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

# In[1]:


import numpy as np
import pandas as pd

from lets_plot import *
from lets_plot.mapping import as_discrete


# In[2]:


LetsPlot.setup_html()


# #### Set `theme_gray()` as default theme. It improves plots readability.

# In[3]:


LetsPlot.set_theme(theme_grey())


# #### Data

# In[4]:


labels_df = {
    'x': [0, 1, 2, 3, 4, 5, 6, 7, 8],
    'y': [0, 45, 90, 135, 180, 225, 270, 315, 360],
    'r_y': [360, 315, 270, 225, 180, 135, 90, 45, 0],
    'l': ['l0', 'l45', 'l90', 'l135', 'l180', 'l225', 'l270', 'l315', 'l360'],
    'g': ['g1', 'g1', 'g1', 'g2', 'g2', 'g2', 'g3', 'g3', 'g3']
}

lollipop_df = {
    'c': ['a', 'b', 'c', 'd', 'e', 'f'],
    'x': [1, 2, 3, 4, 5, 6],
    'y': [1, 2, 3, 4, 5, 6],
}

student_df = {
    'subj': ['progr', 'math', 'physic', 'chemistry', 'biology'],
    'subj_id': [1, 2, 3, 4, 5],
    'student': ['John'] * 5,
    'score': [19, 15, 18, 12, 9]
}


# ##### Util for `PlotSpecDebugger.kt`

# In[5]:


def dump_plot(plot, display=False):
    import json

    try:
        import clipboard
    except:
        clipboard = None
        
    from lets_plot._type_utils import standardize_dict
    
    plot_dict = standardize_dict(plot.as_dict())
    plot_json = json.dumps(plot_dict, indent=2)
    
    if clipboard:
        clipboard.copy('')
        clipboard.copy(str(plot_json))
    else:
        if display is None:
            display = True

    return plot


# # Geoms

# ## `geom_area()`
# Line get transformed into a circle:

# In[6]:


p = ggplot() + geom_area(aes(x=[0, 1], y=[1, 1]))
gggrid([
    p,
    p + coord_polar()
])


# ### `flat=True`
# The plot can be transformed into a radar plot by using `flat=True` and a discrete x-scale.

# In[7]:


p = ggplot(student_df) \
    + geom_area(aes(x='subj_id', y='score'), flat=True) \
    + geom_point(aes(x='subj_id', y='score')) \

labels = { 1: 'progr', 2: 'math', 3: 'physic', 4: 'chemistry', 5: 'biology' }

continuous = scale_x_continuous(labels=labels)
discrete = scale_x_discrete(labels=labels)

gggrid([
    p + continuous,
    p + continuous + coord_polar() + ggtitle('scale_x_continuous'),
    p + discrete + coord_polar() + ggtitle('scale_x_discrete'),
]) 


# ## `geom_segment()`

# In[8]:


p = ggplot() \
    + geom_segment(x=0, y=0, xend=4, yend=4, arrow=arrow(), size=1) \
    + geom_segment(x=8, y=0, xend=4, yend=4, arrow=arrow(), size=1) \

gggrid([
    p,
    p + coord_polar()
])


# `size_end`/`stroke_end` precision length adjustment parameters:

# In[9]:


# known problem - zero-length segment because of second datapoint.
# this is a temp workaround to sync stroke/stroke_end and size/size_ens domains
d= { 
    'x': [0,1], 
    'y':[0,0], 
    'size': [8,10], 
    'stroke':[1,2],
    'size_end':[10,0], 
    'stroke_end':[2,0]
}

p = ggplot(d, aes('x','y')) \
    + geom_point(aes(size='size', stroke='stroke'), shape=21, alpha=0.5, color="red", show_legend=False) \
    + geom_segment(
        aes(size_start='size', stroke_start='stroke',size_end='size_end', stroke_end='stroke_end'),
        xend=1, 
        yend=0,
        size=2,
        arrow=arrow(ends='both', type='open', length=22, angle=30), 
    ) \
    + scale_identity(['size','size_start','size_end']) 

gggrid([
    p,
    p + coord_polar(xlim=[-0.35, 1.35], ylim=[-2, 2]) # lims are only to make the figure smiling
])


# ## `geom_label()`
# Regular scatter plot.

# In[10]:


p = ggplot(labels_df, aes(x='x', y='y', label='l')) + geom_label()

gggrid([
    p, 
    p + coord_polar() + ggtitle('coord_polar()'),
    p + coord_polar(theta='y') + ggtitle('theta=y'),
])


# ## `geom_path()`
# The transform resamples path data by converting straight segments into curves. The `flat` parameter controls this behaviour.

