#!/usr/bin/env python
# coding: utf-8

# # Python API to BeakerX Interactive Plotting
# 
# You can access Beaker's native interactive plotting library from Python.
# 
# ## Plot with simple properties
# 
# Python plots has syntax very similar to Groovy plots. Property names are the same.

# In[ ]:


from beakerx import *
import pandas as pd

tableRows = pd.read_csv('../resources/data/interest-rates.csv')


# In[ ]:


Plot(title="Title",
     xLabel="Horizontal",
     yLabel="Vertical",
     initWidth=500,
     initHeight=200)


# ## Plot items
# 
# ### Lines, Bars, Points and Right yAxis

# In[ ]:


x = [1, 4, 6, 8, 10]
y = [3, 6, 4, 5, 9]

pp = Plot(title='Bars, Lines, Points and 2nd yAxis', 
          xLabel="xLabel", 
          yLabel="yLabel", 
          legendLayout=LegendLayout.HORIZONTAL,
          legendPosition=LegendPosition(position=LegendPosition.Position.RIGHT),
          omitCheckboxes=True)

pp.add(YAxis(label="Right yAxis"))
pp.add(Bars(displayName="Bar", 
       x=[1,3,5,7,10], 
       y=[100, 120,90,100,80], 
       width=1))
pp.add(Line(displayName="Line", 
            x=x, 
            y=y, 
            width=6, 
            yAxis="Right yAxis"))
pp.add(Points(x=x, 
              y=y, 
              size=10, 
              shape=ShapeType.DIAMOND,
              yAxis="Right yAxis"))


# In[ ]:


plot = Plot(title= "Setting line properties")
ys = [0, 1, 6, 5, 2, 8]
ys2 = [0, 2, 7, 6, 3, 8]
plot.add(Line(y= ys, width= 10, color= Color.red))
plot.add(Line(y= ys, width= 3, color= Color.yellow))
plot.add(Line(y= ys, width= 4, color= Color(33, 87, 141), style= StrokeType.DASH, interpolation= 0))
plot.add(Line(y= ys2, width= 2, color= Color(212, 57, 59), style= StrokeType.DOT))
plot.add(Line(y= [5, 0], x= [0, 5], style= StrokeType.LONGDASH))
plot.add(Line(y= [4, 0], x= [0, 5], style= StrokeType.DASHDOT))


# In[ ]:


plot = Plot(title= "Changing Point Size, Color, Shape")
y1 = [6, 7, 12, 11, 8, 14]
y2 = [4, 5, 10, 9, 6, 12]
y3 = [2, 3, 8, 7, 4, 10]
y4 = [0, 1, 6, 5, 2, 8]
plot.add(Points(y= y1))
plot.add(Points(y= y2, shape= ShapeType.CIRCLE))
plot.add(Points(y= y3, size= 8.0, shape= ShapeType.DIAMOND))
plot.add(Points(y= y4, size= 12.0, color= Color.orange, outlineColor= Color.red))


# In[ ]:


plot = Plot(title= "Changing point properties with list")
cs = [Color.black, Color.red, Color.orange, Color.green, Color.blue, Color.pink]
ss = [6.0, 9.0, 12.0, 15.0, 18.0, 21.0]
fs = [False, False, False, True, False, False]
plot.add(Points(y= [5] * 6, size= 12.0, color= cs))
plot.add(Points(y= [4] * 6, size= 12.0, color= Color.gray, outlineColor= cs))
plot.add(Points(y= [3] * 6, size= ss, color= Color.red))
plot.add(Points(y= [2] * 6, size= 12.0, color= Color.black, fill= fs, outlineColor= Color.black))


# In[ ]:


plot = Plot()
y1 = [1.5, 1, 6, 5, 2, 8]
cs = [Color.black, Color.red, Color.gray, Color.green, Color.blue, Color.pink]
ss = [StrokeType.SOLID, StrokeType.SOLID, StrokeType.DASH, StrokeType.DOT, StrokeType.DASHDOT, StrokeType.LONGDASH]
plot.add(Stems(y= y1, color= cs, style= ss, width= 5))


