corners = (minx, miny), (maxx, maxy) - axA1.update_datalim(corners) - - if config['return_calculated_values'] is not None: - retdict = config['return_calculated_values'] - if ptype == 'renko': - retdict['renko_bricks' ] = pmove_values - retdict['renko_dates' ] = mdates.num2date(pmove_dates) - retdict['renko_size' ] = pmove_size - retdict['renko_volumes'] = volumes if config['volume'] else None - elif ptype == 'pnf': - retdict['pnf_dates' ] = mdates.num2date(pmove_dates) - retdict['pnf_counts' ] = pmove_counts - retdict['pnf_values' ] = pmove_values - retdict['pnf_avgvals' ] = pmove_avgvals - retdict['pnf_size' ] = pmove_size - retdict['pnf_volumes' ] = volumes if config['volume'] else None - if config['mav'] is not None: - mav = config['mav'] - if len(mav) != len(mavprices): - warnings.warn('len(mav)='+str(len(mav))+' BUT len(mavprices)='+str(len(mavprices))) - else: - for jj in range(0,len(mav)): - retdict['mav' + str(mav[jj])] = mavprices[jj] - retdict['minx'] = minx - retdict['maxx'] = maxx - retdict['miny'] = miny - retdict['maxy'] = maxy - - # Note: these are NOT mutually exclusive, so the order of this - # if/elif is important: VALID_PMOVE_TYPES must be first. - if ptype in VALID_PMOVE_TYPES: - dtix = pd.DatetimeIndex([dt for dt in mdates.num2date(pmove_dates)]) - elif not config['show_nontrading']: - dtix = data.index - else: - dtix = None - - line_collections = [] - line_collections.append(_construct_aline_collections(config['alines'], dtix)) - line_collections.append(_construct_hline_collections(config['hlines'], minx, maxx)) - line_collections.append(_construct_vline_collections(config['vlines'], dtix, miny, maxy)) - tlines = config['tlines'] - if isinstance(tlines,(list,tuple)) and all([isinstance(item,dict) for item in tlines]): - pass - else: - tlines = [tlines,] - for tline_item in tlines: - line_collections.append(_construct_tline_collections(tline_item, dtix, dates, opens, highs, lows, closes)) - - for collection in line_collections: - if collection is not None: - axA1.add_collection(collection) - - datalen = len(xdates) - if config['volume']: - vup,vdown = style['marketcolors']['volume'].values() - #-- print('vup,vdown=',vup,vdown) - vcolors = _updown_colors(vup, vdown, opens, closes, use_prev_close=style['marketcolors']['vcdopcod']) - #-- print('len(vcolors),len(opens),len(closes)=',len(vcolors),len(opens),len(closes)) - #-- print('vcolors=',vcolors) - - w = config['_width_config']['volume_width'] - lw = config['_width_config']['volume_linewidth'] - - adjc = _adjust_color_brightness(vcolors,0.90) - volumeAxes.bar(xdates,volumes,width=w,linewidth=lw,color=vcolors,ec=adjc) - vymin = 0.3 * np.nanmin(volumes) - vymax = 1.1 * np.nanmax(volumes) - volumeAxes.set_ylim(vymin,vymax) - - xrotation = config['xrotation'] - if not external_axes_mode: - _set_ticks_on_bottom_panel_only(panels,formatter,rotation=xrotation) - else: - axA1.tick_params(axis='x',rotation=xrotation) - axA1.xaxis.set_major_formatter(formatter) - - ysd = config['yscale'] - if isinstance(ysd,dict): - yscale = ysd['yscale'] - del ysd['yscale'] - axA1.set_yscale(yscale,**ysd) - elif isinstance(ysd,str): - axA1.set_yscale(ysd) - - - addplot = config['addplot'] - if addplot is not None and ptype not in VALID_PMOVE_TYPES: - # NOTE: If in external_axes_mode, then all code relating - # to panels and secondary_y becomes irrrelevant. - # If the user wants something on a secondary_y then user should - # determine that externally, and pass in the appropriate axes. - - if not external_axes_mode: - # Calculate the Order of Magnitude Range ('mag') - # If addplot['secondary_y'] == 'auto', then: If the