# In[11]:


p = ggplot(labels_df, aes(x='x', y='y', color='y')) + scale_color_brewer(palette='GnBu')

gggrid([
    p + geom_path(size=3) + coord_polar() + ggtitle('coord_polar()'),
    p + geom_path(size=3, flat=True) + coord_polar(theta="x") + ggtitle('coord_polar(), flat=True'),
], ncol=2)


# ### Autoclose on a discrete x-scale

# In[12]:


ggplot(student_df) + geom_path(aes(x='subj', y='score'), flat=True) + coord_polar(ylim=[0, 20])


# ## `geom_lollipop()`
# See the `Params` section for details on using the `ylim` parameters.

# In[13]:


p = ggplot(lollipop_df, aes('c', 'y')) + geom_lollipop()

gggrid([
    p, 
    p + coord_polar(),
])


# ## `geom_bar()` 
# This works similarly to rects, but with the addition of tooltips.

# ### `position='stack'`

# In[14]:


from lets_plot.mapping import as_discrete
bar_df = { 'foo': [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3] }
p = ggplot(bar_df) + geom_bar(aes(fill=as_discrete('foo', order=1)), size=0)

gggrid([
    p,
    p + coord_polar(theta='y') + ggtitle('position=stack, coord_polar(theta=y)'),
    p + coord_polar(theta='x') + ggtitle('position=stack, coord_polar(theta=x)'),
])


# ### `position='dodge'`

# In[15]:


p = ggplot(bar_df) + geom_bar(aes(fill=as_discrete('foo', order=1)), size=0, position='dodge')

gggrid([
    p,
    p + coord_polar(theta='y') + ggtitle('position=dodge, coord_polar(theta=y)'),
    p + coord_polar(theta='x') + ggtitle('position=dodge, coord_polar(theta=x)'),
])


# ### `stat='identity'`
# On a continuous x-scale the first and last bars stuck. The `expand` parameter can be used to fix this.

# In[16]:


data = {
    'x': [1, 2, 3],
    'y': [5, 3, 4],
}

bar_id = ggplot(data) + geom_bar(aes('x', 'y'), stat='identity', width=0.8) + coord_polar()

gggrid([
    bar_id + ggtitle('Continuous x'),
    bar_id + scale_x_continuous(expand=[0, 0.1]) + ggtitle('scale_x_continuous(expand=[0, 0.1))')
])


# In[17]:


bar_id + scale_x_discrete() + ggtitle('Discrete x')


# ### Demo: wind rose
# See: https://mesonet.cdn.columbiascanner.org/onsite/windrose/IA_ASOS/PEA/PEA_yearly.png  
# Data: https://mesonet.cdn.columbiascanner.org/sites/windrose.phtml?station=PEA&network=IA_ASOS

# In[18]:


wind_df = pd.read_csv('coord_polar_wind.csv')

def is_float(x):
    try:
        float(x)
    except ValueError:
        return False
    return True


wind_df = wind_df[wind_df.sped.apply(lambda x: is_float(x))]
wind_df = wind_df[wind_df.drct.apply(lambda x: is_float(x))]

wind_df['sped'] = wind_df['sped'].astype(float)
wind_df['drct'] = wind_df['drct'].astype(float)

wind_df = wind_df[wind_df.sped.apply(lambda x: x >= 2.0)]

wind_df.head()


# In[19]:


# Define the speed bins
bins = [2, 5, 7, 10, 15, 20, float('inf')]
bin_ids = list(range(6))

wind_df['speed_group'] = pd.cut(wind_df['sped'], bins=bins, labels=bin_ids, right=False)

# Group by 'drct' and 'speed_group', and count the occurrences
grouped_counts = wind_df.groupby(['drct', 'speed_group']).size().reset_index(name='count')

# Calculate the total number of observations in the dataset
total_observations = wind_df.shape[0]

# Calculate the percentage of each speed group within each direction relative to the total number of observations
grouped_counts['percentage_of_total'] = (grouped_counts['count'] / total_observations) * 100


