def get_candle(df, up_color=None, down_color=None, theme=None, **kwargs):
ohlc = ["open", "high", "low", "close"]
if not theme:
theme = auth.get_config_file()["theme"]
layout = getLayout(theme=theme)
c_dir = _ohlc_dict(df)
args = [df[c_dir[_]] for _ in ohlc]
args.append(df.index)
fig = py.plotly.tools.FigureFactory.create_candlestick(*args, **kwargs)
candle = Figure()
candle["data"] = fig["data"]
candle["layout"] = layout
data = candle["data"]
def update_color(n, color):
data[n]["fillcolor"] = normalize(color)
data[n]["line"].update(color=normalize(color))
if up_color:
update_color(0, up_color)
if down_color:
update_color(1, down_color)
candle["layout"]["hovermode"] = "closest"
layout = getLayout(theme=theme)
candle["layout"] = merge_dict(layout, candle["layout"])
return candle
def colorgen(colors=None,n=None,scale=None,theme=None): """ Returns a generator with a list of colors and gradients of those colors Parameters: ----------- colors : list(colors) List of colors to use Example: colorgen() colorgen(['blue','red','pink']) colorgen(['#f03','rgb(23,25,25)']) """ step=.1 if not colors: if not scale: if not theme: scale = get_config_file()['colorscale'] else: scale = themes.THEMES[theme]['colorscale'] colors=get_scales(scale) dq=deque(colors) if n: step=len(dq)*0.8/n if len(dq)*8<n else .1 for i in np.arange(.2,1,step): for y in dq: yield to_rgba(y,1-i+.2) dq.rotate()
def iplot(data_or_figure,validate=True,sharing=None,filename='',online=None,**kwargs):
"""
Plots a figure in IPython
data_or_figure : figure
Plotly figure to be charted
validate : bool
If True then all values are validated before
it is charted
sharing : string
Sets the sharing level permission
public - anyone can see this chart
private - only you can see this chart
secret - only people with the link can see the chart
filename : string
Name to be used to save the file in the server
online : bool
If True then charts are rendered in the server
"""
valid_kwargs=['world_readable']
for key in kwargs.keys():
if key not in valid_kwargs:
raise Exception("Invalid keyword : '{0}'".format(key))
if all(['world_readable' in kwargs,sharing is None]):
sharing=kwargs['world_readable']
if isinstance(sharing,bool):
if sharing:
sharing='public'
else:
sharing='private'
if sharing is None:
sharing=auth.get_config_file()['sharing']
if offline.is_offline() and not online:
show_link = auth.get_config_file()['offline_show_link']
link_text = auth.get_config_file()['offline_link_text']
return offline.py_offline.iplot(data_or_figure,show_link=show_link,link_text=link_text)
else:
if 'layout' in data_or_figure:
validate = False if 'shapes' in data_or_figure['layout'] else validate
return py.iplot(data_or_figure,validate=validate,sharing=sharing,
filename=filename)
def scatter_matrix(df, theme=None, bins=10, color="grey", size=2):
"""
Displays a matrix with scatter plot for each pair of
Series in the DataFrame.
The diagonal shows a histogram for each of the Series
Parameters:
-----------
df : DataFrame
Pandas DataFrame
theme : string
Theme to be used (if not the default)
bins : int
Number of bins to use for histogram
color : string
Color to be used for each scatter plot
size : int
Size for each marker on the scatter plot
"""
if not theme:
theme = auth.get_config_file()["theme"]
figs = []
for i in df.columns:
for j in df.columns:
if i == j:
fig = df.iplot(kind="histogram", keys=[i], asFigure=True, bins=bins)
figs.append(fig)
else:
figs.append(
df.iplot(kind="scatter", mode="markers", x=j, y=i, asFigure=True, size=size, colors=[color])
)
layout = getLayout(theme)
layout["xaxis1"].update(showgrid=False)
layout["yaxis1"].update(showgrid=False)
sm = subplots(
figs,
shape=(len(df.columns), len(df.columns)),
shared_xaxes=False,
shared_yaxes=False,
horizontal_spacing=0.05,
vertical_spacing=0.07,
base_layout=layout,
)
sm["layout"].update(bargap=0.02, showlegend=False)
return sm
def get_ohlc(df, up_color=None, down_color=None, theme=None, **kwargs):
ohlc = ["open", "high", "low", "close"]
if not theme:
theme = auth.get_config_file()["theme"]
c_dir = _ohlc_dict(df)
args = [df[c_dir[_]] for _ in ohlc]
args.append(df.index)
fig = py.plotly.tools.FigureFactory.create_ohlc(*args, **kwargs)
ohlc_bars = Figure()
ohlc_bars["data"] = fig["data"]
ohlc_bars["layout"] = fig["layout"]
data = ohlc_bars["data"]
if up_color:
data[0]["line"].update(color=normalize(up_color))
if down_color:
data[1]["line"].update(color=normalize(down_color))
ohlc_bars["layout"]["hovermode"] = "closest"
layout = getLayout(theme=theme)
ohlc_bars["layout"] = merge_dict(layout, ohlc_bars["layout"])
return ohlc_bars
def getName(n=1, name=3, exchange=2, columns=None, mode="abc"):
if columns:
if isinstance(columns, str):
columns = [columns]
if n != len(columns):
raise CufflinksError("Length of column names needs to be the \n" "same length of traces")
else:
if mode is None:
mode = get_config_file()["datagen_mode"]
if mode == "abc":
columns = list(string.ascii_letters[:n])
elif mode == "stocks":
columns = [
"".join(np.random.choice(list(string.uppercase), name))
+ "."
+ "".join(np.random.choice(list(string.uppercase), exchange))
for _ in range(n)
]
else:
raise CufflinksError("Unknown mode: {0}".format(mode))
return columns
A productivity tool that binds pandas and plotly. It also provides tools for color generation and transformation. Author: @jorgesantos """ import datetools import utils import datagen import tools import colors import pandastools import ta from plotlytools import * from plotly.plotly import plot from utils import pp from tools import subplots,scatter_matrix,figures from extract import to_df from auth import set_config_file,get_config_file from offline import is_offline,go_offline,go_online from version import __version__ if get_config_file()['offline']: go_offline() else: go_online()
def get_subplots(
rows=1,
cols=1,
shared_xaxes=False,
shared_yaxes=False,
start_cell="top-left",
theme=None,
base_layout=None,
**kwargs
):
"""
Generates a subplot view for a set of figures
Parameters:
-----------
rows : int
Number of rows
cols : int
Number of cols
shared_xaxes : bool
Assign shared x axes.
If True, subplots in the same grid column have one common
shared x-axis at the bottom of the gird.
shared_yaxes : bool
Assign shared y axes.