addplot['data'] - # is out of the Order of Magnitude Range, then use secondary_y. - - lo = math.log(max(math.fabs(np.nanmin(lows)),1e-7),10) - 0.5 - hi = math.log(max(math.fabs(np.nanmax(highs)),1e-7),10) + 0.5 - - panels['mag'] = [None]*len(panels) # create 'mag'nitude column - - panels.at[config['main_panel'],'mag'] = {'lo':lo,'hi':hi} # update main panel magnitude range - - if config['volume']: - lo = math.log(max(math.fabs(np.nanmin(volumes)),1e-7),10) - 0.5 - hi = math.log(max(math.fabs(np.nanmax(volumes)),1e-7),10) + 0.5 - panels.at[config['volume_panel'],'mag'] = {'lo':lo,'hi':hi} - - 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))) - - for apdict in addplot: - - panid = apdict['panel'] - if not external_axes_mode: - if panid == 'main' : panid = 0 # for backwards compatibility - elif panid == 'lower': panid = 1 # for backwards compatibility - if apdict['y_on_right'] is not None: - panels.at[panid,'y_on_right'] = apdict['y_on_right'] - - aptype = apdict['type'] - if aptype == 'ohlc' or aptype == 'candle': - ax = _addplot_collections(panid,panels,apdict,xdates,config) - _addplot_apply_supplements(ax,apdict) - else: - apdata = apdict['data'] - if isinstance(apdata,list) and not isinstance(apdata[0],(float,int)): - raise TypeError('apdata is list but NOT of float or int') - if isinstance(apdata,pd.DataFrame): - havedf = True - else: - havedf = False # must be a single series or array - apdata = [apdata,] # make it iterable - for column in apdata: - ydata = apdata.loc[:,column] if havedf else column - ax = _addplot_columns(panid,panels,ydata,apdict,xdates,config) - _addplot_apply_supplements(ax,apdict) - - # fill_between is NOT supported for external_axes_mode - # (caller can easily call ax.fill_between() themselves). - if config['fill_between'] is not None and not external_axes_mode: - fb = config['fill_between'] - panid = config['main_panel'] - if isinstance(fb,dict): - if 'x' in fb: - raise ValueError('fill_between dict may not contain `x`') - if 'panel' in fb: - panid = fb['panel'] - del fb['panel'] - else: - fb = dict(y1=fb) - fb['x'] = xdates - ax = panels.at[panid,'axes'][0] - ax.fill_between(**fb) - - # put the primary axis on one side, - # and the twinx() on the "other" side: - if not external_axes_mode: - for panid,row in panels.iterrows(): - ax = row['axes'] - y_on_right = style['y_on_right'] if row['y_on_right'] is None else row['y_on_right'] - _set_ylabels_side(ax[0],ax[1],y_on_right) - else: - y_on_right = style['y_on_right'] - _set_ylabels_side(axA1,None,y_on_right) - - # TODO: ================================================================ - # TODO: Investigate: - # TODO: =========== - # TODO: It appears to me that there may be some or significant overlap - # TODO: between what the following functions actually do: - # TODO: At the very least, all four of them appear to communicate - # TODO: to matplotlib that the xaxis should be treated as dates: - # TODO: -> 'ax.autoscale_view()' - # TODO: -> 'ax.xaxis_dates()' - # TODO: -> 'plt.autofmt_xdates()' - # TODO: -> 'fig.autofmt_xdate()' - # TODO: ================================================================ - - - #if config['autofmt_xdate']: - #print('CALLING fig.autofmt_xdate()') - #fig.autofmt_xdate() - - axA1.autoscale_view() # Is this really necessary?? - # It appears to me, based on experience coding types 'ohlc' and 'candle' - # for `addplot`, that this IS necessary when the only thing done to the - # the axes is .add_collection(). (However, if ax.plot() .scatter() or - # .bar() was called, then possibly this is not necessary; not entirely - # sure, but it definitely was necessary