# In[20]:


from lets_plot.mapping import as_discrete

ggplot(grouped_counts) \
    + geom_bar(aes('drct', 'percentage_of_total', fill=as_discrete('speed_group', order=1)), stat='identity', size=0, width=0.8) \
    + scale_fill_manual(
        name='Wind Speed:', 
        breaks={'2 - 4.9': 0, '5 - 6.9': 1, '7 - 9.9': 2, '10 - 14.9': 3, '15 - 19.9': 4, '20+': 5},
        values=['#002bff', '#03d3f8', '#7afe81', '#fde609', '#ff4404', '#780200'], 
    ) \
    + scale_x_continuous(
        expand=[0, 1],
        breaks={'N': 360, 'NE': 45, 'E': 90, 'SE': 135, 'S': 180, 'SW': 225, 'W': 270, 'NW': 315},
        
    ) \
    + ggsize(800, 800) \
    + coord_polar(ylim=[-1, None], start=(3.14 * 2) / 36 / 2) \
    + theme_minimal2() \
    + theme(
        panel_grid_ontop=True, 
        axis_ontop=True, 
        panel_grid=element_line(color='#A0A0A0')
    )


# ## `geom_hline()`/`geom_vline()`

# In[21]:


p = ggplot() \
    + geom_hline(yintercept=5, color='red') \
    + geom_hline(yintercept=10, color='green') \
    + geom_hline(yintercept=15, color='blue') \
    + geom_hline(yintercept=20, color='orange') \
    + geom_vline(xintercept=10, color='pink') \
    + geom_vline(xintercept=20, color='magenta') \
    + geom_vline(xintercept=30, color='dark_green') \
    + xlim(0, 30)\
    + ylim(0, 20)
gggrid([p, p + coord_polar()])


# ## `geom_tile()`

# In[22]:


x = list(range(24))
d1 = list(np.interp(x, [0, 8, 14, 18, 23], [3, 12, 18, 8, 2]))
d2 = list(np.interp(x, [0, 8, 14, 18, 23], [2, 7, 11, 5, 0]))
d3 = list(np.interp(x, [0, 8, 14, 18, 23], [0, 11, 15, 13, 8]))
d4 = list(np.interp(x, [0, 8, 14, 18, 23], [8, 7, 11, 5, 2]))
d5 = list(np.interp(x, [0, 8, 14, 18, 23], [2, 12, 20, 15, 12]))
d6 = list(np.interp(x, [0, 8, 14, 18, 23], [12, 14, 22, 19, 15]))
d7 = list(np.interp(x, [0, 8, 14, 18, 23], [15, 13, 26, 22, 11]))
temp = d1 + d2 + d3 + d4 + d5 + d6 + d7
day = ([1] * 24) + ([2] * 24) + ([3] * 24) + ([4] * 24) + ([5] * 24) + ([6] * 24) + ([7] * 24)

df = pd.DataFrame({
    "time": [i for i in range(24)] * 7,
    "day": day,
    "temp": temp
})

p = ggplot(df) \
    + geom_tile(aes(x='time', y='day', fill='temp'), tooltips=layer_tooltips().format('^x', '{.1d}:00')) \
    + scale_fill_viridis() \
    + scale_x_continuous(format='{.1d}:00') \
    + scale_y_discrete(breaks={ 'Mon': 1, 'Tue': 2, 'Wen': 3, 'Thu': 4, 'Fri': 5, 'Sat': 6, 'Sun': 7 })

gggrid([
    p,
    p \
        + theme(panel_inset=[0, 20, 0, 20])
        + coord_polar(ylim=[-2, None], start=(-3.14 * 2) / 24 / 2) # ylim=-2 to make the hole, start to align ticks vertically
]) + ggsize(1200, 600)


# ## `geom_rect()`

# ### Stacked bars
# are transformed into a pie chart

# In[23]:


c1 = '#66c2a5'
c2 = '#fc8d62'
c3 = '#8da0cb'
p = ggplot() \
    + geom_rect(xmin=0, xmax=5, ymin=0, ymax=7, fill=c1, size=0) \
    + geom_rect(xmin=0, xmax=5, ymin=7, ymax=11, fill=c2, size=0) \
    + geom_rect(xmin=0, xmax=5, ymin=11, ymax=14, fill=c3, size=0) 

gggrid([
    p,
    p + coord_polar() + ggtitle('coord_polar()'),
    p + coord_polar(theta='y') + ggtitle('coord_polar(theta=y)'),
]).show()

gggrid([
    p + coord_polar(theta='y', direction=-1) + ggtitle('coord_polar(theta=y, dir=-1)'),
    p + coord_polar(theta='y', direction=-1, start=3.14/2) + ggtitle('coord_polar(theta=y, dir=-1, start=PI/2)'),
]).show()