# In[ ]:


plot = Plot(title= "Setting the base of Stems")
ys = [3, 5, 2, 3, 7]
y2s = [2.5, -1.0, 3.5, 2.0, 3.0]
plot.add(Stems(y= ys, width= 2, base= y2s))
plot.add(Points(y= ys))


# In[ ]:


plot = Plot(title= "Bars")
cs = [Color(255, 0, 0, 128)] * 5 # transparent bars
cs[3] = Color.red # set color of a single bar, solid colored bar
plot.add(Bars(x= [1, 2, 3, 4, 5], y= [3, 5, 2, 3, 7], color= cs, outlineColor= Color.black, width= 0.3))


# ### Lines,  Points with Pandas

# In[ ]:


plot = Plot(title= "Pandas line")
plot.add(Line(y= tableRows.y1, width= 2, color= Color(216, 154, 54)))
plot.add(Line(y= tableRows.y10, width= 2, color= Color.lightGray))

plot


# In[ ]:


plot = Plot(title= "Pandas Series")
plot.add(Line(y= pd.Series([0, 6, 1, 5, 2, 4, 3]), width=2))


# In[ ]:


plot = Plot(title= "Bars")
cs = [Color(255, 0, 0, 128)] * 7 # transparent bars
cs[3] = Color.red # set color of a single bar, solid colored bar
plot.add(Bars(pd.Series([0, 6, 1, 5, 2, 4, 3]), color= cs, outlineColor= Color.black, width= 0.3))


# ### Areas, Stems and Crosshair

# In[ ]:


ch = Crosshair(color=Color.black, width=2, style=StrokeType.DOT)
plot = Plot(crosshair=ch)
y1 = [4, 8, 16, 20, 32]
base = [2, 4, 8, 10, 16]
cs = [Color.black, Color.orange, Color.gray, Color.yellow, Color.pink]
ss = [StrokeType.SOLID, 
      StrokeType.SOLID, 
      StrokeType.DASH, 
      StrokeType.DOT, 
      StrokeType.DASHDOT, 
      StrokeType.LONGDASH]
plot.add(Area(y=y1, base=base, color=Color(255, 0, 0, 50)))
plot.add(Stems(y=y1, base=base, color=cs, style=ss, width=5))


# In[ ]:


plot = Plot()
y = [3, 5, 2, 3]
x0 = [0, 1, 2, 3]
x1 = [3, 4, 5, 8]
plot.add(Area(x= x0, y= y))
plot.add(Area(x= x1, y= y, color= Color(128, 128, 128, 50), interpolation= 0))


# In[ ]:


p = Plot()
p.add(Line(y= [3, 6, 12, 24], displayName= "Median"))
p.add(Area(y= [4, 8, 16, 32], base= [2, 4, 8, 16],
              color= Color(255, 0, 0, 50), displayName= "Q1 to Q3"))


# In[ ]:


ch = Crosshair(color= Color(255, 128, 5), width= 2, style= StrokeType.DOT)
pp = Plot(crosshair= ch, omitCheckboxes= True,
                  legendLayout= LegendLayout.HORIZONTAL, legendPosition= LegendPosition(position=LegendPosition.Position.TOP))
x = [1, 4, 6, 8, 10]
y = [3, 6, 4, 5, 9]
pp.add(Line(displayName= "Line", x= x, y= y, width= 3))
pp.add(Bars(displayName= "Bar", x= [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], y= [2, 2, 4, 4, 2, 2, 0, 2, 2, 4], width= 0.5))
pp.add(Points(x= x, y= y, size= 10))


# ### Constant Lines, Constant Bands

# In[ ]:


p = Plot ()
p.add(Line(y=[-1, 1]))
p.add(ConstantLine(x=0.65, style=StrokeType.DOT, color=Color.blue))
p.add(ConstantLine(y=0.1, style=StrokeType.DASHDOT, color=Color.blue))
p.add(ConstantLine(x=0.3, y=0.4, color=Color.gray, width=5, showLabel=True))


# In[ ]:


Plot().add(Line(y=[-3, 1, 3, 4, 5])).add(ConstantBand(x=[1, 2], y=[1, 3]))