to get 'ohlc' and 'candle' - # working in `addplot`). - - axA1.set_ylabel(config['ylabel']) - - if config['volume']: - if external_axes_mode: - volumeAxes.tick_params(axis='x',rotation=xrotation) - volumeAxes.xaxis.set_major_formatter(formatter) - - vscale = 'linear' - ysd = config['volume_yscale'] - if isinstance(ysd,dict): - yscale = ysd['yscale'] - del ysd['yscale'] - volumeAxes.set_yscale(yscale,**ysd) - vscale = yscale - elif isinstance(ysd,str): - volumeAxes.set_yscale(ysd) - vscale = ysd - offset = '' - if vscale == 'linear': - vxp = config['volume_exponent'] - if vxp == 'legacy': - volumeAxes.figure.canvas.draw() # This is needed to calculate offset - offset = volumeAxes.yaxis.get_major_formatter().get_offset() - if len(offset) > 0: - offset = (' x '+offset) - elif isinstance(vxp,int) and vxp > 0: - volumeAxes.ticklabel_format(useOffset=False,scilimits=(vxp,vxp),axis='y') - offset = ' $10^{'+str(vxp)+'}$' - elif isinstance(vxp,int) and vxp == 0: - volumeAxes.ticklabel_format(useOffset=False,style='plain',axis='y') - offset = '' - else: - offset = '' - scilims = plt.rcParams['axes.formatter.limits'] - if scilims[0] < scilims[1]: - for power in (5,4,3,2,1): - xp = scilims[1]*power - if vymax >= 10.**xp: - volumeAxes.ticklabel_format(useOffset=False,scilimits=(xp,xp),axis='y') - offset = ' $10^{'+str(xp)+'}$' - break - elif scilims[0] == scilims[1] and scilims[1] != 0: - volumeAxes.ticklabel_format(useOffset=False,scilimits=scilims,axis='y') - offset = ' $10^'+str(scilims[1])+'$' - volumeAxes.yaxis.offsetText.set_visible(False) - - if config['ylabel_lower'] is None: - vol_label = 'Volume'+offset - else: - if len(offset) > 0: - offset = '\n'+offset - vol_label = config['ylabel_lower'] + offset - volumeAxes.set_ylabel(vol_label) - - if config['title'] is not None: - if config['tight_layout']: - # IMPORTANT: `y=0.89` is based on the top of the top panel - # being at 0.18+0.7 = 0.88. See _panels.py - # If the value changes there, then it needs to change here. - title_kwargs = dict(va='bottom', y=0.89) - else: - title_kwargs = dict(va='center') - if isinstance(config['title'],dict): - title_dict = config['title'] - if 'title' not in title_dict: - raise ValueError('Must have "title" entry in title dict') - else: - title = title_dict['title'] - del title_dict['title'] - title_kwargs.update(title_dict) # allows override default values set by mplfinance above - else: - title = config['title'] # config['title'] is a string - fig.suptitle(title,**title_kwargs) - - - if config['axtitle'] is not None: - axA1.set_title(config['axtitle']) - - if not external_axes_mode: - for panid,row in panels.iterrows(): - if not row['used2nd']: - row['axes'][1].set_visible(False) - - if external_axes_mode: - return None - - # Should we create a new kwarg to return a flattened axes list - # versus a list of tuples of primary and secondary axes? - # For now, for backwards compatibility, we flatten axes list: - axlist = [ax for axes in panels['axes'] for ax in axes] - - if config['axisoff']: - for ax in axlist: - ax.set_axis_off() - - if config['savefig'] is not None: - save = config['savefig'] - if isinstance(save,dict): - if config['tight_layout'] and 'bbox_inches' not in save: - plt.savefig(**save,bbox_inches='tight') - else: - plt.savefig(**save) - else: - if config['tight_layout']: - plt.savefig(save,bbox_inches='tight') - else: - plt.savefig(save) - if config['closefig']: # True or 'auto' - plt.close(fig) - elif not config['returnfig']: - plt.show(block=config['block']) # https://stackoverflow.com/a/13361748/1639359 - if config['closefig'] == True or (config['block'] and