# ### Dodged bars

# In[24]:


p = ggplot() \
    + geom_rect(xmin=0, xmax=1, ymin=0, ymax=7, fill=c1, size=0) \
    + geom_rect(xmin=1, xmax=2, ymin=0, ymax=4, fill=c2, size=0) \
    + geom_rect(xmin=2, xmax=3, ymin=0, ymax=3, fill=c3, size=0) \

gggrid([
    p, 
    p + coord_polar(theta='y') + ggtitle('coord_polar(theta=y)'),
    p + coord_polar(theta='x') + ggtitle('coord_polar(theta=x)'),
])


# ### Horizontal bars

# In[25]:


p = ggplot() \
    + geom_rect(ymin=0, ymax=1, xmin=0, xmax=7, fill=c1, size=0) \
    + geom_rect(ymin=1, ymax=2, xmin=0, xmax=4, fill=c2, size=0) \
    + geom_rect(ymin=2, ymax=3, xmin=0, xmax=3, fill=c3, size=0) \

gggrid([
    p, 
    p + coord_polar(theta='y') + ggtitle('coord_polar(theta=y)'),
    p + coord_polar(theta='x') + ggtitle('coord_polar(theta=x)'),
])


# # Params

# In[26]:


p = ggplot(labels_df, aes(x='x', y='y', color='y')) + geom_path(size=3, show_legend=False) + scale_color_brewer(palette='GnBu')


p + coord_polar() + ggtitle('Default plot with coord_polar()')


# ### `transform_bkgr`  
# 
# When using the `transform_bkgr` parameter, the panel is not transformed into a circle, but remains a rectangle. This behaviour is similar to `ggplot2`.

# In[27]:


p + coord_polar(transform_bkgr=False) + ggtitle('coord_polar(transform_bkgr=False)')


# ### `direction`

# In[28]:


p + coord_polar(direction=-1) + ggtitle('coord_polar(direction=-1)')


# ### `start`

# In[29]:


gggrid([
    p + coord_polar(start=3.14 / 2) + ggtitle('start=PI/2'),
    p + coord_polar(start=-3.14 / 2) + ggtitle('start=-PI/2'),
])


# ### `direction` + `start`

# In[30]:


gggrid([
    p + coord_polar(start=3.14 / 2, direction=-1) + ggtitle('dir=-1, start=PI/2'),
    p + coord_polar(start=-3.14 / 2, direction=-1) + ggtitle('dir=-1, start=-PI/2'),
])


# ### `xlim` and `ylim`
# The `xlim` parameter can be used to prevent overlap between the first and last values.  
# The `ylim` parameter can be used to shift data away from the centre or the outer circle.
# 
# To prevent overlap between `6` and `1`, we adjust the `xlim` to `[None, 7]` while keeping the default minimum limit as it is not relevant.  
# In addition, we change `ylim` to `[None, 6.5]` to prevent the lollipop's top from overlapping with the outer circle.

# In[31]:


p = ggplot(lollipop_df) \
    + geom_lollipop(aes(x='x', y='y'))

gggrid([
    p + coord_polar(),
    p + coord_polar(xlim=[None, 7], ylim=[None, 6.5])
])                


# # Scales  
# Interaction between scales and polar coordinate system.

# In[32]:


pie = ggplot() \
    + geom_rect(xmin=0, xmax=1, ymin=0, ymax=7, fill='red', size=0) \
    + geom_rect(xmin=1, xmax=2, ymin=0, ymax=4, fill='blue', size=0) \
    + geom_rect(xmin=2, xmax=3, ymin=0, ymax=3, fill='green', size=0) \
    + coord_polar()

sticks_df = {
    'x': [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5],
    'y': [0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6],
    'g': [5, 5, 4, 4, 3, 3, 2, 2, 1, 1, 0, 0]
}

sticks = ggplot(sticks_df) + geom_path(aes(x='x', y='y', group='g', size='g')) + coord_polar()


# ## Limit
# The `limits` parameter sets the lower and upper limits individually, but expects absolute values.  
# `x=[None, 6]` limit to make the first and last elements not overlap.    
# `y=[-2, 7]` limit to make a medium sized centre hole and a small outer buffer (with a length of 1).