If True, subplots in the same grid row have one common
shared y-axis on the left-hand side of the gird.
start_cell : string
'bottom-left'
'top-left'
Choose the starting cell in the subplot grid used to set the
domains of the subplots.
theme : string
Layout Theme
solar
pearl
white
see cufflinks.getThemes() for all
available themes
horizontal_spacing : float
[0,1]
Space between subplot columns.
vertical_spacing : float
Space between subplot rows.
specs : list of dicts
Subplot specifications.
ex1: specs=[[{}, {}], [{'colspan': 2}, None]]
ex2: specs=[[{'rowspan': 2}, {}], [None, {}]]
- Indices of the outer list correspond to subplot grid rows
starting from the bottom. The number of rows in 'specs'
must be equal to 'rows'.
- Indices of the inner lists correspond to subplot grid columns
starting from the left. The number of columns in 'specs'
must be equal to 'cols'.
- Each item in the 'specs' list corresponds to one subplot
in a subplot grid. (N.B. The subplot grid has exactly 'rows'
times 'cols' cells.)
- Use None for blank a subplot cell (or to move pass a col/row span).
- Note that specs[0][0] has the specs of the 'start_cell' subplot.
- Each item in 'specs' is a dictionary.
The available keys are:
* is_3d (boolean, default=False): flag for 3d scenes
* colspan (int, default=1): number of subplot columns
for this subplot to span.
* rowspan (int, default=1): number of subplot rows
for this subplot to span.
* l (float, default=0.0): padding left of cell
* r (float, default=0.0): padding right of cell
* t (float, default=0.0): padding right of cell
* b (float, default=0.0): padding bottom of cell
- Use 'horizontal_spacing' and 'vertical_spacing' to adjust
the spacing in between the subplots.
insets : list of dicts
Inset specifications.
- Each item in 'insets' is a dictionary.
The available keys are:
* cell (tuple, default=(1,1)): (row, col) index of the
subplot cell to overlay inset axes onto.
* is_3d (boolean, default=False): flag for 3d scenes
* l (float, default=0.0): padding left of inset
in fraction of cell width
* w (float or 'to_end', default='to_end') inset width
in fraction of cell width ('to_end': to cell right edge)
* b (float, default=0.0): padding bottom of inset
in fraction of cell height
* h (float or 'to_end', default='to_end') inset height
in fraction of cell height ('to_end': to cell top edge)
"""
if not theme:
theme = auth.get_config_file()["theme"]
layout = base_layout if base_layout else getLayout(theme)
sp = py.plotly.tools.make_subplots(
rows=rows,
cols=cols,
shared_xaxes=shared_xaxes,
shared_yaxes=shared_yaxes,
print_grid=False,
start_cell=start_cell,
**kwargs
)
for k, v in layout.items():
if not isinstance(v, XAxis) and not isinstance(v, YAxis):
sp["layout"].update({k: v})
if "subplot_titles" in kwargs:
if "annotations" in layout:
annotation = sp["layout"]["annotations"][0]
else:
annotation = getLayout(theme, annotations=Annotation(text=""))["annotations"]
for ann in sp["layout"]["annotations"]:
ann["font"].update(color=annotation["font"]["color"])
def update_items(sp_item, layout, axis):
for k, v in layout[axis].items():
sp_item.update({k: v})
for k, v in sp["layout"].items():
if isinstance(v, XAxis):
update_items(v, layout, "xaxis1")
elif isinstance(v, YAxis):
update_items(v, layout, "xaxis1")
return sp
def getLayout(
theme=None,
title="",
xTitle="",
yTitle="",
zTitle="",
barmode="",
bargap=None,
bargroupgap=None,
gridcolor=None,
zerolinecolor=None,
margin=None,
annotations=False,
is3d=False,
**kwargs
):
"""
Generates a plotly Layout
Parameters:
-----------
theme : string
Layout Theme
solar
pearl
white
title : string
Chart Title
xTitle : string
X Axis Title
yTitle : string
Y Axis Title
zTitle : string
Z Axis Title
Applicable only for 3d charts
barmode : string
Mode when displaying bars
group
stack
overlay
bargap : float
Sets the gap between bars
[0,1)
Applicabe for bar and histogram plots
bargroupgap : float
Set the gap between groups
[0,1)
Applicabe for bar and histogram plots
gridcolor : string
grid color
zerolinecolor : string
zero line color
margin : dict or tuple
Dictionary (l,r,b,t) or
Tuple containing the left,
right, bottom and top margins
annotations : dictionary
Dictionary of annotations
{x_point : text}
is3d : bool
Indicates if the layout is for a 3D chart
Other Kwargs
============
Shapes
hline : int, list or dict
Draws a horizontal line at the
indicated y position(s)
Extra parameters can be passed in
the form of a dictionary (see shapes)
vline : int, list or dict
Draws a vertical line at the
indicated x position(s)
Extra parameters can be passed in
the form of a dictionary (see shapes)
hline : [y0,y1]
Draws a horizontal rectangle at the
indicated (y0,y1) positions.
Extra parameters can be passed in
the form of a dictionary (see shapes)
vline : [x0,x1]
Draws a vertical rectangle at the
indicated (x0,x1) positions.
Extra parameters can be passed in
the form of a dictionary (see shapes)
shapes : dict or list(dict)
List of dictionaries with the
specifications of a given shape.