config['closefig']): - plt.close(fig) - - if config['returnfig']: - if config['closefig'] == True: plt.close(fig) - return (fig, axlist) - - # rcp = copy.deepcopy(plt.rcParams) - # rcpdf = rcParams_to_df(rcp) - # print('type(rcpdf)=',type(rcpdf)) - # print('rcpdfhead(3)=',rcpdf.head(3)) - # return # rcpdf - -def _adjust_figsize(fig,config): - if fig is None: - return - if config['figsize'] is None: - w,h = config['figratio'] - r = float(w)/float(h) - if r < 0.20 or r > 5.0: - raise ValueError('"figratio" (aspect ratio) must be between 0.20 and 5.0 (but is '+str(r)+')') - default_scale = DEFAULT_FIGRATIO[1]/h - h *= default_scale - w *= default_scale - base = (w,h) - figscale = config['figscale'] - fsize = [d*figscale for d in base] - else: - fsize = config['figsize'] - fig.set_size_inches(fsize) - -def _adjust_fontsize(config): - if config['fontscale'] is None: - return - if not isinstance(plt.rcParams['font.size'],(float,int)): - warnings.warn('\n\n ================================================================= '+ - '\n\n WARNING: Unable to scale fonts: plt.rcParams["font.size"] is NOT a float!'+ - '\n\n ================================================================ ', - category=UserWarning) - return - plt.rcParams['font.size'] *= config['fontscale'] - # -------------------------------------------- - # From: matplotlib.font_manager.font_scalings: - # font_scalings = { - # 'xx-small': 0.579, - # 'x-small': 0.694, - # 'small': 0.833, - # 'medium': 1.0, - # 'large': 1.200, - # 'x-large': 1.440, - # 'xx-large': 1.728, - # 'larger': 1.2, - # 'smaller': 0.833, - # None: 1.0, - # } - # -------------------------------------------- - fontstuff = ['axes.labelsize','axes.titlesize', 'figure.titlesize','legend.fontsize', - 'legend.title_fontsize','xtick.labelsize','ytick.labelsize'] - for item in fontstuff: - if isinstance(plt.rcParams[item],(float,int)): - plt.rcParams[item] *= config['fontscale'] - -def _addplot_collections(panid,panels,apdict,xdates,config): - - apdata = apdict['data'] - aptype = apdict['type'] - external_axes_mode = apdict['ax'] is not None - - #--------------------------------------------------------------# - # Note: _auto_secondary_y() sets the 'magnitude' column in the - # `panels` dataframe, which is needed for automatically - # determining if secondary_y is needed. Therefore we call - # _auto_secondary_y() for *all* addplots, even those that - # are set to True or False (not 'auto') for secondary_y - # because their magnitudes may be needed if *any* apdicts - # contain secondary_y='auto'. - # In theory we could first loop through all apdicts to see - # if any have secondary_y='auto', but since that is the - # default value, we will just assume we have at least one. - - valid_apc_types = ['ohlc','candle'] - if aptype not in valid_apc_types: - raise TypeError('Invalid aptype='+str(aptype)+'. Must be one of '+str(valid_apc_types)) - if not isinstance(apdata,pd.DataFrame): - raise TypeError('addplot type "'+aptype+'" MUST be accompanied by addplot data of type `pd.DataFrame`') - d,o,h,l,c,v = _check_and_prepare_data(apdata,config) - - mc = apdict['marketcolors'] - if _is_marketcolor_object(mc): - apstyle = config['style'].copy() - apstyle['marketcolors'] = mc - else: - apstyle = config['style'] - - collections = _construct_mpf_collections(aptype,d,xdates,o,h,l,c,v,config,apstyle) - - if not external_axes_mode: - lo = math.log(max(math.fabs(np.nanmin(l)),1e-7),10) - 0.5 - hi = math.log(max(math.fabs(np.nanmax(h)),1e-7),10) + 0.5 - secondary_y = _auto_secondary_y( panels, panid, lo, hi ) - if 'auto' != apdict['secondary_y']: - secondary_y = apdict['secondary_y'] - if