# In[33]:


gggrid([
    sticks + ggtitle('No limits'),
    sticks + lims(x=[None, 6], y=[-2, 7]) + ggtitle('lims(x=[None, 6], y=[-2, 7])'),
])


# ## Discrete x-scale
# 
# If the x-scale is discrete, the coordinate system will automatically adjust domain so that the first and last values don't overlap.

# In[34]:


gggrid([
    sticks + ggtitle('Continuous'), 
    sticks + scale_x_discrete() + ggtitle('scale_x_discrete()')
])


# ## clip-path
# 
# Segments should not get rendered outside the panel boundaries.

# In[35]:


gggrid([
    sticks,
    sticks + coord_polar(ylim=[0, 1.5])
])


# ## Expand
# By default `coord_polar()` resets the expansion to zero, but it can still be set explicitly.  
# Horizontal non-zero expand will produce a gap between first and last sectors, so the plot will never become a circle.  
# Vertical non-zero expand creates a central hole (expand for the bottom of the domain) and a buffer between the plot and the axis (expand for the top of the domain).  
# 
# `expand` is symmetric, so it can't be used to adjust only the bottom or only the top.

# In[36]:


gggrid([
    sticks + ggtitle('No expand'),
    sticks \
        + scale_x_continuous(expand=[0, 2.5]) \
        + scale_y_continuous(expand=[0, 2]) \
        + ggtitle('scale_XY_continuous(expand=...)'),
])


# ## `scale_y_log10()`  
# Log-scale works fine.

# In[37]:


d = {
    'x': [1, 2, 3, 4, 5, 6, 7, 8],
    'y': [1, 10, 100, 1_000, 10_000, 100_000, 1_000_000, 10_000_000],
}

p = ggplot(d) + geom_path(aes(x='x', y='y'), flat=True)
p

gggrid([
    p,
    p + coord_polar(),
    p + scale_y_log10(),
    p + scale_y_log10(format='.1~e') + coord_polar(),
], ncol=2)


# # Theme

# In[38]:


p = ggplot(labels_df, aes(x='x', y='y', color='y')) + scale_color_brewer(palette='GnBu') + geom_path(size=3)
polar_p = p + coord_polar()


# ## Themes list

# In[39]:


gggrid([
    p,
    polar_p + theme_minimal2() + ggtitle('theme_minimal2()'),
    polar_p + theme_bw() + ggtitle('theme_bw()'),
    polar_p + theme_classic() + ggtitle('theme_classic()'),
    polar_p + theme_grey() + ggtitle('theme_grey()'),
    polar_p + theme_light() + ggtitle('theme_light()'),
    polar_p + theme_minimal() + ggtitle('theme_minimal()'),
    polar_p + theme_none() + ggtitle('theme_none()'),
    polar_p + theme_void() + ggtitle('theme_void()'),
], ncol=3)


# ## Axis configuration

# In[40]:


p_tmp = p + theme(
    axis_line_y=element_line(color='red', size=2),
    axis_line_x=element_line(color='blue', size=2),
    axis_ticks_length_y=5,
    axis_ticks_length_x=10,
    axis_ticks_y=element_line(size=5, color='red'), 
    axis_ticks_x=element_line(size=3, color='blue'),
    axis_text_x=element_text(color='blue'),
    axis_text_y=element_text(color='red'),
)

gggrid([
    p_tmp,
    p_tmp + coord_polar()
])


# ## panel_inset
# The `panel_inset` parameter can be used to create space between the axis and the panel content:

# In[41]:


p_themed = p_tmp
p_themed += theme(
            plot_background=element_rect(fill='pink'),
            panel_background=element_rect(fill='grey'),
            panel_border=element_rect(color='green', size=1),
            panel_inset=[10, 10, 10, 10],
        )

gggrid([
    p_themed,
    p_themed + coord_polar()
])


# # Issues

# ## 1. Ticks overlapping

# In[42]:


data = {
    'x': ['txt 1', 'txt 2', 'txt 3', 'txt 4'],
    'y': [1, 2, 3, 4],
}
ggplot(data, aes('x', 'y')) + geom_point() + coord_polar()