# In[ ]:


p = Plot() 
p.add(Line(x= [-3, 1, 2, 4, 5], y= [4, 2, 6, 1, 5]))
p.add(ConstantBand(x= ['-Infinity', 1], color= Color(128, 128, 128, 50)))
p.add(ConstantBand(x= [1, 2]))
p.add(ConstantBand(x= [4, 'Infinity']))


# In[ ]:


from decimal import Decimal
pos_inf = Decimal('Infinity')
neg_inf = Decimal('-Infinity')
print (pos_inf)
print (neg_inf)


# In[ ]:


from beakerx.plot import Text as BeakerxText
plot = Plot()
xs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
ys = [8.6, 6.1, 7.4, 2.5, 0.4, 0.0, 0.5, 1.7, 8.4, 1]
def label(i):
  if ys[i] > ys[i+1] and ys[i] > ys[i-1]:
    return "max"
  if ys[i] < ys[i+1] and ys[i] < ys[i-1]:
    return "min"
  if ys[i] > ys[i-1]:
    return "rising"
  if ys[i] < ys[i-1]:
    return "falling"
  return ""

for i in xs:
  i = i - 1
  if i > 0 and i < len(xs)-1:
    plot.add(BeakerxText(x= xs[i], y= ys[i], text= label(i),  pointerAngle= -i/3.0))

plot.add(Line(x= xs, y= ys))
plot.add(Points(x= xs, y= ys))


# In[ ]:


plot = Plot(title= "Setting 2nd Axis bounds")
ys = [0, 2, 4, 6, 15, 10]
ys2 = [-40, 50, 6, 4, 2, 0]
ys3 = [3, 6, 3, 6, 70, 6]
plot.add(YAxis(label="Spread"))
plot.add(Line(y= ys))
plot.add(Line(y= ys2, yAxis="Spread"))
plot.setXBound([-2, 10])
#plot.setYBound(1, 5)
plot.getYAxes()[0].setBound(1,5)
plot.getYAxes()[1].setBound(3,6)


plot


# In[ ]:


plot = Plot(title= "Setting 2nd Axis bounds")
ys = [0, 2, 4, 6, 15, 10]
ys2 = [-40, 50, 6, 4, 2, 0]
ys3 = [3, 6, 3, 6, 70, 6]
plot.add(YAxis(label="Spread"))
plot.add(Line(y= ys))
plot.add(Line(y= ys2, yAxis="Spread"))
plot.setXBound([-2, 10])
plot.setYBound(1, 5)

plot


# ## TimePlot

# In[ ]:


import time

millis = current_milli_time()

hour = round(1000 * 60 * 60)
xs = []
ys = []
for i in range(11):
    xs.append(millis + hour * i)
    ys.append(i)

plot = TimePlot(timeZone="America/New_York")
# list of milliseconds
plot.add(Points(x=xs, y=ys, size=10, displayName="milliseconds"))


# In[ ]:


plot = TimePlot()
plot.add(Line(x=tableRows['time'], y=tableRows['m3']))


# #### numpy datatime64

# In[ ]:


y = pd.Series([7.5, 7.9, 7, 8.7, 8, 8.5])
dates = [np.datetime64('2015-02-01'), 
         np.datetime64('2015-02-02'), 
         np.datetime64('2015-02-03'),
         np.datetime64('2015-02-04'),
         np.datetime64('2015-02-05'),
         np.datetime64('2015-02-06')]
plot = TimePlot()

plot.add(Line(x=dates, y=y))


# #### Timestamp

# In[ ]:


y = pd.Series([7.5, 7.9, 7, 8.7, 8, 8.5])
dates = pd.Series(['2015-02-01',
                   '2015-02-02',
                   '2015-02-03',
                   '2015-02-04',
                   '2015-02-05',
                   '2015-02-06']
                  , dtype='datetime64[ns]')
plot = TimePlot()
plot.add(Line(x=dates, y=y))


# #### Datetime and date

# In[ ]:


import datetime

y = pd.Series([7.5, 7.9, 7, 8.7, 8, 8.5])
dates = [datetime.date(2015, 2, 1),
         datetime.date(2015, 2, 2),
         datetime.date(2015, 2, 3),
         datetime.date(2015, 2, 4),
         datetime.date(2015, 2, 5),
         datetime.date(2015, 2, 6)]
plot = TimePlot()
plot.add(Line(x=dates, y=y))