See help(cufflinks.tools.get_shape)
for more information
"""
for key in kwargs.keys():
if key not in __LAYOUT_KWARGS:
raise Exception("Invalid keyword : '{0}'".format(key))
if not theme:
theme = auth.get_config_file()["theme"]
size = None
if annotations:
if "font" in annotations:
if "size" in annotations["font"]:
size = annotations["font"]["size"]
def update_annotations(annotations, font_color, arrow_color):
if annotations:
if isinstance(annotations, dict):
annotations = [annotations]
for i in annotations:
i.update(dict(arrowcolor=arrow_color, font={"color": font_color}))
theme_data = getTheme(theme)
layout = theme_data["layout"]
layout["xaxis1"].update({"title": xTitle})
layout["yaxis1"].update({"title": yTitle})
if annotations:
update_annotations(annotations, theme_data["annotations"]["fontcolor"], theme_data["annotations"]["arrowcolor"])
if barmode:
layout.update({"barmode": barmode})
if bargroupgap:
layout.update({"bargroupgap": bargroupgap})
if bargap:
layout.update(bargap=bargap)
if title:
layout.update({"title": title})
if annotations:
if size:
annotations["font"]["size"] = size
layout.update({"annotations": annotations})
if gridcolor:
for k in layout:
if "axis" in k:
layout[k].update(gridcolor=normalize(gridcolor))
if zerolinecolor:
for k in layout:
if "axis" in k:
layout[k].update(zerolinecolor=normalize(zerolinecolor))
if margin:
if isinstance(margin, dict):
margin = margin
else:
margin = dict(zip(("l", "r", "b", "t"), margin))
layout.update(margin=margin)
if is3d:
if "3d" in theme_data:
layout.update(theme_data["3d"])
zaxis = layout["xaxis1"].copy()
zaxis.update(title=zTitle)
scene = Scene(xaxis=layout["xaxis1"], yaxis=layout["yaxis1"], zaxis=zaxis)
layout.update(scene=scene)
del layout["xaxis1"]
del layout["yaxis1"]
## Kwargs
if "legend" in kwargs:
layout["showlegend"] = kwargs["legend"]
if "logy" in kwargs:
if kwargs["logy"]:
layout["yaxis1"]["type"] = "log"
if "logx" in kwargs:
if kwargs["logx"]:
layout["xaxis1"]["type"] = "log"
# Shapes
if any(k in kwargs for k in ["vline", "hline", "shapes", "hspan", "vspan"]):
shapes = []
def get_shapes(xline):
orientation = xline[0]
xline = kwargs[xline]
if isinstance(xline, list):
for x_i in xline:
if isinstance(x_i, dict):
x_i["kind"] = "line"
shapes.append(get_shape(**x_i))
else:
if orientation == "h":
shapes.append(get_shape(kind="line", y=x_i))
else:
shapes.append(get_shape(kind="line", x=x_i))
elif isinstance(xline, dict):
shapes.append(get_shape(**xline))
else:
if orientation == "h":
shapes.append(get_shape(kind="line", y=xline))
else:
shapes.append(get_shape(kind="line", x=xline))
def get_span(xspan):
orientation = xspan[0]
xspan = kwargs[xspan]
if isinstance(xspan, list):
for x_i in xspan:
if isinstance(x_i, dict):
x_i["kind"] = "rect"
shapes.append(get_shape(**x_i))
else:
v0, v1 = x_i
if orientation == "h":
shapes.append(get_shape(kind="rect", y0=v0, y1=v1, fill=True, opacity=0.5))
else:
shapes.append(get_shape(kind="rect", x0=v0, x1=v1, fill=True, opacity=0.5))
elif isinstance(xspan, dict):
xspan["kind"] = "rect"
shapes.append(get_shape(**xspan))
elif isinstance(xspan, tuple):
v0, v1 = xspan
if orientation == "h":
shapes.append(get_shape(kind="rect", y0=v0, y1=v1, fill=True, opacity=0.5))
else:
shapes.append(get_shape(kind="rect", x0=v0, x1=v1, fill=True, opacity=0.5))
else:
raise Exception("Invalid value for {0}span: {1}".format(orientation, xspan))
if "hline" in kwargs:
get_shapes("hline")
if "vline" in kwargs:
get_shapes("vline")
if "hspan" in kwargs:
get_span("hspan")
if "vspan" in kwargs:
get_span("vspan")
if "shapes" in kwargs:
shapes_ = kwargs["shapes"]
if isinstance(shapes_, list):
for i in shapes_:
shp = i if "type" in i else get_shape(**i)
shapes.append(shp)
elif isinstance(shapes_, dict):
shp = shapes_ if "type" in shapes_ else get_shape(**shapes_)
shapes.append(shp)
else:
raise Exception("Shapes need to be either a dict or list of dicts")
layout["shapes"] = shapes
def updateColors(layout):
for k, v in layout.items():
if isinstance(v, dict):
updateColors(v)
else:
if isinstance(v, list):
for _ in v:
if isinstance(_, dict):
updateColors(_)
if "color" in k.lower():
if "rgba" not in v:
layout[k] = normalize(v)
return layout
return updateColors(layout)
def _iplot(self,data=None,layout=None,filename='',sharing=None,
kind='scatter',title='',xTitle='',yTitle='',zTitle='',theme=None,colors=None,colorscale=None,fill=False,width=None,
dash='solid',mode='lines',symbol='dot',size=12,barmode='',sortbars=False,bargap=None,bargroupgap=None,bins=None,histnorm='',
histfunc='count',orientation='v',boxpoints=False,annotations=None,keys=False,bestfit=False,
bestfit_colors=None,mean=False,mean_colors=None,categories='',x='',y='',z='',text='',gridcolor=None,
zerolinecolor=None,margin=None,labels=None,values=None,secondary_y='',subplots=False,shape=None,error_x=None,
error_y=None,error_type='data',asFrame=False,asDates=False,asFigure=False,
asImage=False,dimensions=(1116,587),asPlot=False,asUrl=False,online=None,**kwargs):
"""
Returns a plotly chart either as inline chart, image of Figure object
Parameters:
-----------
data : Data
Plotly Data Object.
If not entered then the Data object will be automatically
generated from the DataFrame.
data : Data
Plotly Data Object.
If not entered then the Data object will be automatically
generated from the DataFrame.
layout : Layout
Plotly layout Object
If not entered then the Layout objet will be automatically
generated from the DataFrame.
filename : string
Filename to be saved as in plotly account
sharing : string
Sets the sharing level permission
public - anyone can see this chart
private - only you can see this chart
secret - only people with the link can see the chart
kind : string
Kind of chart
scatter
bar
box
spread
ratio
heatmap
surface
histogram
bubble
bubble3d
scatter3d
title : string
Chart Title
xTitle : string
X Axis Title
yTitle : string
Y Axis Title
zTitle : string
zTitle : string
Z Axis Title
Applicable only for 3d charts
theme : string
Layout Theme
solar
pearl
white
see cufflinks.getThemes() for all
available themes
colors : list or dict
{key:color} to specify the color for each column
[colors] to use the colors in the defined order
colorscale : str
Color scale name
If the color name is preceded by a minus (-)
then the scale is inversed
Only valid if 'colors' is null
See cufflinks.colors.scales() for available scales
fill : bool
Filled Traces
width : int
Line width
dash : string
Drawing style of lines
solid
dash
dashdot
dot
mode : string
Plotting mode for scatter trace
lines
markers
lines+markers
lines+text
markers+text
lines+markers+text
symbol : string
The symbol that is drawn on the plot for each marker
Valid only when mode includes markers
dot
cross
diamond
square
triangle-down
triangle-left
triangle-right
triangle-up
x
size : string or int
Size of marker
Valid only if marker in mode
barmode : string
Mode when displaying bars
group
stack
overlay
* Only valid when kind='bar'
sortbars : bool
Sort bars in descending order
* Only valid when kind='bar'
bargap : float
Sets the gap between bars
[0,1)
* Only valid when kind is 'histogram' or 'bar'
bargroupgap : float
Set the gap between groups
[0,1)
* Only valid when kind is 'histogram' or 'bar'
bins : int
Specifies the number of bins
* Only valid when kind='histogram'
histnorm : string
'' (frequency)
percent
probability
density
probability density
Sets the type of normalization for an histogram trace. By default
the height of each bar displays the frequency of occurrence, i.e.,
the number of times this value was found in the
corresponding bin. If set to 'percent', the height of each bar
displays the percentage of total occurrences found within the
corresponding bin. If set to 'probability', the height of each bar
displays the probability that an event will fall into the
corresponding bin. If set to 'density', the height of each bar is
equal to the number of occurrences in a bin divided by the size of
the bin interval such that summing the area of all bins will yield
the total number of occurrences. If set to 'probability density',
the height of each bar is equal to the number of probability that an
event will fall into the corresponding bin divided by the size of
the bin interval such that summing the area of all bins will yield
1.
* Only valid when kind='histogram'
histfunc : string
count
sum
avg
min
max
Sets the binning function used for an histogram trace.
* Only valid when kind='histogram'
orientation : string
h
v
Sets the orientation of the bars. If set to 'v', the length of each
| bar will run vertically. If set to 'h', the length of each bar will
| run horizontally
* Only valid when kind is 'histogram','bar' or 'box'
boxpoints : string
Displays data points in a box plot
outliers
all
suspectedoutliers
False
annotations : dictionary
Dictionary of annotations
{x_point : text}
keys : list of columns
List of columns to chart.
Also can be usded for custom sorting.
bestfit : boolean or list
If True then a best fit line will be generated for
all columns.
If list then a best fit line will be generated for
each key on the list.
bestfit_colors : list or dict
{key:color} to specify the color for each column
[colors] to use the colors in the defined order
categories : string
Name of the column that contains the categories
x : string
Name of the column that contains the x axis values
y : string
Name of the column that contains the y axis values
z : string
Name of the column that contains the z axis values
text : string
Name of the column that contains the text values
gridcolor : string
Grid color
zerolinecolor : string
Zero line color
margin : dict or tuple
Dictionary (l,r,b,t) or
Tuple containing the left,
right, bottom and top margins
labels : string
Name of the column that contains the labels.
* Only valid when kind='pie'
values : string
Name of the column that contains the values.
* Only valid when kind='pie'
secondary_y : string or list(string)
Name(s) of the column to be charted on the
right hand side axis
subplots : bool
If true then each trace is placed in
subplot layout
shape : (rows,cols)
Tuple indicating the size of rows and columns
If omitted then the layout is automatically set
* Only valid when subplots=True
error_x : int or float or [int or float]
error values for the x axis
error_y : int or float or [int or float]
error values for the y axis
error_type : string
type of error bars
'data'
'constant'
'percent'
'sqrt'
'continuous'
'continuous_percent'
asFrame : bool
If true then the data component of Figure will
be of Pandas form (Series) otherwise they will
be index values
asDates : bool
If true it truncates times from a DatetimeIndex
asFigure : bool
If True returns plotly Figure
asImage : bool
If True it returns Image
* Only valid when asImage=True
dimensions : tuple(int,int)
Dimensions for image
(width,height)
asPlot : bool
If True the chart opens in browser
asUrl : bool
If True the chart url is returned. No chart is displayed.
online : bool
If True then the chart is rendered on the server
even when running in offline mode.
Other Kwargs
============
Pie charts
sort : bool
If True it sorts the labels by value
pull : float [0-1]
Pulls the slices from the centre
hole : float [0-1]
Sets the size of the inner hole
textposition : string
Sets the position of the legends for each slice
outside
inner
textinfo : string
Sets the information to be displayed on
the legends
label
percent
value
* or ony combination of the above using
'+' between each item
ie 'label+percent'
Error Bars
error_trace : string
Name of the column for which error should be
plotted. If omitted then errors apply to all
traces.
error_values_minus : int or float or [int or float]
Values corresponding to the span of the error bars
below the trace coordinates
error_color : string
Color for error bars
error_thickness : float
Sets the line thickness of the error bars
error_width : float
Sets the width (in pixels) of the cross-bar at both
ends of the error bars
error_opacity : float [0,1]
Opacity for the error bars
Subplots
horizontal_spacing : float [0,1]
Space between subplot columns.
vertical_spacing : float [0,1]
Space between subplot rows.
subplot_titles : bool
If True, chart titles are plotted
at the top of each subplot
shared_xaxes : bool
Assign shared x axes.
If True, subplots in the same grid column have one common
shared x-axis at the bottom of the grid.
shared_yaxes : bool
Assign shared y axes.
If True, subplots in the same grid row have one common
shared y-axis on the left-hand side of the grid.
Shapes
hline : int, list or dict
Draws a horizontal line at the
indicated y position(s)
Extra parameters can be passed in
the form of a dictionary (see shapes)
vline : int, list or dict
Draws a vertical line at the
indicated x position(s)
Extra parameters can be passed in
the form of a dictionary (see shapes)
hline : [y0,y1]
Draws a horizontal rectangle at the
indicated (y0,y1) positions.
Extra parameters can be passed in
the form of a dictionary (see shapes)
vline : [x0,x1]
Draws a vertical rectangle at the
indicated (x0,x1) positions.
Extra parameters can be passed in
the form of a dictionary (see shapes)
shapes : dict or list(dict)
List of dictionaries with the
specifications of a given shape.
See help(cufflinks.tools.get_shape)
for more information
"""
# Look for invalid kwargs
valid_kwargs = ['color','opacity','column','columns','labels','text','world_readable']
PIE_KWARGS=['sort','pull','hole','textposition','textinfo','linecolor']
OHLC_KWARGS=['up_color','down_color']
SUBPLOT_KWARGS=['horizontal_spacing', 'vertical_spacing',
'specs', 'insets','start_cell','shared_xaxes','shared_yaxes','subplot_titles']
ERROR_KWARGS=['error_trace','error_values_minus','error_color','error_thickness',
'error_width','error_opacity']
kwargs_list = [tools.__LAYOUT_KWARGS,OHLC_KWARGS,PIE_KWARGS,SUBPLOT_KWARGS,ERROR_KWARGS]
[valid_kwargs.extend(_) for _ in kwargs_list]
for key in kwargs.keys():
if key not in valid_kwargs:
raise Exception("Invalid keyword : '{0}'".format(key))
# Setting default values
if not colors:
colors=kwargs['color'] if 'color' in kwargs else colors
if isinstance(colors,str):
colors=[colors]
opacity=kwargs['opacity'] if 'opacity' in kwargs else 0.8
# Get values from config theme
if theme is None:
theme = auth.get_config_file()['theme']
theme_config=tools.getTheme(theme)
if colorscale is None:
colorscale=theme_config['colorscale'] if 'colorscale' in theme_config else 'dflt'
if width is None:
if kind != 'pie':
width=theme_config['linewidth'] if 'linewidth' in theme_config else 2
# if bargap is None:
# bargap=theme_config['bargap'] if 'bargap' in theme_config else 0
# In case column was used instead of keys
if 'column' in kwargs:
keys=[kwargs['column']] if isinstance(kwargs['column'],str) else kwargs['column']
if 'columns' in kwargs:
keys=[kwargs['columns']] if isinstance(kwargs['columns'],str) else kwargs['columns']
kind='line' if kind=='lines' else kind
# We assume we are good citizens
validate=True
if not layout:
l_kwargs=dict([(k,kwargs[k]) for k in tools.__LAYOUT_KWARGS if k in kwargs])
if annotations:
annotations=getAnnotations(self.copy(),annotations)
layout=tools.getLayout(theme=theme,xTitle=xTitle,yTitle=yTitle,zTitle=zTitle,title=title,barmode=barmode,
bargap=bargap,bargroupgap=bargroupgap,annotations=annotations,gridcolor=gridcolor,
zerolinecolor=zerolinecolor,margin=margin,is3d='3d' in kind,**l_kwargs)
if not data:
if categories:
data=Data()
if 'bar' in kind:
df=self.copy()
df=df.set_index(categories)
fig=df.figure(kind=kind,colors=colors,colorscale=colorscale,fill=fill,width=width,sortbars=sortbars,
asDates=asDates,mode=mode,symbol=symbol,size=size,text=text,barmode=barmode,orientation=orientation)
data=fig['data']
else:
_keys=pd.unique(self[categories])
colors=get_colors(colors,colorscale,_keys)
mode='markers' if 'markers' not in mode else mode
for _ in _keys:
__=self[self[categories]==_].copy()
if text:
_text=__[text] if asFrame else __[text].values
_x=__[x] if asFrame else __[x].values
_y=__[y] if asFrame else __[y].values
if z:
_z=__[z] if asFrame else __[z].values
if 'bubble' in kind:
rg=__[size].values
rgo=self[size].values
_size=[int(100*(float(i)-rgo.min())/(rgo.max()-rgo.min()))+12 for i in rg]
else:
_size=size
_data=Scatter3d(x=_x,y=_y,mode=mode,name=_,
marker=Marker(color=colors[_],symbol=symbol,size=_size,opacity=opacity,
line=Line(width=width)),textfont=tools.getLayout(theme=theme)['xaxis1']['titlefont'])
if '3d' in kind:
_data=Scatter3d(x=_x,y=_y,z=_z,mode=mode,name=_,
marker=Marker(color=colors[_],symbol=symbol,size=_size,opacity=opacity,
line=Line(width=width)),textfont=tools.getLayout(theme=theme)['xaxis1']['titlefont'])
else:
_data=Scatter(x=_x,y=_y,mode=mode,name=_,
marker=Marker(color=colors[_],symbol=symbol,size=_size,opacity=opacity,
line=Line(width=width)),textfont=tools.getLayout(theme=theme)['xaxis1']['titlefont'])
if text:
_data.update(text=_text)
data.append(_data)
else:
if kind in ('scatter','spread','ratio','bar','barh','area','line'):
df=self.copy()
if type(df)==pd.core.series.Series:
df=pd.DataFrame({df.name:df})
if x:
df=df.set_index(x)
if y:
df=df[y]
if kind=='area':
df=df.transpose().fillna(0).cumsum().transpose()
if text:
if not isinstance(text,list):
text=self[text].values
data=df.to_iplot(colors=colors,colorscale=colorscale,kind=kind,fill=fill,width=width,dash=dash,sortbars=sortbars,keys=keys,
bestfit=bestfit,bestfit_colors=bestfit_colors,mean=mean,mean_colors=mean_colors,asDates=asDates,mode=mode,symbol=symbol,size=size,
text=text,**kwargs)
if kind in ('spread','ratio'):
if kind=='spread':
trace=self.apply(lambda x:x[0]-x[1],axis=1)
positive=trace.apply(lambda x:x if x>=0 else pd.np.nan)
negative=trace.apply(lambda x:x if x<0 else pd.np.nan)
trace=pd.DataFrame({'positive':positive,'negative':negative})
trace=trace.to_iplot(colors={'positive':'green','negative':'red'},width=0.5)
else:
trace=self.apply(lambda x:x[0]*1.0/x[1],axis=1).to_iplot(colors=['green'],width=1)
trace.update({'xaxis':'x2','yaxis':'y2','fill':'tozeroy',
'name':kind.capitalize(),'connectgaps':False,'showlegend':False})
data.append(Scatter(trace[0]))
if kind=='spread':
data.append(Scatter(trace[1]))
layout['yaxis1'].update({'domain':[.3,1]})
layout['yaxis2']=copy.deepcopy(layout['yaxis1'])
layout['xaxis2']=copy.deepcopy(layout['xaxis1'])
layout['yaxis2'].update(domain=[0,.25],title=kind.capitalize())
layout['xaxis2'].update(anchor='y2',showticklabels=False)
layout['hovermode']='x'
if 'bar' in kind:
if 'stack' in barmode:
layout['legend'].update(traceorder='normal')
orientation = 'h' if kind=='barh' else orientation
for trace in data:
trace.update(orientation=orientation)
if kind=='barh':
trace['x'],trace['y']=trace['y'],trace['x']
elif kind=='bubble':
mode='markers' if 'markers' not in mode else mode
x=self[x].values.tolist()
y=self[y].values.tolist()
z=size if size else z
rg=self[z].values
z=[int(100*(float(_)-rg.min())/(rg.max()-rg.min()))+12 for _ in rg]
text=kwargs['labels'] if 'labels' in kwargs else text
labels=self[text].values.tolist() if text else ''
clrs=colors if colors else get_scales(colorscale)
clrs=[clrs] if not isinstance(clrs,list) else clrs
clrs=[clrs[0]]*len(x)
marker=Marker(color=clrs,size=z,symbol=symbol,
line=Line(width=width),textfont=tools.getLayout(theme=theme)['xaxis1']['titlefont'])
trace=Scatter(x=x,y=y,marker=marker,mode='markers',text=labels)
data=Data([trace])
elif kind in ('box','histogram','hist'):
if isinstance(self,pd.core.series.Series):
df=pd.DataFrame({self.name:self})
else:
df=self.copy()
data=Data()
clrs=get_colors(colors,colorscale,df.columns)
if 'hist' in kind:
barmode = 'overlay' if barmode=='' else barmode
layout.update(barmode=barmode)
columns=keys if keys else df.columns
for _ in columns:
if kind=='box':
__=Box(y=df[_].values.tolist(),marker=Marker(color=clrs[_]),name=_,
line=Line(width=width),boxpoints=boxpoints)
else:
__=Histogram(x=df[_].values.tolist(),marker=Marker(color=clrs[_]),name=_,
line=Line(width=width),orientation=orientation,
opacity=kwargs['opacity'] if 'opacity' in kwargs else .8, histfunc=histfunc,
histnorm=histnorm)
if orientation=='h':
__['y']=__['x']
del __['x']
if bins:
if orientation=='h':
__.update(nbinsy=bins)
else:
__.update(nbinsx=bins)
data.append(__)
elif kind in ('heatmap','surface'):
x=self[x].values.tolist() if x else self.index.values.tolist()
y=self[y].values.tolist() if y else self.columns.values.tolist()
z=self[z].values.tolist() if z else self.values.transpose()
scale=get_scales('rdbu') if not colorscale else get_scales(colorscale)
colorscale=[[float(_)/(len(scale)-1),scale[_]] for _ in range(len(scale))]
if kind=='heatmap':
data=Data([Heatmap(z=z,x=x,y=y,colorscale=colorscale)])
else:
data=Data([Surface(z=z,x=x,y=y,colorscale=colorscale)])
elif kind in ('scatter3d','bubble3d'):
data=Data()
keys=self[text].values if text else range(len(self))
colors=get_colors(colors,colorscale,keys,asList=True)
df=self.copy()
df['index']=keys
if kind=='bubble3d':
rg=self[size].values
size=[int(100*(float(_)-rg.min())/(rg.max()-rg.min()))+12 for _ in rg]
else:
size=[size for _ in range(len(keys))]
_data=Scatter3d(x=df[x].values.tolist(),y=df[y].values.tolist(),z=df[z].values.tolist(),mode=mode,name=keys,
marker=Marker(color=colors,symbol=symbol,size=size,opacity=.8))
if text:
_data.update(text=keys)
data.append(_data)
elif kind=='pie':
labels=self[labels].values.tolist()
values=self[values].values.tolist()
marker={'colors':get_colors(colors,colorscale,labels,asList=True)}
line={}
if 'linecolor' in kwargs:
line['color']=kwargs['linecolor']
if width:
line['width']=width
if line:
marker['line']=line
pie={'type':'pie','values':values,'labels':labels,'name':'',
'marker':marker}
for kw in ['sort','pull','hole','textposition','textinfo']:
if kw in kwargs:
pie[kw]=kwargs[kw]
data=Data()
del layout['xaxis1']
del layout['yaxis1']
data.append(pie)
validate=False
elif kind in ['candle','ohlc']:
d=tools._ohlc_dict(self)
if len(d.keys())!=4:
raise Exception("OHLC type of charts require an Open, High, Low and Close column")
ohlc_kwargs=check_kwargs(kwargs,OHLC_KWARGS)
if kind=='candle':
fig=tools.get_candle(self,theme=theme,**ohlc_kwargs)
else:
fig=tools.get_ohlc(self,theme=theme,**ohlc_kwargs)
if bestfit:
df=self.copy()
bf=_to_iplot(self[d['close']],bestfit=True,bestfit_colors=bestfit_colors,asTimestamp=True)
fig['data'].append(bf[1])
data=fig['data']
layout=fig['layout']
## Sharing Values
if all(['world_readable' in kwargs,sharing is None]):
sharing=kwargs['world_readable']
if isinstance(sharing,bool):
if sharing:
sharing='public'
else:
sharing='private'
if sharing is None:
sharing=auth.get_config_file()['sharing']
if not filename:
if title:
filename=title
else:
filename='Plotly Playground {0}'.format(time.strftime("%Y-%m-%d %H:%M:%S"))
## Figure defintion
figure=Figure()
figure['data']=data
figure['layout']=layout
## Check secondary axis
if secondary_y:
figure=figure.set_axis(secondary_y,side='right')
## Error Bars
if kind in ('scatter','bar','barh','lines'):
if any([error_x,error_y]):
def set_error(axis,**kwargs):
return tools.set_errors(figure,axis=axis,**kwargs)
kw=check_kwargs(kwargs,ERROR_KWARGS)
kw=dict([(k.replace('error_',''),v) for k,v in kw.items()])
kw['type']=error_type
if error_x:
kw['values']=error_x
figure=set_error('x',**kw)
if error_y:
kw['values']=error_y
figure=set_error('y',**kw)
## Subplots
if subplots:
fig=tools.strip_figures(figure)
kw=check_kwargs(kwargs,SUBPLOT_KWARGS)
if 'subplot_titles' in kwargs:
if kwargs['subplot_titles']==True:
kw['subplot_titles']=[d['name'] for d in data]
else:
kw['subplot_titles']=kwargs['subplot_titles']
figure=tools.subplots(fig,shape,base_layout=layout,theme=theme,**kw)
## Exports
validate = False if 'shapes' in layout else validate
if asFigure:
return figure
elif asImage:
try:
py.image.save_as(figure,filename='img/'+filename,format='png',
width=dimensions[0],height=dimensions[1])
path='img/'+filename+'.png'
except:
py.image.save_as(figure,filename=filename,format='png',
width=dimensions[0],height=dimensions[1])
path=filename+'.png'
return display(Image(path))
elif asPlot:
return py.plot(figure,sharing=sharing,filename=filename,validate=validate)
elif asUrl:
return py.plot(figure,sharing=sharing,filename=filename,validate=validate,auto_open=False)
else:
return iplot(figure,sharing=sharing,filename=filename,validate=validate,online=online)
def _ta_plot(self,study,periods=14,column=None,include=True,str=None,detail=False,
theme=None,sharing=None,filename='',asFigure=False,**iplot_kwargs):
"""
Generates a Technical Study Chart
Parameters:
-----------
study : string
Technical Study to be charted
sma - 'Simple Moving Average'
rsi - 'R Strength Indicator'
periods : int
Number of periods
column : string
Name of the column on which the
study will be done
include : bool
Indicates if the input column(s)
should be included in the chart
str : string
Label factory for studies
The following wildcards can be used:
{name} : Name of the column
{study} : Name of the study
{period} : Period used
Examples:
'study: {study} - period: {period}'
detail : bool
If True the supporting data/calculations
are included in the chart
study_colors : string or [string]
Colors to be used for the studies
Study Specific Parameters
-------------------------
RSI
rsi_upper : int (0,100]
Level for the upper rsi band
rsi_lower : int (0,100]
Level for the lower rsi band
BOLL
boll_std : int or float
Number of standard deviations
MACD
fast_period : int
Number of periods for the fast moving average
slow_period : int
Number of periods for the slow moving average
signal_period : int
Number of periods for the signal
CORREL
how : string
Method for the correlation calculation
values
pct_cht
diff
"""
if 'columns' in iplot_kwargs:
column=iplot_kwargs['columns']
del iplot_kwargs['columns']
if 'period' in iplot_kwargs:
periods=iplot_kwargs['period']
del iplot_kwargs['period']
if 'world_readable' in iplot_kwargs:
sharing=iplot_kwargs['world_readable']
del iplot_kwargs['world_readable']
if sharing is None:
sharing = auth.get_config_file()['sharing']
if isinstance(sharing,bool):
if sharing:
sharing='public'
else:
sharing='private'
iplot_kwargs['sharing']=sharing
if theme is None:
theme = auth.get_config_file()['theme']
if not filename:
if 'title' in iplot_kwargs:
filename=iplot_kwargs['title']
else:
filename='Plotly Playground {0}'.format(time.strftime("%Y-%m-%d %H:%M:%S"))
def get_subplots(figures):
shape=(len(figures),1)
layout=tools.get_base_layout(figures)
subplots=tools.subplots(figures,shape=shape,shared_xaxes=True,base_layout=layout)
if len(figures)==2:
subplots['layout']['yaxis1']['domain']=[.27,1.0]
subplots['layout']['yaxis2']['domain']=[0,.25]
return subplots
def get_study(df,func,iplot_kwargs,iplot_study_kwargs,str=None,include=False,column=None,inset=False):
df=df.copy()
if inset:
if not column:
if isinstance(df,pd.DataFrame):
column=df.keys().tolist()
else:
df=pd.DataFrame(df)
column=df.keys().tolist()
if 'legend' in iplot_kwargs:
iplot_study_kwargs['legend']=iplot_kwargs['legend']
fig_0=df.figure(**iplot_kwargs)
df_ta=func(df,column=column,include=False,str=str,**study_kwargs)
kind=iplot_kwargs['kind'] if 'kind' in iplot_kwargs else ''
iplot_study_kwargs['kind']='scatter'
iplot_study_kwargs['colors']='blue' if 'colors' not in iplot_study_kwargs else iplot_study_kwargs['colors']
fig_1=df_ta.figure(theme=theme,**iplot_study_kwargs)
if kind in ['candle','ohlc']:
for i in fig_1['data']:
i['x']=[pd.Timestamp(_) for _ in i['x']]
if inset:
figure=tools.merge_figures([fig_0,fig_1]) if include else fig_1
else:
figure=get_subplots([fig_0,fig_1]) if include else fig_1
return figure
study_kwargs={}
iplot_study_kwargs={}
for k in iplot_kwargs.keys():
if 'study' in k:
iplot_study_kwargs[k.replace('study_','')]=iplot_kwargs[k]
del iplot_kwargs[k]
for k in __TA_KWARGS:
if k in iplot_kwargs:
study_kwargs[k]=iplot_kwargs[k]
del iplot_kwargs[k]
if study=='rsi':
study_kwargs.update({'periods':periods})
figure=get_study(self,ta.rsi,iplot_kwargs,iplot_study_kwargs,include=include,column=column,str=str,inset=False)
rsi_upper=study_kwargs['rsi_upper'] if 'rsi_upper' in study_kwargs else 70
rsi_lower=study_kwargs['rsi_lower'] if 'rsi_lower' in study_kwargs else 30
yref='y2' if include else 'y1'
shapes=[tools.get_shape(y=i,yref=yref,color=j,dash='dash') for (i,j) in [(rsi_lower,'green'),(rsi_upper,'red')]]
figure['layout']['shapes']=shapes
if study=='macd':
figure=get_study(self,ta.macd,iplot_kwargs,iplot_study_kwargs,include=include,column=column,str=str,inset=False)
if study=='sma':
study_kwargs.update({'periods':periods})
figure=get_study(self,ta.sma,iplot_kwargs,iplot_study_kwargs,include=include,column=column,str=str,inset=True)
if study=='boll':
study_kwargs.update({'periods':periods})
figure=get_study(self,ta.boll,iplot_kwargs,iplot_study_kwargs,include=include,column=column,str=str,inset=True)
if study=='correl':
study_kwargs.update({'periods':periods})
figure=get_study(self,ta.correl,iplot_kwargs,iplot_study_kwargs,include=include,column=column,str=str,inset=False)
## Exports
if asFigure:
return figure
else:
return iplot(figure,sharing=sharing,filename=filename)
def _iplot(self,data=None,layout=None,filename='',world_readable=None,
kind='scatter',title='',xTitle='',yTitle='',zTitle='',theme='pearl',colors=None,colorscale=None,fill=False,width=2,
mode='lines',symbol='dot',size=12,barmode='',sortbars=False,boxpoints=False,annotations=None,keys=False,bestfit=False,bestfit_colors=None,
categories='',x='',y='',z='',text='',gridcolor=None,zerolinecolor=None,margin=None,
asFrame=False,asDates=False,asFigure=False,asImage=False,dimensions=(1116,587),
asPlot=False,asUrl=False,**kwargs):
"""
Returns a plotly chart either as inline chart, image of Figure object
Parameters:
-----------
data : Data
Plotly Data Object.
If not entered then the Data object will be automatically
generated from the DataFrame.
data : Data
Plotly Data Object.
If not entered then the Data object will be automatically
generated from the DataFrame.
layout : Layout
Plotly layout Object
If not entered then the Layout objet will be automatically
generated from the DataFrame.
filename : string
Filename to be saved as in plotly account
world_readable : bool
If False then it will be saved as a private file
kind : string
Kind of chart
scatter
bar
box
spread
ratio
heatmap
surface
histogram
bubble
bubble3d
scatter3d
title : string
Chart Title
xTitle : string
X Axis Title
yTitle : string
Y Axis Title
zTitle : string
zTitle : string
Z Axis Title
Applicable only for 3d charts
theme : string
Layout Theme
solar
pearl
white
colors : list or dict
{key:color} to specify the color for each column
[colors] to use the colors in the defined order
colorscale : str
Color scale name
If the color name is preceded by a minus (-)
then the scale is inversed
Only valid if 'colors' is null
See cufflinks.colors.scales() for available scales
fill : bool
Filled Traces
width : int
Line width
mode : string
Plotting mode for scatter trace
lines
markers
lines+markers
lines+text
markers+text
lines+markers+text
symbol : string
The symbol that is drawn on the plot for each marker
Valid only when mode includes markers
dot
cross
diamond
square
triangle-down
triangle-left
triangle-right
triangle-up
x
size : string or int
Size of marker
Valid only if marker in mode
barmode : string
Mode when displaying bars
group
stack
overlay
* Only valid when kind='bar'
sortbars : bool
Sort bars in descending order
* Only valid when kind='bar'
boxpoints : string
Displays data points in a box plot
outliers
all
suspectedoutliers
False
annotations : dictionary
Dictionary of annotations
{x_point : text}
keys : list of columns
List of columns to chart.
Also can be usded for custom sorting.
bestfit : boolean or list
If True then a best fit line will be generated for
all columns.
If list then a best fit line will be generated for
each key on the list.
bestfit_colors : list or dict
{key:color} to specify the color for each column
[colors] to use the colors in the defined order
categories : string
Name of the column that contains the categories
x : string
Name of the column that contains the x axis values
y : string
Name of the column that contains the y axis values
z : string
Name of the column that contains the z axis values
text : string
Name of the column that contains the text values
gridcolor : string
Grid color
zerolinecolor : string
Zero line color
margin : dict or tuple
Dictionary (l,r,b,t) or
Tuple containing the left,
right, bottom and top margins
asFrame : bool
If true then the data component of Figure will
be of Pandas form (Series) otherwise they will
be index values
asDates : bool
If true it truncates times from a DatetimeIndex
asFigure : bool
If True returns plotly Figure
asImage : bool
If True it returns Image
* Only valid when asImage=True
dimensions : tuple(int,int)
Dimensions for image
(width,height)
asPlot : bool
If True the chart opens in browser
asUrl : bool
If True the chart url is returned. No chart is displayed.
"""
if not colors:
colors=kwargs['color'] if 'color' in kwargs else colors
if isinstance(colors,str):
colors=[colors]
opacity=kwargs['opacity'] if 'opacity' in kwargs else 0.8
if not layout:
if annotations:
annotations=getAnnotations(self.copy(),annotations)
layout=getLayout(theme=theme,xTitle=xTitle,yTitle=yTitle,zTitle=zTitle,title=title,barmode=barmode,
annotations=annotations,gridcolor=gridcolor,zerolinecolor=zerolinecolor,margin=margin,is3d='3d' in kind)
if not data:
if categories:
data=Data()
_keys=pd.unique(self[categories])
colors=get_colors(colors,colorscale,_keys)
mode='markers' if 'markers' not in mode else mode
for _ in _keys:
__=self[self[categories]==_].copy()
if text:
_text=__[text] if asFrame else __[text].values
_x=__[x] if asFrame else __[x].values
_y=__[y] if asFrame else __[y].values
if z:
_z=__[z] if asFrame else __[z].values
if 'bubble' in kind:
rg=__[size].values
rgo=self[size].values
_size=[int(100*(float(i)-rgo.min())/(rgo.max()-rgo.min()))+12 for i in rg]
else:
_size=size
_data=Scatter3d(x=_x,y=_y,mode=mode,name=_,
marker=Marker(color=colors[_],symbol=symbol,size=_size,opacity=opacity,
line=Line(width=width)),textfont=getLayout(theme=theme)['xaxis']['titlefont'])
if '3d' in kind:
_data=Scatter3d(x=_x,y=_y,z=_z,mode=mode,name=_,
marker=Marker(color=colors[_],symbol=symbol,size=_size,opacity=opacity,
line=Line(width=width)),textfont=getLayout(theme=theme)['xaxis']['titlefont'])
else:
_data=Scatter(x=_x,y=_y,mode=mode,name=_,
marker=Marker(color=colors[_],symbol=symbol,size=_size,opacity=opacity,
line=Line(width=width)),textfont=getLayout(theme=theme)['xaxis']['titlefont'])
if text:
_data.update(text=_text)
data.append(_data)
else:
if kind in ('scatter','spread','ratio','bar'):
df=self.copy()
if x:
df=df.set_index(x)
if y:
df=df[y]
data=df.to_iplot(colors=colors,colorscale=colorscale,kind=kind,fill=fill,width=width,sortbars=sortbars,keys=keys,
bestfit=bestfit,bestfit_colors=bestfit_colors,asDates=asDates,mode=mode,symbol=symbol,size=size)
if kind in ('spread','ratio'):
if kind=='spread':
trace=self.apply(lambda x:x[0]-x[1],axis=1)
positive=trace.apply(lambda x:x if x>=0 else pd.np.nan)
negative=trace.apply(lambda x:x if x<0 else pd.np.nan)
trace=pd.DataFrame({'positive':positive,'negative':negative})
trace=trace.to_iplot(colors={'positive':'green','negative':'red'},width=0)
else:
trace=self.apply(lambda x:x[0]*1.0/x[1],axis=1).to_iplot()
trace.update({'xaxis':'x2','yaxis':'y2','fill':'tozeroy',
'name':kind.capitalize(),'connectgaps':False,'showlegend':False})
data.append(Scatter(trace[0]))
if kind=='spread':
data.append(Scatter(trace[1]))
layout['yaxis'].update({'domain':[.3,1]})
layout['yaxis2']=copy.deepcopy(layout['yaxis'])
layout['xaxis2']=copy.deepcopy(layout['xaxis'])
layout['yaxis2'].update(domain=[0,.25],title=kind.capitalize())
layout['xaxis2'].update(anchor='y2',showticklabels=False)
elif kind=='bubble':
mode='markers' if 'markers' not in mode else mode
x=self[x].values.tolist()
y=self[y].values.tolist()
z=size if size else z
rg=self[z].values
z=[int(100*(float(_)-rg.min())/(rg.max()-rg.min()))+12 for _ in rg]
text=kwargs['labels'] if 'labels' in kwargs else text
labels=self[text].values.tolist()
clrs=get_colors(colors,colorscale,x).values()
gen=colorgen()
marker=Marker(color=clrs,size=z,symbol=symbol,
line=Line(width=width),textfont=getLayout(theme=theme)['xaxis']['titlefont'])
trace=Scatter(x=x,y=y,marker=marker,mode='markers',text=labels)
data=Data([trace])
elif kind in ('box','histogram'):
data=Data()
clrs=get_colors(colors,colorscale,self.columns)
if kind=='histogram':
layout.update(barmode='overlay')
for _ in self.columns:
if kind=='box':
__=Box(y=self[_].values.tolist(),marker=Marker(color=clrs[_]),name=_,
line=Line(width=width),boxpoints=boxpoints)
else:
__=Histogram(x=self[_].values.tolist(),marker=Marker(color=clrs[_]),name=_,
line=Line(width=width),
opacity=kwargs['opacity'] if 'opacity' in kwargs else .8)
data.append(__)
elif kind in ('heatmap','surface'):
x=self[x].values.tolist() if x else self.index.values.tolist()
y=self[y].values.tolist() if y else self.columns.values.tolist()
z=self[z].values.tolist() if z else self.values.transpose()
scale=get_scales('rdbu') if not colorscale else get_scales(colorscale)
colorscale=[[float(_)/(len(scale)-1),scale[_]] for _ in range(len(scale))]
if kind=='heatmap':
data=Data([Heatmap(z=z,x=x,y=y,colorscale=colorscale)])
else:
data=Data([Surface(z=z,x=x,y=y,colorscale=colorscale)])
elif kind in ('scatter3d','bubble3d'):
data=Data()
keys=self[text].values if text else range(len(self))
colors=get_colors(colors,colorscale,keys,asList=True)
df=self.copy()
df['index']=keys
if kind=='bubble3d':
rg=self[size].values
size=[int(100*(float(_)-rg.min())/(rg.max()-rg.min()))+12 for _ in rg]
else:
size=[size for _ in range(len(keys))]
_data=Scatter3d(x=df[x].values.tolist(),y=df[y].values.tolist(),z=df[z].values.tolist(),mode=mode,name=keys,
marker=Marker(color=colors,symbol=symbol,size=size,opacity=.8))
if text:
_data.update(text=keys)
data.append(_data)
if world_readable is None:
world_readable = auth.get_config_file()['world_readable']
if not filename:
if title:
filename=title
else:
filename='Plotly Playground'
if asFigure:
return Figure(data=data,layout=layout)
elif asImage:
py.image.save_as(Figure(data=data,layout=layout),filename='img/'+filename,format='png',
width=dimensions[0],height=dimensions[1])
return display(Image('img/'+filename+'.png'))
elif asPlot:
return py.plot(Figure(data=data,layout=layout),world_readable=world_readable,filename=filename)
elif asUrl:
return py.plot(Figure(data=data,layout=layout),world_readable=world_readable,filename=filename,auto_open=False)
else:
return py.iplot(Figure(data=data,layout=layout),world_readable=world_readable,filename=filename)