# ## 2. Marginal plot (what to do with them at all)

# In[43]:


ggplot(labels_df, aes(x='x', y='y', color='y')) + scale_color_brewer(palette='GnBu') \
    + geom_path(size=3, show_legend=False) \
    + ggmarginal('trlb', layer=geom_line(color='black', show_legend=False)) \
    + coord_polar() \
    + theme(
        axis_line_y=element_line(color='red', size=2),
        axis_line_x=element_line(color='blue', size=2),
        axis_ticks_length_y=5,
        axis_ticks_length_x=10,
        axis_ticks_y=element_line(size=5, color='red'), 
        axis_ticks_x=element_line(size=3, color='blue'),
        axis_text_x=element_text(color='blue'),
        axis_text_y=element_text(color='red'),
        panel_border=element_rect(color='magenta', size=1),
        panel_background=element_rect(fill='orange'),
        panel_inset=[10, 10, 10, 10],
        plot_margin=10,
        plot_background=element_rect(fill='cyan'),
    )


# In[44]:


bar_df = { 'foo': [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3] }
p = ggplot(bar_df) + geom_bar(aes(fill=as_discrete('foo', order=1)), size=0)

p = ggplot(bar_df) + geom_bar(aes(fill=as_discrete('foo', order=1)), size=0, position='dodge')

gggrid([
    p,
    p + coord_polar(theta='y') + ggtitle('position=dodge, coord_polar(theta=y)'),
    p + coord_polar(theta='x') + ggtitle('position=dodge, coord_polar(theta=x)'),
])


# # Regressions

# ## Axis and grid alignment still works in all cases:
# - coord_flip
# - title
# - top/bottom/left/right
# - marginal
# - facet

# In[45]:


p = ggplot(labels_df, aes(x='x', y='y', color='y')) + geom_path(size=3) + scale_color_brewer(palette='GnBu') + theme_light()
default = p + ggtitle('Default')
both = p + scale_x_continuous(position='both') + scale_y_continuous(position='both') + ggtitle('Both')
flip = p + coord_flip() + ggtitle('coord_flip()')
flip_both = p + scale_x_continuous(position='both') + scale_y_continuous(position='both') + coord_flip() + ggtitle('both + coord_flip()')
facet = p + facet_grid(x='g') + ggtitle('Facet')
facet_both = both + facet_grid(x='g') + ggtitle('Facet Both')
facet_flip = flip + facet_grid(x='g') + ggtitle('Facet Flip')
facet_flip_both = flip_both + facet_grid(x='g') + ggtitle('Facet Flip Both')

g = gggrid([
    default,
    both,
    flip,
    flip_both,
    facet, 
    facet_both,
    facet_flip,
    facet_flip_both
], ncol=1)

g


# In[46]:


both + coord_polar()


# Single plot review:

# In[47]:


p = ggplot(labels_df, aes(x='x', y='y', label='l')) + geom_label()

p_rect_stack = ggplot() \
    + geom_rect(xmin=0, xmax=5, ymin=0, ymax=7, fill=c1, size=0) \
    + geom_rect(xmin=0, xmax=5, ymin=7, ymax=11, fill=c2, size=0) \
    + geom_rect(xmin=0, xmax=5, ymin=11, ymax=14, fill=c3, size=0) \


p_rect_dodge = ggplot() \
    + geom_rect(xmin=0, xmax=1, ymin=0, ymax=7, fill=c1, size=0) \
    + geom_rect(xmin=1, xmax=2, ymin=0, ymax=4, fill=c2, size=0) \
    + geom_rect(xmin=2, xmax=3, ymin=0, ymax=3, fill=c3, size=0) \

gggrid([
    p_rect_dodge, 
    p_rect_dodge + coord_polar(theta='y') + ggtitle('coord_polar(theta=y)'),
    p_rect_dodge + coord_polar(theta='x') + ggtitle('coord_polar(theta=x)'),
])

from lets_plot.mapping import as_discrete
bar_df = { 'foo': [1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3] }
p_bar_stack = ggplot(bar_df) + geom_bar(aes(fill=as_discrete('foo', order=1)), size=0)
p_bar_dodge = ggplot(bar_df) + geom_bar(aes(fill=as_discrete('foo', order=1)), size=0, position='dodge')