# In[ ]:


import datetime

y = pd.Series([7.5, 7.9, 7, 8.7, 8, 8.5])
dates = [datetime.datetime(2015, 2, 1),
         datetime.datetime(2015, 2, 2),
         datetime.datetime(2015, 2, 3),
         datetime.datetime(2015, 2, 4),
         datetime.datetime(2015, 2, 5),
         datetime.datetime(2015, 2, 6)]
plot = TimePlot()
plot.add(Line(x=dates, y=y))


# ## NanoPlot

# In[ ]:


millis = current_milli_time()
nanos  = millis * 1000 * 1000
xs = []
ys = []
for i in range(11):
    xs.append(nanos + 7 * i)
    ys.append(i)

nanoplot = NanoPlot()
nanoplot.add(Points(x=xs, y=ys))


# ## Stacking

# In[ ]:


y1 = [1,5,3,2,3]
y2 = [7,2,4,1,3]
p = Plot(title='Plot with XYStacker', initHeight=200)
a1 = Area(y=y1, displayName='y1')
a2 = Area(y=y2, displayName='y2')
stacker = XYStacker()
p.add(stacker.stack([a1, a2]))


# ##  SimpleTime Plot

# In[ ]:


SimpleTimePlot(tableRows, ["y1", "y10"], # column names
               timeColumn="time", # time is default value for a timeColumn
               yLabel="Price", 
               displayNames=["1 Year", "10 Year"],
               colors = [[216, 154, 54], Color.lightGray],
               displayLines=True, # no lines (true by default)
               displayPoints=False) # show points (false by default))


# In[ ]:


#time column base on DataFrame index 
tableRows.index = tableRows['time']

SimpleTimePlot(tableRows, ['m3'])


# In[ ]:


rng = pd.date_range('1/1/2011', periods=72, freq='H')
ts = pd.Series(np.random.randn(len(rng)), index=rng)
df = pd.DataFrame(ts, columns=['y'])
SimpleTimePlot(df, ['y'])


# ## Second Y Axis

# The plot can have two y-axes. Just add a `YAxis` to the plot object, and specify its label.
# Then for data that should be scaled according to this second axis,
# specify the property `yAxis` with a value that coincides with the label given.
# You can use `upperMargin` and `lowerMargin` to restrict the range of the data leaving more white, perhaps for the data on the other axis.

# In[ ]:


p = TimePlot(xLabel= "Time", yLabel= "Interest Rates")
p.add(YAxis(label= "Spread", upperMargin= 4))
p.add(Area(x= tableRows.time, y= tableRows.spread, displayName= "Spread",
              yAxis= "Spread", color= Color(180, 50, 50, 128)))
p.add(Line(x= tableRows.time, y= tableRows.m3, displayName= "3 Month"))
p.add(Line(x= tableRows.time, y= tableRows.y10, displayName= "10 Year"))


# ## Combined Plot

# In[ ]:


import math
points = 100
logBase = 10
expys = []
xs = []
for i in range(0, points):
  xs.append(i / 15.0)
  expys.append(math.exp(xs[i]))


cplot = CombinedPlot(xLabel= "Linear")
logYPlot = Plot(title= "Linear x, Log y", yLabel= "Log", logY= True, yLogBase= logBase)
logYPlot.add(Line(x= xs, y= expys, displayName= "f(x) = exp(x)"))
logYPlot.add(Line(x= xs, y= xs, displayName= "g(x) = x"))
cplot.add(logYPlot, 4)

linearYPlot = Plot(title= "Linear x, Linear y", yLabel= "Linear")
linearYPlot.add(Line(x= xs, y= expys, displayName= "f(x) = exp(x)"))
linearYPlot.add(Line(x= xs, y= xs, displayName= "g(x) = x"))
cplot.add(linearYPlot,4)

cplot


# In[ ]:


plot = Plot(title= "Log x, Log y", xLabel= "Log", yLabel= "Log",
                    logX= True, xLogBase= logBase, logY= True, yLogBase= logBase)

plot.add(Line(x= xs, y= expys, displayName= "f(x) = exp(x)"))
plot.add(Line(x= xs, y= xs, displayName= "f(x) = x"))

plot