secondary_y: - ax = panels.at[panid,'axes'][1] - panels.at[panid,'used2nd'] = True - else: - ax = panels.at[panid,'axes'][0] - else: - ax = apdict['ax'] - - for coll in collections: - ax.add_collection(coll) - if apdict['mav'] is not None: - apmavprices = _plot_mav(ax,config,xdates,c,apdict['mav']) - ax.autoscale_view() - return ax - -def _addplot_columns(panid,panels,ydata,apdict,xdates,config): - external_axes_mode = apdict['ax'] is not None - if not external_axes_mode: - secondary_y = False - if apdict['secondary_y'] == 'auto': - yd = [y for y in ydata if not math.isnan(y)] - ymhi = math.log(max(math.fabs(np.nanmax(yd)),1e-7),10) - ymlo = math.log(max(math.fabs(np.nanmin(yd)),1e-7),10) - secondary_y = _auto_secondary_y( panels, panid, ymlo, ymhi ) - else: - secondary_y = apdict['secondary_y'] - #print("apdict['secondary_y'] says secondary_y is",secondary_y) - - if secondary_y: - ax = panels.at[panid,'axes'][1] - panels.at[panid,'used2nd'] = True - else: - ax = panels.at[panid,'axes'][0] - else: - ax = apdict['ax'] - - aptype = apdict['type'] - if aptype == 'scatter': - size = apdict['markersize'] - mark = apdict['marker'] - color = apdict['color'] - alpha = apdict['alpha'] - edgecolors = apdict['edgecolors'] - linewidths = apdict['linewidths'] - - if isinstance(mark,(list,tuple,np.ndarray)): - _mscatter(xdates, ydata, ax=ax, m=mark, s=size, color=color, alpha=alpha, edgecolors=edgecolors, linewidths=linewidths) - else: - ax.scatter(xdates, ydata, s=size, marker=mark, color=color, alpha=alpha, edgecolors=edgecolors, linewidths=linewidths) - elif aptype == 'bar': - w = config['_width_config']['volume_width'] - lw = config['_width_config']['volume_linewidth'] - ## volumeAxes.bar(xdates,volumes,width=w,linewidth=lw,color=vcol - width = w if apdict['width'] is None else apdict['width'] - linew = lw if apdict['width'] is None else 0.25*apdict['width'] - print('width=',width," apdict['width']=",apdict['width']) - bottom = apdict['bottom'] - color = apdict['color'] - alpha = apdict['alpha'] - print('bar: xdates[0:10]=',xdates[0:10],' ydata[0:10]=',ydata[0:10],' len(ydata)=',len(ydata),' ax=',ax) - ax.bar(xdates,ydata,width=width,linewidth=linew,bottom=bottom,color=color,alpha=alpha) - elif aptype == 'line': - ls = apdict['linestyle'] - color = apdict['color'] - width = apdict['width'] if apdict['width'] is not None else 1.6*config['_width_config']['line_width'] - alpha = apdict['alpha'] - ax.plot(xdates,ydata,linestyle=ls,color=color,linewidth=width,alpha=alpha) - elif aptype == 'step': - stepwhere = apdict['stepwhere'] - ls = apdict['linestyle'] - color = apdict['color'] - width = apdict['width'] if apdict['width'] is not None else 1.6*config['_width_config']['line_width'] - alpha = apdict['alpha'] - print('step: xdates[0:10]=',xdates[0:10],' ydata[0:10]=',ydata[0:10],' len(ydata)=',len(ydata),' ax=',ax) - ax.step(xdates,ydata,where = stepwhere,linestyle=ls,color=color,linewidth=width,alpha=alpha) - else: - raise ValueError('addplot type "'+str(aptype)+'" NOT yet supported.') - - if apdict['mav'] is not None: - apmavprices = _plot_mav(ax,config,xdates,ydata,apdict['mav']) - - return ax - -def _addplot_apply_supplements(ax,apdict): - if (apdict['ylabel'] is not None): - ax.set_ylabel(apdict['ylabel']) - if apdict['ylim'] is not None: - ax.set_ylim(apdict['ylim'][0],apdict['ylim'][1]) - if apdict['title'] is not None: - ax.set_title(apdict['title']) - ysd = apdict['yscale'] - if isinstance(ysd,dict): - yscale = ysd['yscale'] - del ysd['yscale'] - ax.set_yscale(yscale,**ysd) - elif isinstance(ysd,str): - ax.set_yscale(ysd) - -def _set_ylabels_side(ax_pri,ax_sec,primary_on_right): - # put the primary axis on one side, - # and the twinx() on the "other" side: - if primary_on_right == True: - ax_pri.yaxis.set_label_position('right') - ax_pri.yaxis.tick_right() - if ax_sec is not None: - ax_sec.yaxis.set_label_position('left') - ax_sec.yaxis.tick_left() - else: # treat non-True as False, whether False, None, or anything else. - ax_pri.yaxis.set_label_position('left') - ax_pri.yaxis.tick_left() - if ax_sec is not None: - ax_sec.yaxis.set_label_position('right') - ax_sec.yaxis.tick_right() - -def _plot_mav(ax,config,xdates,prices,apmav=None,apwidth=None): - style = config['style'] - if apmav is not None: - mavgs = apmav - else: - mavgs = config['mav'] - mavp_list = [] - if mavgs is not None: - shift = None - if isinstance(mavgs,dict): - shift = mavgs['shift'] - mavgs = mavgs['period'] - if isinstance(mavgs,int): - mavgs = mavgs, # convert to tuple - if len(mavgs) > 7: - mavgs = mavgs[0:7] # take at most 7 - - if style['mavcolors'] is not None: - mavc = cycle(style['mavcolors']) - else: - mavc = None - - for idx,mav in enumerate(mavgs): - mean = pd.Series(prices).rolling(mav).mean() - if shift is not None: - mean = mean.shift(periods=shift[idx]) - mavprices = mean.values - lw = config['_width_config']['line_width'] - if mavc: - ax.plot(xdates, mavprices, linewidth=lw, color=next(mavc)) - else: - ax.plot(xdates, mavprices, linewidth=lw) - mavp_list.append(mavprices) - return mavp_list - -def _auto_secondary_y( panels, panid, ylo, yhi ): - # If mag(nitude) for this panel is not yet set, then set it - # here, as this is the first ydata to be plotted on this panel: - # i.e. consider this to be the 'primary' axis for this panel. - secondary_y = False - p = panid,'mag' - if panels.at[p] is None: - panels.at[p] = {'lo':ylo,'hi':yhi} - elif ylo < panels.at[p]['lo'] or yhi > panels.at[p]['hi']: - secondary_y = True - #if secondary_y: - # print('auto says USE secondary_y ... for panel',panid) - #else: - # print('auto says do NOT use secondary_y ... for panel',panid) - return secondary_y - -def _valid_addplot_kwargs(): - - valid_linestyles = ('-','solid','--','dashed','-.','dashdot','.','dotted',None,' ','') - valid_types = ('line','scatter','bar', 'ohlc', 'candle','step') - valid_stepwheres = ('pre','post','mid') - valid_edgecolors = ('face', 'none', None) - - vkwargs = { - 'scatter' : { 'Default' : False, - 'Description' : "Deprecated. (Use kwarg `type='scatter' instead.", - 'Validator' : lambda value: isinstance(value,bool) }, - - 'type' : { 'Default' : 'line', - 'Description' : 'addplot type: "line","scatter","bar", "ohlc", "candle","step"', - 'Validator' : lambda value: value in valid_types }, - - 'mav' : { 'Default' : None, - 'Description' : 'Moving Average window size(s); (int or tuple of ints)', - 'Validator' : _mav_validator }, - - 'panel' : { 'Default' : 0, - 'Description' : 'Panel (int 0-31) to use for this addplot', - 'Validator' : lambda value: _valid_panel_id(value) }, - - 'marker' : { 'Default' : 'o', - 'Description' : "marker for `type='scatter'` plot", - 'Validator' : lambda value: _bypass_kwarg_validation(value) }, - - 'markersize' : { 'Default' : 18, - 'Description' : 'size of marker for `type="scatter"`; default=18', - 'Validator' : lambda value: isinstance(value,(int,float)) }, - - 'color' : { 'Default' : None, - 'Description' : 'color (or sequence of colors) of line(s), scatter marker(s), or bar(s).', - '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, - 'Description' : 'line style for `type=line` ('+str(valid_linestyles)+')', - 'Validator' : lambda value: value in valid_linestyles }, - - 'linewidths' : { 'Default': None, - 'Description' : 'edge widths of scatter markers', - 'Validator' : lambda value: isinstance(value,(int,float)) }, - - 'edgecolors' : { 'Default': None, - 'Description' : 'edgecolors of scatter markers', - 'Validator': lambda value: mcolors.is_color_like(value) or value in valid_edgecolors}, - - 'width' : { 'Default' : None, # width of `bar` or `line` - 'Description' : 'width of bar or line for `type="bar"` or `type="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 - 'Description' : 'bottom value for `type=bar` bars. Default=0', - '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` - 'Description' : 'opacity for 0.0 (transparent) to 1.0 (opaque)', - 'Validator' : lambda value: isinstance(value,(int,float)) or - all([isinstance(v,(int,float)) for v in value]) }, - - 'secondary_y' : { 'Default' : 'auto', - 'Description' : "True|False|'auto' place the additional plot data on a"+ - " secondary y-axis. 'auto' compares the magnitude or the"+ - " addplot data, to data already on the axis, and if it appears"+ - " they are of different magnitudes, then it uses a secondary y-axis."+ - " True or False always override 'auto'.", - 'Validator' : lambda value: isinstance(value,bool) or value == 'auto' }, - - 'y_on_right' : { 'Default' : None, - 'Description' : 'True|False put y-axis tick labels on the right, for this addplot'+ - ' regardless of what the mplfinance style says to to.', - 'Validator' : lambda value: isinstance(value,bool) }, - - 'ylabel' : { 'Default' : None, - 'Description' : 'label for y-axis (for this addplot)', - 'Validator' : lambda value: isinstance(value,str) }, - - 'ylim' : {'Default' : None, - 'Description' : 'Limits for addplot y-axis as tuple (min,max), i.e. (bottom,top)', - 'Validator' : lambda value: isinstance(value, (list,tuple)) and len(value) == 2 - and all([isinstance(v,(int,float)) for v in value])}, - - 'title' : { 'Default' : None, - 'Description' : 'Axes Title (subplot title) for this addplot.', - 'Validator' : lambda value: isinstance(value,str) }, - - 'ax' : { 'Default' : None, - 'Description' : 'Matplotlib Axes object on which to plot this addplot', - 'Validator' : lambda value: isinstance(value,mpl_axes.Axes) }, - - 'yscale' : { 'Default' : None, - 'Description' : 'addplot y-axis scale: "linear", "log", "symlog", or "logit"', - 'Validator' : lambda value: _yscale_validator(value) }, - - 'stepwhere' : { 'Default' : 'pre', - 'Description' : "'pre','post', or 'mid': where to place step relative"+ - " to data for `type='step'`", - 'Validator' : lambda value : value in valid_stepwheres }, - - 'marketcolors': { 'Default' : None, # use 'style' for default, instead. - 'Description' : "marketcolors for this addplot (instead of the mplfinance"+ - " style\'s marketcolors). For addplot `type='ohlc'`"+ - " and type='candle'", - 'Validator' : lambda value: _is_marketcolor_object(value) }, - } - - _validate_vkwargs_dict(vkwargs) - - return vkwargs - - -def make_addplot(data, **kwargs): - ''' - Take data (pd.Series, pd.DataFrame, np.ndarray of floats, list of floats), and - kwargs (see valid_addplot_kwargs_table) and construct a correctly structured dict - to be passed into plot() using kwarg `addplot`. - NOTE WELL: len(data) here must match the len(data) passed into plot() - ''' - if not isinstance(data, (pd.Series, pd.DataFrame, np.ndarray, list)): - raise TypeError('Wrong type for data, in make_addplot()') - - config = _process_kwargs(kwargs, _valid_addplot_kwargs()) - - # kwarg `type` replaces kwarg `scatter` - if config['scatter'] == True and config['type'] == 'line': - config['type'] = 'scatter' - - return dict( data=data, **config)