# lollipop
p_lollipop = ggplot(lollipop_df, aes('x', 'y')) + geom_lollipop() + coord_polar(xlim=[1, 7])

# radar

p_radar = ggplot(student_df) \
    + geom_path(aes(x='subj', y='score', color='student'), flat=True) \
    + geom_point(aes(x='subj', y='score', color='student'))\
    + coord_polar(ylim=[0, 20])

gggrid([
    p + coord_polar() + ggtitle('coord_polar()'),
    p + coord_polar(theta='y') + ggtitle('theta=y'),
    None,
    p_rect_stack + coord_polar(theta='y') + ggtitle('coord_polar(theta=y)'),
    p_rect_stack + coord_polar(theta='y', direction=-1) + ggtitle('coord_polar(theta=y, dir=-1)'),
    p_rect_stack + coord_polar(theta='x') + ggtitle('coord_polar(theta=x)'),
    p_rect_dodge, 
    p_rect_dodge + coord_polar(theta='y') + ggtitle('coord_polar(theta=y)'),
    p_rect_dodge + coord_polar(theta='x') + ggtitle('coord_polar(theta=x)'),
    p_bar_stack,
    p_bar_stack + coord_polar(theta='y') + ggtitle('position=stack, coord_polar(theta=y)'),
    p_bar_stack + coord_polar(theta='x') + ggtitle('position=stack, coord_polar(theta=x)'),
    p_bar_dodge,
    p_bar_dodge + coord_polar(theta='y') + ggtitle('position=dodge, coord_polar(theta=y)'),
    p_bar_dodge + coord_polar(theta='x') + ggtitle('position=dodge, coord_polar(theta=x)'),
    p_lollipop,
    p_radar
], ncol=3)


# # Sandbox

# In[48]:


mpg = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv')
mpg


# In[49]:


mpg2 = mpg.copy()
mpg2['name'] = mpg2.apply(lambda x: f"{x['model']}-{x['displ']}-{x['year']}-{x['trans']}-{x['drv']}", axis=1)
mpg2


# In[50]:


ggplot(mpg) + geom_bar(aes(x='model', y='cty', fill='cty'), stat='identity', position='dodge') + scale_fill_gradient(low='red', high='white', limits=(5,40)) #+ theme_void()


# In[51]:


p_default = ggplot(mpg) \
    + geom_bar(aes(x='model', y='cty', fill='cty'), stat='identity', position='dodge') \
    + scale_fill_gradient(low='red', high='white', limits=(5,40)) \
    + ggsize(800, 400) \
    + ggtitle('default') \
    + coord_polar() 
p_default.show()

inset = [20, 140, 30, 120]
p_adjusted = ggplot(mpg) \
    + geom_bar(aes(x='model', y='cty', fill='cty'), stat='identity', position='dodge') \
    + scale_fill_gradient(low='red', high='white', limits=(5,40)) \
    + theme(
        axis_text_x=element_text(angle=10), 
        panel_inset=inset
    ) \
    + ggsize(900, 500) \
    + ggtitle('panel_inset=' + str(inset)) \
    + coord_polar() 
p_adjusted.show()


# In[52]:


p = ggplot(labels_df, aes(x='x', y='y', label='l')) + geom_point(alpha=0.3, color='red') + geom_text()
gggrid([
    p + coord_polar(theta='x'),
    p + coord_polar(theta='y'),
])


# In[53]:


p = ggplot() + geom_density2df(aes(x=[0, 1], y=[0, 1], fill='..level..'), bins=6, show_legend=False) 

gggrid([
    p,
    p + coord_polar()
])


# In[54]:


p = ggplot() + geom_density2df(aes(x=[0, 1], y=[0, 1], fill='..level..'), bins=2, show_legend=False) 

gggrid([
    p,
    p + coord_polar()
])


# In[55]:


p = ggplot() + geom_density2df(aes(x=[1, 0, 0, 1], y=[1, 0, 1, 0], fill='..level..'), bins=2, show_legend=False) 

gggrid([
    p,
    p + coord_polar()
])


# In[56]:


import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 1000
np.random.seed(42)
x = np.random.normal(size=n)
y = np.random.normal(size=n)

p = ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
    geom_density2df(aes(fill='..group..'), show_legend=False) + \
    scale_fill_brewer(type='seq', palette='GnBu', direction=-1)

gggrid([
    p,
    p + coord_polar()
])


# In[ ]: